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import unittest from transformers import PegasusConfig, PegasusTokenizer, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_configuration_common import ConfigTester from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html _lowercase = '''platform''' import jax import jax.numpy as jnp import numpy as np from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel @require_flax class __snake_case : """simple docstring""" UpperCamelCase_ = PegasusConfig UpperCamelCase_ = {} UpperCamelCase_ = '''gelu''' def __init__( self : Dict ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Tuple=13 ,lowerCAmelCase__ : Dict=7 ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : List[str]=False ,lowerCAmelCase__ : Dict=99 ,lowerCAmelCase__ : Dict=32 ,lowerCAmelCase__ : Union[str, Any]=5 ,lowerCAmelCase__ : Any=4 ,lowerCAmelCase__ : Dict=37 ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : Optional[int]=0.1 ,lowerCAmelCase__ : List[str]=20 ,lowerCAmelCase__ : Optional[Any]=2 ,lowerCAmelCase__ : int=1 ,lowerCAmelCase__ : str=0 ,) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = parent lowerCAmelCase_ : Union[str, Any] = batch_size lowerCAmelCase_ : Optional[int] = seq_length lowerCAmelCase_ : Optional[Any] = is_training lowerCAmelCase_ : Union[str, Any] = use_labels lowerCAmelCase_ : Optional[int] = vocab_size lowerCAmelCase_ : Optional[int] = hidden_size lowerCAmelCase_ : int = num_hidden_layers lowerCAmelCase_ : Union[str, Any] = num_attention_heads lowerCAmelCase_ : Optional[Any] = intermediate_size lowerCAmelCase_ : Union[str, Any] = hidden_dropout_prob lowerCAmelCase_ : List[Any] = attention_probs_dropout_prob lowerCAmelCase_ : str = max_position_embeddings lowerCAmelCase_ : Union[str, Any] = eos_token_id lowerCAmelCase_ : Union[str, Any] = pad_token_id lowerCAmelCase_ : Tuple = bos_token_id def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : List[Any] = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size ).clip(3 ,self.vocab_size ) lowerCAmelCase_ : Union[str, Any] = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) ,1 ) lowerCAmelCase_ : int = np.concatenate([input_ids, eos_tensor] ,axis=1 ) lowerCAmelCase_ : str = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size ) lowerCAmelCase_ : str = self.config_cls( vocab_size=self.vocab_size ,d_model=self.hidden_size ,encoder_layers=self.num_hidden_layers ,decoder_layers=self.num_hidden_layers ,encoder_attention_heads=self.num_attention_heads ,decoder_attention_heads=self.num_attention_heads ,encoder_ffn_dim=self.intermediate_size ,decoder_ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,eos_token_ids=[2] ,bos_token_id=self.bos_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.pad_token_id ,**self.config_updates ,) lowerCAmelCase_ : Optional[int] = prepare_pegasus_inputs_dict(a_ ,a_ ,a_ ) return config, inputs_dict def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Tuple = 20 lowerCAmelCase_ : int = model_class_name(a_ ) lowerCAmelCase_ : str = model.encode(inputs_dict["input_ids"] ) lowerCAmelCase_ : Dict = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) lowerCAmelCase_ : List[Any] = model.init_cache(decoder_input_ids.shape[0] ,a_ ,a_ ) lowerCAmelCase_ : Union[str, Any] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,dtype="i4" ) lowerCAmelCase_ : Optional[int] = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,) lowerCAmelCase_ : List[Any] = model.decode( decoder_input_ids[:, :-1] ,a_ ,decoder_attention_mask=a_ ,past_key_values=a_ ,decoder_position_ids=a_ ,) lowerCAmelCase_ : str = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype="i4" ) lowerCAmelCase_ : Optional[int] = model.decode( decoder_input_ids[:, -1:] ,a_ ,decoder_attention_mask=a_ ,past_key_values=outputs_cache.past_key_values ,decoder_position_ids=a_ ,) lowerCAmelCase_ : Any = model.decode(a_ ,a_ ) lowerCAmelCase_ : Optional[Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) ) self.parent.assertTrue(diff < 1e-3 ,msg=f'''Max diff is {diff}''' ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : List[Any] ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = 20 lowerCAmelCase_ : Dict = model_class_name(a_ ) lowerCAmelCase_ : str = model.encode(inputs_dict["input_ids"] ) lowerCAmelCase_ : Optional[Any] = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) lowerCAmelCase_ : Dict = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ), ] ,axis=-1 ,) lowerCAmelCase_ : List[str] = model.init_cache(decoder_input_ids.shape[0] ,a_ ,a_ ) lowerCAmelCase_ : Optional[int] = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,) lowerCAmelCase_ : Union[str, Any] = model.decode( decoder_input_ids[:, :-1] ,a_ ,decoder_attention_mask=a_ ,past_key_values=a_ ,decoder_position_ids=a_ ,) lowerCAmelCase_ : Dict = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype="i4" ) lowerCAmelCase_ : int = model.decode( decoder_input_ids[:, -1:] ,a_ ,past_key_values=outputs_cache.past_key_values ,decoder_attention_mask=a_ ,decoder_position_ids=a_ ,) lowerCAmelCase_ : str = model.decode(a_ ,a_ ,decoder_attention_mask=a_ ) lowerCAmelCase_ : Any = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) ) self.parent.assertTrue(diff < 1e-3 ,msg=f'''Max diff is {diff}''' ) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , ): if attention_mask is None: lowerCAmelCase_ : List[str] = np.not_equal(lowerCAmelCase_ , config.pad_token_id).astype(np.inta) if decoder_attention_mask is None: lowerCAmelCase_ : Any = np.concatenate( [ np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta), np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id).astype(np.inta), ] , axis=-1 , ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, } @require_flax class __snake_case ( __lowerCamelCase , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = ( ( FlaxPegasusForConditionalGeneration, FlaxPegasusModel, ) if is_flax_available() else () ) UpperCamelCase_ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else () UpperCamelCase_ = True UpperCamelCase_ = False UpperCamelCase_ = False UpperCamelCase_ = False def UpperCAmelCase_ ( self : List[str] ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Any = FlaxPegasusModelTester(self ) lowerCAmelCase_ : Dict = ConfigTester(self ,config_class=a_ ) def UpperCAmelCase_ ( self : Union[str, Any] ) -> Union[str, Any]: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase_ ( self : Optional[int] ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(a_ ,a_ ,a_ ) def UpperCAmelCase_ ( self : Tuple ) -> str: '''simple docstring''' lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(a_ ,a_ ,a_ ) def UpperCAmelCase_ ( self : Optional[int] ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__ ): lowerCAmelCase_ : str = self._prepare_for_class(a_ ,a_ ) lowerCAmelCase_ : List[Any] = model_class(a_ ) @jax.jit def encode_jitted(lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Union[str, Any]=None ,**lowerCAmelCase__ : int ): return model.encode(input_ids=a_ ,attention_mask=a_ ) with self.subTest("JIT Enabled" ): lowerCAmelCase_ : List[Any] = encode_jitted(**a_ ).to_tuple() with self.subTest("JIT Disabled" ): with jax.disable_jit(): lowerCAmelCase_ : int = encode_jitted(**a_ ).to_tuple() self.assertEqual(len(a_ ) ,len(a_ ) ) for jitted_output, output in zip(a_ ,a_ ): self.assertEqual(jitted_output.shape ,output.shape ) def UpperCAmelCase_ ( self : int ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__ ): lowerCAmelCase_ : Optional[int] = model_class(a_ ) lowerCAmelCase_ : List[str] = model.encode(inputs_dict["input_ids"] ,inputs_dict["attention_mask"] ) lowerCAmelCase_ : List[str] = { "decoder_input_ids": inputs_dict["decoder_input_ids"], "decoder_attention_mask": inputs_dict["decoder_attention_mask"], "encoder_outputs": encoder_outputs, } @jax.jit def decode_jitted(lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ): return model.decode( decoder_input_ids=a_ ,decoder_attention_mask=a_ ,encoder_outputs=a_ ,) with self.subTest("JIT Enabled" ): lowerCAmelCase_ : int = decode_jitted(**a_ ).to_tuple() with self.subTest("JIT Disabled" ): with jax.disable_jit(): lowerCAmelCase_ : Optional[Any] = decode_jitted(**a_ ).to_tuple() self.assertEqual(len(a_ ) ,len(a_ ) ) for jitted_output, output in zip(a_ ,a_ ): self.assertEqual(jitted_output.shape ,output.shape ) @slow def UpperCAmelCase_ ( self : Any ) -> Tuple: '''simple docstring''' for model_class_name in self.all_model_classes: lowerCAmelCase_ : Tuple = model_class_name.from_pretrained("google/pegasus-large" ,from_pt=a_ ) lowerCAmelCase_ : Any = np.ones((1, 1) ) lowerCAmelCase_ : Any = model(a_ ) self.assertIsNotNone(a_ ) @slow def UpperCAmelCase_ ( self : Tuple ) -> str: '''simple docstring''' lowerCAmelCase_ : Tuple = FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum" ) lowerCAmelCase_ : Dict = PegasusTokenizer.from_pretrained("google/pegasus-xsum" ) lowerCAmelCase_ : str = [ " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.", " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ", ] lowerCAmelCase_ : List[str] = [ "California's largest electricity provider has turned off power to hundreds of thousands of customers.", "Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.", ] lowerCAmelCase_ : int = tokenizer(a_ ,return_tensors="np" ,truncation=a_ ,max_length=5_12 ,padding=a_ ) lowerCAmelCase_ : Dict = model.generate(**a_ ,num_beams=2 ).sequences lowerCAmelCase_ : List[Any] = tokenizer.batch_decode(a_ ,skip_special_tokens=a_ ) assert tgt_text == decoded
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from __future__ import annotations def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ): if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1: raise ValueError("You cannot supply more or less than 2 values") elif electron_conc < 0: raise ValueError("Electron concentration cannot be negative in a semiconductor") elif hole_conc < 0: raise ValueError("Hole concentration cannot be negative in a semiconductor") elif intrinsic_conc < 0: raise ValueError( "Intrinsic concentration cannot be negative in a semiconductor") elif electron_conc == 0: return ( "electron_conc", intrinsic_conc**2 / hole_conc, ) elif hole_conc == 0: return ( "hole_conc", intrinsic_conc**2 / electron_conc, ) elif intrinsic_conc == 0: return ( "intrinsic_conc", (electron_conc * hole_conc) ** 0.5, ) else: return (-1, -1) if __name__ == "__main__": import doctest doctest.testmod()
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_lowercase = '''Input must be a string of 8 numbers plus letter''' _lowercase = '''TRWAGMYFPDXBNJZSQVHLCKE''' def UpperCamelCase ( snake_case__): if not isinstance(_snake_case , _snake_case): lowerCAmelCase_ : Optional[Any] = F'''Expected string as input, found {type(_snake_case).__name__}''' raise TypeError(_snake_case) lowerCAmelCase_ : Any = spanish_id.replace("-" , "").upper() if len(_snake_case) != 9: raise ValueError(_snake_case) try: lowerCAmelCase_ : Any = int(spanish_id_clean[0:8]) lowerCAmelCase_ : Dict = spanish_id_clean[8] except ValueError as ex: raise ValueError(_snake_case) from ex if letter.isdigit(): raise ValueError(_snake_case) return letter == LOOKUP_LETTERS[number % 23] if __name__ == "__main__": import doctest doctest.testmod()
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _lowercase = { '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''], '''processing_git''': ['''GitProcessor'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''GitForCausalLM''', '''GitModel''', '''GitPreTrainedModel''', '''GitVisionModel''', ] if TYPE_CHECKING: from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig from .processing_git import GitProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_git import ( GIT_PRETRAINED_MODEL_ARCHIVE_LIST, GitForCausalLM, GitModel, GitPreTrainedModel, GitVisionModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_speech_available, is_torch_available, ) _lowercase = { 'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'], 'processing_trocr': ['TrOCRProcessor'], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ 'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST', 'TrOCRForCausalLM', 'TrOCRPreTrainedModel', ] if TYPE_CHECKING: from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig from .processing_trocr import TrOCRProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments def UpperCamelCase ( ): lowerCAmelCase_ : List[str] = HfArgumentParser(snake_case__) lowerCAmelCase_ : List[Any] = parser.parse_args_into_dataclasses()[0] lowerCAmelCase_ : Optional[int] = TensorFlowBenchmark(args=snake_case__) try: lowerCAmelCase_ : Tuple = parser.parse_args_into_dataclasses()[0] except ValueError as e: lowerCAmelCase_ : Union[str, Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead." lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1]) lowerCAmelCase_ : Union[str, Any] = "" lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1]) lowerCAmelCase_ : Tuple = [] for arg in depreciated_args: # arg[2:] removes '--' if arg[2:] in TensorFlowBenchmark.deprecated_args: # arg[5:] removes '--no_' full_error_msg += arg_error_msg.format(arg[5:]) else: wrong_args.append(snake_case__) if len(snake_case__) > 0: lowerCAmelCase_ : Optional[Any] = full_error_msg + begin_error_msg + str(snake_case__) raise ValueError(snake_case__) benchmark.run() if __name__ == "__main__": main()
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'''simple docstring''' def UpperCamelCase ( snake_case__ , snake_case__): print("\nThe shortest path matrix using Floyd Warshall algorithm\n") for i in range(A_): for j in range(A_): if dist[i][j] != float("inf"): print(int(dist[i][j]) , end="\t") else: print("INF" , end="\t") print() def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : str = [[float("inf") for _ in range(A_)] for _ in range(A_)] for i in range(A_): for j in range(A_): lowerCAmelCase_ : List[Any] = graph[i][j] # check vertex k against all other vertices (i, j) for k in range(A_): # looping through rows of graph array for i in range(A_): # looping through columns of graph array for j in range(A_): if ( dist[i][k] != float("inf") and dist[k][j] != float("inf") and dist[i][k] + dist[k][j] < dist[i][j] ): lowerCAmelCase_ : Optional[Any] = dist[i][k] + dist[k][j] _print_dist(A_ , A_) return dist, v if __name__ == "__main__": _lowercase = int(input('''Enter number of vertices: ''')) _lowercase = int(input('''Enter number of edges: ''')) _lowercase = [[float('''inf''') for i in range(v)] for j in range(v)] for i in range(v): _lowercase = 0.0 # src and dst are indices that must be within the array size graph[e][v] # failure to follow this will result in an error for i in range(e): print('''\nEdge ''', i + 1) _lowercase = int(input('''Enter source:''')) _lowercase = int(input('''Enter destination:''')) _lowercase = float(input('''Enter weight:''')) _lowercase = weight floyd_warshall(graph, v) # Example Input # Enter number of vertices: 3 # Enter number of edges: 2 # # generated graph from vertex and edge inputs # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]] # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]] # specify source, destination and weight for edge #1 # Edge 1 # Enter source:1 # Enter destination:2 # Enter weight:2 # specify source, destination and weight for edge #2 # Edge 2 # Enter source:2 # Enter destination:1 # Enter weight:1 # # Expected Output from the vertice, edge and src, dst, weight inputs!! # 0 INF INF # INF 0 2 # INF 1 0
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_lowercase = { 0: '''0''', 1: '''1''', 2: '''2''', 3: '''3''', 4: '''4''', 5: '''5''', 6: '''6''', 7: '''7''', 8: '''8''', 9: '''9''', 10: '''a''', 11: '''b''', 12: '''c''', 13: '''d''', 14: '''e''', 15: '''f''', } def UpperCamelCase ( snake_case__): assert type(snake_case__) in (int, float) and decimal == int(snake_case__) lowerCAmelCase_ : Optional[Any] = int(snake_case__) lowerCAmelCase_ : Tuple = "" lowerCAmelCase_ : str = False if decimal < 0: lowerCAmelCase_ : Tuple = True decimal *= -1 while decimal > 0: lowerCAmelCase_ , lowerCAmelCase_ : Any = divmod(snake_case__ , 16) lowerCAmelCase_ : Dict = values[remainder] + hexadecimal lowerCAmelCase_ : List[str] = "0x" + hexadecimal if negative: lowerCAmelCase_ : Optional[Any] = "-" + hexadecimal return hexadecimal if __name__ == "__main__": import doctest doctest.testmod()
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from typing import Union import fire import torch from tqdm import tqdm def UpperCamelCase ( snake_case__ , snake_case__ = "cpu" , snake_case__ = None): lowerCAmelCase_ : Tuple = torch.load(snake_case__ , map_location=snake_case__) for k, v in tqdm(state_dict.items()): if not isinstance(snake_case__ , torch.Tensor): raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin") lowerCAmelCase_ : List[str] = v.half() if save_path is None: # overwrite src_path lowerCAmelCase_ : List[str] = src_path torch.save(snake_case__ , snake_case__) if __name__ == "__main__": fire.Fire(convert)
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from pathlib import Path from typing import List from transformers import is_torch_available, is_vision_available from transformers.testing_utils import get_tests_dir, is_tool_test from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText if is_torch_available(): import torch if is_vision_available(): from PIL import Image _lowercase = ['''text''', '''image''', '''audio'''] def UpperCamelCase ( snake_case__): lowerCAmelCase_ : int = [] for input_type in input_types: if input_type == "text": inputs.append("Text input") elif input_type == "image": inputs.append( Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png").resize((5_12, 5_12))) elif input_type == "audio": inputs.append(torch.ones(30_00)) elif isinstance(snake_case__ , snake_case__): inputs.append(create_inputs(snake_case__)) else: raise ValueError(F'''Invalid type requested: {input_type}''') return inputs def UpperCamelCase ( snake_case__): lowerCAmelCase_ : List[Any] = [] for output in outputs: if isinstance(snake_case__ , (str, AgentText)): output_types.append("text") elif isinstance(snake_case__ , (Image.Image, AgentImage)): output_types.append("image") elif isinstance(snake_case__ , (torch.Tensor, AgentAudio)): output_types.append("audio") else: raise ValueError(F'''Invalid output: {output}''') return output_types @is_tool_test class __snake_case : """simple docstring""" def UpperCAmelCase_ ( self : int ) -> int: '''simple docstring''' self.assertTrue(hasattr(self.tool ,"inputs" ) ) self.assertTrue(hasattr(self.tool ,"outputs" ) ) lowerCAmelCase_ : List[Any] = self.tool.inputs for _input in inputs: if isinstance(_input ,lowerCAmelCase__ ): for __input in _input: self.assertTrue(__input in authorized_types ) else: self.assertTrue(_input in authorized_types ) lowerCAmelCase_ : Any = self.tool.outputs for _output in outputs: self.assertTrue(_output in authorized_types ) def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Any = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) # There is a single output if len(self.tool.outputs ) == 1: lowerCAmelCase_ : Optional[int] = [outputs] self.assertListEqual(output_types(lowerCAmelCase__ ) ,self.tool.outputs ) def UpperCAmelCase_ ( self : int ) -> Any: '''simple docstring''' self.assertTrue(hasattr(self.tool ,"description" ) ) self.assertTrue(hasattr(self.tool ,"default_checkpoint" ) ) self.assertTrue(self.tool.description.startswith("This is a tool that" ) ) def UpperCAmelCase_ ( self : List[Any] ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): lowerCAmelCase_ : str = [outputs] self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) ) for output, output_type in zip(lowerCAmelCase__ ,self.tool.outputs ): lowerCAmelCase_ : Tuple = AGENT_TYPE_MAPPING[output_type] self.assertTrue(isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) ) def UpperCAmelCase_ ( self : Any ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Tuple = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = [] for _input, input_type in zip(lowerCAmelCase__ ,self.tool.inputs ): if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] ) else: _inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) ) # Should not raise an error lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): lowerCAmelCase_ : int = [outputs] self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
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import unittest import numpy as np import torch from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device enable_full_determinism() class __snake_case ( unittest.TestCase ): """simple docstring""" @property def UpperCAmelCase_ ( self : Tuple ) -> str: '''simple docstring''' torch.manual_seed(0 ) lowerCAmelCase_ : Union[str, Any] = UNetaDModel( block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=("DownBlock2D", "AttnDownBlock2D") ,up_block_types=("AttnUpBlock2D", "UpBlock2D") ,) return model def UpperCAmelCase_ ( self : int ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : Dict = self.dummy_uncond_unet lowerCAmelCase_ : str = KarrasVeScheduler() lowerCAmelCase_ : Optional[int] = KarrasVePipeline(unet=__a ,scheduler=__a ) pipe.to(__a ) pipe.set_progress_bar_config(disable=__a ) lowerCAmelCase_ : Union[str, Any] = torch.manual_seed(0 ) lowerCAmelCase_ : Any = pipe(num_inference_steps=2 ,generator=__a ,output_type="numpy" ).images lowerCAmelCase_ : int = torch.manual_seed(0 ) lowerCAmelCase_ : List[Any] = pipe(num_inference_steps=2 ,generator=__a ,output_type="numpy" ,return_dict=__a )[0] lowerCAmelCase_ : Optional[Any] = image[0, -3:, -3:, -1] lowerCAmelCase_ : Optional[int] = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 32, 32, 3) lowerCAmelCase_ : int = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2 @slow @require_torch class __snake_case ( unittest.TestCase ): """simple docstring""" def UpperCAmelCase_ ( self : Any ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : int = "google/ncsnpp-celebahq-256" lowerCAmelCase_ : List[str] = UNetaDModel.from_pretrained(__a ) lowerCAmelCase_ : Optional[int] = KarrasVeScheduler() lowerCAmelCase_ : List[Any] = KarrasVePipeline(unet=__a ,scheduler=__a ) pipe.to(__a ) pipe.set_progress_bar_config(disable=__a ) lowerCAmelCase_ : Optional[int] = torch.manual_seed(0 ) lowerCAmelCase_ : Dict = pipe(num_inference_steps=20 ,generator=__a ,output_type="numpy" ).images lowerCAmelCase_ : Dict = image[0, -3:, -3:, -1] assert image.shape == (1, 2_56, 2_56, 3) lowerCAmelCase_ : Union[str, Any] = np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
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import pytest _lowercase = '''__dummy_dataset1__''' _lowercase = ''' import json import os import datasets REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/" URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"} class __DummyDataset1__(datasets.GeneratorBasedBuilder): def _info(self): features = datasets.Features( { "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=[ "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", ] ) ), "langs": datasets.Sequence(datasets.Value("string")), "spans": datasets.Sequence(datasets.Value("string")), } ) return datasets.DatasetInfo(features=features) def _split_generators(self, dl_manager): dl_path = dl_manager.download(URLS) return [ datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}), datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}), ] def _generate_examples(self, filepath): with open(filepath, "r", encoding="utf-8") as f: for i, line in enumerate(f): yield i, json.loads(line) ''' @pytest.fixture def UpperCamelCase ( ): return DATASET_LOADING_SCRIPT_NAME @pytest.fixture def UpperCamelCase ( ): return DATASET_LOADING_SCRIPT_CODE @pytest.fixture def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : List[Any] = dataset_loading_script_name lowerCAmelCase_ : List[str] = tmp_path / "datasets" / script_name script_dir.mkdir(parents=snake_case__) lowerCAmelCase_ : List[Any] = script_dir / F'''{script_name}.py''' with open(snake_case__ , "w") as f: f.write(snake_case__) return str(snake_case__)
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_lowercase = [ '''Audio''', '''Array2D''', '''Array3D''', '''Array4D''', '''Array5D''', '''ClassLabel''', '''Features''', '''Sequence''', '''Value''', '''Image''', '''Translation''', '''TranslationVariableLanguages''', ] from .audio import Audio from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value from .image import Image from .translation import Translation, TranslationVariableLanguages
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import json import os import re import unittest from transformers import CodeGenTokenizer, CodeGenTokenizerFast from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class __snake_case ( snake_case__ , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = CodeGenTokenizer UpperCamelCase_ = CodeGenTokenizerFast UpperCamelCase_ = True UpperCamelCase_ = {'add_prefix_space': True} UpperCamelCase_ = False def UpperCAmelCase_ ( self : str ) -> Tuple: '''simple docstring''' super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt lowerCAmelCase_ : Optional[Any] = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "<unk>", "<|endoftext|>", ] lowerCAmelCase_ : int = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) ) lowerCAmelCase_ : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] lowerCAmelCase_ : List[Any] = {"unk_token": "<unk>"} lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase_ : Tuple = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] ) with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp: fp.write(json.dumps(lowerCAmelCase__ ) + "\n" ) with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp: fp.write("\n".join(lowerCAmelCase__ ) ) def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : str ) -> int: '''simple docstring''' kwargs.update(self.special_tokens_map ) return CodeGenTokenizer.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : Optional[Any] ) -> Tuple: '''simple docstring''' kwargs.update(self.special_tokens_map ) return CodeGenTokenizerFast.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : str ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Tuple = "lower newer" lowerCAmelCase_ : Tuple = "lower newer" return input_text, output_text def UpperCAmelCase_ ( self : Optional[Any] ) -> Any: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map ) lowerCAmelCase_ : Dict = "lower newer" lowerCAmelCase_ : Dict = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"] lowerCAmelCase_ : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = tokens + [tokenizer.unk_token] lowerCAmelCase_ : Union[str, Any] = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]: '''simple docstring''' if not self.test_rust_tokenizer: return lowerCAmelCase_ : Tuple = self.get_tokenizer() lowerCAmelCase_ : Optional[int] = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = "lower newer" # Testing tokenization lowerCAmelCase_ : Tuple = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = rust_tokenizer.tokenize(lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing conversion to ids without special tokens lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = rust_tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing conversion to ids with special tokens lowerCAmelCase_ : int = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing the unknown token lowerCAmelCase_ : Union[str, Any] = tokens + [rust_tokenizer.unk_token] lowerCAmelCase_ : List[str] = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Optional[Any] ) -> List[str]: '''simple docstring''' pass def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Any=15 ) -> str: '''simple docstring''' for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ): lowerCAmelCase_ : Any = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ ) # Simple input lowerCAmelCase_ : int = "This is a simple input" lowerCAmelCase_ : Dict = ["This is a simple input 1", "This is a simple input 2"] lowerCAmelCase_ : str = ("This is a simple input", "This is a pair") lowerCAmelCase_ : Optional[int] = [ ("This is a simple input 1", "This is a simple input 2"), ("This is a simple pair 1", "This is a simple pair 2"), ] # Simple input tests self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Simple input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Simple input self.assertRaises( lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,) # Pair input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Pair input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Pair input self.assertRaises( lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,) def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname ,pad_token="<pad>" ) # Simple input lowerCAmelCase_ : Dict = "This is a simple input" lowerCAmelCase_ : List[str] = ["This is a simple input looooooooong", "This is a simple input"] lowerCAmelCase_ : Any = ("This is a simple input", "This is a pair") lowerCAmelCase_ : List[str] = [ ("This is a simple input loooooong", "This is a simple input"), ("This is a simple pair loooooong", "This is a simple pair"), ] lowerCAmelCase_ : Dict = tokenizer.pad_token_id lowerCAmelCase_ : Union[str, Any] = tokenizer(lowerCAmelCase__ ,padding="max_length" ,max_length=30 ,return_tensors="np" ) lowerCAmelCase_ : Tuple = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" ) lowerCAmelCase_ : Any = tokenizer(*lowerCAmelCase__ ,padding="max_length" ,max_length=60 ,return_tensors="np" ) lowerCAmelCase_ : Optional[int] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" ) # s # test single string max_length padding self.assertEqual(out_s["input_ids"].shape[-1] ,30 ) self.assertTrue(pad_token_id in out_s["input_ids"] ) self.assertTrue(0 in out_s["attention_mask"] ) # s2 # test automatic padding self.assertEqual(out_sa["input_ids"].shape[-1] ,33 ) # long slice doesn't have padding self.assertFalse(pad_token_id in out_sa["input_ids"][0] ) self.assertFalse(0 in out_sa["attention_mask"][0] ) # short slice does have padding self.assertTrue(pad_token_id in out_sa["input_ids"][1] ) self.assertTrue(0 in out_sa["attention_mask"][1] ) # p # test single pair max_length padding self.assertEqual(out_p["input_ids"].shape[-1] ,60 ) self.assertTrue(pad_token_id in out_p["input_ids"] ) self.assertTrue(0 in out_p["attention_mask"] ) # p2 # test automatic padding pair self.assertEqual(out_pa["input_ids"].shape[-1] ,52 ) # long slice pair doesn't have padding self.assertFalse(pad_token_id in out_pa["input_ids"][0] ) self.assertFalse(0 in out_pa["attention_mask"][0] ) # short slice pair does have padding self.assertTrue(pad_token_id in out_pa["input_ids"][1] ) self.assertTrue(0 in out_pa["attention_mask"][1] ) def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any: '''simple docstring''' lowerCAmelCase_ : Any = "$$$" lowerCAmelCase_ : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname ,bos_token=lowerCAmelCase__ ,add_bos_token=lowerCAmelCase__ ) lowerCAmelCase_ : Dict = "This is a simple input" lowerCAmelCase_ : Union[str, Any] = ["This is a simple input 1", "This is a simple input 2"] lowerCAmelCase_ : int = tokenizer.bos_token_id lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ ) self.assertEqual(out_s.input_ids[0] ,lowerCAmelCase__ ) self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) ) lowerCAmelCase_ : List[str] = tokenizer.decode(out_s.input_ids ) lowerCAmelCase_ : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids ) self.assertEqual(decode_s.split()[0] ,lowerCAmelCase__ ) self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) ) @slow def UpperCAmelCase_ ( self : Any ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" ) lowerCAmelCase_ : str = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#" lowerCAmelCase_ : int = "\nif len_a > len_b: result = a\nelse: result = b" lowerCAmelCase_ : Dict = tokenizer.encode(lowerCAmelCase__ ) lowerCAmelCase_ : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"] lowerCAmelCase_ : Union[str, Any] = tokenizer.decode(lowerCAmelCase__ ,truncate_before_pattern=lowerCAmelCase__ ) self.assertEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]: '''simple docstring''' pass
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from math import isclose, sqrt def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : List[str] = point_y / 4 / point_x lowerCAmelCase_ : Optional[int] = 2 * normal_gradient / (1 + normal_gradient * normal_gradient) lowerCAmelCase_ : Optional[int] = (1 - normal_gradient * normal_gradient) / ( 1 + normal_gradient * normal_gradient ) lowerCAmelCase_ : Dict = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient) # to find the next point, solve the simultaeneous equations: # y^2 + 4x^2 = 100 # y - b = m * (x - a) # ==> A x^2 + B x + C = 0 lowerCAmelCase_ : str = outgoing_gradient**2 + 4 lowerCAmelCase_ : Dict = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x) lowerCAmelCase_ : List[Any] = (point_y - outgoing_gradient * point_x) ** 2 - 1_00 lowerCAmelCase_ : Dict = ( -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term) ) / (2 * quadratic_term) lowerCAmelCase_ : Union[str, Any] = ( -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term) ) / (2 * quadratic_term) # two solutions, one of which is our input point lowerCAmelCase_ : Dict = x_minus if isclose(A__ , A__) else x_plus lowerCAmelCase_ : str = point_y + outgoing_gradient * (next_x - point_x) return next_x, next_y, outgoing_gradient def UpperCamelCase ( snake_case__ = 1.4 , snake_case__ = -9.6): lowerCAmelCase_ : Tuple = 0 lowerCAmelCase_ : Any = first_x_coord lowerCAmelCase_ : Tuple = first_y_coord lowerCAmelCase_ : List[str] = (10.1 - point_y) / (0.0 - point_x) while not (-0.01 <= point_x <= 0.01 and point_y > 0): lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = next_point(A__ , A__ , A__) num_reflections += 1 return num_reflections if __name__ == "__main__": print(f"{solution() = }")
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from __future__ import annotations from random import random class __snake_case : """simple docstring""" def __init__( self : Optional[int] ,lowerCAmelCase__ : int | None = None ) -> int: '''simple docstring''' lowerCAmelCase_ : Dict = value lowerCAmelCase_ : Any = random() lowerCAmelCase_ : Node | None = None lowerCAmelCase_ : Node | None = None def __repr__( self : Any ) -> str: '''simple docstring''' from pprint import pformat if self.left is None and self.right is None: return f'''\'{self.value}: {self.prior:.5}\'''' else: return pformat( {f'''{self.value}: {self.prior:.5}''': (self.left, self.right)} ,indent=1 ) def __str__( self : str ) -> str: '''simple docstring''' lowerCAmelCase_ : List[Any] = str(self.value ) + " " lowerCAmelCase_ : List[Any] = str(self.left or "" ) lowerCAmelCase_ : Union[str, Any] = str(self.right or "" ) return value + left + right def UpperCamelCase ( snake_case__ , snake_case__): if root is None: # None tree is split into 2 Nones return None, None elif root.value is None: return None, None else: if value < root.value: lowerCAmelCase_ , lowerCAmelCase_ : Any = split(root.left , snake_case__) return left, root else: lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = split(root.right , snake_case__) return root, right def UpperCamelCase ( snake_case__ , snake_case__): if (not left) or (not right): # If one node is None, return the other return left or right elif left.prior < right.prior: lowerCAmelCase_ : Dict = merge(left.right , snake_case__) return left else: lowerCAmelCase_ : List[str] = merge(snake_case__ , right.left) return right def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : List[Any] = Node(snake_case__) lowerCAmelCase_ , lowerCAmelCase_ : Tuple = split(snake_case__ , snake_case__) return merge(merge(snake_case__ , snake_case__) , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ , lowerCAmelCase_ : List[str] = split(snake_case__ , value - 1) lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = split(snake_case__ , snake_case__) return merge(snake_case__ , snake_case__) def UpperCamelCase ( snake_case__): if not root: # None return else: inorder(root.left) print(root.value , end=",") inorder(root.right) def UpperCamelCase ( snake_case__ , snake_case__): for arg in args.split(): if arg[0] == "+": lowerCAmelCase_ : List[str] = insert(snake_case__ , int(arg[1:])) elif arg[0] == "-": lowerCAmelCase_ : Optional[int] = erase(snake_case__ , int(arg[1:])) else: print("Unknown command") return root def UpperCamelCase ( ): lowerCAmelCase_ : str = None print( "enter numbers to create a tree, + value to add value into treap, " "- value to erase all nodes with value. 'q' to quit. ") lowerCAmelCase_ : str = input() while args != "q": lowerCAmelCase_ : int = interact_treap(snake_case__ , snake_case__) print(snake_case__) lowerCAmelCase_ : str = input() print("good by!") if __name__ == "__main__": import doctest doctest.testmod() main()
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from math import sqrt def UpperCamelCase ( snake_case__ = 1_00_00_00): lowerCAmelCase_ : int = 0 lowerCAmelCase_ : int = 0 lowerCAmelCase_ : int while num_cuboids <= limit: max_cuboid_size += 1 for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1): if sqrt(sum_shortest_sides**2 + max_cuboid_size**2).is_integer(): num_cuboids += ( min(lowerCamelCase__ , sum_shortest_sides // 2) - max(1 , sum_shortest_sides - max_cuboid_size) + 1 ) return max_cuboid_size if __name__ == "__main__": print(f"{solution() = }")
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_funnel import FunnelTokenizer _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''} _lowercase = [ '''small''', '''small-base''', '''medium''', '''medium-base''', '''intermediate''', '''intermediate-base''', '''large''', '''large-base''', '''xlarge''', '''xlarge-base''', ] _lowercase = { '''vocab_file''': { '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''', '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''', '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''', '''funnel-transformer/medium-base''': ( '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt''' ), '''funnel-transformer/intermediate''': ( '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt''' ), '''funnel-transformer/intermediate-base''': ( '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt''' ), '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''', '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''', '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''', '''funnel-transformer/xlarge-base''': ( '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt''' ), }, '''tokenizer_file''': { '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''', '''funnel-transformer/small-base''': ( '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''', '''funnel-transformer/medium-base''': ( '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/intermediate''': ( '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json''' ), '''funnel-transformer/intermediate-base''': ( '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''', '''funnel-transformer/large-base''': ( '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''', '''funnel-transformer/xlarge-base''': ( '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json''' ), }, } _lowercase = {f"funnel-transformer/{name}": 512 for name in _model_names} _lowercase = {f"funnel-transformer/{name}": {'''do_lower_case''': True} for name in _model_names} class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_INIT_CONFIGURATION UpperCamelCase_ = FunnelTokenizer UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = 2 def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : List[str]="<unk>" ,lowerCAmelCase__ : int="<sep>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : List[str]="<cls>" ,lowerCAmelCase__ : Optional[int]="<mask>" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : List[Any]="##" ,**lowerCAmelCase__ : int ,) -> List[Any]: '''simple docstring''' super().__init__( lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,clean_text=lowerCAmelCase__ ,tokenize_chinese_chars=lowerCAmelCase__ ,strip_accents=lowerCAmelCase__ ,wordpieces_prefix=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() ) if ( normalizer_state.get("lowercase" ,lowerCAmelCase__ ) != do_lower_case or normalizer_state.get("strip_accents" ,lowerCAmelCase__ ) != strip_accents or normalizer_state.get("handle_chinese_chars" ,lowerCAmelCase__ ) != tokenize_chinese_chars ): lowerCAmelCase_ : Optional[int] = getattr(lowerCAmelCase__ ,normalizer_state.pop("type" ) ) lowerCAmelCase_ : List[Any] = do_lower_case lowerCAmelCase_ : List[str] = strip_accents lowerCAmelCase_ : Any = tokenize_chinese_chars lowerCAmelCase_ : List[Any] = normalizer_class(**lowerCAmelCase__ ) lowerCAmelCase_ : int = do_lower_case def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str=None ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id] if token_ids_a: output += token_ids_a + [self.sep_token_id] return output def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : str = [self.sep_token_id] lowerCAmelCase_ : Optional[Any] = [self.cls_token_id] if token_ids_a is None: return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ ) return tuple(lowerCAmelCase__ )
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def UpperCamelCase ( snake_case__ = 50): lowerCAmelCase_ : str = [1] * (length + 1) for row_length in range(length + 1): for tile_length in range(2 , 5): for tile_start in range(row_length - tile_length + 1): ways_number[row_length] += ways_number[ row_length - tile_start - tile_length ] return ways_number[length] if __name__ == "__main__": print(f"{solution() = }")
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# tests directory-specific settings - this file is run automatically # by pytest before any tests are run import doctest import sys import warnings from os.path import abspath, dirname, join import _pytest from transformers.testing_utils import HfDoctestModule, HfDocTestParser # allow having multiple repository checkouts and not needing to remember to rerun # 'pip install -e .[dev]' when switching between checkouts and running tests. _lowercase = abspath(join(dirname(__file__), '''src''')) sys.path.insert(1, git_repo_path) # silence FutureWarning warnings in tests since often we can't act on them until # they become normal warnings - i.e. the tests still need to test the current functionality warnings.simplefilter(action='''ignore''', category=FutureWarning) def UpperCamelCase ( snake_case__): config.addinivalue_line( "markers" , "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested") config.addinivalue_line( "markers" , "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested") config.addinivalue_line("markers" , "is_pipeline_test: mark test to run only when pipelines are tested") config.addinivalue_line("markers" , "is_staging_test: mark test to run only in the staging environment") config.addinivalue_line("markers" , "accelerate_tests: mark test that require accelerate") config.addinivalue_line("markers" , "tool_tests: mark the tool tests that are run on their specific schedule") def UpperCamelCase ( snake_case__): from transformers.testing_utils import pytest_addoption_shared pytest_addoption_shared(snake_case__) def UpperCamelCase ( snake_case__): from transformers.testing_utils import pytest_terminal_summary_main lowerCAmelCase_ : int = terminalreporter.config.getoption("--make-reports") if make_reports: pytest_terminal_summary_main(snake_case__ , id=snake_case__) def UpperCamelCase ( snake_case__ , snake_case__): # If no tests are collected, pytest exists with code 5, which makes the CI fail. if exitstatus == 5: lowerCAmelCase_ : List[Any] = 0 # Doctest custom flag to ignore output. _lowercase = doctest.register_optionflag('''IGNORE_RESULT''') _lowercase = doctest.OutputChecker class __snake_case ( snake_case__ ): """simple docstring""" def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Any: '''simple docstring''' if IGNORE_RESULT & optionflags: return True return OutputChecker.check_output(self ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) _lowercase = CustomOutputChecker _lowercase = HfDoctestModule _lowercase = HfDocTestParser
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available _lowercase = { '''configuration_squeezebert''': [ '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SqueezeBertConfig''', '''SqueezeBertOnnxConfig''', ], '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ['''SqueezeBertTokenizerFast'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''SqueezeBertForMaskedLM''', '''SqueezeBertForMultipleChoice''', '''SqueezeBertForQuestionAnswering''', '''SqueezeBertForSequenceClassification''', '''SqueezeBertForTokenClassification''', '''SqueezeBertModel''', '''SqueezeBertModule''', '''SqueezeBertPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_squeezebert import ( SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertOnnxConfig, ) from .tokenization_squeezebert import SqueezeBertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_squeezebert import ( SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, SqueezeBertForMaskedLM, SqueezeBertForMultipleChoice, SqueezeBertForQuestionAnswering, SqueezeBertForSequenceClassification, SqueezeBertForTokenClassification, SqueezeBertModel, SqueezeBertModule, SqueezeBertPreTrainedModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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from __future__ import annotations from collections.abc import Sequence from typing import Literal def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = list(snake_case__) lowerCAmelCase_ : Tuple = list(snake_case__) lowerCAmelCase_ : List[str] = 0 for i in range(len(snake_case__)): if lista[i] != lista[i]: count += 1 lowerCAmelCase_ : Dict = "_" if count > 1: return False else: return "".join(snake_case__) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Union[str, Any] = [] while True: lowerCAmelCase_ : Tuple = ["$"] * len(snake_case__) lowerCAmelCase_ : Tuple = [] for i in range(len(snake_case__)): for j in range(i + 1 , len(snake_case__)): lowerCAmelCase_ : Optional[int] = compare_string(binary[i] , binary[j]) if k is False: lowerCAmelCase_ : str = "*" lowerCAmelCase_ : Tuple = "*" temp.append("X") for i in range(len(snake_case__)): if checka[i] == "$": pi.append(binary[i]) if len(snake_case__) == 0: return pi lowerCAmelCase_ : List[Any] = list(set(snake_case__)) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = [] for minterm in minterms: lowerCAmelCase_ : Dict = "" for _ in range(snake_case__): lowerCAmelCase_ : Dict = str(minterm % 2) + string minterm //= 2 temp.append(snake_case__) return temp def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = list(snake_case__) lowerCAmelCase_ : Dict = list(snake_case__) lowerCAmelCase_ : Dict = 0 for i in range(len(snake_case__)): if lista[i] != lista[i]: count_n += 1 return count_n == count def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = [] lowerCAmelCase_ : Dict = [0] * len(snake_case__) for i in range(len(chart[0])): lowerCAmelCase_ : List[Any] = 0 lowerCAmelCase_ : int = -1 for j in range(len(snake_case__)): if chart[j][i] == 1: count += 1 lowerCAmelCase_ : Optional[int] = j if count == 1: lowerCAmelCase_ : Union[str, Any] = 1 for i in range(len(snake_case__)): if select[i] == 1: for j in range(len(chart[0])): if chart[i][j] == 1: for k in range(len(snake_case__)): lowerCAmelCase_ : Tuple = 0 temp.append(prime_implicants[i]) while True: lowerCAmelCase_ : Optional[Any] = 0 lowerCAmelCase_ : Dict = -1 lowerCAmelCase_ : Tuple = 0 for i in range(len(snake_case__)): lowerCAmelCase_ : Dict = chart[i].count(1) if count_n > max_n: lowerCAmelCase_ : Optional[int] = count_n lowerCAmelCase_ : Optional[Any] = i if max_n == 0: return temp temp.append(prime_implicants[rem]) for i in range(len(chart[0])): if chart[rem][i] == 1: for j in range(len(snake_case__)): lowerCAmelCase_ : Any = 0 def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : str = [[0 for x in range(len(snake_case__))] for x in range(len(snake_case__))] for i in range(len(snake_case__)): lowerCAmelCase_ : Optional[Any] = prime_implicants[i].count("_") for j in range(len(snake_case__)): if is_for_table(prime_implicants[i] , binary[j] , snake_case__): lowerCAmelCase_ : Dict = 1 return chart def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = int(input("Enter the no. of variables\n")) lowerCAmelCase_ : Tuple = [ float(snake_case__) for x in input( "Enter the decimal representation of Minterms 'Spaces Separated'\n").split() ] lowerCAmelCase_ : Any = decimal_to_binary(snake_case__ , snake_case__) lowerCAmelCase_ : Dict = check(snake_case__) print("Prime Implicants are:") print(snake_case__) lowerCAmelCase_ : int = prime_implicant_chart(snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = selection(snake_case__ , snake_case__) print("Essential Prime Implicants are:") print(snake_case__) if __name__ == "__main__": import doctest doctest.testmod() main()
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from .dependency_versions_table import deps from .utils.versions import require_version, require_version_core # define which module versions we always want to check at run time # (usually the ones defined in `install_requires` in setup.py) # # order specific notes: # - tqdm must be checked before tokenizers _lowercase = [ 'python', 'tqdm', 'regex', 'requests', 'packaging', 'filelock', 'numpy', 'tokenizers', 'huggingface-hub', 'safetensors', 'accelerate', 'pyyaml', ] for pkg in pkgs_to_check_at_runtime: if pkg in deps: if pkg == "tokenizers": # must be loaded here, or else tqdm check may fail from .utils import is_tokenizers_available if not is_tokenizers_available(): continue # not required, check version only if installed elif pkg == "accelerate": # must be loaded here, or else tqdm check may fail from .utils import is_accelerate_available # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of # Transformers with PyTorch if not is_accelerate_available(): continue # not required, check version only if installed require_version_core(deps[pkg]) else: raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py") def UpperCamelCase ( snake_case__ , snake_case__=None): require_version(deps[pkg] , snake_case__)
718
import logging import os from typing import Dict, List, Optional, Union import torch import torch.nn as nn from accelerate.utils.imports import ( is_abit_bnb_available, is_abit_bnb_available, is_bnb_available, ) from ..big_modeling import dispatch_model, init_empty_weights from .dataclasses import BnbQuantizationConfig from .modeling import ( find_tied_parameters, get_balanced_memory, infer_auto_device_map, load_checkpoint_in_model, offload_weight, set_module_tensor_to_device, ) if is_bnb_available(): import bitsandbytes as bnb from copy import deepcopy _lowercase = logging.getLogger(__name__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = False , ): lowerCAmelCase_ : List[Any] = bnb_quantization_config.load_in_abit lowerCAmelCase_ : Optional[Any] = bnb_quantization_config.load_in_abit if load_in_abit and not is_abit_bnb_available(): raise ImportError( "You have a version of `bitsandbytes` that is not compatible with 8bit quantization," " make sure you have the latest version of `bitsandbytes` installed.") if load_in_abit and not is_abit_bnb_available(): raise ValueError( "You have a version of `bitsandbytes` that is not compatible with 4bit quantization," "make sure you have the latest version of `bitsandbytes` installed.") lowerCAmelCase_ : List[str] = [] # custom device map if isinstance(snake_case__ , snake_case__) and len(device_map.keys()) > 1: lowerCAmelCase_ : Union[str, Any] = [key for key, value in device_map.items() if value in ["disk", "cpu"]] # We keep some modules such as the lm_head in their original dtype for numerical stability reasons if bnb_quantization_config.skip_modules is None: lowerCAmelCase_ : Union[str, Any] = get_keys_to_not_convert(snake_case__) # add cpu modules to skip modules only for 4-bit modules if load_in_abit: bnb_quantization_config.skip_modules.extend(snake_case__) lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules # We add the modules we want to keep in full precision if bnb_quantization_config.keep_in_fpaa_modules is None: lowerCAmelCase_ : Optional[int] = [] lowerCAmelCase_ : int = bnb_quantization_config.keep_in_fpaa_modules modules_to_not_convert.extend(snake_case__) # compatibility with peft lowerCAmelCase_ : Optional[int] = load_in_abit lowerCAmelCase_ : List[str] = load_in_abit lowerCAmelCase_ : Optional[int] = get_parameter_device(snake_case__) if model_device.type != "meta": # quantization of an already loaded model logger.warning( "It is not recommended to quantize a loaded model. " "The model should be instantiated under the `init_empty_weights` context manager.") lowerCAmelCase_ : Union[str, Any] = replace_with_bnb_layers(snake_case__ , snake_case__ , modules_to_not_convert=snake_case__) # convert param to the right dtype lowerCAmelCase_ : Any = bnb_quantization_config.torch_dtype for name, param in model.state_dict().items(): if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules): param.to(torch.floataa) if param.dtype != torch.floataa: lowerCAmelCase_ : Optional[int] = name.replace(".weight" , "").replace(".bias" , "") lowerCAmelCase_ : Optional[int] = getattr(snake_case__ , snake_case__ , snake_case__) if param is not None: param.to(torch.floataa) elif torch.is_floating_point(snake_case__): param.to(snake_case__) if model_device.type == "cuda": # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda model.cuda(torch.cuda.current_device()) torch.cuda.empty_cache() elif torch.cuda.is_available(): model.to(torch.cuda.current_device()) else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info( F'''The model device type is {model_device.type}. However, cuda is needed for quantization.''' "We move the model to cuda.") return model elif weights_location is None: raise RuntimeError( F'''`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} ''') else: with init_empty_weights(): lowerCAmelCase_ : str = replace_with_bnb_layers( snake_case__ , snake_case__ , modules_to_not_convert=snake_case__) lowerCAmelCase_ : Optional[int] = get_quantized_model_device_map( snake_case__ , snake_case__ , snake_case__ , max_memory=snake_case__ , no_split_module_classes=snake_case__ , ) if offload_state_dict is None and device_map is not None and "disk" in device_map.values(): lowerCAmelCase_ : Optional[Any] = True lowerCAmelCase_ : Optional[int] = any(x in list(device_map.values()) for x in ["cpu", "disk"]) load_checkpoint_in_model( snake_case__ , snake_case__ , snake_case__ , dtype=bnb_quantization_config.torch_dtype , offload_folder=snake_case__ , offload_state_dict=snake_case__ , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , ) return dispatch_model(snake_case__ , device_map=snake_case__ , offload_dir=snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=None): if device_map is None: if torch.cuda.is_available(): lowerCAmelCase_ : Any = {"": torch.cuda.current_device()} else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info("The device_map was not initialized." "Setting device_map to `{'':torch.cuda.current_device()}`.") if isinstance(snake_case__ , snake_case__): if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]: raise ValueError( "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or " "'sequential'.") lowerCAmelCase_ : Dict = {} special_dtypes.update( { name: bnb_quantization_config.torch_dtype for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.skip_modules) }) special_dtypes.update( { name: torch.floataa for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules) }) lowerCAmelCase_ : List[str] = {} lowerCAmelCase_ : Union[str, Any] = special_dtypes lowerCAmelCase_ : Union[str, Any] = no_split_module_classes lowerCAmelCase_ : Any = bnb_quantization_config.target_dtype # get max_memory for each device. if device_map != "sequential": lowerCAmelCase_ : Tuple = get_balanced_memory( snake_case__ , low_zero=(device_map == "balanced_low_0") , max_memory=snake_case__ , **snake_case__ , ) lowerCAmelCase_ : Tuple = max_memory lowerCAmelCase_ : Optional[Any] = infer_auto_device_map(snake_case__ , **snake_case__) if isinstance(snake_case__ , snake_case__): # check if don't have any quantized module on the cpu lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules lowerCAmelCase_ : List[Any] = { key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert } for device in ["cpu", "disk"]: if device in device_map_without_some_modules.values(): if bnb_quantization_config.load_in_abit: raise ValueError( "\n Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n these modules in `torch_dtype`, you need to pass a custom `device_map` to\n `load_and_quantize_model`. Check\n https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n for more details.\n ") else: logger.info( "Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit") del device_map_without_some_modules return device_map def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None): if modules_to_not_convert is None: lowerCAmelCase_ : List[str] = [] lowerCAmelCase_ , lowerCAmelCase_ : Tuple = _replace_with_bnb_layers( snake_case__ , snake_case__ , snake_case__ , snake_case__) if not has_been_replaced: logger.warning( "You are loading your model in 8bit or 4bit but no linear modules were found in your model." " this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers." " Please double check your model architecture, or submit an issue on github if you think this is" " a bug.") return model def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , ): lowerCAmelCase_ : str = False for name, module in model.named_children(): if current_key_name is None: lowerCAmelCase_ : Optional[int] = [] current_key_name.append(snake_case__) if isinstance(snake_case__ , nn.Linear) and name not in modules_to_not_convert: # Check if the current key is not in the `modules_to_not_convert` lowerCAmelCase_ : Optional[int] = ".".join(snake_case__) lowerCAmelCase_ : List[str] = True for key in modules_to_not_convert: if ( (key in current_key_name_str) and (key + "." in current_key_name_str) ) or key == current_key_name_str: lowerCAmelCase_ : List[Any] = False break if proceed: # Load bnb module with empty weight and replace ``nn.Linear` module if bnb_quantization_config.load_in_abit: lowerCAmelCase_ : Tuple = bnb.nn.LinearabitLt( module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=snake_case__ , threshold=bnb_quantization_config.llm_inta_threshold , ) elif bnb_quantization_config.load_in_abit: lowerCAmelCase_ : Dict = bnb.nn.Linearabit( module.in_features , module.out_features , module.bias is not None , bnb_quantization_config.bnb_abit_compute_dtype , compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant , quant_type=bnb_quantization_config.bnb_abit_quant_type , ) else: raise ValueError("load_in_8bit and load_in_4bit can't be both False") lowerCAmelCase_ : List[str] = module.weight.data if module.bias is not None: lowerCAmelCase_ : Any = module.bias.data bnb_module.requires_grad_(snake_case__) setattr(snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = True if len(list(module.children())) > 0: lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = _replace_with_bnb_layers( snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : Optional[int] = has_been_replaced | _has_been_replaced # Remove the last key for recursion current_key_name.pop(-1) return model, has_been_replaced def UpperCamelCase ( snake_case__): # Create a copy of the model with init_empty_weights(): lowerCAmelCase_ : List[Any] = deepcopy(snake_case__) # this has 0 cost since it is done inside `init_empty_weights` context manager` lowerCAmelCase_ : Dict = find_tied_parameters(snake_case__) # For compatibility with Accelerate < 0.18 if isinstance(snake_case__ , snake_case__): lowerCAmelCase_ : List[str] = sum(list(tied_params.values()) , []) + list(tied_params.keys()) else: lowerCAmelCase_ : Optional[Any] = sum(snake_case__ , []) lowerCAmelCase_ : List[Any] = len(snake_case__) > 0 # Check if it is a base model lowerCAmelCase_ : List[str] = False if hasattr(snake_case__ , "base_model_prefix"): lowerCAmelCase_ : Tuple = not hasattr(snake_case__ , model.base_model_prefix) # Ignore this for base models (BertModel, GPT2Model, etc.) if (not has_tied_params) and is_base_model: return [] # otherwise they have an attached head lowerCAmelCase_ : Union[str, Any] = list(model.named_children()) lowerCAmelCase_ : Optional[int] = [list_modules[-1][0]] # add last module together with tied weights lowerCAmelCase_ : Any = set(snake_case__) - set(snake_case__) lowerCAmelCase_ : Tuple = list(set(snake_case__)) + list(snake_case__) # remove ".weight" from the keys lowerCAmelCase_ : List[str] = [".weight", ".bias"] lowerCAmelCase_ : Tuple = [] for name in list_untouched: for name_to_remove in names_to_remove: if name_to_remove in name: lowerCAmelCase_ : str = name.replace(snake_case__ , "") filtered_module_names.append(snake_case__) return filtered_module_names def UpperCamelCase ( snake_case__): for m in model.modules(): if isinstance(snake_case__ , bnb.nn.Linearabit): return True return False def UpperCamelCase ( snake_case__): return next(parameter.parameters()).device def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): # if it is not quantized, we quantize and offload the quantized weights and the SCB stats if fpaa_statistics is None: set_module_tensor_to_device(snake_case__ , snake_case__ , 0 , dtype=snake_case__ , value=snake_case__) lowerCAmelCase_ : str = param_name lowerCAmelCase_ : Tuple = model if "." in tensor_name: lowerCAmelCase_ : Dict = tensor_name.split(".") for split in splits[:-1]: lowerCAmelCase_ : Any = getattr(snake_case__ , snake_case__) if new_module is None: raise ValueError(F'''{module} has no attribute {split}.''') lowerCAmelCase_ : Union[str, Any] = new_module lowerCAmelCase_ : Any = splits[-1] # offload weights lowerCAmelCase_ : List[Any] = False offload_weight(module._parameters[tensor_name] , snake_case__ , snake_case__ , index=snake_case__) if hasattr(module._parameters[tensor_name] , "SCB"): offload_weight( module._parameters[tensor_name].SCB , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__ , ) else: offload_weight(snake_case__ , snake_case__ , snake_case__ , index=snake_case__) offload_weight(snake_case__ , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__) set_module_tensor_to_device(snake_case__ , snake_case__ , "meta" , dtype=snake_case__ , value=torch.empty(*param.size()))
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import copy from ...configuration_utils import PretrainedConfig from ...utils import logging from ..auto import CONFIG_MAPPING _lowercase = logging.get_logger(__name__) _lowercase = { """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""", } class __snake_case ( SCREAMING_SNAKE_CASE__ ): """simple docstring""" UpperCamelCase_ = 'deta' UpperCamelCase_ = { 'hidden_size': 'd_model', 'num_attention_heads': 'encoder_attention_heads', } def __init__( self : Optional[Any] ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : str=9_00 ,lowerCAmelCase__ : Any=20_48 ,lowerCAmelCase__ : List[str]=6 ,lowerCAmelCase__ : Optional[Any]=20_48 ,lowerCAmelCase__ : Union[str, Any]=8 ,lowerCAmelCase__ : Optional[Any]=6 ,lowerCAmelCase__ : Dict=10_24 ,lowerCAmelCase__ : Union[str, Any]=8 ,lowerCAmelCase__ : List[Any]=0.0 ,lowerCAmelCase__ : Any=True ,lowerCAmelCase__ : Optional[int]="relu" ,lowerCAmelCase__ : Any=2_56 ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : List[Any]=0.0 ,lowerCAmelCase__ : Any=0.0 ,lowerCAmelCase__ : str=0.02 ,lowerCAmelCase__ : Dict=1.0 ,lowerCAmelCase__ : int=True ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : List[str]="sine" ,lowerCAmelCase__ : str=5 ,lowerCAmelCase__ : Dict=4 ,lowerCAmelCase__ : str=4 ,lowerCAmelCase__ : Dict=True ,lowerCAmelCase__ : Union[str, Any]=3_00 ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Tuple=1 ,lowerCAmelCase__ : List[str]=5 ,lowerCAmelCase__ : Any=2 ,lowerCAmelCase__ : str=1 ,lowerCAmelCase__ : List[str]=1 ,lowerCAmelCase__ : List[Any]=5 ,lowerCAmelCase__ : str=2 ,lowerCAmelCase__ : Any=0.1 ,lowerCAmelCase__ : Union[str, Any]=0.25 ,**lowerCAmelCase__ : Any ,) -> int: '''simple docstring''' if backbone_config is None: logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." ) lowerCAmelCase_ : Optional[int] = CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] ) else: if isinstance(snake_case__ ,snake_case__ ): lowerCAmelCase_ : Union[str, Any] = backbone_config.pop("model_type" ) lowerCAmelCase_ : Optional[int] = CONFIG_MAPPING[backbone_model_type] lowerCAmelCase_ : Tuple = config_class.from_dict(snake_case__ ) lowerCAmelCase_ : List[str] = backbone_config lowerCAmelCase_ : List[str] = num_queries lowerCAmelCase_ : Optional[int] = max_position_embeddings lowerCAmelCase_ : Union[str, Any] = d_model lowerCAmelCase_ : Tuple = encoder_ffn_dim lowerCAmelCase_ : List[Any] = encoder_layers lowerCAmelCase_ : Dict = encoder_attention_heads lowerCAmelCase_ : List[Any] = decoder_ffn_dim lowerCAmelCase_ : Any = decoder_layers lowerCAmelCase_ : Dict = decoder_attention_heads lowerCAmelCase_ : Optional[int] = dropout lowerCAmelCase_ : List[Any] = attention_dropout lowerCAmelCase_ : List[Any] = activation_dropout lowerCAmelCase_ : int = activation_function lowerCAmelCase_ : Optional[Any] = init_std lowerCAmelCase_ : str = init_xavier_std lowerCAmelCase_ : Tuple = encoder_layerdrop lowerCAmelCase_ : Union[str, Any] = auxiliary_loss lowerCAmelCase_ : Any = position_embedding_type # deformable attributes lowerCAmelCase_ : int = num_feature_levels lowerCAmelCase_ : Union[str, Any] = encoder_n_points lowerCAmelCase_ : str = decoder_n_points lowerCAmelCase_ : str = two_stage lowerCAmelCase_ : Optional[int] = two_stage_num_proposals lowerCAmelCase_ : Any = with_box_refine lowerCAmelCase_ : int = assign_first_stage if two_stage is True and with_box_refine is False: raise ValueError("If two_stage is True, with_box_refine must be True." ) # Hungarian matcher lowerCAmelCase_ : int = class_cost lowerCAmelCase_ : Any = bbox_cost lowerCAmelCase_ : int = giou_cost # Loss coefficients lowerCAmelCase_ : List[Any] = mask_loss_coefficient lowerCAmelCase_ : List[str] = dice_loss_coefficient lowerCAmelCase_ : Optional[int] = bbox_loss_coefficient lowerCAmelCase_ : Any = giou_loss_coefficient lowerCAmelCase_ : Any = eos_coefficient lowerCAmelCase_ : List[str] = focal_alpha super().__init__(is_encoder_decoder=snake_case__ ,**snake_case__ ) @property def UpperCAmelCase_ ( self : Optional[Any] ) -> Any: '''simple docstring''' return self.encoder_attention_heads @property def UpperCAmelCase_ ( self : int ) -> Dict: '''simple docstring''' return self.d_model def UpperCAmelCase_ ( self : int ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : Dict = copy.deepcopy(self.__dict__ ) lowerCAmelCase_ : int = self.backbone_config.to_dict() lowerCAmelCase_ : List[str] = self.__class__.model_type return output
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import copy from typing import Any, Dict, List, Optional, Union import numpy as np import torch from ...audio_utils import mel_filter_bank, spectrogram, window_function from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import TensorType, logging _lowercase = logging.get_logger(__name__) class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = ['input_features', 'is_longer'] def __init__( self : Optional[int] ,lowerCAmelCase__ : List[Any]=64 ,lowerCAmelCase__ : Any=4_80_00 ,lowerCAmelCase__ : Optional[Any]=4_80 ,lowerCAmelCase__ : List[str]=10 ,lowerCAmelCase__ : List[Any]=10_24 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : float = 0 ,lowerCAmelCase__ : float = 1_40_00 ,lowerCAmelCase__ : int = None ,lowerCAmelCase__ : str = "fusion" ,lowerCAmelCase__ : str = "repeatpad" ,**lowerCAmelCase__ : Union[str, Any] ,) -> Union[str, Any]: '''simple docstring''' super().__init__( feature_size=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,padding_value=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : Optional[Any] = top_db lowerCAmelCase_ : str = truncation lowerCAmelCase_ : Tuple = padding lowerCAmelCase_ : str = fft_window_size lowerCAmelCase_ : Dict = (fft_window_size >> 1) + 1 lowerCAmelCase_ : Dict = hop_length lowerCAmelCase_ : Any = max_length_s lowerCAmelCase_ : int = max_length_s * sampling_rate lowerCAmelCase_ : Optional[int] = sampling_rate lowerCAmelCase_ : int = frequency_min lowerCAmelCase_ : Optional[Any] = frequency_max lowerCAmelCase_ : List[Any] = mel_filter_bank( num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm=lowerCAmelCase__ ,mel_scale="htk" ,) lowerCAmelCase_ : List[Any] = mel_filter_bank( num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm="slaney" ,mel_scale="slaney" ,) def UpperCAmelCase_ ( self : Dict ) -> Dict[str, Any]: '''simple docstring''' lowerCAmelCase_ : int = copy.deepcopy(self.__dict__ ) lowerCAmelCase_ : Optional[int] = self.__class__.__name__ if "mel_filters" in output: del output["mel_filters"] if "mel_filters_slaney" in output: del output["mel_filters_slaney"] return output def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Optional[np.array] = None ) -> np.ndarray: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = spectrogram( lowerCAmelCase__ ,window_function(self.fft_window_size ,"hann" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=lowerCAmelCase__ ,log_mel="dB" ,) return log_mel_spectrogram.T def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Tuple ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Tuple = np.array_split(list(range(0 ,total_frames - chunk_frames + 1 ) ) ,3 ) if len(ranges[1] ) == 0: # if the audio is too short, we just use the first chunk lowerCAmelCase_ : List[Any] = [0] if len(ranges[2] ) == 0: # if the audio is too short, we just use the first chunk lowerCAmelCase_ : List[Any] = [0] # randomly choose index for each part lowerCAmelCase_ : str = np.random.choice(ranges[0] ) lowerCAmelCase_ : Optional[Any] = np.random.choice(ranges[1] ) lowerCAmelCase_ : Any = np.random.choice(ranges[2] ) lowerCAmelCase_ : str = mel[idx_front : idx_front + chunk_frames, :] lowerCAmelCase_ : Dict = mel[idx_middle : idx_middle + chunk_frames, :] lowerCAmelCase_ : Optional[Any] = mel[idx_back : idx_back + chunk_frames, :] lowerCAmelCase_ : List[str] = torch.tensor(mel[None, None, :] ) lowerCAmelCase_ : List[Any] = torch.nn.functional.interpolate( lowerCAmelCase__ ,size=[chunk_frames, 64] ,mode="bilinear" ,align_corners=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = mel_shrink[0][0].numpy() lowerCAmelCase_ : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 ) return mel_fusion def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : int ) -> np.array: '''simple docstring''' if waveform.shape[0] > max_length: if truncation == "rand_trunc": lowerCAmelCase_ : List[Any] = True # random crop to max_length (for compatibility) -> this should be handled by self.pad lowerCAmelCase_ : str = len(lowerCAmelCase__ ) - max_length lowerCAmelCase_ : Any = np.random.randint(0 ,overflow + 1 ) lowerCAmelCase_ : Dict = waveform[idx : idx + max_length] lowerCAmelCase_ : List[str] = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :] elif truncation == "fusion": lowerCAmelCase_ : Tuple = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters ) lowerCAmelCase_ : str = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed lowerCAmelCase_ : List[str] = mel.shape[0] if chunk_frames == total_frames: # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length. # In this case, we just use the whole audio. lowerCAmelCase_ : Dict = np.stack([mel, mel, mel, mel] ,axis=0 ) lowerCAmelCase_ : int = False else: lowerCAmelCase_ : str = self._random_mel_fusion(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Any = True else: raise NotImplementedError(f'''data_truncating {truncation} not implemented''' ) else: lowerCAmelCase_ : Dict = False # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding if waveform.shape[0] < max_length: if padding == "repeat": lowerCAmelCase_ : List[Any] = int(max_length / len(lowerCAmelCase__ ) ) lowerCAmelCase_ : int = np.stack(np.tile(lowerCAmelCase__ ,n_repeat + 1 ) )[:max_length] if padding == "repeatpad": lowerCAmelCase_ : Optional[Any] = int(max_length / len(lowerCAmelCase__ ) ) lowerCAmelCase_ : Tuple = np.stack(np.tile(lowerCAmelCase__ ,lowerCAmelCase__ ) ) lowerCAmelCase_ : List[Any] = np.pad(lowerCAmelCase__ ,(0, max_length - waveform.shape[0]) ,mode="constant" ,constant_values=0 ) if truncation == "fusion": lowerCAmelCase_ : int = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters ) lowerCAmelCase_ : Tuple = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 ) else: lowerCAmelCase_ : str = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :] return input_mel, longer def __call__( self : int ,lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCAmelCase__ : str = None ,lowerCAmelCase__ : Optional[str] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[Union[str, TensorType]] = None ,**lowerCAmelCase__ : List[Any] ,) -> BatchFeature: '''simple docstring''' lowerCAmelCase_ : List[str] = truncation if truncation is not None else self.truncation lowerCAmelCase_ : List[Any] = padding if padding else self.padding if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a''' f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input''' f''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' ) else: logger.warning( "It is strongly recommended to pass the `sampling_rate` argument to this function. " "Failing to do so can result in silent errors that might be hard to debug." ) lowerCAmelCase_ : Dict = isinstance(lowerCAmelCase__ ,np.ndarray ) and len(raw_speech.shape ) > 1 if is_batched_numpy and len(raw_speech.shape ) > 2: raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' ) lowerCAmelCase_ : Dict = is_batched_numpy or ( isinstance(lowerCAmelCase__ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) )) ) if is_batched: lowerCAmelCase_ : List[str] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for speech in raw_speech] elif not is_batched and not isinstance(lowerCAmelCase__ ,np.ndarray ): lowerCAmelCase_ : Tuple = np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) elif isinstance(lowerCAmelCase__ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ): lowerCAmelCase_ : Any = raw_speech.astype(np.floataa ) # always return batch if not is_batched: lowerCAmelCase_ : Any = [np.asarray(lowerCAmelCase__ )] # convert to mel spectrogram, truncate and pad if needed. lowerCAmelCase_ : Optional[Any] = [ self._get_input_mel(lowerCAmelCase__ ,max_length if max_length else self.nb_max_samples ,lowerCAmelCase__ ,lowerCAmelCase__ ) for waveform in raw_speech ] lowerCAmelCase_ : str = [] lowerCAmelCase_ : str = [] for mel, longer in padded_inputs: input_mel.append(lowerCAmelCase__ ) is_longer.append(lowerCAmelCase__ ) if truncation == "fusion" and sum(lowerCAmelCase__ ) == 0: # if no audio is longer than 10s, then randomly select one audio to be longer lowerCAmelCase_ : Any = np.random.randint(0 ,len(lowerCAmelCase__ ) ) lowerCAmelCase_ : Dict = True if isinstance(input_mel[0] ,lowerCAmelCase__ ): lowerCAmelCase_ : Optional[int] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for feature in input_mel] # is_longer is a list of bool lowerCAmelCase_ : List[Any] = [[longer] for longer in is_longer] lowerCAmelCase_ : Optional[Any] = {"input_features": input_mel, "is_longer": is_longer} lowerCAmelCase_ : Dict = BatchFeature(lowerCAmelCase__ ) if return_tensors is not None: lowerCAmelCase_ : List[str] = input_features.convert_to_tensors(lowerCAmelCase__ ) return input_features
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import gc import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, StableDiffusionPanoramaPipeline, UNetaDConditionModel, ) from diffusers.utils import slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() @skip_mps class __snake_case ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = StableDiffusionPanoramaPipeline UpperCamelCase_ = TEXT_TO_IMAGE_PARAMS UpperCamelCase_ = TEXT_TO_IMAGE_BATCH_PARAMS UpperCamelCase_ = TEXT_TO_IMAGE_IMAGE_PARAMS UpperCamelCase_ = TEXT_TO_IMAGE_IMAGE_PARAMS def UpperCAmelCase_ ( self : int ) -> List[str]: '''simple docstring''' torch.manual_seed(0 ) lowerCAmelCase_ : int = UNetaDConditionModel( block_out_channels=(32, 64) ,layers_per_block=1 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,cross_attention_dim=32 ,) lowerCAmelCase_ : int = DDIMScheduler() torch.manual_seed(0 ) lowerCAmelCase_ : List[Any] = AutoencoderKL( block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,latent_channels=4 ,) torch.manual_seed(0 ) lowerCAmelCase_ : Any = CLIPTextConfig( bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1e-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=10_00 ,) lowerCAmelCase_ : Any = CLIPTextModel(SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Union[str, Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" ) lowerCAmelCase_ : int = { "unet": unet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "safety_checker": None, "feature_extractor": None, } return components def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[Any]=0 ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : str = torch.manual_seed(SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : List[str] = { "prompt": "a photo of the dolomites", "generator": generator, # Setting height and width to None to prevent OOMs on CPU. "height": None, "width": None, "num_inference_steps": 1, "guidance_scale": 6.0, "output_type": "numpy", } return inputs def UpperCAmelCase_ ( self : str ) -> int: '''simple docstring''' lowerCAmelCase_ : Any = "cpu" # ensure determinism for the device-dependent torch.Generator lowerCAmelCase_ : int = self.get_dummy_components() lowerCAmelCase_ : List[Any] = StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Any = sd_pipe.to(SCREAMING_SNAKE_CASE_ ) sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : str = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Dict = sd_pipe(**SCREAMING_SNAKE_CASE_ ).images lowerCAmelCase_ : Tuple = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) lowerCAmelCase_ : Union[str, Any] = np.array([0.6_186, 0.5_374, 0.4_915, 0.4_135, 0.4_114, 0.4_563, 0.5_128, 0.4_977, 0.4_757] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 def UpperCAmelCase_ ( self : Union[str, Any] ) -> Dict: '''simple docstring''' super().test_inference_batch_consistent(batch_sizes=[1, 2] ) def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]: '''simple docstring''' super().test_inference_batch_single_identical(batch_size=2 ,expected_max_diff=3.2_5e-3 ) def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Dict = "cpu" # ensure determinism for the device-dependent torch.Generator lowerCAmelCase_ : Any = self.get_dummy_components() lowerCAmelCase_ : Optional[Any] = StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Dict = sd_pipe.to(SCREAMING_SNAKE_CASE_ ) sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Dict = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Optional[Any] = "french fries" lowerCAmelCase_ : Tuple = sd_pipe(**SCREAMING_SNAKE_CASE_ ,negative_prompt=SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Optional[int] = output.images lowerCAmelCase_ : int = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) lowerCAmelCase_ : int = np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 def UpperCAmelCase_ ( self : int ) -> str: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = "cpu" # ensure determinism for the device-dependent torch.Generator lowerCAmelCase_ : int = self.get_dummy_components() lowerCAmelCase_ : str = StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : int = sd_pipe.to(SCREAMING_SNAKE_CASE_ ) sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : List[Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : int = sd_pipe(**SCREAMING_SNAKE_CASE_ ,view_batch_size=2 ) lowerCAmelCase_ : str = output.images lowerCAmelCase_ : Dict = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) lowerCAmelCase_ : Any = np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 def UpperCAmelCase_ ( self : int ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Tuple = "cpu" # ensure determinism for the device-dependent torch.Generator lowerCAmelCase_ : str = self.get_dummy_components() lowerCAmelCase_ : Any = EulerAncestralDiscreteScheduler( beta_start=0.00_085 ,beta_end=0.012 ,beta_schedule="scaled_linear" ) lowerCAmelCase_ : Any = StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : int = sd_pipe.to(SCREAMING_SNAKE_CASE_ ) sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : List[str] = sd_pipe(**SCREAMING_SNAKE_CASE_ ).images lowerCAmelCase_ : List[Any] = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) lowerCAmelCase_ : Dict = np.array([0.4_024, 0.6_510, 0.4_901, 0.5_378, 0.5_813, 0.5_622, 0.4_795, 0.4_467, 0.4_952] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 def UpperCAmelCase_ ( self : Optional[int] ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Any = "cpu" # ensure determinism for the device-dependent torch.Generator lowerCAmelCase_ : Union[str, Any] = self.get_dummy_components() lowerCAmelCase_ : Dict = PNDMScheduler( beta_start=0.00_085 ,beta_end=0.012 ,beta_schedule="scaled_linear" ,skip_prk_steps=SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Union[str, Any] = StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Dict = sd_pipe.to(SCREAMING_SNAKE_CASE_ ) sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : int = sd_pipe(**SCREAMING_SNAKE_CASE_ ).images lowerCAmelCase_ : Any = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) lowerCAmelCase_ : str = np.array([0.6_391, 0.6_291, 0.4_861, 0.5_134, 0.5_552, 0.4_578, 0.5_032, 0.5_023, 0.4_539] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 @slow @require_torch_gpu class __snake_case ( unittest.TestCase ): """simple docstring""" def UpperCAmelCase_ ( self : List[Any] ) -> Tuple: '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : str=0 ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : str = torch.manual_seed(SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Optional[int] = { "prompt": "a photo of the dolomites", "generator": generator, "num_inference_steps": 3, "guidance_scale": 7.5, "output_type": "numpy", } return inputs def UpperCAmelCase_ ( self : Tuple ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : List[Any] = "stabilityai/stable-diffusion-2-base" lowerCAmelCase_ : List[str] = DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_ ,subfolder="scheduler" ) lowerCAmelCase_ : Any = StableDiffusionPanoramaPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ ,scheduler=SCREAMING_SNAKE_CASE_ ,safety_checker=SCREAMING_SNAKE_CASE_ ) pipe.to(SCREAMING_SNAKE_CASE_ ) pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) pipe.enable_attention_slicing() lowerCAmelCase_ : int = self.get_inputs() lowerCAmelCase_ : Union[str, Any] = pipe(**SCREAMING_SNAKE_CASE_ ).images lowerCAmelCase_ : Tuple = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 5_12, 20_48, 3) lowerCAmelCase_ : int = np.array( [ 0.36_968_392, 0.27_025_372, 0.32_446_766, 0.28_379_387, 0.36_363_274, 0.30_733_347, 0.27_100_027, 0.27_054_125, 0.25_536_096, ] ) assert np.abs(expected_slice - image_slice ).max() < 1e-2 def UpperCAmelCase_ ( self : int ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = StableDiffusionPanoramaPipeline.from_pretrained( "stabilityai/stable-diffusion-2-base" ,safety_checker=SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : List[Any] = LMSDiscreteScheduler.from_config(pipe.scheduler.config ) pipe.to(SCREAMING_SNAKE_CASE_ ) pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) pipe.enable_attention_slicing() lowerCAmelCase_ : Tuple = self.get_inputs() lowerCAmelCase_ : Optional[Any] = pipe(**SCREAMING_SNAKE_CASE_ ).images lowerCAmelCase_ : Optional[Any] = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 5_12, 20_48, 3) lowerCAmelCase_ : Dict = np.array( [ [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ] ] ) assert np.abs(expected_slice - image_slice ).max() < 1e-3 def UpperCAmelCase_ ( self : List[Any] ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : Dict = 0 def callback_fn(lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Any ) -> None: lowerCAmelCase_ : Tuple = True nonlocal number_of_steps number_of_steps += 1 if step == 1: lowerCAmelCase_ : Union[str, Any] = latents.detach().cpu().numpy() assert latents.shape == (1, 4, 64, 2_56) lowerCAmelCase_ : Optional[int] = latents[0, -3:, -3:, -1] lowerCAmelCase_ : int = np.array( [ 0.18_681_869, 0.33_907_816, 0.5_361_276, 0.14_432_865, -0.02_856_611, -0.73_941_123, 0.23_397_987, 0.47_322_682, -0.37_823_164, ] ) assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2 elif step == 2: lowerCAmelCase_ : Optional[Any] = latents.detach().cpu().numpy() assert latents.shape == (1, 4, 64, 2_56) lowerCAmelCase_ : List[Any] = latents[0, -3:, -3:, -1] lowerCAmelCase_ : Dict = np.array( [ 0.18_539_645, 0.33_987_248, 0.5_378_559, 0.14_437_142, -0.02_455_261, -0.7_338_317, 0.23_990_755, 0.47_356_272, -0.3_786_505, ] ) assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2 lowerCAmelCase_ : Union[str, Any] = False lowerCAmelCase_ : List[str] = "stabilityai/stable-diffusion-2-base" lowerCAmelCase_ : Any = DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_ ,subfolder="scheduler" ) lowerCAmelCase_ : Any = StableDiffusionPanoramaPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ ,scheduler=SCREAMING_SNAKE_CASE_ ,safety_checker=SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Optional[Any] = pipe.to(SCREAMING_SNAKE_CASE_ ) pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) pipe.enable_attention_slicing() lowerCAmelCase_ : int = self.get_inputs() pipe(**SCREAMING_SNAKE_CASE_ ,callback=SCREAMING_SNAKE_CASE_ ,callback_steps=1 ) assert callback_fn.has_been_called assert number_of_steps == 3 def UpperCAmelCase_ ( self : int ) -> Any: '''simple docstring''' torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() torch.cuda.reset_peak_memory_stats() lowerCAmelCase_ : Union[str, Any] = "stabilityai/stable-diffusion-2-base" lowerCAmelCase_ : Dict = DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_ ,subfolder="scheduler" ) lowerCAmelCase_ : Any = StableDiffusionPanoramaPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ ,scheduler=SCREAMING_SNAKE_CASE_ ,safety_checker=SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : List[Any] = pipe.to(SCREAMING_SNAKE_CASE_ ) pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) pipe.enable_attention_slicing(1 ) pipe.enable_sequential_cpu_offload() lowerCAmelCase_ : Any = self.get_inputs() lowerCAmelCase_ : str = pipe(**SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Optional[Any] = torch.cuda.max_memory_allocated() # make sure that less than 5.2 GB is allocated assert mem_bytes < 5.5 * 10**9
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from multiprocessing import Lock, Pipe, Process # lock used to ensure that two processes do not access a pipe at the same time _lowercase = Lock() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): global process_lock # we perform n swaps since after n swaps we know we are sorted # we *could* stop early if we are sorted already, but it takes as long to # find out we are sorted as it does to sort the list with this algorithm for i in range(0 , 10): if (i + position) % 2 == 0 and r_send is not None: # send your value to your right neighbor process_lock.acquire() r_send[1].send(snake_case__) process_lock.release() # receive your right neighbor's value process_lock.acquire() lowerCAmelCase_ : Optional[Any] = rr_cv[0].recv() process_lock.release() # take the lower value since you are on the left lowerCAmelCase_ : Any = min(snake_case__ , snake_case__) elif (i + position) % 2 != 0 and l_send is not None: # send your value to your left neighbor process_lock.acquire() l_send[1].send(snake_case__) process_lock.release() # receive your left neighbor's value process_lock.acquire() lowerCAmelCase_ : str = lr_cv[0].recv() process_lock.release() # take the higher value since you are on the right lowerCAmelCase_ : Dict = max(snake_case__ , snake_case__) # after all swaps are performed, send the values back to main result_pipe[1].send(snake_case__) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Union[str, Any] = [] lowerCAmelCase_ : int = [] # initialize the list of pipes where the values will be retrieved for _ in arr: result_pipe.append(Pipe()) # creates the processes # the first and last process only have one neighbor so they are made outside # of the loop lowerCAmelCase_ : Tuple = Pipe() lowerCAmelCase_ : Optional[int] = Pipe() process_array_.append( Process( target=snake_case__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , )) lowerCAmelCase_ : int = temp_rs lowerCAmelCase_ : List[Any] = temp_rr for i in range(1 , len(snake_case__) - 1): lowerCAmelCase_ : Dict = Pipe() lowerCAmelCase_ : List[str] = Pipe() process_array_.append( Process( target=snake_case__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , )) lowerCAmelCase_ : Dict = temp_rs lowerCAmelCase_ : Optional[Any] = temp_rr process_array_.append( Process( target=snake_case__ , args=( len(snake_case__) - 1, arr[len(snake_case__) - 1], temp_ls, None, temp_lr, None, result_pipe[len(snake_case__) - 1], ) , )) # start the processes for p in process_array_: p.start() # wait for the processes to end and write their values to the list for p in range(0 , len(snake_case__)): lowerCAmelCase_ : Union[str, Any] = result_pipe[p][0].recv() process_array_[p].join() return arr def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = list(range(10 , 0 , -1)) print("Initial List") print(*snake_case__) lowerCAmelCase_ : Tuple = odd_even_transposition(snake_case__) print("Sorted List\n") print(*snake_case__) if __name__ == "__main__": main()
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import unittest import numpy as np import torch from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device enable_full_determinism() class __snake_case ( unittest.TestCase ): """simple docstring""" @property def UpperCAmelCase_ ( self : List[str] ) -> List[str]: '''simple docstring''' torch.manual_seed(0 ) lowerCAmelCase_ : List[str] = UNetaDModel( block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=("DownBlock2D", "AttnDownBlock2D") ,up_block_types=("AttnUpBlock2D", "UpBlock2D") ,) return model def UpperCAmelCase_ ( self : str ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = self.dummy_uncond_unet lowerCAmelCase_ : Optional[int] = KarrasVeScheduler() lowerCAmelCase_ : Optional[int] = KarrasVePipeline(unet=snake_case_ ,scheduler=snake_case_ ) pipe.to(snake_case_ ) pipe.set_progress_bar_config(disable=snake_case_ ) lowerCAmelCase_ : Dict = torch.manual_seed(0 ) lowerCAmelCase_ : Dict = pipe(num_inference_steps=2 ,generator=snake_case_ ,output_type="numpy" ).images lowerCAmelCase_ : Union[str, Any] = torch.manual_seed(0 ) lowerCAmelCase_ : List[str] = pipe(num_inference_steps=2 ,generator=snake_case_ ,output_type="numpy" ,return_dict=snake_case_ )[0] lowerCAmelCase_ : int = image[0, -3:, -3:, -1] lowerCAmelCase_ : List[str] = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 32, 32, 3) lowerCAmelCase_ : Union[str, Any] = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2 @slow @require_torch class __snake_case ( unittest.TestCase ): """simple docstring""" def UpperCAmelCase_ ( self : Optional[Any] ) -> str: '''simple docstring''' lowerCAmelCase_ : List[Any] = "google/ncsnpp-celebahq-256" lowerCAmelCase_ : Tuple = UNetaDModel.from_pretrained(snake_case_ ) lowerCAmelCase_ : Tuple = KarrasVeScheduler() lowerCAmelCase_ : Optional[int] = KarrasVePipeline(unet=snake_case_ ,scheduler=snake_case_ ) pipe.to(snake_case_ ) pipe.set_progress_bar_config(disable=snake_case_ ) lowerCAmelCase_ : Union[str, Any] = torch.manual_seed(0 ) lowerCAmelCase_ : List[str] = pipe(num_inference_steps=20 ,generator=snake_case_ ,output_type="numpy" ).images lowerCAmelCase_ : List[Any] = image[0, -3:, -3:, -1] assert image.shape == (1, 2_56, 2_56, 3) lowerCAmelCase_ : Dict = np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
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from typing import Any def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ): _validation( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) # Creates data structures and fill initial step lowerCAmelCase_ : dict = {} lowerCAmelCase_ : dict = {} for state in states_space: lowerCAmelCase_ : List[Any] = observations_space[0] lowerCAmelCase_ : int = ( initial_probabilities[state] * emission_probabilities[state][observation] ) lowerCAmelCase_ : Dict = None # Fills the data structure with the probabilities of # different transitions and pointers to previous states for o in range(1 , len(snake_case__)): lowerCAmelCase_ : List[Any] = observations_space[o] lowerCAmelCase_ : Optional[Any] = observations_space[o - 1] for state in states_space: # Calculates the argmax for probability function lowerCAmelCase_ : List[Any] = "" lowerCAmelCase_ : Tuple = -1 for k_state in states_space: lowerCAmelCase_ : int = ( probabilities[(k_state, prior_observation)] * transition_probabilities[k_state][state] * emission_probabilities[state][observation] ) if probability > max_probability: lowerCAmelCase_ : List[str] = probability lowerCAmelCase_ : Optional[Any] = k_state # Update probabilities and pointers dicts lowerCAmelCase_ : Union[str, Any] = ( probabilities[(arg_max, prior_observation)] * transition_probabilities[arg_max][state] * emission_probabilities[state][observation] ) lowerCAmelCase_ : Any = arg_max # The final observation lowerCAmelCase_ : List[Any] = observations_space[len(snake_case__) - 1] # argmax for given final observation lowerCAmelCase_ : List[str] = "" lowerCAmelCase_ : List[str] = -1 for k_state in states_space: lowerCAmelCase_ : List[str] = probabilities[(k_state, final_observation)] if probability > max_probability: lowerCAmelCase_ : List[str] = probability lowerCAmelCase_ : Tuple = k_state lowerCAmelCase_ : str = arg_max # Process pointers backwards lowerCAmelCase_ : int = last_state lowerCAmelCase_ : int = [] for o in range(len(snake_case__) - 1 , -1 , -1): result.append(snake_case__) lowerCAmelCase_ : Optional[Any] = pointers[previous, observations_space[o]] result.reverse() return result def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ): _validate_not_empty( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) _validate_lists(snake_case__ , snake_case__) _validate_dicts( snake_case__ , snake_case__ , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ): if not all( [ observations_space, states_space, initial_probabilities, transition_probabilities, emission_probabilities, ]): raise ValueError("There's an empty parameter") def UpperCamelCase ( snake_case__ , snake_case__): _validate_list(snake_case__ , "observations_space") _validate_list(snake_case__ , "states_space") def UpperCamelCase ( snake_case__ , snake_case__): if not isinstance(_object , snake_case__): lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list''' raise ValueError(snake_case__) else: for x in _object: if not isinstance(snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list of strings''' raise ValueError(snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ): _validate_dict(snake_case__ , "initial_probabilities" , snake_case__) _validate_nested_dict(snake_case__ , "transition_probabilities") _validate_nested_dict(snake_case__ , "emission_probabilities") def UpperCamelCase ( snake_case__ , snake_case__): _validate_dict(_object , snake_case__ , snake_case__) for x in _object.values(): _validate_dict(snake_case__ , snake_case__ , snake_case__ , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = False): if not isinstance(_object , snake_case__): lowerCAmelCase_ : List[str] = F'''{var_name} must be a dict''' raise ValueError(snake_case__) if not all(isinstance(snake_case__ , snake_case__) for x in _object): lowerCAmelCase_ : Dict = F'''{var_name} all keys must be strings''' raise ValueError(snake_case__) if not all(isinstance(snake_case__ , snake_case__) for x in _object.values()): lowerCAmelCase_ : Union[str, Any] = "nested dictionary " if nested else "" lowerCAmelCase_ : Any = F'''{var_name} {nested_text}all values must be {value_type.__name__}''' raise ValueError(snake_case__) if __name__ == "__main__": from doctest import testmod testmod()
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import os import unicodedata from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''spiece.model'''} _lowercase = { '''vocab_file''': { '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''', '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''', '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''', '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''', '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''', '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''', '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''', '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''', } } _lowercase = { '''albert-base-v1''': 512, '''albert-large-v1''': 512, '''albert-xlarge-v1''': 512, '''albert-xxlarge-v1''': 512, '''albert-base-v2''': 512, '''albert-large-v2''': 512, '''albert-xlarge-v2''': 512, '''albert-xxlarge-v2''': 512, } _lowercase = '''▁''' class __snake_case ( lowercase__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self : int ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : str=True ,lowerCAmelCase__ : Union[str, Any]=True ,lowerCAmelCase__ : int=False ,lowerCAmelCase__ : Tuple="[CLS]" ,lowerCAmelCase__ : Any="[SEP]" ,lowerCAmelCase__ : Union[str, Any]="<unk>" ,lowerCAmelCase__ : Union[str, Any]="[SEP]" ,lowerCAmelCase__ : Optional[Any]="<pad>" ,lowerCAmelCase__ : str="[CLS]" ,lowerCAmelCase__ : Dict="[MASK]" ,lowerCAmelCase__ : Optional[Dict[str, Any]] = None ,**lowerCAmelCase__ : Optional[int] ,) -> str: '''simple docstring''' lowerCAmelCase_ : str = ( AddedToken(UpperCAmelCase__ ,lstrip=UpperCAmelCase__ ,rstrip=UpperCAmelCase__ ,normalized=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ ,UpperCAmelCase__ ) else mask_token ) lowerCAmelCase_ : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( do_lower_case=UpperCAmelCase__ ,remove_space=UpperCAmelCase__ ,keep_accents=UpperCAmelCase__ ,bos_token=UpperCAmelCase__ ,eos_token=UpperCAmelCase__ ,unk_token=UpperCAmelCase__ ,sep_token=UpperCAmelCase__ ,pad_token=UpperCAmelCase__ ,cls_token=UpperCAmelCase__ ,mask_token=UpperCAmelCase__ ,sp_model_kwargs=self.sp_model_kwargs ,**UpperCAmelCase__ ,) lowerCAmelCase_ : int = do_lower_case lowerCAmelCase_ : List[str] = remove_space lowerCAmelCase_ : Any = keep_accents lowerCAmelCase_ : Dict = vocab_file lowerCAmelCase_ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(UpperCAmelCase__ ) @property def UpperCAmelCase_ ( self : Optional[Any] ) -> str: '''simple docstring''' return len(self.sp_model ) def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = {self.convert_ids_to_tokens(UpperCAmelCase__ ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self : Optional[Any] ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = self.__dict__.copy() lowerCAmelCase_ : str = None return state def __setstate__( self : Tuple ,lowerCAmelCase__ : Optional[Any] ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = d # for backward compatibility if not hasattr(self ,"sp_model_kwargs" ): lowerCAmelCase_ : Union[str, Any] = {} lowerCAmelCase_ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Optional[Any] ) -> List[str]: '''simple docstring''' if self.remove_space: lowerCAmelCase_ : Tuple = ''' '''.join(inputs.strip().split() ) else: lowerCAmelCase_ : Optional[int] = inputs lowerCAmelCase_ : int = outputs.replace("``" ,"\"" ).replace("\'\'" ,"\"" ) if not self.keep_accents: lowerCAmelCase_ : List[Any] = unicodedata.normalize("NFKD" ,UpperCAmelCase__ ) lowerCAmelCase_ : Optional[int] = ''''''.join([c for c in outputs if not unicodedata.combining(UpperCAmelCase__ )] ) if self.do_lower_case: lowerCAmelCase_ : Any = outputs.lower() return outputs def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = self.preprocess_text(UpperCAmelCase__ ) lowerCAmelCase_ : Tuple = self.sp_model.encode(UpperCAmelCase__ ,out_type=UpperCAmelCase__ ) lowerCAmelCase_ : Dict = [] for piece in pieces: if len(UpperCAmelCase__ ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit(): lowerCAmelCase_ : Tuple = self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCAmelCase__ ,"" ) ) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: if len(cur_pieces[0] ) == 1: lowerCAmelCase_ : int = cur_pieces[1:] else: lowerCAmelCase_ : Any = cur_pieces[0][1:] cur_pieces.append(piece[-1] ) new_pieces.extend(UpperCAmelCase__ ) else: new_pieces.append(UpperCAmelCase__ ) return new_pieces def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> str: '''simple docstring''' return self.sp_model.PieceToId(UpperCAmelCase__ ) def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : List[Any] ) -> int: '''simple docstring''' return self.sp_model.IdToPiece(UpperCAmelCase__ ) def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[Any] ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Any = [] lowerCAmelCase_ : str = '''''' lowerCAmelCase_ : List[str] = False for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: out_string += " " out_string += self.sp_model.decode(UpperCAmelCase__ ) + token lowerCAmelCase_ : str = True lowerCAmelCase_ : Tuple = [] else: current_sub_tokens.append(UpperCAmelCase__ ) lowerCAmelCase_ : Optional[int] = False out_string += self.sp_model.decode(UpperCAmelCase__ ) return out_string.strip() def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Tuple = [self.sep_token_id] lowerCAmelCase_ : int = [self.cls_token_id] if token_ids_a is None: return cls + token_ids_a + sep return cls + token_ids_a + sep + token_ids_a + sep def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> Tuple: '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=UpperCAmelCase__ ,token_ids_a=UpperCAmelCase__ ,already_has_special_tokens=UpperCAmelCase__ ) if token_ids_a is not None: return [1] + ([0] * len(UpperCAmelCase__ )) + [1] + ([0] * len(UpperCAmelCase__ )) + [1] return [1] + ([0] * len(UpperCAmelCase__ )) + [1] def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> str: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = [self.sep_token_id] lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> List[Any]: '''simple docstring''' if not os.path.isdir(UpperCAmelCase__ ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return lowerCAmelCase_ : Union[str, Any] = os.path.join( UpperCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase__ ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file ,UpperCAmelCase__ ) elif not os.path.isfile(self.vocab_file ): with open(UpperCAmelCase__ ,"wb" ) as fi: lowerCAmelCase_ : Any = self.sp_model.serialized_model_proto() fi.write(UpperCAmelCase__ ) return (out_vocab_file,)
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor from ..utils import is_datasets_available from .base import PipelineTool if is_datasets_available(): from datasets import load_dataset class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = 'microsoft/speecht5_tts' UpperCamelCase_ = ( 'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the ' 'text to read (in English) and returns a waveform object containing the sound.' ) UpperCamelCase_ = 'text_reader' UpperCamelCase_ = SpeechTaProcessor UpperCamelCase_ = SpeechTaForTextToSpeech UpperCamelCase_ = SpeechTaHifiGan UpperCamelCase_ = ['text'] UpperCamelCase_ = ['audio'] def UpperCAmelCase_ ( self : Dict ) -> Any: '''simple docstring''' if self.post_processor is None: lowerCAmelCase_ : Any = "microsoft/speecht5_hifigan" super().setup() def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Any = self.pre_processor(text=lowerCAmelCase__ ,return_tensors="pt" ,truncation=lowerCAmelCase__ ) if speaker_embeddings is None: if not is_datasets_available(): raise ImportError("Datasets needs to be installed if not passing speaker embeddings." ) lowerCAmelCase_ : str = load_dataset("Matthijs/cmu-arctic-xvectors" ,split="validation" ) lowerCAmelCase_ : List[Any] = torch.tensor(embeddings_dataset[73_05]["xvector"] ).unsqueeze(0 ) return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings} def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]: '''simple docstring''' with torch.no_grad(): return self.model.generate_speech(**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : str ) -> Any: '''simple docstring''' with torch.no_grad(): return self.post_processor(lowerCAmelCase__ ).cpu().detach()
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from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = { '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''', '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''', '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''', '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''', # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2 } class __snake_case ( __UpperCAmelCase ): """simple docstring""" UpperCamelCase_ = "mobilenet_v2" def __init__( self : Tuple ,lowerCAmelCase__ : List[str]=3 ,lowerCAmelCase__ : Union[str, Any]=2_24 ,lowerCAmelCase__ : Optional[Any]=1.0 ,lowerCAmelCase__ : Dict=8 ,lowerCAmelCase__ : int=8 ,lowerCAmelCase__ : Any=6 ,lowerCAmelCase__ : Tuple=32 ,lowerCAmelCase__ : Union[str, Any]=True ,lowerCAmelCase__ : Dict=True ,lowerCAmelCase__ : Any="relu6" ,lowerCAmelCase__ : Any=True ,lowerCAmelCase__ : Dict=0.8 ,lowerCAmelCase__ : str=0.02 ,lowerCAmelCase__ : Dict=0.001 ,lowerCAmelCase__ : Any=2_55 ,**lowerCAmelCase__ : Dict ,) -> Tuple: '''simple docstring''' super().__init__(**_lowerCamelCase ) if depth_multiplier <= 0: raise ValueError("depth_multiplier must be greater than zero." ) lowerCAmelCase_ : Dict = num_channels lowerCAmelCase_ : Dict = image_size lowerCAmelCase_ : Optional[int] = depth_multiplier lowerCAmelCase_ : Optional[int] = depth_divisible_by lowerCAmelCase_ : List[str] = min_depth lowerCAmelCase_ : Dict = expand_ratio lowerCAmelCase_ : int = output_stride lowerCAmelCase_ : Dict = first_layer_is_expansion lowerCAmelCase_ : str = finegrained_output lowerCAmelCase_ : Optional[int] = hidden_act lowerCAmelCase_ : int = tf_padding lowerCAmelCase_ : Union[str, Any] = classifier_dropout_prob lowerCAmelCase_ : int = initializer_range lowerCAmelCase_ : Tuple = layer_norm_eps lowerCAmelCase_ : Dict = semantic_loss_ignore_index class __snake_case ( __UpperCAmelCase ): """simple docstring""" UpperCamelCase_ = version.parse('1.11' ) @property def UpperCAmelCase_ ( self : Optional[int] ) -> int: '''simple docstring''' return OrderedDict([("pixel_values", {0: "batch"})] ) @property def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any: '''simple docstring''' if self.task == "image-classification": return OrderedDict([("logits", {0: "batch"})] ) else: return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] ) @property def UpperCAmelCase_ ( self : Tuple ) -> int: '''simple docstring''' return 1e-4
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import argparse import collections import json import os import re import string import sys import numpy as np _lowercase = re.compile(r'''\b(a|an|the)\b''', re.UNICODE) _lowercase = None def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0.") parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file.") parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions.") parser.add_argument( "--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout).") parser.add_argument( "--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer.") parser.add_argument( "--na-prob-thresh" , "-t" , type=snake_case__ , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , ) parser.add_argument( "--out-image-dir" , "-p" , metavar="out_images" , default=snake_case__ , help="Save precision-recall curves to directory.") parser.add_argument("--verbose" , "-v" , action="store_true") if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() def UpperCamelCase ( snake_case__): lowerCAmelCase_ : str = {} for article in dataset: for p in article["paragraphs"]: for qa in p["qas"]: lowerCAmelCase_ : Dict = bool(qa["answers"]["text"]) return qid_to_has_ans def UpperCamelCase ( snake_case__): def remove_articles(snake_case__): return ARTICLES_REGEX.sub(" " , snake_case__) def white_space_fix(snake_case__): return " ".join(text.split()) def remove_punc(snake_case__): lowerCAmelCase_ : Optional[int] = set(string.punctuation) return "".join(ch for ch in text if ch not in exclude) def lower(snake_case__): return text.lower() return white_space_fix(remove_articles(remove_punc(lower(snake_case__)))) def UpperCamelCase ( snake_case__): if not s: return [] return normalize_answer(snake_case__).split() def UpperCamelCase ( snake_case__ , snake_case__): return int(normalize_answer(snake_case__) == normalize_answer(snake_case__)) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = get_tokens(snake_case__) lowerCAmelCase_ : Union[str, Any] = get_tokens(snake_case__) lowerCAmelCase_ : Any = collections.Counter(snake_case__) & collections.Counter(snake_case__) lowerCAmelCase_ : Dict = sum(common.values()) if len(snake_case__) == 0 or len(snake_case__) == 0: # If either is no-answer, then F1 is 1 if they agree, 0 otherwise return int(gold_toks == pred_toks) if num_same == 0: return 0 lowerCAmelCase_ : List[Any] = 1.0 * num_same / len(snake_case__) lowerCAmelCase_ : int = 1.0 * num_same / len(snake_case__) lowerCAmelCase_ : List[Any] = (2 * precision * recall) / (precision + recall) return fa def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Tuple = {} lowerCAmelCase_ : int = {} for article in dataset: for p in article["paragraphs"]: for qa in p["qas"]: lowerCAmelCase_ : int = qa["id"] lowerCAmelCase_ : Any = [t for t in qa["answers"]["text"] if normalize_answer(snake_case__)] if not gold_answers: # For unanswerable questions, only correct answer is empty string lowerCAmelCase_ : Any = [""] if qid not in preds: print(F'''Missing prediction for {qid}''') continue lowerCAmelCase_ : Tuple = preds[qid] # Take max over all gold answers lowerCAmelCase_ : Any = max(compute_exact(snake_case__ , snake_case__) for a in gold_answers) lowerCAmelCase_ : Optional[Any] = max(compute_fa(snake_case__ , snake_case__) for a in gold_answers) return exact_scores, fa_scores def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Dict = {} for qid, s in scores.items(): lowerCAmelCase_ : List[Any] = na_probs[qid] > na_prob_thresh if pred_na: lowerCAmelCase_ : List[str] = float(not qid_to_has_ans[qid]) else: lowerCAmelCase_ : Union[str, Any] = s return new_scores def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None): if not qid_list: lowerCAmelCase_ : Any = len(snake_case__) return collections.OrderedDict( [ ("exact", 100.0 * sum(exact_scores.values()) / total), ("f1", 100.0 * sum(fa_scores.values()) / total), ("total", total), ]) else: lowerCAmelCase_ : Tuple = len(snake_case__) return collections.OrderedDict( [ ("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total), ("f1", 100.0 * sum(fa_scores[k] for k in qid_list) / total), ("total", total), ]) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): for k in new_eval: lowerCAmelCase_ : Union[str, Any] = new_eval[k] def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): plt.step(snake_case__ , snake_case__ , color="b" , alpha=0.2 , where="post") plt.fill_between(snake_case__ , snake_case__ , step="post" , alpha=0.2 , color="b") plt.xlabel("Recall") plt.ylabel("Precision") plt.xlim([0.0, 1.05]) plt.ylim([0.0, 1.05]) plt.title(snake_case__) plt.savefig(snake_case__) plt.clf() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__=None , snake_case__=None): lowerCAmelCase_ : List[Any] = sorted(snake_case__ , key=lambda snake_case__: na_probs[k]) lowerCAmelCase_ : Dict = 0.0 lowerCAmelCase_ : int = 1.0 lowerCAmelCase_ : List[str] = 0.0 lowerCAmelCase_ : Tuple = [1.0] lowerCAmelCase_ : Tuple = [0.0] lowerCAmelCase_ : Dict = 0.0 for i, qid in enumerate(snake_case__): if qid_to_has_ans[qid]: true_pos += scores[qid] lowerCAmelCase_ : str = true_pos / float(i + 1) lowerCAmelCase_ : Union[str, Any] = true_pos / float(snake_case__) if i == len(snake_case__) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]: # i.e., if we can put a threshold after this point avg_prec += cur_p * (cur_r - recalls[-1]) precisions.append(snake_case__) recalls.append(snake_case__) if out_image: plot_pr_curve(snake_case__ , snake_case__ , snake_case__ , snake_case__) return {"ap": 100.0 * avg_prec} def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): if out_image_dir and not os.path.exists(snake_case__): os.makedirs(snake_case__) lowerCAmelCase_ : Any = sum(1 for v in qid_to_has_ans.values() if v) if num_true_pos == 0: return lowerCAmelCase_ : Any = make_precision_recall_eval( snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_exact.png") , title="Precision-Recall curve for Exact Match score" , ) lowerCAmelCase_ : Dict = make_precision_recall_eval( snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_f1.png") , title="Precision-Recall curve for F1 score" , ) lowerCAmelCase_ : Dict = {k: float(snake_case__) for k, v in qid_to_has_ans.items()} lowerCAmelCase_ : str = make_precision_recall_eval( snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_oracle.png") , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , ) merge_eval(snake_case__ , snake_case__ , "pr_exact") merge_eval(snake_case__ , snake_case__ , "pr_f1") merge_eval(snake_case__ , snake_case__ , "pr_oracle") def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): if not qid_list: return lowerCAmelCase_ : Optional[Any] = [na_probs[k] for k in qid_list] lowerCAmelCase_ : Dict = np.ones_like(snake_case__) / float(len(snake_case__)) plt.hist(snake_case__ , weights=snake_case__ , bins=20 , range=(0.0, 1.0)) plt.xlabel("Model probability of no-answer") plt.ylabel("Proportion of dataset") plt.title(F'''Histogram of no-answer probability: {name}''') plt.savefig(os.path.join(snake_case__ , F'''na_prob_hist_{name}.png''')) plt.clf() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Dict = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]) lowerCAmelCase_ : str = num_no_ans lowerCAmelCase_ : List[str] = cur_score lowerCAmelCase_ : List[Any] = 0.0 lowerCAmelCase_ : str = sorted(snake_case__ , key=lambda snake_case__: na_probs[k]) for i, qid in enumerate(snake_case__): if qid not in scores: continue if qid_to_has_ans[qid]: lowerCAmelCase_ : Union[str, Any] = scores[qid] else: if preds[qid]: lowerCAmelCase_ : List[Any] = -1 else: lowerCAmelCase_ : List[str] = 0 cur_score += diff if cur_score > best_score: lowerCAmelCase_ : Optional[Any] = cur_score lowerCAmelCase_ : Optional[int] = na_probs[qid] return 100.0 * best_score / len(snake_case__), best_thresh def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ , lowerCAmelCase_ : Dict = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = best_exact lowerCAmelCase_ : List[str] = exact_thresh lowerCAmelCase_ : Any = best_fa lowerCAmelCase_ : List[str] = fa_thresh def UpperCamelCase ( ): with open(OPTS.data_file) as f: lowerCAmelCase_ : Optional[int] = json.load(snake_case__) lowerCAmelCase_ : List[Any] = dataset_json["data"] with open(OPTS.pred_file) as f: lowerCAmelCase_ : int = json.load(snake_case__) if OPTS.na_prob_file: with open(OPTS.na_prob_file) as f: lowerCAmelCase_ : Optional[int] = json.load(snake_case__) else: lowerCAmelCase_ : List[Any] = {k: 0.0 for k in preds} lowerCAmelCase_ : Tuple = make_qid_to_has_ans(snake_case__) # maps qid to True/False lowerCAmelCase_ : Any = [k for k, v in qid_to_has_ans.items() if v] lowerCAmelCase_ : List[str] = [k for k, v in qid_to_has_ans.items() if not v] lowerCAmelCase_ , lowerCAmelCase_ : Dict = get_raw_scores(snake_case__ , snake_case__) lowerCAmelCase_ : str = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh) lowerCAmelCase_ : Dict = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh) lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__) if has_ans_qids: lowerCAmelCase_ : str = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__) merge_eval(snake_case__ , snake_case__ , "HasAns") if no_ans_qids: lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__) merge_eval(snake_case__ , snake_case__ , "NoAns") if OPTS.na_prob_file: find_all_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__) if OPTS.na_prob_file and OPTS.out_image_dir: run_precision_recall_analysis(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , OPTS.out_image_dir) histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "hasAns") histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "noAns") if OPTS.out_file: with open(OPTS.out_file , "w") as f: json.dump(snake_case__ , snake_case__) else: print(json.dumps(snake_case__ , indent=2)) if __name__ == "__main__": _lowercase = parse_args() if OPTS.out_image_dir: import matplotlib matplotlib.use('''Agg''') import matplotlib.pyplot as plt main()
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from typing import Optional from .. import Features, NamedSplit from ..packaged_modules.text.text import Text from ..utils.typing import NestedDataStructureLike, PathLike from .abc import AbstractDatasetReader class __snake_case ( lowerCamelCase__ ): """simple docstring""" def __init__( self : Dict ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[str] = None ,lowerCAmelCase__ : List[Any] = None ,lowerCAmelCase__ : Tuple = None ,lowerCAmelCase__ : int = False ,lowerCAmelCase__ : Any = False ,lowerCAmelCase__ : int = None ,**lowerCAmelCase__ : List[Any] ,) -> Union[str, Any]: '''simple docstring''' super().__init__( __lowerCamelCase ,split=__lowerCamelCase ,features=__lowerCamelCase ,cache_dir=__lowerCamelCase ,keep_in_memory=__lowerCamelCase ,streaming=__lowerCamelCase ,num_proc=__lowerCamelCase ,**__lowerCamelCase ,) lowerCAmelCase_ : Optional[Any] = path_or_paths if isinstance(__lowerCamelCase ,__lowerCamelCase ) else {self.split: path_or_paths} lowerCAmelCase_ : int = Text( cache_dir=__lowerCamelCase ,data_files=__lowerCamelCase ,features=__lowerCamelCase ,**__lowerCamelCase ,) def UpperCAmelCase_ ( self : str ) -> Tuple: '''simple docstring''' if self.streaming: lowerCAmelCase_ : Tuple = self.builder.as_streaming_dataset(split=self.split ) # Build regular (map-style) dataset else: lowerCAmelCase_ : List[str] = None lowerCAmelCase_ : Tuple = None lowerCAmelCase_ : List[str] = None lowerCAmelCase_ : Optional[Any] = None self.builder.download_and_prepare( download_config=__lowerCamelCase ,download_mode=__lowerCamelCase ,verification_mode=__lowerCamelCase ,base_path=__lowerCamelCase ,num_proc=self.num_proc ,) lowerCAmelCase_ : str = self.builder.as_dataset( split=self.split ,verification_mode=__lowerCamelCase ,in_memory=self.keep_in_memory ) return dataset
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from math import sqrt def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[int] = 0 for i in range(1 , int(sqrt(snake_case__) + 1)): if n % i == 0 and i != sqrt(snake_case__): total += i + n // i elif i == sqrt(snake_case__): total += i return total - n def UpperCamelCase ( snake_case__ = 1_00_00): lowerCAmelCase_ : int = sum( i for i in range(1 , snake_case__) if sum_of_divisors(sum_of_divisors(snake_case__)) == i and sum_of_divisors(snake_case__) != i) return total if __name__ == "__main__": print(solution(int(str(input()).strip())))
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from __future__ import annotations import copy import inspect import unittest import numpy as np from transformers import is_tf_available, is_vision_available from transformers.models.auto import get_values from transformers.testing_utils import require_tf, slow from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST, TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, LayoutLMvaConfig, TFLayoutLMvaForQuestionAnswering, TFLayoutLMvaForSequenceClassification, TFLayoutLMvaForTokenClassification, TFLayoutLMvaModel, ) if is_vision_available(): from PIL import Image from transformers import LayoutLMvaImageProcessor class __snake_case : """simple docstring""" def __init__( self : Dict ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Union[str, Any]=2 ,lowerCAmelCase__ : Optional[Any]=3 ,lowerCAmelCase__ : str=4 ,lowerCAmelCase__ : Optional[Any]=2 ,lowerCAmelCase__ : Tuple=7 ,lowerCAmelCase__ : Union[str, Any]=True ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : Dict=True ,lowerCAmelCase__ : Union[str, Any]=True ,lowerCAmelCase__ : int=99 ,lowerCAmelCase__ : List[Any]=36 ,lowerCAmelCase__ : str=2 ,lowerCAmelCase__ : Union[str, Any]=4 ,lowerCAmelCase__ : Any=37 ,lowerCAmelCase__ : List[str]="gelu" ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : Tuple=0.1 ,lowerCAmelCase__ : Optional[int]=5_12 ,lowerCAmelCase__ : Dict=16 ,lowerCAmelCase__ : Tuple=2 ,lowerCAmelCase__ : Tuple=0.02 ,lowerCAmelCase__ : Optional[Any]=6 ,lowerCAmelCase__ : Any=6 ,lowerCAmelCase__ : List[str]=3 ,lowerCAmelCase__ : Optional[Any]=4 ,lowerCAmelCase__ : int=None ,lowerCAmelCase__ : int=10_00 ,) -> Tuple: '''simple docstring''' lowerCAmelCase_ : int = parent lowerCAmelCase_ : List[str] = batch_size lowerCAmelCase_ : Optional[int] = num_channels lowerCAmelCase_ : Any = image_size lowerCAmelCase_ : Any = patch_size lowerCAmelCase_ : Dict = is_training lowerCAmelCase_ : Any = use_input_mask lowerCAmelCase_ : Dict = use_token_type_ids lowerCAmelCase_ : List[str] = use_labels lowerCAmelCase_ : int = vocab_size lowerCAmelCase_ : List[str] = hidden_size lowerCAmelCase_ : List[Any] = num_hidden_layers lowerCAmelCase_ : Optional[int] = num_attention_heads lowerCAmelCase_ : Tuple = intermediate_size lowerCAmelCase_ : List[str] = hidden_act lowerCAmelCase_ : Dict = hidden_dropout_prob lowerCAmelCase_ : Optional[Any] = attention_probs_dropout_prob lowerCAmelCase_ : Union[str, Any] = max_position_embeddings lowerCAmelCase_ : int = type_vocab_size lowerCAmelCase_ : List[str] = type_sequence_label_size lowerCAmelCase_ : List[str] = initializer_range lowerCAmelCase_ : List[str] = coordinate_size lowerCAmelCase_ : Union[str, Any] = shape_size lowerCAmelCase_ : Any = num_labels lowerCAmelCase_ : List[str] = num_choices lowerCAmelCase_ : int = scope lowerCAmelCase_ : Optional[Any] = range_bbox # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token) lowerCAmelCase_ : Optional[int] = text_seq_length lowerCAmelCase_ : List[Any] = (image_size // patch_size) ** 2 + 1 lowerCAmelCase_ : Union[str, Any] = self.text_seq_length + self.image_seq_length def UpperCAmelCase_ ( self : Any ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : List[Any] = ids_tensor([self.batch_size, self.text_seq_length] ,self.vocab_size ) lowerCAmelCase_ : List[str] = ids_tensor([self.batch_size, self.text_seq_length, 4] ,self.range_bbox ) lowerCAmelCase_ : Union[str, Any] = bbox.numpy() # Ensure that bbox is legal for i in range(bbox.shape[0] ): for j in range(bbox.shape[1] ): if bbox[i, j, 3] < bbox[i, j, 1]: lowerCAmelCase_ : Union[str, Any] = bbox[i, j, 3] lowerCAmelCase_ : Tuple = bbox[i, j, 1] lowerCAmelCase_ : int = tmp_coordinate if bbox[i, j, 2] < bbox[i, j, 0]: lowerCAmelCase_ : List[str] = bbox[i, j, 2] lowerCAmelCase_ : Dict = bbox[i, j, 0] lowerCAmelCase_ : str = tmp_coordinate lowerCAmelCase_ : Optional[Any] = tf.constant(_lowercase ) lowerCAmelCase_ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) lowerCAmelCase_ : Any = None if self.use_input_mask: lowerCAmelCase_ : str = random_attention_mask([self.batch_size, self.text_seq_length] ) lowerCAmelCase_ : str = None if self.use_token_type_ids: lowerCAmelCase_ : int = ids_tensor([self.batch_size, self.text_seq_length] ,self.type_vocab_size ) lowerCAmelCase_ : str = None lowerCAmelCase_ : Dict = None if self.use_labels: lowerCAmelCase_ : str = ids_tensor([self.batch_size] ,self.type_sequence_label_size ) lowerCAmelCase_ : Union[str, Any] = ids_tensor([self.batch_size, self.text_seq_length] ,self.num_labels ) lowerCAmelCase_ : Dict = LayoutLMvaConfig( vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,initializer_range=self.initializer_range ,coordinate_size=self.coordinate_size ,shape_size=self.shape_size ,input_size=self.image_size ,patch_size=self.patch_size ,) return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Optional[int] = TFLayoutLMvaModel(config=_lowercase ) # text + image lowerCAmelCase_ : List[Any] = model(_lowercase ,pixel_values=_lowercase ,training=_lowercase ) lowerCAmelCase_ : Tuple = model( _lowercase ,bbox=_lowercase ,pixel_values=_lowercase ,attention_mask=_lowercase ,token_type_ids=_lowercase ,training=_lowercase ,) lowerCAmelCase_ : Optional[int] = model(_lowercase ,bbox=_lowercase ,pixel_values=_lowercase ,training=_lowercase ) self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) ) # text only lowerCAmelCase_ : List[Any] = model(_lowercase ,training=_lowercase ) self.parent.assertEqual( result.last_hidden_state.shape ,(self.batch_size, self.text_seq_length, self.hidden_size) ) # image only lowerCAmelCase_ : Any = model({"pixel_values": pixel_values} ,training=_lowercase ) self.parent.assertEqual( result.last_hidden_state.shape ,(self.batch_size, self.image_seq_length, self.hidden_size) ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Dict ) -> Dict: '''simple docstring''' lowerCAmelCase_ : int = self.num_labels lowerCAmelCase_ : Dict = TFLayoutLMvaForSequenceClassification(config=_lowercase ) lowerCAmelCase_ : Optional[int] = model( _lowercase ,bbox=_lowercase ,pixel_values=_lowercase ,attention_mask=_lowercase ,token_type_ids=_lowercase ,labels=_lowercase ,training=_lowercase ,) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : List[Any] ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Dict = self.num_labels lowerCAmelCase_ : List[str] = TFLayoutLMvaForTokenClassification(config=_lowercase ) lowerCAmelCase_ : int = model( _lowercase ,bbox=_lowercase ,pixel_values=_lowercase ,attention_mask=_lowercase ,token_type_ids=_lowercase ,labels=_lowercase ,training=_lowercase ,) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.text_seq_length, self.num_labels) ) def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : int ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : Optional[int] = 2 lowerCAmelCase_ : int = TFLayoutLMvaForQuestionAnswering(config=_lowercase ) lowerCAmelCase_ : List[str] = model( _lowercase ,bbox=_lowercase ,pixel_values=_lowercase ,attention_mask=_lowercase ,token_type_ids=_lowercase ,start_positions=_lowercase ,end_positions=_lowercase ,training=_lowercase ,) self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) ) def UpperCAmelCase_ ( self : Tuple ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : List[str] = self.prepare_config_and_inputs() ((lowerCAmelCase_) , (lowerCAmelCase_) , (lowerCAmelCase_) , (lowerCAmelCase_) , (lowerCAmelCase_) , (lowerCAmelCase_) , (lowerCAmelCase_) , (lowerCAmelCase_)) : Union[str, Any] = config_and_inputs lowerCAmelCase_ : Optional[int] = { "input_ids": input_ids, "bbox": bbox, "pixel_values": pixel_values, "token_type_ids": token_type_ids, "attention_mask": input_mask, } return config, inputs_dict @require_tf class __snake_case ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = ( ( TFLayoutLMvaModel, TFLayoutLMvaForQuestionAnswering, TFLayoutLMvaForSequenceClassification, TFLayoutLMvaForTokenClassification, ) if is_tf_available() else () ) UpperCamelCase_ = ( {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel} if is_tf_available() else {} ) UpperCamelCase_ = False UpperCamelCase_ = False UpperCamelCase_ = False def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Union[str, Any] ) -> List[str]: '''simple docstring''' return True def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Union[str, Any]=False ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Dict = copy.deepcopy(_lowercase ) if model_class in get_values(_lowercase ): lowerCAmelCase_ : str = { k: tf.tile(tf.expand_dims(_lowercase ,1 ) ,(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) ) if isinstance(_lowercase ,tf.Tensor ) and v.ndim > 0 else v for k, v in inputs_dict.items() } if return_labels: if model_class in get_values(_lowercase ): lowerCAmelCase_ : Optional[Any] = tf.ones(self.model_tester.batch_size ,dtype=tf.intaa ) elif model_class in get_values(_lowercase ): lowerCAmelCase_ : Union[str, Any] = tf.zeros(self.model_tester.batch_size ,dtype=tf.intaa ) lowerCAmelCase_ : int = tf.zeros(self.model_tester.batch_size ,dtype=tf.intaa ) elif model_class in get_values(_lowercase ): lowerCAmelCase_ : Optional[int] = tf.zeros(self.model_tester.batch_size ,dtype=tf.intaa ) elif model_class in get_values(_lowercase ): lowerCAmelCase_ : Any = tf.zeros( (self.model_tester.batch_size, self.model_tester.text_seq_length) ,dtype=tf.intaa ) return inputs_dict def UpperCAmelCase_ ( self : Dict ) -> Any: '''simple docstring''' lowerCAmelCase_ : Any = TFLayoutLMvaModelTester(self ) lowerCAmelCase_ : Dict = ConfigTester(self ,config_class=_lowercase ,hidden_size=37 ) def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase_ ( self : int ) -> Dict: '''simple docstring''' lowerCAmelCase_ , lowerCAmelCase_ : Dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: lowerCAmelCase_ : List[str] = model_class(_lowercase ) if getattr(_lowercase ,"hf_compute_loss" ,_lowercase ): # The number of elements in the loss should be the same as the number of elements in the label lowerCAmelCase_ : str = self._prepare_for_class(inputs_dict.copy() ,_lowercase ,return_labels=_lowercase ) lowerCAmelCase_ : Tuple = prepared_for_class[ sorted(prepared_for_class.keys() - inputs_dict.keys() ,reverse=_lowercase )[0] ] lowerCAmelCase_ : Union[str, Any] = added_label.shape.as_list()[:1] # Test that model correctly compute the loss with kwargs lowerCAmelCase_ : int = self._prepare_for_class(inputs_dict.copy() ,_lowercase ,return_labels=_lowercase ) lowerCAmelCase_ : List[str] = prepared_for_class.pop("input_ids" ) lowerCAmelCase_ : List[Any] = model(_lowercase ,**_lowercase )[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] ) # Test that model correctly compute the loss when we mask some positions lowerCAmelCase_ : List[str] = self._prepare_for_class(inputs_dict.copy() ,_lowercase ,return_labels=_lowercase ) lowerCAmelCase_ : List[str] = prepared_for_class.pop("input_ids" ) if "labels" in prepared_for_class: lowerCAmelCase_ : Dict = prepared_for_class["labels"].numpy() if len(labels.shape ) > 1 and labels.shape[1] != 1: lowerCAmelCase_ : Dict = -1_00 lowerCAmelCase_ : Dict = tf.convert_to_tensor(_lowercase ) lowerCAmelCase_ : Any = model(_lowercase ,**_lowercase )[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] ) self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) ) # Test that model correctly compute the loss with a dict lowerCAmelCase_ : str = self._prepare_for_class(inputs_dict.copy() ,_lowercase ,return_labels=_lowercase ) lowerCAmelCase_ : Dict = model(_lowercase )[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] ) # Test that model correctly compute the loss with a tuple lowerCAmelCase_ : str = self._prepare_for_class(inputs_dict.copy() ,_lowercase ,return_labels=_lowercase ) # Get keys that were added with the _prepare_for_class function lowerCAmelCase_ : Union[str, Any] = prepared_for_class.keys() - inputs_dict.keys() lowerCAmelCase_ : List[str] = inspect.signature(model.call ).parameters lowerCAmelCase_ : Any = list(signature.keys() ) # Create a dictionary holding the location of the tensors in the tuple lowerCAmelCase_ : Tuple = {0: "input_ids"} for label_key in label_keys: lowerCAmelCase_ : Any = signature_names.index(_lowercase ) lowerCAmelCase_ : Optional[int] = label_key lowerCAmelCase_ : int = sorted(tuple_index_mapping.items() ) # Initialize a list with their default values, update the values and convert to a tuple lowerCAmelCase_ : Tuple = [] for name in signature_names: if name != "kwargs": list_input.append(signature[name].default ) for index, value in sorted_tuple_index_mapping: lowerCAmelCase_ : Optional[Any] = prepared_for_class[value] lowerCAmelCase_ : Optional[int] = tuple(_lowercase ) # Send to model lowerCAmelCase_ : str = model(tuple_input[:-1] )[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] ) def UpperCAmelCase_ ( self : Dict ) -> Union[str, Any]: '''simple docstring''' ( ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ) : List[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ) def UpperCAmelCase_ ( self : str ) -> Dict: '''simple docstring''' ( ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ) : int = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: lowerCAmelCase_ : str = type self.model_tester.create_and_check_model(_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ) def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]: '''simple docstring''' ( ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ) : Dict = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification( _lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ) def UpperCAmelCase_ ( self : List[str] ) -> List[str]: '''simple docstring''' ( ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ) : str = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification( _lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ) def UpperCAmelCase_ ( self : List[str] ) -> str: '''simple docstring''' ( ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ( lowerCAmelCase_ ) , ) : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering( _lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ) @slow def UpperCAmelCase_ ( self : Optional[Any] ) -> Any: '''simple docstring''' for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: lowerCAmelCase_ : List[str] = TFLayoutLMvaModel.from_pretrained(_lowercase ) self.assertIsNotNone(_lowercase ) def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_tf class __snake_case ( unittest.TestCase ): """simple docstring""" @cached_property def UpperCAmelCase_ ( self : int ) -> str: '''simple docstring''' return LayoutLMvaImageProcessor(apply_ocr=_lowercase ) if is_vision_available() else None @slow def UpperCAmelCase_ ( self : Any ) -> Any: '''simple docstring''' lowerCAmelCase_ : str = TFLayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" ) lowerCAmelCase_ : Dict = self.default_image_processor lowerCAmelCase_ : Optional[Any] = prepare_img() lowerCAmelCase_ : Dict = image_processor(images=_lowercase ,return_tensors="tf" ).pixel_values lowerCAmelCase_ : List[str] = tf.constant([[1, 2]] ) lowerCAmelCase_ : List[str] = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) ,axis=0 ) # forward pass lowerCAmelCase_ : Union[str, Any] = model(input_ids=_lowercase ,bbox=_lowercase ,pixel_values=_lowercase ,training=_lowercase ) # verify the logits lowerCAmelCase_ : Optional[Any] = (1, 1_99, 7_68) self.assertEqual(outputs.last_hidden_state.shape ,_lowercase ) lowerCAmelCase_ : int = tf.constant( [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]] ) self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] ,_lowercase ,atol=1e-4 ) )
703
from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_speech_available, is_tf_available, is_torch_available, ) _lowercase = { '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''], '''processing_speech_to_text''': ['''Speech2TextProcessor'''], } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ['''Speech2TextTokenizer'''] try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ['''Speech2TextFeatureExtractor'''] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''TFSpeech2TextForConditionalGeneration''', '''TFSpeech2TextModel''', '''TFSpeech2TextPreTrainedModel''', ] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''Speech2TextForConditionalGeneration''', '''Speech2TextModel''', '''Speech2TextPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig from .processing_speech_to_text import SpeechaTextProcessor try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_speech_to_text import SpeechaTextTokenizer try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_speech_to_text import ( TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, TFSpeechaTextForConditionalGeneration, TFSpeechaTextModel, TFSpeechaTextPreTrainedModel, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_speech_to_text import ( SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, SpeechaTextForConditionalGeneration, SpeechaTextModel, SpeechaTextPreTrainedModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
683
0
import numpy as np from cva import destroyAllWindows, imread, imshow, waitKey class __snake_case : """simple docstring""" def __init__( self : Any ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : Optional[int] ) -> Optional[int]: '''simple docstring''' if dst_width < 0 or dst_height < 0: raise ValueError("Destination width/height should be > 0" ) lowerCAmelCase_ : List[str] = img lowerCAmelCase_ : str = img.shape[1] lowerCAmelCase_ : Tuple = img.shape[0] lowerCAmelCase_ : Union[str, Any] = dst_width lowerCAmelCase_ : str = dst_height lowerCAmelCase_ : str = self.src_w / self.dst_w lowerCAmelCase_ : List[Any] = self.src_h / self.dst_h lowerCAmelCase_ : str = ( np.ones((self.dst_h, self.dst_w, 3) ,np.uinta ) * 2_55 ) def UpperCAmelCase_ ( self : Optional[Any] ) -> List[Any]: '''simple docstring''' for i in range(self.dst_h ): for j in range(self.dst_w ): lowerCAmelCase_ : Optional[int] = self.img[self.get_y(lowercase_ )][self.get_x(lowercase_ )] def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[str] ) -> Optional[Any]: '''simple docstring''' return int(self.ratio_x * x ) def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : Optional[int] ) -> str: '''simple docstring''' return int(self.ratio_y * y ) if __name__ == "__main__": _lowercase , _lowercase = 800, 600 _lowercase = imread('''image_data/lena.jpg''', 1) _lowercase = NearestNeighbour(im, dst_w, dst_h) n.process() imshow( f"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output ) waitKey(0) destroyAllWindows()
704
import json import os from functools import lru_cache from typing import List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''} _lowercase = { '''vocab_file''': { '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''', '''allenai/longformer-large-4096''': ( '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json''' ), '''allenai/longformer-large-4096-finetuned-triviaqa''': ( '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json''' ), '''allenai/longformer-base-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json''' ), '''allenai/longformer-large-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json''' ), }, '''merges_file''': { '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''', '''allenai/longformer-large-4096''': ( '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt''' ), '''allenai/longformer-large-4096-finetuned-triviaqa''': ( '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt''' ), '''allenai/longformer-base-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt''' ), '''allenai/longformer-large-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt''' ), }, } _lowercase = { '''allenai/longformer-base-4096''': 4096, '''allenai/longformer-large-4096''': 4096, '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096, '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096, '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096, } @lru_cache() # Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode def UpperCamelCase ( ): lowerCAmelCase_ : str = ( list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1)) ) lowerCAmelCase_ : Tuple = bs[:] lowerCAmelCase_ : Dict = 0 for b in range(2**8): if b not in bs: bs.append(snake_case__) cs.append(2**8 + n) n += 1 lowerCAmelCase_ : Union[str, Any] = [chr(snake_case__) for n in cs] return dict(zip(snake_case__ , snake_case__)) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[Any] = set() lowerCAmelCase_ : List[Any] = word[0] for char in word[1:]: pairs.add((prev_char, char)) lowerCAmelCase_ : Union[str, Any] = char return pairs class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = ['input_ids', 'attention_mask'] def __init__( self : str ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[Any]="replace" ,lowerCAmelCase__ : Dict="<s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : Optional[Any]="<s>" ,lowerCAmelCase__ : List[Any]="<unk>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : int="<mask>" ,lowerCAmelCase__ : Any=False ,**lowerCAmelCase__ : int ,) -> Dict: '''simple docstring''' lowerCAmelCase_ : Optional[int] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token lowerCAmelCase_ : Dict = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else pad_token # Mask token behave like a normal word, i.e. include the space before it lowerCAmelCase_ : Optional[Any] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else mask_token super().__init__( errors=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,**lowerCAmelCase__ ,) with open(lowerCAmelCase__ ,encoding="utf-8" ) as vocab_handle: lowerCAmelCase_ : List[Any] = json.load(lowerCAmelCase__ ) lowerCAmelCase_ : Dict = {v: k for k, v in self.encoder.items()} lowerCAmelCase_ : List[Any] = errors # how to handle errors in decoding lowerCAmelCase_ : Optional[Any] = bytes_to_unicode() lowerCAmelCase_ : int = {v: k for k, v in self.byte_encoder.items()} with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle: lowerCAmelCase_ : Union[str, Any] = merges_handle.read().split("\n" )[1:-1] lowerCAmelCase_ : Dict = [tuple(merge.split() ) for merge in bpe_merges] lowerCAmelCase_ : Dict = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) ) lowerCAmelCase_ : Any = {} lowerCAmelCase_ : int = add_prefix_space # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions lowerCAmelCase_ : Optional[Any] = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" ) @property def UpperCAmelCase_ ( self : Optional[int] ) -> Any: '''simple docstring''' return len(self.encoder ) def UpperCAmelCase_ ( self : Any ) -> Optional[int]: '''simple docstring''' return dict(self.encoder ,**self.added_tokens_encoder ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[str] ) -> List[Any]: '''simple docstring''' if token in self.cache: return self.cache[token] lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = get_pairs(lowerCAmelCase__ ) if not pairs: return token while True: lowerCAmelCase_ : Dict = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) ) if bigram not in self.bpe_ranks: break lowerCAmelCase_ , lowerCAmelCase_ : Dict = bigram lowerCAmelCase_ : Optional[Any] = [] lowerCAmelCase_ : Any = 0 while i < len(lowerCAmelCase__ ): try: lowerCAmelCase_ : Optional[int] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ ) except ValueError: new_word.extend(word[i:] ) break else: new_word.extend(word[i:j] ) lowerCAmelCase_ : Tuple = j if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second: new_word.append(first + second ) i += 2 else: new_word.append(word[i] ) i += 1 lowerCAmelCase_ : Optional[Any] = tuple(lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = new_word if len(lowerCAmelCase__ ) == 1: break else: lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = " ".join(lowerCAmelCase__ ) lowerCAmelCase_ : Any = word return word def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Dict = [] for token in re.findall(self.pat ,lowerCAmelCase__ ): lowerCAmelCase_ : List[str] = "".join( self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__ ).split(" " ) ) return bpe_tokens def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ) -> Tuple: '''simple docstring''' return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]: '''simple docstring''' return self.decoder.get(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Any: '''simple docstring''' lowerCAmelCase_ : Optional[int] = "".join(lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors ) return text def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' if not os.path.isdir(lowerCAmelCase__ ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return lowerCAmelCase_ : Optional[Any] = os.path.join( lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase_ : Tuple = os.path.join( lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as f: f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowerCAmelCase__ ,ensure_ascii=lowerCAmelCase__ ) + "\n" ) lowerCAmelCase_ : Tuple = 0 with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as writer: writer.write("#version: 0.2\n" ) for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda lowerCAmelCase__ : kv[1] ): if index != token_index: logger.warning( f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.''' " Please check that the tokenizer is not corrupted!" ) lowerCAmelCase_ : Optional[Any] = token_index writer.write(" ".join(lowerCAmelCase__ ) + "\n" ) index += 1 return vocab_file, merge_file def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] lowerCAmelCase_ : List[Any] = [self.cls_token_id] lowerCAmelCase_ : List[str] = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]: '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=lowerCAmelCase__ ,token_ids_a=lowerCAmelCase__ ,already_has_special_tokens=lowerCAmelCase__ ) if token_ids_a is None: return [1] + ([0] * len(lowerCAmelCase__ )) + [1] return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1] def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : List[str] = [self.sep_token_id] lowerCAmelCase_ : Tuple = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Optional[int] ) -> Any: '''simple docstring''' lowerCAmelCase_ : int = kwargs.pop("add_prefix_space" ,self.add_prefix_space ) if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__ ) > 0 and not text[0].isspace()): lowerCAmelCase_ : Union[str, Any] = " " + text return (text, kwargs)
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_torch_available, ) _lowercase = { "configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ "FALCON_PRETRAINED_MODEL_ARCHIVE_LIST", "FalconForCausalLM", "FalconModel", "FalconPreTrainedModel", "FalconForSequenceClassification", "FalconForTokenClassification", "FalconForQuestionAnswering", ] if TYPE_CHECKING: from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_falcon import ( FALCON_PRETRAINED_MODEL_ARCHIVE_LIST, FalconForCausalLM, FalconForQuestionAnswering, FalconForSequenceClassification, FalconForTokenClassification, FalconModel, FalconPreTrainedModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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from collections.abc import Iterable from typing import Any class __snake_case : """simple docstring""" def __init__( self : Optional[Any] ,lowerCAmelCase__ : int | None = None ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : Dict = value lowerCAmelCase_ : Node | None = None # Added in order to delete a node easier lowerCAmelCase_ : Node | None = None lowerCAmelCase_ : Node | None = None def __repr__( self : Union[str, Any] ) -> str: '''simple docstring''' from pprint import pformat if self.left is None and self.right is None: return str(self.value ) return pformat({f'''{self.value}''': (self.left, self.right)} ,indent=1 ) class __snake_case : """simple docstring""" def __init__( self : Optional[Any] ,lowerCAmelCase__ : Node | None = None ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : List[Any] = root def __str__( self : Dict ) -> str: '''simple docstring''' return str(self.root ) def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Node ,lowerCAmelCase__ : Node | None ) -> None: '''simple docstring''' if new_children is not None: # reset its kids lowerCAmelCase_ : Optional[int] = node.parent if node.parent is not None: # reset its parent if self.is_right(lowerCAmelCase__ ): # If it is the right children lowerCAmelCase_ : List[Any] = new_children else: lowerCAmelCase_ : List[Any] = new_children else: lowerCAmelCase_ : Any = new_children def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Node ) -> bool: '''simple docstring''' if node.parent and node.parent.right: return node == node.parent.right return False def UpperCAmelCase_ ( self : List[str] ) -> bool: '''simple docstring''' return self.root is None def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Union[str, Any] ) -> None: '''simple docstring''' lowerCAmelCase_ : str = Node(lowerCAmelCase__ ) # create a new Node if self.empty(): # if Tree is empty lowerCAmelCase_ : Optional[int] = new_node # set its root else: # Tree is not empty lowerCAmelCase_ : List[Any] = self.root # from root if parent_node is None: return while True: # While we don't get to a leaf if value < parent_node.value: # We go left if parent_node.left is None: lowerCAmelCase_ : Dict = new_node # We insert the new node in a leaf break else: lowerCAmelCase_ : List[str] = parent_node.left else: if parent_node.right is None: lowerCAmelCase_ : Dict = new_node break else: lowerCAmelCase_ : str = parent_node.right lowerCAmelCase_ : Optional[int] = parent_node def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : Tuple ) -> None: '''simple docstring''' for value in values: self.__insert(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Optional[int] ) -> Node | None: '''simple docstring''' if self.empty(): raise IndexError("Warning: Tree is empty! please use another." ) else: lowerCAmelCase_ : Dict = self.root # use lazy evaluation here to avoid NoneType Attribute error while node is not None and node.value is not value: lowerCAmelCase_ : Union[str, Any] = node.left if value < node.value else node.right return node def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None: '''simple docstring''' if node is None: if self.root is None: return None lowerCAmelCase_ : Dict = self.root if not self.empty(): while node.right is not None: lowerCAmelCase_ : Union[str, Any] = node.right return node def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None: '''simple docstring''' if node is None: lowerCAmelCase_ : Dict = self.root if self.root is None: return None if not self.empty(): lowerCAmelCase_ : Dict = self.root while node.left is not None: lowerCAmelCase_ : Union[str, Any] = node.left return node def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : int ) -> None: '''simple docstring''' lowerCAmelCase_ : Dict = self.search(lowerCAmelCase__ ) # Look for the node with that label if node is not None: if node.left is None and node.right is None: # If it has no children self.__reassign_nodes(lowerCAmelCase__ ,lowerCAmelCase__ ) elif node.left is None: # Has only right children self.__reassign_nodes(lowerCAmelCase__ ,node.right ) elif node.right is None: # Has only left children self.__reassign_nodes(lowerCAmelCase__ ,node.left ) else: lowerCAmelCase_ : int = self.get_max( node.left ) # Gets the max value of the left branch self.remove(tmp_node.value ) # type: ignore lowerCAmelCase_ : Any = ( tmp_node.value # type: ignore ) # Assigns the value to the node to delete and keep tree structure def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Node | None ) -> Iterable: '''simple docstring''' if node is not None: yield node # Preorder Traversal yield from self.preorder_traverse(node.left ) yield from self.preorder_traverse(node.right ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Dict=None ) -> Any: '''simple docstring''' if traversal_function is None: return self.preorder_traverse(self.root ) else: return traversal_function(self.root ) def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : list ,lowerCAmelCase__ : Node | None ) -> None: '''simple docstring''' if node: self.inorder(lowerCAmelCase__ ,node.left ) arr.append(node.value ) self.inorder(lowerCAmelCase__ ,node.right ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Node ) -> int: '''simple docstring''' lowerCAmelCase_ : list[int] = [] self.inorder(lowerCAmelCase__ ,lowerCAmelCase__ ) # append all values to list using inorder traversal return arr[k - 1] def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[Any] = [] if curr_node is not None: lowerCAmelCase_ : Dict = postorder(curr_node.left) + postorder(curr_node.right) + [curr_node] return node_list def UpperCamelCase ( ): lowerCAmelCase_ : Tuple = (8, 3, 6, 1, 10, 14, 13, 4, 7) lowerCAmelCase_ : Tuple = BinarySearchTree() for i in testlist: t.insert(snake_case__) # Prints all the elements of the list in order traversal print(snake_case__) if t.search(6) is not None: print("The value 6 exists") else: print("The value 6 doesn't exist") if t.search(-1) is not None: print("The value -1 exists") else: print("The value -1 doesn't exist") if not t.empty(): print("Max Value: " , t.get_max().value) # type: ignore print("Min Value: " , t.get_min().value) # type: ignore for i in testlist: t.remove(snake_case__) print(snake_case__) if __name__ == "__main__": import doctest doctest.testmod(verbose=True)
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'''simple docstring''' import unittest from transformers import ( MODEL_FOR_OBJECT_DETECTION_MAPPING, AutoFeatureExtractor, AutoModelForObjectDetection, ObjectDetectionPipeline, is_vision_available, pipeline, ) from transformers.testing_utils import ( is_pipeline_test, nested_simplify, require_pytesseract, require_tf, require_timm, require_torch, require_vision, slow, ) from .test_pipelines_common import ANY if is_vision_available(): from PIL import Image else: class __snake_case : """simple docstring""" @staticmethod def UpperCAmelCase_ ( *lowerCAmelCase__ : List[Any] ,**lowerCAmelCase__ : List[Any] ) -> Any: '''simple docstring''' pass @is_pipeline_test @require_vision @require_timm @require_torch class __snake_case ( unittest.TestCase ): """simple docstring""" UpperCamelCase_ = MODEL_FOR_OBJECT_DETECTION_MAPPING def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[Any] ) -> int: '''simple docstring''' lowerCAmelCase_ : Dict = ObjectDetectionPipeline(model=lowerCAmelCase__ ,image_processor=lowerCAmelCase__ ) return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"] def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[int] ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" ,threshold=0.0 ) self.assertGreater(len(lowerCAmelCase__ ) ,0 ) for detected_object in outputs: self.assertEqual( lowerCAmelCase__ ,{ "score": ANY(lowerCAmelCase__ ), "label": ANY(lowerCAmelCase__ ), "box": {"xmin": ANY(lowerCAmelCase__ ), "ymin": ANY(lowerCAmelCase__ ), "xmax": ANY(lowerCAmelCase__ ), "ymax": ANY(lowerCAmelCase__ )}, } ,) import datasets lowerCAmelCase_ : Tuple = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" ,"image" ,split="test" ) lowerCAmelCase_ : int = [ Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ), "http://images.cocodataset.org/val2017/000000039769.jpg", # RGBA dataset[0]["file"], # LA dataset[1]["file"], # L dataset[2]["file"], ] lowerCAmelCase_ : Optional[Any] = object_detector(lowerCAmelCase__ ,threshold=0.0 ) self.assertEqual(len(lowerCAmelCase__ ) ,len(lowerCAmelCase__ ) ) for outputs in batch_outputs: self.assertGreater(len(lowerCAmelCase__ ) ,0 ) for detected_object in outputs: self.assertEqual( lowerCAmelCase__ ,{ "score": ANY(lowerCAmelCase__ ), "label": ANY(lowerCAmelCase__ ), "box": {"xmin": ANY(lowerCAmelCase__ ), "ymin": ANY(lowerCAmelCase__ ), "xmax": ANY(lowerCAmelCase__ ), "ymax": ANY(lowerCAmelCase__ )}, } ,) @require_tf @unittest.skip("Object detection not implemented in TF" ) def UpperCAmelCase_ ( self : int ) -> str: '''simple docstring''' pass @require_torch def UpperCAmelCase_ ( self : List[str] ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : str = "hf-internal-testing/tiny-detr-mobilenetsv3" lowerCAmelCase_ : Union[str, Any] = AutoModelForObjectDetection.from_pretrained(lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = AutoFeatureExtractor.from_pretrained(lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = ObjectDetectionPipeline(model=lowerCAmelCase__ ,feature_extractor=lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" ,threshold=0.0 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.3_376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}}, {"score": 0.3_376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}}, ] ,) lowerCAmelCase_ : List[str] = object_detector( [ "http://images.cocodataset.org/val2017/000000039769.jpg", "http://images.cocodataset.org/val2017/000000039769.jpg", ] ,threshold=0.0 ,) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ [ {"score": 0.3_376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}}, {"score": 0.3_376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}}, ], [ {"score": 0.3_376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}}, {"score": 0.3_376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}}, ], ] ,) @require_torch @slow def UpperCAmelCase_ ( self : List[str] ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Optional[int] = "facebook/detr-resnet-50" lowerCAmelCase_ : Union[str, Any] = AutoModelForObjectDetection.from_pretrained(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = AutoFeatureExtractor.from_pretrained(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = ObjectDetectionPipeline(model=lowerCAmelCase__ ,feature_extractor=lowerCAmelCase__ ) lowerCAmelCase_ : str = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.9_982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}}, {"score": 0.9_960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}}, {"score": 0.9_955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}}, {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}}, {"score": 0.9_987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}}, ] ,) lowerCAmelCase_ : Optional[Any] = object_detector( [ "http://images.cocodataset.org/val2017/000000039769.jpg", "http://images.cocodataset.org/val2017/000000039769.jpg", ] ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ [ {"score": 0.9_982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}}, {"score": 0.9_960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}}, {"score": 0.9_955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}}, {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}}, {"score": 0.9_987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}}, ], [ {"score": 0.9_982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}}, {"score": 0.9_960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}}, {"score": 0.9_955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}}, {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}}, {"score": 0.9_987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}}, ], ] ,) @require_torch @slow def UpperCAmelCase_ ( self : List[str] ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : int = "facebook/detr-resnet-50" lowerCAmelCase_ : List[str] = pipeline("object-detection" ,model=lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.9_982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}}, {"score": 0.9_960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}}, {"score": 0.9_955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}}, {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}}, {"score": 0.9_987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}}, ] ,) lowerCAmelCase_ : Optional[Any] = object_detector( [ "http://images.cocodataset.org/val2017/000000039769.jpg", "http://images.cocodataset.org/val2017/000000039769.jpg", ] ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ [ {"score": 0.9_982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}}, {"score": 0.9_960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}}, {"score": 0.9_955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}}, {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}}, {"score": 0.9_987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}}, ], [ {"score": 0.9_982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}}, {"score": 0.9_960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}}, {"score": 0.9_955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}}, {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}}, {"score": 0.9_987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}}, ], ] ,) @require_torch @slow def UpperCAmelCase_ ( self : int ) -> int: '''simple docstring''' lowerCAmelCase_ : Optional[int] = 0.9_985 lowerCAmelCase_ : Tuple = "facebook/detr-resnet-50" lowerCAmelCase_ : Optional[Any] = pipeline("object-detection" ,model=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" ,threshold=lowerCAmelCase__ ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}}, {"score": 0.9_987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}}, ] ,) @require_torch @require_pytesseract @slow def UpperCAmelCase_ ( self : List[str] ) -> Dict: '''simple docstring''' lowerCAmelCase_ : int = "Narsil/layoutlmv3-finetuned-funsd" lowerCAmelCase_ : List[Any] = 0.9_993 lowerCAmelCase_ : Tuple = pipeline("object-detection" ,model=lowerCAmelCase__ ,threshold=lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = object_detector( "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.9_993, "label": "I-ANSWER", "box": {"xmin": 2_94, "ymin": 2_54, "xmax": 3_43, "ymax": 2_64}}, {"score": 0.9_993, "label": "I-ANSWER", "box": {"xmin": 2_94, "ymin": 2_54, "xmax": 3_43, "ymax": 2_64}}, ] ,)
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class __snake_case : """simple docstring""" def __init__( self : Optional[int] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None: '''simple docstring''' lowerCAmelCase_ : dict[str, RadixNode] = {} # A node will be a leaf if the tree contains its word lowerCAmelCase_ : int = is_leaf lowerCAmelCase_ : Optional[Any] = prefix def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ) -> tuple[str, str, str]: '''simple docstring''' lowerCAmelCase_ : Any = 0 for q, w in zip(self.prefix ,lowerCAmelCase__ ): if q != w: break x += 1 return self.prefix[:x], self.prefix[x:], word[x:] def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : list[str] ) -> None: '''simple docstring''' for word in words: self.insert(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ) -> None: '''simple docstring''' if self.prefix == word: lowerCAmelCase_ : Optional[Any] = True # Case 2: The node has no edges that have a prefix to the word # Solution: We create an edge from the current node to a new one # containing the word elif word[0] not in self.nodes: lowerCAmelCase_ : List[Any] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ ) else: lowerCAmelCase_ : Tuple = self.nodes[word[0]] lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = incoming_node.match( lowerCAmelCase__ ) # Case 3: The node prefix is equal to the matching # Solution: We insert remaining word on the next node if remaining_prefix == "": self.nodes[matching_string[0]].insert(lowerCAmelCase__ ) # Case 4: The word is greater equal to the matching # Solution: Create a node in between both nodes, change # prefixes and add the new node for the remaining word else: lowerCAmelCase_ : Optional[int] = remaining_prefix lowerCAmelCase_ : Optional[int] = self.nodes[matching_string[0]] lowerCAmelCase_ : List[Any] = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Dict = aux_node if remaining_word == "": lowerCAmelCase_ : List[str] = True else: self.nodes[matching_string[0]].insert(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool: '''simple docstring''' lowerCAmelCase_ : Any = self.nodes.get(word[0] ,lowerCAmelCase__ ) if not incoming_node: return False else: lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match( lowerCAmelCase__ ) # If there is remaining prefix, the word can't be on the tree if remaining_prefix != "": return False # This applies when the word and the prefix are equal elif remaining_word == "": return incoming_node.is_leaf # We have word remaining so we check the next node else: return incoming_node.find(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool: '''simple docstring''' lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ ) if not incoming_node: return False else: lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = incoming_node.match( lowerCAmelCase__ ) # If there is remaining prefix, the word can't be on the tree if remaining_prefix != "": return False # We have word remaining so we check the next node elif remaining_word != "": return incoming_node.delete(lowerCAmelCase__ ) else: # If it is not a leaf, we don't have to delete if not incoming_node.is_leaf: return False else: # We delete the nodes if no edges go from it if len(incoming_node.nodes ) == 0: del self.nodes[word[0]] # We merge the current node with its only child if len(self.nodes ) == 1 and not self.is_leaf: lowerCAmelCase_ : str = list(self.nodes.values() )[0] lowerCAmelCase_ : Tuple = merging_node.is_leaf self.prefix += merging_node.prefix lowerCAmelCase_ : Optional[int] = merging_node.nodes # If there is more than 1 edge, we just mark it as non-leaf elif len(incoming_node.nodes ) > 1: lowerCAmelCase_ : Optional[Any] = False # If there is 1 edge, we merge it with its child else: lowerCAmelCase_ : Tuple = list(incoming_node.nodes.values() )[0] lowerCAmelCase_ : Union[str, Any] = merging_node.is_leaf incoming_node.prefix += merging_node.prefix lowerCAmelCase_ : str = merging_node.nodes return True def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : int = 0 ) -> None: '''simple docstring''' if self.prefix != "": print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" ) for value in self.nodes.values(): value.print_tree(height + 1 ) def UpperCamelCase ( ): lowerCAmelCase_ : Dict = "banana bananas bandana band apple all beast".split() lowerCAmelCase_ : List[Any] = RadixNode() root.insert_many(snake_case__) assert all(root.find(snake_case__) for word in words) assert not root.find("bandanas") assert not root.find("apps") root.delete("all") assert not root.find("all") root.delete("banana") assert not root.find("banana") assert root.find("bananas") return True def UpperCamelCase ( ): assert test_trie() def UpperCamelCase ( ): lowerCAmelCase_ : List[str] = RadixNode() lowerCAmelCase_ : Optional[Any] = "banana bananas bandanas bandana band apple all beast".split() root.insert_many(snake_case__) print("Words:" , snake_case__) print("Tree:") root.print_tree() if __name__ == "__main__": main()
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import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import ViTImageProcessor class __snake_case ( unittest.TestCase ): """simple docstring""" def __init__( self : List[Any] ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : str=13 ,lowerCAmelCase__ : Dict=3 ,lowerCAmelCase__ : Optional[int]=2_24 ,lowerCAmelCase__ : Optional[int]=30 ,lowerCAmelCase__ : int=4_00 ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : int=None ,lowerCAmelCase__ : List[str]=True ,lowerCAmelCase__ : str=[0.5, 0.5, 0.5] ,lowerCAmelCase__ : Any=[0.5, 0.5, 0.5] ,) -> Any: '''simple docstring''' lowerCAmelCase_ : int = size if size is not None else {"height": 18, "width": 18} lowerCAmelCase_ : Optional[Any] = parent lowerCAmelCase_ : Union[str, Any] = batch_size lowerCAmelCase_ : int = num_channels lowerCAmelCase_ : int = image_size lowerCAmelCase_ : Tuple = min_resolution lowerCAmelCase_ : Tuple = max_resolution lowerCAmelCase_ : Tuple = do_resize lowerCAmelCase_ : int = size lowerCAmelCase_ : Dict = do_normalize lowerCAmelCase_ : Any = image_mean lowerCAmelCase_ : List[str] = image_std def UpperCAmelCase_ ( self : int ) -> Optional[Any]: '''simple docstring''' return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "size": self.size, } @require_torch @require_vision class __snake_case ( snake_case__ , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = ViTImageProcessor if is_vision_available() else None def UpperCAmelCase_ ( self : int ) -> str: '''simple docstring''' lowerCAmelCase_ : List[str] = EfficientFormerImageProcessorTester(self ) @property def UpperCAmelCase_ ( self : Optional[int] ) -> Tuple: '''simple docstring''' return self.image_proc_tester.prepare_image_processor_dict() def UpperCAmelCase_ ( self : str ) -> str: '''simple docstring''' lowerCAmelCase_ : Any = self.image_processing_class(**self.image_processor_dict ) self.assertTrue(hasattr(__UpperCamelCase ,"image_mean" ) ) self.assertTrue(hasattr(__UpperCamelCase ,"image_std" ) ) self.assertTrue(hasattr(__UpperCamelCase ,"do_normalize" ) ) self.assertTrue(hasattr(__UpperCamelCase ,"do_resize" ) ) self.assertTrue(hasattr(__UpperCamelCase ,"size" ) ) def UpperCAmelCase_ ( self : Optional[int] ) -> Dict: '''simple docstring''' pass def UpperCAmelCase_ ( self : Dict ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : str = self.image_processing_class(**self.image_processor_dict ) # create random PIL images lowerCAmelCase_ : Optional[int] = prepare_image_inputs(self.image_proc_tester ,equal_resolution=__UpperCamelCase ) for image in image_inputs: self.assertIsInstance(__UpperCamelCase ,Image.Image ) # Test not batched input lowerCAmelCase_ : Tuple = image_processor(image_inputs[0] ,return_tensors="pt" ).pixel_values self.assertEqual( encoded_images.shape ,( 1, self.image_proc_tester.num_channels, self.image_proc_tester.size["height"], self.image_proc_tester.size["width"], ) ,) # Test batched lowerCAmelCase_ : List[Any] = image_processor(__UpperCamelCase ,return_tensors="pt" ).pixel_values self.assertEqual( encoded_images.shape ,( self.image_proc_tester.batch_size, self.image_proc_tester.num_channels, self.image_proc_tester.size["height"], self.image_proc_tester.size["width"], ) ,) def UpperCAmelCase_ ( self : List[str] ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Optional[int] = self.image_processing_class(**self.image_processor_dict ) # create random numpy tensors lowerCAmelCase_ : Optional[int] = prepare_image_inputs(self.image_proc_tester ,equal_resolution=__UpperCamelCase ,numpify=__UpperCamelCase ) for image in image_inputs: self.assertIsInstance(__UpperCamelCase ,np.ndarray ) # Test not batched input lowerCAmelCase_ : Tuple = image_processor(image_inputs[0] ,return_tensors="pt" ).pixel_values self.assertEqual( encoded_images.shape ,( 1, self.image_proc_tester.num_channels, self.image_proc_tester.size["height"], self.image_proc_tester.size["width"], ) ,) # Test batched lowerCAmelCase_ : List[Any] = image_processor(__UpperCamelCase ,return_tensors="pt" ).pixel_values self.assertEqual( encoded_images.shape ,( self.image_proc_tester.batch_size, self.image_proc_tester.num_channels, self.image_proc_tester.size["height"], self.image_proc_tester.size["width"], ) ,) def UpperCAmelCase_ ( self : Tuple ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : int = self.image_processing_class(**self.image_processor_dict ) # create random PyTorch tensors lowerCAmelCase_ : int = prepare_image_inputs(self.image_proc_tester ,equal_resolution=__UpperCamelCase ,torchify=__UpperCamelCase ) for image in image_inputs: self.assertIsInstance(__UpperCamelCase ,torch.Tensor ) # Test not batched input lowerCAmelCase_ : Dict = image_processor(image_inputs[0] ,return_tensors="pt" ).pixel_values self.assertEqual( encoded_images.shape ,( 1, self.image_proc_tester.num_channels, self.image_proc_tester.size["height"], self.image_proc_tester.size["width"], ) ,) # Test batched lowerCAmelCase_ : str = image_processor(__UpperCamelCase ,return_tensors="pt" ).pixel_values self.assertEqual( encoded_images.shape ,( self.image_proc_tester.batch_size, self.image_proc_tester.num_channels, self.image_proc_tester.size["height"], self.image_proc_tester.size["width"], ) ,)
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from __future__ import annotations def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ): if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1: raise ValueError("You cannot supply more or less than 2 values") elif electron_conc < 0: raise ValueError("Electron concentration cannot be negative in a semiconductor") elif hole_conc < 0: raise ValueError("Hole concentration cannot be negative in a semiconductor") elif intrinsic_conc < 0: raise ValueError( "Intrinsic concentration cannot be negative in a semiconductor") elif electron_conc == 0: return ( "electron_conc", intrinsic_conc**2 / hole_conc, ) elif hole_conc == 0: return ( "hole_conc", intrinsic_conc**2 / electron_conc, ) elif intrinsic_conc == 0: return ( "intrinsic_conc", (electron_conc * hole_conc) ** 0.5, ) else: return (-1, -1) if __name__ == "__main__": import doctest doctest.testmod()
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# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import platform import numpy as np import psutil import torch from accelerate import __version__ as version from accelerate.commands.config import default_config_file, load_config_from_file from ..utils import is_npu_available, is_xpu_available def UpperCamelCase ( snake_case__=None): if subparsers is not None: lowerCAmelCase_ : Any = subparsers.add_parser("env") else: lowerCAmelCase_ : int = argparse.ArgumentParser("Accelerate env command") parser.add_argument( "--config_file" , default=snake_case__ , help="The config file to use for the default values in the launching script.") if subparsers is not None: parser.set_defaults(func=snake_case__) return parser def UpperCamelCase ( snake_case__): lowerCAmelCase_ : List[str] = torch.__version__ lowerCAmelCase_ : List[Any] = torch.cuda.is_available() lowerCAmelCase_ : List[Any] = is_xpu_available() lowerCAmelCase_ : int = is_npu_available() lowerCAmelCase_ : Optional[int] = """Not found""" # Get the default from the config file. if args.config_file is not None or os.path.isfile(snake_case__): lowerCAmelCase_ : List[str] = load_config_from_file(args.config_file).to_dict() lowerCAmelCase_ : Optional[int] = { """`Accelerate` version""": version, """Platform""": platform.platform(), """Python version""": platform.python_version(), """Numpy version""": np.__version__, """PyTorch version (GPU?)""": F'''{pt_version} ({pt_cuda_available})''', """PyTorch XPU available""": str(snake_case__), """PyTorch NPU available""": str(snake_case__), """System RAM""": F'''{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB''', } if pt_cuda_available: lowerCAmelCase_ : str = torch.cuda.get_device_name() print("\nCopy-and-paste the text below in your GitHub issue\n") print("\n".join([F'''- {prop}: {val}''' for prop, val in info.items()])) print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:") lowerCAmelCase_ : Optional[int] = ( """\n""".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]) if isinstance(snake_case__ , snake_case__) else F'''\t{accelerate_config}''' ) print(snake_case__) lowerCAmelCase_ : Dict = accelerate_config return info def UpperCamelCase ( ): lowerCAmelCase_ : Any = env_command_parser() lowerCAmelCase_ : Union[str, Any] = parser.parse_args() env_command(snake_case__) return 0 if __name__ == "__main__": raise SystemExit(main())
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _lowercase = { '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''], '''processing_git''': ['''GitProcessor'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''GitForCausalLM''', '''GitModel''', '''GitPreTrainedModel''', '''GitVisionModel''', ] if TYPE_CHECKING: from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig from .processing_git import GitProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_git import ( GIT_PRETRAINED_MODEL_ARCHIVE_LIST, GitForCausalLM, GitModel, GitPreTrainedModel, GitVisionModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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from ..utils import DummyObject, requires_backends class __snake_case ( metaclass=__A ): """simple docstring""" UpperCamelCase_ = ["""onnx"""] def __init__( self : List[str] ,*lowerCAmelCase__ : Union[str, Any] ,**lowerCAmelCase__ : Any ) -> Union[str, Any]: '''simple docstring''' requires_backends(self ,["onnx"] ) @classmethod def UpperCAmelCase_ ( cls : Optional[int] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : str ) -> str: '''simple docstring''' requires_backends(cls ,["onnx"] ) @classmethod def UpperCAmelCase_ ( cls : Optional[Any] ,*lowerCAmelCase__ : Any ,**lowerCAmelCase__ : Tuple ) -> Dict: '''simple docstring''' requires_backends(cls ,["onnx"] )
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from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments def UpperCamelCase ( ): lowerCAmelCase_ : List[str] = HfArgumentParser(snake_case__) lowerCAmelCase_ : List[Any] = parser.parse_args_into_dataclasses()[0] lowerCAmelCase_ : Optional[int] = TensorFlowBenchmark(args=snake_case__) try: lowerCAmelCase_ : Tuple = parser.parse_args_into_dataclasses()[0] except ValueError as e: lowerCAmelCase_ : Union[str, Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead." lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1]) lowerCAmelCase_ : Union[str, Any] = "" lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1]) lowerCAmelCase_ : Tuple = [] for arg in depreciated_args: # arg[2:] removes '--' if arg[2:] in TensorFlowBenchmark.deprecated_args: # arg[5:] removes '--no_' full_error_msg += arg_error_msg.format(arg[5:]) else: wrong_args.append(snake_case__) if len(snake_case__) > 0: lowerCAmelCase_ : Optional[Any] = full_error_msg + begin_error_msg + str(snake_case__) raise ValueError(snake_case__) benchmark.run() if __name__ == "__main__": main()
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'''simple docstring''' import argparse import json from typing import List from ltp import LTP from transformers import BertTokenizer def UpperCamelCase ( snake_case__): # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) # # Note that the CJK Unicode block is NOT all Japanese and Korean characters, # despite its name. The modern Korean Hangul alphabet is a different block, # as is Japanese Hiragana and Katakana. Those alphabets are used to write # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ( (cp >= 0x4e_00 and cp <= 0x9f_ff) or (cp >= 0x34_00 and cp <= 0x4d_bf) # or (cp >= 0x2_00_00 and cp <= 0x2_a6_df) # or (cp >= 0x2_a7_00 and cp <= 0x2_b7_3f) # or (cp >= 0x2_b7_40 and cp <= 0x2_b8_1f) # or (cp >= 0x2_b8_20 and cp <= 0x2_ce_af) # or (cp >= 0xf9_00 and cp <= 0xfa_ff) or (cp >= 0x2_f8_00 and cp <= 0x2_fa_1f) # ): # return True return False def UpperCamelCase ( snake_case__): # word like '180' or '身高' or '神' for char in word: lowerCAmelCase_ : Optional[int] = ord(snake_case__) if not _is_chinese_char(snake_case__): return 0 return 1 def UpperCamelCase ( snake_case__): lowerCAmelCase_ : List[Any] = set() for token in tokens: lowerCAmelCase_ : int = len(snake_case__) > 1 and is_chinese(snake_case__) if chinese_word: word_set.add(snake_case__) lowerCAmelCase_ : Any = list(snake_case__) return word_list def UpperCamelCase ( snake_case__ , snake_case__): if not chinese_word_set: return bert_tokens lowerCAmelCase_ : Tuple = max([len(snake_case__) for w in chinese_word_set]) lowerCAmelCase_ : Dict = bert_tokens lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = 0, len(snake_case__) while start < end: lowerCAmelCase_ : List[str] = True if is_chinese(bert_word[start]): lowerCAmelCase_ : List[str] = min(end - start , snake_case__) for i in range(snake_case__ , 1 , -1): lowerCAmelCase_ : List[str] = "".join(bert_word[start : start + i]) if whole_word in chinese_word_set: for j in range(start + 1 , start + i): lowerCAmelCase_ : List[Any] = "##" + bert_word[j] lowerCAmelCase_ : Dict = start + i lowerCAmelCase_ : Tuple = False break if single_word: start += 1 return bert_word def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Tuple = [] for i in range(0 , len(snake_case__) , 1_00): lowerCAmelCase_ : Dict = ltp_tokenizer.seg(lines[i : i + 1_00])[0] lowerCAmelCase_ : Optional[Any] = [get_chinese_word(snake_case__) for r in res] ltp_res.extend(snake_case__) assert len(snake_case__) == len(snake_case__) lowerCAmelCase_ : int = [] for i in range(0 , len(snake_case__) , 1_00): lowerCAmelCase_ : Union[str, Any] = bert_tokenizer(lines[i : i + 1_00] , add_special_tokens=snake_case__ , truncation=snake_case__ , max_length=5_12) bert_res.extend(res["input_ids"]) assert len(snake_case__) == len(snake_case__) lowerCAmelCase_ : int = [] for input_ids, chinese_word in zip(snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = [] for id in input_ids: lowerCAmelCase_ : Optional[int] = bert_tokenizer._convert_id_to_token(snake_case__) input_tokens.append(snake_case__) lowerCAmelCase_ : Dict = add_sub_symbol(snake_case__ , snake_case__) lowerCAmelCase_ : str = [] # We only save pos of chinese subwords start with ##, which mean is part of a whole word. for i, token in enumerate(snake_case__): if token[:2] == "##": lowerCAmelCase_ : Optional[int] = token[2:] # save chinese tokens' pos if len(snake_case__) == 1 and _is_chinese_char(ord(snake_case__)): ref_id.append(snake_case__) ref_ids.append(snake_case__) assert len(snake_case__) == len(snake_case__) return ref_ids def UpperCamelCase ( snake_case__): # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm) # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp) with open(args.file_name , "r" , encoding="utf-8") as f: lowerCAmelCase_ : str = f.readlines() lowerCAmelCase_ : Union[str, Any] = [line.strip() for line in data if len(snake_case__) > 0 and not line.isspace()] # avoid delimiter like '\u2029' lowerCAmelCase_ : List[Any] = LTP(args.ltp) # faster in GPU device lowerCAmelCase_ : int = BertTokenizer.from_pretrained(args.bert) lowerCAmelCase_ : List[Any] = prepare_ref(snake_case__ , snake_case__ , snake_case__) with open(args.save_path , "w" , encoding="utf-8") as f: lowerCAmelCase_ : Dict = [json.dumps(snake_case__) + "\n" for ref in ref_ids] f.writelines(snake_case__) if __name__ == "__main__": _lowercase = argparse.ArgumentParser(description='''prepare_chinese_ref''') parser.add_argument( '''--file_name''', type=str, default='''./resources/chinese-demo.txt''', help='''file need process, same as training data in lm''', ) parser.add_argument( '''--ltp''', type=str, default='''./resources/ltp''', help='''resources for LTP tokenizer, usually a path''' ) parser.add_argument('''--bert''', type=str, default='''./resources/robert''', help='''resources for Bert tokenizer''') parser.add_argument('''--save_path''', type=str, default='''./resources/ref.txt''', help='''path to save res''') _lowercase = parser.parse_args() main(args)
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_lowercase = { 0: '''0''', 1: '''1''', 2: '''2''', 3: '''3''', 4: '''4''', 5: '''5''', 6: '''6''', 7: '''7''', 8: '''8''', 9: '''9''', 10: '''a''', 11: '''b''', 12: '''c''', 13: '''d''', 14: '''e''', 15: '''f''', } def UpperCamelCase ( snake_case__): assert type(snake_case__) in (int, float) and decimal == int(snake_case__) lowerCAmelCase_ : Optional[Any] = int(snake_case__) lowerCAmelCase_ : Tuple = "" lowerCAmelCase_ : str = False if decimal < 0: lowerCAmelCase_ : Tuple = True decimal *= -1 while decimal > 0: lowerCAmelCase_ , lowerCAmelCase_ : Any = divmod(snake_case__ , 16) lowerCAmelCase_ : Dict = values[remainder] + hexadecimal lowerCAmelCase_ : List[str] = "0x" + hexadecimal if negative: lowerCAmelCase_ : Optional[Any] = "-" + hexadecimal return hexadecimal if __name__ == "__main__": import doctest doctest.testmod()
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from __future__ import annotations from random import random from typing import Generic, TypeVar _lowercase = TypeVar('''KT''') _lowercase = TypeVar('''VT''') class __snake_case ( Generic[KT, VT] ): """simple docstring""" def __init__( self : Tuple ,lowerCAmelCase__ : KT | str = "root" ,lowerCAmelCase__ : VT | None = None ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Optional[int] = key lowerCAmelCase_ : List[Any] = value lowerCAmelCase_ : List[str] = [] def __repr__( self : int ) -> List[str]: '''simple docstring''' return f'''Node({self.key}: {self.value})''' @property def UpperCAmelCase_ ( self : str ) -> Optional[Any]: '''simple docstring''' return len(self.forward ) class __snake_case ( Generic[KT, VT] ): """simple docstring""" def __init__( self : Tuple ,lowerCAmelCase__ : float = 0.5 ,lowerCAmelCase__ : int = 16 ) -> Dict: '''simple docstring''' lowerCAmelCase_ : str = Node[KT, VT]() lowerCAmelCase_ : Union[str, Any] = 0 lowerCAmelCase_ : Any = p lowerCAmelCase_ : Optional[int] = max_level def __str__( self : int ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = list(self ) if len(UpperCamelCase__ ) == 0: return f'''SkipList(level={self.level})''' lowerCAmelCase_ : str = max((len(str(UpperCamelCase__ ) ) for item in items) ,default=4 ) lowerCAmelCase_ : List[Any] = max(UpperCamelCase__ ,4 ) + 4 lowerCAmelCase_ : Union[str, Any] = self.head lowerCAmelCase_ : Dict = [] lowerCAmelCase_ : List[str] = node.forward.copy() lines.append(f'''[{node.key}]'''.ljust(UpperCamelCase__ ,"-" ) + "* " * len(UpperCamelCase__ ) ) lines.append(" " * label_size + "| " * len(UpperCamelCase__ ) ) while len(node.forward ) != 0: lowerCAmelCase_ : List[str] = node.forward[0] lines.append( f'''[{node.key}]'''.ljust(UpperCamelCase__ ,"-" ) + " ".join(str(n.key ) if n.key == node.key else "|" for n in forwards ) ) lines.append(" " * label_size + "| " * len(UpperCamelCase__ ) ) lowerCAmelCase_ : List[str] = node.forward lines.append("None".ljust(UpperCamelCase__ ) + "* " * len(UpperCamelCase__ ) ) return f'''SkipList(level={self.level})\n''' + "\n".join(UpperCamelCase__ ) def __iter__( self : Any ) -> Any: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = self.head while len(node.forward ) != 0: yield node.forward[0].key lowerCAmelCase_ : List[str] = node.forward[0] def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : Tuple = 1 while random() < self.p and level < self.max_level: level += 1 return level def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : Any ) -> int: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = [] lowerCAmelCase_ : Optional[int] = self.head for i in reversed(range(self.level ) ): # i < node.level - When node level is lesser than `i` decrement `i`. # node.forward[i].key < key - Jumping to node with key value higher # or equal to searched key would result # in skipping searched key. while i < node.level and node.forward[i].key < key: lowerCAmelCase_ : Union[str, Any] = node.forward[i] # Each leftmost node (relative to searched node) will potentially have to # be updated. update_vector.append(UpperCamelCase__ ) update_vector.reverse() # Note that we were inserting values in reverse order. # len(node.forward) != 0 - If current node doesn't contain any further # references then searched key is not present. # node.forward[0].key == key - Next node key should be equal to search key # if key is present. if len(node.forward ) != 0 and node.forward[0].key == key: return node.forward[0], update_vector else: return None, update_vector def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : KT ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ , lowerCAmelCase_ : Tuple = self._locate_node(UpperCamelCase__ ) if node is not None: for i, update_node in enumerate(UpperCamelCase__ ): # Remove or replace all references to removed node. if update_node.level > i and update_node.forward[i].key == key: if node.level > i: lowerCAmelCase_ : Optional[int] = node.forward[i] else: lowerCAmelCase_ : List[str] = update_node.forward[:i] def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : KT ,lowerCAmelCase__ : VT ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = self._locate_node(UpperCamelCase__ ) if node is not None: lowerCAmelCase_ : Any = value else: lowerCAmelCase_ : List[Any] = self.random_level() if level > self.level: # After level increase we have to add additional nodes to head. for _ in range(self.level - 1 ,UpperCamelCase__ ): update_vector.append(self.head ) lowerCAmelCase_ : Optional[int] = level lowerCAmelCase_ : List[Any] = Node(UpperCamelCase__ ,UpperCamelCase__ ) for i, update_node in enumerate(update_vector[:level] ): # Change references to pass through new node. if update_node.level > i: new_node.forward.append(update_node.forward[i] ) if update_node.level < i + 1: update_node.forward.append(UpperCamelCase__ ) else: lowerCAmelCase_ : Dict = new_node def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : VT ) -> List[str]: '''simple docstring''' lowerCAmelCase_ , lowerCAmelCase_ : str = self._locate_node(UpperCamelCase__ ) if node is not None: return node.value return None def UpperCamelCase ( ): lowerCAmelCase_ : int = SkipList() skip_list.insert("Key1" , 3) skip_list.insert("Key2" , 12) skip_list.insert("Key3" , 41) skip_list.insert("Key4" , -19) lowerCAmelCase_ : List[Any] = skip_list.head lowerCAmelCase_ : Optional[Any] = {} while node.level != 0: lowerCAmelCase_ : str = node.forward[0] lowerCAmelCase_ : int = node.value assert len(snake_case__) == 4 assert all_values["Key1"] == 3 assert all_values["Key2"] == 12 assert all_values["Key3"] == 41 assert all_values["Key4"] == -19 def UpperCamelCase ( ): lowerCAmelCase_ : List[str] = SkipList() skip_list.insert("Key1" , 10) skip_list.insert("Key1" , 12) skip_list.insert("Key5" , 7) skip_list.insert("Key7" , 10) skip_list.insert("Key10" , 5) skip_list.insert("Key7" , 7) skip_list.insert("Key5" , 5) skip_list.insert("Key10" , 10) lowerCAmelCase_ : Dict = skip_list.head lowerCAmelCase_ : int = {} while node.level != 0: lowerCAmelCase_ : int = node.forward[0] lowerCAmelCase_ : str = node.value if len(snake_case__) != 4: print() assert len(snake_case__) == 4 assert all_values["Key1"] == 12 assert all_values["Key7"] == 7 assert all_values["Key5"] == 5 assert all_values["Key10"] == 10 def UpperCamelCase ( ): lowerCAmelCase_ : List[str] = SkipList() assert skip_list.find("Some key") is None def UpperCamelCase ( ): lowerCAmelCase_ : Optional[int] = SkipList() skip_list.insert("Key2" , 20) assert skip_list.find("Key2") == 20 skip_list.insert("Some Key" , 10) skip_list.insert("Key2" , 8) skip_list.insert("V" , 13) assert skip_list.find("Y") is None assert skip_list.find("Key2") == 8 assert skip_list.find("Some Key") == 10 assert skip_list.find("V") == 13 def UpperCamelCase ( ): lowerCAmelCase_ : Union[str, Any] = SkipList() skip_list.delete("Some key") assert len(skip_list.head.forward) == 0 def UpperCamelCase ( ): lowerCAmelCase_ : List[Any] = SkipList() skip_list.insert("Key1" , 12) skip_list.insert("V" , 13) skip_list.insert("X" , 14) skip_list.insert("Key2" , 15) skip_list.delete("V") skip_list.delete("Key2") assert skip_list.find("V") is None assert skip_list.find("Key2") is None def UpperCamelCase ( ): lowerCAmelCase_ : Dict = SkipList() skip_list.insert("Key1" , 12) skip_list.insert("V" , 13) skip_list.insert("X" , 14) skip_list.insert("Key2" , 15) skip_list.delete("V") assert skip_list.find("V") is None assert skip_list.find("X") == 14 assert skip_list.find("Key1") == 12 assert skip_list.find("Key2") == 15 skip_list.delete("X") assert skip_list.find("V") is None assert skip_list.find("X") is None assert skip_list.find("Key1") == 12 assert skip_list.find("Key2") == 15 skip_list.delete("Key1") assert skip_list.find("V") is None assert skip_list.find("X") is None assert skip_list.find("Key1") is None assert skip_list.find("Key2") == 15 skip_list.delete("Key2") assert skip_list.find("V") is None assert skip_list.find("X") is None assert skip_list.find("Key1") is None assert skip_list.find("Key2") is None def UpperCamelCase ( ): lowerCAmelCase_ : Any = SkipList() skip_list.insert("Key1" , 12) skip_list.insert("V" , 13) skip_list.insert("X" , 1_42) skip_list.insert("Key2" , 15) skip_list.delete("X") def traverse_keys(snake_case__): yield node.key for forward_node in node.forward: yield from traverse_keys(snake_case__) assert len(set(traverse_keys(skip_list.head))) == 4 def UpperCamelCase ( ): def is_sorted(snake_case__): return all(next_item >= item for item, next_item in zip(snake_case__ , lst[1:])) lowerCAmelCase_ : Dict = SkipList() for i in range(10): skip_list.insert(snake_case__ , snake_case__) assert is_sorted(list(snake_case__)) skip_list.delete(5) skip_list.delete(8) skip_list.delete(2) assert is_sorted(list(snake_case__)) skip_list.insert(-12 , -12) skip_list.insert(77 , 77) assert is_sorted(list(snake_case__)) def UpperCamelCase ( ): for _ in range(1_00): # Repeat test 100 times due to the probabilistic nature of skip list # random values == random bugs test_insert() test_insert_overrides_existing_value() test_searching_empty_list_returns_none() test_search() test_deleting_item_from_empty_list_do_nothing() test_deleted_items_are_not_founded_by_find_method() test_delete_removes_only_given_key() test_delete_doesnt_leave_dead_nodes() test_iter_always_yields_sorted_values() def UpperCamelCase ( ): lowerCAmelCase_ : List[Any] = SkipList() skip_list.insert(2 , "2") skip_list.insert(4 , "4") skip_list.insert(6 , "4") skip_list.insert(4 , "5") skip_list.insert(8 , "4") skip_list.insert(9 , "4") skip_list.delete(4) print(snake_case__) if __name__ == "__main__": import doctest doctest.testmod() main()
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from pathlib import Path from typing import List from transformers import is_torch_available, is_vision_available from transformers.testing_utils import get_tests_dir, is_tool_test from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText if is_torch_available(): import torch if is_vision_available(): from PIL import Image _lowercase = ['''text''', '''image''', '''audio'''] def UpperCamelCase ( snake_case__): lowerCAmelCase_ : int = [] for input_type in input_types: if input_type == "text": inputs.append("Text input") elif input_type == "image": inputs.append( Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png").resize((5_12, 5_12))) elif input_type == "audio": inputs.append(torch.ones(30_00)) elif isinstance(snake_case__ , snake_case__): inputs.append(create_inputs(snake_case__)) else: raise ValueError(F'''Invalid type requested: {input_type}''') return inputs def UpperCamelCase ( snake_case__): lowerCAmelCase_ : List[Any] = [] for output in outputs: if isinstance(snake_case__ , (str, AgentText)): output_types.append("text") elif isinstance(snake_case__ , (Image.Image, AgentImage)): output_types.append("image") elif isinstance(snake_case__ , (torch.Tensor, AgentAudio)): output_types.append("audio") else: raise ValueError(F'''Invalid output: {output}''') return output_types @is_tool_test class __snake_case : """simple docstring""" def UpperCAmelCase_ ( self : int ) -> int: '''simple docstring''' self.assertTrue(hasattr(self.tool ,"inputs" ) ) self.assertTrue(hasattr(self.tool ,"outputs" ) ) lowerCAmelCase_ : List[Any] = self.tool.inputs for _input in inputs: if isinstance(_input ,lowerCAmelCase__ ): for __input in _input: self.assertTrue(__input in authorized_types ) else: self.assertTrue(_input in authorized_types ) lowerCAmelCase_ : Any = self.tool.outputs for _output in outputs: self.assertTrue(_output in authorized_types ) def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Any = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) # There is a single output if len(self.tool.outputs ) == 1: lowerCAmelCase_ : Optional[int] = [outputs] self.assertListEqual(output_types(lowerCAmelCase__ ) ,self.tool.outputs ) def UpperCAmelCase_ ( self : int ) -> Any: '''simple docstring''' self.assertTrue(hasattr(self.tool ,"description" ) ) self.assertTrue(hasattr(self.tool ,"default_checkpoint" ) ) self.assertTrue(self.tool.description.startswith("This is a tool that" ) ) def UpperCAmelCase_ ( self : List[Any] ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): lowerCAmelCase_ : str = [outputs] self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) ) for output, output_type in zip(lowerCAmelCase__ ,self.tool.outputs ): lowerCAmelCase_ : Tuple = AGENT_TYPE_MAPPING[output_type] self.assertTrue(isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) ) def UpperCAmelCase_ ( self : Any ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Tuple = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = [] for _input, input_type in zip(lowerCAmelCase__ ,self.tool.inputs ): if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] ) else: _inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) ) # Should not raise an error lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): lowerCAmelCase_ : int = [outputs] self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
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from __future__ import annotations def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = [] lowerCAmelCase_ , lowerCAmelCase_ : int = input_list[low:mid], input_list[mid : high + 1] while left and right: result.append((left if left[0] <= right[0] else right).pop(0)) lowerCAmelCase_ : Dict = result + left + right return input_list def UpperCamelCase ( snake_case__): if len(__lowerCAmelCase) <= 1: return input_list lowerCAmelCase_ : Dict = list(__lowerCAmelCase) # iteration for two-way merging lowerCAmelCase_ : Optional[int] = 2 while p <= len(__lowerCAmelCase): # getting low, high and middle value for merge-sort of single list for i in range(0 , len(__lowerCAmelCase) , __lowerCAmelCase): lowerCAmelCase_ : Tuple = i lowerCAmelCase_ : Optional[Any] = i + p - 1 lowerCAmelCase_ : Tuple = (low + high + 1) // 2 lowerCAmelCase_ : Union[str, Any] = merge(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase) # final merge of last two parts if p * 2 >= len(__lowerCAmelCase): lowerCAmelCase_ : int = i lowerCAmelCase_ : Union[str, Any] = merge(__lowerCAmelCase , 0 , __lowerCAmelCase , len(__lowerCAmelCase) - 1) break p *= 2 return input_list if __name__ == "__main__": _lowercase = input('''Enter numbers separated by a comma:\n''').strip() if user_input == "": _lowercase = [] else: _lowercase = [int(item.strip()) for item in user_input.split(''',''')] print(iter_merge_sort(unsorted))
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import pytest _lowercase = '''__dummy_dataset1__''' _lowercase = ''' import json import os import datasets REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/" URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"} class __DummyDataset1__(datasets.GeneratorBasedBuilder): def _info(self): features = datasets.Features( { "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=[ "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", ] ) ), "langs": datasets.Sequence(datasets.Value("string")), "spans": datasets.Sequence(datasets.Value("string")), } ) return datasets.DatasetInfo(features=features) def _split_generators(self, dl_manager): dl_path = dl_manager.download(URLS) return [ datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}), datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}), ] def _generate_examples(self, filepath): with open(filepath, "r", encoding="utf-8") as f: for i, line in enumerate(f): yield i, json.loads(line) ''' @pytest.fixture def UpperCamelCase ( ): return DATASET_LOADING_SCRIPT_NAME @pytest.fixture def UpperCamelCase ( ): return DATASET_LOADING_SCRIPT_CODE @pytest.fixture def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : List[Any] = dataset_loading_script_name lowerCAmelCase_ : List[str] = tmp_path / "datasets" / script_name script_dir.mkdir(parents=snake_case__) lowerCAmelCase_ : List[Any] = script_dir / F'''{script_name}.py''' with open(snake_case__ , "w") as f: f.write(snake_case__) return str(snake_case__)
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import argparse from collections import OrderedDict from pathlib import Path import torch from transformers import ( VisualBertConfig, VisualBertForMultipleChoice, VisualBertForPreTraining, VisualBertForQuestionAnswering, VisualBertForVisualReasoning, ) from transformers.utils import logging logging.set_verbosity_info() _lowercase = logging.get_logger(__name__) _lowercase = [ ('''bert.bert''', '''visual_bert'''), ('''bert.cls''', '''cls'''), ('''bert.classifier''', '''cls'''), ('''token_type_embeddings_visual''', '''visual_token_type_embeddings'''), ('''position_embeddings_visual''', '''visual_position_embeddings'''), ('''projection''', '''visual_projection'''), ] _lowercase = [ '''nlvr2_coco_pre_trained.th''', '''nlvr2_fine_tuned.th''', '''nlvr2_pre_trained.th''', '''vcr_coco_pre_train.th''', '''vcr_fine_tune.th''', '''vcr_pre_train.th''', '''vqa_coco_pre_trained.th''', '''vqa_fine_tuned.th''', '''vqa_pre_trained.th''', ] def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Tuple = torch.load(a_ , map_location="cpu") return sd def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=rename_keys_prefix): lowerCAmelCase_ : Any = OrderedDict() lowerCAmelCase_ : Optional[Any] = torch.arange(config.max_position_embeddings).expand((1, -1)) # detector_d = OrderedDict() for key in d: if "detector" in key: # detector_d[key.replace('detector.','')] = d[key] continue lowerCAmelCase_ : Any = key for name_pair in rename_keys_prefix: lowerCAmelCase_ : Union[str, Any] = new_key.replace(name_pair[0] , name_pair[1]) lowerCAmelCase_ : Union[str, Any] = d[key] if key == "bert.cls.predictions.decoder.weight": # Old bert code didn't have `decoder.bias`, but was added separately lowerCAmelCase_ : Optional[Any] = new_d['''cls.predictions.bias'''] return new_d @torch.no_grad() def UpperCamelCase ( snake_case__ , snake_case__): assert ( checkpoint_path.split("/")[-1] in ACCEPTABLE_CHECKPOINTS ), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.''' # Get Config if "pre" in checkpoint_path: lowerCAmelCase_ : Any = '''pretraining''' if "vcr" in checkpoint_path: lowerCAmelCase_ : Tuple = {'''visual_embedding_dim''': 5_12} elif "vqa_advanced" in checkpoint_path: lowerCAmelCase_ : Union[str, Any] = {'''visual_embedding_dim''': 20_48} elif "vqa" in checkpoint_path: lowerCAmelCase_ : Tuple = {'''visual_embedding_dim''': 20_48} elif "nlvr" in checkpoint_path: lowerCAmelCase_ : str = {'''visual_embedding_dim''': 10_24} else: raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.''') else: if "vcr" in checkpoint_path: lowerCAmelCase_ : Dict = {'''visual_embedding_dim''': 5_12} lowerCAmelCase_ : List[str] = '''multichoice''' elif "vqa_advanced" in checkpoint_path: lowerCAmelCase_ : Any = {'''visual_embedding_dim''': 20_48} lowerCAmelCase_ : Any = '''vqa_advanced''' elif "vqa" in checkpoint_path: lowerCAmelCase_ : Tuple = {'''visual_embedding_dim''': 20_48, '''num_labels''': 31_29} lowerCAmelCase_ : Tuple = '''vqa''' elif "nlvr" in checkpoint_path: lowerCAmelCase_ : List[Any] = { '''visual_embedding_dim''': 10_24, '''num_labels''': 2, } lowerCAmelCase_ : str = '''nlvr''' lowerCAmelCase_ : Any = VisualBertConfig(**a_) # Load State Dict lowerCAmelCase_ : Union[str, Any] = load_state_dict(a_) lowerCAmelCase_ : List[str] = get_new_dict(a_ , a_) if model_type == "pretraining": lowerCAmelCase_ : Optional[Any] = VisualBertForPreTraining(a_) elif model_type == "vqa": lowerCAmelCase_ : Union[str, Any] = VisualBertForQuestionAnswering(a_) elif model_type == "nlvr": lowerCAmelCase_ : Any = VisualBertForVisualReasoning(a_) elif model_type == "multichoice": lowerCAmelCase_ : str = VisualBertForMultipleChoice(a_) model.load_state_dict(a_) # Save Checkpoints Path(a_).mkdir(exist_ok=a_) model.save_pretrained(a_) if __name__ == "__main__": _lowercase = argparse.ArgumentParser() # Required parameters parser.add_argument('''orig_checkpoint_path''', type=str, help='''A path to .th on local filesystem.''') parser.add_argument('''pytorch_dump_folder_path''', type=str, help='''Path to the output PyTorch model.''') _lowercase = parser.parse_args() convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
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import json import os import re import unittest from transformers import CodeGenTokenizer, CodeGenTokenizerFast from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class __snake_case ( snake_case__ , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = CodeGenTokenizer UpperCamelCase_ = CodeGenTokenizerFast UpperCamelCase_ = True UpperCamelCase_ = {'add_prefix_space': True} UpperCamelCase_ = False def UpperCAmelCase_ ( self : str ) -> Tuple: '''simple docstring''' super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt lowerCAmelCase_ : Optional[Any] = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "<unk>", "<|endoftext|>", ] lowerCAmelCase_ : int = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) ) lowerCAmelCase_ : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] lowerCAmelCase_ : List[Any] = {"unk_token": "<unk>"} lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase_ : Tuple = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] ) with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp: fp.write(json.dumps(lowerCAmelCase__ ) + "\n" ) with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp: fp.write("\n".join(lowerCAmelCase__ ) ) def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : str ) -> int: '''simple docstring''' kwargs.update(self.special_tokens_map ) return CodeGenTokenizer.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : Optional[Any] ) -> Tuple: '''simple docstring''' kwargs.update(self.special_tokens_map ) return CodeGenTokenizerFast.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : str ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Tuple = "lower newer" lowerCAmelCase_ : Tuple = "lower newer" return input_text, output_text def UpperCAmelCase_ ( self : Optional[Any] ) -> Any: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map ) lowerCAmelCase_ : Dict = "lower newer" lowerCAmelCase_ : Dict = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"] lowerCAmelCase_ : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = tokens + [tokenizer.unk_token] lowerCAmelCase_ : Union[str, Any] = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]: '''simple docstring''' if not self.test_rust_tokenizer: return lowerCAmelCase_ : Tuple = self.get_tokenizer() lowerCAmelCase_ : Optional[int] = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = "lower newer" # Testing tokenization lowerCAmelCase_ : Tuple = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = rust_tokenizer.tokenize(lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing conversion to ids without special tokens lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = rust_tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing conversion to ids with special tokens lowerCAmelCase_ : int = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing the unknown token lowerCAmelCase_ : Union[str, Any] = tokens + [rust_tokenizer.unk_token] lowerCAmelCase_ : List[str] = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Optional[Any] ) -> List[str]: '''simple docstring''' pass def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Any=15 ) -> str: '''simple docstring''' for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ): lowerCAmelCase_ : Any = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ ) # Simple input lowerCAmelCase_ : int = "This is a simple input" lowerCAmelCase_ : Dict = ["This is a simple input 1", "This is a simple input 2"] lowerCAmelCase_ : str = ("This is a simple input", "This is a pair") lowerCAmelCase_ : Optional[int] = [ ("This is a simple input 1", "This is a simple input 2"), ("This is a simple pair 1", "This is a simple pair 2"), ] # Simple input tests self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Simple input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Simple input self.assertRaises( lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,) # Pair input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Pair input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Pair input self.assertRaises( lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,) def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname ,pad_token="<pad>" ) # Simple input lowerCAmelCase_ : Dict = "This is a simple input" lowerCAmelCase_ : List[str] = ["This is a simple input looooooooong", "This is a simple input"] lowerCAmelCase_ : Any = ("This is a simple input", "This is a pair") lowerCAmelCase_ : List[str] = [ ("This is a simple input loooooong", "This is a simple input"), ("This is a simple pair loooooong", "This is a simple pair"), ] lowerCAmelCase_ : Dict = tokenizer.pad_token_id lowerCAmelCase_ : Union[str, Any] = tokenizer(lowerCAmelCase__ ,padding="max_length" ,max_length=30 ,return_tensors="np" ) lowerCAmelCase_ : Tuple = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" ) lowerCAmelCase_ : Any = tokenizer(*lowerCAmelCase__ ,padding="max_length" ,max_length=60 ,return_tensors="np" ) lowerCAmelCase_ : Optional[int] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" ) # s # test single string max_length padding self.assertEqual(out_s["input_ids"].shape[-1] ,30 ) self.assertTrue(pad_token_id in out_s["input_ids"] ) self.assertTrue(0 in out_s["attention_mask"] ) # s2 # test automatic padding self.assertEqual(out_sa["input_ids"].shape[-1] ,33 ) # long slice doesn't have padding self.assertFalse(pad_token_id in out_sa["input_ids"][0] ) self.assertFalse(0 in out_sa["attention_mask"][0] ) # short slice does have padding self.assertTrue(pad_token_id in out_sa["input_ids"][1] ) self.assertTrue(0 in out_sa["attention_mask"][1] ) # p # test single pair max_length padding self.assertEqual(out_p["input_ids"].shape[-1] ,60 ) self.assertTrue(pad_token_id in out_p["input_ids"] ) self.assertTrue(0 in out_p["attention_mask"] ) # p2 # test automatic padding pair self.assertEqual(out_pa["input_ids"].shape[-1] ,52 ) # long slice pair doesn't have padding self.assertFalse(pad_token_id in out_pa["input_ids"][0] ) self.assertFalse(0 in out_pa["attention_mask"][0] ) # short slice pair does have padding self.assertTrue(pad_token_id in out_pa["input_ids"][1] ) self.assertTrue(0 in out_pa["attention_mask"][1] ) def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any: '''simple docstring''' lowerCAmelCase_ : Any = "$$$" lowerCAmelCase_ : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname ,bos_token=lowerCAmelCase__ ,add_bos_token=lowerCAmelCase__ ) lowerCAmelCase_ : Dict = "This is a simple input" lowerCAmelCase_ : Union[str, Any] = ["This is a simple input 1", "This is a simple input 2"] lowerCAmelCase_ : int = tokenizer.bos_token_id lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ ) self.assertEqual(out_s.input_ids[0] ,lowerCAmelCase__ ) self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) ) lowerCAmelCase_ : List[str] = tokenizer.decode(out_s.input_ids ) lowerCAmelCase_ : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids ) self.assertEqual(decode_s.split()[0] ,lowerCAmelCase__ ) self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) ) @slow def UpperCAmelCase_ ( self : Any ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" ) lowerCAmelCase_ : str = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#" lowerCAmelCase_ : int = "\nif len_a > len_b: result = a\nelse: result = b" lowerCAmelCase_ : Dict = tokenizer.encode(lowerCAmelCase__ ) lowerCAmelCase_ : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"] lowerCAmelCase_ : Union[str, Any] = tokenizer.decode(lowerCAmelCase__ ,truncate_before_pattern=lowerCAmelCase__ ) self.assertEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]: '''simple docstring''' pass
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import logging import os from dataclasses import dataclass, field from typing import Dict, Optional import datasets import numpy as np import tensorflow as tf from transformers import ( AutoConfig, AutoTokenizer, EvalPrediction, HfArgumentParser, PreTrainedTokenizer, TFAutoModelForSequenceClassification, TFTrainer, TFTrainingArguments, ) from transformers.utils import logging as hf_logging hf_logging.set_verbosity_info() hf_logging.enable_default_handler() hf_logging.enable_explicit_format() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ = None , ): lowerCAmelCase_ : Tuple = {} if train_file is not None: lowerCAmelCase_ : List[Any] = [train_file] if eval_file is not None: lowerCAmelCase_ : Dict = [eval_file] if test_file is not None: lowerCAmelCase_ : List[Any] = [test_file] lowerCAmelCase_ : Optional[Any] = datasets.load_dataset("csv" , data_files=UpperCAmelCase__) lowerCAmelCase_ : Dict = list(ds[list(files.keys())[0]].features.keys()) lowerCAmelCase_ : int = features_name.pop(UpperCAmelCase__) lowerCAmelCase_ : Tuple = list(set(ds[list(files.keys())[0]][label_name])) lowerCAmelCase_ : Any = {label: i for i, label in enumerate(UpperCAmelCase__)} lowerCAmelCase_ : List[Any] = tokenizer.model_input_names lowerCAmelCase_ : Optional[Any] = {} if len(UpperCAmelCase__) == 1: for k in files.keys(): lowerCAmelCase_ : Any = ds[k].map( lambda snake_case__: tokenizer.batch_encode_plus( example[features_name[0]] , truncation=UpperCAmelCase__ , max_length=UpperCAmelCase__ , padding="max_length") , batched=UpperCAmelCase__ , ) elif len(UpperCAmelCase__) == 2: for k in files.keys(): lowerCAmelCase_ : List[Any] = ds[k].map( lambda snake_case__: tokenizer.batch_encode_plus( (example[features_name[0]], example[features_name[1]]) , truncation=UpperCAmelCase__ , max_length=UpperCAmelCase__ , padding="max_length" , ) , batched=UpperCAmelCase__ , ) def gen_train(): for ex in transformed_ds[datasets.Split.TRAIN]: lowerCAmelCase_ : Optional[Any] = {k: v for k, v in ex.items() if k in input_names} lowerCAmelCase_ : Any = labelaid[ex[label_name]] yield (d, label) def gen_val(): for ex in transformed_ds[datasets.Split.VALIDATION]: lowerCAmelCase_ : Tuple = {k: v for k, v in ex.items() if k in input_names} lowerCAmelCase_ : Optional[Any] = labelaid[ex[label_name]] yield (d, label) def gen_test(): for ex in transformed_ds[datasets.Split.TEST]: lowerCAmelCase_ : Optional[Any] = {k: v for k, v in ex.items() if k in input_names} lowerCAmelCase_ : Any = labelaid[ex[label_name]] yield (d, label) lowerCAmelCase_ : Optional[int] = ( tf.data.Dataset.from_generator( UpperCAmelCase__ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])) , ) if datasets.Split.TRAIN in transformed_ds else None ) if train_ds is not None: lowerCAmelCase_ : List[str] = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]))) lowerCAmelCase_ : Union[str, Any] = ( tf.data.Dataset.from_generator( UpperCAmelCase__ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])) , ) if datasets.Split.VALIDATION in transformed_ds else None ) if val_ds is not None: lowerCAmelCase_ : Any = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]))) lowerCAmelCase_ : List[Any] = ( tf.data.Dataset.from_generator( UpperCAmelCase__ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])) , ) if datasets.Split.TEST in transformed_ds else None ) if test_ds is not None: lowerCAmelCase_ : List[Any] = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]))) return train_ds, val_ds, test_ds, labelaid _lowercase = logging.getLogger(__name__) @dataclass class __snake_case : """simple docstring""" UpperCamelCase_ = field(metadata={'help': 'Which column contains the label'} ) UpperCamelCase_ = field(default=_a , metadata={'help': 'The path of the training file'} ) UpperCamelCase_ = field(default=_a , metadata={'help': 'The path of the development file'} ) UpperCamelCase_ = field(default=_a , metadata={'help': 'The path of the test file'} ) UpperCamelCase_ = field( default=1_2_8 , metadata={ 'help': ( 'The maximum total input sequence length after tokenization. Sequences longer ' 'than this will be truncated, sequences shorter will be padded.' ) } , ) UpperCamelCase_ = field( default=_a , metadata={'help': 'Overwrite the cached training and evaluation sets'} ) @dataclass class __snake_case : """simple docstring""" UpperCamelCase_ = field( metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} ) UpperCamelCase_ = field( default=_a , metadata={'help': 'Pretrained config name or path if not the same as model_name'} ) UpperCamelCase_ = field( default=_a , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} ) UpperCamelCase_ = field(default=_a , metadata={'help': 'Set this flag to use fast tokenization.'} ) # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script, # or just modify its tokenizer_config.json. UpperCamelCase_ = field( default=_a , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , ) def UpperCamelCase ( ): lowerCAmelCase_ : int = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)) lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = parser.parse_args_into_dataclasses() if ( os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use''' " --overwrite_output_dir to overcome.") # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO , ) logger.info( F'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1)}, ''' F'''16-bits training: {training_args.fpaa}''') logger.info(F'''Training/evaluation parameters {training_args}''') # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. lowerCAmelCase_ : Dict = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , ) lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Tuple = get_tfds( train_file=data_args.train_file , eval_file=data_args.dev_file , test_file=data_args.test_file , tokenizer=UpperCAmelCase__ , label_column_id=data_args.label_column_id , max_seq_length=data_args.max_seq_length , ) lowerCAmelCase_ : Tuple = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=len(UpperCAmelCase__) , labelaid=UpperCAmelCase__ , idalabel={id: label for label, id in labelaid.items()} , finetuning_task="text-classification" , cache_dir=model_args.cache_dir , ) with training_args.strategy.scope(): lowerCAmelCase_ : List[Any] = TFAutoModelForSequenceClassification.from_pretrained( model_args.model_name_or_path , from_pt=bool(".bin" in model_args.model_name_or_path) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , ) def compute_metrics(snake_case__) -> Dict: lowerCAmelCase_ : Union[str, Any] = np.argmax(p.predictions , axis=1) return {"acc": (preds == p.label_ids).mean()} # Initialize our Trainer lowerCAmelCase_ : Optional[int] = TFTrainer( model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , ) # Training if training_args.do_train: trainer.train() trainer.save_model() tokenizer.save_pretrained(training_args.output_dir) # Evaluation lowerCAmelCase_ : Tuple = {} if training_args.do_eval: logger.info("*** Evaluate ***") lowerCAmelCase_ : List[str] = trainer.evaluate() lowerCAmelCase_ : Dict = os.path.join(training_args.output_dir , "eval_results.txt") with open(UpperCAmelCase__ , "w") as writer: logger.info("***** Eval results *****") for key, value in result.items(): logger.info(F''' {key} = {value}''') writer.write(F'''{key} = {value}\n''') results.update(UpperCAmelCase__) return results if __name__ == "__main__": main()
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from __future__ import annotations from random import random class __snake_case : """simple docstring""" def __init__( self : Optional[int] ,lowerCAmelCase__ : int | None = None ) -> int: '''simple docstring''' lowerCAmelCase_ : Dict = value lowerCAmelCase_ : Any = random() lowerCAmelCase_ : Node | None = None lowerCAmelCase_ : Node | None = None def __repr__( self : Any ) -> str: '''simple docstring''' from pprint import pformat if self.left is None and self.right is None: return f'''\'{self.value}: {self.prior:.5}\'''' else: return pformat( {f'''{self.value}: {self.prior:.5}''': (self.left, self.right)} ,indent=1 ) def __str__( self : str ) -> str: '''simple docstring''' lowerCAmelCase_ : List[Any] = str(self.value ) + " " lowerCAmelCase_ : List[Any] = str(self.left or "" ) lowerCAmelCase_ : Union[str, Any] = str(self.right or "" ) return value + left + right def UpperCamelCase ( snake_case__ , snake_case__): if root is None: # None tree is split into 2 Nones return None, None elif root.value is None: return None, None else: if value < root.value: lowerCAmelCase_ , lowerCAmelCase_ : Any = split(root.left , snake_case__) return left, root else: lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = split(root.right , snake_case__) return root, right def UpperCamelCase ( snake_case__ , snake_case__): if (not left) or (not right): # If one node is None, return the other return left or right elif left.prior < right.prior: lowerCAmelCase_ : Dict = merge(left.right , snake_case__) return left else: lowerCAmelCase_ : List[str] = merge(snake_case__ , right.left) return right def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : List[Any] = Node(snake_case__) lowerCAmelCase_ , lowerCAmelCase_ : Tuple = split(snake_case__ , snake_case__) return merge(merge(snake_case__ , snake_case__) , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ , lowerCAmelCase_ : List[str] = split(snake_case__ , value - 1) lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = split(snake_case__ , snake_case__) return merge(snake_case__ , snake_case__) def UpperCamelCase ( snake_case__): if not root: # None return else: inorder(root.left) print(root.value , end=",") inorder(root.right) def UpperCamelCase ( snake_case__ , snake_case__): for arg in args.split(): if arg[0] == "+": lowerCAmelCase_ : List[str] = insert(snake_case__ , int(arg[1:])) elif arg[0] == "-": lowerCAmelCase_ : Optional[int] = erase(snake_case__ , int(arg[1:])) else: print("Unknown command") return root def UpperCamelCase ( ): lowerCAmelCase_ : str = None print( "enter numbers to create a tree, + value to add value into treap, " "- value to erase all nodes with value. 'q' to quit. ") lowerCAmelCase_ : str = input() while args != "q": lowerCAmelCase_ : int = interact_treap(snake_case__ , snake_case__) print(snake_case__) lowerCAmelCase_ : str = input() print("good by!") if __name__ == "__main__": import doctest doctest.testmod() main()
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from ...configuration_utils import PretrainedConfig class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = 'bert-generation' def __init__( self : str ,lowerCAmelCase__ : List[Any]=5_03_58 ,lowerCAmelCase__ : str=10_24 ,lowerCAmelCase__ : Optional[int]=24 ,lowerCAmelCase__ : Union[str, Any]=16 ,lowerCAmelCase__ : int=40_96 ,lowerCAmelCase__ : Dict="gelu" ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : str=0.1 ,lowerCAmelCase__ : Any=5_12 ,lowerCAmelCase__ : List[str]=0.02 ,lowerCAmelCase__ : Tuple=1e-1_2 ,lowerCAmelCase__ : Optional[int]=0 ,lowerCAmelCase__ : List[Any]=2 ,lowerCAmelCase__ : Tuple=1 ,lowerCAmelCase__ : Any="absolute" ,lowerCAmelCase__ : Optional[Any]=True ,**lowerCAmelCase__ : Union[str, Any] ,) -> str: '''simple docstring''' super().__init__(pad_token_id=__UpperCamelCase ,bos_token_id=__UpperCamelCase ,eos_token_id=__UpperCamelCase ,**__UpperCamelCase ) lowerCAmelCase_ : str = vocab_size lowerCAmelCase_ : List[str] = hidden_size lowerCAmelCase_ : str = num_hidden_layers lowerCAmelCase_ : Optional[int] = num_attention_heads lowerCAmelCase_ : Optional[Any] = hidden_act lowerCAmelCase_ : int = intermediate_size lowerCAmelCase_ : int = hidden_dropout_prob lowerCAmelCase_ : Optional[Any] = attention_probs_dropout_prob lowerCAmelCase_ : Optional[int] = max_position_embeddings lowerCAmelCase_ : Optional[int] = initializer_range lowerCAmelCase_ : Optional[int] = layer_norm_eps lowerCAmelCase_ : Union[str, Any] = position_embedding_type lowerCAmelCase_ : List[Any] = use_cache
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_funnel import FunnelTokenizer _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''} _lowercase = [ '''small''', '''small-base''', '''medium''', '''medium-base''', '''intermediate''', '''intermediate-base''', '''large''', '''large-base''', '''xlarge''', '''xlarge-base''', ] _lowercase = { '''vocab_file''': { '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''', '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''', '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''', '''funnel-transformer/medium-base''': ( '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt''' ), '''funnel-transformer/intermediate''': ( '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt''' ), '''funnel-transformer/intermediate-base''': ( '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt''' ), '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''', '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''', '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''', '''funnel-transformer/xlarge-base''': ( '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt''' ), }, '''tokenizer_file''': { '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''', '''funnel-transformer/small-base''': ( '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''', '''funnel-transformer/medium-base''': ( '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/intermediate''': ( '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json''' ), '''funnel-transformer/intermediate-base''': ( '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''', '''funnel-transformer/large-base''': ( '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''', '''funnel-transformer/xlarge-base''': ( '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json''' ), }, } _lowercase = {f"funnel-transformer/{name}": 512 for name in _model_names} _lowercase = {f"funnel-transformer/{name}": {'''do_lower_case''': True} for name in _model_names} class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_INIT_CONFIGURATION UpperCamelCase_ = FunnelTokenizer UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = 2 def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : List[str]="<unk>" ,lowerCAmelCase__ : int="<sep>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : List[str]="<cls>" ,lowerCAmelCase__ : Optional[int]="<mask>" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : List[Any]="##" ,**lowerCAmelCase__ : int ,) -> List[Any]: '''simple docstring''' super().__init__( lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,clean_text=lowerCAmelCase__ ,tokenize_chinese_chars=lowerCAmelCase__ ,strip_accents=lowerCAmelCase__ ,wordpieces_prefix=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() ) if ( normalizer_state.get("lowercase" ,lowerCAmelCase__ ) != do_lower_case or normalizer_state.get("strip_accents" ,lowerCAmelCase__ ) != strip_accents or normalizer_state.get("handle_chinese_chars" ,lowerCAmelCase__ ) != tokenize_chinese_chars ): lowerCAmelCase_ : Optional[int] = getattr(lowerCAmelCase__ ,normalizer_state.pop("type" ) ) lowerCAmelCase_ : List[Any] = do_lower_case lowerCAmelCase_ : List[str] = strip_accents lowerCAmelCase_ : Any = tokenize_chinese_chars lowerCAmelCase_ : List[Any] = normalizer_class(**lowerCAmelCase__ ) lowerCAmelCase_ : int = do_lower_case def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str=None ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id] if token_ids_a: output += token_ids_a + [self.sep_token_id] return output def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : str = [self.sep_token_id] lowerCAmelCase_ : Optional[Any] = [self.cls_token_id] if token_ids_a is None: return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ ) return tuple(lowerCAmelCase__ )
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import math def UpperCamelCase ( snake_case__): return math.sqrt(_UpperCamelCase) * math.sqrt(_UpperCamelCase) == num def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Dict = 0 lowerCAmelCase_ : Optional[int] = n while left <= right: lowerCAmelCase_ : List[Any] = (left + right) // 2 if mid**2 == n: return True elif mid**2 > n: lowerCAmelCase_ : Any = mid - 1 else: lowerCAmelCase_ : List[Any] = mid + 1 return False if __name__ == "__main__": import doctest doctest.testmod()
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# tests directory-specific settings - this file is run automatically # by pytest before any tests are run import doctest import sys import warnings from os.path import abspath, dirname, join import _pytest from transformers.testing_utils import HfDoctestModule, HfDocTestParser # allow having multiple repository checkouts and not needing to remember to rerun # 'pip install -e .[dev]' when switching between checkouts and running tests. _lowercase = abspath(join(dirname(__file__), '''src''')) sys.path.insert(1, git_repo_path) # silence FutureWarning warnings in tests since often we can't act on them until # they become normal warnings - i.e. the tests still need to test the current functionality warnings.simplefilter(action='''ignore''', category=FutureWarning) def UpperCamelCase ( snake_case__): config.addinivalue_line( "markers" , "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested") config.addinivalue_line( "markers" , "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested") config.addinivalue_line("markers" , "is_pipeline_test: mark test to run only when pipelines are tested") config.addinivalue_line("markers" , "is_staging_test: mark test to run only in the staging environment") config.addinivalue_line("markers" , "accelerate_tests: mark test that require accelerate") config.addinivalue_line("markers" , "tool_tests: mark the tool tests that are run on their specific schedule") def UpperCamelCase ( snake_case__): from transformers.testing_utils import pytest_addoption_shared pytest_addoption_shared(snake_case__) def UpperCamelCase ( snake_case__): from transformers.testing_utils import pytest_terminal_summary_main lowerCAmelCase_ : int = terminalreporter.config.getoption("--make-reports") if make_reports: pytest_terminal_summary_main(snake_case__ , id=snake_case__) def UpperCamelCase ( snake_case__ , snake_case__): # If no tests are collected, pytest exists with code 5, which makes the CI fail. if exitstatus == 5: lowerCAmelCase_ : List[Any] = 0 # Doctest custom flag to ignore output. _lowercase = doctest.register_optionflag('''IGNORE_RESULT''') _lowercase = doctest.OutputChecker class __snake_case ( snake_case__ ): """simple docstring""" def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Any: '''simple docstring''' if IGNORE_RESULT & optionflags: return True return OutputChecker.check_output(self ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) _lowercase = CustomOutputChecker _lowercase = HfDoctestModule _lowercase = HfDocTestParser
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from __future__ import annotations from collections.abc import Iterator class __snake_case : """simple docstring""" def __init__( self : str ,lowerCAmelCase__ : int ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : List[str] = value lowerCAmelCase_ : int = None lowerCAmelCase_ : Optional[int] = None class __snake_case : """simple docstring""" def __init__( self : Optional[Any] ,lowerCAmelCase__ : Union[str, Any] ) -> str: '''simple docstring''' lowerCAmelCase_ : List[str] = tree def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[str] ) -> str: '''simple docstring''' if node is None: return 0 return node.value + ( self.depth_first_search(node.left ) + self.depth_first_search(node.right ) ) def __iter__( self : List[Any] ) -> Tuple: '''simple docstring''' yield self.depth_first_search(self.tree ) if __name__ == "__main__": import doctest doctest.testmod()
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from __future__ import annotations from collections.abc import Sequence from typing import Literal def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = list(snake_case__) lowerCAmelCase_ : Tuple = list(snake_case__) lowerCAmelCase_ : List[str] = 0 for i in range(len(snake_case__)): if lista[i] != lista[i]: count += 1 lowerCAmelCase_ : Dict = "_" if count > 1: return False else: return "".join(snake_case__) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Union[str, Any] = [] while True: lowerCAmelCase_ : Tuple = ["$"] * len(snake_case__) lowerCAmelCase_ : Tuple = [] for i in range(len(snake_case__)): for j in range(i + 1 , len(snake_case__)): lowerCAmelCase_ : Optional[int] = compare_string(binary[i] , binary[j]) if k is False: lowerCAmelCase_ : str = "*" lowerCAmelCase_ : Tuple = "*" temp.append("X") for i in range(len(snake_case__)): if checka[i] == "$": pi.append(binary[i]) if len(snake_case__) == 0: return pi lowerCAmelCase_ : List[Any] = list(set(snake_case__)) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = [] for minterm in minterms: lowerCAmelCase_ : Dict = "" for _ in range(snake_case__): lowerCAmelCase_ : Dict = str(minterm % 2) + string minterm //= 2 temp.append(snake_case__) return temp def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = list(snake_case__) lowerCAmelCase_ : Dict = list(snake_case__) lowerCAmelCase_ : Dict = 0 for i in range(len(snake_case__)): if lista[i] != lista[i]: count_n += 1 return count_n == count def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = [] lowerCAmelCase_ : Dict = [0] * len(snake_case__) for i in range(len(chart[0])): lowerCAmelCase_ : List[Any] = 0 lowerCAmelCase_ : int = -1 for j in range(len(snake_case__)): if chart[j][i] == 1: count += 1 lowerCAmelCase_ : Optional[int] = j if count == 1: lowerCAmelCase_ : Union[str, Any] = 1 for i in range(len(snake_case__)): if select[i] == 1: for j in range(len(chart[0])): if chart[i][j] == 1: for k in range(len(snake_case__)): lowerCAmelCase_ : Tuple = 0 temp.append(prime_implicants[i]) while True: lowerCAmelCase_ : Optional[Any] = 0 lowerCAmelCase_ : Dict = -1 lowerCAmelCase_ : Tuple = 0 for i in range(len(snake_case__)): lowerCAmelCase_ : Dict = chart[i].count(1) if count_n > max_n: lowerCAmelCase_ : Optional[int] = count_n lowerCAmelCase_ : Optional[Any] = i if max_n == 0: return temp temp.append(prime_implicants[rem]) for i in range(len(chart[0])): if chart[rem][i] == 1: for j in range(len(snake_case__)): lowerCAmelCase_ : Any = 0 def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : str = [[0 for x in range(len(snake_case__))] for x in range(len(snake_case__))] for i in range(len(snake_case__)): lowerCAmelCase_ : Optional[Any] = prime_implicants[i].count("_") for j in range(len(snake_case__)): if is_for_table(prime_implicants[i] , binary[j] , snake_case__): lowerCAmelCase_ : Dict = 1 return chart def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = int(input("Enter the no. of variables\n")) lowerCAmelCase_ : Tuple = [ float(snake_case__) for x in input( "Enter the decimal representation of Minterms 'Spaces Separated'\n").split() ] lowerCAmelCase_ : Any = decimal_to_binary(snake_case__ , snake_case__) lowerCAmelCase_ : Dict = check(snake_case__) print("Prime Implicants are:") print(snake_case__) lowerCAmelCase_ : int = prime_implicant_chart(snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = selection(snake_case__ , snake_case__) print("Essential Prime Implicants are:") print(snake_case__) if __name__ == "__main__": import doctest doctest.testmod() main()
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import argparse import os import gluonnlp as nlp import mxnet as mx import numpy as np import torch from gluonnlp.base import get_home_dir from gluonnlp.model.bert import BERTEncoder from gluonnlp.model.utils import _load_vocab from gluonnlp.vocab import Vocab from packaging import version from torch import nn from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer from transformers.models.bert.modeling_bert import ( BertIntermediate, BertLayer, BertOutput, BertSelfAttention, BertSelfOutput, ) from transformers.utils import logging if version.parse(nlp.__version__) != version.parse('''0.8.3'''): raise Exception('''requires gluonnlp == 0.8.3''') if version.parse(mx.__version__) != version.parse('''1.5.0'''): raise Exception('''requires mxnet == 1.5.0''') logging.set_verbosity_info() _lowercase = logging.get_logger(__name__) _lowercase = "The Nymphenburg Palace is a beautiful palace in Munich!" def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Any = { '''attention_cell''': '''multi_head''', '''num_layers''': 4, '''units''': 10_24, '''hidden_size''': 7_68, '''max_length''': 5_12, '''num_heads''': 8, '''scaled''': True, '''dropout''': 0.1, '''use_residual''': True, '''embed_size''': 10_24, '''embed_dropout''': 0.1, '''word_embed''': None, '''layer_norm_eps''': 1e-5, '''token_type_vocab_size''': 2, } lowerCAmelCase_ : Dict = bort_4_8_768_1024_hparams # Let's construct the original Bort model here # Taken from official BERT implementation, see: # https://github.com/alexa/bort/blob/master/bort/bort.py lowerCAmelCase_ : Tuple = BERTEncoder( attention_cell=predefined_args["attention_cell"] , num_layers=predefined_args["num_layers"] , units=predefined_args["units"] , hidden_size=predefined_args["hidden_size"] , max_length=predefined_args["max_length"] , num_heads=predefined_args["num_heads"] , scaled=predefined_args["scaled"] , dropout=predefined_args["dropout"] , output_attention=snake_case__ , output_all_encodings=snake_case__ , use_residual=predefined_args["use_residual"] , activation=predefined_args.get("activation" , "gelu") , layer_norm_eps=predefined_args.get("layer_norm_eps" , snake_case__) , ) # Vocab information needs to be fetched first # It's the same as RoBERTa, so RobertaTokenizer can be used later lowerCAmelCase_ : int = '''openwebtext_ccnews_stories_books_cased''' # Specify download folder to Gluonnlp's vocab lowerCAmelCase_ : Union[str, Any] = os.path.join(get_home_dir() , "models") lowerCAmelCase_ : Union[str, Any] = _load_vocab(snake_case__ , snake_case__ , snake_case__ , cls=snake_case__) lowerCAmelCase_ : Optional[Any] = nlp.model.BERTModel( snake_case__ , len(snake_case__) , units=predefined_args["units"] , embed_size=predefined_args["embed_size"] , embed_dropout=predefined_args["embed_dropout"] , word_embed=predefined_args["word_embed"] , use_pooler=snake_case__ , use_token_type_embed=snake_case__ , token_type_vocab_size=predefined_args["token_type_vocab_size"] , use_classifier=snake_case__ , use_decoder=snake_case__ , ) original_bort.load_parameters(snake_case__ , cast_dtype=snake_case__ , ignore_extra=snake_case__) lowerCAmelCase_ : Any = original_bort._collect_params_with_prefix() # Build our config 🤗 lowerCAmelCase_ : Optional[int] = { '''architectures''': ['''BertForMaskedLM'''], '''attention_probs_dropout_prob''': predefined_args['''dropout'''], '''hidden_act''': '''gelu''', '''hidden_dropout_prob''': predefined_args['''dropout'''], '''hidden_size''': predefined_args['''embed_size'''], '''initializer_range''': 0.02, '''intermediate_size''': predefined_args['''hidden_size'''], '''layer_norm_eps''': predefined_args['''layer_norm_eps'''], '''max_position_embeddings''': predefined_args['''max_length'''], '''model_type''': '''bort''', '''num_attention_heads''': predefined_args['''num_heads'''], '''num_hidden_layers''': predefined_args['''num_layers'''], '''pad_token_id''': 1, # 2 = BERT, 1 = RoBERTa '''type_vocab_size''': 1, # 2 = BERT, 1 = RoBERTa '''vocab_size''': len(snake_case__), } lowerCAmelCase_ : Union[str, Any] = BertConfig.from_dict(snake_case__) lowerCAmelCase_ : Dict = BertForMaskedLM(snake_case__) hf_bort_model.eval() # Parameter mapping table (Gluonnlp to Transformers) # * denotes layer index # # | Gluon Parameter | Transformers Parameter # | -------------------------------------------------------------- | ---------------------- # | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias` # | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight` # | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight` # | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight` # | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias` # | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight` # | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias` # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight` # | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias` # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight` # | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias` # | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight` # | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias` # | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight` # | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias` # | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight` # | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias` # | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight` # | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias` # | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight` # Helper function to convert MXNET Arrays to PyTorch def to_torch(snake_case__) -> nn.Parameter: return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy())) # Check param shapes and map new HF param back def check_and_map_params(snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = hf_param.shape lowerCAmelCase_ : List[Any] = to_torch(params[gluon_param]) lowerCAmelCase_ : List[Any] = gluon_param.shape assert ( shape_hf == shape_gluon ), F'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers''' return gluon_param lowerCAmelCase_ : Union[str, Any] = check_and_map_params( hf_bort_model.bert.embeddings.word_embeddings.weight , "word_embed.0.weight") lowerCAmelCase_ : Dict = check_and_map_params( hf_bort_model.bert.embeddings.position_embeddings.weight , "encoder.position_weight") lowerCAmelCase_ : int = check_and_map_params( hf_bort_model.bert.embeddings.LayerNorm.bias , "encoder.layer_norm.beta") lowerCAmelCase_ : List[str] = check_and_map_params( hf_bort_model.bert.embeddings.LayerNorm.weight , "encoder.layer_norm.gamma") # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them) lowerCAmelCase_ : int = torch.zeros_like( hf_bort_model.bert.embeddings.token_type_embeddings.weight.data) for i in range(hf_bort_config.num_hidden_layers): lowerCAmelCase_ : BertLayer = hf_bort_model.bert.encoder.layer[i] # self attention lowerCAmelCase_ : BertSelfAttention = layer.attention.self lowerCAmelCase_ : int = check_and_map_params( self_attn.key.bias.data , F'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias''') lowerCAmelCase_ : Optional[Any] = check_and_map_params( self_attn.key.weight.data , F'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight''') lowerCAmelCase_ : Tuple = check_and_map_params( self_attn.query.bias.data , F'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias''') lowerCAmelCase_ : str = check_and_map_params( self_attn.query.weight.data , F'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight''') lowerCAmelCase_ : List[str] = check_and_map_params( self_attn.value.bias.data , F'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias''') lowerCAmelCase_ : Dict = check_and_map_params( self_attn.value.weight.data , F'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight''') # self attention output lowerCAmelCase_ : BertSelfOutput = layer.attention.output lowerCAmelCase_ : Any = check_and_map_params( self_output.dense.bias , F'''encoder.transformer_cells.{i}.proj.bias''') lowerCAmelCase_ : Optional[int] = check_and_map_params( self_output.dense.weight , F'''encoder.transformer_cells.{i}.proj.weight''') lowerCAmelCase_ : List[str] = check_and_map_params( self_output.LayerNorm.bias , F'''encoder.transformer_cells.{i}.layer_norm.beta''') lowerCAmelCase_ : Dict = check_and_map_params( self_output.LayerNorm.weight , F'''encoder.transformer_cells.{i}.layer_norm.gamma''') # intermediate lowerCAmelCase_ : BertIntermediate = layer.intermediate lowerCAmelCase_ : Optional[int] = check_and_map_params( intermediate.dense.bias , F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias''') lowerCAmelCase_ : Tuple = check_and_map_params( intermediate.dense.weight , F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight''') # output lowerCAmelCase_ : BertOutput = layer.output lowerCAmelCase_ : str = check_and_map_params( bert_output.dense.bias , F'''encoder.transformer_cells.{i}.ffn.ffn_2.bias''') lowerCAmelCase_ : Any = check_and_map_params( bert_output.dense.weight , F'''encoder.transformer_cells.{i}.ffn.ffn_2.weight''') lowerCAmelCase_ : List[Any] = check_and_map_params( bert_output.LayerNorm.bias , F'''encoder.transformer_cells.{i}.ffn.layer_norm.beta''') lowerCAmelCase_ : List[str] = check_and_map_params( bert_output.LayerNorm.weight , F'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma''') # Save space and energy 🎄 hf_bort_model.half() # Compare output of both models lowerCAmelCase_ : Dict = RobertaTokenizer.from_pretrained("roberta-base") lowerCAmelCase_ : Union[str, Any] = tokenizer.encode_plus(snake_case__)['''input_ids'''] # Get gluon output lowerCAmelCase_ : Any = mx.nd.array([input_ids]) lowerCAmelCase_ : int = original_bort(inputs=snake_case__ , token_types=[]) # Get Transformer output (save and reload model again) hf_bort_model.save_pretrained(snake_case__) lowerCAmelCase_ : List[Any] = BertModel.from_pretrained(snake_case__) hf_bort_model.eval() lowerCAmelCase_ : Union[str, Any] = tokenizer.encode_plus(snake_case__ , return_tensors="pt") lowerCAmelCase_ : Any = hf_bort_model(**snake_case__)[0] lowerCAmelCase_ : str = output_gluon[0].asnumpy() lowerCAmelCase_ : int = output_hf[0].detach().numpy() lowerCAmelCase_ : Dict = np.max(np.abs(hf_layer - gluon_layer)).item() lowerCAmelCase_ : Optional[int] = np.allclose(snake_case__ , snake_case__ , atol=1e-3) if success: print("✔️ Both model do output the same tensors") else: print("❌ Both model do **NOT** output the same tensors") print("Absolute difference is:" , snake_case__) if __name__ == "__main__": _lowercase = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.''' ) parser.add_argument( '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.''' ) _lowercase = parser.parse_args() convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
718
import logging import os from typing import Dict, List, Optional, Union import torch import torch.nn as nn from accelerate.utils.imports import ( is_abit_bnb_available, is_abit_bnb_available, is_bnb_available, ) from ..big_modeling import dispatch_model, init_empty_weights from .dataclasses import BnbQuantizationConfig from .modeling import ( find_tied_parameters, get_balanced_memory, infer_auto_device_map, load_checkpoint_in_model, offload_weight, set_module_tensor_to_device, ) if is_bnb_available(): import bitsandbytes as bnb from copy import deepcopy _lowercase = logging.getLogger(__name__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = False , ): lowerCAmelCase_ : List[Any] = bnb_quantization_config.load_in_abit lowerCAmelCase_ : Optional[Any] = bnb_quantization_config.load_in_abit if load_in_abit and not is_abit_bnb_available(): raise ImportError( "You have a version of `bitsandbytes` that is not compatible with 8bit quantization," " make sure you have the latest version of `bitsandbytes` installed.") if load_in_abit and not is_abit_bnb_available(): raise ValueError( "You have a version of `bitsandbytes` that is not compatible with 4bit quantization," "make sure you have the latest version of `bitsandbytes` installed.") lowerCAmelCase_ : List[str] = [] # custom device map if isinstance(snake_case__ , snake_case__) and len(device_map.keys()) > 1: lowerCAmelCase_ : Union[str, Any] = [key for key, value in device_map.items() if value in ["disk", "cpu"]] # We keep some modules such as the lm_head in their original dtype for numerical stability reasons if bnb_quantization_config.skip_modules is None: lowerCAmelCase_ : Union[str, Any] = get_keys_to_not_convert(snake_case__) # add cpu modules to skip modules only for 4-bit modules if load_in_abit: bnb_quantization_config.skip_modules.extend(snake_case__) lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules # We add the modules we want to keep in full precision if bnb_quantization_config.keep_in_fpaa_modules is None: lowerCAmelCase_ : Optional[int] = [] lowerCAmelCase_ : int = bnb_quantization_config.keep_in_fpaa_modules modules_to_not_convert.extend(snake_case__) # compatibility with peft lowerCAmelCase_ : Optional[int] = load_in_abit lowerCAmelCase_ : List[str] = load_in_abit lowerCAmelCase_ : Optional[int] = get_parameter_device(snake_case__) if model_device.type != "meta": # quantization of an already loaded model logger.warning( "It is not recommended to quantize a loaded model. " "The model should be instantiated under the `init_empty_weights` context manager.") lowerCAmelCase_ : Union[str, Any] = replace_with_bnb_layers(snake_case__ , snake_case__ , modules_to_not_convert=snake_case__) # convert param to the right dtype lowerCAmelCase_ : Any = bnb_quantization_config.torch_dtype for name, param in model.state_dict().items(): if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules): param.to(torch.floataa) if param.dtype != torch.floataa: lowerCAmelCase_ : Optional[int] = name.replace(".weight" , "").replace(".bias" , "") lowerCAmelCase_ : Optional[int] = getattr(snake_case__ , snake_case__ , snake_case__) if param is not None: param.to(torch.floataa) elif torch.is_floating_point(snake_case__): param.to(snake_case__) if model_device.type == "cuda": # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda model.cuda(torch.cuda.current_device()) torch.cuda.empty_cache() elif torch.cuda.is_available(): model.to(torch.cuda.current_device()) else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info( F'''The model device type is {model_device.type}. However, cuda is needed for quantization.''' "We move the model to cuda.") return model elif weights_location is None: raise RuntimeError( F'''`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} ''') else: with init_empty_weights(): lowerCAmelCase_ : str = replace_with_bnb_layers( snake_case__ , snake_case__ , modules_to_not_convert=snake_case__) lowerCAmelCase_ : Optional[int] = get_quantized_model_device_map( snake_case__ , snake_case__ , snake_case__ , max_memory=snake_case__ , no_split_module_classes=snake_case__ , ) if offload_state_dict is None and device_map is not None and "disk" in device_map.values(): lowerCAmelCase_ : Optional[Any] = True lowerCAmelCase_ : Optional[int] = any(x in list(device_map.values()) for x in ["cpu", "disk"]) load_checkpoint_in_model( snake_case__ , snake_case__ , snake_case__ , dtype=bnb_quantization_config.torch_dtype , offload_folder=snake_case__ , offload_state_dict=snake_case__ , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , ) return dispatch_model(snake_case__ , device_map=snake_case__ , offload_dir=snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=None): if device_map is None: if torch.cuda.is_available(): lowerCAmelCase_ : Any = {"": torch.cuda.current_device()} else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info("The device_map was not initialized." "Setting device_map to `{'':torch.cuda.current_device()}`.") if isinstance(snake_case__ , snake_case__): if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]: raise ValueError( "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or " "'sequential'.") lowerCAmelCase_ : Dict = {} special_dtypes.update( { name: bnb_quantization_config.torch_dtype for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.skip_modules) }) special_dtypes.update( { name: torch.floataa for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules) }) lowerCAmelCase_ : List[str] = {} lowerCAmelCase_ : Union[str, Any] = special_dtypes lowerCAmelCase_ : Union[str, Any] = no_split_module_classes lowerCAmelCase_ : Any = bnb_quantization_config.target_dtype # get max_memory for each device. if device_map != "sequential": lowerCAmelCase_ : Tuple = get_balanced_memory( snake_case__ , low_zero=(device_map == "balanced_low_0") , max_memory=snake_case__ , **snake_case__ , ) lowerCAmelCase_ : Tuple = max_memory lowerCAmelCase_ : Optional[Any] = infer_auto_device_map(snake_case__ , **snake_case__) if isinstance(snake_case__ , snake_case__): # check if don't have any quantized module on the cpu lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules lowerCAmelCase_ : List[Any] = { key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert } for device in ["cpu", "disk"]: if device in device_map_without_some_modules.values(): if bnb_quantization_config.load_in_abit: raise ValueError( "\n Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n these modules in `torch_dtype`, you need to pass a custom `device_map` to\n `load_and_quantize_model`. Check\n https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n for more details.\n ") else: logger.info( "Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit") del device_map_without_some_modules return device_map def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None): if modules_to_not_convert is None: lowerCAmelCase_ : List[str] = [] lowerCAmelCase_ , lowerCAmelCase_ : Tuple = _replace_with_bnb_layers( snake_case__ , snake_case__ , snake_case__ , snake_case__) if not has_been_replaced: logger.warning( "You are loading your model in 8bit or 4bit but no linear modules were found in your model." " this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers." " Please double check your model architecture, or submit an issue on github if you think this is" " a bug.") return model def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , ): lowerCAmelCase_ : str = False for name, module in model.named_children(): if current_key_name is None: lowerCAmelCase_ : Optional[int] = [] current_key_name.append(snake_case__) if isinstance(snake_case__ , nn.Linear) and name not in modules_to_not_convert: # Check if the current key is not in the `modules_to_not_convert` lowerCAmelCase_ : Optional[int] = ".".join(snake_case__) lowerCAmelCase_ : List[str] = True for key in modules_to_not_convert: if ( (key in current_key_name_str) and (key + "." in current_key_name_str) ) or key == current_key_name_str: lowerCAmelCase_ : List[Any] = False break if proceed: # Load bnb module with empty weight and replace ``nn.Linear` module if bnb_quantization_config.load_in_abit: lowerCAmelCase_ : Tuple = bnb.nn.LinearabitLt( module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=snake_case__ , threshold=bnb_quantization_config.llm_inta_threshold , ) elif bnb_quantization_config.load_in_abit: lowerCAmelCase_ : Dict = bnb.nn.Linearabit( module.in_features , module.out_features , module.bias is not None , bnb_quantization_config.bnb_abit_compute_dtype , compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant , quant_type=bnb_quantization_config.bnb_abit_quant_type , ) else: raise ValueError("load_in_8bit and load_in_4bit can't be both False") lowerCAmelCase_ : List[str] = module.weight.data if module.bias is not None: lowerCAmelCase_ : Any = module.bias.data bnb_module.requires_grad_(snake_case__) setattr(snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = True if len(list(module.children())) > 0: lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = _replace_with_bnb_layers( snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : Optional[int] = has_been_replaced | _has_been_replaced # Remove the last key for recursion current_key_name.pop(-1) return model, has_been_replaced def UpperCamelCase ( snake_case__): # Create a copy of the model with init_empty_weights(): lowerCAmelCase_ : List[Any] = deepcopy(snake_case__) # this has 0 cost since it is done inside `init_empty_weights` context manager` lowerCAmelCase_ : Dict = find_tied_parameters(snake_case__) # For compatibility with Accelerate < 0.18 if isinstance(snake_case__ , snake_case__): lowerCAmelCase_ : List[str] = sum(list(tied_params.values()) , []) + list(tied_params.keys()) else: lowerCAmelCase_ : Optional[Any] = sum(snake_case__ , []) lowerCAmelCase_ : List[Any] = len(snake_case__) > 0 # Check if it is a base model lowerCAmelCase_ : List[str] = False if hasattr(snake_case__ , "base_model_prefix"): lowerCAmelCase_ : Tuple = not hasattr(snake_case__ , model.base_model_prefix) # Ignore this for base models (BertModel, GPT2Model, etc.) if (not has_tied_params) and is_base_model: return [] # otherwise they have an attached head lowerCAmelCase_ : Union[str, Any] = list(model.named_children()) lowerCAmelCase_ : Optional[int] = [list_modules[-1][0]] # add last module together with tied weights lowerCAmelCase_ : Any = set(snake_case__) - set(snake_case__) lowerCAmelCase_ : Tuple = list(set(snake_case__)) + list(snake_case__) # remove ".weight" from the keys lowerCAmelCase_ : List[str] = [".weight", ".bias"] lowerCAmelCase_ : Tuple = [] for name in list_untouched: for name_to_remove in names_to_remove: if name_to_remove in name: lowerCAmelCase_ : str = name.replace(snake_case__ , "") filtered_module_names.append(snake_case__) return filtered_module_names def UpperCamelCase ( snake_case__): for m in model.modules(): if isinstance(snake_case__ , bnb.nn.Linearabit): return True return False def UpperCamelCase ( snake_case__): return next(parameter.parameters()).device def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): # if it is not quantized, we quantize and offload the quantized weights and the SCB stats if fpaa_statistics is None: set_module_tensor_to_device(snake_case__ , snake_case__ , 0 , dtype=snake_case__ , value=snake_case__) lowerCAmelCase_ : str = param_name lowerCAmelCase_ : Tuple = model if "." in tensor_name: lowerCAmelCase_ : Dict = tensor_name.split(".") for split in splits[:-1]: lowerCAmelCase_ : Any = getattr(snake_case__ , snake_case__) if new_module is None: raise ValueError(F'''{module} has no attribute {split}.''') lowerCAmelCase_ : Union[str, Any] = new_module lowerCAmelCase_ : Any = splits[-1] # offload weights lowerCAmelCase_ : List[Any] = False offload_weight(module._parameters[tensor_name] , snake_case__ , snake_case__ , index=snake_case__) if hasattr(module._parameters[tensor_name] , "SCB"): offload_weight( module._parameters[tensor_name].SCB , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__ , ) else: offload_weight(snake_case__ , snake_case__ , snake_case__ , index=snake_case__) offload_weight(snake_case__ , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__) set_module_tensor_to_device(snake_case__ , snake_case__ , "meta" , dtype=snake_case__ , value=torch.empty(*param.size()))
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from typing import List, Optional import numpy as np from ...processing_utils import ProcessorMixin from ...utils import to_numpy class __snake_case ( UpperCamelCase_ ): """simple docstring""" UpperCamelCase_ = 'EncodecFeatureExtractor' UpperCamelCase_ = ('T5Tokenizer', 'T5TokenizerFast') def __init__( self : int ,lowerCAmelCase__ : str ,lowerCAmelCase__ : str ) -> List[Any]: '''simple docstring''' super().__init__(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Any = self.feature_extractor lowerCAmelCase_ : Any = False def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : int=None ,lowerCAmelCase__ : List[str]=True ) -> Tuple: '''simple docstring''' return self.tokenizer.get_decoder_prompt_ids(task=lowerCAmelCase__ ,language=lowerCAmelCase__ ,no_timestamps=lowerCAmelCase__ ) def __call__( self : Optional[Any] ,*lowerCAmelCase__ : int ,**lowerCAmelCase__ : Optional[int] ) -> List[Any]: '''simple docstring''' if self._in_target_context_manager: return self.current_processor(*lowerCAmelCase__ ,**lowerCAmelCase__ ) lowerCAmelCase_ : str = kwargs.pop("audio" ,lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = kwargs.pop("sampling_rate" ,lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = kwargs.pop("text" ,lowerCAmelCase__ ) if len(lowerCAmelCase__ ) > 0: lowerCAmelCase_ : Union[str, Any] = args[0] lowerCAmelCase_ : Optional[int] = args[1:] if audio is None and text is None: raise ValueError("You need to specify either an `audio` or `text` input to process." ) if text is not None: lowerCAmelCase_ : List[Any] = self.tokenizer(lowerCAmelCase__ ,**lowerCAmelCase__ ) if audio is not None: lowerCAmelCase_ : List[str] = self.feature_extractor(lowerCAmelCase__ ,*lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,**lowerCAmelCase__ ) if audio is None: return inputs elif text is None: return audio_inputs else: lowerCAmelCase_ : List[Any] = audio_inputs['''input_values'''] if "padding_mask" in audio_inputs: lowerCAmelCase_ : Optional[int] = audio_inputs['''padding_mask'''] return inputs def UpperCAmelCase_ ( self : Any ,*lowerCAmelCase__ : Tuple ,**lowerCAmelCase__ : Any ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : int = kwargs.pop("audio" ,lowerCAmelCase__ ) lowerCAmelCase_ : int = kwargs.pop("padding_mask" ,lowerCAmelCase__ ) if len(lowerCAmelCase__ ) > 0: lowerCAmelCase_ : Optional[int] = args[0] lowerCAmelCase_ : Dict = args[1:] if audio_values is not None: return self._decode_audio(lowerCAmelCase__ ,padding_mask=lowerCAmelCase__ ) else: return self.tokenizer.batch_decode(*lowerCAmelCase__ ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[Any] ,*lowerCAmelCase__ : str ,**lowerCAmelCase__ : Any ) -> List[Any]: '''simple docstring''' return self.tokenizer.decode(*lowerCAmelCase__ ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : List[Any] = None ) -> List[np.ndarray]: '''simple docstring''' lowerCAmelCase_ : Dict = to_numpy(lowerCAmelCase__ ) lowerCAmelCase_ : List[Any] = audio_values.shape if padding_mask is None: return list(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = to_numpy(lowerCAmelCase__ ) # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding** # token (so that the generated audio values are **not** treated as padded tokens) lowerCAmelCase_ : Union[str, Any] = seq_len - padding_mask.shape[-1] lowerCAmelCase_ : List[str] = 1 - self.feature_extractor.padding_value lowerCAmelCase_ : str = np.pad(lowerCAmelCase__ ,((0, 0), (0, difference)) ,"constant" ,constant_values=lowerCAmelCase__ ) lowerCAmelCase_ : int = audio_values.tolist() for i in range(lowerCAmelCase__ ): lowerCAmelCase_ : Optional[Any] = np.asarray(audio_values[i] )[ padding_mask[i][None, :] != self.feature_extractor.padding_value ] lowerCAmelCase_ : int = sliced_audio.reshape(lowerCAmelCase__ ,-1 ) return audio_values
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import copy from typing import Any, Dict, List, Optional, Union import numpy as np import torch from ...audio_utils import mel_filter_bank, spectrogram, window_function from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import TensorType, logging _lowercase = logging.get_logger(__name__) class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = ['input_features', 'is_longer'] def __init__( self : Optional[int] ,lowerCAmelCase__ : List[Any]=64 ,lowerCAmelCase__ : Any=4_80_00 ,lowerCAmelCase__ : Optional[Any]=4_80 ,lowerCAmelCase__ : List[str]=10 ,lowerCAmelCase__ : List[Any]=10_24 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : float = 0 ,lowerCAmelCase__ : float = 1_40_00 ,lowerCAmelCase__ : int = None ,lowerCAmelCase__ : str = "fusion" ,lowerCAmelCase__ : str = "repeatpad" ,**lowerCAmelCase__ : Union[str, Any] ,) -> Union[str, Any]: '''simple docstring''' super().__init__( feature_size=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,padding_value=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : Optional[Any] = top_db lowerCAmelCase_ : str = truncation lowerCAmelCase_ : Tuple = padding lowerCAmelCase_ : str = fft_window_size lowerCAmelCase_ : Dict = (fft_window_size >> 1) + 1 lowerCAmelCase_ : Dict = hop_length lowerCAmelCase_ : Any = max_length_s lowerCAmelCase_ : int = max_length_s * sampling_rate lowerCAmelCase_ : Optional[int] = sampling_rate lowerCAmelCase_ : int = frequency_min lowerCAmelCase_ : Optional[Any] = frequency_max lowerCAmelCase_ : List[Any] = mel_filter_bank( num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm=lowerCAmelCase__ ,mel_scale="htk" ,) lowerCAmelCase_ : List[Any] = mel_filter_bank( num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm="slaney" ,mel_scale="slaney" ,) def UpperCAmelCase_ ( self : Dict ) -> Dict[str, Any]: '''simple docstring''' lowerCAmelCase_ : int = copy.deepcopy(self.__dict__ ) lowerCAmelCase_ : Optional[int] = self.__class__.__name__ if "mel_filters" in output: del output["mel_filters"] if "mel_filters_slaney" in output: del output["mel_filters_slaney"] return output def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Optional[np.array] = None ) -> np.ndarray: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = spectrogram( lowerCAmelCase__ ,window_function(self.fft_window_size ,"hann" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=lowerCAmelCase__ ,log_mel="dB" ,) return log_mel_spectrogram.T def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Tuple ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Tuple = np.array_split(list(range(0 ,total_frames - chunk_frames + 1 ) ) ,3 ) if len(ranges[1] ) == 0: # if the audio is too short, we just use the first chunk lowerCAmelCase_ : List[Any] = [0] if len(ranges[2] ) == 0: # if the audio is too short, we just use the first chunk lowerCAmelCase_ : List[Any] = [0] # randomly choose index for each part lowerCAmelCase_ : str = np.random.choice(ranges[0] ) lowerCAmelCase_ : Optional[Any] = np.random.choice(ranges[1] ) lowerCAmelCase_ : Any = np.random.choice(ranges[2] ) lowerCAmelCase_ : str = mel[idx_front : idx_front + chunk_frames, :] lowerCAmelCase_ : Dict = mel[idx_middle : idx_middle + chunk_frames, :] lowerCAmelCase_ : Optional[Any] = mel[idx_back : idx_back + chunk_frames, :] lowerCAmelCase_ : List[str] = torch.tensor(mel[None, None, :] ) lowerCAmelCase_ : List[Any] = torch.nn.functional.interpolate( lowerCAmelCase__ ,size=[chunk_frames, 64] ,mode="bilinear" ,align_corners=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = mel_shrink[0][0].numpy() lowerCAmelCase_ : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 ) return mel_fusion def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : int ) -> np.array: '''simple docstring''' if waveform.shape[0] > max_length: if truncation == "rand_trunc": lowerCAmelCase_ : List[Any] = True # random crop to max_length (for compatibility) -> this should be handled by self.pad lowerCAmelCase_ : str = len(lowerCAmelCase__ ) - max_length lowerCAmelCase_ : Any = np.random.randint(0 ,overflow + 1 ) lowerCAmelCase_ : Dict = waveform[idx : idx + max_length] lowerCAmelCase_ : List[str] = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :] elif truncation == "fusion": lowerCAmelCase_ : Tuple = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters ) lowerCAmelCase_ : str = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed lowerCAmelCase_ : List[str] = mel.shape[0] if chunk_frames == total_frames: # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length. # In this case, we just use the whole audio. lowerCAmelCase_ : Dict = np.stack([mel, mel, mel, mel] ,axis=0 ) lowerCAmelCase_ : int = False else: lowerCAmelCase_ : str = self._random_mel_fusion(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Any = True else: raise NotImplementedError(f'''data_truncating {truncation} not implemented''' ) else: lowerCAmelCase_ : Dict = False # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding if waveform.shape[0] < max_length: if padding == "repeat": lowerCAmelCase_ : List[Any] = int(max_length / len(lowerCAmelCase__ ) ) lowerCAmelCase_ : int = np.stack(np.tile(lowerCAmelCase__ ,n_repeat + 1 ) )[:max_length] if padding == "repeatpad": lowerCAmelCase_ : Optional[Any] = int(max_length / len(lowerCAmelCase__ ) ) lowerCAmelCase_ : Tuple = np.stack(np.tile(lowerCAmelCase__ ,lowerCAmelCase__ ) ) lowerCAmelCase_ : List[Any] = np.pad(lowerCAmelCase__ ,(0, max_length - waveform.shape[0]) ,mode="constant" ,constant_values=0 ) if truncation == "fusion": lowerCAmelCase_ : int = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters ) lowerCAmelCase_ : Tuple = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 ) else: lowerCAmelCase_ : str = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :] return input_mel, longer def __call__( self : int ,lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCAmelCase__ : str = None ,lowerCAmelCase__ : Optional[str] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[Union[str, TensorType]] = None ,**lowerCAmelCase__ : List[Any] ,) -> BatchFeature: '''simple docstring''' lowerCAmelCase_ : List[str] = truncation if truncation is not None else self.truncation lowerCAmelCase_ : List[Any] = padding if padding else self.padding if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a''' f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input''' f''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' ) else: logger.warning( "It is strongly recommended to pass the `sampling_rate` argument to this function. " "Failing to do so can result in silent errors that might be hard to debug." ) lowerCAmelCase_ : Dict = isinstance(lowerCAmelCase__ ,np.ndarray ) and len(raw_speech.shape ) > 1 if is_batched_numpy and len(raw_speech.shape ) > 2: raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' ) lowerCAmelCase_ : Dict = is_batched_numpy or ( isinstance(lowerCAmelCase__ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) )) ) if is_batched: lowerCAmelCase_ : List[str] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for speech in raw_speech] elif not is_batched and not isinstance(lowerCAmelCase__ ,np.ndarray ): lowerCAmelCase_ : Tuple = np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) elif isinstance(lowerCAmelCase__ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ): lowerCAmelCase_ : Any = raw_speech.astype(np.floataa ) # always return batch if not is_batched: lowerCAmelCase_ : Any = [np.asarray(lowerCAmelCase__ )] # convert to mel spectrogram, truncate and pad if needed. lowerCAmelCase_ : Optional[Any] = [ self._get_input_mel(lowerCAmelCase__ ,max_length if max_length else self.nb_max_samples ,lowerCAmelCase__ ,lowerCAmelCase__ ) for waveform in raw_speech ] lowerCAmelCase_ : str = [] lowerCAmelCase_ : str = [] for mel, longer in padded_inputs: input_mel.append(lowerCAmelCase__ ) is_longer.append(lowerCAmelCase__ ) if truncation == "fusion" and sum(lowerCAmelCase__ ) == 0: # if no audio is longer than 10s, then randomly select one audio to be longer lowerCAmelCase_ : Any = np.random.randint(0 ,len(lowerCAmelCase__ ) ) lowerCAmelCase_ : Dict = True if isinstance(input_mel[0] ,lowerCAmelCase__ ): lowerCAmelCase_ : Optional[int] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for feature in input_mel] # is_longer is a list of bool lowerCAmelCase_ : List[Any] = [[longer] for longer in is_longer] lowerCAmelCase_ : Optional[Any] = {"input_features": input_mel, "is_longer": is_longer} lowerCAmelCase_ : Dict = BatchFeature(lowerCAmelCase__ ) if return_tensors is not None: lowerCAmelCase_ : List[str] = input_features.convert_to_tensors(lowerCAmelCase__ ) return input_features
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import inspect import unittest from transformers import MobileViTConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import MobileViTImageProcessor class __snake_case ( lowercase_ ): """simple docstring""" def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Any = self.config_class(**self.inputs_dict ) self.parent.assertTrue(hasattr(lowerCAmelCase__ ,"hidden_sizes" ) ) self.parent.assertTrue(hasattr(lowerCAmelCase__ ,"neck_hidden_sizes" ) ) self.parent.assertTrue(hasattr(lowerCAmelCase__ ,"num_attention_heads" ) ) class __snake_case : """simple docstring""" def __init__( self : Any ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Union[str, Any]=13 ,lowerCAmelCase__ : Optional[Any]=32 ,lowerCAmelCase__ : List[Any]=2 ,lowerCAmelCase__ : Any=3 ,lowerCAmelCase__ : Any=6_40 ,lowerCAmelCase__ : List[str]=4 ,lowerCAmelCase__ : List[Any]="silu" ,lowerCAmelCase__ : Optional[Any]=3 ,lowerCAmelCase__ : Any=32 ,lowerCAmelCase__ : int=0.1 ,lowerCAmelCase__ : Union[str, Any]=0.1 ,lowerCAmelCase__ : Optional[int]=0.1 ,lowerCAmelCase__ : str=0.02 ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : Dict=True ,lowerCAmelCase__ : str=10 ,lowerCAmelCase__ : Any=None ,) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Dict = parent lowerCAmelCase_ : Dict = batch_size lowerCAmelCase_ : str = image_size lowerCAmelCase_ : int = patch_size lowerCAmelCase_ : str = num_channels lowerCAmelCase_ : Optional[int] = last_hidden_size lowerCAmelCase_ : Dict = num_attention_heads lowerCAmelCase_ : Optional[Any] = hidden_act lowerCAmelCase_ : List[Any] = conv_kernel_size lowerCAmelCase_ : Dict = output_stride lowerCAmelCase_ : Optional[int] = hidden_dropout_prob lowerCAmelCase_ : Dict = attention_probs_dropout_prob lowerCAmelCase_ : Tuple = classifier_dropout_prob lowerCAmelCase_ : int = use_labels lowerCAmelCase_ : List[str] = is_training lowerCAmelCase_ : Tuple = num_labels lowerCAmelCase_ : List[Any] = initializer_range lowerCAmelCase_ : Dict = scope def UpperCAmelCase_ ( self : List[str] ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) lowerCAmelCase_ : str = None lowerCAmelCase_ : Dict = None if self.use_labels: lowerCAmelCase_ : Dict = ids_tensor([self.batch_size] ,self.num_labels ) lowerCAmelCase_ : Dict = ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels ) lowerCAmelCase_ : Union[str, Any] = self.get_config() return config, pixel_values, labels, pixel_labels def UpperCAmelCase_ ( self : int ) -> str: '''simple docstring''' return MobileViTConfig( image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,num_attention_heads=self.num_attention_heads ,hidden_act=self.hidden_act ,conv_kernel_size=self.conv_kernel_size ,output_stride=self.output_stride ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,classifier_dropout_prob=self.classifier_dropout_prob ,initializer_range=self.initializer_range ,) def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : Any ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Optional[int] = MobileViTModel(config=lowerCAmelCase__ ) model.to(lowerCAmelCase__ ) model.eval() lowerCAmelCase_ : Dict = model(lowerCAmelCase__ ) self.parent.assertEqual( result.last_hidden_state.shape ,( self.batch_size, self.last_hidden_size, self.image_size // self.output_stride, self.image_size // self.output_stride, ) ,) def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Optional[Any] ) -> str: '''simple docstring''' lowerCAmelCase_ : Dict = self.num_labels lowerCAmelCase_ : List[Any] = MobileViTForImageClassification(lowerCAmelCase__ ) model.to(lowerCAmelCase__ ) model.eval() lowerCAmelCase_ : str = model(lowerCAmelCase__ ,labels=lowerCAmelCase__ ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) ) def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : int ) -> Any: '''simple docstring''' lowerCAmelCase_ : int = self.num_labels lowerCAmelCase_ : Any = MobileViTForSemanticSegmentation(lowerCAmelCase__ ) model.to(lowerCAmelCase__ ) model.eval() lowerCAmelCase_ : Optional[Any] = model(lowerCAmelCase__ ) self.parent.assertEqual( result.logits.shape ,( self.batch_size, self.num_labels, self.image_size // self.output_stride, self.image_size // self.output_stride, ) ,) lowerCAmelCase_ : Union[str, Any] = model(lowerCAmelCase__ ,labels=lowerCAmelCase__ ) self.parent.assertEqual( result.logits.shape ,( self.batch_size, self.num_labels, self.image_size // self.output_stride, self.image_size // self.output_stride, ) ,) def UpperCAmelCase_ ( self : Optional[Any] ) -> Any: '''simple docstring''' lowerCAmelCase_ : Tuple = self.prepare_config_and_inputs() lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = config_and_inputs lowerCAmelCase_ : List[Any] = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class __snake_case ( lowercase_ , lowercase_ , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = ( (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation) if is_torch_available() else () ) UpperCamelCase_ = ( { '''feature-extraction''': MobileViTModel, '''image-classification''': MobileViTForImageClassification, '''image-segmentation''': MobileViTForSemanticSegmentation, } if is_torch_available() else {} ) UpperCamelCase_ = False UpperCamelCase_ = False UpperCamelCase_ = False UpperCamelCase_ = False def UpperCAmelCase_ ( self : Any ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : Dict = MobileViTModelTester(self ) lowerCAmelCase_ : List[Any] = MobileViTConfigTester(self ,config_class=lowerCAmelCase__ ,has_text_modality=lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Dict ) -> Union[str, Any]: '''simple docstring''' self.config_tester.run_common_tests() @unittest.skip(reason="MobileViT does not use inputs_embeds" ) def UpperCAmelCase_ ( self : Any ) -> Tuple: '''simple docstring''' pass @unittest.skip(reason="MobileViT does not support input and output embeddings" ) def UpperCAmelCase_ ( self : Optional[Any] ) -> List[str]: '''simple docstring''' pass @unittest.skip(reason="MobileViT does not output attentions" ) def UpperCAmelCase_ ( self : List[Any] ) -> Optional[Any]: '''simple docstring''' pass def UpperCAmelCase_ ( self : str ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ , lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: lowerCAmelCase_ : int = model_class(lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic lowerCAmelCase_ : Tuple = [*signature.parameters.keys()] lowerCAmelCase_ : Dict = ["pixel_values"] self.assertListEqual(arg_names[:1] ,lowerCAmelCase__ ) @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." ) def UpperCAmelCase_ ( self : str ) -> Tuple: '''simple docstring''' pass def UpperCAmelCase_ ( self : Optional[Any] ) -> str: '''simple docstring''' lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any: '''simple docstring''' def check_hidden_states_output(lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[int] ): lowerCAmelCase_ : int = model_class(lowerCAmelCase__ ) model.to(lowerCAmelCase__ ) model.eval() with torch.no_grad(): lowerCAmelCase_ : Optional[Any] = model(**self._prepare_for_class(lowerCAmelCase__ ,lowerCAmelCase__ ) ) lowerCAmelCase_ : Dict = outputs.hidden_states lowerCAmelCase_ : Tuple = 5 self.assertEqual(len(lowerCAmelCase__ ) ,lowerCAmelCase__ ) # MobileViT's feature maps are of shape (batch_size, num_channels, height, width) # with the width and height being successively divided by 2. lowerCAmelCase_ : Any = 2 for i in range(len(lowerCAmelCase__ ) ): self.assertListEqual( list(hidden_states[i].shape[-2:] ) ,[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] ,) divisor *= 2 self.assertEqual(self.model_tester.output_stride ,divisor // 2 ) lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: lowerCAmelCase_ : List[Any] = True check_hidden_states_output(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] lowerCAmelCase_ : List[Any] = True check_hidden_states_output(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[Any] ) -> Any: '''simple docstring''' lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__ ) def UpperCAmelCase_ ( self : str ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__ ) @slow def UpperCAmelCase_ ( self : Dict ) -> int: '''simple docstring''' for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: lowerCAmelCase_ : int = MobileViTModel.from_pretrained(lowerCAmelCase__ ) self.assertIsNotNone(lowerCAmelCase__ ) def UpperCamelCase ( ): lowerCAmelCase_ : List[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class __snake_case ( unittest.TestCase ): """simple docstring""" @cached_property def UpperCAmelCase_ ( self : Union[str, Any] ) -> str: '''simple docstring''' return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small" ) if is_vision_available() else None @slow def UpperCAmelCase_ ( self : int ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : List[Any] = MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small" ).to(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = self.default_image_processor lowerCAmelCase_ : Any = prepare_img() lowerCAmelCase_ : Dict = image_processor(images=lowerCAmelCase__ ,return_tensors="pt" ).to(lowerCAmelCase__ ) # forward pass with torch.no_grad(): lowerCAmelCase_ : int = model(**lowerCAmelCase__ ) # verify the logits lowerCAmelCase_ : Any = torch.Size((1, 10_00) ) self.assertEqual(outputs.logits.shape ,lowerCAmelCase__ ) lowerCAmelCase_ : Any = torch.tensor([-1.9_364, -1.2_327, -0.4_653] ).to(lowerCAmelCase__ ) self.assertTrue(torch.allclose(outputs.logits[0, :3] ,lowerCAmelCase__ ,atol=1e-4 ) ) @slow def UpperCAmelCase_ ( self : List[str] ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small" ) lowerCAmelCase_ : Tuple = model.to(lowerCAmelCase__ ) lowerCAmelCase_ : Any = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small" ) lowerCAmelCase_ : Dict = prepare_img() lowerCAmelCase_ : str = image_processor(images=lowerCAmelCase__ ,return_tensors="pt" ).to(lowerCAmelCase__ ) # forward pass with torch.no_grad(): lowerCAmelCase_ : Optional[int] = model(**lowerCAmelCase__ ) lowerCAmelCase_ : str = outputs.logits # verify the logits lowerCAmelCase_ : Dict = torch.Size((1, 21, 32, 32) ) self.assertEqual(logits.shape ,lowerCAmelCase__ ) lowerCAmelCase_ : List[Any] = torch.tensor( [ [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]], [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]], [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]], ] ,device=lowerCAmelCase__ ,) self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,lowerCAmelCase__ ,atol=1e-4 ) ) @slow def UpperCAmelCase_ ( self : Optional[Any] ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Tuple = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small" ) lowerCAmelCase_ : Optional[int] = model.to(lowerCAmelCase__ ) lowerCAmelCase_ : int = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small" ) lowerCAmelCase_ : Optional[Any] = prepare_img() lowerCAmelCase_ : Any = image_processor(images=lowerCAmelCase__ ,return_tensors="pt" ).to(lowerCAmelCase__ ) # forward pass with torch.no_grad(): lowerCAmelCase_ : Tuple = model(**lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = outputs.logits.detach().cpu() lowerCAmelCase_ : Union[str, Any] = image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__ ,target_sizes=[(50, 60)] ) lowerCAmelCase_ : int = torch.Size((50, 60) ) self.assertEqual(segmentation[0].shape ,lowerCAmelCase__ ) lowerCAmelCase_ : List[Any] = image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__ ) lowerCAmelCase_ : Dict = torch.Size((32, 32) ) self.assertEqual(segmentation[0].shape ,lowerCAmelCase__ )
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from multiprocessing import Lock, Pipe, Process # lock used to ensure that two processes do not access a pipe at the same time _lowercase = Lock() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): global process_lock # we perform n swaps since after n swaps we know we are sorted # we *could* stop early if we are sorted already, but it takes as long to # find out we are sorted as it does to sort the list with this algorithm for i in range(0 , 10): if (i + position) % 2 == 0 and r_send is not None: # send your value to your right neighbor process_lock.acquire() r_send[1].send(snake_case__) process_lock.release() # receive your right neighbor's value process_lock.acquire() lowerCAmelCase_ : Optional[Any] = rr_cv[0].recv() process_lock.release() # take the lower value since you are on the left lowerCAmelCase_ : Any = min(snake_case__ , snake_case__) elif (i + position) % 2 != 0 and l_send is not None: # send your value to your left neighbor process_lock.acquire() l_send[1].send(snake_case__) process_lock.release() # receive your left neighbor's value process_lock.acquire() lowerCAmelCase_ : str = lr_cv[0].recv() process_lock.release() # take the higher value since you are on the right lowerCAmelCase_ : Dict = max(snake_case__ , snake_case__) # after all swaps are performed, send the values back to main result_pipe[1].send(snake_case__) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Union[str, Any] = [] lowerCAmelCase_ : int = [] # initialize the list of pipes where the values will be retrieved for _ in arr: result_pipe.append(Pipe()) # creates the processes # the first and last process only have one neighbor so they are made outside # of the loop lowerCAmelCase_ : Tuple = Pipe() lowerCAmelCase_ : Optional[int] = Pipe() process_array_.append( Process( target=snake_case__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , )) lowerCAmelCase_ : int = temp_rs lowerCAmelCase_ : List[Any] = temp_rr for i in range(1 , len(snake_case__) - 1): lowerCAmelCase_ : Dict = Pipe() lowerCAmelCase_ : List[str] = Pipe() process_array_.append( Process( target=snake_case__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , )) lowerCAmelCase_ : Dict = temp_rs lowerCAmelCase_ : Optional[Any] = temp_rr process_array_.append( Process( target=snake_case__ , args=( len(snake_case__) - 1, arr[len(snake_case__) - 1], temp_ls, None, temp_lr, None, result_pipe[len(snake_case__) - 1], ) , )) # start the processes for p in process_array_: p.start() # wait for the processes to end and write their values to the list for p in range(0 , len(snake_case__)): lowerCAmelCase_ : Union[str, Any] = result_pipe[p][0].recv() process_array_[p].join() return arr def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = list(range(10 , 0 , -1)) print("Initial List") print(*snake_case__) lowerCAmelCase_ : Tuple = odd_even_transposition(snake_case__) print("Sorted List\n") print(*snake_case__) if __name__ == "__main__": main()
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from __future__ import annotations import matplotlib.pyplot as plt # type: ignore import numpy # initial triangle of Koch snowflake _lowercase = numpy.array([0, 0]) _lowercase = numpy.array([0.5, 0.8_660_254]) _lowercase = numpy.array([1, 0]) _lowercase = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1] def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : int = initial_vectors for _ in range(lowerCamelCase__): lowerCAmelCase_ : Optional[Any] = iteration_step(lowerCamelCase__) return vectors def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Dict = [] for i, start_vector in enumerate(vectors[:-1]): lowerCAmelCase_ : Any = vectors[i + 1] new_vectors.append(lowerCamelCase__) lowerCAmelCase_ : Optional[Any] = end_vector - start_vector new_vectors.append(start_vector + difference_vector / 3) new_vectors.append( start_vector + difference_vector / 3 + rotate(difference_vector / 3 , 60)) new_vectors.append(start_vector + difference_vector * 2 / 3) new_vectors.append(vectors[-1]) return new_vectors def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Tuple = numpy.radians(lowerCamelCase__) lowerCAmelCase_ , lowerCAmelCase_ : str = numpy.cos(lowerCamelCase__), numpy.sin(lowerCamelCase__) lowerCAmelCase_ : List[str] = numpy.array(((c, -s), (s, c))) return numpy.dot(lowerCamelCase__ , lowerCamelCase__) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : str = plt.gca() axes.set_aspect("equal") # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all # y-coordinates as inputs, which are constructed from the vector-list using # zip() lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = zip(*lowerCamelCase__) plt.plot(lowerCamelCase__ , lowerCamelCase__) plt.show() if __name__ == "__main__": import doctest doctest.testmod() _lowercase = iterate(INITIAL_VECTORS, 5) plot(processed_vectors)
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from typing import Any def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ): _validation( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) # Creates data structures and fill initial step lowerCAmelCase_ : dict = {} lowerCAmelCase_ : dict = {} for state in states_space: lowerCAmelCase_ : List[Any] = observations_space[0] lowerCAmelCase_ : int = ( initial_probabilities[state] * emission_probabilities[state][observation] ) lowerCAmelCase_ : Dict = None # Fills the data structure with the probabilities of # different transitions and pointers to previous states for o in range(1 , len(snake_case__)): lowerCAmelCase_ : List[Any] = observations_space[o] lowerCAmelCase_ : Optional[Any] = observations_space[o - 1] for state in states_space: # Calculates the argmax for probability function lowerCAmelCase_ : List[Any] = "" lowerCAmelCase_ : Tuple = -1 for k_state in states_space: lowerCAmelCase_ : int = ( probabilities[(k_state, prior_observation)] * transition_probabilities[k_state][state] * emission_probabilities[state][observation] ) if probability > max_probability: lowerCAmelCase_ : List[str] = probability lowerCAmelCase_ : Optional[Any] = k_state # Update probabilities and pointers dicts lowerCAmelCase_ : Union[str, Any] = ( probabilities[(arg_max, prior_observation)] * transition_probabilities[arg_max][state] * emission_probabilities[state][observation] ) lowerCAmelCase_ : Any = arg_max # The final observation lowerCAmelCase_ : List[Any] = observations_space[len(snake_case__) - 1] # argmax for given final observation lowerCAmelCase_ : List[str] = "" lowerCAmelCase_ : List[str] = -1 for k_state in states_space: lowerCAmelCase_ : List[str] = probabilities[(k_state, final_observation)] if probability > max_probability: lowerCAmelCase_ : List[str] = probability lowerCAmelCase_ : Tuple = k_state lowerCAmelCase_ : str = arg_max # Process pointers backwards lowerCAmelCase_ : int = last_state lowerCAmelCase_ : int = [] for o in range(len(snake_case__) - 1 , -1 , -1): result.append(snake_case__) lowerCAmelCase_ : Optional[Any] = pointers[previous, observations_space[o]] result.reverse() return result def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ): _validate_not_empty( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) _validate_lists(snake_case__ , snake_case__) _validate_dicts( snake_case__ , snake_case__ , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ): if not all( [ observations_space, states_space, initial_probabilities, transition_probabilities, emission_probabilities, ]): raise ValueError("There's an empty parameter") def UpperCamelCase ( snake_case__ , snake_case__): _validate_list(snake_case__ , "observations_space") _validate_list(snake_case__ , "states_space") def UpperCamelCase ( snake_case__ , snake_case__): if not isinstance(_object , snake_case__): lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list''' raise ValueError(snake_case__) else: for x in _object: if not isinstance(snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list of strings''' raise ValueError(snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ): _validate_dict(snake_case__ , "initial_probabilities" , snake_case__) _validate_nested_dict(snake_case__ , "transition_probabilities") _validate_nested_dict(snake_case__ , "emission_probabilities") def UpperCamelCase ( snake_case__ , snake_case__): _validate_dict(_object , snake_case__ , snake_case__) for x in _object.values(): _validate_dict(snake_case__ , snake_case__ , snake_case__ , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = False): if not isinstance(_object , snake_case__): lowerCAmelCase_ : List[str] = F'''{var_name} must be a dict''' raise ValueError(snake_case__) if not all(isinstance(snake_case__ , snake_case__) for x in _object): lowerCAmelCase_ : Dict = F'''{var_name} all keys must be strings''' raise ValueError(snake_case__) if not all(isinstance(snake_case__ , snake_case__) for x in _object.values()): lowerCAmelCase_ : Union[str, Any] = "nested dictionary " if nested else "" lowerCAmelCase_ : Any = F'''{var_name} {nested_text}all values must be {value_type.__name__}''' raise ValueError(snake_case__) if __name__ == "__main__": from doctest import testmod testmod()
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_lowercase = { '''a''': '''AAAAA''', '''b''': '''AAAAB''', '''c''': '''AAABA''', '''d''': '''AAABB''', '''e''': '''AABAA''', '''f''': '''AABAB''', '''g''': '''AABBA''', '''h''': '''AABBB''', '''i''': '''ABAAA''', '''j''': '''BBBAA''', '''k''': '''ABAAB''', '''l''': '''ABABA''', '''m''': '''ABABB''', '''n''': '''ABBAA''', '''o''': '''ABBAB''', '''p''': '''ABBBA''', '''q''': '''ABBBB''', '''r''': '''BAAAA''', '''s''': '''BAAAB''', '''t''': '''BAABA''', '''u''': '''BAABB''', '''v''': '''BBBAB''', '''w''': '''BABAA''', '''x''': '''BABAB''', '''y''': '''BABBA''', '''z''': '''BABBB''', ''' ''': ''' ''', } _lowercase = {value: key for key, value in encode_dict.items()} def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[int] = "" for letter in word.lower(): if letter.isalpha() or letter == " ": encoded += encode_dict[letter] else: raise Exception("encode() accepts only letters of the alphabet and spaces") return encoded def UpperCamelCase ( snake_case__): if set(snake_case__) - {"A", "B", " "} != set(): raise Exception("decode() accepts only 'A', 'B' and spaces") lowerCAmelCase_ : Optional[Any] = "" for word in coded.split(): while len(snake_case__) != 0: decoded += decode_dict[word[:5]] lowerCAmelCase_ : Any = word[5:] decoded += " " return decoded.strip() if __name__ == "__main__": from doctest import testmod testmod()
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor from ..utils import is_datasets_available from .base import PipelineTool if is_datasets_available(): from datasets import load_dataset class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = 'microsoft/speecht5_tts' UpperCamelCase_ = ( 'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the ' 'text to read (in English) and returns a waveform object containing the sound.' ) UpperCamelCase_ = 'text_reader' UpperCamelCase_ = SpeechTaProcessor UpperCamelCase_ = SpeechTaForTextToSpeech UpperCamelCase_ = SpeechTaHifiGan UpperCamelCase_ = ['text'] UpperCamelCase_ = ['audio'] def UpperCAmelCase_ ( self : Dict ) -> Any: '''simple docstring''' if self.post_processor is None: lowerCAmelCase_ : Any = "microsoft/speecht5_hifigan" super().setup() def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Any = self.pre_processor(text=lowerCAmelCase__ ,return_tensors="pt" ,truncation=lowerCAmelCase__ ) if speaker_embeddings is None: if not is_datasets_available(): raise ImportError("Datasets needs to be installed if not passing speaker embeddings." ) lowerCAmelCase_ : str = load_dataset("Matthijs/cmu-arctic-xvectors" ,split="validation" ) lowerCAmelCase_ : List[Any] = torch.tensor(embeddings_dataset[73_05]["xvector"] ).unsqueeze(0 ) return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings} def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]: '''simple docstring''' with torch.no_grad(): return self.model.generate_speech(**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : str ) -> Any: '''simple docstring''' with torch.no_grad(): return self.post_processor(lowerCAmelCase__ ).cpu().detach()
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from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = { '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''', '''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/config.json''', '''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/config.json''', '''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/config.json''', '''bert-base-multilingual-uncased''': '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json''', '''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json''', '''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/config.json''', '''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/config.json''', '''bert-large-uncased-whole-word-masking''': ( '''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json''' ), '''bert-large-cased-whole-word-masking''': ( '''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json''' ), '''bert-large-uncased-whole-word-masking-finetuned-squad''': ( '''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json''' ), '''bert-large-cased-whole-word-masking-finetuned-squad''': ( '''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json''' ), '''bert-base-cased-finetuned-mrpc''': '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json''', '''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json''', '''bert-base-german-dbmdz-uncased''': '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json''', '''cl-tohoku/bert-base-japanese''': '''https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json''', '''cl-tohoku/bert-base-japanese-whole-word-masking''': ( '''https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json''' ), '''cl-tohoku/bert-base-japanese-char''': ( '''https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json''' ), '''cl-tohoku/bert-base-japanese-char-whole-word-masking''': ( '''https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json''' ), '''TurkuNLP/bert-base-finnish-cased-v1''': ( '''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json''' ), '''TurkuNLP/bert-base-finnish-uncased-v1''': ( '''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json''' ), '''wietsedv/bert-base-dutch-cased''': '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json''', # See all BERT models at https://huggingface.co/models?filter=bert } class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = 'bert' def __init__( self : List[str] ,lowerCAmelCase__ : List[Any]=3_05_22 ,lowerCAmelCase__ : List[str]=7_68 ,lowerCAmelCase__ : Optional[int]=12 ,lowerCAmelCase__ : List[Any]=12 ,lowerCAmelCase__ : int=30_72 ,lowerCAmelCase__ : List[Any]="gelu" ,lowerCAmelCase__ : Tuple=0.1 ,lowerCAmelCase__ : Tuple=0.1 ,lowerCAmelCase__ : List[str]=5_12 ,lowerCAmelCase__ : Optional[int]=2 ,lowerCAmelCase__ : Any=0.02 ,lowerCAmelCase__ : Union[str, Any]=1e-1_2 ,lowerCAmelCase__ : List[Any]=0 ,lowerCAmelCase__ : Any="absolute" ,lowerCAmelCase__ : Any=True ,lowerCAmelCase__ : Optional[int]=None ,**lowerCAmelCase__ : Optional[Any] ,) -> str: '''simple docstring''' super().__init__(pad_token_id=lowerCAmelCase__ ,**lowerCAmelCase__ ) lowerCAmelCase_ : Any = vocab_size lowerCAmelCase_ : Optional[Any] = hidden_size lowerCAmelCase_ : Tuple = num_hidden_layers lowerCAmelCase_ : int = num_attention_heads lowerCAmelCase_ : str = hidden_act lowerCAmelCase_ : List[str] = intermediate_size lowerCAmelCase_ : Any = hidden_dropout_prob lowerCAmelCase_ : str = attention_probs_dropout_prob lowerCAmelCase_ : str = max_position_embeddings lowerCAmelCase_ : int = type_vocab_size lowerCAmelCase_ : str = initializer_range lowerCAmelCase_ : Optional[int] = layer_norm_eps lowerCAmelCase_ : Dict = position_embedding_type lowerCAmelCase_ : Optional[Any] = use_cache lowerCAmelCase_ : Tuple = classifier_dropout class __snake_case ( snake_case__ ): """simple docstring""" @property def UpperCAmelCase_ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]: '''simple docstring''' if self.task == "multiple-choice": lowerCAmelCase_ : Any = {0: "batch", 1: "choice", 2: "sequence"} else: lowerCAmelCase_ : Union[str, Any] = {0: "batch", 1: "sequence"} return OrderedDict( [ ("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis), ] )
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import argparse import collections import json import os import re import string import sys import numpy as np _lowercase = re.compile(r'''\b(a|an|the)\b''', re.UNICODE) _lowercase = None def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0.") parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file.") parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions.") parser.add_argument( "--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout).") parser.add_argument( "--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer.") parser.add_argument( "--na-prob-thresh" , "-t" , type=snake_case__ , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , ) parser.add_argument( "--out-image-dir" , "-p" , metavar="out_images" , default=snake_case__ , help="Save precision-recall curves to directory.") parser.add_argument("--verbose" , "-v" , action="store_true") if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() def UpperCamelCase ( snake_case__): lowerCAmelCase_ : str = {} for article in dataset: for p in article["paragraphs"]: for qa in p["qas"]: lowerCAmelCase_ : Dict = bool(qa["answers"]["text"]) return qid_to_has_ans def UpperCamelCase ( snake_case__): def remove_articles(snake_case__): return ARTICLES_REGEX.sub(" " , snake_case__) def white_space_fix(snake_case__): return " ".join(text.split()) def remove_punc(snake_case__): lowerCAmelCase_ : Optional[int] = set(string.punctuation) return "".join(ch for ch in text if ch not in exclude) def lower(snake_case__): return text.lower() return white_space_fix(remove_articles(remove_punc(lower(snake_case__)))) def UpperCamelCase ( snake_case__): if not s: return [] return normalize_answer(snake_case__).split() def UpperCamelCase ( snake_case__ , snake_case__): return int(normalize_answer(snake_case__) == normalize_answer(snake_case__)) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = get_tokens(snake_case__) lowerCAmelCase_ : Union[str, Any] = get_tokens(snake_case__) lowerCAmelCase_ : Any = collections.Counter(snake_case__) & collections.Counter(snake_case__) lowerCAmelCase_ : Dict = sum(common.values()) if len(snake_case__) == 0 or len(snake_case__) == 0: # If either is no-answer, then F1 is 1 if they agree, 0 otherwise return int(gold_toks == pred_toks) if num_same == 0: return 0 lowerCAmelCase_ : List[Any] = 1.0 * num_same / len(snake_case__) lowerCAmelCase_ : int = 1.0 * num_same / len(snake_case__) lowerCAmelCase_ : List[Any] = (2 * precision * recall) / (precision + recall) return fa def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Tuple = {} lowerCAmelCase_ : int = {} for article in dataset: for p in article["paragraphs"]: for qa in p["qas"]: lowerCAmelCase_ : int = qa["id"] lowerCAmelCase_ : Any = [t for t in qa["answers"]["text"] if normalize_answer(snake_case__)] if not gold_answers: # For unanswerable questions, only correct answer is empty string lowerCAmelCase_ : Any = [""] if qid not in preds: print(F'''Missing prediction for {qid}''') continue lowerCAmelCase_ : Tuple = preds[qid] # Take max over all gold answers lowerCAmelCase_ : Any = max(compute_exact(snake_case__ , snake_case__) for a in gold_answers) lowerCAmelCase_ : Optional[Any] = max(compute_fa(snake_case__ , snake_case__) for a in gold_answers) return exact_scores, fa_scores def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Dict = {} for qid, s in scores.items(): lowerCAmelCase_ : List[Any] = na_probs[qid] > na_prob_thresh if pred_na: lowerCAmelCase_ : List[str] = float(not qid_to_has_ans[qid]) else: lowerCAmelCase_ : Union[str, Any] = s return new_scores def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None): if not qid_list: lowerCAmelCase_ : Any = len(snake_case__) return collections.OrderedDict( [ ("exact", 100.0 * sum(exact_scores.values()) / total), ("f1", 100.0 * sum(fa_scores.values()) / total), ("total", total), ]) else: lowerCAmelCase_ : Tuple = len(snake_case__) return collections.OrderedDict( [ ("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total), ("f1", 100.0 * sum(fa_scores[k] for k in qid_list) / total), ("total", total), ]) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): for k in new_eval: lowerCAmelCase_ : Union[str, Any] = new_eval[k] def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): plt.step(snake_case__ , snake_case__ , color="b" , alpha=0.2 , where="post") plt.fill_between(snake_case__ , snake_case__ , step="post" , alpha=0.2 , color="b") plt.xlabel("Recall") plt.ylabel("Precision") plt.xlim([0.0, 1.05]) plt.ylim([0.0, 1.05]) plt.title(snake_case__) plt.savefig(snake_case__) plt.clf() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__=None , snake_case__=None): lowerCAmelCase_ : List[Any] = sorted(snake_case__ , key=lambda snake_case__: na_probs[k]) lowerCAmelCase_ : Dict = 0.0 lowerCAmelCase_ : int = 1.0 lowerCAmelCase_ : List[str] = 0.0 lowerCAmelCase_ : Tuple = [1.0] lowerCAmelCase_ : Tuple = [0.0] lowerCAmelCase_ : Dict = 0.0 for i, qid in enumerate(snake_case__): if qid_to_has_ans[qid]: true_pos += scores[qid] lowerCAmelCase_ : str = true_pos / float(i + 1) lowerCAmelCase_ : Union[str, Any] = true_pos / float(snake_case__) if i == len(snake_case__) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]: # i.e., if we can put a threshold after this point avg_prec += cur_p * (cur_r - recalls[-1]) precisions.append(snake_case__) recalls.append(snake_case__) if out_image: plot_pr_curve(snake_case__ , snake_case__ , snake_case__ , snake_case__) return {"ap": 100.0 * avg_prec} def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): if out_image_dir and not os.path.exists(snake_case__): os.makedirs(snake_case__) lowerCAmelCase_ : Any = sum(1 for v in qid_to_has_ans.values() if v) if num_true_pos == 0: return lowerCAmelCase_ : Any = make_precision_recall_eval( snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_exact.png") , title="Precision-Recall curve for Exact Match score" , ) lowerCAmelCase_ : Dict = make_precision_recall_eval( snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_f1.png") , title="Precision-Recall curve for F1 score" , ) lowerCAmelCase_ : Dict = {k: float(snake_case__) for k, v in qid_to_has_ans.items()} lowerCAmelCase_ : str = make_precision_recall_eval( snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_oracle.png") , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , ) merge_eval(snake_case__ , snake_case__ , "pr_exact") merge_eval(snake_case__ , snake_case__ , "pr_f1") merge_eval(snake_case__ , snake_case__ , "pr_oracle") def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): if not qid_list: return lowerCAmelCase_ : Optional[Any] = [na_probs[k] for k in qid_list] lowerCAmelCase_ : Dict = np.ones_like(snake_case__) / float(len(snake_case__)) plt.hist(snake_case__ , weights=snake_case__ , bins=20 , range=(0.0, 1.0)) plt.xlabel("Model probability of no-answer") plt.ylabel("Proportion of dataset") plt.title(F'''Histogram of no-answer probability: {name}''') plt.savefig(os.path.join(snake_case__ , F'''na_prob_hist_{name}.png''')) plt.clf() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Dict = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]) lowerCAmelCase_ : str = num_no_ans lowerCAmelCase_ : List[str] = cur_score lowerCAmelCase_ : List[Any] = 0.0 lowerCAmelCase_ : str = sorted(snake_case__ , key=lambda snake_case__: na_probs[k]) for i, qid in enumerate(snake_case__): if qid not in scores: continue if qid_to_has_ans[qid]: lowerCAmelCase_ : Union[str, Any] = scores[qid] else: if preds[qid]: lowerCAmelCase_ : List[Any] = -1 else: lowerCAmelCase_ : List[str] = 0 cur_score += diff if cur_score > best_score: lowerCAmelCase_ : Optional[Any] = cur_score lowerCAmelCase_ : Optional[int] = na_probs[qid] return 100.0 * best_score / len(snake_case__), best_thresh def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ , lowerCAmelCase_ : Dict = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = best_exact lowerCAmelCase_ : List[str] = exact_thresh lowerCAmelCase_ : Any = best_fa lowerCAmelCase_ : List[str] = fa_thresh def UpperCamelCase ( ): with open(OPTS.data_file) as f: lowerCAmelCase_ : Optional[int] = json.load(snake_case__) lowerCAmelCase_ : List[Any] = dataset_json["data"] with open(OPTS.pred_file) as f: lowerCAmelCase_ : int = json.load(snake_case__) if OPTS.na_prob_file: with open(OPTS.na_prob_file) as f: lowerCAmelCase_ : Optional[int] = json.load(snake_case__) else: lowerCAmelCase_ : List[Any] = {k: 0.0 for k in preds} lowerCAmelCase_ : Tuple = make_qid_to_has_ans(snake_case__) # maps qid to True/False lowerCAmelCase_ : Any = [k for k, v in qid_to_has_ans.items() if v] lowerCAmelCase_ : List[str] = [k for k, v in qid_to_has_ans.items() if not v] lowerCAmelCase_ , lowerCAmelCase_ : Dict = get_raw_scores(snake_case__ , snake_case__) lowerCAmelCase_ : str = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh) lowerCAmelCase_ : Dict = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh) lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__) if has_ans_qids: lowerCAmelCase_ : str = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__) merge_eval(snake_case__ , snake_case__ , "HasAns") if no_ans_qids: lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__) merge_eval(snake_case__ , snake_case__ , "NoAns") if OPTS.na_prob_file: find_all_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__) if OPTS.na_prob_file and OPTS.out_image_dir: run_precision_recall_analysis(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , OPTS.out_image_dir) histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "hasAns") histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "noAns") if OPTS.out_file: with open(OPTS.out_file , "w") as f: json.dump(snake_case__ , snake_case__) else: print(json.dumps(snake_case__ , indent=2)) if __name__ == "__main__": _lowercase = parse_args() if OPTS.out_image_dir: import matplotlib matplotlib.use('''Agg''') import matplotlib.pyplot as plt main()
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from __future__ import annotations from math import pi from typing import Protocol import matplotlib.pyplot as plt import numpy as np class __snake_case ( snake_case__ ): """simple docstring""" def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : float ) -> float: '''simple docstring''' return 0.0 def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Tuple = min([-20, np.min(fft_results[1 : samplerate // 2 - 1])]) lowerCAmelCase_ : List[str] = max([20, np.max(fft_results[1 : samplerate // 2 - 1])]) return lowest, highest def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Tuple = 5_12 lowerCAmelCase_ : Union[str, Any] = [1] + [0] * (size - 1) lowerCAmelCase_ : Any = [filter_type.process(snake_case__) for item in inputs] lowerCAmelCase_ : Optional[int] = [0] * (samplerate - size) # zero-padding outputs += filler lowerCAmelCase_ : Optional[Any] = np.abs(np.fft.fft(snake_case__)) lowerCAmelCase_ : Any = 20 * np.logaa(snake_case__) # Frequencies on log scale from 24 to nyquist frequency plt.xlim(24 , samplerate / 2 - 1) plt.xlabel("Frequency (Hz)") plt.xscale("log") # Display within reasonable bounds lowerCAmelCase_ : Optional[Any] = get_bounds(snake_case__ , snake_case__) plt.ylim(max([-80, bounds[0]]) , min([80, bounds[1]])) plt.ylabel("Gain (dB)") plt.plot(snake_case__) plt.show() def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : List[Any] = 5_12 lowerCAmelCase_ : Tuple = [1] + [0] * (size - 1) lowerCAmelCase_ : Optional[Any] = [filter_type.process(snake_case__) for item in inputs] lowerCAmelCase_ : str = [0] * (samplerate - size) # zero-padding outputs += filler lowerCAmelCase_ : str = np.angle(np.fft.fft(snake_case__)) # Frequencies on log scale from 24 to nyquist frequency plt.xlim(24 , samplerate / 2 - 1) plt.xlabel("Frequency (Hz)") plt.xscale("log") plt.ylim(-2 * pi , 2 * pi) plt.ylabel("Phase shift (Radians)") plt.plot(np.unwrap(snake_case__ , -2 * pi)) plt.show()
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from math import sqrt def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[int] = 0 for i in range(1 , int(sqrt(snake_case__) + 1)): if n % i == 0 and i != sqrt(snake_case__): total += i + n // i elif i == sqrt(snake_case__): total += i return total - n def UpperCamelCase ( snake_case__ = 1_00_00): lowerCAmelCase_ : int = sum( i for i in range(1 , snake_case__) if sum_of_divisors(sum_of_divisors(snake_case__)) == i and sum_of_divisors(snake_case__) != i) return total if __name__ == "__main__": print(solution(int(str(input()).strip())))
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def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Tuple = 0 while num > 0: digit_sum += num % 10 num //= 10 return digit_sum def UpperCamelCase ( snake_case__ = 1_00): lowerCAmelCase_ : str = 1 lowerCAmelCase_ : Tuple = 2 for i in range(2 , max_n + 1): lowerCAmelCase_ : Union[str, Any] = pre_numerator lowerCAmelCase_ : int = 2 * i // 3 if i % 3 == 0 else 1 lowerCAmelCase_ : Union[str, Any] = cur_numerator lowerCAmelCase_ : List[Any] = e_cont * pre_numerator + temp return sum_digits(snake_case__) if __name__ == "__main__": print(f"{solution() = }")
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_speech_available, is_tf_available, is_torch_available, ) _lowercase = { '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''], '''processing_speech_to_text''': ['''Speech2TextProcessor'''], } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ['''Speech2TextTokenizer'''] try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ['''Speech2TextFeatureExtractor'''] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''TFSpeech2TextForConditionalGeneration''', '''TFSpeech2TextModel''', '''TFSpeech2TextPreTrainedModel''', ] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''Speech2TextForConditionalGeneration''', '''Speech2TextModel''', '''Speech2TextPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig from .processing_speech_to_text import SpeechaTextProcessor try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_speech_to_text import SpeechaTextTokenizer try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_speech_to_text import ( TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, TFSpeechaTextForConditionalGeneration, TFSpeechaTextModel, TFSpeechaTextPreTrainedModel, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_speech_to_text import ( SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, SpeechaTextForConditionalGeneration, SpeechaTextModel, SpeechaTextPreTrainedModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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from __future__ import annotations import unittest from transformers import FunnelConfig, is_tf_available from transformers.testing_utils import require_tf from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( TFFunnelBaseModel, TFFunnelForMaskedLM, TFFunnelForMultipleChoice, TFFunnelForPreTraining, TFFunnelForQuestionAnswering, TFFunnelForSequenceClassification, TFFunnelForTokenClassification, TFFunnelModel, ) class __snake_case : """simple docstring""" def __init__( self : Optional[int] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Tuple=13 ,lowerCAmelCase__ : int=7 ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : List[Any]=True ,lowerCAmelCase__ : List[str]=True ,lowerCAmelCase__ : List[str]=99 ,lowerCAmelCase__ : Dict=[1, 1, 2] ,lowerCAmelCase__ : int=1 ,lowerCAmelCase__ : Tuple=32 ,lowerCAmelCase__ : List[str]=4 ,lowerCAmelCase__ : Optional[Any]=8 ,lowerCAmelCase__ : Dict=37 ,lowerCAmelCase__ : Optional[Any]="gelu_new" ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : Union[str, Any]=0.1 ,lowerCAmelCase__ : Any=0.0 ,lowerCAmelCase__ : Optional[int]=5_12 ,lowerCAmelCase__ : List[Any]=3 ,lowerCAmelCase__ : Tuple=0.02 ,lowerCAmelCase__ : Optional[Any]=3 ,lowerCAmelCase__ : List[str]=4 ,lowerCAmelCase__ : str=None ,lowerCAmelCase__ : List[Any]=False ,) -> Any: '''simple docstring''' lowerCAmelCase_ : Any = parent lowerCAmelCase_ : Optional[int] = batch_size lowerCAmelCase_ : str = seq_length lowerCAmelCase_ : Optional[int] = is_training lowerCAmelCase_ : Union[str, Any] = use_input_mask lowerCAmelCase_ : List[str] = use_token_type_ids lowerCAmelCase_ : List[str] = use_labels lowerCAmelCase_ : Optional[Any] = vocab_size lowerCAmelCase_ : List[str] = block_sizes lowerCAmelCase_ : Dict = num_decoder_layers lowerCAmelCase_ : List[Any] = d_model lowerCAmelCase_ : Optional[Any] = n_head lowerCAmelCase_ : List[Any] = d_head lowerCAmelCase_ : List[str] = d_inner lowerCAmelCase_ : str = hidden_act lowerCAmelCase_ : str = hidden_dropout lowerCAmelCase_ : Any = attention_dropout lowerCAmelCase_ : Optional[Any] = activation_dropout lowerCAmelCase_ : str = max_position_embeddings lowerCAmelCase_ : int = type_vocab_size lowerCAmelCase_ : Optional[int] = 2 lowerCAmelCase_ : List[Any] = num_labels lowerCAmelCase_ : Optional[Any] = num_choices lowerCAmelCase_ : List[str] = scope lowerCAmelCase_ : int = initializer_std # Used in the tests to check the size of the first attention layer lowerCAmelCase_ : Optional[Any] = n_head # Used in the tests to check the size of the first hidden state lowerCAmelCase_ : Tuple = self.d_model # Used in the tests to check the number of output hidden states/attentions lowerCAmelCase_ : List[Any] = sum(self.block_sizes ) + (0 if base else self.num_decoder_layers) # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with # the last hidden state of the first block (which is the first hidden state of the decoder). if not base: lowerCAmelCase_ : Tuple = self.num_hidden_layers + 2 def UpperCAmelCase_ ( self : Optional[Any] ) -> int: '''simple docstring''' lowerCAmelCase_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size ) lowerCAmelCase_ : Optional[int] = None if self.use_input_mask: lowerCAmelCase_ : int = random_attention_mask([self.batch_size, self.seq_length] ) lowerCAmelCase_ : List[str] = None if self.use_token_type_ids: lowerCAmelCase_ : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size ) lowerCAmelCase_ : int = None lowerCAmelCase_ : Any = None lowerCAmelCase_ : Tuple = None if self.use_labels: lowerCAmelCase_ : List[str] = ids_tensor([self.batch_size] ,self.type_sequence_label_size ) lowerCAmelCase_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels ) lowerCAmelCase_ : int = ids_tensor([self.batch_size] ,self.num_choices ) lowerCAmelCase_ : str = FunnelConfig( vocab_size=self.vocab_size ,block_sizes=self.block_sizes ,num_decoder_layers=self.num_decoder_layers ,d_model=self.d_model ,n_head=self.n_head ,d_head=self.d_head ,d_inner=self.d_inner ,hidden_act=self.hidden_act ,hidden_dropout=self.hidden_dropout ,attention_dropout=self.attention_dropout ,activation_dropout=self.activation_dropout ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,initializer_std=self.initializer_std ,) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : Any ,) -> int: '''simple docstring''' lowerCAmelCase_ : Dict = TFFunnelModel(config=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} lowerCAmelCase_ : str = model(lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = [input_ids, input_mask] lowerCAmelCase_ : Optional[Any] = model(lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = model(lowerCAmelCase__ ) self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.d_model) ) lowerCAmelCase_ : Optional[Any] = False lowerCAmelCase_ : Tuple = TFFunnelModel(config=lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = model(lowerCAmelCase__ ) self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.d_model) ) lowerCAmelCase_ : str = False lowerCAmelCase_ : int = TFFunnelModel(config=lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = model(lowerCAmelCase__ ) self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.d_model) ) def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : str ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Optional[int] ,) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Optional[int] = TFFunnelBaseModel(config=lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} lowerCAmelCase_ : List[str] = model(lowerCAmelCase__ ) lowerCAmelCase_ : int = [input_ids, input_mask] lowerCAmelCase_ : str = model(lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = model(lowerCAmelCase__ ) self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, 2, self.d_model) ) lowerCAmelCase_ : Optional[Any] = False lowerCAmelCase_ : List[str] = TFFunnelBaseModel(config=lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = model(lowerCAmelCase__ ) self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, 3, self.d_model) ) lowerCAmelCase_ : Tuple = False lowerCAmelCase_ : str = TFFunnelBaseModel(config=lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = model(lowerCAmelCase__ ) self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, 2, self.d_model) ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Any ,) -> int: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = TFFunnelForPreTraining(config=lowerCAmelCase__ ) lowerCAmelCase_ : str = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} lowerCAmelCase_ : List[Any] = model(lowerCAmelCase__ ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length) ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Dict ,) -> Dict: '''simple docstring''' lowerCAmelCase_ : Any = TFFunnelForMaskedLM(config=lowerCAmelCase__ ) lowerCAmelCase_ : Any = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} lowerCAmelCase_ : Any = model(lowerCAmelCase__ ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) ) def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : List[Any] ,) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Optional[int] = self.num_labels lowerCAmelCase_ : List[str] = TFFunnelForSequenceClassification(config=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} lowerCAmelCase_ : Tuple = model(lowerCAmelCase__ ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) ) def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Tuple ,) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Tuple = self.num_choices lowerCAmelCase_ : Optional[int] = TFFunnelForMultipleChoice(config=lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = tf.tile(tf.expand_dims(lowerCAmelCase__ ,1 ) ,(1, self.num_choices, 1) ) lowerCAmelCase_ : int = tf.tile(tf.expand_dims(lowerCAmelCase__ ,1 ) ,(1, self.num_choices, 1) ) lowerCAmelCase_ : Optional[int] = tf.tile(tf.expand_dims(lowerCAmelCase__ ,1 ) ,(1, self.num_choices, 1) ) lowerCAmelCase_ : Optional[Any] = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } lowerCAmelCase_ : Any = model(lowerCAmelCase__ ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) ) def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Optional[Any] ,) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = self.num_labels lowerCAmelCase_ : int = TFFunnelForTokenClassification(config=lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} lowerCAmelCase_ : Any = model(lowerCAmelCase__ ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) ) def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Union[str, Any] ,) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = TFFunnelForQuestionAnswering(config=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} lowerCAmelCase_ : Dict = model(lowerCAmelCase__ ) self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) ) def UpperCAmelCase_ ( self : Tuple ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Any = self.prepare_config_and_inputs() ( lowerCAmelCase_ ) : Tuple = config_and_inputs lowerCAmelCase_ : Union[str, Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_tf class __snake_case ( snake_case__ , snake_case__ , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = ( ( TFFunnelModel, TFFunnelForMaskedLM, TFFunnelForPreTraining, TFFunnelForQuestionAnswering, TFFunnelForTokenClassification, ) if is_tf_available() else () ) UpperCamelCase_ = ( { 'feature-extraction': (TFFunnelBaseModel, TFFunnelModel), 'fill-mask': TFFunnelForMaskedLM, 'question-answering': TFFunnelForQuestionAnswering, 'text-classification': TFFunnelForSequenceClassification, 'token-classification': TFFunnelForTokenClassification, 'zero-shot': TFFunnelForSequenceClassification, } if is_tf_available() else {} ) UpperCamelCase_ = False UpperCamelCase_ = False def UpperCAmelCase_ ( self : Any ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = TFFunnelModelTester(self ) lowerCAmelCase_ : Optional[int] = ConfigTester(self ,config_class=lowerCAmelCase__ ) def UpperCAmelCase_ ( self : List[Any] ) -> Tuple: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase_ ( self : Any ) -> str: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Dict ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__ ) def UpperCAmelCase_ ( self : int ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Dict ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__ ) @require_tf class __snake_case ( snake_case__ , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = ( (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else () ) UpperCamelCase_ = False UpperCamelCase_ = False def UpperCAmelCase_ ( self : Dict ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : List[Any] = TFFunnelModelTester(self ,base=lowerCAmelCase__ ) lowerCAmelCase_ : str = ConfigTester(self ,config_class=lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[Any]: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_base_model(*lowerCAmelCase__ ) def UpperCAmelCase_ ( self : List[str] ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__ ) def UpperCAmelCase_ ( self : str ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__ )
704
import json import os from functools import lru_cache from typing import List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''} _lowercase = { '''vocab_file''': { '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''', '''allenai/longformer-large-4096''': ( '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json''' ), '''allenai/longformer-large-4096-finetuned-triviaqa''': ( '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json''' ), '''allenai/longformer-base-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json''' ), '''allenai/longformer-large-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json''' ), }, '''merges_file''': { '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''', '''allenai/longformer-large-4096''': ( '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt''' ), '''allenai/longformer-large-4096-finetuned-triviaqa''': ( '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt''' ), '''allenai/longformer-base-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt''' ), '''allenai/longformer-large-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt''' ), }, } _lowercase = { '''allenai/longformer-base-4096''': 4096, '''allenai/longformer-large-4096''': 4096, '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096, '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096, '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096, } @lru_cache() # Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode def UpperCamelCase ( ): lowerCAmelCase_ : str = ( list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1)) ) lowerCAmelCase_ : Tuple = bs[:] lowerCAmelCase_ : Dict = 0 for b in range(2**8): if b not in bs: bs.append(snake_case__) cs.append(2**8 + n) n += 1 lowerCAmelCase_ : Union[str, Any] = [chr(snake_case__) for n in cs] return dict(zip(snake_case__ , snake_case__)) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[Any] = set() lowerCAmelCase_ : List[Any] = word[0] for char in word[1:]: pairs.add((prev_char, char)) lowerCAmelCase_ : Union[str, Any] = char return pairs class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = ['input_ids', 'attention_mask'] def __init__( self : str ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[Any]="replace" ,lowerCAmelCase__ : Dict="<s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : Optional[Any]="<s>" ,lowerCAmelCase__ : List[Any]="<unk>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : int="<mask>" ,lowerCAmelCase__ : Any=False ,**lowerCAmelCase__ : int ,) -> Dict: '''simple docstring''' lowerCAmelCase_ : Optional[int] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token lowerCAmelCase_ : Dict = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else pad_token # Mask token behave like a normal word, i.e. include the space before it lowerCAmelCase_ : Optional[Any] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else mask_token super().__init__( errors=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,**lowerCAmelCase__ ,) with open(lowerCAmelCase__ ,encoding="utf-8" ) as vocab_handle: lowerCAmelCase_ : List[Any] = json.load(lowerCAmelCase__ ) lowerCAmelCase_ : Dict = {v: k for k, v in self.encoder.items()} lowerCAmelCase_ : List[Any] = errors # how to handle errors in decoding lowerCAmelCase_ : Optional[Any] = bytes_to_unicode() lowerCAmelCase_ : int = {v: k for k, v in self.byte_encoder.items()} with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle: lowerCAmelCase_ : Union[str, Any] = merges_handle.read().split("\n" )[1:-1] lowerCAmelCase_ : Dict = [tuple(merge.split() ) for merge in bpe_merges] lowerCAmelCase_ : Dict = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) ) lowerCAmelCase_ : Any = {} lowerCAmelCase_ : int = add_prefix_space # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions lowerCAmelCase_ : Optional[Any] = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" ) @property def UpperCAmelCase_ ( self : Optional[int] ) -> Any: '''simple docstring''' return len(self.encoder ) def UpperCAmelCase_ ( self : Any ) -> Optional[int]: '''simple docstring''' return dict(self.encoder ,**self.added_tokens_encoder ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[str] ) -> List[Any]: '''simple docstring''' if token in self.cache: return self.cache[token] lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = get_pairs(lowerCAmelCase__ ) if not pairs: return token while True: lowerCAmelCase_ : Dict = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) ) if bigram not in self.bpe_ranks: break lowerCAmelCase_ , lowerCAmelCase_ : Dict = bigram lowerCAmelCase_ : Optional[Any] = [] lowerCAmelCase_ : Any = 0 while i < len(lowerCAmelCase__ ): try: lowerCAmelCase_ : Optional[int] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ ) except ValueError: new_word.extend(word[i:] ) break else: new_word.extend(word[i:j] ) lowerCAmelCase_ : Tuple = j if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second: new_word.append(first + second ) i += 2 else: new_word.append(word[i] ) i += 1 lowerCAmelCase_ : Optional[Any] = tuple(lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = new_word if len(lowerCAmelCase__ ) == 1: break else: lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = " ".join(lowerCAmelCase__ ) lowerCAmelCase_ : Any = word return word def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Dict = [] for token in re.findall(self.pat ,lowerCAmelCase__ ): lowerCAmelCase_ : List[str] = "".join( self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__ ).split(" " ) ) return bpe_tokens def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ) -> Tuple: '''simple docstring''' return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]: '''simple docstring''' return self.decoder.get(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Any: '''simple docstring''' lowerCAmelCase_ : Optional[int] = "".join(lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors ) return text def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' if not os.path.isdir(lowerCAmelCase__ ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return lowerCAmelCase_ : Optional[Any] = os.path.join( lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase_ : Tuple = os.path.join( lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as f: f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowerCAmelCase__ ,ensure_ascii=lowerCAmelCase__ ) + "\n" ) lowerCAmelCase_ : Tuple = 0 with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as writer: writer.write("#version: 0.2\n" ) for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda lowerCAmelCase__ : kv[1] ): if index != token_index: logger.warning( f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.''' " Please check that the tokenizer is not corrupted!" ) lowerCAmelCase_ : Optional[Any] = token_index writer.write(" ".join(lowerCAmelCase__ ) + "\n" ) index += 1 return vocab_file, merge_file def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] lowerCAmelCase_ : List[Any] = [self.cls_token_id] lowerCAmelCase_ : List[str] = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]: '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=lowerCAmelCase__ ,token_ids_a=lowerCAmelCase__ ,already_has_special_tokens=lowerCAmelCase__ ) if token_ids_a is None: return [1] + ([0] * len(lowerCAmelCase__ )) + [1] return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1] def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : List[str] = [self.sep_token_id] lowerCAmelCase_ : Tuple = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Optional[int] ) -> Any: '''simple docstring''' lowerCAmelCase_ : int = kwargs.pop("add_prefix_space" ,self.add_prefix_space ) if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__ ) > 0 and not text[0].isspace()): lowerCAmelCase_ : Union[str, Any] = " " + text return (text, kwargs)
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'''simple docstring''' def UpperCamelCase ( snake_case__ , snake_case__): return base * power(snake_case__ , (exponent - 1)) if exponent else 1 if __name__ == "__main__": print('''Raise base to the power of exponent using recursion...''') _lowercase = int(input('''Enter the base: ''').strip()) _lowercase = int(input('''Enter the exponent: ''').strip()) _lowercase = power(base, abs(exponent)) if exponent < 0: # power() does not properly deal w/ negative exponents _lowercase = 1 / result print(f"{base} to the power of {exponent} is {result}")
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from collections.abc import Iterable from typing import Any class __snake_case : """simple docstring""" def __init__( self : Optional[Any] ,lowerCAmelCase__ : int | None = None ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : Dict = value lowerCAmelCase_ : Node | None = None # Added in order to delete a node easier lowerCAmelCase_ : Node | None = None lowerCAmelCase_ : Node | None = None def __repr__( self : Union[str, Any] ) -> str: '''simple docstring''' from pprint import pformat if self.left is None and self.right is None: return str(self.value ) return pformat({f'''{self.value}''': (self.left, self.right)} ,indent=1 ) class __snake_case : """simple docstring""" def __init__( self : Optional[Any] ,lowerCAmelCase__ : Node | None = None ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : List[Any] = root def __str__( self : Dict ) -> str: '''simple docstring''' return str(self.root ) def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Node ,lowerCAmelCase__ : Node | None ) -> None: '''simple docstring''' if new_children is not None: # reset its kids lowerCAmelCase_ : Optional[int] = node.parent if node.parent is not None: # reset its parent if self.is_right(lowerCAmelCase__ ): # If it is the right children lowerCAmelCase_ : List[Any] = new_children else: lowerCAmelCase_ : List[Any] = new_children else: lowerCAmelCase_ : Any = new_children def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Node ) -> bool: '''simple docstring''' if node.parent and node.parent.right: return node == node.parent.right return False def UpperCAmelCase_ ( self : List[str] ) -> bool: '''simple docstring''' return self.root is None def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Union[str, Any] ) -> None: '''simple docstring''' lowerCAmelCase_ : str = Node(lowerCAmelCase__ ) # create a new Node if self.empty(): # if Tree is empty lowerCAmelCase_ : Optional[int] = new_node # set its root else: # Tree is not empty lowerCAmelCase_ : List[Any] = self.root # from root if parent_node is None: return while True: # While we don't get to a leaf if value < parent_node.value: # We go left if parent_node.left is None: lowerCAmelCase_ : Dict = new_node # We insert the new node in a leaf break else: lowerCAmelCase_ : List[str] = parent_node.left else: if parent_node.right is None: lowerCAmelCase_ : Dict = new_node break else: lowerCAmelCase_ : str = parent_node.right lowerCAmelCase_ : Optional[int] = parent_node def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : Tuple ) -> None: '''simple docstring''' for value in values: self.__insert(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Optional[int] ) -> Node | None: '''simple docstring''' if self.empty(): raise IndexError("Warning: Tree is empty! please use another." ) else: lowerCAmelCase_ : Dict = self.root # use lazy evaluation here to avoid NoneType Attribute error while node is not None and node.value is not value: lowerCAmelCase_ : Union[str, Any] = node.left if value < node.value else node.right return node def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None: '''simple docstring''' if node is None: if self.root is None: return None lowerCAmelCase_ : Dict = self.root if not self.empty(): while node.right is not None: lowerCAmelCase_ : Union[str, Any] = node.right return node def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None: '''simple docstring''' if node is None: lowerCAmelCase_ : Dict = self.root if self.root is None: return None if not self.empty(): lowerCAmelCase_ : Dict = self.root while node.left is not None: lowerCAmelCase_ : Union[str, Any] = node.left return node def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : int ) -> None: '''simple docstring''' lowerCAmelCase_ : Dict = self.search(lowerCAmelCase__ ) # Look for the node with that label if node is not None: if node.left is None and node.right is None: # If it has no children self.__reassign_nodes(lowerCAmelCase__ ,lowerCAmelCase__ ) elif node.left is None: # Has only right children self.__reassign_nodes(lowerCAmelCase__ ,node.right ) elif node.right is None: # Has only left children self.__reassign_nodes(lowerCAmelCase__ ,node.left ) else: lowerCAmelCase_ : int = self.get_max( node.left ) # Gets the max value of the left branch self.remove(tmp_node.value ) # type: ignore lowerCAmelCase_ : Any = ( tmp_node.value # type: ignore ) # Assigns the value to the node to delete and keep tree structure def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Node | None ) -> Iterable: '''simple docstring''' if node is not None: yield node # Preorder Traversal yield from self.preorder_traverse(node.left ) yield from self.preorder_traverse(node.right ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Dict=None ) -> Any: '''simple docstring''' if traversal_function is None: return self.preorder_traverse(self.root ) else: return traversal_function(self.root ) def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : list ,lowerCAmelCase__ : Node | None ) -> None: '''simple docstring''' if node: self.inorder(lowerCAmelCase__ ,node.left ) arr.append(node.value ) self.inorder(lowerCAmelCase__ ,node.right ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Node ) -> int: '''simple docstring''' lowerCAmelCase_ : list[int] = [] self.inorder(lowerCAmelCase__ ,lowerCAmelCase__ ) # append all values to list using inorder traversal return arr[k - 1] def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[Any] = [] if curr_node is not None: lowerCAmelCase_ : Dict = postorder(curr_node.left) + postorder(curr_node.right) + [curr_node] return node_list def UpperCamelCase ( ): lowerCAmelCase_ : Tuple = (8, 3, 6, 1, 10, 14, 13, 4, 7) lowerCAmelCase_ : Tuple = BinarySearchTree() for i in testlist: t.insert(snake_case__) # Prints all the elements of the list in order traversal print(snake_case__) if t.search(6) is not None: print("The value 6 exists") else: print("The value 6 doesn't exist") if t.search(-1) is not None: print("The value -1 exists") else: print("The value -1 doesn't exist") if not t.empty(): print("Max Value: " , t.get_max().value) # type: ignore print("Min Value: " , t.get_min().value) # type: ignore for i in testlist: t.remove(snake_case__) print(snake_case__) if __name__ == "__main__": import doctest doctest.testmod(verbose=True)
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'''simple docstring''' import argparse import os import pickle import sys import torch from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging logging.set_verbosity_info() # We do this to be able to load python 2 datasets pickles # See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918 _lowercase = data_utils.TransfoXLTokenizer _lowercase = data_utils.TransfoXLCorpus _lowercase = data_utils _lowercase = data_utils def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): if transfo_xl_dataset_file: # Convert a pre-processed corpus (see original TensorFlow repo) with open(snake_case__ , "rb") as fp: lowerCAmelCase_ : List[Any] = pickle.load(snake_case__ , encoding="latin1") # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term) lowerCAmelCase_ : List[str] = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"] print(F'''Save vocabulary to {pytorch_vocab_dump_path}''') lowerCAmelCase_ : Tuple = corpus.vocab.__dict__ torch.save(snake_case__ , snake_case__) lowerCAmelCase_ : int = corpus.__dict__ corpus_dict_no_vocab.pop("vocab" , snake_case__) lowerCAmelCase_ : List[Any] = pytorch_dump_folder_path + "/" + CORPUS_NAME print(F'''Save dataset to {pytorch_dataset_dump_path}''') torch.save(snake_case__ , snake_case__) if tf_checkpoint_path: # Convert a pre-trained TensorFlow model lowerCAmelCase_ : Tuple = os.path.abspath(snake_case__) lowerCAmelCase_ : str = os.path.abspath(snake_case__) print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.''') # Initialise PyTorch model if transfo_xl_config_file == "": lowerCAmelCase_ : Tuple = TransfoXLConfig() else: lowerCAmelCase_ : Tuple = TransfoXLConfig.from_json_file(snake_case__) print(F'''Building PyTorch model from configuration: {config}''') lowerCAmelCase_ : Union[str, Any] = TransfoXLLMHeadModel(snake_case__) lowerCAmelCase_ : List[str] = load_tf_weights_in_transfo_xl(snake_case__ , snake_case__ , snake_case__) # Save pytorch-model lowerCAmelCase_ : int = os.path.join(snake_case__ , snake_case__) lowerCAmelCase_ : Optional[int] = os.path.join(snake_case__ , snake_case__) print(F'''Save PyTorch model to {os.path.abspath(snake_case__)}''') torch.save(model.state_dict() , snake_case__) print(F'''Save configuration file to {os.path.abspath(snake_case__)}''') with open(snake_case__ , "w" , encoding="utf-8") as f: f.write(config.to_json_string()) if __name__ == "__main__": _lowercase = argparse.ArgumentParser() parser.add_argument( '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the folder to store the PyTorch model or dataset/vocab.''', ) parser.add_argument( '''--tf_checkpoint_path''', default='''''', type=str, help='''An optional path to a TensorFlow checkpoint path to be converted.''', ) parser.add_argument( '''--transfo_xl_config_file''', default='''''', type=str, help=( '''An optional config json file corresponding to the pre-trained BERT model. \n''' '''This specifies the model architecture.''' ), ) parser.add_argument( '''--transfo_xl_dataset_file''', default='''''', type=str, help='''An optional dataset file to be converted in a vocabulary.''', ) _lowercase = parser.parse_args() convert_transfo_xl_checkpoint_to_pytorch( args.tf_checkpoint_path, args.transfo_xl_config_file, args.pytorch_dump_folder_path, args.transfo_xl_dataset_file, )
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class __snake_case : """simple docstring""" def __init__( self : Optional[int] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None: '''simple docstring''' lowerCAmelCase_ : dict[str, RadixNode] = {} # A node will be a leaf if the tree contains its word lowerCAmelCase_ : int = is_leaf lowerCAmelCase_ : Optional[Any] = prefix def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ) -> tuple[str, str, str]: '''simple docstring''' lowerCAmelCase_ : Any = 0 for q, w in zip(self.prefix ,lowerCAmelCase__ ): if q != w: break x += 1 return self.prefix[:x], self.prefix[x:], word[x:] def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : list[str] ) -> None: '''simple docstring''' for word in words: self.insert(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ) -> None: '''simple docstring''' if self.prefix == word: lowerCAmelCase_ : Optional[Any] = True # Case 2: The node has no edges that have a prefix to the word # Solution: We create an edge from the current node to a new one # containing the word elif word[0] not in self.nodes: lowerCAmelCase_ : List[Any] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ ) else: lowerCAmelCase_ : Tuple = self.nodes[word[0]] lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = incoming_node.match( lowerCAmelCase__ ) # Case 3: The node prefix is equal to the matching # Solution: We insert remaining word on the next node if remaining_prefix == "": self.nodes[matching_string[0]].insert(lowerCAmelCase__ ) # Case 4: The word is greater equal to the matching # Solution: Create a node in between both nodes, change # prefixes and add the new node for the remaining word else: lowerCAmelCase_ : Optional[int] = remaining_prefix lowerCAmelCase_ : Optional[int] = self.nodes[matching_string[0]] lowerCAmelCase_ : List[Any] = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Dict = aux_node if remaining_word == "": lowerCAmelCase_ : List[str] = True else: self.nodes[matching_string[0]].insert(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool: '''simple docstring''' lowerCAmelCase_ : Any = self.nodes.get(word[0] ,lowerCAmelCase__ ) if not incoming_node: return False else: lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match( lowerCAmelCase__ ) # If there is remaining prefix, the word can't be on the tree if remaining_prefix != "": return False # This applies when the word and the prefix are equal elif remaining_word == "": return incoming_node.is_leaf # We have word remaining so we check the next node else: return incoming_node.find(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool: '''simple docstring''' lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ ) if not incoming_node: return False else: lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = incoming_node.match( lowerCAmelCase__ ) # If there is remaining prefix, the word can't be on the tree if remaining_prefix != "": return False # We have word remaining so we check the next node elif remaining_word != "": return incoming_node.delete(lowerCAmelCase__ ) else: # If it is not a leaf, we don't have to delete if not incoming_node.is_leaf: return False else: # We delete the nodes if no edges go from it if len(incoming_node.nodes ) == 0: del self.nodes[word[0]] # We merge the current node with its only child if len(self.nodes ) == 1 and not self.is_leaf: lowerCAmelCase_ : str = list(self.nodes.values() )[0] lowerCAmelCase_ : Tuple = merging_node.is_leaf self.prefix += merging_node.prefix lowerCAmelCase_ : Optional[int] = merging_node.nodes # If there is more than 1 edge, we just mark it as non-leaf elif len(incoming_node.nodes ) > 1: lowerCAmelCase_ : Optional[Any] = False # If there is 1 edge, we merge it with its child else: lowerCAmelCase_ : Tuple = list(incoming_node.nodes.values() )[0] lowerCAmelCase_ : Union[str, Any] = merging_node.is_leaf incoming_node.prefix += merging_node.prefix lowerCAmelCase_ : str = merging_node.nodes return True def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : int = 0 ) -> None: '''simple docstring''' if self.prefix != "": print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" ) for value in self.nodes.values(): value.print_tree(height + 1 ) def UpperCamelCase ( ): lowerCAmelCase_ : Dict = "banana bananas bandana band apple all beast".split() lowerCAmelCase_ : List[Any] = RadixNode() root.insert_many(snake_case__) assert all(root.find(snake_case__) for word in words) assert not root.find("bandanas") assert not root.find("apps") root.delete("all") assert not root.find("all") root.delete("banana") assert not root.find("banana") assert root.find("bananas") return True def UpperCamelCase ( ): assert test_trie() def UpperCamelCase ( ): lowerCAmelCase_ : List[str] = RadixNode() lowerCAmelCase_ : Optional[Any] = "banana bananas bandanas bandana band apple all beast".split() root.insert_many(snake_case__) print("Words:" , snake_case__) print("Tree:") root.print_tree() if __name__ == "__main__": main()
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available _lowercase = { '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''', '''LongT5EncoderModel''', '''LongT5ForConditionalGeneration''', '''LongT5Model''', '''LongT5PreTrainedModel''', ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''FlaxLongT5ForConditionalGeneration''', '''FlaxLongT5Model''', '''FlaxLongT5PreTrainedModel''', ] if TYPE_CHECKING: from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_longta import ( LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST, LongTaEncoderModel, LongTaForConditionalGeneration, LongTaModel, LongTaPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_longta import ( FlaxLongTaForConditionalGeneration, FlaxLongTaModel, FlaxLongTaPreTrainedModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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from __future__ import annotations def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ): if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1: raise ValueError("You cannot supply more or less than 2 values") elif electron_conc < 0: raise ValueError("Electron concentration cannot be negative in a semiconductor") elif hole_conc < 0: raise ValueError("Hole concentration cannot be negative in a semiconductor") elif intrinsic_conc < 0: raise ValueError( "Intrinsic concentration cannot be negative in a semiconductor") elif electron_conc == 0: return ( "electron_conc", intrinsic_conc**2 / hole_conc, ) elif hole_conc == 0: return ( "hole_conc", intrinsic_conc**2 / electron_conc, ) elif intrinsic_conc == 0: return ( "intrinsic_conc", (electron_conc * hole_conc) ** 0.5, ) else: return (-1, -1) if __name__ == "__main__": import doctest doctest.testmod()
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from __future__ import annotations from random import random class __snake_case : """simple docstring""" def __init__( self : Optional[int] ,lowerCAmelCase__ : int | None = None ) -> int: '''simple docstring''' lowerCAmelCase_ : Dict = value lowerCAmelCase_ : Any = random() lowerCAmelCase_ : Node | None = None lowerCAmelCase_ : Node | None = None def __repr__( self : Any ) -> str: '''simple docstring''' from pprint import pformat if self.left is None and self.right is None: return f'''\'{self.value}: {self.prior:.5}\'''' else: return pformat( {f'''{self.value}: {self.prior:.5}''': (self.left, self.right)} ,indent=1 ) def __str__( self : str ) -> str: '''simple docstring''' lowerCAmelCase_ : List[Any] = str(self.value ) + " " lowerCAmelCase_ : List[Any] = str(self.left or "" ) lowerCAmelCase_ : Union[str, Any] = str(self.right or "" ) return value + left + right def UpperCamelCase ( snake_case__ , snake_case__): if root is None: # None tree is split into 2 Nones return None, None elif root.value is None: return None, None else: if value < root.value: lowerCAmelCase_ : Any = split(root.left , snake_case__) return left, root else: lowerCAmelCase_ : Optional[Any] = split(root.right , snake_case__) return root, right def UpperCamelCase ( snake_case__ , snake_case__): if (not left) or (not right): # If one node is None, return the other return left or right elif left.prior < right.prior: lowerCAmelCase_ : Dict = merge(left.right , snake_case__) return left else: lowerCAmelCase_ : List[str] = merge(snake_case__ , right.left) return right def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : List[Any] = Node(snake_case__) lowerCAmelCase_ : Tuple = split(snake_case__ , snake_case__) return merge(merge(snake_case__ , snake_case__) , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : List[str] = split(snake_case__ , value - 1) lowerCAmelCase_ : Union[str, Any] = split(snake_case__ , snake_case__) return merge(snake_case__ , snake_case__) def UpperCamelCase ( snake_case__): if not root: # None return else: inorder(root.left) print(root.value , end=",") inorder(root.right) def UpperCamelCase ( snake_case__ , snake_case__): for arg in args.split(): if arg[0] == "+": lowerCAmelCase_ : List[str] = insert(snake_case__ , int(arg[1:])) elif arg[0] == "-": lowerCAmelCase_ : Optional[int] = erase(snake_case__ , int(arg[1:])) else: print("Unknown command") return root def UpperCamelCase ( ): lowerCAmelCase_ : str = None print( "enter numbers to create a tree, + value to add value into treap, " "- value to erase all nodes with value. 'q' to quit. ") lowerCAmelCase_ : str = input() while args != "q": lowerCAmelCase_ : int = interact_treap(snake_case__ , snake_case__) print(snake_case__) lowerCAmelCase_ : str = input() print("good by!") if __name__ == "__main__": import doctest doctest.testmod() main()
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _lowercase = { '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''], '''processing_git''': ['''GitProcessor'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''GitForCausalLM''', '''GitModel''', '''GitPreTrainedModel''', '''GitVisionModel''', ] if TYPE_CHECKING: from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig from .processing_git import GitProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_git import ( GIT_PRETRAINED_MODEL_ARCHIVE_LIST, GitForCausalLM, GitModel, GitPreTrainedModel, GitVisionModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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def UpperCamelCase ( snake_case__ , snake_case__): return numa ^ numa < 0 if __name__ == "__main__": import doctest doctest.testmod()
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from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments def UpperCamelCase ( ): lowerCAmelCase_ : List[str] = HfArgumentParser(snake_case__) lowerCAmelCase_ : List[Any] = parser.parse_args_into_dataclasses()[0] lowerCAmelCase_ : Optional[int] = TensorFlowBenchmark(args=snake_case__) try: lowerCAmelCase_ : Tuple = parser.parse_args_into_dataclasses()[0] except ValueError as e: lowerCAmelCase_ : Union[str, Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead." lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1]) lowerCAmelCase_ : Union[str, Any] = "" lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1]) lowerCAmelCase_ : Tuple = [] for arg in depreciated_args: # arg[2:] removes '--' if arg[2:] in TensorFlowBenchmark.deprecated_args: # arg[5:] removes '--no_' full_error_msg += arg_error_msg.format(arg[5:]) else: wrong_args.append(snake_case__) if len(snake_case__) > 0: lowerCAmelCase_ : Optional[Any] = full_error_msg + begin_error_msg + str(snake_case__) raise ValueError(snake_case__) benchmark.run() if __name__ == "__main__": main()
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'''simple docstring''' from __future__ import annotations from collections.abc import Iterator from typing import Generic, TypeVar _lowercase = TypeVar('''T''') class __snake_case ( Generic[T] ): """simple docstring""" def __init__( self : Union[str, Any] ,lowerCAmelCase__ : T ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : List[Any] = data lowerCAmelCase_ : Node[T] | None = None def __str__( self : Optional[int] ) -> str: '''simple docstring''' return f'''{self.data}''' class __snake_case ( Generic[T] ): """simple docstring""" def __init__( self : str ) -> None: '''simple docstring''' lowerCAmelCase_ : Node[T] | None = None def __iter__( self : str ) -> Iterator[T]: '''simple docstring''' lowerCAmelCase_ : List[str] = self.top while node: yield node.data lowerCAmelCase_ : str = node.next def __str__( self : List[str] ) -> str: '''simple docstring''' return "->".join([str(lowerCAmelCase__ ) for item in self] ) def __len__( self : Dict ) -> int: '''simple docstring''' return len(tuple(iter(self ) ) ) def UpperCAmelCase_ ( self : List[Any] ) -> bool: '''simple docstring''' return self.top is None def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : T ) -> None: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = Node(lowerCAmelCase__ ) if not self.is_empty(): lowerCAmelCase_ : Dict = self.top lowerCAmelCase_ : Optional[int] = node def UpperCAmelCase_ ( self : List[Any] ) -> T: '''simple docstring''' if self.is_empty(): raise IndexError("pop from empty stack" ) assert isinstance(self.top ,lowerCAmelCase__ ) lowerCAmelCase_ : int = self.top lowerCAmelCase_ : Union[str, Any] = self.top.next return pop_node.data def UpperCAmelCase_ ( self : Optional[int] ) -> T: '''simple docstring''' if self.is_empty(): raise IndexError("peek from empty stack" ) assert self.top is not None return self.top.data def UpperCAmelCase_ ( self : Dict ) -> None: '''simple docstring''' lowerCAmelCase_ : str = None if __name__ == "__main__": from doctest import testmod testmod()
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_lowercase = { 0: '''0''', 1: '''1''', 2: '''2''', 3: '''3''', 4: '''4''', 5: '''5''', 6: '''6''', 7: '''7''', 8: '''8''', 9: '''9''', 10: '''a''', 11: '''b''', 12: '''c''', 13: '''d''', 14: '''e''', 15: '''f''', } def UpperCamelCase ( snake_case__): assert type(snake_case__) in (int, float) and decimal == int(snake_case__) lowerCAmelCase_ : Optional[Any] = int(snake_case__) lowerCAmelCase_ : Tuple = "" lowerCAmelCase_ : str = False if decimal < 0: lowerCAmelCase_ : Tuple = True decimal *= -1 while decimal > 0: lowerCAmelCase_ , lowerCAmelCase_ : Any = divmod(snake_case__ , 16) lowerCAmelCase_ : Dict = values[remainder] + hexadecimal lowerCAmelCase_ : List[str] = "0x" + hexadecimal if negative: lowerCAmelCase_ : Optional[Any] = "-" + hexadecimal return hexadecimal if __name__ == "__main__": import doctest doctest.testmod()
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from manim import * class __snake_case ( snake_case__ ): """simple docstring""" def UpperCAmelCase_ ( self : Tuple ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = Rectangle(height=0.5 ,width=0.5 ) lowerCAmelCase_ : int = Rectangle(height=0.46 ,width=0.46 ).set_stroke(width=0 ) lowerCAmelCase_ : Optional[Any] = [mem.copy() for i in range(6 )] lowerCAmelCase_ : Optional[Any] = [mem.copy() for i in range(6 )] lowerCAmelCase_ : int = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0 ) lowerCAmelCase_ : Union[str, Any] = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0 ) lowerCAmelCase_ : List[Any] = VGroup(lowerCAmelCase__ ,lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0 ) lowerCAmelCase_ : Optional[int] = Text("CPU" ,font_size=24 ) lowerCAmelCase_ : Union[str, Any] = Group(lowerCAmelCase__ ,lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0.5 ,aligned_edge=lowerCAmelCase__ ) cpu.move_to([-2.5, -0.5, 0] ) self.add(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = [mem.copy() for i in range(4 )] lowerCAmelCase_ : Optional[Any] = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0 ) lowerCAmelCase_ : Dict = Text("GPU" ,font_size=24 ) lowerCAmelCase_ : List[Any] = Group(lowerCAmelCase__ ,lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0.5 ,aligned_edge=lowerCAmelCase__ ) gpu.move_to([-1, -1, 0] ) self.add(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = [mem.copy() for i in range(6 )] lowerCAmelCase_ : Any = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0 ) lowerCAmelCase_ : Dict = Text("Model" ,font_size=24 ) lowerCAmelCase_ : Tuple = Group(lowerCAmelCase__ ,lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0.5 ,aligned_edge=lowerCAmelCase__ ) model.move_to([3, -1.0, 0] ) self.add(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = [] for i, rect in enumerate(lowerCAmelCase__ ): rect.set_stroke(lowerCAmelCase__ ) # target = fill.copy().set_fill(YELLOW, opacity=0.7) # target.move_to(rect) # self.add(target) lowerCAmelCase_ : int = Rectangle(height=0.46 / 4 ,width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(lowerCAmelCase__ ,opacity=0.7 ) if i == 0: cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) ,buff=0.02 ,direction=lowerCAmelCase__ ) cpu_target.set_x(cpu_target.get_x() + 0.1 ) elif i == 3: cpu_target.next_to(cpu_targs[0] ,direction=lowerCAmelCase__ ,buff=0.0 ) else: cpu_target.next_to(cpu_targs[i - 1] ,direction=lowerCAmelCase__ ,buff=0.0 ) self.add(lowerCAmelCase__ ) cpu_targs.append(lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = [mem.copy() for i in range(6 )] lowerCAmelCase_ : Union[str, Any] = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0 ) lowerCAmelCase_ : Optional[int] = Text("Loaded Checkpoint" ,font_size=24 ) lowerCAmelCase_ : Tuple = Group(lowerCAmelCase__ ,lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,aligned_edge=lowerCAmelCase__ ,buff=0.4 ) checkpoint.move_to([3, 0.5, 0] ) lowerCAmelCase_ : Optional[int] = Square(side_length=2.2 ) key.move_to([-5, 2, 0] ) lowerCAmelCase_ : List[Any] = MarkupText( f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' ,font_size=18 ,) key_text.move_to([-5, 2.4, 0] ) self.add(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Dict = MarkupText( f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' ,font_size=18 ,) blue_text.next_to(lowerCAmelCase__ ,DOWN * 2.4 ,aligned_edge=key_text.get_left() ) lowerCAmelCase_ : Dict = MarkupText( f'''Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.''' ,font_size=24 ,) step_a.move_to([2, 2, 0] ) self.play(Write(lowerCAmelCase__ ) ,Write(lowerCAmelCase__ ) ) self.play(Write(lowerCAmelCase__ ,run_time=1 ) ,Create(lowerCAmelCase__ ,run_time=1 ) ) lowerCAmelCase_ : Union[str, Any] = [] lowerCAmelCase_ : List[Any] = [] for i, rect in enumerate(lowerCAmelCase__ ): lowerCAmelCase_ : Tuple = fill.copy().set_fill(lowerCAmelCase__ ,opacity=0.7 ) target.move_to(lowerCAmelCase__ ) first_animations.append(GrowFromCenter(lowerCAmelCase__ ,run_time=1 ) ) lowerCAmelCase_ : Tuple = target.copy() cpu_target.generate_target() if i < 5: cpu_target.target.move_to(cpu_left_col_base[i + 1] ) else: cpu_target.target.move_to(cpu_right_col_base[i - 5] ) second_animations.append(MoveToTarget(lowerCAmelCase__ ,run_time=1.5 ) ) self.play(*lowerCAmelCase__ ) self.play(*lowerCAmelCase__ ) self.wait()
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from pathlib import Path from typing import List from transformers import is_torch_available, is_vision_available from transformers.testing_utils import get_tests_dir, is_tool_test from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText if is_torch_available(): import torch if is_vision_available(): from PIL import Image _lowercase = ['''text''', '''image''', '''audio'''] def UpperCamelCase ( snake_case__): lowerCAmelCase_ : int = [] for input_type in input_types: if input_type == "text": inputs.append("Text input") elif input_type == "image": inputs.append( Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png").resize((5_12, 5_12))) elif input_type == "audio": inputs.append(torch.ones(30_00)) elif isinstance(snake_case__ , snake_case__): inputs.append(create_inputs(snake_case__)) else: raise ValueError(F'''Invalid type requested: {input_type}''') return inputs def UpperCamelCase ( snake_case__): lowerCAmelCase_ : List[Any] = [] for output in outputs: if isinstance(snake_case__ , (str, AgentText)): output_types.append("text") elif isinstance(snake_case__ , (Image.Image, AgentImage)): output_types.append("image") elif isinstance(snake_case__ , (torch.Tensor, AgentAudio)): output_types.append("audio") else: raise ValueError(F'''Invalid output: {output}''') return output_types @is_tool_test class __snake_case : """simple docstring""" def UpperCAmelCase_ ( self : int ) -> int: '''simple docstring''' self.assertTrue(hasattr(self.tool ,"inputs" ) ) self.assertTrue(hasattr(self.tool ,"outputs" ) ) lowerCAmelCase_ : List[Any] = self.tool.inputs for _input in inputs: if isinstance(_input ,lowerCAmelCase__ ): for __input in _input: self.assertTrue(__input in authorized_types ) else: self.assertTrue(_input in authorized_types ) lowerCAmelCase_ : Any = self.tool.outputs for _output in outputs: self.assertTrue(_output in authorized_types ) def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Any = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) # There is a single output if len(self.tool.outputs ) == 1: lowerCAmelCase_ : Optional[int] = [outputs] self.assertListEqual(output_types(lowerCAmelCase__ ) ,self.tool.outputs ) def UpperCAmelCase_ ( self : int ) -> Any: '''simple docstring''' self.assertTrue(hasattr(self.tool ,"description" ) ) self.assertTrue(hasattr(self.tool ,"default_checkpoint" ) ) self.assertTrue(self.tool.description.startswith("This is a tool that" ) ) def UpperCAmelCase_ ( self : List[Any] ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): lowerCAmelCase_ : str = [outputs] self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) ) for output, output_type in zip(lowerCAmelCase__ ,self.tool.outputs ): lowerCAmelCase_ : Tuple = AGENT_TYPE_MAPPING[output_type] self.assertTrue(isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) ) def UpperCAmelCase_ ( self : Any ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Tuple = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = [] for _input, input_type in zip(lowerCAmelCase__ ,self.tool.inputs ): if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] ) else: _inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) ) # Should not raise an error lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): lowerCAmelCase_ : int = [outputs] self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _lowercase = { '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''], '''processing_git''': ['''GitProcessor'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''GitForCausalLM''', '''GitModel''', '''GitPreTrainedModel''', '''GitVisionModel''', ] if TYPE_CHECKING: from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig from .processing_git import GitProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_git import ( GIT_PRETRAINED_MODEL_ARCHIVE_LIST, GitForCausalLM, GitModel, GitPreTrainedModel, GitVisionModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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import pytest _lowercase = '''__dummy_dataset1__''' _lowercase = ''' import json import os import datasets REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/" URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"} class __DummyDataset1__(datasets.GeneratorBasedBuilder): def _info(self): features = datasets.Features( { "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=[ "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", ] ) ), "langs": datasets.Sequence(datasets.Value("string")), "spans": datasets.Sequence(datasets.Value("string")), } ) return datasets.DatasetInfo(features=features) def _split_generators(self, dl_manager): dl_path = dl_manager.download(URLS) return [ datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}), datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}), ] def _generate_examples(self, filepath): with open(filepath, "r", encoding="utf-8") as f: for i, line in enumerate(f): yield i, json.loads(line) ''' @pytest.fixture def UpperCamelCase ( ): return DATASET_LOADING_SCRIPT_NAME @pytest.fixture def UpperCamelCase ( ): return DATASET_LOADING_SCRIPT_CODE @pytest.fixture def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : List[Any] = dataset_loading_script_name lowerCAmelCase_ : List[str] = tmp_path / "datasets" / script_name script_dir.mkdir(parents=snake_case__) lowerCAmelCase_ : List[Any] = script_dir / F'''{script_name}.py''' with open(snake_case__ , "w") as f: f.write(snake_case__) return str(snake_case__)
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import unittest import numpy as np import torch from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device enable_full_determinism() class __snake_case ( unittest.TestCase ): """simple docstring""" @property def UpperCAmelCase_ ( self : Any ) -> str: '''simple docstring''' torch.manual_seed(0 ) lowerCAmelCase_ : Union[str, Any] = UNetaDModel( block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=("DownBlock2D", "AttnDownBlock2D") ,up_block_types=("AttnUpBlock2D", "UpBlock2D") ,) return model def UpperCAmelCase_ ( self : Optional[int] ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : int = self.dummy_uncond_unet lowerCAmelCase_ : Any = PNDMScheduler() lowerCAmelCase_ : List[str] = PNDMPipeline(unet=lowerCAmelCase__ ,scheduler=lowerCAmelCase__ ) pndm.to(lowerCAmelCase__ ) pndm.set_progress_bar_config(disable=lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = torch.manual_seed(0 ) lowerCAmelCase_ : List[str] = pndm(generator=lowerCAmelCase__ ,num_inference_steps=20 ,output_type="numpy" ).images lowerCAmelCase_ : int = torch.manual_seed(0 ) lowerCAmelCase_ : int = pndm(generator=lowerCAmelCase__ ,num_inference_steps=20 ,output_type="numpy" ,return_dict=lowerCAmelCase__ )[0] lowerCAmelCase_ : str = image[0, -3:, -3:, -1] lowerCAmelCase_ : Any = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 32, 32, 3) lowerCAmelCase_ : Optional[int] = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2 @slow @require_torch class __snake_case ( unittest.TestCase ): """simple docstring""" def UpperCAmelCase_ ( self : str ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Tuple = "google/ddpm-cifar10-32" lowerCAmelCase_ : Optional[int] = UNetaDModel.from_pretrained(lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = PNDMScheduler() lowerCAmelCase_ : Dict = PNDMPipeline(unet=lowerCAmelCase__ ,scheduler=lowerCAmelCase__ ) pndm.to(lowerCAmelCase__ ) pndm.set_progress_bar_config(disable=lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = torch.manual_seed(0 ) lowerCAmelCase_ : Dict = pndm(generator=lowerCAmelCase__ ,output_type="numpy" ).images lowerCAmelCase_ : int = image[0, -3:, -3:, -1] assert image.shape == (1, 32, 32, 3) lowerCAmelCase_ : Union[str, Any] = np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
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import json import os import re import unittest from transformers import CodeGenTokenizer, CodeGenTokenizerFast from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class __snake_case ( snake_case__ , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = CodeGenTokenizer UpperCamelCase_ = CodeGenTokenizerFast UpperCamelCase_ = True UpperCamelCase_ = {'add_prefix_space': True} UpperCamelCase_ = False def UpperCAmelCase_ ( self : str ) -> Tuple: '''simple docstring''' super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt lowerCAmelCase_ : Optional[Any] = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "<unk>", "<|endoftext|>", ] lowerCAmelCase_ : int = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) ) lowerCAmelCase_ : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] lowerCAmelCase_ : List[Any] = {"unk_token": "<unk>"} lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase_ : Tuple = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] ) with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp: fp.write(json.dumps(lowerCAmelCase__ ) + "\n" ) with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp: fp.write("\n".join(lowerCAmelCase__ ) ) def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : str ) -> int: '''simple docstring''' kwargs.update(self.special_tokens_map ) return CodeGenTokenizer.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : Optional[Any] ) -> Tuple: '''simple docstring''' kwargs.update(self.special_tokens_map ) return CodeGenTokenizerFast.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : str ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Tuple = "lower newer" lowerCAmelCase_ : Tuple = "lower newer" return input_text, output_text def UpperCAmelCase_ ( self : Optional[Any] ) -> Any: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map ) lowerCAmelCase_ : Dict = "lower newer" lowerCAmelCase_ : Dict = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"] lowerCAmelCase_ : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = tokens + [tokenizer.unk_token] lowerCAmelCase_ : Union[str, Any] = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]: '''simple docstring''' if not self.test_rust_tokenizer: return lowerCAmelCase_ : Tuple = self.get_tokenizer() lowerCAmelCase_ : Optional[int] = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = "lower newer" # Testing tokenization lowerCAmelCase_ : Tuple = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = rust_tokenizer.tokenize(lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing conversion to ids without special tokens lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = rust_tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing conversion to ids with special tokens lowerCAmelCase_ : int = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing the unknown token lowerCAmelCase_ : Union[str, Any] = tokens + [rust_tokenizer.unk_token] lowerCAmelCase_ : List[str] = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Optional[Any] ) -> List[str]: '''simple docstring''' pass def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Any=15 ) -> str: '''simple docstring''' for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ): lowerCAmelCase_ : Any = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ ) # Simple input lowerCAmelCase_ : int = "This is a simple input" lowerCAmelCase_ : Dict = ["This is a simple input 1", "This is a simple input 2"] lowerCAmelCase_ : str = ("This is a simple input", "This is a pair") lowerCAmelCase_ : Optional[int] = [ ("This is a simple input 1", "This is a simple input 2"), ("This is a simple pair 1", "This is a simple pair 2"), ] # Simple input tests self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Simple input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Simple input self.assertRaises( lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,) # Pair input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Pair input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Pair input self.assertRaises( lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,) def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname ,pad_token="<pad>" ) # Simple input lowerCAmelCase_ : Dict = "This is a simple input" lowerCAmelCase_ : List[str] = ["This is a simple input looooooooong", "This is a simple input"] lowerCAmelCase_ : Any = ("This is a simple input", "This is a pair") lowerCAmelCase_ : List[str] = [ ("This is a simple input loooooong", "This is a simple input"), ("This is a simple pair loooooong", "This is a simple pair"), ] lowerCAmelCase_ : Dict = tokenizer.pad_token_id lowerCAmelCase_ : Union[str, Any] = tokenizer(lowerCAmelCase__ ,padding="max_length" ,max_length=30 ,return_tensors="np" ) lowerCAmelCase_ : Tuple = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" ) lowerCAmelCase_ : Any = tokenizer(*lowerCAmelCase__ ,padding="max_length" ,max_length=60 ,return_tensors="np" ) lowerCAmelCase_ : Optional[int] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" ) # s # test single string max_length padding self.assertEqual(out_s["input_ids"].shape[-1] ,30 ) self.assertTrue(pad_token_id in out_s["input_ids"] ) self.assertTrue(0 in out_s["attention_mask"] ) # s2 # test automatic padding self.assertEqual(out_sa["input_ids"].shape[-1] ,33 ) # long slice doesn't have padding self.assertFalse(pad_token_id in out_sa["input_ids"][0] ) self.assertFalse(0 in out_sa["attention_mask"][0] ) # short slice does have padding self.assertTrue(pad_token_id in out_sa["input_ids"][1] ) self.assertTrue(0 in out_sa["attention_mask"][1] ) # p # test single pair max_length padding self.assertEqual(out_p["input_ids"].shape[-1] ,60 ) self.assertTrue(pad_token_id in out_p["input_ids"] ) self.assertTrue(0 in out_p["attention_mask"] ) # p2 # test automatic padding pair self.assertEqual(out_pa["input_ids"].shape[-1] ,52 ) # long slice pair doesn't have padding self.assertFalse(pad_token_id in out_pa["input_ids"][0] ) self.assertFalse(0 in out_pa["attention_mask"][0] ) # short slice pair does have padding self.assertTrue(pad_token_id in out_pa["input_ids"][1] ) self.assertTrue(0 in out_pa["attention_mask"][1] ) def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any: '''simple docstring''' lowerCAmelCase_ : Any = "$$$" lowerCAmelCase_ : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname ,bos_token=lowerCAmelCase__ ,add_bos_token=lowerCAmelCase__ ) lowerCAmelCase_ : Dict = "This is a simple input" lowerCAmelCase_ : Union[str, Any] = ["This is a simple input 1", "This is a simple input 2"] lowerCAmelCase_ : int = tokenizer.bos_token_id lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ ) self.assertEqual(out_s.input_ids[0] ,lowerCAmelCase__ ) self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) ) lowerCAmelCase_ : List[str] = tokenizer.decode(out_s.input_ids ) lowerCAmelCase_ : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids ) self.assertEqual(decode_s.split()[0] ,lowerCAmelCase__ ) self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) ) @slow def UpperCAmelCase_ ( self : Any ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" ) lowerCAmelCase_ : str = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#" lowerCAmelCase_ : int = "\nif len_a > len_b: result = a\nelse: result = b" lowerCAmelCase_ : Dict = tokenizer.encode(lowerCAmelCase__ ) lowerCAmelCase_ : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"] lowerCAmelCase_ : Union[str, Any] = tokenizer.decode(lowerCAmelCase__ ,truncate_before_pattern=lowerCAmelCase__ ) self.assertEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]: '''simple docstring''' pass
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_distilbert import DistilBertTokenizer _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''} _lowercase = { '''vocab_file''': { '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''', '''distilbert-base-uncased-distilled-squad''': ( '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt''' ), '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''', '''distilbert-base-cased-distilled-squad''': ( '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt''' ), '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''', '''distilbert-base-multilingual-cased''': ( '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt''' ), }, '''tokenizer_file''': { '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''', '''distilbert-base-uncased-distilled-squad''': ( '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json''' ), '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''', '''distilbert-base-cased-distilled-squad''': ( '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json''' ), '''distilbert-base-german-cased''': ( '''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json''' ), '''distilbert-base-multilingual-cased''': ( '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json''' ), }, } _lowercase = { '''distilbert-base-uncased''': 512, '''distilbert-base-uncased-distilled-squad''': 512, '''distilbert-base-cased''': 512, '''distilbert-base-cased-distilled-squad''': 512, '''distilbert-base-german-cased''': 512, '''distilbert-base-multilingual-cased''': 512, } _lowercase = { '''distilbert-base-uncased''': {'''do_lower_case''': True}, '''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True}, '''distilbert-base-cased''': {'''do_lower_case''': False}, '''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False}, '''distilbert-base-german-cased''': {'''do_lower_case''': False}, '''distilbert-base-multilingual-cased''': {'''do_lower_case''': False}, } class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = PRETRAINED_INIT_CONFIGURATION UpperCamelCase_ = ['input_ids', 'attention_mask'] UpperCamelCase_ = DistilBertTokenizer def __init__( self : Optional[int] ,lowerCAmelCase__ : Tuple=None ,lowerCAmelCase__ : List[str]=None ,lowerCAmelCase__ : Dict=True ,lowerCAmelCase__ : Any="[UNK]" ,lowerCAmelCase__ : int="[SEP]" ,lowerCAmelCase__ : str="[PAD]" ,lowerCAmelCase__ : List[Any]="[CLS]" ,lowerCAmelCase__ : Dict="[MASK]" ,lowerCAmelCase__ : Any=True ,lowerCAmelCase__ : Any=None ,**lowerCAmelCase__ : Tuple ,): '''simple docstring''' super().__init__( lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,tokenize_chinese_chars=lowerCAmelCase__ ,strip_accents=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : Tuple = json.loads(self.backend_tokenizer.normalizer.__getstate__() ) if ( normalizer_state.get("lowercase" ,lowerCAmelCase__ ) != do_lower_case or normalizer_state.get("strip_accents" ,lowerCAmelCase__ ) != strip_accents or normalizer_state.get("handle_chinese_chars" ,lowerCAmelCase__ ) != tokenize_chinese_chars ): lowerCAmelCase_ : List[Any] = getattr(lowerCAmelCase__ ,normalizer_state.pop("type" ) ) lowerCAmelCase_ : Optional[int] = do_lower_case lowerCAmelCase_ : Dict = strip_accents lowerCAmelCase_ : Any = tokenize_chinese_chars lowerCAmelCase_ : Union[str, Any] = normalizer_class(**lowerCAmelCase__ ) lowerCAmelCase_ : List[Any] = do_lower_case def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : Optional[int]=None ): '''simple docstring''' lowerCAmelCase_ : Optional[int] = [self.cls_token_id] + token_ids_a + [self.sep_token_id] if token_ids_a: output += token_ids_a + [self.sep_token_id] return output def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ): '''simple docstring''' lowerCAmelCase_ : Tuple = [self.sep_token_id] lowerCAmelCase_ : Tuple = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ): '''simple docstring''' lowerCAmelCase_ : int = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ ) return tuple(lowerCAmelCase__ )
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from __future__ import annotations from random import random class __snake_case : """simple docstring""" def __init__( self : Optional[int] ,lowerCAmelCase__ : int | None = None ) -> int: '''simple docstring''' lowerCAmelCase_ : Dict = value lowerCAmelCase_ : Any = random() lowerCAmelCase_ : Node | None = None lowerCAmelCase_ : Node | None = None def __repr__( self : Any ) -> str: '''simple docstring''' from pprint import pformat if self.left is None and self.right is None: return f'''\'{self.value}: {self.prior:.5}\'''' else: return pformat( {f'''{self.value}: {self.prior:.5}''': (self.left, self.right)} ,indent=1 ) def __str__( self : str ) -> str: '''simple docstring''' lowerCAmelCase_ : List[Any] = str(self.value ) + " " lowerCAmelCase_ : List[Any] = str(self.left or "" ) lowerCAmelCase_ : Union[str, Any] = str(self.right or "" ) return value + left + right def UpperCamelCase ( snake_case__ , snake_case__): if root is None: # None tree is split into 2 Nones return None, None elif root.value is None: return None, None else: if value < root.value: lowerCAmelCase_ , lowerCAmelCase_ : Any = split(root.left , snake_case__) return left, root else: lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = split(root.right , snake_case__) return root, right def UpperCamelCase ( snake_case__ , snake_case__): if (not left) or (not right): # If one node is None, return the other return left or right elif left.prior < right.prior: lowerCAmelCase_ : Dict = merge(left.right , snake_case__) return left else: lowerCAmelCase_ : List[str] = merge(snake_case__ , right.left) return right def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : List[Any] = Node(snake_case__) lowerCAmelCase_ , lowerCAmelCase_ : Tuple = split(snake_case__ , snake_case__) return merge(merge(snake_case__ , snake_case__) , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ , lowerCAmelCase_ : List[str] = split(snake_case__ , value - 1) lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = split(snake_case__ , snake_case__) return merge(snake_case__ , snake_case__) def UpperCamelCase ( snake_case__): if not root: # None return else: inorder(root.left) print(root.value , end=",") inorder(root.right) def UpperCamelCase ( snake_case__ , snake_case__): for arg in args.split(): if arg[0] == "+": lowerCAmelCase_ : List[str] = insert(snake_case__ , int(arg[1:])) elif arg[0] == "-": lowerCAmelCase_ : Optional[int] = erase(snake_case__ , int(arg[1:])) else: print("Unknown command") return root def UpperCamelCase ( ): lowerCAmelCase_ : str = None print( "enter numbers to create a tree, + value to add value into treap, " "- value to erase all nodes with value. 'q' to quit. ") lowerCAmelCase_ : str = input() while args != "q": lowerCAmelCase_ : int = interact_treap(snake_case__ , snake_case__) print(snake_case__) lowerCAmelCase_ : str = input() print("good by!") if __name__ == "__main__": import doctest doctest.testmod() main()
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import inspect import unittest from transformers import MobileNetVaConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import MobileNetVaForImageClassification, MobileNetVaModel from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import MobileNetVaImageProcessor class __snake_case ( snake_case__ ): """simple docstring""" def UpperCAmelCase_ ( self : int ) -> Dict: '''simple docstring''' lowerCAmelCase_ : int = self.config_class(**self.inputs_dict ) self.parent.assertTrue(hasattr(lowerCAmelCase__ ,"tf_padding" ) ) self.parent.assertTrue(hasattr(lowerCAmelCase__ ,"depth_multiplier" ) ) class __snake_case : """simple docstring""" def __init__( self : List[Any] ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : int=13 ,lowerCAmelCase__ : List[Any]=3 ,lowerCAmelCase__ : str=32 ,lowerCAmelCase__ : List[str]=0.25 ,lowerCAmelCase__ : int=8 ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : Any=10_24 ,lowerCAmelCase__ : Optional[Any]=32 ,lowerCAmelCase__ : Tuple="relu6" ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : Tuple=0.02 ,lowerCAmelCase__ : int=True ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Any=10 ,lowerCAmelCase__ : Optional[int]=None ,) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : Dict = parent lowerCAmelCase_ : List[str] = batch_size lowerCAmelCase_ : Optional[int] = num_channels lowerCAmelCase_ : Any = image_size lowerCAmelCase_ : int = depth_multiplier lowerCAmelCase_ : Optional[Any] = min_depth lowerCAmelCase_ : List[str] = tf_padding lowerCAmelCase_ : Optional[Any] = int(last_hidden_size * depth_multiplier ) lowerCAmelCase_ : Optional[Any] = output_stride lowerCAmelCase_ : Optional[Any] = hidden_act lowerCAmelCase_ : str = classifier_dropout_prob lowerCAmelCase_ : Tuple = use_labels lowerCAmelCase_ : List[Any] = is_training lowerCAmelCase_ : Optional[Any] = num_labels lowerCAmelCase_ : Optional[Any] = initializer_range lowerCAmelCase_ : List[str] = scope def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) lowerCAmelCase_ : List[str] = None lowerCAmelCase_ : List[Any] = None if self.use_labels: lowerCAmelCase_ : List[str] = ids_tensor([self.batch_size] ,self.num_labels ) lowerCAmelCase_ : str = ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels ) lowerCAmelCase_ : int = self.get_config() return config, pixel_values, labels, pixel_labels def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]: '''simple docstring''' return MobileNetVaConfig( num_channels=self.num_channels ,image_size=self.image_size ,depth_multiplier=self.depth_multiplier ,min_depth=self.min_depth ,tf_padding=self.tf_padding ,hidden_act=self.hidden_act ,classifier_dropout_prob=self.classifier_dropout_prob ,initializer_range=self.initializer_range ,) def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[int] ) -> int: '''simple docstring''' lowerCAmelCase_ : List[str] = MobileNetVaModel(config=lowerCAmelCase__ ) model.to(lowerCAmelCase__ ) model.eval() lowerCAmelCase_ : Dict = model(lowerCAmelCase__ ) self.parent.assertEqual( result.last_hidden_state.shape ,( self.batch_size, self.last_hidden_size, self.image_size // self.output_stride, self.image_size // self.output_stride, ) ,) def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Tuple ) -> Dict: '''simple docstring''' lowerCAmelCase_ : str = self.num_labels lowerCAmelCase_ : str = MobileNetVaForImageClassification(lowerCAmelCase__ ) model.to(lowerCAmelCase__ ) model.eval() lowerCAmelCase_ : Tuple = model(lowerCAmelCase__ ,labels=lowerCAmelCase__ ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) ) def UpperCAmelCase_ ( self : Optional[Any] ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = self.prepare_config_and_inputs() lowerCAmelCase_ : int = config_and_inputs lowerCAmelCase_ : Optional[int] = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class __snake_case ( snake_case__ , snake_case__ , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else () UpperCamelCase_ = ( {'feature-extraction': MobileNetVaModel, 'image-classification': MobileNetVaForImageClassification} if is_torch_available() else {} ) UpperCamelCase_ = False UpperCamelCase_ = False UpperCamelCase_ = False UpperCamelCase_ = False def UpperCAmelCase_ ( self : str ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : int = MobileNetVaModelTester(self ) lowerCAmelCase_ : Union[str, Any] = MobileNetVaConfigTester(self ,config_class=lowerCAmelCase__ ,has_text_modality=lowerCAmelCase__ ) def UpperCAmelCase_ ( self : List[str] ) -> List[Any]: '''simple docstring''' self.config_tester.run_common_tests() @unittest.skip(reason="MobileNetV1 does not use inputs_embeds" ) def UpperCAmelCase_ ( self : Optional[Any] ) -> Tuple: '''simple docstring''' pass @unittest.skip(reason="MobileNetV1 does not support input and output embeddings" ) def UpperCAmelCase_ ( self : Dict ) -> Dict: '''simple docstring''' pass @unittest.skip(reason="MobileNetV1 does not output attentions" ) def UpperCAmelCase_ ( self : str ) -> List[Any]: '''simple docstring''' pass def UpperCAmelCase_ ( self : List[str] ) -> str: '''simple docstring''' lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: lowerCAmelCase_ : Dict = model_class(lowerCAmelCase__ ) lowerCAmelCase_ : List[Any] = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic lowerCAmelCase_ : Tuple = [*signature.parameters.keys()] lowerCAmelCase_ : Optional[Any] = ["pixel_values"] self.assertListEqual(arg_names[:1] ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : str ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*lowerCAmelCase__ ) def UpperCAmelCase_ ( self : str ) -> Dict: '''simple docstring''' def check_hidden_states_output(lowerCAmelCase__ : str ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Union[str, Any] ): lowerCAmelCase_ : Optional[int] = model_class(lowerCAmelCase__ ) model.to(lowerCAmelCase__ ) model.eval() with torch.no_grad(): lowerCAmelCase_ : Optional[Any] = model(**self._prepare_for_class(lowerCAmelCase__ ,lowerCAmelCase__ ) ) lowerCAmelCase_ : Dict = outputs.hidden_states lowerCAmelCase_ : List[str] = 26 self.assertEqual(len(lowerCAmelCase__ ) ,lowerCAmelCase__ ) lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: lowerCAmelCase_ : List[str] = True check_hidden_states_output(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] lowerCAmelCase_ : str = True check_hidden_states_output(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : List[str] ) -> Dict: '''simple docstring''' lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__ ) @slow def UpperCAmelCase_ ( self : Tuple ) -> Tuple: '''simple docstring''' for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: lowerCAmelCase_ : Tuple = MobileNetVaModel.from_pretrained(lowerCAmelCase__ ) self.assertIsNotNone(lowerCAmelCase__ ) def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class __snake_case ( unittest.TestCase ): """simple docstring""" @cached_property def UpperCAmelCase_ ( self : Dict ) -> Optional[Any]: '''simple docstring''' return ( MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224" ) if is_vision_available() else None ) @slow def UpperCAmelCase_ ( self : Optional[Any] ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Tuple = MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224" ).to(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = self.default_image_processor lowerCAmelCase_ : Union[str, Any] = prepare_img() lowerCAmelCase_ : Any = image_processor(images=lowerCAmelCase__ ,return_tensors="pt" ).to(lowerCAmelCase__ ) # forward pass with torch.no_grad(): lowerCAmelCase_ : List[str] = model(**lowerCAmelCase__ ) # verify the logits lowerCAmelCase_ : str = torch.Size((1, 10_01) ) self.assertEqual(outputs.logits.shape ,lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = torch.tensor([-4.1_739, -1.1_233, 3.1_205] ).to(lowerCAmelCase__ ) self.assertTrue(torch.allclose(outputs.logits[0, :3] ,lowerCAmelCase__ ,atol=1e-4 ) )
715
import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_funnel import FunnelTokenizer _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''} _lowercase = [ '''small''', '''small-base''', '''medium''', '''medium-base''', '''intermediate''', '''intermediate-base''', '''large''', '''large-base''', '''xlarge''', '''xlarge-base''', ] _lowercase = { '''vocab_file''': { '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''', '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''', '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''', '''funnel-transformer/medium-base''': ( '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt''' ), '''funnel-transformer/intermediate''': ( '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt''' ), '''funnel-transformer/intermediate-base''': ( '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt''' ), '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''', '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''', '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''', '''funnel-transformer/xlarge-base''': ( '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt''' ), }, '''tokenizer_file''': { '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''', '''funnel-transformer/small-base''': ( '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''', '''funnel-transformer/medium-base''': ( '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/intermediate''': ( '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json''' ), '''funnel-transformer/intermediate-base''': ( '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''', '''funnel-transformer/large-base''': ( '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''', '''funnel-transformer/xlarge-base''': ( '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json''' ), }, } _lowercase = {f"funnel-transformer/{name}": 512 for name in _model_names} _lowercase = {f"funnel-transformer/{name}": {'''do_lower_case''': True} for name in _model_names} class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_INIT_CONFIGURATION UpperCamelCase_ = FunnelTokenizer UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = 2 def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : List[str]="<unk>" ,lowerCAmelCase__ : int="<sep>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : List[str]="<cls>" ,lowerCAmelCase__ : Optional[int]="<mask>" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : List[Any]="##" ,**lowerCAmelCase__ : int ,) -> List[Any]: '''simple docstring''' super().__init__( lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,clean_text=lowerCAmelCase__ ,tokenize_chinese_chars=lowerCAmelCase__ ,strip_accents=lowerCAmelCase__ ,wordpieces_prefix=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() ) if ( normalizer_state.get("lowercase" ,lowerCAmelCase__ ) != do_lower_case or normalizer_state.get("strip_accents" ,lowerCAmelCase__ ) != strip_accents or normalizer_state.get("handle_chinese_chars" ,lowerCAmelCase__ ) != tokenize_chinese_chars ): lowerCAmelCase_ : Optional[int] = getattr(lowerCAmelCase__ ,normalizer_state.pop("type" ) ) lowerCAmelCase_ : List[Any] = do_lower_case lowerCAmelCase_ : List[str] = strip_accents lowerCAmelCase_ : Any = tokenize_chinese_chars lowerCAmelCase_ : List[Any] = normalizer_class(**lowerCAmelCase__ ) lowerCAmelCase_ : int = do_lower_case def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str=None ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id] if token_ids_a: output += token_ids_a + [self.sep_token_id] return output def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : str = [self.sep_token_id] lowerCAmelCase_ : Optional[Any] = [self.cls_token_id] if token_ids_a is None: return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ ) return tuple(lowerCAmelCase__ )
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0
import json import os from functools import lru_cache from typing import List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''} _lowercase = { '''vocab_file''': { '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''', '''allenai/longformer-large-4096''': ( '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json''' ), '''allenai/longformer-large-4096-finetuned-triviaqa''': ( '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json''' ), '''allenai/longformer-base-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json''' ), '''allenai/longformer-large-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json''' ), }, '''merges_file''': { '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''', '''allenai/longformer-large-4096''': ( '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt''' ), '''allenai/longformer-large-4096-finetuned-triviaqa''': ( '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt''' ), '''allenai/longformer-base-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt''' ), '''allenai/longformer-large-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt''' ), }, } _lowercase = { '''allenai/longformer-base-4096''': 4096, '''allenai/longformer-large-4096''': 4096, '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096, '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096, '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096, } @lru_cache() # Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode def UpperCamelCase ( ): lowerCAmelCase_ : str = ( list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1)) ) lowerCAmelCase_ : Tuple = bs[:] lowerCAmelCase_ : Dict = 0 for b in range(2**8): if b not in bs: bs.append(snake_case__) cs.append(2**8 + n) n += 1 lowerCAmelCase_ : Union[str, Any] = [chr(snake_case__) for n in cs] return dict(zip(snake_case__ , snake_case__)) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[Any] = set() lowerCAmelCase_ : List[Any] = word[0] for char in word[1:]: pairs.add((prev_char, char)) lowerCAmelCase_ : Union[str, Any] = char return pairs class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = ['input_ids', 'attention_mask'] def __init__( self : str ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[Any]="replace" ,lowerCAmelCase__ : Dict="<s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : Optional[Any]="<s>" ,lowerCAmelCase__ : List[Any]="<unk>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : int="<mask>" ,lowerCAmelCase__ : Any=False ,**lowerCAmelCase__ : int ,) -> Dict: '''simple docstring''' lowerCAmelCase_ : Optional[int] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token lowerCAmelCase_ : Dict = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else pad_token # Mask token behave like a normal word, i.e. include the space before it lowerCAmelCase_ : Optional[Any] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else mask_token super().__init__( errors=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,**lowerCAmelCase__ ,) with open(lowerCAmelCase__ ,encoding="utf-8" ) as vocab_handle: lowerCAmelCase_ : List[Any] = json.load(lowerCAmelCase__ ) lowerCAmelCase_ : Dict = {v: k for k, v in self.encoder.items()} lowerCAmelCase_ : List[Any] = errors # how to handle errors in decoding lowerCAmelCase_ : Optional[Any] = bytes_to_unicode() lowerCAmelCase_ : int = {v: k for k, v in self.byte_encoder.items()} with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle: lowerCAmelCase_ : Union[str, Any] = merges_handle.read().split("\n" )[1:-1] lowerCAmelCase_ : Dict = [tuple(merge.split() ) for merge in bpe_merges] lowerCAmelCase_ : Dict = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) ) lowerCAmelCase_ : Any = {} lowerCAmelCase_ : int = add_prefix_space # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions lowerCAmelCase_ : Optional[Any] = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" ) @property def UpperCAmelCase_ ( self : Optional[int] ) -> Any: '''simple docstring''' return len(self.encoder ) def UpperCAmelCase_ ( self : Any ) -> Optional[int]: '''simple docstring''' return dict(self.encoder ,**self.added_tokens_encoder ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[str] ) -> List[Any]: '''simple docstring''' if token in self.cache: return self.cache[token] lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = get_pairs(lowerCAmelCase__ ) if not pairs: return token while True: lowerCAmelCase_ : Dict = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) ) if bigram not in self.bpe_ranks: break lowerCAmelCase_ : Dict = bigram lowerCAmelCase_ : Optional[Any] = [] lowerCAmelCase_ : Any = 0 while i < len(lowerCAmelCase__ ): try: lowerCAmelCase_ : Optional[int] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ ) except ValueError: new_word.extend(word[i:] ) break else: new_word.extend(word[i:j] ) lowerCAmelCase_ : Tuple = j if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second: new_word.append(first + second ) i += 2 else: new_word.append(word[i] ) i += 1 lowerCAmelCase_ : Optional[Any] = tuple(lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = new_word if len(lowerCAmelCase__ ) == 1: break else: lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = " ".join(lowerCAmelCase__ ) lowerCAmelCase_ : Any = word return word def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Dict = [] for token in re.findall(self.pat ,lowerCAmelCase__ ): lowerCAmelCase_ : List[str] = "".join( self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__ ).split(" " ) ) return bpe_tokens def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ) -> Tuple: '''simple docstring''' return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]: '''simple docstring''' return self.decoder.get(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Any: '''simple docstring''' lowerCAmelCase_ : Optional[int] = "".join(lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors ) return text def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' if not os.path.isdir(lowerCAmelCase__ ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return lowerCAmelCase_ : Optional[Any] = os.path.join( lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase_ : Tuple = os.path.join( lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as f: f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowerCAmelCase__ ,ensure_ascii=lowerCAmelCase__ ) + "\n" ) lowerCAmelCase_ : Tuple = 0 with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as writer: writer.write("#version: 0.2\n" ) for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda lowerCAmelCase__ : kv[1] ): if index != token_index: logger.warning( f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.''' " Please check that the tokenizer is not corrupted!" ) lowerCAmelCase_ : Optional[Any] = token_index writer.write(" ".join(lowerCAmelCase__ ) + "\n" ) index += 1 return vocab_file, merge_file def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] lowerCAmelCase_ : List[Any] = [self.cls_token_id] lowerCAmelCase_ : List[str] = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]: '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=lowerCAmelCase__ ,token_ids_a=lowerCAmelCase__ ,already_has_special_tokens=lowerCAmelCase__ ) if token_ids_a is None: return [1] + ([0] * len(lowerCAmelCase__ )) + [1] return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1] def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : List[str] = [self.sep_token_id] lowerCAmelCase_ : Tuple = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Optional[int] ) -> Any: '''simple docstring''' lowerCAmelCase_ : int = kwargs.pop("add_prefix_space" ,self.add_prefix_space ) if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__ ) > 0 and not text[0].isspace()): lowerCAmelCase_ : Union[str, Any] = " " + text return (text, kwargs)
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# tests directory-specific settings - this file is run automatically # by pytest before any tests are run import doctest import sys import warnings from os.path import abspath, dirname, join import _pytest from transformers.testing_utils import HfDoctestModule, HfDocTestParser # allow having multiple repository checkouts and not needing to remember to rerun # 'pip install -e .[dev]' when switching between checkouts and running tests. _lowercase = abspath(join(dirname(__file__), '''src''')) sys.path.insert(1, git_repo_path) # silence FutureWarning warnings in tests since often we can't act on them until # they become normal warnings - i.e. the tests still need to test the current functionality warnings.simplefilter(action='''ignore''', category=FutureWarning) def UpperCamelCase ( snake_case__): config.addinivalue_line( "markers" , "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested") config.addinivalue_line( "markers" , "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested") config.addinivalue_line("markers" , "is_pipeline_test: mark test to run only when pipelines are tested") config.addinivalue_line("markers" , "is_staging_test: mark test to run only in the staging environment") config.addinivalue_line("markers" , "accelerate_tests: mark test that require accelerate") config.addinivalue_line("markers" , "tool_tests: mark the tool tests that are run on their specific schedule") def UpperCamelCase ( snake_case__): from transformers.testing_utils import pytest_addoption_shared pytest_addoption_shared(snake_case__) def UpperCamelCase ( snake_case__): from transformers.testing_utils import pytest_terminal_summary_main lowerCAmelCase_ : int = terminalreporter.config.getoption("--make-reports") if make_reports: pytest_terminal_summary_main(snake_case__ , id=snake_case__) def UpperCamelCase ( snake_case__ , snake_case__): # If no tests are collected, pytest exists with code 5, which makes the CI fail. if exitstatus == 5: lowerCAmelCase_ : List[Any] = 0 # Doctest custom flag to ignore output. _lowercase = doctest.register_optionflag('''IGNORE_RESULT''') _lowercase = doctest.OutputChecker class __snake_case ( snake_case__ ): """simple docstring""" def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Any: '''simple docstring''' if IGNORE_RESULT & optionflags: return True return OutputChecker.check_output(self ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) _lowercase = CustomOutputChecker _lowercase = HfDoctestModule _lowercase = HfDocTestParser
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available, is_vision_available, ) _lowercase = { '''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ['''MobileViTFeatureExtractor'''] _lowercase = ['''MobileViTImageProcessor'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''MobileViTForImageClassification''', '''MobileViTForSemanticSegmentation''', '''MobileViTModel''', '''MobileViTPreTrainedModel''', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''TFMobileViTForImageClassification''', '''TFMobileViTForSemanticSegmentation''', '''TFMobileViTModel''', '''TFMobileViTPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_mobilevit import MobileViTFeatureExtractor from .image_processing_mobilevit import MobileViTImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_mobilevit import ( MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST, MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel, MobileViTPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_mobilevit import ( TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel, TFMobileViTPreTrainedModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
717
from __future__ import annotations from collections.abc import Sequence from typing import Literal def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = list(snake_case__) lowerCAmelCase_ : Tuple = list(snake_case__) lowerCAmelCase_ : List[str] = 0 for i in range(len(snake_case__)): if lista[i] != lista[i]: count += 1 lowerCAmelCase_ : Dict = "_" if count > 1: return False else: return "".join(snake_case__) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Union[str, Any] = [] while True: lowerCAmelCase_ : Tuple = ["$"] * len(snake_case__) lowerCAmelCase_ : Tuple = [] for i in range(len(snake_case__)): for j in range(i + 1 , len(snake_case__)): lowerCAmelCase_ : Optional[int] = compare_string(binary[i] , binary[j]) if k is False: lowerCAmelCase_ : str = "*" lowerCAmelCase_ : Tuple = "*" temp.append("X") for i in range(len(snake_case__)): if checka[i] == "$": pi.append(binary[i]) if len(snake_case__) == 0: return pi lowerCAmelCase_ : List[Any] = list(set(snake_case__)) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = [] for minterm in minterms: lowerCAmelCase_ : Dict = "" for _ in range(snake_case__): lowerCAmelCase_ : Dict = str(minterm % 2) + string minterm //= 2 temp.append(snake_case__) return temp def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = list(snake_case__) lowerCAmelCase_ : Dict = list(snake_case__) lowerCAmelCase_ : Dict = 0 for i in range(len(snake_case__)): if lista[i] != lista[i]: count_n += 1 return count_n == count def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = [] lowerCAmelCase_ : Dict = [0] * len(snake_case__) for i in range(len(chart[0])): lowerCAmelCase_ : List[Any] = 0 lowerCAmelCase_ : int = -1 for j in range(len(snake_case__)): if chart[j][i] == 1: count += 1 lowerCAmelCase_ : Optional[int] = j if count == 1: lowerCAmelCase_ : Union[str, Any] = 1 for i in range(len(snake_case__)): if select[i] == 1: for j in range(len(chart[0])): if chart[i][j] == 1: for k in range(len(snake_case__)): lowerCAmelCase_ : Tuple = 0 temp.append(prime_implicants[i]) while True: lowerCAmelCase_ : Optional[Any] = 0 lowerCAmelCase_ : Dict = -1 lowerCAmelCase_ : Tuple = 0 for i in range(len(snake_case__)): lowerCAmelCase_ : Dict = chart[i].count(1) if count_n > max_n: lowerCAmelCase_ : Optional[int] = count_n lowerCAmelCase_ : Optional[Any] = i if max_n == 0: return temp temp.append(prime_implicants[rem]) for i in range(len(chart[0])): if chart[rem][i] == 1: for j in range(len(snake_case__)): lowerCAmelCase_ : Any = 0 def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : str = [[0 for x in range(len(snake_case__))] for x in range(len(snake_case__))] for i in range(len(snake_case__)): lowerCAmelCase_ : Optional[Any] = prime_implicants[i].count("_") for j in range(len(snake_case__)): if is_for_table(prime_implicants[i] , binary[j] , snake_case__): lowerCAmelCase_ : Dict = 1 return chart def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = int(input("Enter the no. of variables\n")) lowerCAmelCase_ : Tuple = [ float(snake_case__) for x in input( "Enter the decimal representation of Minterms 'Spaces Separated'\n").split() ] lowerCAmelCase_ : Any = decimal_to_binary(snake_case__ , snake_case__) lowerCAmelCase_ : Dict = check(snake_case__) print("Prime Implicants are:") print(snake_case__) lowerCAmelCase_ : int = prime_implicant_chart(snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = selection(snake_case__ , snake_case__) print("Essential Prime Implicants are:") print(snake_case__) if __name__ == "__main__": import doctest doctest.testmod() main()
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from __future__ import annotations _lowercase = list[tuple[int, int]] _lowercase = [ [0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [1, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0], ] _lowercase = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right class __snake_case : """simple docstring""" def __init__( self : List[Any] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : int ,lowerCAmelCase__ : int ,lowerCAmelCase__ : int ,lowerCAmelCase__ : float ,lowerCAmelCase__ : Node | None ,) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Any = pos_x lowerCAmelCase_ : Tuple = pos_y lowerCAmelCase_ : Union[str, Any] = (pos_y, pos_x) lowerCAmelCase_ : Optional[int] = goal_x lowerCAmelCase_ : Any = goal_y lowerCAmelCase_ : int = g_cost lowerCAmelCase_ : Optional[Any] = parent lowerCAmelCase_ : Optional[Any] = self.calculate_heuristic() def UpperCAmelCase_ ( self : Any ) -> float: '''simple docstring''' lowerCAmelCase_ : Dict = abs(self.pos_x - self.goal_x ) lowerCAmelCase_ : Any = abs(self.pos_y - self.goal_y ) return dx + dy def __lt__( self : List[str] ,lowerCAmelCase__ : Dict ) -> bool: '''simple docstring''' return self.f_cost < other.f_cost class __snake_case : """simple docstring""" def __init__( self : Optional[Any] ,lowerCAmelCase__ : tuple[int, int] ,lowerCAmelCase__ : tuple[int, int] ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : List[str] = Node(start[1] ,start[0] ,goal[1] ,goal[0] ,0 ,lowerCAmelCase__ ) lowerCAmelCase_ : List[Any] = Node(goal[1] ,goal[0] ,goal[1] ,goal[0] ,9_99_99 ,lowerCAmelCase__ ) lowerCAmelCase_ : List[Any] = [self.start] lowerCAmelCase_ : list[Node] = [] lowerCAmelCase_ : Union[str, Any] = False def UpperCAmelCase_ ( self : Any ) -> Path | None: '''simple docstring''' while self.open_nodes: # Open Nodes are sorted using __lt__ self.open_nodes.sort() lowerCAmelCase_ : Optional[int] = self.open_nodes.pop(0 ) if current_node.pos == self.target.pos: lowerCAmelCase_ : Optional[Any] = True return self.retrace_path(lowerCAmelCase__ ) self.closed_nodes.append(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = self.get_successors(lowerCAmelCase__ ) for child_node in successors: if child_node in self.closed_nodes: continue if child_node not in self.open_nodes: self.open_nodes.append(lowerCAmelCase__ ) else: # retrieve the best current path lowerCAmelCase_ : List[str] = self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase__ ) ) if child_node.g_cost < better_node.g_cost: self.open_nodes.append(lowerCAmelCase__ ) else: self.open_nodes.append(lowerCAmelCase__ ) if not self.reached: return [self.start.pos] return None def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Node ) -> list[Node]: '''simple docstring''' lowerCAmelCase_ : Any = [] for action in delta: lowerCAmelCase_ : Any = parent.pos_x + action[1] lowerCAmelCase_ : Optional[Any] = parent.pos_y + action[0] if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(lowerCAmelCase__ ) - 1): continue if grid[pos_y][pos_x] != 0: continue successors.append( Node( lowerCAmelCase__ ,lowerCAmelCase__ ,self.target.pos_y ,self.target.pos_x ,parent.g_cost + 1 ,lowerCAmelCase__ ,) ) return successors def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Node | None ) -> Path: '''simple docstring''' lowerCAmelCase_ : int = node lowerCAmelCase_ : Tuple = [] while current_node is not None: path.append((current_node.pos_y, current_node.pos_x) ) lowerCAmelCase_ : str = current_node.parent path.reverse() return path if __name__ == "__main__": _lowercase = (0, 0) _lowercase = (len(grid) - 1, len(grid[0]) - 1) for elem in grid: print(elem) print('''------''') _lowercase = GreedyBestFirst(init, goal) _lowercase = greedy_bf.search() if path: for pos_x, pos_y in path: _lowercase = 2 for elem in grid: print(elem)
718
import logging import os from typing import Dict, List, Optional, Union import torch import torch.nn as nn from accelerate.utils.imports import ( is_abit_bnb_available, is_abit_bnb_available, is_bnb_available, ) from ..big_modeling import dispatch_model, init_empty_weights from .dataclasses import BnbQuantizationConfig from .modeling import ( find_tied_parameters, get_balanced_memory, infer_auto_device_map, load_checkpoint_in_model, offload_weight, set_module_tensor_to_device, ) if is_bnb_available(): import bitsandbytes as bnb from copy import deepcopy _lowercase = logging.getLogger(__name__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = False , ): lowerCAmelCase_ : List[Any] = bnb_quantization_config.load_in_abit lowerCAmelCase_ : Optional[Any] = bnb_quantization_config.load_in_abit if load_in_abit and not is_abit_bnb_available(): raise ImportError( "You have a version of `bitsandbytes` that is not compatible with 8bit quantization," " make sure you have the latest version of `bitsandbytes` installed.") if load_in_abit and not is_abit_bnb_available(): raise ValueError( "You have a version of `bitsandbytes` that is not compatible with 4bit quantization," "make sure you have the latest version of `bitsandbytes` installed.") lowerCAmelCase_ : List[str] = [] # custom device map if isinstance(snake_case__ , snake_case__) and len(device_map.keys()) > 1: lowerCAmelCase_ : Union[str, Any] = [key for key, value in device_map.items() if value in ["disk", "cpu"]] # We keep some modules such as the lm_head in their original dtype for numerical stability reasons if bnb_quantization_config.skip_modules is None: lowerCAmelCase_ : Union[str, Any] = get_keys_to_not_convert(snake_case__) # add cpu modules to skip modules only for 4-bit modules if load_in_abit: bnb_quantization_config.skip_modules.extend(snake_case__) lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules # We add the modules we want to keep in full precision if bnb_quantization_config.keep_in_fpaa_modules is None: lowerCAmelCase_ : Optional[int] = [] lowerCAmelCase_ : int = bnb_quantization_config.keep_in_fpaa_modules modules_to_not_convert.extend(snake_case__) # compatibility with peft lowerCAmelCase_ : Optional[int] = load_in_abit lowerCAmelCase_ : List[str] = load_in_abit lowerCAmelCase_ : Optional[int] = get_parameter_device(snake_case__) if model_device.type != "meta": # quantization of an already loaded model logger.warning( "It is not recommended to quantize a loaded model. " "The model should be instantiated under the `init_empty_weights` context manager.") lowerCAmelCase_ : Union[str, Any] = replace_with_bnb_layers(snake_case__ , snake_case__ , modules_to_not_convert=snake_case__) # convert param to the right dtype lowerCAmelCase_ : Any = bnb_quantization_config.torch_dtype for name, param in model.state_dict().items(): if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules): param.to(torch.floataa) if param.dtype != torch.floataa: lowerCAmelCase_ : Optional[int] = name.replace(".weight" , "").replace(".bias" , "") lowerCAmelCase_ : Optional[int] = getattr(snake_case__ , snake_case__ , snake_case__) if param is not None: param.to(torch.floataa) elif torch.is_floating_point(snake_case__): param.to(snake_case__) if model_device.type == "cuda": # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda model.cuda(torch.cuda.current_device()) torch.cuda.empty_cache() elif torch.cuda.is_available(): model.to(torch.cuda.current_device()) else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info( F'''The model device type is {model_device.type}. However, cuda is needed for quantization.''' "We move the model to cuda.") return model elif weights_location is None: raise RuntimeError( F'''`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} ''') else: with init_empty_weights(): lowerCAmelCase_ : str = replace_with_bnb_layers( snake_case__ , snake_case__ , modules_to_not_convert=snake_case__) lowerCAmelCase_ : Optional[int] = get_quantized_model_device_map( snake_case__ , snake_case__ , snake_case__ , max_memory=snake_case__ , no_split_module_classes=snake_case__ , ) if offload_state_dict is None and device_map is not None and "disk" in device_map.values(): lowerCAmelCase_ : Optional[Any] = True lowerCAmelCase_ : Optional[int] = any(x in list(device_map.values()) for x in ["cpu", "disk"]) load_checkpoint_in_model( snake_case__ , snake_case__ , snake_case__ , dtype=bnb_quantization_config.torch_dtype , offload_folder=snake_case__ , offload_state_dict=snake_case__ , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , ) return dispatch_model(snake_case__ , device_map=snake_case__ , offload_dir=snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=None): if device_map is None: if torch.cuda.is_available(): lowerCAmelCase_ : Any = {"": torch.cuda.current_device()} else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info("The device_map was not initialized." "Setting device_map to `{'':torch.cuda.current_device()}`.") if isinstance(snake_case__ , snake_case__): if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]: raise ValueError( "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or " "'sequential'.") lowerCAmelCase_ : Dict = {} special_dtypes.update( { name: bnb_quantization_config.torch_dtype for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.skip_modules) }) special_dtypes.update( { name: torch.floataa for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules) }) lowerCAmelCase_ : List[str] = {} lowerCAmelCase_ : Union[str, Any] = special_dtypes lowerCAmelCase_ : Union[str, Any] = no_split_module_classes lowerCAmelCase_ : Any = bnb_quantization_config.target_dtype # get max_memory for each device. if device_map != "sequential": lowerCAmelCase_ : Tuple = get_balanced_memory( snake_case__ , low_zero=(device_map == "balanced_low_0") , max_memory=snake_case__ , **snake_case__ , ) lowerCAmelCase_ : Tuple = max_memory lowerCAmelCase_ : Optional[Any] = infer_auto_device_map(snake_case__ , **snake_case__) if isinstance(snake_case__ , snake_case__): # check if don't have any quantized module on the cpu lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules lowerCAmelCase_ : List[Any] = { key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert } for device in ["cpu", "disk"]: if device in device_map_without_some_modules.values(): if bnb_quantization_config.load_in_abit: raise ValueError( "\n Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n these modules in `torch_dtype`, you need to pass a custom `device_map` to\n `load_and_quantize_model`. Check\n https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n for more details.\n ") else: logger.info( "Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit") del device_map_without_some_modules return device_map def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None): if modules_to_not_convert is None: lowerCAmelCase_ : List[str] = [] lowerCAmelCase_ , lowerCAmelCase_ : Tuple = _replace_with_bnb_layers( snake_case__ , snake_case__ , snake_case__ , snake_case__) if not has_been_replaced: logger.warning( "You are loading your model in 8bit or 4bit but no linear modules were found in your model." " this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers." " Please double check your model architecture, or submit an issue on github if you think this is" " a bug.") return model def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , ): lowerCAmelCase_ : str = False for name, module in model.named_children(): if current_key_name is None: lowerCAmelCase_ : Optional[int] = [] current_key_name.append(snake_case__) if isinstance(snake_case__ , nn.Linear) and name not in modules_to_not_convert: # Check if the current key is not in the `modules_to_not_convert` lowerCAmelCase_ : Optional[int] = ".".join(snake_case__) lowerCAmelCase_ : List[str] = True for key in modules_to_not_convert: if ( (key in current_key_name_str) and (key + "." in current_key_name_str) ) or key == current_key_name_str: lowerCAmelCase_ : List[Any] = False break if proceed: # Load bnb module with empty weight and replace ``nn.Linear` module if bnb_quantization_config.load_in_abit: lowerCAmelCase_ : Tuple = bnb.nn.LinearabitLt( module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=snake_case__ , threshold=bnb_quantization_config.llm_inta_threshold , ) elif bnb_quantization_config.load_in_abit: lowerCAmelCase_ : Dict = bnb.nn.Linearabit( module.in_features , module.out_features , module.bias is not None , bnb_quantization_config.bnb_abit_compute_dtype , compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant , quant_type=bnb_quantization_config.bnb_abit_quant_type , ) else: raise ValueError("load_in_8bit and load_in_4bit can't be both False") lowerCAmelCase_ : List[str] = module.weight.data if module.bias is not None: lowerCAmelCase_ : Any = module.bias.data bnb_module.requires_grad_(snake_case__) setattr(snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = True if len(list(module.children())) > 0: lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = _replace_with_bnb_layers( snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : Optional[int] = has_been_replaced | _has_been_replaced # Remove the last key for recursion current_key_name.pop(-1) return model, has_been_replaced def UpperCamelCase ( snake_case__): # Create a copy of the model with init_empty_weights(): lowerCAmelCase_ : List[Any] = deepcopy(snake_case__) # this has 0 cost since it is done inside `init_empty_weights` context manager` lowerCAmelCase_ : Dict = find_tied_parameters(snake_case__) # For compatibility with Accelerate < 0.18 if isinstance(snake_case__ , snake_case__): lowerCAmelCase_ : List[str] = sum(list(tied_params.values()) , []) + list(tied_params.keys()) else: lowerCAmelCase_ : Optional[Any] = sum(snake_case__ , []) lowerCAmelCase_ : List[Any] = len(snake_case__) > 0 # Check if it is a base model lowerCAmelCase_ : List[str] = False if hasattr(snake_case__ , "base_model_prefix"): lowerCAmelCase_ : Tuple = not hasattr(snake_case__ , model.base_model_prefix) # Ignore this for base models (BertModel, GPT2Model, etc.) if (not has_tied_params) and is_base_model: return [] # otherwise they have an attached head lowerCAmelCase_ : Union[str, Any] = list(model.named_children()) lowerCAmelCase_ : Optional[int] = [list_modules[-1][0]] # add last module together with tied weights lowerCAmelCase_ : Any = set(snake_case__) - set(snake_case__) lowerCAmelCase_ : Tuple = list(set(snake_case__)) + list(snake_case__) # remove ".weight" from the keys lowerCAmelCase_ : List[str] = [".weight", ".bias"] lowerCAmelCase_ : Tuple = [] for name in list_untouched: for name_to_remove in names_to_remove: if name_to_remove in name: lowerCAmelCase_ : str = name.replace(snake_case__ , "") filtered_module_names.append(snake_case__) return filtered_module_names def UpperCamelCase ( snake_case__): for m in model.modules(): if isinstance(snake_case__ , bnb.nn.Linearabit): return True return False def UpperCamelCase ( snake_case__): return next(parameter.parameters()).device def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): # if it is not quantized, we quantize and offload the quantized weights and the SCB stats if fpaa_statistics is None: set_module_tensor_to_device(snake_case__ , snake_case__ , 0 , dtype=snake_case__ , value=snake_case__) lowerCAmelCase_ : str = param_name lowerCAmelCase_ : Tuple = model if "." in tensor_name: lowerCAmelCase_ : Dict = tensor_name.split(".") for split in splits[:-1]: lowerCAmelCase_ : Any = getattr(snake_case__ , snake_case__) if new_module is None: raise ValueError(F'''{module} has no attribute {split}.''') lowerCAmelCase_ : Union[str, Any] = new_module lowerCAmelCase_ : Any = splits[-1] # offload weights lowerCAmelCase_ : List[Any] = False offload_weight(module._parameters[tensor_name] , snake_case__ , snake_case__ , index=snake_case__) if hasattr(module._parameters[tensor_name] , "SCB"): offload_weight( module._parameters[tensor_name].SCB , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__ , ) else: offload_weight(snake_case__ , snake_case__ , snake_case__ , index=snake_case__) offload_weight(snake_case__ , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__) set_module_tensor_to_device(snake_case__ , snake_case__ , "meta" , dtype=snake_case__ , value=torch.empty(*param.size()))
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import warnings from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = ['image_processor', 'tokenizer'] UpperCamelCase_ = 'CLIPImageProcessor' UpperCamelCase_ = ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast') def __init__( self : str ,lowerCAmelCase__ : Union[str, Any]=None ,lowerCAmelCase__ : Union[str, Any]=None ,**lowerCAmelCase__ : List[str] ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = None if "feature_extractor" in kwargs: warnings.warn( "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" " instead." ,lowerCAmelCase__ ,) lowerCAmelCase_ : str = kwargs.pop("feature_extractor" ) lowerCAmelCase_ : List[Any] = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError("You need to specify an `image_processor`." ) if tokenizer is None: raise ValueError("You need to specify a `tokenizer`." ) super().__init__(lowerCAmelCase__ ,lowerCAmelCase__ ) def __call__( self : int ,lowerCAmelCase__ : Tuple=None ,lowerCAmelCase__ : List[Any]=None ,lowerCAmelCase__ : Optional[Any]=None ,**lowerCAmelCase__ : int ) -> str: '''simple docstring''' if text is None and images is None: raise ValueError("You have to specify either text or images. Both cannot be none." ) if text is not None: lowerCAmelCase_ : int = self.tokenizer(lowerCAmelCase__ ,return_tensors=lowerCAmelCase__ ,**lowerCAmelCase__ ) if images is not None: lowerCAmelCase_ : Any = self.image_processor(lowerCAmelCase__ ,return_tensors=lowerCAmelCase__ ,**lowerCAmelCase__ ) if text is not None and images is not None: lowerCAmelCase_ : Optional[int] = image_features.pixel_values return encoding elif text is not None: return encoding else: return BatchEncoding(data=dict(**lowerCAmelCase__ ) ,tensor_type=lowerCAmelCase__ ) def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : Tuple ,**lowerCAmelCase__ : List[Any] ) -> Optional[Any]: '''simple docstring''' return self.tokenizer.batch_decode(*lowerCAmelCase__ ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : Dict ,**lowerCAmelCase__ : List[Any] ) -> Any: '''simple docstring''' return self.tokenizer.decode(*lowerCAmelCase__ ,**lowerCAmelCase__ ) @property def UpperCAmelCase_ ( self : str ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : List[Any] = self.tokenizer.model_input_names lowerCAmelCase_ : Union[str, Any] = self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
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import copy from typing import Any, Dict, List, Optional, Union import numpy as np import torch from ...audio_utils import mel_filter_bank, spectrogram, window_function from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import TensorType, logging _lowercase = logging.get_logger(__name__) class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = ['input_features', 'is_longer'] def __init__( self : Optional[int] ,lowerCAmelCase__ : List[Any]=64 ,lowerCAmelCase__ : Any=4_80_00 ,lowerCAmelCase__ : Optional[Any]=4_80 ,lowerCAmelCase__ : List[str]=10 ,lowerCAmelCase__ : List[Any]=10_24 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : float = 0 ,lowerCAmelCase__ : float = 1_40_00 ,lowerCAmelCase__ : int = None ,lowerCAmelCase__ : str = "fusion" ,lowerCAmelCase__ : str = "repeatpad" ,**lowerCAmelCase__ : Union[str, Any] ,) -> Union[str, Any]: '''simple docstring''' super().__init__( feature_size=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,padding_value=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : Optional[Any] = top_db lowerCAmelCase_ : str = truncation lowerCAmelCase_ : Tuple = padding lowerCAmelCase_ : str = fft_window_size lowerCAmelCase_ : Dict = (fft_window_size >> 1) + 1 lowerCAmelCase_ : Dict = hop_length lowerCAmelCase_ : Any = max_length_s lowerCAmelCase_ : int = max_length_s * sampling_rate lowerCAmelCase_ : Optional[int] = sampling_rate lowerCAmelCase_ : int = frequency_min lowerCAmelCase_ : Optional[Any] = frequency_max lowerCAmelCase_ : List[Any] = mel_filter_bank( num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm=lowerCAmelCase__ ,mel_scale="htk" ,) lowerCAmelCase_ : List[Any] = mel_filter_bank( num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm="slaney" ,mel_scale="slaney" ,) def UpperCAmelCase_ ( self : Dict ) -> Dict[str, Any]: '''simple docstring''' lowerCAmelCase_ : int = copy.deepcopy(self.__dict__ ) lowerCAmelCase_ : Optional[int] = self.__class__.__name__ if "mel_filters" in output: del output["mel_filters"] if "mel_filters_slaney" in output: del output["mel_filters_slaney"] return output def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Optional[np.array] = None ) -> np.ndarray: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = spectrogram( lowerCAmelCase__ ,window_function(self.fft_window_size ,"hann" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=lowerCAmelCase__ ,log_mel="dB" ,) return log_mel_spectrogram.T def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Tuple ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Tuple = np.array_split(list(range(0 ,total_frames - chunk_frames + 1 ) ) ,3 ) if len(ranges[1] ) == 0: # if the audio is too short, we just use the first chunk lowerCAmelCase_ : List[Any] = [0] if len(ranges[2] ) == 0: # if the audio is too short, we just use the first chunk lowerCAmelCase_ : List[Any] = [0] # randomly choose index for each part lowerCAmelCase_ : str = np.random.choice(ranges[0] ) lowerCAmelCase_ : Optional[Any] = np.random.choice(ranges[1] ) lowerCAmelCase_ : Any = np.random.choice(ranges[2] ) lowerCAmelCase_ : str = mel[idx_front : idx_front + chunk_frames, :] lowerCAmelCase_ : Dict = mel[idx_middle : idx_middle + chunk_frames, :] lowerCAmelCase_ : Optional[Any] = mel[idx_back : idx_back + chunk_frames, :] lowerCAmelCase_ : List[str] = torch.tensor(mel[None, None, :] ) lowerCAmelCase_ : List[Any] = torch.nn.functional.interpolate( lowerCAmelCase__ ,size=[chunk_frames, 64] ,mode="bilinear" ,align_corners=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = mel_shrink[0][0].numpy() lowerCAmelCase_ : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 ) return mel_fusion def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : int ) -> np.array: '''simple docstring''' if waveform.shape[0] > max_length: if truncation == "rand_trunc": lowerCAmelCase_ : List[Any] = True # random crop to max_length (for compatibility) -> this should be handled by self.pad lowerCAmelCase_ : str = len(lowerCAmelCase__ ) - max_length lowerCAmelCase_ : Any = np.random.randint(0 ,overflow + 1 ) lowerCAmelCase_ : Dict = waveform[idx : idx + max_length] lowerCAmelCase_ : List[str] = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :] elif truncation == "fusion": lowerCAmelCase_ : Tuple = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters ) lowerCAmelCase_ : str = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed lowerCAmelCase_ : List[str] = mel.shape[0] if chunk_frames == total_frames: # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length. # In this case, we just use the whole audio. lowerCAmelCase_ : Dict = np.stack([mel, mel, mel, mel] ,axis=0 ) lowerCAmelCase_ : int = False else: lowerCAmelCase_ : str = self._random_mel_fusion(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Any = True else: raise NotImplementedError(f'''data_truncating {truncation} not implemented''' ) else: lowerCAmelCase_ : Dict = False # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding if waveform.shape[0] < max_length: if padding == "repeat": lowerCAmelCase_ : List[Any] = int(max_length / len(lowerCAmelCase__ ) ) lowerCAmelCase_ : int = np.stack(np.tile(lowerCAmelCase__ ,n_repeat + 1 ) )[:max_length] if padding == "repeatpad": lowerCAmelCase_ : Optional[Any] = int(max_length / len(lowerCAmelCase__ ) ) lowerCAmelCase_ : Tuple = np.stack(np.tile(lowerCAmelCase__ ,lowerCAmelCase__ ) ) lowerCAmelCase_ : List[Any] = np.pad(lowerCAmelCase__ ,(0, max_length - waveform.shape[0]) ,mode="constant" ,constant_values=0 ) if truncation == "fusion": lowerCAmelCase_ : int = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters ) lowerCAmelCase_ : Tuple = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 ) else: lowerCAmelCase_ : str = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :] return input_mel, longer def __call__( self : int ,lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCAmelCase__ : str = None ,lowerCAmelCase__ : Optional[str] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[Union[str, TensorType]] = None ,**lowerCAmelCase__ : List[Any] ,) -> BatchFeature: '''simple docstring''' lowerCAmelCase_ : List[str] = truncation if truncation is not None else self.truncation lowerCAmelCase_ : List[Any] = padding if padding else self.padding if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a''' f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input''' f''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' ) else: logger.warning( "It is strongly recommended to pass the `sampling_rate` argument to this function. " "Failing to do so can result in silent errors that might be hard to debug." ) lowerCAmelCase_ : Dict = isinstance(lowerCAmelCase__ ,np.ndarray ) and len(raw_speech.shape ) > 1 if is_batched_numpy and len(raw_speech.shape ) > 2: raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' ) lowerCAmelCase_ : Dict = is_batched_numpy or ( isinstance(lowerCAmelCase__ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) )) ) if is_batched: lowerCAmelCase_ : List[str] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for speech in raw_speech] elif not is_batched and not isinstance(lowerCAmelCase__ ,np.ndarray ): lowerCAmelCase_ : Tuple = np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) elif isinstance(lowerCAmelCase__ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ): lowerCAmelCase_ : Any = raw_speech.astype(np.floataa ) # always return batch if not is_batched: lowerCAmelCase_ : Any = [np.asarray(lowerCAmelCase__ )] # convert to mel spectrogram, truncate and pad if needed. lowerCAmelCase_ : Optional[Any] = [ self._get_input_mel(lowerCAmelCase__ ,max_length if max_length else self.nb_max_samples ,lowerCAmelCase__ ,lowerCAmelCase__ ) for waveform in raw_speech ] lowerCAmelCase_ : str = [] lowerCAmelCase_ : str = [] for mel, longer in padded_inputs: input_mel.append(lowerCAmelCase__ ) is_longer.append(lowerCAmelCase__ ) if truncation == "fusion" and sum(lowerCAmelCase__ ) == 0: # if no audio is longer than 10s, then randomly select one audio to be longer lowerCAmelCase_ : Any = np.random.randint(0 ,len(lowerCAmelCase__ ) ) lowerCAmelCase_ : Dict = True if isinstance(input_mel[0] ,lowerCAmelCase__ ): lowerCAmelCase_ : Optional[int] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for feature in input_mel] # is_longer is a list of bool lowerCAmelCase_ : List[Any] = [[longer] for longer in is_longer] lowerCAmelCase_ : Optional[Any] = {"input_features": input_mel, "is_longer": is_longer} lowerCAmelCase_ : Dict = BatchFeature(lowerCAmelCase__ ) if return_tensors is not None: lowerCAmelCase_ : List[str] = input_features.convert_to_tensors(lowerCAmelCase__ ) return input_features
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_lowercase : int = [0, 2, 4, 6, 8] _lowercase : int = [1, 3, 5, 7, 9] def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): if remaining_length == 0: if digits[0] == 0 or digits[-1] == 0: return 0 for i in range(length // 2 - 1 , -1 , -1): remainder += digits[i] + digits[length - i - 1] if remainder % 2 == 0: return 0 remainder //= 10 return 1 if remaining_length == 1: if remainder % 2 == 0: return 0 lowerCAmelCase_ : Tuple = 0 for digit in range(10): lowerCAmelCase_ : Optional[int] = digit result += reversible_numbers( 0 , (remainder + 2 * digit) // 10 , snake_case__ , snake_case__) return result lowerCAmelCase_ : str = 0 for digita in range(10): lowerCAmelCase_ : int = digita if (remainder + digita) % 2 == 0: lowerCAmelCase_ : str = ODD_DIGITS else: lowerCAmelCase_ : str = EVEN_DIGITS for digita in other_parity_digits: lowerCAmelCase_ : Dict = digita result += reversible_numbers( remaining_length - 2 , (remainder + digita + digita) // 10 , snake_case__ , snake_case__ , ) return result def UpperCamelCase ( snake_case__ = 9): lowerCAmelCase_ : Tuple = 0 for length in range(1 , max_power + 1): result += reversible_numbers(snake_case__ , 0 , [0] * length , snake_case__) return result if __name__ == "__main__": print(f"{solution() = }")
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from multiprocessing import Lock, Pipe, Process # lock used to ensure that two processes do not access a pipe at the same time _lowercase = Lock() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): global process_lock # we perform n swaps since after n swaps we know we are sorted # we *could* stop early if we are sorted already, but it takes as long to # find out we are sorted as it does to sort the list with this algorithm for i in range(0 , 10): if (i + position) % 2 == 0 and r_send is not None: # send your value to your right neighbor process_lock.acquire() r_send[1].send(snake_case__) process_lock.release() # receive your right neighbor's value process_lock.acquire() lowerCAmelCase_ : Optional[Any] = rr_cv[0].recv() process_lock.release() # take the lower value since you are on the left lowerCAmelCase_ : Any = min(snake_case__ , snake_case__) elif (i + position) % 2 != 0 and l_send is not None: # send your value to your left neighbor process_lock.acquire() l_send[1].send(snake_case__) process_lock.release() # receive your left neighbor's value process_lock.acquire() lowerCAmelCase_ : str = lr_cv[0].recv() process_lock.release() # take the higher value since you are on the right lowerCAmelCase_ : Dict = max(snake_case__ , snake_case__) # after all swaps are performed, send the values back to main result_pipe[1].send(snake_case__) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Union[str, Any] = [] lowerCAmelCase_ : int = [] # initialize the list of pipes where the values will be retrieved for _ in arr: result_pipe.append(Pipe()) # creates the processes # the first and last process only have one neighbor so they are made outside # of the loop lowerCAmelCase_ : Tuple = Pipe() lowerCAmelCase_ : Optional[int] = Pipe() process_array_.append( Process( target=snake_case__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , )) lowerCAmelCase_ : int = temp_rs lowerCAmelCase_ : List[Any] = temp_rr for i in range(1 , len(snake_case__) - 1): lowerCAmelCase_ : Dict = Pipe() lowerCAmelCase_ : List[str] = Pipe() process_array_.append( Process( target=snake_case__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , )) lowerCAmelCase_ : Dict = temp_rs lowerCAmelCase_ : Optional[Any] = temp_rr process_array_.append( Process( target=snake_case__ , args=( len(snake_case__) - 1, arr[len(snake_case__) - 1], temp_ls, None, temp_lr, None, result_pipe[len(snake_case__) - 1], ) , )) # start the processes for p in process_array_: p.start() # wait for the processes to end and write their values to the list for p in range(0 , len(snake_case__)): lowerCAmelCase_ : Union[str, Any] = result_pipe[p][0].recv() process_array_[p].join() return arr def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = list(range(10 , 0 , -1)) print("Initial List") print(*snake_case__) lowerCAmelCase_ : Tuple = odd_even_transposition(snake_case__) print("Sorted List\n") print(*snake_case__) if __name__ == "__main__": main()
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import random import torch from huggingface_hub import HfApi from diffusers import UNetaDModel _lowercase = HfApi() _lowercase = {} # fmt: off _lowercase = torch.tensor([ -0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467, 1.2_342, -2.2_485, 0.4_636, 0.8_076, -0.7_991, 0.3_969, 0.8_498, 0.9_189, -1.8_887, -3.3_522, 0.7_639, 0.2_040, 0.6_271, -2.7_148, -1.6_316, 3.0_839, 0.3_186, 0.2_721, -0.9_759, -1.2_461, 2.6_257, 1.3_557 ]) _lowercase = torch.tensor([ -2.3_639, -2.5_344, 0.0_054, -0.6_674, 1.5_990, 1.0_158, 0.3_124, -2.1_436, 1.8_795, -2.5_429, -0.1_566, -0.3_973, 1.2_490, 2.6_447, 1.2_283, -0.5_208, -2.8_154, -3.5_119, 2.3_838, 1.2_033, 1.7_201, -2.1_256, -1.4_576, 2.7_948, 2.4_204, -0.9_752, -1.2_546, 0.8_027, 3.2_758, 3.1_365 ]) _lowercase = torch.tensor([ -0.6_531, -0.6_891, -0.3_172, -0.5_375, -0.9_140, -0.5_367, -0.1_175, -0.7_869, -0.3_808, -0.4_513, -0.2_098, -0.0_083, 0.3_183, 0.5_140, 0.2_247, -0.1_304, -0.1_302, -0.2_802, -0.2_084, -0.2_025, -0.4_967, -0.4_873, -0.0_861, 0.6_925, 0.0_250, 0.1_290, -0.1_543, 0.6_316, 1.0_460, 1.4_943 ]) _lowercase = torch.tensor([ 0.0_911, 0.1_107, 0.0_182, 0.0_435, -0.0_805, -0.0_608, 0.0_381, 0.2_172, -0.0_280, 0.1_327, -0.0_299, -0.0_255, -0.0_050, -0.1_170, -0.1_046, 0.0_309, 0.1_367, 0.1_728, -0.0_533, -0.0_748, -0.0_534, 0.1_624, 0.0_384, -0.1_805, -0.0_707, 0.0_642, 0.0_220, -0.0_134, -0.1_333, -0.1_505 ]) _lowercase = torch.tensor([ 0.1_321, 0.1_337, 0.0_440, 0.0_622, -0.0_591, -0.0_370, 0.0_503, 0.2_133, -0.0_177, 0.1_415, -0.0_116, -0.0_112, 0.0_044, -0.0_980, -0.0_789, 0.0_395, 0.1_502, 0.1_785, -0.0_488, -0.0_514, -0.0_404, 0.1_539, 0.0_454, -0.1_559, -0.0_665, 0.0_659, 0.0_383, -0.0_005, -0.1_266, -0.1_386 ]) _lowercase = torch.tensor([ 0.1_154, 0.1_218, 0.0_307, 0.0_526, -0.0_711, -0.0_541, 0.0_366, 0.2_078, -0.0_267, 0.1_317, -0.0_226, -0.0_193, -0.0_014, -0.1_055, -0.0_902, 0.0_330, 0.1_391, 0.1_709, -0.0_562, -0.0_693, -0.0_560, 0.1_482, 0.0_381, -0.1_683, -0.0_681, 0.0_661, 0.0_331, -0.0_046, -0.1_268, -0.1_431 ]) _lowercase = torch.tensor([ 0.1_192, 0.1_240, 0.0_414, 0.0_606, -0.0_557, -0.0_412, 0.0_430, 0.2_042, -0.0_200, 0.1_385, -0.0_115, -0.0_132, 0.0_017, -0.0_965, -0.0_802, 0.0_398, 0.1_433, 0.1_747, -0.0_458, -0.0_533, -0.0_407, 0.1_545, 0.0_419, -0.1_574, -0.0_645, 0.0_626, 0.0_341, -0.0_010, -0.1_199, -0.1_390 ]) _lowercase = torch.tensor([ 0.1_075, 0.1_074, 0.0_205, 0.0_431, -0.0_774, -0.0_607, 0.0_298, 0.2_042, -0.0_320, 0.1_267, -0.0_281, -0.0_250, -0.0_064, -0.1_091, -0.0_946, 0.0_290, 0.1_328, 0.1_650, -0.0_580, -0.0_738, -0.0_586, 0.1_440, 0.0_337, -0.1_746, -0.0_712, 0.0_605, 0.0_250, -0.0_099, -0.1_316, -0.1_473 ]) _lowercase = torch.tensor([ -1.4_572, -2.0_481, -0.0_414, -0.6_005, 1.4_136, 0.5_848, 0.4_028, -2.7_330, 1.2_212, -2.1_228, 0.2_155, 0.4_039, 0.7_662, 2.0_535, 0.7_477, -0.3_243, -2.1_758, -2.7_648, 1.6_947, 0.7_026, 1.2_338, -1.6_078, -0.8_682, 2.2_810, 1.8_574, -0.5_718, -0.5_586, -0.0_186, 2.3_415, 2.1_251]) _lowercase = torch.tensor([ -1.3_690, -1.9_720, -0.4_090, -0.6_966, 1.4_660, 0.9_938, -0.1_385, -2.7_324, 0.7_736, -1.8_917, 0.2_923, 0.4_293, 0.1_693, 1.4_112, 1.1_887, -0.3_181, -2.2_160, -2.6_381, 1.3_170, 0.8_163, 0.9_240, -1.6_544, -0.6_099, 2.5_259, 1.6_430, -0.9_090, -0.9_392, -0.0_126, 2.4_268, 2.3_266 ]) _lowercase = torch.tensor([ -1.3_525, -1.9_628, -0.3_956, -0.6_860, 1.4_664, 1.0_014, -0.1_259, -2.7_212, 0.7_772, -1.8_811, 0.2_996, 0.4_388, 0.1_704, 1.4_029, 1.1_701, -0.3_027, -2.2_053, -2.6_287, 1.3_350, 0.8_131, 0.9_274, -1.6_292, -0.6_098, 2.5_131, 1.6_505, -0.8_958, -0.9_298, -0.0_151, 2.4_257, 2.3_355 ]) _lowercase = torch.tensor([ -2.0_585, -2.7_897, -0.2_850, -0.8_940, 1.9_052, 0.5_702, 0.6_345, -3.8_959, 1.5_932, -3.2_319, 0.1_974, 0.0_287, 1.7_566, 2.6_543, 0.8_387, -0.5_351, -3.2_736, -4.3_375, 2.9_029, 1.6_390, 1.4_640, -2.1_701, -1.9_013, 2.9_341, 3.4_981, -0.6_255, -1.1_644, -0.1_591, 3.7_097, 3.2_066 ]) _lowercase = torch.tensor([ -2.3_139, -2.5_594, -0.0_197, -0.6_785, 1.7_001, 1.1_606, 0.3_075, -2.1_740, 1.8_071, -2.5_630, -0.0_926, -0.3_811, 1.2_116, 2.6_246, 1.2_731, -0.5_398, -2.8_153, -3.6_140, 2.3_893, 1.3_262, 1.6_258, -2.1_856, -1.3_267, 2.8_395, 2.3_779, -1.0_623, -1.2_468, 0.8_959, 3.3_367, 3.2_243 ]) _lowercase = torch.tensor([ -2.0_628, -2.7_667, -0.2_089, -0.8_263, 2.0_539, 0.5_992, 0.6_495, -3.8_336, 1.6_025, -3.2_817, 0.1_721, -0.0_633, 1.7_516, 2.7_039, 0.8_100, -0.5_908, -3.2_113, -4.4_343, 2.9_257, 1.3_632, 1.5_562, -2.1_489, -1.9_894, 3.0_560, 3.3_396, -0.7_328, -1.0_417, 0.0_383, 3.7_093, 3.2_343 ]) _lowercase = torch.tensor([ -1.4_574, -2.0_569, -0.0_473, -0.6_117, 1.4_018, 0.5_769, 0.4_129, -2.7_344, 1.2_241, -2.1_397, 0.2_000, 0.3_937, 0.7_616, 2.0_453, 0.7_324, -0.3_391, -2.1_746, -2.7_744, 1.6_963, 0.6_921, 1.2_187, -1.6_172, -0.8_877, 2.2_439, 1.8_471, -0.5_839, -0.5_605, -0.0_464, 2.3_250, 2.1_219 ]) # fmt: on _lowercase = api.list_models(filter='''diffusers''') for mod in models: if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256": _lowercase = '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1] print(f"Started running {mod.modelId}!!!") if mod.modelId.startswith('''CompVis'''): _lowercase = UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''') else: _lowercase = UNetaDModel.from_pretrained(local_checkpoint) torch.manual_seed(0) random.seed(0) _lowercase = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size) _lowercase = torch.tensor([10] * noise.shape[0]) with torch.no_grad(): _lowercase = model(noise, time_step).sample assert torch.allclose( logits[0, 0, 0, :30], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1E-3 ) print(f"{mod.modelId} has passed successfully!!!")
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from typing import Any def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ): _validation( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) # Creates data structures and fill initial step lowerCAmelCase_ : dict = {} lowerCAmelCase_ : dict = {} for state in states_space: lowerCAmelCase_ : List[Any] = observations_space[0] lowerCAmelCase_ : int = ( initial_probabilities[state] * emission_probabilities[state][observation] ) lowerCAmelCase_ : Dict = None # Fills the data structure with the probabilities of # different transitions and pointers to previous states for o in range(1 , len(snake_case__)): lowerCAmelCase_ : List[Any] = observations_space[o] lowerCAmelCase_ : Optional[Any] = observations_space[o - 1] for state in states_space: # Calculates the argmax for probability function lowerCAmelCase_ : List[Any] = "" lowerCAmelCase_ : Tuple = -1 for k_state in states_space: lowerCAmelCase_ : int = ( probabilities[(k_state, prior_observation)] * transition_probabilities[k_state][state] * emission_probabilities[state][observation] ) if probability > max_probability: lowerCAmelCase_ : List[str] = probability lowerCAmelCase_ : Optional[Any] = k_state # Update probabilities and pointers dicts lowerCAmelCase_ : Union[str, Any] = ( probabilities[(arg_max, prior_observation)] * transition_probabilities[arg_max][state] * emission_probabilities[state][observation] ) lowerCAmelCase_ : Any = arg_max # The final observation lowerCAmelCase_ : List[Any] = observations_space[len(snake_case__) - 1] # argmax for given final observation lowerCAmelCase_ : List[str] = "" lowerCAmelCase_ : List[str] = -1 for k_state in states_space: lowerCAmelCase_ : List[str] = probabilities[(k_state, final_observation)] if probability > max_probability: lowerCAmelCase_ : List[str] = probability lowerCAmelCase_ : Tuple = k_state lowerCAmelCase_ : str = arg_max # Process pointers backwards lowerCAmelCase_ : int = last_state lowerCAmelCase_ : int = [] for o in range(len(snake_case__) - 1 , -1 , -1): result.append(snake_case__) lowerCAmelCase_ : Optional[Any] = pointers[previous, observations_space[o]] result.reverse() return result def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ): _validate_not_empty( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) _validate_lists(snake_case__ , snake_case__) _validate_dicts( snake_case__ , snake_case__ , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ): if not all( [ observations_space, states_space, initial_probabilities, transition_probabilities, emission_probabilities, ]): raise ValueError("There's an empty parameter") def UpperCamelCase ( snake_case__ , snake_case__): _validate_list(snake_case__ , "observations_space") _validate_list(snake_case__ , "states_space") def UpperCamelCase ( snake_case__ , snake_case__): if not isinstance(_object , snake_case__): lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list''' raise ValueError(snake_case__) else: for x in _object: if not isinstance(snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list of strings''' raise ValueError(snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ): _validate_dict(snake_case__ , "initial_probabilities" , snake_case__) _validate_nested_dict(snake_case__ , "transition_probabilities") _validate_nested_dict(snake_case__ , "emission_probabilities") def UpperCamelCase ( snake_case__ , snake_case__): _validate_dict(_object , snake_case__ , snake_case__) for x in _object.values(): _validate_dict(snake_case__ , snake_case__ , snake_case__ , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = False): if not isinstance(_object , snake_case__): lowerCAmelCase_ : List[str] = F'''{var_name} must be a dict''' raise ValueError(snake_case__) if not all(isinstance(snake_case__ , snake_case__) for x in _object): lowerCAmelCase_ : Dict = F'''{var_name} all keys must be strings''' raise ValueError(snake_case__) if not all(isinstance(snake_case__ , snake_case__) for x in _object.values()): lowerCAmelCase_ : Union[str, Any] = "nested dictionary " if nested else "" lowerCAmelCase_ : Any = F'''{var_name} {nested_text}all values must be {value_type.__name__}''' raise ValueError(snake_case__) if __name__ == "__main__": from doctest import testmod testmod()
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def UpperCamelCase ( snake_case__ , snake_case__): while a != 0: lowerCAmelCase_ : str = b % a, a return b def UpperCamelCase ( snake_case__ , snake_case__): if gcd(snake_case__ , snake_case__) != 1: lowerCAmelCase_ : Any = F'''mod inverse of {a!r} and {m!r} does not exist''' raise ValueError(snake_case__) lowerCAmelCase_ : Dict = 1, 0, a lowerCAmelCase_ : Dict = 0, 1, m while va != 0: lowerCAmelCase_ : List[Any] = ua // va lowerCAmelCase_ : List[Any] = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va return ua % m
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor from ..utils import is_datasets_available from .base import PipelineTool if is_datasets_available(): from datasets import load_dataset class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = 'microsoft/speecht5_tts' UpperCamelCase_ = ( 'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the ' 'text to read (in English) and returns a waveform object containing the sound.' ) UpperCamelCase_ = 'text_reader' UpperCamelCase_ = SpeechTaProcessor UpperCamelCase_ = SpeechTaForTextToSpeech UpperCamelCase_ = SpeechTaHifiGan UpperCamelCase_ = ['text'] UpperCamelCase_ = ['audio'] def UpperCAmelCase_ ( self : Dict ) -> Any: '''simple docstring''' if self.post_processor is None: lowerCAmelCase_ : Any = "microsoft/speecht5_hifigan" super().setup() def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Any = self.pre_processor(text=lowerCAmelCase__ ,return_tensors="pt" ,truncation=lowerCAmelCase__ ) if speaker_embeddings is None: if not is_datasets_available(): raise ImportError("Datasets needs to be installed if not passing speaker embeddings." ) lowerCAmelCase_ : str = load_dataset("Matthijs/cmu-arctic-xvectors" ,split="validation" ) lowerCAmelCase_ : List[Any] = torch.tensor(embeddings_dataset[73_05]["xvector"] ).unsqueeze(0 ) return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings} def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]: '''simple docstring''' with torch.no_grad(): return self.model.generate_speech(**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : str ) -> Any: '''simple docstring''' with torch.no_grad(): return self.post_processor(lowerCAmelCase__ ).cpu().detach()
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from graphs.minimum_spanning_tree_kruskal import kruskal def UpperCamelCase ( ): lowerCAmelCase_ : Union[str, Any] = 9 lowerCAmelCase_ : Dict = [ [0, 1, 4], [0, 7, 8], [1, 2, 8], [7, 8, 7], [7, 6, 1], [2, 8, 2], [8, 6, 6], [2, 3, 7], [2, 5, 4], [6, 5, 2], [3, 5, 14], [3, 4, 9], [5, 4, 10], [1, 7, 11], ] lowerCAmelCase_ : Union[str, Any] = kruskal(snake_case__ , snake_case__) lowerCAmelCase_ : Union[str, Any] = [ [7, 6, 1], [2, 8, 2], [6, 5, 2], [0, 1, 4], [2, 5, 4], [2, 3, 7], [0, 7, 8], [3, 4, 9], ] assert sorted(snake_case__) == sorted(snake_case__)
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import argparse import collections import json import os import re import string import sys import numpy as np _lowercase = re.compile(r'''\b(a|an|the)\b''', re.UNICODE) _lowercase = None def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0.") parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file.") parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions.") parser.add_argument( "--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout).") parser.add_argument( "--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer.") parser.add_argument( "--na-prob-thresh" , "-t" , type=snake_case__ , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , ) parser.add_argument( "--out-image-dir" , "-p" , metavar="out_images" , default=snake_case__ , help="Save precision-recall curves to directory.") parser.add_argument("--verbose" , "-v" , action="store_true") if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() def UpperCamelCase ( snake_case__): lowerCAmelCase_ : str = {} for article in dataset: for p in article["paragraphs"]: for qa in p["qas"]: lowerCAmelCase_ : Dict = bool(qa["answers"]["text"]) return qid_to_has_ans def UpperCamelCase ( snake_case__): def remove_articles(snake_case__): return ARTICLES_REGEX.sub(" " , snake_case__) def white_space_fix(snake_case__): return " ".join(text.split()) def remove_punc(snake_case__): lowerCAmelCase_ : Optional[int] = set(string.punctuation) return "".join(ch for ch in text if ch not in exclude) def lower(snake_case__): return text.lower() return white_space_fix(remove_articles(remove_punc(lower(snake_case__)))) def UpperCamelCase ( snake_case__): if not s: return [] return normalize_answer(snake_case__).split() def UpperCamelCase ( snake_case__ , snake_case__): return int(normalize_answer(snake_case__) == normalize_answer(snake_case__)) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = get_tokens(snake_case__) lowerCAmelCase_ : Union[str, Any] = get_tokens(snake_case__) lowerCAmelCase_ : Any = collections.Counter(snake_case__) & collections.Counter(snake_case__) lowerCAmelCase_ : Dict = sum(common.values()) if len(snake_case__) == 0 or len(snake_case__) == 0: # If either is no-answer, then F1 is 1 if they agree, 0 otherwise return int(gold_toks == pred_toks) if num_same == 0: return 0 lowerCAmelCase_ : List[Any] = 1.0 * num_same / len(snake_case__) lowerCAmelCase_ : int = 1.0 * num_same / len(snake_case__) lowerCAmelCase_ : List[Any] = (2 * precision * recall) / (precision + recall) return fa def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Tuple = {} lowerCAmelCase_ : int = {} for article in dataset: for p in article["paragraphs"]: for qa in p["qas"]: lowerCAmelCase_ : int = qa["id"] lowerCAmelCase_ : Any = [t for t in qa["answers"]["text"] if normalize_answer(snake_case__)] if not gold_answers: # For unanswerable questions, only correct answer is empty string lowerCAmelCase_ : Any = [""] if qid not in preds: print(F'''Missing prediction for {qid}''') continue lowerCAmelCase_ : Tuple = preds[qid] # Take max over all gold answers lowerCAmelCase_ : Any = max(compute_exact(snake_case__ , snake_case__) for a in gold_answers) lowerCAmelCase_ : Optional[Any] = max(compute_fa(snake_case__ , snake_case__) for a in gold_answers) return exact_scores, fa_scores def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Dict = {} for qid, s in scores.items(): lowerCAmelCase_ : List[Any] = na_probs[qid] > na_prob_thresh if pred_na: lowerCAmelCase_ : List[str] = float(not qid_to_has_ans[qid]) else: lowerCAmelCase_ : Union[str, Any] = s return new_scores def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None): if not qid_list: lowerCAmelCase_ : Any = len(snake_case__) return collections.OrderedDict( [ ("exact", 100.0 * sum(exact_scores.values()) / total), ("f1", 100.0 * sum(fa_scores.values()) / total), ("total", total), ]) else: lowerCAmelCase_ : Tuple = len(snake_case__) return collections.OrderedDict( [ ("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total), ("f1", 100.0 * sum(fa_scores[k] for k in qid_list) / total), ("total", total), ]) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): for k in new_eval: lowerCAmelCase_ : Union[str, Any] = new_eval[k] def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): plt.step(snake_case__ , snake_case__ , color="b" , alpha=0.2 , where="post") plt.fill_between(snake_case__ , snake_case__ , step="post" , alpha=0.2 , color="b") plt.xlabel("Recall") plt.ylabel("Precision") plt.xlim([0.0, 1.05]) plt.ylim([0.0, 1.05]) plt.title(snake_case__) plt.savefig(snake_case__) plt.clf() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__=None , snake_case__=None): lowerCAmelCase_ : List[Any] = sorted(snake_case__ , key=lambda snake_case__: na_probs[k]) lowerCAmelCase_ : Dict = 0.0 lowerCAmelCase_ : int = 1.0 lowerCAmelCase_ : List[str] = 0.0 lowerCAmelCase_ : Tuple = [1.0] lowerCAmelCase_ : Tuple = [0.0] lowerCAmelCase_ : Dict = 0.0 for i, qid in enumerate(snake_case__): if qid_to_has_ans[qid]: true_pos += scores[qid] lowerCAmelCase_ : str = true_pos / float(i + 1) lowerCAmelCase_ : Union[str, Any] = true_pos / float(snake_case__) if i == len(snake_case__) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]: # i.e., if we can put a threshold after this point avg_prec += cur_p * (cur_r - recalls[-1]) precisions.append(snake_case__) recalls.append(snake_case__) if out_image: plot_pr_curve(snake_case__ , snake_case__ , snake_case__ , snake_case__) return {"ap": 100.0 * avg_prec} def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): if out_image_dir and not os.path.exists(snake_case__): os.makedirs(snake_case__) lowerCAmelCase_ : Any = sum(1 for v in qid_to_has_ans.values() if v) if num_true_pos == 0: return lowerCAmelCase_ : Any = make_precision_recall_eval( snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_exact.png") , title="Precision-Recall curve for Exact Match score" , ) lowerCAmelCase_ : Dict = make_precision_recall_eval( snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_f1.png") , title="Precision-Recall curve for F1 score" , ) lowerCAmelCase_ : Dict = {k: float(snake_case__) for k, v in qid_to_has_ans.items()} lowerCAmelCase_ : str = make_precision_recall_eval( snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_oracle.png") , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , ) merge_eval(snake_case__ , snake_case__ , "pr_exact") merge_eval(snake_case__ , snake_case__ , "pr_f1") merge_eval(snake_case__ , snake_case__ , "pr_oracle") def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): if not qid_list: return lowerCAmelCase_ : Optional[Any] = [na_probs[k] for k in qid_list] lowerCAmelCase_ : Dict = np.ones_like(snake_case__) / float(len(snake_case__)) plt.hist(snake_case__ , weights=snake_case__ , bins=20 , range=(0.0, 1.0)) plt.xlabel("Model probability of no-answer") plt.ylabel("Proportion of dataset") plt.title(F'''Histogram of no-answer probability: {name}''') plt.savefig(os.path.join(snake_case__ , F'''na_prob_hist_{name}.png''')) plt.clf() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Dict = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]) lowerCAmelCase_ : str = num_no_ans lowerCAmelCase_ : List[str] = cur_score lowerCAmelCase_ : List[Any] = 0.0 lowerCAmelCase_ : str = sorted(snake_case__ , key=lambda snake_case__: na_probs[k]) for i, qid in enumerate(snake_case__): if qid not in scores: continue if qid_to_has_ans[qid]: lowerCAmelCase_ : Union[str, Any] = scores[qid] else: if preds[qid]: lowerCAmelCase_ : List[Any] = -1 else: lowerCAmelCase_ : List[str] = 0 cur_score += diff if cur_score > best_score: lowerCAmelCase_ : Optional[Any] = cur_score lowerCAmelCase_ : Optional[int] = na_probs[qid] return 100.0 * best_score / len(snake_case__), best_thresh def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ , lowerCAmelCase_ : Dict = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = best_exact lowerCAmelCase_ : List[str] = exact_thresh lowerCAmelCase_ : Any = best_fa lowerCAmelCase_ : List[str] = fa_thresh def UpperCamelCase ( ): with open(OPTS.data_file) as f: lowerCAmelCase_ : Optional[int] = json.load(snake_case__) lowerCAmelCase_ : List[Any] = dataset_json["data"] with open(OPTS.pred_file) as f: lowerCAmelCase_ : int = json.load(snake_case__) if OPTS.na_prob_file: with open(OPTS.na_prob_file) as f: lowerCAmelCase_ : Optional[int] = json.load(snake_case__) else: lowerCAmelCase_ : List[Any] = {k: 0.0 for k in preds} lowerCAmelCase_ : Tuple = make_qid_to_has_ans(snake_case__) # maps qid to True/False lowerCAmelCase_ : Any = [k for k, v in qid_to_has_ans.items() if v] lowerCAmelCase_ : List[str] = [k for k, v in qid_to_has_ans.items() if not v] lowerCAmelCase_ , lowerCAmelCase_ : Dict = get_raw_scores(snake_case__ , snake_case__) lowerCAmelCase_ : str = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh) lowerCAmelCase_ : Dict = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh) lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__) if has_ans_qids: lowerCAmelCase_ : str = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__) merge_eval(snake_case__ , snake_case__ , "HasAns") if no_ans_qids: lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__) merge_eval(snake_case__ , snake_case__ , "NoAns") if OPTS.na_prob_file: find_all_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__) if OPTS.na_prob_file and OPTS.out_image_dir: run_precision_recall_analysis(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , OPTS.out_image_dir) histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "hasAns") histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "noAns") if OPTS.out_file: with open(OPTS.out_file , "w") as f: json.dump(snake_case__ , snake_case__) else: print(json.dumps(snake_case__ , indent=2)) if __name__ == "__main__": _lowercase = parse_args() if OPTS.out_image_dir: import matplotlib matplotlib.use('''Agg''') import matplotlib.pyplot as plt main()
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from __future__ import annotations from collections import namedtuple def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = namedtuple("result" , "name value") if (voltage, current, power).count(0) != 1: raise ValueError("Only one argument must be 0") elif power < 0: raise ValueError( "Power cannot be negative in any electrical/electronics system") elif voltage == 0: return result("voltage" , power / current) elif current == 0: return result("current" , power / voltage) elif power == 0: return result("power" , float(round(abs(voltage * current) , 2))) else: raise ValueError("Exactly one argument must be 0") if __name__ == "__main__": import doctest doctest.testmod()
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from math import sqrt def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[int] = 0 for i in range(1 , int(sqrt(snake_case__) + 1)): if n % i == 0 and i != sqrt(snake_case__): total += i + n // i elif i == sqrt(snake_case__): total += i return total - n def UpperCamelCase ( snake_case__ = 1_00_00): lowerCAmelCase_ : int = sum( i for i in range(1 , snake_case__) if sum_of_divisors(sum_of_divisors(snake_case__)) == i and sum_of_divisors(snake_case__) != i) return total if __name__ == "__main__": print(solution(int(str(input()).strip())))
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import gc import random import unittest import numpy as np import torch from transformers import XLMRobertaTokenizer from diffusers import ( AltDiffusionImgaImgPipeline, AutoencoderKL, PNDMScheduler, UNetaDConditionModel, ) from diffusers.image_processor import VaeImageProcessor from diffusers.pipelines.alt_diffusion.modeling_roberta_series import ( RobertaSeriesConfig, RobertaSeriesModelWithTransformation, ) from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu enable_full_determinism() class __snake_case ( unittest.TestCase ): """simple docstring""" def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]: '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() @property def UpperCAmelCase_ ( self : str ) -> str: '''simple docstring''' lowerCAmelCase_ : str = 1 lowerCAmelCase_ : List[str] = 3 lowerCAmelCase_ : List[Any] = (32, 32) lowerCAmelCase_ : Tuple = floats_tensor((batch_size, num_channels) + sizes ,rng=random.Random(0 ) ).to(lowerCAmelCase__ ) return image @property def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[str]: '''simple docstring''' torch.manual_seed(0 ) lowerCAmelCase_ : int = UNetaDConditionModel( block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,cross_attention_dim=32 ,) return model @property def UpperCAmelCase_ ( self : Tuple ) -> Tuple: '''simple docstring''' torch.manual_seed(0 ) lowerCAmelCase_ : List[Any] = AutoencoderKL( block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,latent_channels=4 ,) return model @property def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]: '''simple docstring''' torch.manual_seed(0 ) lowerCAmelCase_ : int = RobertaSeriesConfig( hidden_size=32 ,project_dim=32 ,intermediate_size=37 ,layer_norm_eps=1e-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=50_06 ,) return RobertaSeriesModelWithTransformation(lowerCAmelCase__ ) @property def UpperCAmelCase_ ( self : Dict ) -> Optional[int]: '''simple docstring''' def extract(*lowerCAmelCase__ : Optional[Any] ,**lowerCAmelCase__ : str ): class __snake_case : """simple docstring""" def __init__( self : Union[str, Any] ) -> int: '''simple docstring''' lowerCAmelCase_ : int = torch.ones([0] ) def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : List[Any] ) -> Tuple: '''simple docstring''' self.pixel_values.to(lowerCAmelCase__ ) return self return Out() return extract def UpperCAmelCase_ ( self : Dict ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : str = "cpu" # ensure determinism for the device-dependent torch.Generator lowerCAmelCase_ : Optional[Any] = self.dummy_cond_unet lowerCAmelCase_ : str = PNDMScheduler(skip_prk_steps=lowerCAmelCase__ ) lowerCAmelCase_ : str = self.dummy_vae lowerCAmelCase_ : List[Any] = self.dummy_text_encoder lowerCAmelCase_ : Union[str, Any] = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" ) lowerCAmelCase_ : List[Any] = 77 lowerCAmelCase_ : Union[str, Any] = self.dummy_image.to(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = init_image / 2 + 0.5 # make sure here that pndm scheduler skips prk lowerCAmelCase_ : str = AltDiffusionImgaImgPipeline( unet=lowerCAmelCase__ ,scheduler=lowerCAmelCase__ ,vae=lowerCAmelCase__ ,text_encoder=lowerCAmelCase__ ,tokenizer=lowerCAmelCase__ ,safety_checker=lowerCAmelCase__ ,feature_extractor=self.dummy_extractor ,) lowerCAmelCase_ : List[str] = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor ,do_normalize=lowerCAmelCase__ ) lowerCAmelCase_ : Any = alt_pipe.to(lowerCAmelCase__ ) alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = "A painting of a squirrel eating a burger" lowerCAmelCase_ : List[str] = torch.Generator(device=lowerCAmelCase__ ).manual_seed(0 ) lowerCAmelCase_ : Dict = alt_pipe( [prompt] ,generator=lowerCAmelCase__ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="np" ,image=lowerCAmelCase__ ,) lowerCAmelCase_ : Union[str, Any] = output.images lowerCAmelCase_ : str = torch.Generator(device=lowerCAmelCase__ ).manual_seed(0 ) lowerCAmelCase_ : Optional[Any] = alt_pipe( [prompt] ,generator=lowerCAmelCase__ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="np" ,image=lowerCAmelCase__ ,return_dict=lowerCAmelCase__ ,)[0] lowerCAmelCase_ : List[Any] = image[0, -3:, -3:, -1] lowerCAmelCase_ : Tuple = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 32, 32, 3) lowerCAmelCase_ : List[Any] = np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3 assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5e-3 @unittest.skipIf(torch_device != "cuda" ,"This test requires a GPU" ) def UpperCAmelCase_ ( self : Union[str, Any] ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = self.dummy_cond_unet lowerCAmelCase_ : Optional[int] = PNDMScheduler(skip_prk_steps=lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = self.dummy_vae lowerCAmelCase_ : Any = self.dummy_text_encoder lowerCAmelCase_ : Any = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" ) lowerCAmelCase_ : Tuple = 77 lowerCAmelCase_ : Optional[int] = self.dummy_image.to(lowerCAmelCase__ ) # put models in fp16 lowerCAmelCase_ : Dict = unet.half() lowerCAmelCase_ : Any = vae.half() lowerCAmelCase_ : Union[str, Any] = bert.half() # make sure here that pndm scheduler skips prk lowerCAmelCase_ : List[str] = AltDiffusionImgaImgPipeline( unet=lowerCAmelCase__ ,scheduler=lowerCAmelCase__ ,vae=lowerCAmelCase__ ,text_encoder=lowerCAmelCase__ ,tokenizer=lowerCAmelCase__ ,safety_checker=lowerCAmelCase__ ,feature_extractor=self.dummy_extractor ,) lowerCAmelCase_ : Tuple = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor ,do_normalize=lowerCAmelCase__ ) lowerCAmelCase_ : List[Any] = alt_pipe.to(lowerCAmelCase__ ) alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__ ) lowerCAmelCase_ : Any = "A painting of a squirrel eating a burger" lowerCAmelCase_ : str = torch.manual_seed(0 ) lowerCAmelCase_ : List[str] = alt_pipe( [prompt] ,generator=lowerCAmelCase__ ,num_inference_steps=2 ,output_type="np" ,image=lowerCAmelCase__ ,).images assert image.shape == (1, 32, 32, 3) @unittest.skipIf(torch_device != "cuda" ,"This test requires a GPU" ) def UpperCAmelCase_ ( self : List[str] ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : Dict = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/img2img/sketch-mountains-input.jpg" ) # resize to resolution that is divisible by 8 but not 16 or 32 lowerCAmelCase_ : Dict = init_image.resize((7_60, 5_04) ) lowerCAmelCase_ : Union[str, Any] = "BAAI/AltDiffusion" lowerCAmelCase_ : str = AltDiffusionImgaImgPipeline.from_pretrained( lowerCAmelCase__ ,safety_checker=lowerCAmelCase__ ,) pipe.to(lowerCAmelCase__ ) pipe.set_progress_bar_config(disable=lowerCAmelCase__ ) pipe.enable_attention_slicing() lowerCAmelCase_ : Tuple = "A fantasy landscape, trending on artstation" lowerCAmelCase_ : List[str] = torch.manual_seed(0 ) lowerCAmelCase_ : str = pipe( prompt=lowerCAmelCase__ ,image=lowerCAmelCase__ ,strength=0.75 ,guidance_scale=7.5 ,generator=lowerCAmelCase__ ,output_type="np" ,) lowerCAmelCase_ : Union[str, Any] = output.images[0] lowerCAmelCase_ : int = image[2_55:2_58, 3_83:3_86, -1] assert image.shape == (5_04, 7_60, 3) lowerCAmelCase_ : Tuple = np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 @slow @require_torch_gpu class __snake_case ( unittest.TestCase ): """simple docstring""" def UpperCAmelCase_ ( self : Dict ) -> Optional[Any]: '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : Dict = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/img2img/sketch-mountains-input.jpg" ) lowerCAmelCase_ : str = init_image.resize((7_68, 5_12) ) lowerCAmelCase_ : Optional[int] = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy" ) lowerCAmelCase_ : List[Any] = "BAAI/AltDiffusion" lowerCAmelCase_ : Union[str, Any] = AltDiffusionImgaImgPipeline.from_pretrained( lowerCAmelCase__ ,safety_checker=lowerCAmelCase__ ,) pipe.to(lowerCAmelCase__ ) pipe.set_progress_bar_config(disable=lowerCAmelCase__ ) pipe.enable_attention_slicing() lowerCAmelCase_ : List[str] = "A fantasy landscape, trending on artstation" lowerCAmelCase_ : Union[str, Any] = torch.manual_seed(0 ) lowerCAmelCase_ : Union[str, Any] = pipe( prompt=lowerCAmelCase__ ,image=lowerCAmelCase__ ,strength=0.75 ,guidance_scale=7.5 ,generator=lowerCAmelCase__ ,output_type="np" ,) lowerCAmelCase_ : Any = output.images[0] assert image.shape == (5_12, 7_68, 3) # img2img is flaky across GPUs even in fp32, so using MAE here assert np.abs(expected_image - image ).max() < 1e-2
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_speech_available, is_tf_available, is_torch_available, ) _lowercase = { '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''], '''processing_speech_to_text''': ['''Speech2TextProcessor'''], } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ['''Speech2TextTokenizer'''] try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ['''Speech2TextFeatureExtractor'''] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''TFSpeech2TextForConditionalGeneration''', '''TFSpeech2TextModel''', '''TFSpeech2TextPreTrainedModel''', ] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''Speech2TextForConditionalGeneration''', '''Speech2TextModel''', '''Speech2TextPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig from .processing_speech_to_text import SpeechaTextProcessor try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_speech_to_text import SpeechaTextTokenizer try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_speech_to_text import ( TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, TFSpeechaTextForConditionalGeneration, TFSpeechaTextModel, TFSpeechaTextPreTrainedModel, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_speech_to_text import ( SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, SpeechaTextForConditionalGeneration, SpeechaTextModel, SpeechaTextPreTrainedModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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0
from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import Features, Value from .base import TaskTemplate @dataclass(frozen=snake_case__ ) class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = field(default='language-modeling' , metadata={'include_in_asdict_even_if_is_default': True} ) UpperCamelCase_ = Features({'text': Value('string' )} ) UpperCamelCase_ = Features({} ) UpperCamelCase_ = 'text' @property def UpperCAmelCase_ ( self : List[Any] ) -> Dict[str, str]: '''simple docstring''' return {self.text_column: "text"}
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import json import os from functools import lru_cache from typing import List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''} _lowercase = { '''vocab_file''': { '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''', '''allenai/longformer-large-4096''': ( '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json''' ), '''allenai/longformer-large-4096-finetuned-triviaqa''': ( '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json''' ), '''allenai/longformer-base-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json''' ), '''allenai/longformer-large-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json''' ), }, '''merges_file''': { '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''', '''allenai/longformer-large-4096''': ( '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt''' ), '''allenai/longformer-large-4096-finetuned-triviaqa''': ( '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt''' ), '''allenai/longformer-base-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt''' ), '''allenai/longformer-large-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt''' ), }, } _lowercase = { '''allenai/longformer-base-4096''': 4096, '''allenai/longformer-large-4096''': 4096, '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096, '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096, '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096, } @lru_cache() # Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode def UpperCamelCase ( ): lowerCAmelCase_ : str = ( list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1)) ) lowerCAmelCase_ : Tuple = bs[:] lowerCAmelCase_ : Dict = 0 for b in range(2**8): if b not in bs: bs.append(snake_case__) cs.append(2**8 + n) n += 1 lowerCAmelCase_ : Union[str, Any] = [chr(snake_case__) for n in cs] return dict(zip(snake_case__ , snake_case__)) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[Any] = set() lowerCAmelCase_ : List[Any] = word[0] for char in word[1:]: pairs.add((prev_char, char)) lowerCAmelCase_ : Union[str, Any] = char return pairs class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = ['input_ids', 'attention_mask'] def __init__( self : str ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[Any]="replace" ,lowerCAmelCase__ : Dict="<s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : Optional[Any]="<s>" ,lowerCAmelCase__ : List[Any]="<unk>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : int="<mask>" ,lowerCAmelCase__ : Any=False ,**lowerCAmelCase__ : int ,) -> Dict: '''simple docstring''' lowerCAmelCase_ : Optional[int] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token lowerCAmelCase_ : Dict = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else pad_token # Mask token behave like a normal word, i.e. include the space before it lowerCAmelCase_ : Optional[Any] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else mask_token super().__init__( errors=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,**lowerCAmelCase__ ,) with open(lowerCAmelCase__ ,encoding="utf-8" ) as vocab_handle: lowerCAmelCase_ : List[Any] = json.load(lowerCAmelCase__ ) lowerCAmelCase_ : Dict = {v: k for k, v in self.encoder.items()} lowerCAmelCase_ : List[Any] = errors # how to handle errors in decoding lowerCAmelCase_ : Optional[Any] = bytes_to_unicode() lowerCAmelCase_ : int = {v: k for k, v in self.byte_encoder.items()} with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle: lowerCAmelCase_ : Union[str, Any] = merges_handle.read().split("\n" )[1:-1] lowerCAmelCase_ : Dict = [tuple(merge.split() ) for merge in bpe_merges] lowerCAmelCase_ : Dict = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) ) lowerCAmelCase_ : Any = {} lowerCAmelCase_ : int = add_prefix_space # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions lowerCAmelCase_ : Optional[Any] = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" ) @property def UpperCAmelCase_ ( self : Optional[int] ) -> Any: '''simple docstring''' return len(self.encoder ) def UpperCAmelCase_ ( self : Any ) -> Optional[int]: '''simple docstring''' return dict(self.encoder ,**self.added_tokens_encoder ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[str] ) -> List[Any]: '''simple docstring''' if token in self.cache: return self.cache[token] lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = get_pairs(lowerCAmelCase__ ) if not pairs: return token while True: lowerCAmelCase_ : Dict = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) ) if bigram not in self.bpe_ranks: break lowerCAmelCase_ , lowerCAmelCase_ : Dict = bigram lowerCAmelCase_ : Optional[Any] = [] lowerCAmelCase_ : Any = 0 while i < len(lowerCAmelCase__ ): try: lowerCAmelCase_ : Optional[int] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ ) except ValueError: new_word.extend(word[i:] ) break else: new_word.extend(word[i:j] ) lowerCAmelCase_ : Tuple = j if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second: new_word.append(first + second ) i += 2 else: new_word.append(word[i] ) i += 1 lowerCAmelCase_ : Optional[Any] = tuple(lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = new_word if len(lowerCAmelCase__ ) == 1: break else: lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = " ".join(lowerCAmelCase__ ) lowerCAmelCase_ : Any = word return word def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Dict = [] for token in re.findall(self.pat ,lowerCAmelCase__ ): lowerCAmelCase_ : List[str] = "".join( self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__ ).split(" " ) ) return bpe_tokens def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ) -> Tuple: '''simple docstring''' return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]: '''simple docstring''' return self.decoder.get(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Any: '''simple docstring''' lowerCAmelCase_ : Optional[int] = "".join(lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors ) return text def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' if not os.path.isdir(lowerCAmelCase__ ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return lowerCAmelCase_ : Optional[Any] = os.path.join( lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase_ : Tuple = os.path.join( lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as f: f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowerCAmelCase__ ,ensure_ascii=lowerCAmelCase__ ) + "\n" ) lowerCAmelCase_ : Tuple = 0 with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as writer: writer.write("#version: 0.2\n" ) for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda lowerCAmelCase__ : kv[1] ): if index != token_index: logger.warning( f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.''' " Please check that the tokenizer is not corrupted!" ) lowerCAmelCase_ : Optional[Any] = token_index writer.write(" ".join(lowerCAmelCase__ ) + "\n" ) index += 1 return vocab_file, merge_file def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] lowerCAmelCase_ : List[Any] = [self.cls_token_id] lowerCAmelCase_ : List[str] = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]: '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=lowerCAmelCase__ ,token_ids_a=lowerCAmelCase__ ,already_has_special_tokens=lowerCAmelCase__ ) if token_ids_a is None: return [1] + ([0] * len(lowerCAmelCase__ )) + [1] return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1] def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : List[str] = [self.sep_token_id] lowerCAmelCase_ : Tuple = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Optional[int] ) -> Any: '''simple docstring''' lowerCAmelCase_ : int = kwargs.pop("add_prefix_space" ,self.add_prefix_space ) if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__ ) > 0 and not text[0].isspace()): lowerCAmelCase_ : Union[str, Any] = " " + text return (text, kwargs)
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'''simple docstring''' import json import os from typing import Dict, List, Optional, Tuple from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = { '''vocab_file''': '''vocab.json''', '''tokenizer_config_file''': '''tokenizer_config.json''', '''merges_file''': '''merges.txt''', } _lowercase = { '''vocab_file''': { '''facebook/s2t-wav2vec2-large-en-de''': ( '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json''' ), }, '''tokenizer_config_file''': { '''facebook/s2t-wav2vec2-large-en-de''': ( '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json''' ), }, '''merges_file''': { '''facebook/s2t-wav2vec2-large-en-de''': ( '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt''' ), }, } _lowercase = '''</w>''' _lowercase = '''@@ ''' def UpperCamelCase ( snake_case__): lowerCAmelCase_ : List[str] = set() lowerCAmelCase_ : Dict = word[0] for char in word[1:]: pairs.add((prev_char, char)) lowerCAmelCase_ : Tuple = char return pairs # Speech2Text2 has no max input length _lowercase = {'''facebook/s2t-wav2vec2-large-en-de''': 1024} class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = ['input_ids', 'attention_mask'] def __init__( self : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[Any]="<s>" ,lowerCAmelCase__ : Optional[Any]="<pad>" ,lowerCAmelCase__ : Optional[int]="</s>" ,lowerCAmelCase__ : Union[str, Any]="<unk>" ,lowerCAmelCase__ : Optional[Any]=False ,lowerCAmelCase__ : Dict=None ,**lowerCAmelCase__ : Any ,) -> Tuple: '''simple docstring''' super().__init__( unk_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : List[Any] = do_lower_case with open(lowerCAmelCase__ ,encoding="utf-8" ) as vocab_handle: lowerCAmelCase_ : Any = json.load(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = {v: k for k, v in self.encoder.items()} if merges_file is None: logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.''' ) lowerCAmelCase_ : List[Any] = None lowerCAmelCase_ : str = None else: with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle: lowerCAmelCase_ : Union[str, Any] = merges_handle.read().split("\n" )[:-1] lowerCAmelCase_ : Dict = [tuple(merge.split()[:2] ) for merge in merges] lowerCAmelCase_ : Tuple = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) ) lowerCAmelCase_ : Union[str, Any] = {} @property def UpperCAmelCase_ ( self : Union[str, Any] ) -> int: '''simple docstring''' return len(self.decoder ) def UpperCAmelCase_ ( self : str ) -> Dict: '''simple docstring''' return dict(self.encoder ,**self.added_tokens_encoder ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Dict ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : Any = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,) if token in self.cache: return self.cache[token] lowerCAmelCase_ : Any = get_pairs(lowerCAmelCase__ ) if not pairs: return token while True: lowerCAmelCase_ : Optional[int] = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) ) if bigram not in self.bpe_ranks: break lowerCAmelCase_ : List[str] = bigram lowerCAmelCase_ : List[Any] = [] lowerCAmelCase_ : List[str] = 0 while i < len(lowerCAmelCase__ ): try: lowerCAmelCase_ : Union[str, Any] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ ) except ValueError: new_word.extend(word[i:] ) break else: new_word.extend(word[i:j] ) lowerCAmelCase_ : Optional[Any] = j if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second: new_word.append(first + second ) i += 2 else: new_word.append(word[i] ) i += 1 lowerCAmelCase_ : Tuple = tuple(lowerCAmelCase__ ) lowerCAmelCase_ : str = new_word if len(lowerCAmelCase__ ) == 1: break else: lowerCAmelCase_ : str = get_pairs(lowerCAmelCase__ ) lowerCAmelCase_ : List[Any] = " ".join(lowerCAmelCase__ ) if word == "\n " + BPE_TOKEN_MERGES: lowerCAmelCase_ : Union[str, Any] = "\n" + BPE_TOKEN_MERGES if word.endswith(lowerCAmelCase__ ): lowerCAmelCase_ : List[Any] = word.replace(lowerCAmelCase__ ,"" ) lowerCAmelCase_ : str = word.replace(" " ,lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = word return word def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> Dict: '''simple docstring''' if self.bpe_ranks is None: raise ValueError( "This tokenizer was instantiated without a `merges.txt` file, so" " that it can only be used for decoding, not for encoding." "Make sure to provide `merges.txt` file at instantiation to enable " "encoding." ) if self.do_lower_case: lowerCAmelCase_ : Union[str, Any] = text.lower() lowerCAmelCase_ : Optional[int] = text.split() lowerCAmelCase_ : List[Any] = [] for token in text: if token: split_tokens.extend(list(self.bpe(lowerCAmelCase__ ).split(" " ) ) ) return split_tokens def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : str ) -> int: '''simple docstring''' return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) ) def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : int ) -> str: '''simple docstring''' lowerCAmelCase_ : Tuple = self.decoder.get(lowerCAmelCase__ ,self.unk_token ) return result def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[str] ) -> str: '''simple docstring''' lowerCAmelCase_ : Any = " ".join(lowerCAmelCase__ ) # make sure @@ tokens are concatenated lowerCAmelCase_ : str = "".join(string.split(lowerCAmelCase__ ) ) return string def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' if not os.path.isdir(lowerCAmelCase__ ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return lowerCAmelCase_ : List[Any] = os.path.join( lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase_ : Tuple = os.path.join( lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as f: f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowerCAmelCase__ ,ensure_ascii=lowerCAmelCase__ ) + "\n" ) lowerCAmelCase_ : Dict = 0 if self.bpe_ranks is None: return (vocab_file,) with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as writer: for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda lowerCAmelCase__ : kv[1] ): if index != token_index: logger.warning( f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.''' " Please check that the tokenizer is not corrupted!" ) lowerCAmelCase_ : int = token_index writer.write(" ".join(lowerCAmelCase__ ) + "\n" ) index += 1 return (vocab_file, merges_file)
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from collections.abc import Iterable from typing import Any class __snake_case : """simple docstring""" def __init__( self : Optional[Any] ,lowerCAmelCase__ : int | None = None ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : Dict = value lowerCAmelCase_ : Node | None = None # Added in order to delete a node easier lowerCAmelCase_ : Node | None = None lowerCAmelCase_ : Node | None = None def __repr__( self : Union[str, Any] ) -> str: '''simple docstring''' from pprint import pformat if self.left is None and self.right is None: return str(self.value ) return pformat({f'''{self.value}''': (self.left, self.right)} ,indent=1 ) class __snake_case : """simple docstring""" def __init__( self : Optional[Any] ,lowerCAmelCase__ : Node | None = None ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : List[Any] = root def __str__( self : Dict ) -> str: '''simple docstring''' return str(self.root ) def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Node ,lowerCAmelCase__ : Node | None ) -> None: '''simple docstring''' if new_children is not None: # reset its kids lowerCAmelCase_ : Optional[int] = node.parent if node.parent is not None: # reset its parent if self.is_right(lowerCAmelCase__ ): # If it is the right children lowerCAmelCase_ : List[Any] = new_children else: lowerCAmelCase_ : List[Any] = new_children else: lowerCAmelCase_ : Any = new_children def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Node ) -> bool: '''simple docstring''' if node.parent and node.parent.right: return node == node.parent.right return False def UpperCAmelCase_ ( self : List[str] ) -> bool: '''simple docstring''' return self.root is None def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Union[str, Any] ) -> None: '''simple docstring''' lowerCAmelCase_ : str = Node(lowerCAmelCase__ ) # create a new Node if self.empty(): # if Tree is empty lowerCAmelCase_ : Optional[int] = new_node # set its root else: # Tree is not empty lowerCAmelCase_ : List[Any] = self.root # from root if parent_node is None: return while True: # While we don't get to a leaf if value < parent_node.value: # We go left if parent_node.left is None: lowerCAmelCase_ : Dict = new_node # We insert the new node in a leaf break else: lowerCAmelCase_ : List[str] = parent_node.left else: if parent_node.right is None: lowerCAmelCase_ : Dict = new_node break else: lowerCAmelCase_ : str = parent_node.right lowerCAmelCase_ : Optional[int] = parent_node def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : Tuple ) -> None: '''simple docstring''' for value in values: self.__insert(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Optional[int] ) -> Node | None: '''simple docstring''' if self.empty(): raise IndexError("Warning: Tree is empty! please use another." ) else: lowerCAmelCase_ : Dict = self.root # use lazy evaluation here to avoid NoneType Attribute error while node is not None and node.value is not value: lowerCAmelCase_ : Union[str, Any] = node.left if value < node.value else node.right return node def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None: '''simple docstring''' if node is None: if self.root is None: return None lowerCAmelCase_ : Dict = self.root if not self.empty(): while node.right is not None: lowerCAmelCase_ : Union[str, Any] = node.right return node def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None: '''simple docstring''' if node is None: lowerCAmelCase_ : Dict = self.root if self.root is None: return None if not self.empty(): lowerCAmelCase_ : Dict = self.root while node.left is not None: lowerCAmelCase_ : Union[str, Any] = node.left return node def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : int ) -> None: '''simple docstring''' lowerCAmelCase_ : Dict = self.search(lowerCAmelCase__ ) # Look for the node with that label if node is not None: if node.left is None and node.right is None: # If it has no children self.__reassign_nodes(lowerCAmelCase__ ,lowerCAmelCase__ ) elif node.left is None: # Has only right children self.__reassign_nodes(lowerCAmelCase__ ,node.right ) elif node.right is None: # Has only left children self.__reassign_nodes(lowerCAmelCase__ ,node.left ) else: lowerCAmelCase_ : int = self.get_max( node.left ) # Gets the max value of the left branch self.remove(tmp_node.value ) # type: ignore lowerCAmelCase_ : Any = ( tmp_node.value # type: ignore ) # Assigns the value to the node to delete and keep tree structure def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Node | None ) -> Iterable: '''simple docstring''' if node is not None: yield node # Preorder Traversal yield from self.preorder_traverse(node.left ) yield from self.preorder_traverse(node.right ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Dict=None ) -> Any: '''simple docstring''' if traversal_function is None: return self.preorder_traverse(self.root ) else: return traversal_function(self.root ) def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : list ,lowerCAmelCase__ : Node | None ) -> None: '''simple docstring''' if node: self.inorder(lowerCAmelCase__ ,node.left ) arr.append(node.value ) self.inorder(lowerCAmelCase__ ,node.right ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Node ) -> int: '''simple docstring''' lowerCAmelCase_ : list[int] = [] self.inorder(lowerCAmelCase__ ,lowerCAmelCase__ ) # append all values to list using inorder traversal return arr[k - 1] def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[Any] = [] if curr_node is not None: lowerCAmelCase_ : Dict = postorder(curr_node.left) + postorder(curr_node.right) + [curr_node] return node_list def UpperCamelCase ( ): lowerCAmelCase_ : Tuple = (8, 3, 6, 1, 10, 14, 13, 4, 7) lowerCAmelCase_ : Tuple = BinarySearchTree() for i in testlist: t.insert(snake_case__) # Prints all the elements of the list in order traversal print(snake_case__) if t.search(6) is not None: print("The value 6 exists") else: print("The value 6 doesn't exist") if t.search(-1) is not None: print("The value -1 exists") else: print("The value -1 doesn't exist") if not t.empty(): print("Max Value: " , t.get_max().value) # type: ignore print("Min Value: " , t.get_min().value) # type: ignore for i in testlist: t.remove(snake_case__) print(snake_case__) if __name__ == "__main__": import doctest doctest.testmod(verbose=True)
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'''simple docstring''' import json from typing import List, Optional, Tuple from tokenizers import pre_tokenizers, processors from ...tokenization_utils_base import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_mvp import MvpTokenizer _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''} # See all MVP models at https://huggingface.co/models?filter=mvp _lowercase = { '''vocab_file''': { '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''', }, '''added_tokens.json''': { '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''', }, '''merges_file''': { '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''', }, '''tokenizer_file''': { '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''', }, } _lowercase = { '''RUCAIBox/mvp''': 1024, } class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = ['input_ids', 'attention_mask'] UpperCamelCase_ = MvpTokenizer def __init__( self : Any ,lowerCAmelCase__ : Dict=None ,lowerCAmelCase__ : int=None ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Union[str, Any]="replace" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]="</s>" ,lowerCAmelCase__ : List[Any]="<s>" ,lowerCAmelCase__ : Optional[Any]="<unk>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : Tuple="<mask>" ,lowerCAmelCase__ : Any=False ,lowerCAmelCase__ : Optional[Any]=True ,**lowerCAmelCase__ : Optional[Any] ,) -> Union[str, Any]: '''simple docstring''' super().__init__( lowerCAmelCase__ ,lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,errors=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,trim_offsets=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : Optional[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() ) if pre_tok_state.get("add_prefix_space" ,lowerCAmelCase__ ) != add_prefix_space: lowerCAmelCase_ : List[str] = getattr(lowerCAmelCase__ ,pre_tok_state.pop("type" ) ) lowerCAmelCase_ : Tuple = add_prefix_space lowerCAmelCase_ : List[Any] = pre_tok_class(**lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = add_prefix_space # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__` lowerCAmelCase_ : Optional[Any] = "post_processor" lowerCAmelCase_ : Union[str, Any] = getattr(self.backend_tokenizer ,lowerCAmelCase__ ,lowerCAmelCase__ ) if tokenizer_component_instance: lowerCAmelCase_ : List[Any] = json.loads(tokenizer_component_instance.__getstate__() ) # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` if "sep" in state: lowerCAmelCase_ : int = tuple(state["sep"] ) if "cls" in state: lowerCAmelCase_ : List[str] = tuple(state["cls"] ) lowerCAmelCase_ : List[Any] = False if state.get("add_prefix_space" ,lowerCAmelCase__ ) != add_prefix_space: lowerCAmelCase_ : Tuple = add_prefix_space lowerCAmelCase_ : Tuple = True if state.get("trim_offsets" ,lowerCAmelCase__ ) != trim_offsets: lowerCAmelCase_ : str = trim_offsets lowerCAmelCase_ : int = True if changes_to_apply: lowerCAmelCase_ : List[str] = getattr(lowerCAmelCase__ ,state.pop("type" ) ) lowerCAmelCase_ : Dict = component_class(**lowerCAmelCase__ ) setattr(self.backend_tokenizer ,lowerCAmelCase__ ,lowerCAmelCase__ ) @property def UpperCAmelCase_ ( self : Any ) -> str: '''simple docstring''' if self._mask_token is None: if self.verbose: logger.error("Using mask_token, but it is not set yet." ) return None return str(self._mask_token ) @mask_token.setter def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else value lowerCAmelCase_ : Optional[int] = value def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : int ,**lowerCAmelCase__ : Optional[Any] ) -> BatchEncoding: '''simple docstring''' lowerCAmelCase_ : Dict = kwargs.get("is_split_into_words" ,lowerCAmelCase__ ) if is_split_into_words and not self.add_prefix_space: raise ValueError( f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True ''' "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*lowerCAmelCase__ ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Union[str, Any] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Union[str, Any] ) -> BatchEncoding: '''simple docstring''' lowerCAmelCase_ : List[str] = kwargs.get("is_split_into_words" ,lowerCAmelCase__ ) if is_split_into_words and not self.add_prefix_space: raise ValueError( f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True ''' "to use it with pretokenized inputs." ) return super()._encode_plus(*lowerCAmelCase__ ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ ) return tuple(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Dict=None ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id] if token_ids_a is None: return output return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id] def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : int = [self.sep_token_id] lowerCAmelCase_ : str = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
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class __snake_case : """simple docstring""" def __init__( self : Optional[int] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None: '''simple docstring''' lowerCAmelCase_ : dict[str, RadixNode] = {} # A node will be a leaf if the tree contains its word lowerCAmelCase_ : int = is_leaf lowerCAmelCase_ : Optional[Any] = prefix def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ) -> tuple[str, str, str]: '''simple docstring''' lowerCAmelCase_ : Any = 0 for q, w in zip(self.prefix ,lowerCAmelCase__ ): if q != w: break x += 1 return self.prefix[:x], self.prefix[x:], word[x:] def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : list[str] ) -> None: '''simple docstring''' for word in words: self.insert(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ) -> None: '''simple docstring''' if self.prefix == word: lowerCAmelCase_ : Optional[Any] = True # Case 2: The node has no edges that have a prefix to the word # Solution: We create an edge from the current node to a new one # containing the word elif word[0] not in self.nodes: lowerCAmelCase_ : List[Any] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ ) else: lowerCAmelCase_ : Tuple = self.nodes[word[0]] lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = incoming_node.match( lowerCAmelCase__ ) # Case 3: The node prefix is equal to the matching # Solution: We insert remaining word on the next node if remaining_prefix == "": self.nodes[matching_string[0]].insert(lowerCAmelCase__ ) # Case 4: The word is greater equal to the matching # Solution: Create a node in between both nodes, change # prefixes and add the new node for the remaining word else: lowerCAmelCase_ : Optional[int] = remaining_prefix lowerCAmelCase_ : Optional[int] = self.nodes[matching_string[0]] lowerCAmelCase_ : List[Any] = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Dict = aux_node if remaining_word == "": lowerCAmelCase_ : List[str] = True else: self.nodes[matching_string[0]].insert(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool: '''simple docstring''' lowerCAmelCase_ : Any = self.nodes.get(word[0] ,lowerCAmelCase__ ) if not incoming_node: return False else: lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match( lowerCAmelCase__ ) # If there is remaining prefix, the word can't be on the tree if remaining_prefix != "": return False # This applies when the word and the prefix are equal elif remaining_word == "": return incoming_node.is_leaf # We have word remaining so we check the next node else: return incoming_node.find(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool: '''simple docstring''' lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ ) if not incoming_node: return False else: lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = incoming_node.match( lowerCAmelCase__ ) # If there is remaining prefix, the word can't be on the tree if remaining_prefix != "": return False # We have word remaining so we check the next node elif remaining_word != "": return incoming_node.delete(lowerCAmelCase__ ) else: # If it is not a leaf, we don't have to delete if not incoming_node.is_leaf: return False else: # We delete the nodes if no edges go from it if len(incoming_node.nodes ) == 0: del self.nodes[word[0]] # We merge the current node with its only child if len(self.nodes ) == 1 and not self.is_leaf: lowerCAmelCase_ : str = list(self.nodes.values() )[0] lowerCAmelCase_ : Tuple = merging_node.is_leaf self.prefix += merging_node.prefix lowerCAmelCase_ : Optional[int] = merging_node.nodes # If there is more than 1 edge, we just mark it as non-leaf elif len(incoming_node.nodes ) > 1: lowerCAmelCase_ : Optional[Any] = False # If there is 1 edge, we merge it with its child else: lowerCAmelCase_ : Tuple = list(incoming_node.nodes.values() )[0] lowerCAmelCase_ : Union[str, Any] = merging_node.is_leaf incoming_node.prefix += merging_node.prefix lowerCAmelCase_ : str = merging_node.nodes return True def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : int = 0 ) -> None: '''simple docstring''' if self.prefix != "": print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" ) for value in self.nodes.values(): value.print_tree(height + 1 ) def UpperCamelCase ( ): lowerCAmelCase_ : Dict = "banana bananas bandana band apple all beast".split() lowerCAmelCase_ : List[Any] = RadixNode() root.insert_many(snake_case__) assert all(root.find(snake_case__) for word in words) assert not root.find("bandanas") assert not root.find("apps") root.delete("all") assert not root.find("all") root.delete("banana") assert not root.find("banana") assert root.find("bananas") return True def UpperCamelCase ( ): assert test_trie() def UpperCamelCase ( ): lowerCAmelCase_ : List[str] = RadixNode() lowerCAmelCase_ : Optional[Any] = "banana bananas bandanas bandana band apple all beast".split() root.insert_many(snake_case__) print("Words:" , snake_case__) print("Tree:") root.print_tree() if __name__ == "__main__": main()
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import numpy as np from PIL import Image def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Any = np.array(snake_case__) if arr.shape[0] != arr.shape[1]: raise ValueError("The input array is not a square matrix") lowerCAmelCase_ : str = 0 lowerCAmelCase_ : List[str] = 0 lowerCAmelCase_ : List[Any] = 0 lowerCAmelCase_ : int = 0 # compute the shape of the output matrix lowerCAmelCase_ : Optional[int] = (arr.shape[0] - size) // stride + 1 # initialize the output matrix with zeros of shape maxpool_shape lowerCAmelCase_ : Optional[Any] = np.zeros((maxpool_shape, maxpool_shape)) while i < arr.shape[0]: if i + size > arr.shape[0]: # if the end of the matrix is reached, break break while j < arr.shape[1]: # if the end of the matrix is reached, break if j + size > arr.shape[1]: break # compute the maximum of the pooling matrix lowerCAmelCase_ : str = np.max(arr[i : i + size, j : j + size]) # shift the pooling matrix by stride of column pixels j += stride mat_j += 1 # shift the pooling matrix by stride of row pixels i += stride mat_i += 1 # reset the column index to 0 lowerCAmelCase_ : Optional[int] = 0 lowerCAmelCase_ : List[str] = 0 return updated_arr def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = np.array(snake_case__) if arr.shape[0] != arr.shape[1]: raise ValueError("The input array is not a square matrix") lowerCAmelCase_ : List[str] = 0 lowerCAmelCase_ : int = 0 lowerCAmelCase_ : List[Any] = 0 lowerCAmelCase_ : str = 0 # compute the shape of the output matrix lowerCAmelCase_ : Tuple = (arr.shape[0] - size) // stride + 1 # initialize the output matrix with zeros of shape avgpool_shape lowerCAmelCase_ : Dict = np.zeros((avgpool_shape, avgpool_shape)) while i < arr.shape[0]: # if the end of the matrix is reached, break if i + size > arr.shape[0]: break while j < arr.shape[1]: # if the end of the matrix is reached, break if j + size > arr.shape[1]: break # compute the average of the pooling matrix lowerCAmelCase_ : Optional[Any] = int(np.average(arr[i : i + size, j : j + size])) # shift the pooling matrix by stride of column pixels j += stride mat_j += 1 # shift the pooling matrix by stride of row pixels i += stride mat_i += 1 # reset the column index to 0 lowerCAmelCase_ : Union[str, Any] = 0 lowerCAmelCase_ : List[Any] = 0 return updated_arr # Main Function if __name__ == "__main__": from doctest import testmod testmod(name='''avgpooling''', verbose=True) # Loading the image _lowercase = Image.open('''path_to_image''') # Converting the image to numpy array and maxpooling, displaying the result # Ensure that the image is a square matrix Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show() # Converting the image to numpy array and averagepooling, displaying the result # Ensure that the image is a square matrix Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
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from __future__ import annotations def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ): if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1: raise ValueError("You cannot supply more or less than 2 values") elif electron_conc < 0: raise ValueError("Electron concentration cannot be negative in a semiconductor") elif hole_conc < 0: raise ValueError("Hole concentration cannot be negative in a semiconductor") elif intrinsic_conc < 0: raise ValueError( "Intrinsic concentration cannot be negative in a semiconductor") elif electron_conc == 0: return ( "electron_conc", intrinsic_conc**2 / hole_conc, ) elif hole_conc == 0: return ( "hole_conc", intrinsic_conc**2 / electron_conc, ) elif intrinsic_conc == 0: return ( "intrinsic_conc", (electron_conc * hole_conc) ** 0.5, ) else: return (-1, -1) if __name__ == "__main__": import doctest doctest.testmod()
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import argparse import fairseq import torch from torch import nn from transformers import ( MBartaaTokenizer, MBartConfig, MBartForCausalLM, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel, WavaVecaConfig, WavaVecaFeatureExtractor, WavaVecaModel, logging, ) logging.set_verbosity_info() _lowercase = logging.get_logger(__name__) _lowercase = { '''post_extract_proj''': '''feature_projection.projection''', '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''', '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''', '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''', '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''', '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''', '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''', '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''', '''fc2''': '''encoder.layers.*.feed_forward.output_dense''', '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''', '''encoder.layer_norm''': '''encoder.layer_norm''', '''w2v_model.layer_norm''': '''feature_projection.layer_norm''', '''quantizer.weight_proj''': '''quantizer.weight_proj''', '''quantizer.vars''': '''quantizer.codevectors''', '''project_q''': '''project_q''', '''final_proj''': '''project_hid''', '''w2v_encoder.proj''': '''lm_head''', '''mask_emb''': '''masked_spec_embed''', } _lowercase = [ '''lm_head''', '''quantizer.weight_proj''', '''quantizer.codevectors''', '''project_q''', '''project_hid''', ] def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): for attribute in key.split("."): lowerCAmelCase_ : str = getattr(snake_case__ , snake_case__) if weight_type is not None: lowerCAmelCase_ : int = getattr(snake_case__ , snake_case__).shape else: lowerCAmelCase_ : List[str] = hf_pointer.shape assert hf_shape == value.shape, ( F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be''' F''' {value.shape} for {full_name}''' ) if weight_type == "weight": lowerCAmelCase_ : int = value elif weight_type == "weight_g": lowerCAmelCase_ : int = value elif weight_type == "weight_v": lowerCAmelCase_ : Dict = value elif weight_type == "bias": lowerCAmelCase_ : Tuple = value else: lowerCAmelCase_ : int = value logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''') def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Any = [] lowerCAmelCase_ : Optional[Any] = fairseq_model.state_dict() lowerCAmelCase_ : List[Any] = hf_model.feature_extractor lowerCAmelCase_ : Any = hf_model.adapter for name, value in fairseq_dict.items(): lowerCAmelCase_ : Any = False if "conv_layers" in name: load_conv_layer( snake_case__ , snake_case__ , snake_case__ , snake_case__ , hf_model.config.feat_extract_norm == "group" , ) lowerCAmelCase_ : Any = True elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]): load_adapter(snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : List[Any] = True else: for key, mapped_key in MAPPING.items(): if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]: lowerCAmelCase_ : List[Any] = True if "*" in mapped_key: lowerCAmelCase_ : Optional[int] = name.split(snake_case__)[0].split(".")[-2] lowerCAmelCase_ : int = mapped_key.replace("*" , snake_case__) if "weight_g" in name: lowerCAmelCase_ : Any = "weight_g" elif "weight_v" in name: lowerCAmelCase_ : int = "weight_v" elif "bias" in name: lowerCAmelCase_ : Optional[int] = "bias" elif "weight" in name: lowerCAmelCase_ : str = "weight" else: lowerCAmelCase_ : Optional[Any] = None set_recursively(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__) continue if not is_used: unused_weights.append(snake_case__) logger.warning(F'''Unused weights: {unused_weights}''') def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = full_name.split("conv_layers.")[-1] lowerCAmelCase_ : Union[str, Any] = name.split(".") lowerCAmelCase_ : Any = int(items[0]) lowerCAmelCase_ : Any = int(items[1]) if type_id == 0: if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' ) lowerCAmelCase_ : List[str] = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''') elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' ) lowerCAmelCase_ : str = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''') elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, ( F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was''' " found." ) lowerCAmelCase_ : List[Any] = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''') elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.''' ) lowerCAmelCase_ : Union[str, Any] = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''') else: unused_weights.append(snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : int = full_name.split("adaptor.")[-1] lowerCAmelCase_ : Dict = name.split(".") if items[1].isdigit(): lowerCAmelCase_ : Any = int(items[1]) else: lowerCAmelCase_ : int = None if "adaptor" not in full_name: if "proj_ln" in full_name: # has to be layer norm if "bias" in name: assert ( value.shape == adapter.proj_layer_norm.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.''' lowerCAmelCase_ : str = value logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''') if "weight" in name: assert ( value.shape == adapter.proj_layer_norm.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.''' lowerCAmelCase_ : Union[str, Any] = value else: # has to be projection layer if "bias" in name: assert ( value.shape == adapter.proj.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.''' lowerCAmelCase_ : List[str] = value logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''') if "weight" in name: assert ( value.shape == adapter.proj.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.''' lowerCAmelCase_ : str = value logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''') elif isinstance(snake_case__ , snake_case__): if "bias" in name: assert ( value.shape == adapter.layers[layer_id].conv.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.''' lowerCAmelCase_ : Dict = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''') elif "weight" in name: assert ( value.shape == adapter.layers[layer_id].conv.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.''' lowerCAmelCase_ : Dict = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''') else: unused_weights.append(snake_case__) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : List[Any] = emb.weight.shape lowerCAmelCase_ : Optional[int] = nn.Linear(snake_case__ , snake_case__ , bias=snake_case__) lowerCAmelCase_ : Optional[int] = emb.weight.data return lin_layer @torch.no_grad() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ): lowerCAmelCase_ : List[Any] = WavaVecaConfig.from_pretrained( snake_case__ , add_adapter=snake_case__ , adapter_stride=snake_case__ , adapter_kernel_size=snake_case__ , use_auth_token=snake_case__ , output_hidden_size=snake_case__ , ) lowerCAmelCase_ : Dict = MBartConfig.from_pretrained(snake_case__) # load model lowerCAmelCase_ : List[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={ "config_yaml": config_yaml_path, "data": "/".join(dict_path.split("/")[:-1]), "w2v_path": checkpoint_path, "load_pretrained_decoder_from": None, } , ) lowerCAmelCase_ : List[Any] = model[0].eval() # load feature extractor lowerCAmelCase_ : List[Any] = WavaVecaFeatureExtractor.from_pretrained(snake_case__ , use_auth_token=snake_case__) # set weights for wav2vec2 encoder lowerCAmelCase_ : int = WavaVecaModel(snake_case__) recursively_load_weights_wavaveca(model.encoder , snake_case__) # load decoder weights lowerCAmelCase_ : Tuple = MBartForCausalLM(snake_case__) lowerCAmelCase_ : Any = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=snake_case__) logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''') logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''') lowerCAmelCase_ : int = SpeechEncoderDecoderModel(encoder=snake_case__ , decoder=snake_case__) lowerCAmelCase_ : int = False lowerCAmelCase_ : Optional[int] = MBartaaTokenizer(snake_case__) tokenizer.save_pretrained(snake_case__) lowerCAmelCase_ : int = hf_wavavec.config.to_dict() lowerCAmelCase_ : List[Any] = tokenizer.pad_token_id lowerCAmelCase_ : int = tokenizer.bos_token_id lowerCAmelCase_ : Tuple = tokenizer.eos_token_id lowerCAmelCase_ : Tuple = "mbart50" lowerCAmelCase_ : Dict = "wav2vec2" lowerCAmelCase_ : Any = tokenizer.eos_token_id lowerCAmelCase_ : List[Any] = 25_00_04 lowerCAmelCase_ : Any = tokenizer.eos_token_id lowerCAmelCase_ : Dict = SpeechEncoderDecoderConfig.from_dict(snake_case__) hf_wavavec.save_pretrained(snake_case__) feature_extractor.save_pretrained(snake_case__) if __name__ == "__main__": _lowercase = argparse.ArgumentParser() parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''') parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''') parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''') parser.add_argument('''--config_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''') parser.add_argument( '''--encoder_config_path''', default='''facebook/wav2vec2-xls-r-1b''', type=str, help='''Path to hf encoder wav2vec2 checkpoint config''', ) parser.add_argument( '''--decoder_config_path''', default='''facebook/mbart-large-50-one-to-many-mmt''', type=str, help='''Path to hf decoder checkpoint config''', ) parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''') parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''') parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''') parser.add_argument('''--encoder_output_dim''', default=1024, type=int, help='''encoder output dim''') parser.add_argument('''--start_token_id''', default=250004, type=int, help='''`decoder_start_token_id` of model config''') _lowercase = parser.parse_args() convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.dict_path, args.config_yaml_path, encoder_config_path=args.encoder_config_path, decoder_config_path=args.decoder_config_path, add_adapter=args.add_adapter, adapter_kernel_size=args.adapter_kernel_size, adapter_stride=args.adapter_stride, decoder_start_token_id=args.start_token_id, encoder_output_dim=args.encoder_output_dim, )
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _lowercase = { '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''], '''processing_git''': ['''GitProcessor'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''GitForCausalLM''', '''GitModel''', '''GitPreTrainedModel''', '''GitVisionModel''', ] if TYPE_CHECKING: from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig from .processing_git import GitProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_git import ( GIT_PRETRAINED_MODEL_ARCHIVE_LIST, GitForCausalLM, GitModel, GitPreTrainedModel, GitVisionModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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import unittest from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available from transformers.pipelines import pipeline from transformers.pipelines.document_question_answering import apply_tesseract from transformers.testing_utils import ( is_pipeline_test, nested_simplify, require_detectrona, require_pytesseract, require_tf, require_torch, require_vision, slow, ) from .test_pipelines_common import ANY if is_vision_available(): from PIL import Image from transformers.image_utils import load_image else: class __snake_case : """simple docstring""" @staticmethod def UpperCAmelCase_ ( *lowerCAmelCase__ : Any ,**lowerCAmelCase__ : Dict ) -> Tuple: '''simple docstring''' pass def UpperCamelCase ( snake_case__): return None # This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace, # so we can expect it to be available. _lowercase = ( '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png''' ) @is_pipeline_test @require_torch @require_vision class __snake_case ( unittest.TestCase ): """simple docstring""" UpperCamelCase_ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING @require_pytesseract @require_vision def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Dict = pipeline( "document-question-answering" ,model=lowerCAmelCase__ ,tokenizer=lowerCAmelCase__ ,image_processor=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = INVOICE_URL lowerCAmelCase_ : List[Any] = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) ,lowerCAmelCase__ ,"" ) ) ) lowerCAmelCase_ : List[Any] = "What is the placebo?" lowerCAmelCase_ : Optional[int] = [ { "image": load_image(lowerCAmelCase__ ), "question": question, }, { "image": image, "question": question, }, { "image": image, "question": question, "word_boxes": word_boxes, }, ] return dqa_pipeline, examples def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str ) -> str: '''simple docstring''' lowerCAmelCase_ : Dict = dqa_pipeline(lowerCAmelCase__ ,top_k=2 ) self.assertEqual( lowerCAmelCase__ ,[ [ {"score": ANY(lowerCAmelCase__ ), "answer": ANY(lowerCAmelCase__ ), "start": ANY(lowerCAmelCase__ ), "end": ANY(lowerCAmelCase__ )}, {"score": ANY(lowerCAmelCase__ ), "answer": ANY(lowerCAmelCase__ ), "start": ANY(lowerCAmelCase__ ), "end": ANY(lowerCAmelCase__ )}, ] ] * 3 ,) @require_torch @require_detectrona @require_pytesseract def UpperCAmelCase_ ( self : List[str] ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Dict = pipeline("document-question-answering" ,model="hf-internal-testing/tiny-random-layoutlmv2" ) lowerCAmelCase_ : int = INVOICE_URL lowerCAmelCase_ : int = "How many cats are there?" lowerCAmelCase_ : List[str] = [ {"score": 0.0_001, "answer": "oy 2312/2019", "start": 38, "end": 39}, {"score": 0.0_001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40}, ] lowerCAmelCase_ : Optional[int] = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 ) self.assertEqual(nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = dqa_pipeline({"image": image, "question": question} ,top_k=2 ) self.assertEqual(nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,lowerCAmelCase__ ) # This image does not detect ANY text in it, meaning layoutlmv2 should fail. # Empty answer probably lowerCAmelCase_ : Dict = "./tests/fixtures/tests_samples/COCO/000000039769.png" lowerCAmelCase_ : Dict = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 ) self.assertEqual(lowerCAmelCase__ ,[] ) # We can optionnally pass directly the words and bounding boxes lowerCAmelCase_ : Optional[Any] = "./tests/fixtures/tests_samples/COCO/000000039769.png" lowerCAmelCase_ : Any = [] lowerCAmelCase_ : int = [] lowerCAmelCase_ : List[Any] = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,words=lowerCAmelCase__ ,boxes=lowerCAmelCase__ ,top_k=2 ) self.assertEqual(lowerCAmelCase__ ,[] ) @slow @require_torch @require_detectrona @require_pytesseract def UpperCAmelCase_ ( self : str ) -> str: '''simple docstring''' lowerCAmelCase_ : Tuple = pipeline( "document-question-answering" ,model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" ,revision="9977165" ,) lowerCAmelCase_ : Any = INVOICE_URL lowerCAmelCase_ : Optional[Any] = "What is the invoice number?" lowerCAmelCase_ : List[str] = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16}, {"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16}, ] ,) lowerCAmelCase_ : Dict = dqa_pipeline({"image": image, "question": question} ,top_k=2 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16}, {"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16}, ] ,) lowerCAmelCase_ : List[str] = dqa_pipeline( [{"image": image, "question": question}, {"image": image, "question": question}] ,top_k=2 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ [ {"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16}, {"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16}, ], ] * 2 ,) @slow @require_torch @require_detectrona @require_pytesseract def UpperCAmelCase_ ( self : Tuple ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Optional[int] = pipeline( "document-question-answering" ,model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" ,revision="9977165" ,max_seq_len=50 ,) lowerCAmelCase_ : int = INVOICE_URL lowerCAmelCase_ : Dict = "What is the invoice number?" lowerCAmelCase_ : Dict = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23}, {"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16}, ] ,) lowerCAmelCase_ : str = dqa_pipeline({"image": image, "question": question} ,top_k=2 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23}, {"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16}, ] ,) lowerCAmelCase_ : Any = dqa_pipeline( [{"image": image, "question": question}, {"image": image, "question": question}] ,top_k=2 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ [ {"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23}, {"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16}, ] ] * 2 ,) @slow @require_torch @require_pytesseract @require_vision def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = AutoTokenizer.from_pretrained( "impira/layoutlm-document-qa" ,revision="3dc6de3" ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = pipeline( "document-question-answering" ,model="impira/layoutlm-document-qa" ,tokenizer=lowerCAmelCase__ ,revision="3dc6de3" ,) lowerCAmelCase_ : str = INVOICE_URL lowerCAmelCase_ : Tuple = "What is the invoice number?" lowerCAmelCase_ : Tuple = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16}, {"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23}, ] ,) lowerCAmelCase_ : Optional[Any] = dqa_pipeline({"image": image, "question": question} ,top_k=2 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16}, {"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23}, ] ,) lowerCAmelCase_ : str = dqa_pipeline( [{"image": image, "question": question}, {"image": image, "question": question}] ,top_k=2 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ [ {"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16}, {"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23}, ] ] * 2 ,) lowerCAmelCase_ : Tuple = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) ,lowerCAmelCase__ ,"" ) ) ) # This model should also work if `image` is set to None lowerCAmelCase_ : Optional[int] = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} ,top_k=2 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16}, {"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23}, ] ,) @slow @require_torch @require_pytesseract @require_vision def UpperCAmelCase_ ( self : Tuple ) -> int: '''simple docstring''' lowerCAmelCase_ : Dict = AutoTokenizer.from_pretrained( "impira/layoutlm-document-qa" ,revision="3dc6de3" ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = pipeline( "document-question-answering" ,model="impira/layoutlm-document-qa" ,tokenizer=lowerCAmelCase__ ,revision="3dc6de3" ,max_seq_len=50 ,) lowerCAmelCase_ : Any = INVOICE_URL lowerCAmelCase_ : int = "What is the invoice number?" lowerCAmelCase_ : Optional[int] = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16}, {"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16}, ] ,) lowerCAmelCase_ : List[str] = dqa_pipeline( [{"image": image, "question": question}, {"image": image, "question": question}] ,top_k=2 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ [ {"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16}, {"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16}, ] ] * 2 ,) lowerCAmelCase_ : Optional[int] = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) ,lowerCAmelCase__ ,"" ) ) ) # This model should also work if `image` is set to None lowerCAmelCase_ : Union[str, Any] = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} ,top_k=2 ) self.assertEqual( nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[ {"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16}, {"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16}, ] ,) @slow @require_torch def UpperCAmelCase_ ( self : int ) -> Any: '''simple docstring''' lowerCAmelCase_ : int = pipeline( "document-question-answering" ,model="naver-clova-ix/donut-base-finetuned-docvqa" ,tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa" ) ,feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa" ,) lowerCAmelCase_ : int = INVOICE_URL lowerCAmelCase_ : Optional[Any] = "What is the invoice number?" lowerCAmelCase_ : List[Any] = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 ) self.assertEqual(nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[{"answer": "us-001"}] ) @require_tf @unittest.skip("Document question answering not implemented in TF" ) def UpperCAmelCase_ ( self : List[Any] ) -> Dict: '''simple docstring''' pass
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from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments def UpperCamelCase ( ): lowerCAmelCase_ : List[str] = HfArgumentParser(snake_case__) lowerCAmelCase_ : List[Any] = parser.parse_args_into_dataclasses()[0] lowerCAmelCase_ : Optional[int] = TensorFlowBenchmark(args=snake_case__) try: lowerCAmelCase_ : Tuple = parser.parse_args_into_dataclasses()[0] except ValueError as e: lowerCAmelCase_ : Union[str, Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead." lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1]) lowerCAmelCase_ : Union[str, Any] = "" lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1]) lowerCAmelCase_ : Tuple = [] for arg in depreciated_args: # arg[2:] removes '--' if arg[2:] in TensorFlowBenchmark.deprecated_args: # arg[5:] removes '--no_' full_error_msg += arg_error_msg.format(arg[5:]) else: wrong_args.append(snake_case__) if len(snake_case__) > 0: lowerCAmelCase_ : Optional[Any] = full_error_msg + begin_error_msg + str(snake_case__) raise ValueError(snake_case__) benchmark.run() if __name__ == "__main__": main()
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'''simple docstring''' import torch from diffusers import DPMSolverSDEScheduler from diffusers.utils import torch_device from diffusers.utils.testing_utils import require_torchsde from .test_schedulers import SchedulerCommonTest @require_torchsde class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = (DPMSolverSDEScheduler,) UpperCamelCase_ = 1_0 def UpperCAmelCase_ ( self : List[Any] ,**lowerCAmelCase__ : Tuple ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = { "num_train_timesteps": 11_00, "beta_start": 0.0_001, "beta_end": 0.02, "beta_schedule": "linear", "noise_sampler_seed": 0, } config.update(**lowerCAmelCase__ ) return config def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]: '''simple docstring''' for timesteps in [10, 50, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]: '''simple docstring''' for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,[0.0_002, 0.002, 0.02] ): self.check_over_configs(beta_start=lowerCAmelCase__ ,beta_end=lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Dict ) -> Tuple: '''simple docstring''' for schedule in ["linear", "scaled_linear"]: self.check_over_configs(beta_schedule=lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Dict ) -> Optional[int]: '''simple docstring''' for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=lowerCAmelCase__ ) def UpperCAmelCase_ ( self : int ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Dict = self.scheduler_classes[0] lowerCAmelCase_ : Union[str, Any] = self.get_scheduler_config() lowerCAmelCase_ : Any = scheduler_class(**lowerCAmelCase__ ) scheduler.set_timesteps(self.num_inference_steps ) lowerCAmelCase_ : Any = self.dummy_model() lowerCAmelCase_ : Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma lowerCAmelCase_ : int = sample.to(lowerCAmelCase__ ) for i, t in enumerate(scheduler.timesteps ): lowerCAmelCase_ : Optional[int] = scheduler.scale_model_input(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = model(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = scheduler.step(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Any = output.prev_sample lowerCAmelCase_ : Union[str, Any] = torch.sum(torch.abs(lowerCAmelCase__ ) ) lowerCAmelCase_ : int = torch.mean(torch.abs(lowerCAmelCase__ ) ) if torch_device in ["mps"]: assert abs(result_sum.item() - 167.47_821_044_921_875 ) < 1e-2 assert abs(result_mean.item() - 0.2_178_705_964_565_277 ) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 171.59_352_111_816_406 ) < 1e-2 assert abs(result_mean.item() - 0.22_342_906_892_299_652 ) < 1e-3 else: assert abs(result_sum.item() - 162.52_383_422_851_562 ) < 1e-2 assert abs(result_mean.item() - 0.211_619_570_851_326 ) < 1e-3 def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = self.scheduler_classes[0] lowerCAmelCase_ : Optional[Any] = self.get_scheduler_config(prediction_type="v_prediction" ) lowerCAmelCase_ : Optional[int] = scheduler_class(**lowerCAmelCase__ ) scheduler.set_timesteps(self.num_inference_steps ) lowerCAmelCase_ : int = self.dummy_model() lowerCAmelCase_ : int = self.dummy_sample_deter * scheduler.init_noise_sigma lowerCAmelCase_ : Any = sample.to(lowerCAmelCase__ ) for i, t in enumerate(scheduler.timesteps ): lowerCAmelCase_ : Optional[int] = scheduler.scale_model_input(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = model(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = scheduler.step(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = output.prev_sample lowerCAmelCase_ : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) ) lowerCAmelCase_ : Union[str, Any] = torch.mean(torch.abs(lowerCAmelCase__ ) ) if torch_device in ["mps"]: assert abs(result_sum.item() - 124.77_149_200_439_453 ) < 1e-2 assert abs(result_mean.item() - 0.16_226_289_014_816_284 ) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 128.1_663_360_595_703 ) < 1e-2 assert abs(result_mean.item() - 0.16_688_326_001_167_297 ) < 1e-3 else: assert abs(result_sum.item() - 119.8_487_548_828_125 ) < 1e-2 assert abs(result_mean.item() - 0.1_560_530_662_536_621 ) < 1e-3 def UpperCAmelCase_ ( self : List[Any] ) -> int: '''simple docstring''' lowerCAmelCase_ : str = self.scheduler_classes[0] lowerCAmelCase_ : Optional[int] = self.get_scheduler_config() lowerCAmelCase_ : Dict = scheduler_class(**lowerCAmelCase__ ) scheduler.set_timesteps(self.num_inference_steps ,device=lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = self.dummy_model() lowerCAmelCase_ : List[Any] = self.dummy_sample_deter.to(lowerCAmelCase__ ) * scheduler.init_noise_sigma for t in scheduler.timesteps: lowerCAmelCase_ : Optional[int] = scheduler.scale_model_input(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = model(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = scheduler.step(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = output.prev_sample lowerCAmelCase_ : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) ) lowerCAmelCase_ : Union[str, Any] = torch.mean(torch.abs(lowerCAmelCase__ ) ) if torch_device in ["mps"]: assert abs(result_sum.item() - 167.46_957_397_460_938 ) < 1e-2 assert abs(result_mean.item() - 0.21_805_934_607_982_635 ) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 171.59_353_637_695_312 ) < 1e-2 assert abs(result_mean.item() - 0.22_342_908_382_415_771 ) < 1e-3 else: assert abs(result_sum.item() - 162.52_383_422_851_562 ) < 1e-2 assert abs(result_mean.item() - 0.211_619_570_851_326 ) < 1e-3 def UpperCAmelCase_ ( self : str ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Optional[int] = self.scheduler_classes[0] lowerCAmelCase_ : int = self.get_scheduler_config() lowerCAmelCase_ : List[str] = scheduler_class(**lowerCAmelCase__ ,use_karras_sigmas=lowerCAmelCase__ ) scheduler.set_timesteps(self.num_inference_steps ,device=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = self.dummy_model() lowerCAmelCase_ : int = self.dummy_sample_deter.to(lowerCAmelCase__ ) * scheduler.init_noise_sigma lowerCAmelCase_ : List[str] = sample.to(lowerCAmelCase__ ) for t in scheduler.timesteps: lowerCAmelCase_ : Any = scheduler.scale_model_input(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : int = model(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : int = scheduler.step(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = output.prev_sample lowerCAmelCase_ : Dict = torch.sum(torch.abs(lowerCAmelCase__ ) ) lowerCAmelCase_ : List[str] = torch.mean(torch.abs(lowerCAmelCase__ ) ) if torch_device in ["mps"]: assert abs(result_sum.item() - 176.66_974_135_742_188 ) < 1e-2 assert abs(result_mean.item() - 0.23_003_872_730_981_811 ) < 1e-2 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 177.63_653_564_453_125 ) < 1e-2 assert abs(result_mean.item() - 0.23_003_872_730_981_811 ) < 1e-2 else: assert abs(result_sum.item() - 170.3_135_223_388_672 ) < 1e-2 assert abs(result_mean.item() - 0.23_003_872_730_981_811 ) < 1e-2
710
_lowercase = { 0: '''0''', 1: '''1''', 2: '''2''', 3: '''3''', 4: '''4''', 5: '''5''', 6: '''6''', 7: '''7''', 8: '''8''', 9: '''9''', 10: '''a''', 11: '''b''', 12: '''c''', 13: '''d''', 14: '''e''', 15: '''f''', } def UpperCamelCase ( snake_case__): assert type(snake_case__) in (int, float) and decimal == int(snake_case__) lowerCAmelCase_ : Optional[Any] = int(snake_case__) lowerCAmelCase_ : Tuple = "" lowerCAmelCase_ : str = False if decimal < 0: lowerCAmelCase_ : Tuple = True decimal *= -1 while decimal > 0: lowerCAmelCase_ , lowerCAmelCase_ : Any = divmod(snake_case__ , 16) lowerCAmelCase_ : Dict = values[remainder] + hexadecimal lowerCAmelCase_ : List[str] = "0x" + hexadecimal if negative: lowerCAmelCase_ : Optional[Any] = "-" + hexadecimal return hexadecimal if __name__ == "__main__": import doctest doctest.testmod()
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from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments def UpperCamelCase ( ): lowerCAmelCase_ : List[str] = HfArgumentParser(snake_case__) lowerCAmelCase_ : List[Any] = parser.parse_args_into_dataclasses()[0] lowerCAmelCase_ : Optional[int] = TensorFlowBenchmark(args=snake_case__) try: lowerCAmelCase_ : Tuple = parser.parse_args_into_dataclasses()[0] except ValueError as e: lowerCAmelCase_ : Union[str, Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead." lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1]) lowerCAmelCase_ : Union[str, Any] = "" lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1]) lowerCAmelCase_ : Tuple = [] for arg in depreciated_args: # arg[2:] removes '--' if arg[2:] in TensorFlowBenchmark.deprecated_args: # arg[5:] removes '--no_' full_error_msg += arg_error_msg.format(arg[5:]) else: wrong_args.append(snake_case__) if len(snake_case__) > 0: lowerCAmelCase_ : Optional[Any] = full_error_msg + begin_error_msg + str(snake_case__) raise ValueError(snake_case__) benchmark.run() if __name__ == "__main__": main()
711
from pathlib import Path from typing import List from transformers import is_torch_available, is_vision_available from transformers.testing_utils import get_tests_dir, is_tool_test from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText if is_torch_available(): import torch if is_vision_available(): from PIL import Image _lowercase = ['''text''', '''image''', '''audio'''] def UpperCamelCase ( snake_case__): lowerCAmelCase_ : int = [] for input_type in input_types: if input_type == "text": inputs.append("Text input") elif input_type == "image": inputs.append( Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png").resize((5_12, 5_12))) elif input_type == "audio": inputs.append(torch.ones(30_00)) elif isinstance(snake_case__ , snake_case__): inputs.append(create_inputs(snake_case__)) else: raise ValueError(F'''Invalid type requested: {input_type}''') return inputs def UpperCamelCase ( snake_case__): lowerCAmelCase_ : List[Any] = [] for output in outputs: if isinstance(snake_case__ , (str, AgentText)): output_types.append("text") elif isinstance(snake_case__ , (Image.Image, AgentImage)): output_types.append("image") elif isinstance(snake_case__ , (torch.Tensor, AgentAudio)): output_types.append("audio") else: raise ValueError(F'''Invalid output: {output}''') return output_types @is_tool_test class __snake_case : """simple docstring""" def UpperCAmelCase_ ( self : int ) -> int: '''simple docstring''' self.assertTrue(hasattr(self.tool ,"inputs" ) ) self.assertTrue(hasattr(self.tool ,"outputs" ) ) lowerCAmelCase_ : List[Any] = self.tool.inputs for _input in inputs: if isinstance(_input ,lowerCAmelCase__ ): for __input in _input: self.assertTrue(__input in authorized_types ) else: self.assertTrue(_input in authorized_types ) lowerCAmelCase_ : Any = self.tool.outputs for _output in outputs: self.assertTrue(_output in authorized_types ) def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Any = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) # There is a single output if len(self.tool.outputs ) == 1: lowerCAmelCase_ : Optional[int] = [outputs] self.assertListEqual(output_types(lowerCAmelCase__ ) ,self.tool.outputs ) def UpperCAmelCase_ ( self : int ) -> Any: '''simple docstring''' self.assertTrue(hasattr(self.tool ,"description" ) ) self.assertTrue(hasattr(self.tool ,"default_checkpoint" ) ) self.assertTrue(self.tool.description.startswith("This is a tool that" ) ) def UpperCAmelCase_ ( self : List[Any] ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): lowerCAmelCase_ : str = [outputs] self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) ) for output, output_type in zip(lowerCAmelCase__ ,self.tool.outputs ): lowerCAmelCase_ : Tuple = AGENT_TYPE_MAPPING[output_type] self.assertTrue(isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) ) def UpperCAmelCase_ ( self : Any ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Tuple = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = [] for _input, input_type in zip(lowerCAmelCase__ ,self.tool.inputs ): if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] ) else: _inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) ) # Should not raise an error lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): lowerCAmelCase_ : int = [outputs] self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
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from ...configuration_utils import PretrainedConfig from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''} class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = 'openai-gpt' UpperCamelCase_ = { 'max_position_embeddings': 'n_positions', 'hidden_size': 'n_embd', 'num_attention_heads': 'n_head', 'num_hidden_layers': 'n_layer', } def __init__( self : Optional[int] ,lowerCAmelCase__ : Dict=4_04_78 ,lowerCAmelCase__ : Tuple=5_12 ,lowerCAmelCase__ : Dict=7_68 ,lowerCAmelCase__ : List[Any]=12 ,lowerCAmelCase__ : List[Any]=12 ,lowerCAmelCase__ : Optional[int]="gelu" ,lowerCAmelCase__ : Any=0.1 ,lowerCAmelCase__ : Optional[Any]=0.1 ,lowerCAmelCase__ : List[str]=0.1 ,lowerCAmelCase__ : Dict=1e-5 ,lowerCAmelCase__ : Union[str, Any]=0.02 ,lowerCAmelCase__ : Union[str, Any]="cls_index" ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Union[str, Any]=None ,lowerCAmelCase__ : List[str]=True ,lowerCAmelCase__ : int=0.1 ,**lowerCAmelCase__ : str ,) -> Dict: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = vocab_size lowerCAmelCase_ : Optional[int] = n_positions lowerCAmelCase_ : Tuple = n_embd lowerCAmelCase_ : Any = n_layer lowerCAmelCase_ : Union[str, Any] = n_head lowerCAmelCase_ : Optional[int] = afn lowerCAmelCase_ : Optional[Any] = resid_pdrop lowerCAmelCase_ : Union[str, Any] = embd_pdrop lowerCAmelCase_ : Union[str, Any] = attn_pdrop lowerCAmelCase_ : List[str] = layer_norm_epsilon lowerCAmelCase_ : Any = initializer_range lowerCAmelCase_ : str = summary_type lowerCAmelCase_ : Union[str, Any] = summary_use_proj lowerCAmelCase_ : Any = summary_activation lowerCAmelCase_ : Dict = summary_first_dropout lowerCAmelCase_ : Any = summary_proj_to_labels super().__init__(**lowerCAmelCase__ )
712
import pytest _lowercase = '''__dummy_dataset1__''' _lowercase = ''' import json import os import datasets REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/" URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"} class __DummyDataset1__(datasets.GeneratorBasedBuilder): def _info(self): features = datasets.Features( { "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=[ "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", ] ) ), "langs": datasets.Sequence(datasets.Value("string")), "spans": datasets.Sequence(datasets.Value("string")), } ) return datasets.DatasetInfo(features=features) def _split_generators(self, dl_manager): dl_path = dl_manager.download(URLS) return [ datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}), datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}), ] def _generate_examples(self, filepath): with open(filepath, "r", encoding="utf-8") as f: for i, line in enumerate(f): yield i, json.loads(line) ''' @pytest.fixture def UpperCamelCase ( ): return DATASET_LOADING_SCRIPT_NAME @pytest.fixture def UpperCamelCase ( ): return DATASET_LOADING_SCRIPT_CODE @pytest.fixture def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : List[Any] = dataset_loading_script_name lowerCAmelCase_ : List[str] = tmp_path / "datasets" / script_name script_dir.mkdir(parents=snake_case__) lowerCAmelCase_ : List[Any] = script_dir / F'''{script_name}.py''' with open(snake_case__ , "w") as f: f.write(snake_case__) return str(snake_case__)
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# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _lowercase = { '''configuration_xmod''': [ '''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XmodConfig''', '''XmodOnnxConfig''', ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''', '''XmodForCausalLM''', '''XmodForMaskedLM''', '''XmodForMultipleChoice''', '''XmodForQuestionAnswering''', '''XmodForSequenceClassification''', '''XmodForTokenClassification''', '''XmodModel''', '''XmodPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_xmod import ( XMOD_PRETRAINED_MODEL_ARCHIVE_LIST, XmodForCausalLM, XmodForMaskedLM, XmodForMultipleChoice, XmodForQuestionAnswering, XmodForSequenceClassification, XmodForTokenClassification, XmodModel, XmodPreTrainedModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
713
import json import os import re import unittest from transformers import CodeGenTokenizer, CodeGenTokenizerFast from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class __snake_case ( snake_case__ , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = CodeGenTokenizer UpperCamelCase_ = CodeGenTokenizerFast UpperCamelCase_ = True UpperCamelCase_ = {'add_prefix_space': True} UpperCamelCase_ = False def UpperCAmelCase_ ( self : str ) -> Tuple: '''simple docstring''' super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt lowerCAmelCase_ : Optional[Any] = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "<unk>", "<|endoftext|>", ] lowerCAmelCase_ : int = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) ) lowerCAmelCase_ : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] lowerCAmelCase_ : List[Any] = {"unk_token": "<unk>"} lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase_ : Tuple = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] ) with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp: fp.write(json.dumps(lowerCAmelCase__ ) + "\n" ) with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp: fp.write("\n".join(lowerCAmelCase__ ) ) def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : str ) -> int: '''simple docstring''' kwargs.update(self.special_tokens_map ) return CodeGenTokenizer.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : Optional[Any] ) -> Tuple: '''simple docstring''' kwargs.update(self.special_tokens_map ) return CodeGenTokenizerFast.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : str ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Tuple = "lower newer" lowerCAmelCase_ : Tuple = "lower newer" return input_text, output_text def UpperCAmelCase_ ( self : Optional[Any] ) -> Any: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map ) lowerCAmelCase_ : Dict = "lower newer" lowerCAmelCase_ : Dict = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"] lowerCAmelCase_ : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = tokens + [tokenizer.unk_token] lowerCAmelCase_ : Union[str, Any] = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]: '''simple docstring''' if not self.test_rust_tokenizer: return lowerCAmelCase_ : Tuple = self.get_tokenizer() lowerCAmelCase_ : Optional[int] = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = "lower newer" # Testing tokenization lowerCAmelCase_ : Tuple = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = rust_tokenizer.tokenize(lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing conversion to ids without special tokens lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = rust_tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing conversion to ids with special tokens lowerCAmelCase_ : int = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing the unknown token lowerCAmelCase_ : Union[str, Any] = tokens + [rust_tokenizer.unk_token] lowerCAmelCase_ : List[str] = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Optional[Any] ) -> List[str]: '''simple docstring''' pass def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Any=15 ) -> str: '''simple docstring''' for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ): lowerCAmelCase_ : Any = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ ) # Simple input lowerCAmelCase_ : int = "This is a simple input" lowerCAmelCase_ : Dict = ["This is a simple input 1", "This is a simple input 2"] lowerCAmelCase_ : str = ("This is a simple input", "This is a pair") lowerCAmelCase_ : Optional[int] = [ ("This is a simple input 1", "This is a simple input 2"), ("This is a simple pair 1", "This is a simple pair 2"), ] # Simple input tests self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Simple input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Simple input self.assertRaises( lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,) # Pair input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Pair input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Pair input self.assertRaises( lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,) def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname ,pad_token="<pad>" ) # Simple input lowerCAmelCase_ : Dict = "This is a simple input" lowerCAmelCase_ : List[str] = ["This is a simple input looooooooong", "This is a simple input"] lowerCAmelCase_ : Any = ("This is a simple input", "This is a pair") lowerCAmelCase_ : List[str] = [ ("This is a simple input loooooong", "This is a simple input"), ("This is a simple pair loooooong", "This is a simple pair"), ] lowerCAmelCase_ : Dict = tokenizer.pad_token_id lowerCAmelCase_ : Union[str, Any] = tokenizer(lowerCAmelCase__ ,padding="max_length" ,max_length=30 ,return_tensors="np" ) lowerCAmelCase_ : Tuple = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" ) lowerCAmelCase_ : Any = tokenizer(*lowerCAmelCase__ ,padding="max_length" ,max_length=60 ,return_tensors="np" ) lowerCAmelCase_ : Optional[int] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" ) # s # test single string max_length padding self.assertEqual(out_s["input_ids"].shape[-1] ,30 ) self.assertTrue(pad_token_id in out_s["input_ids"] ) self.assertTrue(0 in out_s["attention_mask"] ) # s2 # test automatic padding self.assertEqual(out_sa["input_ids"].shape[-1] ,33 ) # long slice doesn't have padding self.assertFalse(pad_token_id in out_sa["input_ids"][0] ) self.assertFalse(0 in out_sa["attention_mask"][0] ) # short slice does have padding self.assertTrue(pad_token_id in out_sa["input_ids"][1] ) self.assertTrue(0 in out_sa["attention_mask"][1] ) # p # test single pair max_length padding self.assertEqual(out_p["input_ids"].shape[-1] ,60 ) self.assertTrue(pad_token_id in out_p["input_ids"] ) self.assertTrue(0 in out_p["attention_mask"] ) # p2 # test automatic padding pair self.assertEqual(out_pa["input_ids"].shape[-1] ,52 ) # long slice pair doesn't have padding self.assertFalse(pad_token_id in out_pa["input_ids"][0] ) self.assertFalse(0 in out_pa["attention_mask"][0] ) # short slice pair does have padding self.assertTrue(pad_token_id in out_pa["input_ids"][1] ) self.assertTrue(0 in out_pa["attention_mask"][1] ) def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any: '''simple docstring''' lowerCAmelCase_ : Any = "$$$" lowerCAmelCase_ : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname ,bos_token=lowerCAmelCase__ ,add_bos_token=lowerCAmelCase__ ) lowerCAmelCase_ : Dict = "This is a simple input" lowerCAmelCase_ : Union[str, Any] = ["This is a simple input 1", "This is a simple input 2"] lowerCAmelCase_ : int = tokenizer.bos_token_id lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ ) self.assertEqual(out_s.input_ids[0] ,lowerCAmelCase__ ) self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) ) lowerCAmelCase_ : List[str] = tokenizer.decode(out_s.input_ids ) lowerCAmelCase_ : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids ) self.assertEqual(decode_s.split()[0] ,lowerCAmelCase__ ) self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) ) @slow def UpperCAmelCase_ ( self : Any ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" ) lowerCAmelCase_ : str = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#" lowerCAmelCase_ : int = "\nif len_a > len_b: result = a\nelse: result = b" lowerCAmelCase_ : Dict = tokenizer.encode(lowerCAmelCase__ ) lowerCAmelCase_ : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"] lowerCAmelCase_ : Union[str, Any] = tokenizer.decode(lowerCAmelCase__ ,truncate_before_pattern=lowerCAmelCase__ ) self.assertEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]: '''simple docstring''' pass
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from ...configuration_utils import PretrainedConfig from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = { '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': ( '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json''' ), } class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = 'gptsan-japanese' UpperCamelCase_ = [ 'past_key_values', ] UpperCamelCase_ = { 'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers', } def __init__( self : int ,lowerCAmelCase__ : List[Any]=3_60_00 ,lowerCAmelCase__ : Union[str, Any]=12_80 ,lowerCAmelCase__ : int=10_24 ,lowerCAmelCase__ : Optional[int]=81_92 ,lowerCAmelCase__ : Optional[Any]=40_96 ,lowerCAmelCase__ : str=1_28 ,lowerCAmelCase__ : Union[str, Any]=10 ,lowerCAmelCase__ : Optional[Any]=0 ,lowerCAmelCase__ : str=16 ,lowerCAmelCase__ : List[Any]=16 ,lowerCAmelCase__ : Dict=1_28 ,lowerCAmelCase__ : Dict=0.0 ,lowerCAmelCase__ : List[str]=1e-5 ,lowerCAmelCase__ : Any=False ,lowerCAmelCase__ : List[str]=0.0 ,lowerCAmelCase__ : Optional[int]="float32" ,lowerCAmelCase__ : Optional[int]=False ,lowerCAmelCase__ : int=False ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : List[str]=0.002 ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Optional[int]=3_59_98 ,lowerCAmelCase__ : List[Any]=3_59_95 ,lowerCAmelCase__ : Dict=3_59_99 ,**lowerCAmelCase__ : Optional[int] ,): '''simple docstring''' lowerCAmelCase_ : str = vocab_size lowerCAmelCase_ : Any = max_position_embeddings lowerCAmelCase_ : str = d_model lowerCAmelCase_ : List[str] = d_ff lowerCAmelCase_ : Tuple = d_ext lowerCAmelCase_ : Optional[Any] = d_spout lowerCAmelCase_ : Optional[int] = num_switch_layers lowerCAmelCase_ : Optional[int] = num_ext_layers lowerCAmelCase_ : Tuple = num_switch_layers + num_ext_layers lowerCAmelCase_ : List[Any] = num_heads lowerCAmelCase_ : Tuple = num_experts lowerCAmelCase_ : Optional[int] = expert_capacity lowerCAmelCase_ : Optional[int] = dropout_rate lowerCAmelCase_ : int = layer_norm_epsilon lowerCAmelCase_ : List[str] = router_bias lowerCAmelCase_ : Optional[Any] = router_jitter_noise lowerCAmelCase_ : Optional[Any] = router_dtype lowerCAmelCase_ : Any = router_ignore_padding_tokens lowerCAmelCase_ : int = output_hidden_states lowerCAmelCase_ : Tuple = output_attentions lowerCAmelCase_ : List[str] = initializer_factor lowerCAmelCase_ : Dict = output_router_logits lowerCAmelCase_ : Optional[int] = use_cache super().__init__( separator_token_id=lowerCAmelCase__ ,pad_token_id=lowerCAmelCase__ ,eos_token_id=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
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from __future__ import annotations from random import random class __snake_case : """simple docstring""" def __init__( self : Optional[int] ,lowerCAmelCase__ : int | None = None ) -> int: '''simple docstring''' lowerCAmelCase_ : Dict = value lowerCAmelCase_ : Any = random() lowerCAmelCase_ : Node | None = None lowerCAmelCase_ : Node | None = None def __repr__( self : Any ) -> str: '''simple docstring''' from pprint import pformat if self.left is None and self.right is None: return f'''\'{self.value}: {self.prior:.5}\'''' else: return pformat( {f'''{self.value}: {self.prior:.5}''': (self.left, self.right)} ,indent=1 ) def __str__( self : str ) -> str: '''simple docstring''' lowerCAmelCase_ : List[Any] = str(self.value ) + " " lowerCAmelCase_ : List[Any] = str(self.left or "" ) lowerCAmelCase_ : Union[str, Any] = str(self.right or "" ) return value + left + right def UpperCamelCase ( snake_case__ , snake_case__): if root is None: # None tree is split into 2 Nones return None, None elif root.value is None: return None, None else: if value < root.value: lowerCAmelCase_ , lowerCAmelCase_ : Any = split(root.left , snake_case__) return left, root else: lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = split(root.right , snake_case__) return root, right def UpperCamelCase ( snake_case__ , snake_case__): if (not left) or (not right): # If one node is None, return the other return left or right elif left.prior < right.prior: lowerCAmelCase_ : Dict = merge(left.right , snake_case__) return left else: lowerCAmelCase_ : List[str] = merge(snake_case__ , right.left) return right def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : List[Any] = Node(snake_case__) lowerCAmelCase_ , lowerCAmelCase_ : Tuple = split(snake_case__ , snake_case__) return merge(merge(snake_case__ , snake_case__) , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ , lowerCAmelCase_ : List[str] = split(snake_case__ , value - 1) lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = split(snake_case__ , snake_case__) return merge(snake_case__ , snake_case__) def UpperCamelCase ( snake_case__): if not root: # None return else: inorder(root.left) print(root.value , end=",") inorder(root.right) def UpperCamelCase ( snake_case__ , snake_case__): for arg in args.split(): if arg[0] == "+": lowerCAmelCase_ : List[str] = insert(snake_case__ , int(arg[1:])) elif arg[0] == "-": lowerCAmelCase_ : Optional[int] = erase(snake_case__ , int(arg[1:])) else: print("Unknown command") return root def UpperCamelCase ( ): lowerCAmelCase_ : str = None print( "enter numbers to create a tree, + value to add value into treap, " "- value to erase all nodes with value. 'q' to quit. ") lowerCAmelCase_ : str = input() while args != "q": lowerCAmelCase_ : int = interact_treap(snake_case__ , snake_case__) print(snake_case__) lowerCAmelCase_ : str = input() print("good by!") if __name__ == "__main__": import doctest doctest.testmod() main()
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from urllib.parse import quote import pytest from datasets.utils.hub import hf_hub_url @pytest.mark.parametrize("repo_id" , ["canonical_dataset_name", "org-name/dataset-name"]) @pytest.mark.parametrize("path" , ["filename.csv", "filename with blanks.csv"]) @pytest.mark.parametrize("revision" , [None, "v2"]) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Tuple = hf_hub_url(repo_id=snake_case__ , path=snake_case__ , revision=snake_case__) assert url == F'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(snake_case__)}'''
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_funnel import FunnelTokenizer _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''} _lowercase = [ '''small''', '''small-base''', '''medium''', '''medium-base''', '''intermediate''', '''intermediate-base''', '''large''', '''large-base''', '''xlarge''', '''xlarge-base''', ] _lowercase = { '''vocab_file''': { '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''', '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''', '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''', '''funnel-transformer/medium-base''': ( '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt''' ), '''funnel-transformer/intermediate''': ( '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt''' ), '''funnel-transformer/intermediate-base''': ( '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt''' ), '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''', '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''', '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''', '''funnel-transformer/xlarge-base''': ( '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt''' ), }, '''tokenizer_file''': { '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''', '''funnel-transformer/small-base''': ( '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''', '''funnel-transformer/medium-base''': ( '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/intermediate''': ( '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json''' ), '''funnel-transformer/intermediate-base''': ( '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''', '''funnel-transformer/large-base''': ( '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''', '''funnel-transformer/xlarge-base''': ( '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json''' ), }, } _lowercase = {f"funnel-transformer/{name}": 512 for name in _model_names} _lowercase = {f"funnel-transformer/{name}": {'''do_lower_case''': True} for name in _model_names} class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_INIT_CONFIGURATION UpperCamelCase_ = FunnelTokenizer UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = 2 def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : List[str]="<unk>" ,lowerCAmelCase__ : int="<sep>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : List[str]="<cls>" ,lowerCAmelCase__ : Optional[int]="<mask>" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : List[Any]="##" ,**lowerCAmelCase__ : int ,) -> List[Any]: '''simple docstring''' super().__init__( lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,clean_text=lowerCAmelCase__ ,tokenize_chinese_chars=lowerCAmelCase__ ,strip_accents=lowerCAmelCase__ ,wordpieces_prefix=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() ) if ( normalizer_state.get("lowercase" ,lowerCAmelCase__ ) != do_lower_case or normalizer_state.get("strip_accents" ,lowerCAmelCase__ ) != strip_accents or normalizer_state.get("handle_chinese_chars" ,lowerCAmelCase__ ) != tokenize_chinese_chars ): lowerCAmelCase_ : Optional[int] = getattr(lowerCAmelCase__ ,normalizer_state.pop("type" ) ) lowerCAmelCase_ : List[Any] = do_lower_case lowerCAmelCase_ : List[str] = strip_accents lowerCAmelCase_ : Any = tokenize_chinese_chars lowerCAmelCase_ : List[Any] = normalizer_class(**lowerCAmelCase__ ) lowerCAmelCase_ : int = do_lower_case def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str=None ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id] if token_ids_a: output += token_ids_a + [self.sep_token_id] return output def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : str = [self.sep_token_id] lowerCAmelCase_ : Optional[Any] = [self.cls_token_id] if token_ids_a is None: return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ ) return tuple(lowerCAmelCase__ )
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import os import jsonlines import numpy as np from tqdm import tqdm _lowercase = 2048 _lowercase = 4096 _lowercase = 42 _lowercase = os.environ.pop('''PROCESS_TRAIN''', '''false''') _lowercase = {'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4} def UpperCamelCase ( snake_case__): def choose_first(snake_case__ , snake_case__=False): assert isinstance(snake_case__ , snake_case__) if len(snake_case__) == 1: lowerCAmelCase_ : Tuple = answer[0] return {k: [answer[k]] for k in answer} if is_long_answer else answer for a in answer: if is_long_answer: lowerCAmelCase_ : Tuple = {k: [a[k]] for k in a} if len(a["start_token"]) > 0: break return a lowerCAmelCase_ : Union[str, Any] = {"id": example["id"]} lowerCAmelCase_ : Optional[int] = example["annotations"] lowerCAmelCase_ : Union[str, Any] = annotation["yes_no_answer"] if 0 in yes_no_answer or 1 in yes_no_answer: lowerCAmelCase_ : Any = ["yes"] if 1 in yes_no_answer else ["no"] lowerCAmelCase_ : str = [] lowerCAmelCase_ : Tuple = [] lowerCAmelCase_ : Tuple = ["<cls>"] else: lowerCAmelCase_ : Tuple = ["short"] lowerCAmelCase_ : Dict = choose_first(annotation["short_answers"]) if len(out["start_token"]) == 0: # answer will be long if short is not available lowerCAmelCase_ : str = ["long"] lowerCAmelCase_ : Union[str, Any] = choose_first(annotation["long_answer"] , is_long_answer=snake_case__) lowerCAmelCase_ : Any = [] answer.update(snake_case__) # disregard some samples if len(answer["start_token"]) > 1 or answer["start_token"] == answer["end_token"]: lowerCAmelCase_ : Optional[Any] = True else: lowerCAmelCase_ : Optional[int] = False lowerCAmelCase_ : str = ["start_token", "end_token", "start_byte", "end_byte", "text"] if not all(isinstance(answer[k] , snake_case__) for k in cols): raise ValueError("Issue in ID" , example["id"]) return answer def UpperCamelCase ( snake_case__ , snake_case__=False): lowerCAmelCase_ : int = _get_single_answer(snake_case__) # bytes are of no use del answer["start_byte"] del answer["end_byte"] # handle yes_no answers explicitly if answer["category"][0] in ["yes", "no"]: # category is list with one element lowerCAmelCase_ : Dict = example["document"]["tokens"] lowerCAmelCase_ : str = [] for i in range(len(doc["token"])): if not doc["is_html"][i]: context.append(doc["token"][i]) return { "context": " ".join(snake_case__), "answer": { "start_token": -1_00, # ignore index in cross-entropy "end_token": -1_00, # ignore index in cross-entropy "category": answer["category"], "span": answer["category"], # extra }, } # later, help in removing all no answers if answer["start_token"] == [-1]: return { "context": "None", "answer": { "start_token": -1, "end_token": -1, "category": "null", "span": "None", # extra }, } # handling normal samples lowerCAmelCase_ : Optional[int] = ["start_token", "end_token"] answer.update({k: answer[k][0] if len(answer[k]) > 0 else answer[k] for k in cols}) # e.g. [10] == 10 lowerCAmelCase_ : List[str] = example["document"]["tokens"] lowerCAmelCase_ : int = answer["start_token"] lowerCAmelCase_ : Union[str, Any] = answer["end_token"] lowerCAmelCase_ : str = [] for i in range(len(doc["token"])): if not doc["is_html"][i]: context.append(doc["token"][i]) else: if answer["start_token"] > i: start_token -= 1 if answer["end_token"] > i: end_token -= 1 lowerCAmelCase_ : Any = " ".join(context[start_token:end_token]) # checking above code if assertion: lowerCAmelCase_ : List[Any] = doc["is_html"][answer["start_token"] : answer["end_token"]] lowerCAmelCase_ : Any = doc["token"][answer["start_token"] : answer["end_token"]] lowerCAmelCase_ : Optional[int] = " ".join([old[i] for i in range(len(snake_case__)) if not is_html[i]]) if new != old: print("ID:" , example["id"]) print("New:" , snake_case__ , end="\n") print("Old:" , snake_case__ , end="\n\n") return { "context": " ".join(snake_case__), "answer": { "start_token": start_token, "end_token": end_token - 1, # this makes it inclusive "category": answer["category"], # either long or short "span": new, # extra }, } def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=20_48 , snake_case__=40_96 , snake_case__=True): # overlap will be of doc_stride - q_len lowerCAmelCase_ : Any = get_context_and_ans(snake_case__ , assertion=snake_case__) lowerCAmelCase_ : Union[str, Any] = out["answer"] # later, removing these samples if answer["start_token"] == -1: return { "example_id": example["id"], "input_ids": [[-1]], "labels": { "start_token": [-1], "end_token": [-1], "category": ["null"], }, } lowerCAmelCase_ : Dict = tokenizer(example["question"]["text"] , out["context"]).input_ids lowerCAmelCase_ : Optional[Any] = input_ids.index(tokenizer.sep_token_id) + 1 # return yes/no if answer["category"][0] in ["yes", "no"]: # category is list with one element lowerCAmelCase_ : Optional[int] = [] lowerCAmelCase_ : Any = [] lowerCAmelCase_ : List[str] = input_ids[:q_len] lowerCAmelCase_ : Union[str, Any] = range(snake_case__ , len(snake_case__) , max_length - doc_stride) for i in doc_start_indices: lowerCAmelCase_ : List[str] = i + max_length - q_len lowerCAmelCase_ : int = input_ids[i:end_index] inputs.append(q_indices + slice) category.append(answer["category"][0]) if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": [-1_00] * len(snake_case__), "end_token": [-1_00] * len(snake_case__), "category": category, }, } lowerCAmelCase_ : Optional[Any] = out["context"].split() lowerCAmelCase_ : List[str] = splitted_context[answer["end_token"]] lowerCAmelCase_ : Optional[int] = len( tokenizer( " ".join(splitted_context[: answer["start_token"]]) , add_special_tokens=snake_case__ , ).input_ids) lowerCAmelCase_ : int = len( tokenizer(" ".join(splitted_context[: answer["end_token"]]) , add_special_tokens=snake_case__).input_ids) answer["start_token"] += q_len answer["end_token"] += q_len # fixing end token lowerCAmelCase_ : Union[str, Any] = len(tokenizer(snake_case__ , add_special_tokens=snake_case__).input_ids) if num_sub_tokens > 1: answer["end_token"] += num_sub_tokens - 1 lowerCAmelCase_ : Dict = input_ids[answer["start_token"] : answer["end_token"] + 1] # right & left are inclusive lowerCAmelCase_ : Any = answer["start_token"] lowerCAmelCase_ : List[Any] = answer["end_token"] if assertion: lowerCAmelCase_ : Optional[int] = tokenizer.decode(snake_case__) if answer["span"] != new: print("ISSUE IN TOKENIZATION") print("OLD:" , answer["span"]) print("NEW:" , snake_case__ , end="\n\n") if len(snake_case__) <= max_length: return { "example_id": example["id"], "input_ids": [input_ids], "labels": { "start_token": [answer["start_token"]], "end_token": [answer["end_token"]], "category": answer["category"], }, } lowerCAmelCase_ : str = input_ids[:q_len] lowerCAmelCase_ : int = range(snake_case__ , len(snake_case__) , max_length - doc_stride) lowerCAmelCase_ : Optional[int] = [] lowerCAmelCase_ : Union[str, Any] = [] lowerCAmelCase_ : str = [] lowerCAmelCase_ : Optional[Any] = [] # null, yes, no, long, short for i in doc_start_indices: lowerCAmelCase_ : List[Any] = i + max_length - q_len lowerCAmelCase_ : Any = input_ids[i:end_index] inputs.append(q_indices + slice) assert len(inputs[-1]) <= max_length, "Issue in truncating length" if start_token >= i and end_token <= end_index - 1: lowerCAmelCase_ : List[str] = start_token - i + q_len lowerCAmelCase_ : Dict = end_token - i + q_len answers_category.append(answer["category"][0]) # ["short"] -> "short" else: lowerCAmelCase_ : Optional[int] = -1_00 lowerCAmelCase_ : str = -1_00 answers_category.append("null") lowerCAmelCase_ : Any = inputs[-1][start_token : end_token + 1] answers_start_token.append(snake_case__) answers_end_token.append(snake_case__) if assertion: if new != old and new != [tokenizer.cls_token_id]: print("ISSUE in strided for ID:" , example["id"]) print("New:" , tokenizer.decode(snake_case__)) print("Old:" , tokenizer.decode(snake_case__) , end="\n\n") if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": answers_start_token, "end_token": answers_end_token, "category": answers_category, }, } def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=20_48 , snake_case__=40_96 , snake_case__=False): lowerCAmelCase_ : Union[str, Any] = get_strided_contexts_and_ans( snake_case__ , snake_case__ , doc_stride=snake_case__ , max_length=snake_case__ , assertion=snake_case__ , ) return example def UpperCamelCase ( snake_case__ , snake_case__): with jsonlines.open(snake_case__ , "a") as writer: for example in tqdm(snake_case__ , total=len(snake_case__) , desc="Saving samples ... "): lowerCAmelCase_ : List[str] = example["labels"] for ids, start, end, cat in zip( example["input_ids"] , labels["start_token"] , labels["end_token"] , labels["category"] , ): if start == -1 and end == -1: continue # leave waste samples with no answer if cat == "null" and np.random.rand() < 0.6: continue # removing 50 % samples writer.write( { "input_ids": ids, "start_token": start, "end_token": end, "category": CATEGORY_MAPPING[cat], }) if __name__ == "__main__": from datasets import load_dataset from transformers import BigBirdTokenizer _lowercase = load_dataset('''natural_questions''') _lowercase = BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''') _lowercase = data['''train''' if PROCESS_TRAIN == '''true''' else '''validation'''] _lowercase = { '''tokenizer''': tokenizer, '''doc_stride''': DOC_STRIDE, '''max_length''': MAX_LENGTH, '''assertion''': False, } _lowercase = data.map(prepare_inputs, fn_kwargs=fn_kwargs) _lowercase = data.remove_columns(['''annotations''', '''document''', '''id''', '''question''']) print(data) np.random.seed(SEED) _lowercase = '''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl''' save_to_disk(data, file_name=cache_file_name)
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# tests directory-specific settings - this file is run automatically # by pytest before any tests are run import doctest import sys import warnings from os.path import abspath, dirname, join import _pytest from transformers.testing_utils import HfDoctestModule, HfDocTestParser # allow having multiple repository checkouts and not needing to remember to rerun # 'pip install -e .[dev]' when switching between checkouts and running tests. _lowercase = abspath(join(dirname(__file__), '''src''')) sys.path.insert(1, git_repo_path) # silence FutureWarning warnings in tests since often we can't act on them until # they become normal warnings - i.e. the tests still need to test the current functionality warnings.simplefilter(action='''ignore''', category=FutureWarning) def UpperCamelCase ( snake_case__): config.addinivalue_line( "markers" , "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested") config.addinivalue_line( "markers" , "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested") config.addinivalue_line("markers" , "is_pipeline_test: mark test to run only when pipelines are tested") config.addinivalue_line("markers" , "is_staging_test: mark test to run only in the staging environment") config.addinivalue_line("markers" , "accelerate_tests: mark test that require accelerate") config.addinivalue_line("markers" , "tool_tests: mark the tool tests that are run on their specific schedule") def UpperCamelCase ( snake_case__): from transformers.testing_utils import pytest_addoption_shared pytest_addoption_shared(snake_case__) def UpperCamelCase ( snake_case__): from transformers.testing_utils import pytest_terminal_summary_main lowerCAmelCase_ : int = terminalreporter.config.getoption("--make-reports") if make_reports: pytest_terminal_summary_main(snake_case__ , id=snake_case__) def UpperCamelCase ( snake_case__ , snake_case__): # If no tests are collected, pytest exists with code 5, which makes the CI fail. if exitstatus == 5: lowerCAmelCase_ : List[Any] = 0 # Doctest custom flag to ignore output. _lowercase = doctest.register_optionflag('''IGNORE_RESULT''') _lowercase = doctest.OutputChecker class __snake_case ( snake_case__ ): """simple docstring""" def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Any: '''simple docstring''' if IGNORE_RESULT & optionflags: return True return OutputChecker.check_output(self ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) _lowercase = CustomOutputChecker _lowercase = HfDoctestModule _lowercase = HfDocTestParser
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from __future__ import annotations from collections.abc import Sequence from typing import Literal def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = list(snake_case__) lowerCAmelCase_ : Tuple = list(snake_case__) lowerCAmelCase_ : List[str] = 0 for i in range(len(snake_case__)): if lista[i] != lista[i]: count += 1 lowerCAmelCase_ : Dict = "_" if count > 1: return False else: return "".join(snake_case__) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Union[str, Any] = [] while True: lowerCAmelCase_ : Tuple = ["$"] * len(snake_case__) lowerCAmelCase_ : Tuple = [] for i in range(len(snake_case__)): for j in range(i + 1 , len(snake_case__)): lowerCAmelCase_ : Optional[int] = compare_string(binary[i] , binary[j]) if k is False: lowerCAmelCase_ : str = "*" lowerCAmelCase_ : Tuple = "*" temp.append("X") for i in range(len(snake_case__)): if checka[i] == "$": pi.append(binary[i]) if len(snake_case__) == 0: return pi lowerCAmelCase_ : List[Any] = list(set(snake_case__)) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = [] for minterm in minterms: lowerCAmelCase_ : Dict = "" for _ in range(snake_case__): lowerCAmelCase_ : Dict = str(minterm % 2) + string minterm //= 2 temp.append(snake_case__) return temp def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = list(snake_case__) lowerCAmelCase_ : Dict = list(snake_case__) lowerCAmelCase_ : Dict = 0 for i in range(len(snake_case__)): if lista[i] != lista[i]: count_n += 1 return count_n == count def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = [] lowerCAmelCase_ : Dict = [0] * len(snake_case__) for i in range(len(chart[0])): lowerCAmelCase_ : List[Any] = 0 lowerCAmelCase_ : int = -1 for j in range(len(snake_case__)): if chart[j][i] == 1: count += 1 lowerCAmelCase_ : Optional[int] = j if count == 1: lowerCAmelCase_ : Union[str, Any] = 1 for i in range(len(snake_case__)): if select[i] == 1: for j in range(len(chart[0])): if chart[i][j] == 1: for k in range(len(snake_case__)): lowerCAmelCase_ : Tuple = 0 temp.append(prime_implicants[i]) while True: lowerCAmelCase_ : Optional[Any] = 0 lowerCAmelCase_ : Dict = -1 lowerCAmelCase_ : Tuple = 0 for i in range(len(snake_case__)): lowerCAmelCase_ : Dict = chart[i].count(1) if count_n > max_n: lowerCAmelCase_ : Optional[int] = count_n lowerCAmelCase_ : Optional[Any] = i if max_n == 0: return temp temp.append(prime_implicants[rem]) for i in range(len(chart[0])): if chart[rem][i] == 1: for j in range(len(snake_case__)): lowerCAmelCase_ : Any = 0 def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : str = [[0 for x in range(len(snake_case__))] for x in range(len(snake_case__))] for i in range(len(snake_case__)): lowerCAmelCase_ : Optional[Any] = prime_implicants[i].count("_") for j in range(len(snake_case__)): if is_for_table(prime_implicants[i] , binary[j] , snake_case__): lowerCAmelCase_ : Dict = 1 return chart def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = int(input("Enter the no. of variables\n")) lowerCAmelCase_ : Tuple = [ float(snake_case__) for x in input( "Enter the decimal representation of Minterms 'Spaces Separated'\n").split() ] lowerCAmelCase_ : Any = decimal_to_binary(snake_case__ , snake_case__) lowerCAmelCase_ : Dict = check(snake_case__) print("Prime Implicants are:") print(snake_case__) lowerCAmelCase_ : int = prime_implicant_chart(snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = selection(snake_case__ , snake_case__) print("Essential Prime Implicants are:") print(snake_case__) if __name__ == "__main__": import doctest doctest.testmod() main()
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from __future__ import annotations from collections.abc import Sequence from typing import Literal def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = list(snake_case__) lowerCAmelCase_ : Tuple = list(snake_case__) lowerCAmelCase_ : List[str] = 0 for i in range(len(snake_case__)): if lista[i] != lista[i]: count += 1 lowerCAmelCase_ : Dict = "_" if count > 1: return False else: return "".join(snake_case__) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Union[str, Any] = [] while True: lowerCAmelCase_ : Tuple = ["$"] * len(snake_case__) lowerCAmelCase_ : Tuple = [] for i in range(len(snake_case__)): for j in range(i + 1 , len(snake_case__)): lowerCAmelCase_ : Optional[int] = compare_string(binary[i] , binary[j]) if k is False: lowerCAmelCase_ : str = "*" lowerCAmelCase_ : Tuple = "*" temp.append("X") for i in range(len(snake_case__)): if checka[i] == "$": pi.append(binary[i]) if len(snake_case__) == 0: return pi lowerCAmelCase_ : List[Any] = list(set(snake_case__)) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = [] for minterm in minterms: lowerCAmelCase_ : Dict = "" for _ in range(snake_case__): lowerCAmelCase_ : Dict = str(minterm % 2) + string minterm //= 2 temp.append(snake_case__) return temp def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = list(snake_case__) lowerCAmelCase_ : Dict = list(snake_case__) lowerCAmelCase_ : Dict = 0 for i in range(len(snake_case__)): if lista[i] != lista[i]: count_n += 1 return count_n == count def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = [] lowerCAmelCase_ : Dict = [0] * len(snake_case__) for i in range(len(chart[0])): lowerCAmelCase_ : List[Any] = 0 lowerCAmelCase_ : int = -1 for j in range(len(snake_case__)): if chart[j][i] == 1: count += 1 lowerCAmelCase_ : Optional[int] = j if count == 1: lowerCAmelCase_ : Union[str, Any] = 1 for i in range(len(snake_case__)): if select[i] == 1: for j in range(len(chart[0])): if chart[i][j] == 1: for k in range(len(snake_case__)): lowerCAmelCase_ : Tuple = 0 temp.append(prime_implicants[i]) while True: lowerCAmelCase_ : Optional[Any] = 0 lowerCAmelCase_ : Dict = -1 lowerCAmelCase_ : Tuple = 0 for i in range(len(snake_case__)): lowerCAmelCase_ : Dict = chart[i].count(1) if count_n > max_n: lowerCAmelCase_ : Optional[int] = count_n lowerCAmelCase_ : Optional[Any] = i if max_n == 0: return temp temp.append(prime_implicants[rem]) for i in range(len(chart[0])): if chart[rem][i] == 1: for j in range(len(snake_case__)): lowerCAmelCase_ : Any = 0 def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : str = [[0 for x in range(len(snake_case__))] for x in range(len(snake_case__))] for i in range(len(snake_case__)): lowerCAmelCase_ : Optional[Any] = prime_implicants[i].count("_") for j in range(len(snake_case__)): if is_for_table(prime_implicants[i] , binary[j] , snake_case__): lowerCAmelCase_ : Dict = 1 return chart def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = int(input("Enter the no. of variables\n")) lowerCAmelCase_ : Tuple = [ float(snake_case__) for x in input( "Enter the decimal representation of Minterms 'Spaces Separated'\n").split() ] lowerCAmelCase_ : Any = decimal_to_binary(snake_case__ , snake_case__) lowerCAmelCase_ : Dict = check(snake_case__) print("Prime Implicants are:") print(snake_case__) lowerCAmelCase_ : int = prime_implicant_chart(snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = selection(snake_case__ , snake_case__) print("Essential Prime Implicants are:") print(snake_case__) if __name__ == "__main__": import doctest doctest.testmod() main()
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from __future__ import annotations _lowercase = [True] * 1000001 _lowercase = 2 while i * i <= 1000000: if seive[i]: for j in range(i * i, 1000001, i): _lowercase = False i += 1 def UpperCamelCase ( snake_case__): return seive[n] def UpperCamelCase ( snake_case__): return any(digit in "02468" for digit in str(snake_case__)) def UpperCamelCase ( snake_case__ = 1_00_00_00): lowerCAmelCase_ : Union[str, Any] = [2] # result already includes the number 2. for num in range(3 , limit + 1 , 2): if is_prime(snake_case__) and not contains_an_even_digit(snake_case__): lowerCAmelCase_ : Optional[Any] = str(snake_case__) lowerCAmelCase_ : str = [int(str_num[j:] + str_num[:j]) for j in range(len(snake_case__))] if all(is_prime(snake_case__) for i in list_nums): result.append(snake_case__) return result def UpperCamelCase ( ): return len(find_circular_primes()) if __name__ == "__main__": print(f"{len(find_circular_primes()) = }")
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import logging import os from typing import Dict, List, Optional, Union import torch import torch.nn as nn from accelerate.utils.imports import ( is_abit_bnb_available, is_abit_bnb_available, is_bnb_available, ) from ..big_modeling import dispatch_model, init_empty_weights from .dataclasses import BnbQuantizationConfig from .modeling import ( find_tied_parameters, get_balanced_memory, infer_auto_device_map, load_checkpoint_in_model, offload_weight, set_module_tensor_to_device, ) if is_bnb_available(): import bitsandbytes as bnb from copy import deepcopy _lowercase = logging.getLogger(__name__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = False , ): lowerCAmelCase_ : List[Any] = bnb_quantization_config.load_in_abit lowerCAmelCase_ : Optional[Any] = bnb_quantization_config.load_in_abit if load_in_abit and not is_abit_bnb_available(): raise ImportError( "You have a version of `bitsandbytes` that is not compatible with 8bit quantization," " make sure you have the latest version of `bitsandbytes` installed.") if load_in_abit and not is_abit_bnb_available(): raise ValueError( "You have a version of `bitsandbytes` that is not compatible with 4bit quantization," "make sure you have the latest version of `bitsandbytes` installed.") lowerCAmelCase_ : List[str] = [] # custom device map if isinstance(snake_case__ , snake_case__) and len(device_map.keys()) > 1: lowerCAmelCase_ : Union[str, Any] = [key for key, value in device_map.items() if value in ["disk", "cpu"]] # We keep some modules such as the lm_head in their original dtype for numerical stability reasons if bnb_quantization_config.skip_modules is None: lowerCAmelCase_ : Union[str, Any] = get_keys_to_not_convert(snake_case__) # add cpu modules to skip modules only for 4-bit modules if load_in_abit: bnb_quantization_config.skip_modules.extend(snake_case__) lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules # We add the modules we want to keep in full precision if bnb_quantization_config.keep_in_fpaa_modules is None: lowerCAmelCase_ : Optional[int] = [] lowerCAmelCase_ : int = bnb_quantization_config.keep_in_fpaa_modules modules_to_not_convert.extend(snake_case__) # compatibility with peft lowerCAmelCase_ : Optional[int] = load_in_abit lowerCAmelCase_ : List[str] = load_in_abit lowerCAmelCase_ : Optional[int] = get_parameter_device(snake_case__) if model_device.type != "meta": # quantization of an already loaded model logger.warning( "It is not recommended to quantize a loaded model. " "The model should be instantiated under the `init_empty_weights` context manager.") lowerCAmelCase_ : Union[str, Any] = replace_with_bnb_layers(snake_case__ , snake_case__ , modules_to_not_convert=snake_case__) # convert param to the right dtype lowerCAmelCase_ : Any = bnb_quantization_config.torch_dtype for name, param in model.state_dict().items(): if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules): param.to(torch.floataa) if param.dtype != torch.floataa: lowerCAmelCase_ : Optional[int] = name.replace(".weight" , "").replace(".bias" , "") lowerCAmelCase_ : Optional[int] = getattr(snake_case__ , snake_case__ , snake_case__) if param is not None: param.to(torch.floataa) elif torch.is_floating_point(snake_case__): param.to(snake_case__) if model_device.type == "cuda": # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda model.cuda(torch.cuda.current_device()) torch.cuda.empty_cache() elif torch.cuda.is_available(): model.to(torch.cuda.current_device()) else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info( F'''The model device type is {model_device.type}. However, cuda is needed for quantization.''' "We move the model to cuda.") return model elif weights_location is None: raise RuntimeError( F'''`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} ''') else: with init_empty_weights(): lowerCAmelCase_ : str = replace_with_bnb_layers( snake_case__ , snake_case__ , modules_to_not_convert=snake_case__) lowerCAmelCase_ : Optional[int] = get_quantized_model_device_map( snake_case__ , snake_case__ , snake_case__ , max_memory=snake_case__ , no_split_module_classes=snake_case__ , ) if offload_state_dict is None and device_map is not None and "disk" in device_map.values(): lowerCAmelCase_ : Optional[Any] = True lowerCAmelCase_ : Optional[int] = any(x in list(device_map.values()) for x in ["cpu", "disk"]) load_checkpoint_in_model( snake_case__ , snake_case__ , snake_case__ , dtype=bnb_quantization_config.torch_dtype , offload_folder=snake_case__ , offload_state_dict=snake_case__ , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , ) return dispatch_model(snake_case__ , device_map=snake_case__ , offload_dir=snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=None): if device_map is None: if torch.cuda.is_available(): lowerCAmelCase_ : Any = {"": torch.cuda.current_device()} else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info("The device_map was not initialized." "Setting device_map to `{'':torch.cuda.current_device()}`.") if isinstance(snake_case__ , snake_case__): if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]: raise ValueError( "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or " "'sequential'.") lowerCAmelCase_ : Dict = {} special_dtypes.update( { name: bnb_quantization_config.torch_dtype for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.skip_modules) }) special_dtypes.update( { name: torch.floataa for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules) }) lowerCAmelCase_ : List[str] = {} lowerCAmelCase_ : Union[str, Any] = special_dtypes lowerCAmelCase_ : Union[str, Any] = no_split_module_classes lowerCAmelCase_ : Any = bnb_quantization_config.target_dtype # get max_memory for each device. if device_map != "sequential": lowerCAmelCase_ : Tuple = get_balanced_memory( snake_case__ , low_zero=(device_map == "balanced_low_0") , max_memory=snake_case__ , **snake_case__ , ) lowerCAmelCase_ : Tuple = max_memory lowerCAmelCase_ : Optional[Any] = infer_auto_device_map(snake_case__ , **snake_case__) if isinstance(snake_case__ , snake_case__): # check if don't have any quantized module on the cpu lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules lowerCAmelCase_ : List[Any] = { key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert } for device in ["cpu", "disk"]: if device in device_map_without_some_modules.values(): if bnb_quantization_config.load_in_abit: raise ValueError( "\n Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n these modules in `torch_dtype`, you need to pass a custom `device_map` to\n `load_and_quantize_model`. Check\n https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n for more details.\n ") else: logger.info( "Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit") del device_map_without_some_modules return device_map def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None): if modules_to_not_convert is None: lowerCAmelCase_ : List[str] = [] lowerCAmelCase_ , lowerCAmelCase_ : Tuple = _replace_with_bnb_layers( snake_case__ , snake_case__ , snake_case__ , snake_case__) if not has_been_replaced: logger.warning( "You are loading your model in 8bit or 4bit but no linear modules were found in your model." " this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers." " Please double check your model architecture, or submit an issue on github if you think this is" " a bug.") return model def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , ): lowerCAmelCase_ : str = False for name, module in model.named_children(): if current_key_name is None: lowerCAmelCase_ : Optional[int] = [] current_key_name.append(snake_case__) if isinstance(snake_case__ , nn.Linear) and name not in modules_to_not_convert: # Check if the current key is not in the `modules_to_not_convert` lowerCAmelCase_ : Optional[int] = ".".join(snake_case__) lowerCAmelCase_ : List[str] = True for key in modules_to_not_convert: if ( (key in current_key_name_str) and (key + "." in current_key_name_str) ) or key == current_key_name_str: lowerCAmelCase_ : List[Any] = False break if proceed: # Load bnb module with empty weight and replace ``nn.Linear` module if bnb_quantization_config.load_in_abit: lowerCAmelCase_ : Tuple = bnb.nn.LinearabitLt( module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=snake_case__ , threshold=bnb_quantization_config.llm_inta_threshold , ) elif bnb_quantization_config.load_in_abit: lowerCAmelCase_ : Dict = bnb.nn.Linearabit( module.in_features , module.out_features , module.bias is not None , bnb_quantization_config.bnb_abit_compute_dtype , compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant , quant_type=bnb_quantization_config.bnb_abit_quant_type , ) else: raise ValueError("load_in_8bit and load_in_4bit can't be both False") lowerCAmelCase_ : List[str] = module.weight.data if module.bias is not None: lowerCAmelCase_ : Any = module.bias.data bnb_module.requires_grad_(snake_case__) setattr(snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = True if len(list(module.children())) > 0: lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = _replace_with_bnb_layers( snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : Optional[int] = has_been_replaced | _has_been_replaced # Remove the last key for recursion current_key_name.pop(-1) return model, has_been_replaced def UpperCamelCase ( snake_case__): # Create a copy of the model with init_empty_weights(): lowerCAmelCase_ : List[Any] = deepcopy(snake_case__) # this has 0 cost since it is done inside `init_empty_weights` context manager` lowerCAmelCase_ : Dict = find_tied_parameters(snake_case__) # For compatibility with Accelerate < 0.18 if isinstance(snake_case__ , snake_case__): lowerCAmelCase_ : List[str] = sum(list(tied_params.values()) , []) + list(tied_params.keys()) else: lowerCAmelCase_ : Optional[Any] = sum(snake_case__ , []) lowerCAmelCase_ : List[Any] = len(snake_case__) > 0 # Check if it is a base model lowerCAmelCase_ : List[str] = False if hasattr(snake_case__ , "base_model_prefix"): lowerCAmelCase_ : Tuple = not hasattr(snake_case__ , model.base_model_prefix) # Ignore this for base models (BertModel, GPT2Model, etc.) if (not has_tied_params) and is_base_model: return [] # otherwise they have an attached head lowerCAmelCase_ : Union[str, Any] = list(model.named_children()) lowerCAmelCase_ : Optional[int] = [list_modules[-1][0]] # add last module together with tied weights lowerCAmelCase_ : Any = set(snake_case__) - set(snake_case__) lowerCAmelCase_ : Tuple = list(set(snake_case__)) + list(snake_case__) # remove ".weight" from the keys lowerCAmelCase_ : List[str] = [".weight", ".bias"] lowerCAmelCase_ : Tuple = [] for name in list_untouched: for name_to_remove in names_to_remove: if name_to_remove in name: lowerCAmelCase_ : str = name.replace(snake_case__ , "") filtered_module_names.append(snake_case__) return filtered_module_names def UpperCamelCase ( snake_case__): for m in model.modules(): if isinstance(snake_case__ , bnb.nn.Linearabit): return True return False def UpperCamelCase ( snake_case__): return next(parameter.parameters()).device def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): # if it is not quantized, we quantize and offload the quantized weights and the SCB stats if fpaa_statistics is None: set_module_tensor_to_device(snake_case__ , snake_case__ , 0 , dtype=snake_case__ , value=snake_case__) lowerCAmelCase_ : str = param_name lowerCAmelCase_ : Tuple = model if "." in tensor_name: lowerCAmelCase_ : Dict = tensor_name.split(".") for split in splits[:-1]: lowerCAmelCase_ : Any = getattr(snake_case__ , snake_case__) if new_module is None: raise ValueError(F'''{module} has no attribute {split}.''') lowerCAmelCase_ : Union[str, Any] = new_module lowerCAmelCase_ : Any = splits[-1] # offload weights lowerCAmelCase_ : List[Any] = False offload_weight(module._parameters[tensor_name] , snake_case__ , snake_case__ , index=snake_case__) if hasattr(module._parameters[tensor_name] , "SCB"): offload_weight( module._parameters[tensor_name].SCB , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__ , ) else: offload_weight(snake_case__ , snake_case__ , snake_case__ , index=snake_case__) offload_weight(snake_case__ , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__) set_module_tensor_to_device(snake_case__ , snake_case__ , "meta" , dtype=snake_case__ , value=torch.empty(*param.size()))
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0
import logging import os from typing import Dict, List, Optional, Union import torch import torch.nn as nn from accelerate.utils.imports import ( is_abit_bnb_available, is_abit_bnb_available, is_bnb_available, ) from ..big_modeling import dispatch_model, init_empty_weights from .dataclasses import BnbQuantizationConfig from .modeling import ( find_tied_parameters, get_balanced_memory, infer_auto_device_map, load_checkpoint_in_model, offload_weight, set_module_tensor_to_device, ) if is_bnb_available(): import bitsandbytes as bnb from copy import deepcopy _lowercase = logging.getLogger(__name__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = False , ): lowerCAmelCase_ : List[Any] = bnb_quantization_config.load_in_abit lowerCAmelCase_ : Optional[Any] = bnb_quantization_config.load_in_abit if load_in_abit and not is_abit_bnb_available(): raise ImportError( "You have a version of `bitsandbytes` that is not compatible with 8bit quantization," " make sure you have the latest version of `bitsandbytes` installed.") if load_in_abit and not is_abit_bnb_available(): raise ValueError( "You have a version of `bitsandbytes` that is not compatible with 4bit quantization," "make sure you have the latest version of `bitsandbytes` installed.") lowerCAmelCase_ : List[str] = [] # custom device map if isinstance(snake_case__ , snake_case__) and len(device_map.keys()) > 1: lowerCAmelCase_ : Union[str, Any] = [key for key, value in device_map.items() if value in ["disk", "cpu"]] # We keep some modules such as the lm_head in their original dtype for numerical stability reasons if bnb_quantization_config.skip_modules is None: lowerCAmelCase_ : Union[str, Any] = get_keys_to_not_convert(snake_case__) # add cpu modules to skip modules only for 4-bit modules if load_in_abit: bnb_quantization_config.skip_modules.extend(snake_case__) lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules # We add the modules we want to keep in full precision if bnb_quantization_config.keep_in_fpaa_modules is None: lowerCAmelCase_ : Optional[int] = [] lowerCAmelCase_ : int = bnb_quantization_config.keep_in_fpaa_modules modules_to_not_convert.extend(snake_case__) # compatibility with peft lowerCAmelCase_ : Optional[int] = load_in_abit lowerCAmelCase_ : List[str] = load_in_abit lowerCAmelCase_ : Optional[int] = get_parameter_device(snake_case__) if model_device.type != "meta": # quantization of an already loaded model logger.warning( "It is not recommended to quantize a loaded model. " "The model should be instantiated under the `init_empty_weights` context manager.") lowerCAmelCase_ : Union[str, Any] = replace_with_bnb_layers(snake_case__ , snake_case__ , modules_to_not_convert=snake_case__) # convert param to the right dtype lowerCAmelCase_ : Any = bnb_quantization_config.torch_dtype for name, param in model.state_dict().items(): if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules): param.to(torch.floataa) if param.dtype != torch.floataa: lowerCAmelCase_ : Optional[int] = name.replace(".weight" , "").replace(".bias" , "") lowerCAmelCase_ : Optional[int] = getattr(snake_case__ , snake_case__ , snake_case__) if param is not None: param.to(torch.floataa) elif torch.is_floating_point(snake_case__): param.to(snake_case__) if model_device.type == "cuda": # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda model.cuda(torch.cuda.current_device()) torch.cuda.empty_cache() elif torch.cuda.is_available(): model.to(torch.cuda.current_device()) else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info( F'''The model device type is {model_device.type}. However, cuda is needed for quantization.''' "We move the model to cuda.") return model elif weights_location is None: raise RuntimeError( F'''`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} ''') else: with init_empty_weights(): lowerCAmelCase_ : str = replace_with_bnb_layers( snake_case__ , snake_case__ , modules_to_not_convert=snake_case__) lowerCAmelCase_ : Optional[int] = get_quantized_model_device_map( snake_case__ , snake_case__ , snake_case__ , max_memory=snake_case__ , no_split_module_classes=snake_case__ , ) if offload_state_dict is None and device_map is not None and "disk" in device_map.values(): lowerCAmelCase_ : Optional[Any] = True lowerCAmelCase_ : Optional[int] = any(x in list(device_map.values()) for x in ["cpu", "disk"]) load_checkpoint_in_model( snake_case__ , snake_case__ , snake_case__ , dtype=bnb_quantization_config.torch_dtype , offload_folder=snake_case__ , offload_state_dict=snake_case__ , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , ) return dispatch_model(snake_case__ , device_map=snake_case__ , offload_dir=snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=None): if device_map is None: if torch.cuda.is_available(): lowerCAmelCase_ : Any = {"": torch.cuda.current_device()} else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info("The device_map was not initialized." "Setting device_map to `{'':torch.cuda.current_device()}`.") if isinstance(snake_case__ , snake_case__): if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]: raise ValueError( "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or " "'sequential'.") lowerCAmelCase_ : Dict = {} special_dtypes.update( { name: bnb_quantization_config.torch_dtype for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.skip_modules) }) special_dtypes.update( { name: torch.floataa for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules) }) lowerCAmelCase_ : List[str] = {} lowerCAmelCase_ : Union[str, Any] = special_dtypes lowerCAmelCase_ : Union[str, Any] = no_split_module_classes lowerCAmelCase_ : Any = bnb_quantization_config.target_dtype # get max_memory for each device. if device_map != "sequential": lowerCAmelCase_ : Tuple = get_balanced_memory( snake_case__ , low_zero=(device_map == "balanced_low_0") , max_memory=snake_case__ , **snake_case__ , ) lowerCAmelCase_ : Tuple = max_memory lowerCAmelCase_ : Optional[Any] = infer_auto_device_map(snake_case__ , **snake_case__) if isinstance(snake_case__ , snake_case__): # check if don't have any quantized module on the cpu lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules lowerCAmelCase_ : List[Any] = { key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert } for device in ["cpu", "disk"]: if device in device_map_without_some_modules.values(): if bnb_quantization_config.load_in_abit: raise ValueError( "\n Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n these modules in `torch_dtype`, you need to pass a custom `device_map` to\n `load_and_quantize_model`. Check\n https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n for more details.\n ") else: logger.info( "Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit") del device_map_without_some_modules return device_map def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None): if modules_to_not_convert is None: lowerCAmelCase_ : List[str] = [] lowerCAmelCase_ : Tuple = _replace_with_bnb_layers( snake_case__ , snake_case__ , snake_case__ , snake_case__) if not has_been_replaced: logger.warning( "You are loading your model in 8bit or 4bit but no linear modules were found in your model." " this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers." " Please double check your model architecture, or submit an issue on github if you think this is" " a bug.") return model def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , ): lowerCAmelCase_ : str = False for name, module in model.named_children(): if current_key_name is None: lowerCAmelCase_ : Optional[int] = [] current_key_name.append(snake_case__) if isinstance(snake_case__ , nn.Linear) and name not in modules_to_not_convert: # Check if the current key is not in the `modules_to_not_convert` lowerCAmelCase_ : Optional[int] = ".".join(snake_case__) lowerCAmelCase_ : List[str] = True for key in modules_to_not_convert: if ( (key in current_key_name_str) and (key + "." in current_key_name_str) ) or key == current_key_name_str: lowerCAmelCase_ : List[Any] = False break if proceed: # Load bnb module with empty weight and replace ``nn.Linear` module if bnb_quantization_config.load_in_abit: lowerCAmelCase_ : Tuple = bnb.nn.LinearabitLt( module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=snake_case__ , threshold=bnb_quantization_config.llm_inta_threshold , ) elif bnb_quantization_config.load_in_abit: lowerCAmelCase_ : Dict = bnb.nn.Linearabit( module.in_features , module.out_features , module.bias is not None , bnb_quantization_config.bnb_abit_compute_dtype , compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant , quant_type=bnb_quantization_config.bnb_abit_quant_type , ) else: raise ValueError("load_in_8bit and load_in_4bit can't be both False") lowerCAmelCase_ : List[str] = module.weight.data if module.bias is not None: lowerCAmelCase_ : Any = module.bias.data bnb_module.requires_grad_(snake_case__) setattr(snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = True if len(list(module.children())) > 0: lowerCAmelCase_ : List[Any] = _replace_with_bnb_layers( snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : Optional[int] = has_been_replaced | _has_been_replaced # Remove the last key for recursion current_key_name.pop(-1) return model, has_been_replaced def UpperCamelCase ( snake_case__): # Create a copy of the model with init_empty_weights(): lowerCAmelCase_ : List[Any] = deepcopy(snake_case__) # this has 0 cost since it is done inside `init_empty_weights` context manager` lowerCAmelCase_ : Dict = find_tied_parameters(snake_case__) # For compatibility with Accelerate < 0.18 if isinstance(snake_case__ , snake_case__): lowerCAmelCase_ : List[str] = sum(list(tied_params.values()) , []) + list(tied_params.keys()) else: lowerCAmelCase_ : Optional[Any] = sum(snake_case__ , []) lowerCAmelCase_ : List[Any] = len(snake_case__) > 0 # Check if it is a base model lowerCAmelCase_ : List[str] = False if hasattr(snake_case__ , "base_model_prefix"): lowerCAmelCase_ : Tuple = not hasattr(snake_case__ , model.base_model_prefix) # Ignore this for base models (BertModel, GPT2Model, etc.) if (not has_tied_params) and is_base_model: return [] # otherwise they have an attached head lowerCAmelCase_ : Union[str, Any] = list(model.named_children()) lowerCAmelCase_ : Optional[int] = [list_modules[-1][0]] # add last module together with tied weights lowerCAmelCase_ : Any = set(snake_case__) - set(snake_case__) lowerCAmelCase_ : Tuple = list(set(snake_case__)) + list(snake_case__) # remove ".weight" from the keys lowerCAmelCase_ : List[str] = [".weight", ".bias"] lowerCAmelCase_ : Tuple = [] for name in list_untouched: for name_to_remove in names_to_remove: if name_to_remove in name: lowerCAmelCase_ : str = name.replace(snake_case__ , "") filtered_module_names.append(snake_case__) return filtered_module_names def UpperCamelCase ( snake_case__): for m in model.modules(): if isinstance(snake_case__ , bnb.nn.Linearabit): return True return False def UpperCamelCase ( snake_case__): return next(parameter.parameters()).device def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): # if it is not quantized, we quantize and offload the quantized weights and the SCB stats if fpaa_statistics is None: set_module_tensor_to_device(snake_case__ , snake_case__ , 0 , dtype=snake_case__ , value=snake_case__) lowerCAmelCase_ : str = param_name lowerCAmelCase_ : Tuple = model if "." in tensor_name: lowerCAmelCase_ : Dict = tensor_name.split(".") for split in splits[:-1]: lowerCAmelCase_ : Any = getattr(snake_case__ , snake_case__) if new_module is None: raise ValueError(F'''{module} has no attribute {split}.''') lowerCAmelCase_ : Union[str, Any] = new_module lowerCAmelCase_ : Any = splits[-1] # offload weights lowerCAmelCase_ : List[Any] = False offload_weight(module._parameters[tensor_name] , snake_case__ , snake_case__ , index=snake_case__) if hasattr(module._parameters[tensor_name] , "SCB"): offload_weight( module._parameters[tensor_name].SCB , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__ , ) else: offload_weight(snake_case__ , snake_case__ , snake_case__ , index=snake_case__) offload_weight(snake_case__ , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__) set_module_tensor_to_device(snake_case__ , snake_case__ , "meta" , dtype=snake_case__ , value=torch.empty(*param.size()))
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import copy from typing import Any, Dict, List, Optional, Union import numpy as np import torch from ...audio_utils import mel_filter_bank, spectrogram, window_function from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import TensorType, logging _lowercase = logging.get_logger(__name__) class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = ['input_features', 'is_longer'] def __init__( self : Optional[int] ,lowerCAmelCase__ : List[Any]=64 ,lowerCAmelCase__ : Any=4_80_00 ,lowerCAmelCase__ : Optional[Any]=4_80 ,lowerCAmelCase__ : List[str]=10 ,lowerCAmelCase__ : List[Any]=10_24 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : float = 0 ,lowerCAmelCase__ : float = 1_40_00 ,lowerCAmelCase__ : int = None ,lowerCAmelCase__ : str = "fusion" ,lowerCAmelCase__ : str = "repeatpad" ,**lowerCAmelCase__ : Union[str, Any] ,) -> Union[str, Any]: '''simple docstring''' super().__init__( feature_size=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,padding_value=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : Optional[Any] = top_db lowerCAmelCase_ : str = truncation lowerCAmelCase_ : Tuple = padding lowerCAmelCase_ : str = fft_window_size lowerCAmelCase_ : Dict = (fft_window_size >> 1) + 1 lowerCAmelCase_ : Dict = hop_length lowerCAmelCase_ : Any = max_length_s lowerCAmelCase_ : int = max_length_s * sampling_rate lowerCAmelCase_ : Optional[int] = sampling_rate lowerCAmelCase_ : int = frequency_min lowerCAmelCase_ : Optional[Any] = frequency_max lowerCAmelCase_ : List[Any] = mel_filter_bank( num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm=lowerCAmelCase__ ,mel_scale="htk" ,) lowerCAmelCase_ : List[Any] = mel_filter_bank( num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm="slaney" ,mel_scale="slaney" ,) def UpperCAmelCase_ ( self : Dict ) -> Dict[str, Any]: '''simple docstring''' lowerCAmelCase_ : int = copy.deepcopy(self.__dict__ ) lowerCAmelCase_ : Optional[int] = self.__class__.__name__ if "mel_filters" in output: del output["mel_filters"] if "mel_filters_slaney" in output: del output["mel_filters_slaney"] return output def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Optional[np.array] = None ) -> np.ndarray: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = spectrogram( lowerCAmelCase__ ,window_function(self.fft_window_size ,"hann" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=lowerCAmelCase__ ,log_mel="dB" ,) return log_mel_spectrogram.T def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Tuple ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Tuple = np.array_split(list(range(0 ,total_frames - chunk_frames + 1 ) ) ,3 ) if len(ranges[1] ) == 0: # if the audio is too short, we just use the first chunk lowerCAmelCase_ : List[Any] = [0] if len(ranges[2] ) == 0: # if the audio is too short, we just use the first chunk lowerCAmelCase_ : List[Any] = [0] # randomly choose index for each part lowerCAmelCase_ : str = np.random.choice(ranges[0] ) lowerCAmelCase_ : Optional[Any] = np.random.choice(ranges[1] ) lowerCAmelCase_ : Any = np.random.choice(ranges[2] ) lowerCAmelCase_ : str = mel[idx_front : idx_front + chunk_frames, :] lowerCAmelCase_ : Dict = mel[idx_middle : idx_middle + chunk_frames, :] lowerCAmelCase_ : Optional[Any] = mel[idx_back : idx_back + chunk_frames, :] lowerCAmelCase_ : List[str] = torch.tensor(mel[None, None, :] ) lowerCAmelCase_ : List[Any] = torch.nn.functional.interpolate( lowerCAmelCase__ ,size=[chunk_frames, 64] ,mode="bilinear" ,align_corners=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = mel_shrink[0][0].numpy() lowerCAmelCase_ : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 ) return mel_fusion def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : int ) -> np.array: '''simple docstring''' if waveform.shape[0] > max_length: if truncation == "rand_trunc": lowerCAmelCase_ : List[Any] = True # random crop to max_length (for compatibility) -> this should be handled by self.pad lowerCAmelCase_ : str = len(lowerCAmelCase__ ) - max_length lowerCAmelCase_ : Any = np.random.randint(0 ,overflow + 1 ) lowerCAmelCase_ : Dict = waveform[idx : idx + max_length] lowerCAmelCase_ : List[str] = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :] elif truncation == "fusion": lowerCAmelCase_ : Tuple = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters ) lowerCAmelCase_ : str = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed lowerCAmelCase_ : List[str] = mel.shape[0] if chunk_frames == total_frames: # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length. # In this case, we just use the whole audio. lowerCAmelCase_ : Dict = np.stack([mel, mel, mel, mel] ,axis=0 ) lowerCAmelCase_ : int = False else: lowerCAmelCase_ : str = self._random_mel_fusion(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Any = True else: raise NotImplementedError(f'''data_truncating {truncation} not implemented''' ) else: lowerCAmelCase_ : Dict = False # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding if waveform.shape[0] < max_length: if padding == "repeat": lowerCAmelCase_ : List[Any] = int(max_length / len(lowerCAmelCase__ ) ) lowerCAmelCase_ : int = np.stack(np.tile(lowerCAmelCase__ ,n_repeat + 1 ) )[:max_length] if padding == "repeatpad": lowerCAmelCase_ : Optional[Any] = int(max_length / len(lowerCAmelCase__ ) ) lowerCAmelCase_ : Tuple = np.stack(np.tile(lowerCAmelCase__ ,lowerCAmelCase__ ) ) lowerCAmelCase_ : List[Any] = np.pad(lowerCAmelCase__ ,(0, max_length - waveform.shape[0]) ,mode="constant" ,constant_values=0 ) if truncation == "fusion": lowerCAmelCase_ : int = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters ) lowerCAmelCase_ : Tuple = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 ) else: lowerCAmelCase_ : str = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :] return input_mel, longer def __call__( self : int ,lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCAmelCase__ : str = None ,lowerCAmelCase__ : Optional[str] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[Union[str, TensorType]] = None ,**lowerCAmelCase__ : List[Any] ,) -> BatchFeature: '''simple docstring''' lowerCAmelCase_ : List[str] = truncation if truncation is not None else self.truncation lowerCAmelCase_ : List[Any] = padding if padding else self.padding if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a''' f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input''' f''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' ) else: logger.warning( "It is strongly recommended to pass the `sampling_rate` argument to this function. " "Failing to do so can result in silent errors that might be hard to debug." ) lowerCAmelCase_ : Dict = isinstance(lowerCAmelCase__ ,np.ndarray ) and len(raw_speech.shape ) > 1 if is_batched_numpy and len(raw_speech.shape ) > 2: raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' ) lowerCAmelCase_ : Dict = is_batched_numpy or ( isinstance(lowerCAmelCase__ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) )) ) if is_batched: lowerCAmelCase_ : List[str] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for speech in raw_speech] elif not is_batched and not isinstance(lowerCAmelCase__ ,np.ndarray ): lowerCAmelCase_ : Tuple = np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) elif isinstance(lowerCAmelCase__ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ): lowerCAmelCase_ : Any = raw_speech.astype(np.floataa ) # always return batch if not is_batched: lowerCAmelCase_ : Any = [np.asarray(lowerCAmelCase__ )] # convert to mel spectrogram, truncate and pad if needed. lowerCAmelCase_ : Optional[Any] = [ self._get_input_mel(lowerCAmelCase__ ,max_length if max_length else self.nb_max_samples ,lowerCAmelCase__ ,lowerCAmelCase__ ) for waveform in raw_speech ] lowerCAmelCase_ : str = [] lowerCAmelCase_ : str = [] for mel, longer in padded_inputs: input_mel.append(lowerCAmelCase__ ) is_longer.append(lowerCAmelCase__ ) if truncation == "fusion" and sum(lowerCAmelCase__ ) == 0: # if no audio is longer than 10s, then randomly select one audio to be longer lowerCAmelCase_ : Any = np.random.randint(0 ,len(lowerCAmelCase__ ) ) lowerCAmelCase_ : Dict = True if isinstance(input_mel[0] ,lowerCAmelCase__ ): lowerCAmelCase_ : Optional[int] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for feature in input_mel] # is_longer is a list of bool lowerCAmelCase_ : List[Any] = [[longer] for longer in is_longer] lowerCAmelCase_ : Optional[Any] = {"input_features": input_mel, "is_longer": is_longer} lowerCAmelCase_ : Dict = BatchFeature(lowerCAmelCase__ ) if return_tensors is not None: lowerCAmelCase_ : List[str] = input_features.convert_to_tensors(lowerCAmelCase__ ) return input_features
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0
import json from typing import List, Optional, Tuple from tokenizers import pre_tokenizers, processors from ...tokenization_utils_base import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_bart import BartTokenizer _lowercase : int = logging.get_logger(__name__) _lowercase : Any = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''} # See all BART models at https://huggingface.co/models?filter=bart _lowercase : Optional[int] = { '''vocab_file''': { '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''', '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''', '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''', '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''', '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''', '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''', }, '''merges_file''': { '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''', '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''', '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''', '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''', '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''', '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''', }, '''tokenizer_file''': { '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''', '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''', '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''', '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''', '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''', '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''', }, } _lowercase : Union[str, Any] = { '''facebook/bart-base''': 1024, '''facebook/bart-large''': 1024, '''facebook/bart-large-mnli''': 1024, '''facebook/bart-large-cnn''': 1024, '''facebook/bart-large-xsum''': 1024, '''yjernite/bart_eli5''': 1024, } class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = ['input_ids', 'attention_mask'] UpperCamelCase_ = BartTokenizer def __init__( self : str ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Optional[Any]=None ,lowerCAmelCase__ : Optional[Any]=None ,lowerCAmelCase__ : Dict="replace" ,lowerCAmelCase__ : Dict="<s>" ,lowerCAmelCase__ : Tuple="</s>" ,lowerCAmelCase__ : int="</s>" ,lowerCAmelCase__ : int="<s>" ,lowerCAmelCase__ : Optional[int]="<unk>" ,lowerCAmelCase__ : List[str]="<pad>" ,lowerCAmelCase__ : Dict="<mask>" ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : Tuple=True ,**lowerCAmelCase__ : str ,) -> int: '''simple docstring''' super().__init__( lowerCAmelCase__ ,lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,errors=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,trim_offsets=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : Optional[int] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() ) if pre_tok_state.get("add_prefix_space" ,lowerCAmelCase__ ) != add_prefix_space: lowerCAmelCase_ : List[Any] = getattr(lowerCAmelCase__ ,pre_tok_state.pop("type" ) ) lowerCAmelCase_ : List[Any] = add_prefix_space lowerCAmelCase_ : List[str] = pre_tok_class(**lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = add_prefix_space # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__` lowerCAmelCase_ : int = "post_processor" lowerCAmelCase_ : str = getattr(self.backend_tokenizer ,lowerCAmelCase__ ,lowerCAmelCase__ ) if tokenizer_component_instance: lowerCAmelCase_ : str = json.loads(tokenizer_component_instance.__getstate__() ) # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` if "sep" in state: lowerCAmelCase_ : Union[str, Any] = tuple(state["sep"] ) if "cls" in state: lowerCAmelCase_ : Optional[Any] = tuple(state["cls"] ) lowerCAmelCase_ : Optional[int] = False if state.get("add_prefix_space" ,lowerCAmelCase__ ) != add_prefix_space: lowerCAmelCase_ : Union[str, Any] = add_prefix_space lowerCAmelCase_ : str = True if state.get("trim_offsets" ,lowerCAmelCase__ ) != trim_offsets: lowerCAmelCase_ : Optional[Any] = trim_offsets lowerCAmelCase_ : str = True if changes_to_apply: lowerCAmelCase_ : List[str] = getattr(lowerCAmelCase__ ,state.pop("type" ) ) lowerCAmelCase_ : List[Any] = component_class(**lowerCAmelCase__ ) setattr(self.backend_tokenizer ,lowerCAmelCase__ ,lowerCAmelCase__ ) @property def UpperCAmelCase_ ( self : Dict ) -> str: '''simple docstring''' if self._mask_token is None: if self.verbose: logger.error("Using mask_token, but it is not set yet." ) return None return str(self._mask_token ) @mask_token.setter def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[str] ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else value lowerCAmelCase_ : Optional[Any] = value def UpperCAmelCase_ ( self : str ,*lowerCAmelCase__ : Union[str, Any] ,**lowerCAmelCase__ : List[Any] ) -> BatchEncoding: '''simple docstring''' lowerCAmelCase_ : Dict = kwargs.get("is_split_into_words" ,lowerCAmelCase__ ) if is_split_into_words and not self.add_prefix_space: raise ValueError( f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True ''' "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*lowerCAmelCase__ ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Dict ,*lowerCAmelCase__ : List[Any] ,**lowerCAmelCase__ : Dict ) -> BatchEncoding: '''simple docstring''' lowerCAmelCase_ : Optional[int] = kwargs.get("is_split_into_words" ,lowerCAmelCase__ ) if is_split_into_words and not self.add_prefix_space: raise ValueError( f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True ''' "to use it with pretokenized inputs." ) return super()._encode_plus(*lowerCAmelCase__ ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' lowerCAmelCase_ : Tuple = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ ) return tuple(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : List[str]=None ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id] if token_ids_a is None: return output return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id] def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : List[str] = [self.sep_token_id] lowerCAmelCase_ : Any = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
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from multiprocessing import Lock, Pipe, Process # lock used to ensure that two processes do not access a pipe at the same time _lowercase = Lock() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): global process_lock # we perform n swaps since after n swaps we know we are sorted # we *could* stop early if we are sorted already, but it takes as long to # find out we are sorted as it does to sort the list with this algorithm for i in range(0 , 10): if (i + position) % 2 == 0 and r_send is not None: # send your value to your right neighbor process_lock.acquire() r_send[1].send(snake_case__) process_lock.release() # receive your right neighbor's value process_lock.acquire() lowerCAmelCase_ : Optional[Any] = rr_cv[0].recv() process_lock.release() # take the lower value since you are on the left lowerCAmelCase_ : Any = min(snake_case__ , snake_case__) elif (i + position) % 2 != 0 and l_send is not None: # send your value to your left neighbor process_lock.acquire() l_send[1].send(snake_case__) process_lock.release() # receive your left neighbor's value process_lock.acquire() lowerCAmelCase_ : str = lr_cv[0].recv() process_lock.release() # take the higher value since you are on the right lowerCAmelCase_ : Dict = max(snake_case__ , snake_case__) # after all swaps are performed, send the values back to main result_pipe[1].send(snake_case__) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Union[str, Any] = [] lowerCAmelCase_ : int = [] # initialize the list of pipes where the values will be retrieved for _ in arr: result_pipe.append(Pipe()) # creates the processes # the first and last process only have one neighbor so they are made outside # of the loop lowerCAmelCase_ : Tuple = Pipe() lowerCAmelCase_ : Optional[int] = Pipe() process_array_.append( Process( target=snake_case__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , )) lowerCAmelCase_ : int = temp_rs lowerCAmelCase_ : List[Any] = temp_rr for i in range(1 , len(snake_case__) - 1): lowerCAmelCase_ : Dict = Pipe() lowerCAmelCase_ : List[str] = Pipe() process_array_.append( Process( target=snake_case__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , )) lowerCAmelCase_ : Dict = temp_rs lowerCAmelCase_ : Optional[Any] = temp_rr process_array_.append( Process( target=snake_case__ , args=( len(snake_case__) - 1, arr[len(snake_case__) - 1], temp_ls, None, temp_lr, None, result_pipe[len(snake_case__) - 1], ) , )) # start the processes for p in process_array_: p.start() # wait for the processes to end and write their values to the list for p in range(0 , len(snake_case__)): lowerCAmelCase_ : Union[str, Any] = result_pipe[p][0].recv() process_array_[p].join() return arr def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = list(range(10 , 0 , -1)) print("Initial List") print(*snake_case__) lowerCAmelCase_ : Tuple = odd_even_transposition(snake_case__) print("Sorted List\n") print(*snake_case__) if __name__ == "__main__": main()
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from collections import defaultdict def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Tuple = first_str.lower().strip() lowerCAmelCase_ : Optional[Any] = second_str.lower().strip() # Remove whitespace lowerCAmelCase_ : Union[str, Any] = first_str.replace(" " , "") lowerCAmelCase_ : Union[str, Any] = second_str.replace(" " , "") # Strings of different lengths are not anagrams if len(snake_case__) != len(snake_case__): return False # Default values for count should be 0 lowerCAmelCase_ : defaultdict[str, int] = defaultdict(snake_case__) # For each character in input strings, # increment count in the corresponding for i in range(len(snake_case__)): count[first_str[i]] += 1 count[second_str[i]] -= 1 return all(_count == 0 for _count in count.values()) if __name__ == "__main__": from doctest import testmod testmod() _lowercase = input('''Enter the first string ''').strip() _lowercase = input('''Enter the second string ''').strip() _lowercase = check_anagrams(input_a, input_b) print(f"{input_a} and {input_b} are {'' if status else 'not '}anagrams.")
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from typing import Any def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ): _validation( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) # Creates data structures and fill initial step lowerCAmelCase_ : dict = {} lowerCAmelCase_ : dict = {} for state in states_space: lowerCAmelCase_ : List[Any] = observations_space[0] lowerCAmelCase_ : int = ( initial_probabilities[state] * emission_probabilities[state][observation] ) lowerCAmelCase_ : Dict = None # Fills the data structure with the probabilities of # different transitions and pointers to previous states for o in range(1 , len(snake_case__)): lowerCAmelCase_ : List[Any] = observations_space[o] lowerCAmelCase_ : Optional[Any] = observations_space[o - 1] for state in states_space: # Calculates the argmax for probability function lowerCAmelCase_ : List[Any] = "" lowerCAmelCase_ : Tuple = -1 for k_state in states_space: lowerCAmelCase_ : int = ( probabilities[(k_state, prior_observation)] * transition_probabilities[k_state][state] * emission_probabilities[state][observation] ) if probability > max_probability: lowerCAmelCase_ : List[str] = probability lowerCAmelCase_ : Optional[Any] = k_state # Update probabilities and pointers dicts lowerCAmelCase_ : Union[str, Any] = ( probabilities[(arg_max, prior_observation)] * transition_probabilities[arg_max][state] * emission_probabilities[state][observation] ) lowerCAmelCase_ : Any = arg_max # The final observation lowerCAmelCase_ : List[Any] = observations_space[len(snake_case__) - 1] # argmax for given final observation lowerCAmelCase_ : List[str] = "" lowerCAmelCase_ : List[str] = -1 for k_state in states_space: lowerCAmelCase_ : List[str] = probabilities[(k_state, final_observation)] if probability > max_probability: lowerCAmelCase_ : List[str] = probability lowerCAmelCase_ : Tuple = k_state lowerCAmelCase_ : str = arg_max # Process pointers backwards lowerCAmelCase_ : int = last_state lowerCAmelCase_ : int = [] for o in range(len(snake_case__) - 1 , -1 , -1): result.append(snake_case__) lowerCAmelCase_ : Optional[Any] = pointers[previous, observations_space[o]] result.reverse() return result def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ): _validate_not_empty( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) _validate_lists(snake_case__ , snake_case__) _validate_dicts( snake_case__ , snake_case__ , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ): if not all( [ observations_space, states_space, initial_probabilities, transition_probabilities, emission_probabilities, ]): raise ValueError("There's an empty parameter") def UpperCamelCase ( snake_case__ , snake_case__): _validate_list(snake_case__ , "observations_space") _validate_list(snake_case__ , "states_space") def UpperCamelCase ( snake_case__ , snake_case__): if not isinstance(_object , snake_case__): lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list''' raise ValueError(snake_case__) else: for x in _object: if not isinstance(snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list of strings''' raise ValueError(snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ): _validate_dict(snake_case__ , "initial_probabilities" , snake_case__) _validate_nested_dict(snake_case__ , "transition_probabilities") _validate_nested_dict(snake_case__ , "emission_probabilities") def UpperCamelCase ( snake_case__ , snake_case__): _validate_dict(_object , snake_case__ , snake_case__) for x in _object.values(): _validate_dict(snake_case__ , snake_case__ , snake_case__ , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = False): if not isinstance(_object , snake_case__): lowerCAmelCase_ : List[str] = F'''{var_name} must be a dict''' raise ValueError(snake_case__) if not all(isinstance(snake_case__ , snake_case__) for x in _object): lowerCAmelCase_ : Dict = F'''{var_name} all keys must be strings''' raise ValueError(snake_case__) if not all(isinstance(snake_case__ , snake_case__) for x in _object.values()): lowerCAmelCase_ : Union[str, Any] = "nested dictionary " if nested else "" lowerCAmelCase_ : Any = F'''{var_name} {nested_text}all values must be {value_type.__name__}''' raise ValueError(snake_case__) if __name__ == "__main__": from doctest import testmod testmod()
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_lowercase = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5] _lowercase = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5] _lowercase = { 0: '''Sunday''', 1: '''Monday''', 2: '''Tuesday''', 3: '''Wednesday''', 4: '''Thursday''', 5: '''Friday''', 6: '''Saturday''', } def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): assert len(str(snake_case__)) > 2, "year should be in YYYY format" assert 1 <= month <= 12, "month should be between 1 to 12" assert 1 <= day <= 31, "day should be between 1 to 31" # Doomsday algorithm: lowerCAmelCase_ : int = year // 1_00 lowerCAmelCase_ : Any = (5 * (century % 4) + 2) % 7 lowerCAmelCase_ : int = year % 1_00 lowerCAmelCase_ : Tuple = centurian % 12 lowerCAmelCase_ : List[str] = ( (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor ) % 7 lowerCAmelCase_ : Union[str, Any] = ( DOOMSDAY_NOT_LEAP[month - 1] if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0) else DOOMSDAY_LEAP[month - 1] ) lowerCAmelCase_ : str = (dooms_day + day - day_anchor) % 7 return WEEK_DAY_NAMES[week_day] if __name__ == "__main__": import doctest doctest.testmod()
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor from ..utils import is_datasets_available from .base import PipelineTool if is_datasets_available(): from datasets import load_dataset class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = 'microsoft/speecht5_tts' UpperCamelCase_ = ( 'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the ' 'text to read (in English) and returns a waveform object containing the sound.' ) UpperCamelCase_ = 'text_reader' UpperCamelCase_ = SpeechTaProcessor UpperCamelCase_ = SpeechTaForTextToSpeech UpperCamelCase_ = SpeechTaHifiGan UpperCamelCase_ = ['text'] UpperCamelCase_ = ['audio'] def UpperCAmelCase_ ( self : Dict ) -> Any: '''simple docstring''' if self.post_processor is None: lowerCAmelCase_ : Any = "microsoft/speecht5_hifigan" super().setup() def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Any = self.pre_processor(text=lowerCAmelCase__ ,return_tensors="pt" ,truncation=lowerCAmelCase__ ) if speaker_embeddings is None: if not is_datasets_available(): raise ImportError("Datasets needs to be installed if not passing speaker embeddings." ) lowerCAmelCase_ : str = load_dataset("Matthijs/cmu-arctic-xvectors" ,split="validation" ) lowerCAmelCase_ : List[Any] = torch.tensor(embeddings_dataset[73_05]["xvector"] ).unsqueeze(0 ) return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings} def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]: '''simple docstring''' with torch.no_grad(): return self.model.generate_speech(**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : str ) -> Any: '''simple docstring''' with torch.no_grad(): return self.post_processor(lowerCAmelCase__ ).cpu().detach()
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import copy import re class __snake_case : """simple docstring""" UpperCamelCase_ = 'hp' UpperCamelCase_ = {} UpperCamelCase_ = None @classmethod def UpperCAmelCase_ ( cls : List[str] ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Tuple ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : Dict = prefix lowerCAmelCase_ : List[Any] = defaults cls.build_naming_info() @staticmethod def UpperCAmelCase_ ( lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : List[str] ) -> List[Any]: '''simple docstring''' if len(lowerCAmelCase__ ) == 0: return "" lowerCAmelCase_ : Dict = None if any(char.isdigit() for char in word ): raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number''' ) if word in info["short_word"]: return info["short_word"][word] for prefix_len in range(1 ,len(lowerCAmelCase__ ) + 1 ): lowerCAmelCase_ : Tuple = word[:prefix_len] if prefix in info["reverse_short_word"]: continue else: lowerCAmelCase_ : Any = prefix break if short_word is None: # Paranoid fallback def int_to_alphabetic(lowerCAmelCase__ : Union[str, Any] ): lowerCAmelCase_ : str = "" while integer != 0: lowerCAmelCase_ : Dict = chr(ord("A" ) + integer % 10 ) + s integer //= 10 return s lowerCAmelCase_ : Tuple = 0 while True: lowerCAmelCase_ : Optional[Any] = word + "#" + int_to_alphabetic(lowerCAmelCase__ ) if sword in info["reverse_short_word"]: continue else: lowerCAmelCase_ : str = sword break lowerCAmelCase_ : Union[str, Any] = short_word lowerCAmelCase_ : Dict = word return short_word @staticmethod def UpperCAmelCase_ ( lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[str] ) -> int: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = param_name.split("_" ) lowerCAmelCase_ : Dict = [TrialShortNamer.shortname_for_word(lowerCAmelCase__ ,lowerCAmelCase__ ) for word in words] # We try to create a separatorless short name, but if there is a collision we have to fallback # to a separated short name lowerCAmelCase_ : Tuple = ["", "_"] for separator in separators: lowerCAmelCase_ : List[Any] = separator.join(lowerCAmelCase__ ) if shortname not in info["reverse_short_param"]: lowerCAmelCase_ : List[str] = shortname lowerCAmelCase_ : Optional[int] = param_name return shortname return param_name @staticmethod def UpperCAmelCase_ ( lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : str ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Dict = TrialShortNamer.shortname_for_key(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Any = short_name lowerCAmelCase_ : Union[str, Any] = param_name @classmethod def UpperCAmelCase_ ( cls : Any ) -> Optional[int]: '''simple docstring''' if cls.NAMING_INFO is not None: return lowerCAmelCase_ : Optional[int] = { "short_word": {}, "reverse_short_word": {}, "short_param": {}, "reverse_short_param": {}, } lowerCAmelCase_ : Optional[int] = list(cls.DEFAULTS.keys() ) for k in field_keys: cls.add_new_param_name(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = info @classmethod def UpperCAmelCase_ ( cls : Any ,lowerCAmelCase__ : Tuple ) -> str: '''simple docstring''' cls.build_naming_info() assert cls.PREFIX is not None lowerCAmelCase_ : Tuple = [copy.copy(cls.PREFIX )] for k, v in params.items(): if k not in cls.DEFAULTS: raise Exception(f'''You should provide a default value for the param name {k} with value {v}''' ) if v == cls.DEFAULTS[k]: # The default value is not added to the name continue lowerCAmelCase_ : Optional[int] = cls.NAMING_INFO["short_param"][k] if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): lowerCAmelCase_ : Any = 1 if v else 0 lowerCAmelCase_ : List[Any] = "" if isinstance(lowerCAmelCase__ ,(int, float) ) else "-" lowerCAmelCase_ : Dict = f'''{key}{sep}{v}''' name.append(lowerCAmelCase__ ) return "_".join(lowerCAmelCase__ ) @classmethod def UpperCAmelCase_ ( cls : int ,lowerCAmelCase__ : Any ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : List[str] = repr[len(cls.PREFIX ) + 1 :] if repr == "": lowerCAmelCase_ : Union[str, Any] = [] else: lowerCAmelCase_ : Union[str, Any] = repr.split("_" ) lowerCAmelCase_ : Tuple = {} for value in values: if "-" in value: lowerCAmelCase_ : Tuple = value.split("-" ) else: lowerCAmelCase_ : List[Any] = re.sub("[0-9.]" ,"" ,lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = float(re.sub("[^0-9.]" ,"" ,lowerCAmelCase__ ) ) lowerCAmelCase_ : str = cls.NAMING_INFO["reverse_short_param"][p_k] lowerCAmelCase_ : int = p_v for k in cls.DEFAULTS: if k not in parameters: lowerCAmelCase_ : List[Any] = cls.DEFAULTS[k] return parameters
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import argparse import collections import json import os import re import string import sys import numpy as np _lowercase = re.compile(r'''\b(a|an|the)\b''', re.UNICODE) _lowercase = None def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0.") parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file.") parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions.") parser.add_argument( "--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout).") parser.add_argument( "--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer.") parser.add_argument( "--na-prob-thresh" , "-t" , type=snake_case__ , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , ) parser.add_argument( "--out-image-dir" , "-p" , metavar="out_images" , default=snake_case__ , help="Save precision-recall curves to directory.") parser.add_argument("--verbose" , "-v" , action="store_true") if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() def UpperCamelCase ( snake_case__): lowerCAmelCase_ : str = {} for article in dataset: for p in article["paragraphs"]: for qa in p["qas"]: lowerCAmelCase_ : Dict = bool(qa["answers"]["text"]) return qid_to_has_ans def UpperCamelCase ( snake_case__): def remove_articles(snake_case__): return ARTICLES_REGEX.sub(" " , snake_case__) def white_space_fix(snake_case__): return " ".join(text.split()) def remove_punc(snake_case__): lowerCAmelCase_ : Optional[int] = set(string.punctuation) return "".join(ch for ch in text if ch not in exclude) def lower(snake_case__): return text.lower() return white_space_fix(remove_articles(remove_punc(lower(snake_case__)))) def UpperCamelCase ( snake_case__): if not s: return [] return normalize_answer(snake_case__).split() def UpperCamelCase ( snake_case__ , snake_case__): return int(normalize_answer(snake_case__) == normalize_answer(snake_case__)) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = get_tokens(snake_case__) lowerCAmelCase_ : Union[str, Any] = get_tokens(snake_case__) lowerCAmelCase_ : Any = collections.Counter(snake_case__) & collections.Counter(snake_case__) lowerCAmelCase_ : Dict = sum(common.values()) if len(snake_case__) == 0 or len(snake_case__) == 0: # If either is no-answer, then F1 is 1 if they agree, 0 otherwise return int(gold_toks == pred_toks) if num_same == 0: return 0 lowerCAmelCase_ : List[Any] = 1.0 * num_same / len(snake_case__) lowerCAmelCase_ : int = 1.0 * num_same / len(snake_case__) lowerCAmelCase_ : List[Any] = (2 * precision * recall) / (precision + recall) return fa def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Tuple = {} lowerCAmelCase_ : int = {} for article in dataset: for p in article["paragraphs"]: for qa in p["qas"]: lowerCAmelCase_ : int = qa["id"] lowerCAmelCase_ : Any = [t for t in qa["answers"]["text"] if normalize_answer(snake_case__)] if not gold_answers: # For unanswerable questions, only correct answer is empty string lowerCAmelCase_ : Any = [""] if qid not in preds: print(F'''Missing prediction for {qid}''') continue lowerCAmelCase_ : Tuple = preds[qid] # Take max over all gold answers lowerCAmelCase_ : Any = max(compute_exact(snake_case__ , snake_case__) for a in gold_answers) lowerCAmelCase_ : Optional[Any] = max(compute_fa(snake_case__ , snake_case__) for a in gold_answers) return exact_scores, fa_scores def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Dict = {} for qid, s in scores.items(): lowerCAmelCase_ : List[Any] = na_probs[qid] > na_prob_thresh if pred_na: lowerCAmelCase_ : List[str] = float(not qid_to_has_ans[qid]) else: lowerCAmelCase_ : Union[str, Any] = s return new_scores def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None): if not qid_list: lowerCAmelCase_ : Any = len(snake_case__) return collections.OrderedDict( [ ("exact", 100.0 * sum(exact_scores.values()) / total), ("f1", 100.0 * sum(fa_scores.values()) / total), ("total", total), ]) else: lowerCAmelCase_ : Tuple = len(snake_case__) return collections.OrderedDict( [ ("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total), ("f1", 100.0 * sum(fa_scores[k] for k in qid_list) / total), ("total", total), ]) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): for k in new_eval: lowerCAmelCase_ : Union[str, Any] = new_eval[k] def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): plt.step(snake_case__ , snake_case__ , color="b" , alpha=0.2 , where="post") plt.fill_between(snake_case__ , snake_case__ , step="post" , alpha=0.2 , color="b") plt.xlabel("Recall") plt.ylabel("Precision") plt.xlim([0.0, 1.05]) plt.ylim([0.0, 1.05]) plt.title(snake_case__) plt.savefig(snake_case__) plt.clf() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__=None , snake_case__=None): lowerCAmelCase_ : List[Any] = sorted(snake_case__ , key=lambda snake_case__: na_probs[k]) lowerCAmelCase_ : Dict = 0.0 lowerCAmelCase_ : int = 1.0 lowerCAmelCase_ : List[str] = 0.0 lowerCAmelCase_ : Tuple = [1.0] lowerCAmelCase_ : Tuple = [0.0] lowerCAmelCase_ : Dict = 0.0 for i, qid in enumerate(snake_case__): if qid_to_has_ans[qid]: true_pos += scores[qid] lowerCAmelCase_ : str = true_pos / float(i + 1) lowerCAmelCase_ : Union[str, Any] = true_pos / float(snake_case__) if i == len(snake_case__) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]: # i.e., if we can put a threshold after this point avg_prec += cur_p * (cur_r - recalls[-1]) precisions.append(snake_case__) recalls.append(snake_case__) if out_image: plot_pr_curve(snake_case__ , snake_case__ , snake_case__ , snake_case__) return {"ap": 100.0 * avg_prec} def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): if out_image_dir and not os.path.exists(snake_case__): os.makedirs(snake_case__) lowerCAmelCase_ : Any = sum(1 for v in qid_to_has_ans.values() if v) if num_true_pos == 0: return lowerCAmelCase_ : Any = make_precision_recall_eval( snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_exact.png") , title="Precision-Recall curve for Exact Match score" , ) lowerCAmelCase_ : Dict = make_precision_recall_eval( snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_f1.png") , title="Precision-Recall curve for F1 score" , ) lowerCAmelCase_ : Dict = {k: float(snake_case__) for k, v in qid_to_has_ans.items()} lowerCAmelCase_ : str = make_precision_recall_eval( snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_oracle.png") , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , ) merge_eval(snake_case__ , snake_case__ , "pr_exact") merge_eval(snake_case__ , snake_case__ , "pr_f1") merge_eval(snake_case__ , snake_case__ , "pr_oracle") def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): if not qid_list: return lowerCAmelCase_ : Optional[Any] = [na_probs[k] for k in qid_list] lowerCAmelCase_ : Dict = np.ones_like(snake_case__) / float(len(snake_case__)) plt.hist(snake_case__ , weights=snake_case__ , bins=20 , range=(0.0, 1.0)) plt.xlabel("Model probability of no-answer") plt.ylabel("Proportion of dataset") plt.title(F'''Histogram of no-answer probability: {name}''') plt.savefig(os.path.join(snake_case__ , F'''na_prob_hist_{name}.png''')) plt.clf() def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Dict = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]) lowerCAmelCase_ : str = num_no_ans lowerCAmelCase_ : List[str] = cur_score lowerCAmelCase_ : List[Any] = 0.0 lowerCAmelCase_ : str = sorted(snake_case__ , key=lambda snake_case__: na_probs[k]) for i, qid in enumerate(snake_case__): if qid not in scores: continue if qid_to_has_ans[qid]: lowerCAmelCase_ : Union[str, Any] = scores[qid] else: if preds[qid]: lowerCAmelCase_ : List[Any] = -1 else: lowerCAmelCase_ : List[str] = 0 cur_score += diff if cur_score > best_score: lowerCAmelCase_ : Optional[Any] = cur_score lowerCAmelCase_ : Optional[int] = na_probs[qid] return 100.0 * best_score / len(snake_case__), best_thresh def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ , lowerCAmelCase_ : Dict = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = best_exact lowerCAmelCase_ : List[str] = exact_thresh lowerCAmelCase_ : Any = best_fa lowerCAmelCase_ : List[str] = fa_thresh def UpperCamelCase ( ): with open(OPTS.data_file) as f: lowerCAmelCase_ : Optional[int] = json.load(snake_case__) lowerCAmelCase_ : List[Any] = dataset_json["data"] with open(OPTS.pred_file) as f: lowerCAmelCase_ : int = json.load(snake_case__) if OPTS.na_prob_file: with open(OPTS.na_prob_file) as f: lowerCAmelCase_ : Optional[int] = json.load(snake_case__) else: lowerCAmelCase_ : List[Any] = {k: 0.0 for k in preds} lowerCAmelCase_ : Tuple = make_qid_to_has_ans(snake_case__) # maps qid to True/False lowerCAmelCase_ : Any = [k for k, v in qid_to_has_ans.items() if v] lowerCAmelCase_ : List[str] = [k for k, v in qid_to_has_ans.items() if not v] lowerCAmelCase_ , lowerCAmelCase_ : Dict = get_raw_scores(snake_case__ , snake_case__) lowerCAmelCase_ : str = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh) lowerCAmelCase_ : Dict = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh) lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__) if has_ans_qids: lowerCAmelCase_ : str = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__) merge_eval(snake_case__ , snake_case__ , "HasAns") if no_ans_qids: lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__) merge_eval(snake_case__ , snake_case__ , "NoAns") if OPTS.na_prob_file: find_all_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__) if OPTS.na_prob_file and OPTS.out_image_dir: run_precision_recall_analysis(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , OPTS.out_image_dir) histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "hasAns") histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "noAns") if OPTS.out_file: with open(OPTS.out_file , "w") as f: json.dump(snake_case__ , snake_case__) else: print(json.dumps(snake_case__ , indent=2)) if __name__ == "__main__": _lowercase = parse_args() if OPTS.out_image_dir: import matplotlib matplotlib.use('''Agg''') import matplotlib.pyplot as plt main()
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def UpperCamelCase ( snake_case__ , snake_case__): return price * (1 + tax_rate) if __name__ == "__main__": print(f"{price_plus_tax(100, 0.25) = }") print(f"{price_plus_tax(125.50, 0.05) = }")
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from math import sqrt def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[int] = 0 for i in range(1 , int(sqrt(snake_case__) + 1)): if n % i == 0 and i != sqrt(snake_case__): total += i + n // i elif i == sqrt(snake_case__): total += i return total - n def UpperCamelCase ( snake_case__ = 1_00_00): lowerCAmelCase_ : int = sum( i for i in range(1 , snake_case__) if sum_of_divisors(sum_of_divisors(snake_case__)) == i and sum_of_divisors(snake_case__) != i) return total if __name__ == "__main__": print(solution(int(str(input()).strip())))
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# Author: OMKAR PATHAK, Nwachukwu Chidiebere # Use a Python dictionary to construct the graph. from __future__ import annotations from pprint import pformat from typing import Generic, TypeVar _lowercase = TypeVar('''T''') class __snake_case ( Generic[T] ): """simple docstring""" def __init__( self : Union[str, Any] ,lowerCAmelCase__ : bool = True ) -> None: '''simple docstring''' lowerCAmelCase_ : dict[T, list[T]] = {} # dictionary of lists lowerCAmelCase_ : Optional[int] = directed def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : T ,lowerCAmelCase__ : T ) -> GraphAdjacencyList[T]: '''simple docstring''' if not self.directed: # For undirected graphs # if both source vertex and destination vertex are both present in the # adjacency list, add destination vertex to source vertex list of adjacent # vertices and add source vertex to destination vertex list of adjacent # vertices. if source_vertex in self.adj_list and destination_vertex in self.adj_list: self.adj_list[source_vertex].append(lowerCAmelCase__ ) self.adj_list[destination_vertex].append(lowerCAmelCase__ ) # if only source vertex is present in adjacency list, add destination vertex # to source vertex list of adjacent vertices, then create a new vertex with # destination vertex as key and assign a list containing the source vertex # as it's first adjacent vertex. elif source_vertex in self.adj_list: self.adj_list[source_vertex].append(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = [source_vertex] # if only destination vertex is present in adjacency list, add source vertex # to destination vertex list of adjacent vertices, then create a new vertex # with source vertex as key and assign a list containing the source vertex # as it's first adjacent vertex. elif destination_vertex in self.adj_list: self.adj_list[destination_vertex].append(lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = [destination_vertex] # if both source vertex and destination vertex are not present in adjacency # list, create a new vertex with source vertex as key and assign a list # containing the destination vertex as it's first adjacent vertex also # create a new vertex with destination vertex as key and assign a list # containing the source vertex as it's first adjacent vertex. else: lowerCAmelCase_ : Any = [destination_vertex] lowerCAmelCase_ : Optional[Any] = [source_vertex] else: # For directed graphs # if both source vertex and destination vertex are present in adjacency # list, add destination vertex to source vertex list of adjacent vertices. if source_vertex in self.adj_list and destination_vertex in self.adj_list: self.adj_list[source_vertex].append(lowerCAmelCase__ ) # if only source vertex is present in adjacency list, add destination # vertex to source vertex list of adjacent vertices and create a new vertex # with destination vertex as key, which has no adjacent vertex elif source_vertex in self.adj_list: self.adj_list[source_vertex].append(lowerCAmelCase__ ) lowerCAmelCase_ : int = [] # if only destination vertex is present in adjacency list, create a new # vertex with source vertex as key and assign a list containing destination # vertex as first adjacent vertex elif destination_vertex in self.adj_list: lowerCAmelCase_ : List[Any] = [destination_vertex] # if both source vertex and destination vertex are not present in adjacency # list, create a new vertex with source vertex as key and a list containing # destination vertex as it's first adjacent vertex. Then create a new vertex # with destination vertex as key, which has no adjacent vertex else: lowerCAmelCase_ : List[Any] = [destination_vertex] lowerCAmelCase_ : Union[str, Any] = [] return self def __repr__( self : List[Any] ) -> str: '''simple docstring''' return pformat(self.adj_list )
703
from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_speech_available, is_tf_available, is_torch_available, ) _lowercase = { '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''], '''processing_speech_to_text''': ['''Speech2TextProcessor'''], } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ['''Speech2TextTokenizer'''] try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ['''Speech2TextFeatureExtractor'''] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''TFSpeech2TextForConditionalGeneration''', '''TFSpeech2TextModel''', '''TFSpeech2TextPreTrainedModel''', ] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''Speech2TextForConditionalGeneration''', '''Speech2TextModel''', '''Speech2TextPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig from .processing_speech_to_text import SpeechaTextProcessor try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_speech_to_text import SpeechaTextTokenizer try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_speech_to_text import ( TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, TFSpeechaTextForConditionalGeneration, TFSpeechaTextModel, TFSpeechaTextPreTrainedModel, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_speech_to_text import ( SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, SpeechaTextForConditionalGeneration, SpeechaTextModel, SpeechaTextPreTrainedModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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import copy from ...configuration_utils import PretrainedConfig from ...utils import logging from ..auto.configuration_auto import CONFIG_MAPPING _lowercase = logging.get_logger(__name__) class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = 'upernet' def __init__( self : Dict ,lowerCAmelCase__ : int=None ,lowerCAmelCase__ : Tuple=5_12 ,lowerCAmelCase__ : str=0.02 ,lowerCAmelCase__ : List[str]=[1, 2, 3, 6] ,lowerCAmelCase__ : str=True ,lowerCAmelCase__ : Union[str, Any]=0.4 ,lowerCAmelCase__ : Tuple=3_84 ,lowerCAmelCase__ : Dict=2_56 ,lowerCAmelCase__ : Dict=1 ,lowerCAmelCase__ : int=False ,lowerCAmelCase__ : Tuple=2_55 ,**lowerCAmelCase__ : Optional[int] ,) -> Tuple: '''simple docstring''' super().__init__(**lowerCAmelCase__ ) if backbone_config is None: logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." ) lowerCAmelCase_ : List[Any] = CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"] ) elif isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): lowerCAmelCase_ : Dict = backbone_config.get("model_type" ) lowerCAmelCase_ : List[Any] = CONFIG_MAPPING[backbone_model_type] lowerCAmelCase_ : str = config_class.from_dict(lowerCAmelCase__ ) lowerCAmelCase_ : List[str] = backbone_config lowerCAmelCase_ : List[str] = hidden_size lowerCAmelCase_ : Optional[Any] = initializer_range lowerCAmelCase_ : Union[str, Any] = pool_scales lowerCAmelCase_ : Any = use_auxiliary_head lowerCAmelCase_ : int = auxiliary_loss_weight lowerCAmelCase_ : Union[str, Any] = auxiliary_in_channels lowerCAmelCase_ : List[Any] = auxiliary_channels lowerCAmelCase_ : Union[str, Any] = auxiliary_num_convs lowerCAmelCase_ : Optional[int] = auxiliary_concat_input lowerCAmelCase_ : int = loss_ignore_index def UpperCAmelCase_ ( self : int ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Dict = copy.deepcopy(self.__dict__ ) lowerCAmelCase_ : List[str] = self.backbone_config.to_dict() lowerCAmelCase_ : Optional[int] = self.__class__.model_type return output
704
import json import os from functools import lru_cache from typing import List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''} _lowercase = { '''vocab_file''': { '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''', '''allenai/longformer-large-4096''': ( '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json''' ), '''allenai/longformer-large-4096-finetuned-triviaqa''': ( '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json''' ), '''allenai/longformer-base-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json''' ), '''allenai/longformer-large-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json''' ), }, '''merges_file''': { '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''', '''allenai/longformer-large-4096''': ( '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt''' ), '''allenai/longformer-large-4096-finetuned-triviaqa''': ( '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt''' ), '''allenai/longformer-base-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt''' ), '''allenai/longformer-large-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt''' ), }, } _lowercase = { '''allenai/longformer-base-4096''': 4096, '''allenai/longformer-large-4096''': 4096, '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096, '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096, '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096, } @lru_cache() # Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode def UpperCamelCase ( ): lowerCAmelCase_ : str = ( list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1)) ) lowerCAmelCase_ : Tuple = bs[:] lowerCAmelCase_ : Dict = 0 for b in range(2**8): if b not in bs: bs.append(snake_case__) cs.append(2**8 + n) n += 1 lowerCAmelCase_ : Union[str, Any] = [chr(snake_case__) for n in cs] return dict(zip(snake_case__ , snake_case__)) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[Any] = set() lowerCAmelCase_ : List[Any] = word[0] for char in word[1:]: pairs.add((prev_char, char)) lowerCAmelCase_ : Union[str, Any] = char return pairs class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = ['input_ids', 'attention_mask'] def __init__( self : str ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[Any]="replace" ,lowerCAmelCase__ : Dict="<s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : Optional[Any]="<s>" ,lowerCAmelCase__ : List[Any]="<unk>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : int="<mask>" ,lowerCAmelCase__ : Any=False ,**lowerCAmelCase__ : int ,) -> Dict: '''simple docstring''' lowerCAmelCase_ : Optional[int] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token lowerCAmelCase_ : Dict = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else pad_token # Mask token behave like a normal word, i.e. include the space before it lowerCAmelCase_ : Optional[Any] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else mask_token super().__init__( errors=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,**lowerCAmelCase__ ,) with open(lowerCAmelCase__ ,encoding="utf-8" ) as vocab_handle: lowerCAmelCase_ : List[Any] = json.load(lowerCAmelCase__ ) lowerCAmelCase_ : Dict = {v: k for k, v in self.encoder.items()} lowerCAmelCase_ : List[Any] = errors # how to handle errors in decoding lowerCAmelCase_ : Optional[Any] = bytes_to_unicode() lowerCAmelCase_ : int = {v: k for k, v in self.byte_encoder.items()} with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle: lowerCAmelCase_ : Union[str, Any] = merges_handle.read().split("\n" )[1:-1] lowerCAmelCase_ : Dict = [tuple(merge.split() ) for merge in bpe_merges] lowerCAmelCase_ : Dict = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) ) lowerCAmelCase_ : Any = {} lowerCAmelCase_ : int = add_prefix_space # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions lowerCAmelCase_ : Optional[Any] = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" ) @property def UpperCAmelCase_ ( self : Optional[int] ) -> Any: '''simple docstring''' return len(self.encoder ) def UpperCAmelCase_ ( self : Any ) -> Optional[int]: '''simple docstring''' return dict(self.encoder ,**self.added_tokens_encoder ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[str] ) -> List[Any]: '''simple docstring''' if token in self.cache: return self.cache[token] lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = get_pairs(lowerCAmelCase__ ) if not pairs: return token while True: lowerCAmelCase_ : Dict = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) ) if bigram not in self.bpe_ranks: break lowerCAmelCase_ , lowerCAmelCase_ : Dict = bigram lowerCAmelCase_ : Optional[Any] = [] lowerCAmelCase_ : Any = 0 while i < len(lowerCAmelCase__ ): try: lowerCAmelCase_ : Optional[int] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ ) except ValueError: new_word.extend(word[i:] ) break else: new_word.extend(word[i:j] ) lowerCAmelCase_ : Tuple = j if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second: new_word.append(first + second ) i += 2 else: new_word.append(word[i] ) i += 1 lowerCAmelCase_ : Optional[Any] = tuple(lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = new_word if len(lowerCAmelCase__ ) == 1: break else: lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = " ".join(lowerCAmelCase__ ) lowerCAmelCase_ : Any = word return word def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Dict: '''simple docstring''' lowerCAmelCase_ : Dict = [] for token in re.findall(self.pat ,lowerCAmelCase__ ): lowerCAmelCase_ : List[str] = "".join( self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__ ).split(" " ) ) return bpe_tokens def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ) -> Tuple: '''simple docstring''' return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]: '''simple docstring''' return self.decoder.get(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Any: '''simple docstring''' lowerCAmelCase_ : Optional[int] = "".join(lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors ) return text def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' if not os.path.isdir(lowerCAmelCase__ ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return lowerCAmelCase_ : Optional[Any] = os.path.join( lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase_ : Tuple = os.path.join( lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as f: f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowerCAmelCase__ ,ensure_ascii=lowerCAmelCase__ ) + "\n" ) lowerCAmelCase_ : Tuple = 0 with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as writer: writer.write("#version: 0.2\n" ) for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda lowerCAmelCase__ : kv[1] ): if index != token_index: logger.warning( f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.''' " Please check that the tokenizer is not corrupted!" ) lowerCAmelCase_ : Optional[Any] = token_index writer.write(" ".join(lowerCAmelCase__ ) + "\n" ) index += 1 return vocab_file, merge_file def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] lowerCAmelCase_ : List[Any] = [self.cls_token_id] lowerCAmelCase_ : List[str] = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]: '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=lowerCAmelCase__ ,token_ids_a=lowerCAmelCase__ ,already_has_special_tokens=lowerCAmelCase__ ) if token_ids_a is None: return [1] + ([0] * len(lowerCAmelCase__ )) + [1] return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1] def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : List[str] = [self.sep_token_id] lowerCAmelCase_ : Tuple = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Optional[int] ) -> Any: '''simple docstring''' lowerCAmelCase_ : int = kwargs.pop("add_prefix_space" ,self.add_prefix_space ) if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__ ) > 0 and not text[0].isspace()): lowerCAmelCase_ : Union[str, Any] = " " + text return (text, kwargs)
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'''simple docstring''' # Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available _lowercase = { '''configuration_cpmant''': ['''CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CpmAntConfig'''], '''tokenization_cpmant''': ['''CpmAntTokenizer'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''CpmAntForCausalLM''', '''CpmAntModel''', '''CpmAntPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig from .tokenization_cpmant import CpmAntTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_cpmant import ( CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST, CpmAntForCausalLM, CpmAntModel, CpmAntPreTrainedModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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from collections.abc import Iterable from typing import Any class __snake_case : """simple docstring""" def __init__( self : Optional[Any] ,lowerCAmelCase__ : int | None = None ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : Dict = value lowerCAmelCase_ : Node | None = None # Added in order to delete a node easier lowerCAmelCase_ : Node | None = None lowerCAmelCase_ : Node | None = None def __repr__( self : Union[str, Any] ) -> str: '''simple docstring''' from pprint import pformat if self.left is None and self.right is None: return str(self.value ) return pformat({f'''{self.value}''': (self.left, self.right)} ,indent=1 ) class __snake_case : """simple docstring""" def __init__( self : Optional[Any] ,lowerCAmelCase__ : Node | None = None ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : List[Any] = root def __str__( self : Dict ) -> str: '''simple docstring''' return str(self.root ) def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Node ,lowerCAmelCase__ : Node | None ) -> None: '''simple docstring''' if new_children is not None: # reset its kids lowerCAmelCase_ : Optional[int] = node.parent if node.parent is not None: # reset its parent if self.is_right(lowerCAmelCase__ ): # If it is the right children lowerCAmelCase_ : List[Any] = new_children else: lowerCAmelCase_ : List[Any] = new_children else: lowerCAmelCase_ : Any = new_children def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Node ) -> bool: '''simple docstring''' if node.parent and node.parent.right: return node == node.parent.right return False def UpperCAmelCase_ ( self : List[str] ) -> bool: '''simple docstring''' return self.root is None def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Union[str, Any] ) -> None: '''simple docstring''' lowerCAmelCase_ : str = Node(lowerCAmelCase__ ) # create a new Node if self.empty(): # if Tree is empty lowerCAmelCase_ : Optional[int] = new_node # set its root else: # Tree is not empty lowerCAmelCase_ : List[Any] = self.root # from root if parent_node is None: return while True: # While we don't get to a leaf if value < parent_node.value: # We go left if parent_node.left is None: lowerCAmelCase_ : Dict = new_node # We insert the new node in a leaf break else: lowerCAmelCase_ : List[str] = parent_node.left else: if parent_node.right is None: lowerCAmelCase_ : Dict = new_node break else: lowerCAmelCase_ : str = parent_node.right lowerCAmelCase_ : Optional[int] = parent_node def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : Tuple ) -> None: '''simple docstring''' for value in values: self.__insert(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Optional[int] ) -> Node | None: '''simple docstring''' if self.empty(): raise IndexError("Warning: Tree is empty! please use another." ) else: lowerCAmelCase_ : Dict = self.root # use lazy evaluation here to avoid NoneType Attribute error while node is not None and node.value is not value: lowerCAmelCase_ : Union[str, Any] = node.left if value < node.value else node.right return node def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None: '''simple docstring''' if node is None: if self.root is None: return None lowerCAmelCase_ : Dict = self.root if not self.empty(): while node.right is not None: lowerCAmelCase_ : Union[str, Any] = node.right return node def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None: '''simple docstring''' if node is None: lowerCAmelCase_ : Dict = self.root if self.root is None: return None if not self.empty(): lowerCAmelCase_ : Dict = self.root while node.left is not None: lowerCAmelCase_ : Union[str, Any] = node.left return node def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : int ) -> None: '''simple docstring''' lowerCAmelCase_ : Dict = self.search(lowerCAmelCase__ ) # Look for the node with that label if node is not None: if node.left is None and node.right is None: # If it has no children self.__reassign_nodes(lowerCAmelCase__ ,lowerCAmelCase__ ) elif node.left is None: # Has only right children self.__reassign_nodes(lowerCAmelCase__ ,node.right ) elif node.right is None: # Has only left children self.__reassign_nodes(lowerCAmelCase__ ,node.left ) else: lowerCAmelCase_ : int = self.get_max( node.left ) # Gets the max value of the left branch self.remove(tmp_node.value ) # type: ignore lowerCAmelCase_ : Any = ( tmp_node.value # type: ignore ) # Assigns the value to the node to delete and keep tree structure def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Node | None ) -> Iterable: '''simple docstring''' if node is not None: yield node # Preorder Traversal yield from self.preorder_traverse(node.left ) yield from self.preorder_traverse(node.right ) def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Dict=None ) -> Any: '''simple docstring''' if traversal_function is None: return self.preorder_traverse(self.root ) else: return traversal_function(self.root ) def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : list ,lowerCAmelCase__ : Node | None ) -> None: '''simple docstring''' if node: self.inorder(lowerCAmelCase__ ,node.left ) arr.append(node.value ) self.inorder(lowerCAmelCase__ ,node.right ) def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Node ) -> int: '''simple docstring''' lowerCAmelCase_ : list[int] = [] self.inorder(lowerCAmelCase__ ,lowerCAmelCase__ ) # append all values to list using inorder traversal return arr[k - 1] def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[Any] = [] if curr_node is not None: lowerCAmelCase_ : Dict = postorder(curr_node.left) + postorder(curr_node.right) + [curr_node] return node_list def UpperCamelCase ( ): lowerCAmelCase_ : Tuple = (8, 3, 6, 1, 10, 14, 13, 4, 7) lowerCAmelCase_ : Tuple = BinarySearchTree() for i in testlist: t.insert(snake_case__) # Prints all the elements of the list in order traversal print(snake_case__) if t.search(6) is not None: print("The value 6 exists") else: print("The value 6 doesn't exist") if t.search(-1) is not None: print("The value -1 exists") else: print("The value -1 doesn't exist") if not t.empty(): print("Max Value: " , t.get_max().value) # type: ignore print("Min Value: " , t.get_min().value) # type: ignore for i in testlist: t.remove(snake_case__) print(snake_case__) if __name__ == "__main__": import doctest doctest.testmod(verbose=True)
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'''simple docstring''' def UpperCamelCase ( snake_case__ = 1_00_00_00): lowerCAmelCase_ : int = [i - 1 for i in range(limit + 1)] for i in range(2 , limit + 1): if phi[i] == i - 1: for j in range(2 * i , limit + 1 , snake_case__): phi[j] -= phi[j] // i return sum(phi[2 : limit + 1]) if __name__ == "__main__": print(solution())
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class __snake_case : """simple docstring""" def __init__( self : Optional[int] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None: '''simple docstring''' lowerCAmelCase_ : dict[str, RadixNode] = {} # A node will be a leaf if the tree contains its word lowerCAmelCase_ : int = is_leaf lowerCAmelCase_ : Optional[Any] = prefix def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ) -> tuple[str, str, str]: '''simple docstring''' lowerCAmelCase_ : Any = 0 for q, w in zip(self.prefix ,lowerCAmelCase__ ): if q != w: break x += 1 return self.prefix[:x], self.prefix[x:], word[x:] def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : list[str] ) -> None: '''simple docstring''' for word in words: self.insert(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ) -> None: '''simple docstring''' if self.prefix == word: lowerCAmelCase_ : Optional[Any] = True # Case 2: The node has no edges that have a prefix to the word # Solution: We create an edge from the current node to a new one # containing the word elif word[0] not in self.nodes: lowerCAmelCase_ : List[Any] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ ) else: lowerCAmelCase_ : Tuple = self.nodes[word[0]] lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = incoming_node.match( lowerCAmelCase__ ) # Case 3: The node prefix is equal to the matching # Solution: We insert remaining word on the next node if remaining_prefix == "": self.nodes[matching_string[0]].insert(lowerCAmelCase__ ) # Case 4: The word is greater equal to the matching # Solution: Create a node in between both nodes, change # prefixes and add the new node for the remaining word else: lowerCAmelCase_ : Optional[int] = remaining_prefix lowerCAmelCase_ : Optional[int] = self.nodes[matching_string[0]] lowerCAmelCase_ : List[Any] = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Dict = aux_node if remaining_word == "": lowerCAmelCase_ : List[str] = True else: self.nodes[matching_string[0]].insert(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool: '''simple docstring''' lowerCAmelCase_ : Any = self.nodes.get(word[0] ,lowerCAmelCase__ ) if not incoming_node: return False else: lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match( lowerCAmelCase__ ) # If there is remaining prefix, the word can't be on the tree if remaining_prefix != "": return False # This applies when the word and the prefix are equal elif remaining_word == "": return incoming_node.is_leaf # We have word remaining so we check the next node else: return incoming_node.find(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool: '''simple docstring''' lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ ) if not incoming_node: return False else: lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = incoming_node.match( lowerCAmelCase__ ) # If there is remaining prefix, the word can't be on the tree if remaining_prefix != "": return False # We have word remaining so we check the next node elif remaining_word != "": return incoming_node.delete(lowerCAmelCase__ ) else: # If it is not a leaf, we don't have to delete if not incoming_node.is_leaf: return False else: # We delete the nodes if no edges go from it if len(incoming_node.nodes ) == 0: del self.nodes[word[0]] # We merge the current node with its only child if len(self.nodes ) == 1 and not self.is_leaf: lowerCAmelCase_ : str = list(self.nodes.values() )[0] lowerCAmelCase_ : Tuple = merging_node.is_leaf self.prefix += merging_node.prefix lowerCAmelCase_ : Optional[int] = merging_node.nodes # If there is more than 1 edge, we just mark it as non-leaf elif len(incoming_node.nodes ) > 1: lowerCAmelCase_ : Optional[Any] = False # If there is 1 edge, we merge it with its child else: lowerCAmelCase_ : Tuple = list(incoming_node.nodes.values() )[0] lowerCAmelCase_ : Union[str, Any] = merging_node.is_leaf incoming_node.prefix += merging_node.prefix lowerCAmelCase_ : str = merging_node.nodes return True def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : int = 0 ) -> None: '''simple docstring''' if self.prefix != "": print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" ) for value in self.nodes.values(): value.print_tree(height + 1 ) def UpperCamelCase ( ): lowerCAmelCase_ : Dict = "banana bananas bandana band apple all beast".split() lowerCAmelCase_ : List[Any] = RadixNode() root.insert_many(snake_case__) assert all(root.find(snake_case__) for word in words) assert not root.find("bandanas") assert not root.find("apps") root.delete("all") assert not root.find("all") root.delete("banana") assert not root.find("banana") assert root.find("bananas") return True def UpperCamelCase ( ): assert test_trie() def UpperCamelCase ( ): lowerCAmelCase_ : List[str] = RadixNode() lowerCAmelCase_ : Optional[Any] = "banana bananas bandanas bandana band apple all beast".split() root.insert_many(snake_case__) print("Words:" , snake_case__) print("Tree:") root.print_tree() if __name__ == "__main__": main()
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import collections import os import re from pathlib import Path _lowercase = '''src/transformers''' # Matches is_xxx_available() _lowercase = re.compile(r'''is\_([a-z_]*)_available()''') # Catches a one-line _import_struct = {xxx} _lowercase = re.compile(r'''^_import_structure\s+=\s+\{([^\}]+)\}''') # Catches a line with a key-values pattern: "bla": ["foo", "bar"] _lowercase = re.compile(r'''\s+"\S*":\s+\[([^\]]*)\]''') # Catches a line if not is_foo_available _lowercase = re.compile(r'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''') # Catches a line _import_struct["bla"].append("foo") _lowercase = re.compile(r'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''') # Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"] _lowercase = re.compile(r'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''') # Catches a line with an object between quotes and a comma: "MyModel", _lowercase = re.compile(r'''^\s+"([^"]+)",''') # Catches a line with objects between brackets only: ["foo", "bar"], _lowercase = re.compile(r'''^\s+\[([^\]]+)\]''') # Catches a line with from foo import bar, bla, boo _lowercase = re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''') # Catches a line with try: _lowercase = re.compile(r'''^\s*try:''') # Catches a line with else: _lowercase = re.compile(r'''^\s*else:''') def UpperCamelCase ( snake_case__): if _re_test_backend.search(snake_case__) is None: return None lowerCAmelCase_ : Optional[Any] = [b[0] for b in _re_backend.findall(snake_case__)] backends.sort() return "_and_".join(snake_case__) def UpperCamelCase ( snake_case__): with open(snake_case__ , "r" , encoding="utf-8" , newline="\n") as f: lowerCAmelCase_ : Any = f.readlines() lowerCAmelCase_ : int = 0 while line_index < len(snake_case__) and not lines[line_index].startswith("_import_structure = {"): line_index += 1 # If this is a traditional init, just return. if line_index >= len(snake_case__): return None # First grab the objects without a specific backend in _import_structure lowerCAmelCase_ : Union[str, Any] = [] while not lines[line_index].startswith("if TYPE_CHECKING") and find_backend(lines[line_index]) is None: lowerCAmelCase_ : Union[str, Any] = lines[line_index] # If we have everything on a single line, let's deal with it. if _re_one_line_import_struct.search(snake_case__): lowerCAmelCase_ : List[Any] = _re_one_line_import_struct.search(snake_case__).groups()[0] lowerCAmelCase_ : List[str] = re.findall(R"\[([^\]]+)\]" , snake_case__) for imp in imports: objects.extend([obj[1:-1] for obj in imp.split(", ")]) line_index += 1 continue lowerCAmelCase_ : int = _re_import_struct_key_value.search(snake_case__) if single_line_import_search is not None: lowerCAmelCase_ : Any = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", ") if len(snake_case__) > 0] objects.extend(snake_case__) elif line.startswith(" " * 8 + "\""): objects.append(line[9:-3]) line_index += 1 lowerCAmelCase_ : str = {"none": objects} # Let's continue with backend-specific objects in _import_structure while not lines[line_index].startswith("if TYPE_CHECKING"): # If the line is an if not is_backend_available, we grab all objects associated. lowerCAmelCase_ : List[Any] = find_backend(lines[line_index]) # Check if the backend declaration is inside a try block: if _re_try.search(lines[line_index - 1]) is None: lowerCAmelCase_ : Tuple = None if backend is not None: line_index += 1 # Scroll until we hit the else block of try-except-else while _re_else.search(lines[line_index]) is None: line_index += 1 line_index += 1 lowerCAmelCase_ : Tuple = [] # Until we unindent, add backend objects to the list while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 4): lowerCAmelCase_ : Tuple = lines[line_index] if _re_import_struct_add_one.search(snake_case__) is not None: objects.append(_re_import_struct_add_one.search(snake_case__).groups()[0]) elif _re_import_struct_add_many.search(snake_case__) is not None: lowerCAmelCase_ : Dict = _re_import_struct_add_many.search(snake_case__).groups()[0].split(", ") lowerCAmelCase_ : Optional[int] = [obj[1:-1] for obj in imports if len(snake_case__) > 0] objects.extend(snake_case__) elif _re_between_brackets.search(snake_case__) is not None: lowerCAmelCase_ : Any = _re_between_brackets.search(snake_case__).groups()[0].split(", ") lowerCAmelCase_ : Optional[int] = [obj[1:-1] for obj in imports if len(snake_case__) > 0] objects.extend(snake_case__) elif _re_quote_object.search(snake_case__) is not None: objects.append(_re_quote_object.search(snake_case__).groups()[0]) elif line.startswith(" " * 8 + "\""): objects.append(line[9:-3]) elif line.startswith(" " * 12 + "\""): objects.append(line[13:-3]) line_index += 1 lowerCAmelCase_ : int = objects else: line_index += 1 # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend lowerCAmelCase_ : Optional[Any] = [] while ( line_index < len(snake_case__) and find_backend(lines[line_index]) is None and not lines[line_index].startswith("else") ): lowerCAmelCase_ : Optional[Any] = lines[line_index] lowerCAmelCase_ : Tuple = _re_import.search(snake_case__) if single_line_import_search is not None: objects.extend(single_line_import_search.groups()[0].split(", ")) elif line.startswith(" " * 8): objects.append(line[8:-2]) line_index += 1 lowerCAmelCase_ : Optional[Any] = {"none": objects} # Let's continue with backend-specific objects while line_index < len(snake_case__): # If the line is an if is_backend_available, we grab all objects associated. lowerCAmelCase_ : List[Any] = find_backend(lines[line_index]) # Check if the backend declaration is inside a try block: if _re_try.search(lines[line_index - 1]) is None: lowerCAmelCase_ : List[Any] = None if backend is not None: line_index += 1 # Scroll until we hit the else block of try-except-else while _re_else.search(lines[line_index]) is None: line_index += 1 line_index += 1 lowerCAmelCase_ : Tuple = [] # Until we unindent, add backend objects to the list while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 8): lowerCAmelCase_ : Optional[Any] = lines[line_index] lowerCAmelCase_ : int = _re_import.search(snake_case__) if single_line_import_search is not None: objects.extend(single_line_import_search.groups()[0].split(", ")) elif line.startswith(" " * 12): objects.append(line[12:-2]) line_index += 1 lowerCAmelCase_ : int = objects else: line_index += 1 return import_dict_objects, type_hint_objects def UpperCamelCase ( snake_case__ , snake_case__): def find_duplicates(snake_case__): return [k for k, v in collections.Counter(snake_case__).items() if v > 1] if list(import_dict_objects.keys()) != list(type_hint_objects.keys()): return ["Both sides of the init do not have the same backends!"] lowerCAmelCase_ : Tuple = [] for key in import_dict_objects.keys(): lowerCAmelCase_ : Dict = find_duplicates(import_dict_objects[key]) if duplicate_imports: errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''') lowerCAmelCase_ : Union[str, Any] = find_duplicates(type_hint_objects[key]) if duplicate_type_hints: errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''') if sorted(set(import_dict_objects[key])) != sorted(set(type_hint_objects[key])): lowerCAmelCase_ : Tuple = "base imports" if key == "none" else F'''{key} backend''' errors.append(F'''Differences for {name}:''') for a in type_hint_objects[key]: if a not in import_dict_objects[key]: errors.append(F''' {a} in TYPE_HINT but not in _import_structure.''') for a in import_dict_objects[key]: if a not in type_hint_objects[key]: errors.append(F''' {a} in _import_structure but not in TYPE_HINT.''') return errors def UpperCamelCase ( ): lowerCAmelCase_ : Optional[int] = [] for root, _, files in os.walk(snake_case__): if "__init__.py" in files: lowerCAmelCase_ : int = os.path.join(snake_case__ , "__init__.py") lowerCAmelCase_ : str = parse_init(snake_case__) if objects is not None: lowerCAmelCase_ : Tuple = analyze_results(*snake_case__) if len(snake_case__) > 0: lowerCAmelCase_ : Dict = F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}''' failures.append("\n".join(snake_case__)) if len(snake_case__) > 0: raise ValueError("\n\n".join(snake_case__)) def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = [] for path, directories, files in os.walk(snake_case__): for folder in directories: # Ignore private modules if folder.startswith("_"): directories.remove(snake_case__) continue # Ignore leftovers from branches (empty folders apart from pycache) if len(list((Path(snake_case__) / folder).glob("*.py"))) == 0: continue lowerCAmelCase_ : Union[str, Any] = str((Path(snake_case__) / folder).relative_to(snake_case__)) lowerCAmelCase_ : List[Any] = short_path.replace(os.path.sep , ".") submodules.append(snake_case__) for fname in files: if fname == "__init__.py": continue lowerCAmelCase_ : Dict = str((Path(snake_case__) / fname).relative_to(snake_case__)) lowerCAmelCase_ : Any = short_path.replace(".py" , "").replace(os.path.sep , ".") if len(submodule.split(".")) == 1: submodules.append(snake_case__) return submodules _lowercase = [ '''convert_pytorch_checkpoint_to_tf2''', '''modeling_flax_pytorch_utils''', '''models.esm.openfold_utils''', ] def UpperCamelCase ( ): # This is to make sure the transformers module imported is the one in the repo. from transformers.utils import direct_transformers_import lowerCAmelCase_ : int = direct_transformers_import(snake_case__) lowerCAmelCase_ : Optional[int] = set(transformers._import_structure.keys()) # This contains all the base keys of the _import_structure object defined in the init, but if the user is missing # some optional dependencies, they may not have all of them. Thus we read the init to read all additions and # (potentiall re-) add them. with open(os.path.join(snake_case__ , "__init__.py") , "r") as f: lowerCAmelCase_ : Any = f.read() import_structure_keys.update(set(re.findall(R"import_structure\[\"([^\"]*)\"\]" , snake_case__))) lowerCAmelCase_ : Dict = [ module for module in get_transformers_submodules() if module not in IGNORE_SUBMODULES and module not in import_structure_keys ] if len(snake_case__) > 0: lowerCAmelCase_ : str = "\n".join(F'''- {module}''' for module in module_not_registered) raise ValueError( "The following submodules are not properly registed in the main init of Transformers:\n" F'''{list_of_modules}\n''' "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.") if __name__ == "__main__": check_all_inits() check_submodules()
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from __future__ import annotations def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ): if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1: raise ValueError("You cannot supply more or less than 2 values") elif electron_conc < 0: raise ValueError("Electron concentration cannot be negative in a semiconductor") elif hole_conc < 0: raise ValueError("Hole concentration cannot be negative in a semiconductor") elif intrinsic_conc < 0: raise ValueError( "Intrinsic concentration cannot be negative in a semiconductor") elif electron_conc == 0: return ( "electron_conc", intrinsic_conc**2 / hole_conc, ) elif hole_conc == 0: return ( "hole_conc", intrinsic_conc**2 / electron_conc, ) elif intrinsic_conc == 0: return ( "intrinsic_conc", (electron_conc * hole_conc) ** 0.5, ) else: return (-1, -1) if __name__ == "__main__": import doctest doctest.testmod()
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import math def UpperCamelCase ( snake_case__ , snake_case__ = 0 , snake_case__ = 0): lowerCAmelCase_ : List[str] = end or len(snake_case__) for i in range(snake_case__ , snake_case__): lowerCAmelCase_ : List[str] = i lowerCAmelCase_ : Optional[Any] = array[i] while temp_index != start and temp_index_value < array[temp_index - 1]: lowerCAmelCase_ : Dict = array[temp_index - 1] temp_index -= 1 lowerCAmelCase_ : Optional[int] = temp_index_value return array def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): # Max Heap lowerCAmelCase_ : Optional[Any] = index lowerCAmelCase_ : int = 2 * index + 1 # Left Node lowerCAmelCase_ : Optional[Any] = 2 * index + 2 # Right Node if left_index < heap_size and array[largest] < array[left_index]: lowerCAmelCase_ : Dict = left_index if right_index < heap_size and array[largest] < array[right_index]: lowerCAmelCase_ : Union[str, Any] = right_index if largest != index: lowerCAmelCase_ : Any = array[largest], array[index] heapify(snake_case__ , snake_case__ , snake_case__) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : int = len(snake_case__) for i in range(n // 2 , -1 , -1): heapify(snake_case__ , snake_case__ , snake_case__) for i in range(n - 1 , 0 , -1): lowerCAmelCase_ : List[Any] = array[0], array[i] heapify(snake_case__ , 0 , snake_case__) return array def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): if (array[first_index] > array[middle_index]) != ( array[first_index] > array[last_index] ): return array[first_index] elif (array[middle_index] > array[first_index]) != ( array[middle_index] > array[last_index] ): return array[middle_index] else: return array[last_index] def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Any = low lowerCAmelCase_ : List[str] = high while True: while array[i] < pivot: i += 1 j -= 1 while pivot < array[j]: j -= 1 if i >= j: return i lowerCAmelCase_ : List[str] = array[j], array[i] i += 1 def UpperCamelCase ( snake_case__): if len(snake_case__) == 0: return array lowerCAmelCase_ : List[Any] = 2 * math.ceil(math.loga(len(snake_case__))) lowerCAmelCase_ : Any = 16 return intro_sort(snake_case__ , 0 , len(snake_case__) , snake_case__ , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): while end - start > size_threshold: if max_depth == 0: return heap_sort(snake_case__) max_depth -= 1 lowerCAmelCase_ : int = median_of_a(snake_case__ , snake_case__ , start + ((end - start) // 2) + 1 , end - 1) lowerCAmelCase_ : Union[str, Any] = partition(snake_case__ , snake_case__ , snake_case__ , snake_case__) intro_sort(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : int = p return insertion_sort(snake_case__ , snake_case__ , snake_case__) if __name__ == "__main__": import doctest doctest.testmod() _lowercase = input('''Enter numbers separated by a comma : ''').strip() _lowercase = [float(item) for item in user_input.split(''',''')] print(sort(unsorted))
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _lowercase = { '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''], '''processing_git''': ['''GitProcessor'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''GitForCausalLM''', '''GitModel''', '''GitPreTrainedModel''', '''GitVisionModel''', ] if TYPE_CHECKING: from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig from .processing_git import GitProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_git import ( GIT_PRETRAINED_MODEL_ARCHIVE_LIST, GitForCausalLM, GitModel, GitPreTrainedModel, GitVisionModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_xlnet import XLNetTokenizer else: _lowercase = None _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''} _lowercase = { '''vocab_file''': { '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''', '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''', }, '''tokenizer_file''': { '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json''', '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json''', }, } _lowercase = { '''xlnet-base-cased''': None, '''xlnet-large-cased''': None, } _lowercase = '''▁''' # Segments (not really needed) _lowercase = 0 _lowercase = 1 _lowercase = 2 _lowercase = 3 _lowercase = 4 class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = 'left' UpperCamelCase_ = XLNetTokenizer def __init__( self : Dict ,lowerCAmelCase__ : int=None ,lowerCAmelCase__ : int=None ,lowerCAmelCase__ : int=False ,lowerCAmelCase__ : List[Any]=True ,lowerCAmelCase__ : int=False ,lowerCAmelCase__ : str="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Dict="<unk>" ,lowerCAmelCase__ : Optional[int]="<sep>" ,lowerCAmelCase__ : int="<pad>" ,lowerCAmelCase__ : Optional[Any]="<cls>" ,lowerCAmelCase__ : Union[str, Any]="<mask>" ,lowerCAmelCase__ : Tuple=["<eop>", "<eod>"] ,**lowerCAmelCase__ : Dict ,) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else mask_token super().__init__( vocab_file=lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,remove_space=lowerCAmelCase__ ,keep_accents=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,additional_special_tokens=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : Union[str, Any] = 3 lowerCAmelCase_ : List[str] = do_lower_case lowerCAmelCase_ : Tuple = remove_space lowerCAmelCase_ : List[str] = keep_accents lowerCAmelCase_ : Optional[Any] = vocab_file lowerCAmelCase_ : Tuple = False if not self.vocab_file else True def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : str = [self.sep_token_id] lowerCAmelCase_ : Tuple = [self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : Any = [self.sep_token_id] lowerCAmelCase_ : Any = [2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' if not self.can_save_slow_tokenizer: raise ValueError( "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow " "tokenizer." ) if not os.path.isdir(lowerCAmelCase__ ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return lowerCAmelCase_ : Optional[Any] = os.path.join( lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase__ ): copyfile(self.vocab_file ,lowerCAmelCase__ ) return (out_vocab_file,)
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from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments def UpperCamelCase ( ): lowerCAmelCase_ : List[str] = HfArgumentParser(snake_case__) lowerCAmelCase_ : List[Any] = parser.parse_args_into_dataclasses()[0] lowerCAmelCase_ : Optional[int] = TensorFlowBenchmark(args=snake_case__) try: lowerCAmelCase_ : Tuple = parser.parse_args_into_dataclasses()[0] except ValueError as e: lowerCAmelCase_ : Union[str, Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead." lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1]) lowerCAmelCase_ : Union[str, Any] = "" lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1]) lowerCAmelCase_ : Tuple = [] for arg in depreciated_args: # arg[2:] removes '--' if arg[2:] in TensorFlowBenchmark.deprecated_args: # arg[5:] removes '--no_' full_error_msg += arg_error_msg.format(arg[5:]) else: wrong_args.append(snake_case__) if len(snake_case__) > 0: lowerCAmelCase_ : Optional[Any] = full_error_msg + begin_error_msg + str(snake_case__) raise ValueError(snake_case__) benchmark.run() if __name__ == "__main__": main()
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_speech_available, is_tf_available, is_torch_available, ) _lowercase = { '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''], '''processing_speech_to_text''': ['''Speech2TextProcessor'''], } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ['''Speech2TextTokenizer'''] try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ['''Speech2TextFeatureExtractor'''] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''TFSpeech2TextForConditionalGeneration''', '''TFSpeech2TextModel''', '''TFSpeech2TextPreTrainedModel''', ] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''Speech2TextForConditionalGeneration''', '''Speech2TextModel''', '''Speech2TextPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig from .processing_speech_to_text import SpeechaTextProcessor try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_speech_to_text import SpeechaTextTokenizer try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_speech_to_text import ( TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, TFSpeechaTextForConditionalGeneration, TFSpeechaTextModel, TFSpeechaTextPreTrainedModel, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_speech_to_text import ( SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, SpeechaTextForConditionalGeneration, SpeechaTextModel, SpeechaTextPreTrainedModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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_lowercase = { 0: '''0''', 1: '''1''', 2: '''2''', 3: '''3''', 4: '''4''', 5: '''5''', 6: '''6''', 7: '''7''', 8: '''8''', 9: '''9''', 10: '''a''', 11: '''b''', 12: '''c''', 13: '''d''', 14: '''e''', 15: '''f''', } def UpperCamelCase ( snake_case__): assert type(snake_case__) in (int, float) and decimal == int(snake_case__) lowerCAmelCase_ : Optional[Any] = int(snake_case__) lowerCAmelCase_ : Tuple = "" lowerCAmelCase_ : str = False if decimal < 0: lowerCAmelCase_ : Tuple = True decimal *= -1 while decimal > 0: lowerCAmelCase_ , lowerCAmelCase_ : Any = divmod(snake_case__ , 16) lowerCAmelCase_ : Dict = values[remainder] + hexadecimal lowerCAmelCase_ : List[str] = "0x" + hexadecimal if negative: lowerCAmelCase_ : Optional[Any] = "-" + hexadecimal return hexadecimal if __name__ == "__main__": import doctest doctest.testmod()
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import logging import os import sys from pathlib import Path from unittest.mock import patch from parameterized import parameterized from run_eval import run_generate from run_eval_search import run_search from transformers.testing_utils import CaptureStdout, TestCasePlus, slow from utils import ROUGE_KEYS logging.basicConfig(level=logging.DEBUG) _lowercase = logging.getLogger() def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Tuple = "\n".join(snake_case__) Path(snake_case__).open("w").writelines(snake_case__) _lowercase = '''patrickvonplaten/t5-tiny-random''' _lowercase = '''sshleifer/bart-tiny-random''' _lowercase = '''sshleifer/tiny-mbart''' _lowercase = logging.StreamHandler(sys.stdout) logger.addHandler(stream_handler) logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks class __snake_case ( snake_case__ ): """simple docstring""" def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]: '''simple docstring''' lowerCAmelCase_ : Dict = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source" lowerCAmelCase_ : Optional[Any] = input_file_name.parent / "utest_output.txt" assert not output_file_name.exists() lowerCAmelCase_ : List[Any] = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."] _dump_articles(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : str = str(Path(self.get_auto_remove_tmp_dir() ) / "scores.json" ) lowerCAmelCase_ : str = "translation_en_to_de" if model == T5_TINY else "summarization" lowerCAmelCase_ : int = f''' run_eval_search.py {model} {input_file_name} {output_file_name} --score_path {score_path} --task {task} --num_beams 2 --length_penalty 2.0 '''.split() with patch.object(lowerCAmelCase__ ,"argv" ,lowerCAmelCase__ ): run_generate() assert Path(lowerCAmelCase__ ).exists() # os.remove(Path(output_file_name)) def UpperCAmelCase_ ( self : Tuple ) -> Tuple: '''simple docstring''' self.run_eval_tester(lowerCAmelCase__ ) @parameterized.expand([BART_TINY, MBART_TINY] ) @slow def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Any ) -> List[Any]: '''simple docstring''' self.run_eval_tester(lowerCAmelCase__ ) @parameterized.expand([T5_TINY, MBART_TINY] ) @slow def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[str] ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : Optional[int] = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source" lowerCAmelCase_ : Optional[int] = input_file_name.parent / "utest_output.txt" assert not output_file_name.exists() lowerCAmelCase_ : Dict = { "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"], "de": [ "Maschinelles Lernen ist großartig, oder?", "Ich esse gerne Bananen", "Morgen ist wieder ein toller Tag!", ], } lowerCAmelCase_ : str = Path(self.get_auto_remove_tmp_dir() ) lowerCAmelCase_ : Union[str, Any] = str(tmp_dir / "scores.json" ) lowerCAmelCase_ : Union[str, Any] = str(tmp_dir / "val.target" ) _dump_articles(lowerCAmelCase__ ,text["en"] ) _dump_articles(lowerCAmelCase__ ,text["de"] ) lowerCAmelCase_ : int = "translation_en_to_de" if model == T5_TINY else "summarization" lowerCAmelCase_ : str = f''' run_eval_search.py {model} {str(lowerCAmelCase__ )} {str(lowerCAmelCase__ )} --score_path {score_path} --reference_path {reference_path} --task {task} '''.split() testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"] ) with patch.object(lowerCAmelCase__ ,"argv" ,lowerCAmelCase__ ): with CaptureStdout() as cs: run_search() lowerCAmelCase_ : Dict = [" num_beams | length_penalty", model, "Best score args"] lowerCAmelCase_ : Any = ["Info"] if "translation" in task: expected_strings.append("bleu" ) else: expected_strings.extend(lowerCAmelCase__ ) for w in expected_strings: assert w in cs.out for w in un_expected_strings: assert w not in cs.out assert Path(lowerCAmelCase__ ).exists() os.remove(Path(lowerCAmelCase__ ) )
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from pathlib import Path from typing import List from transformers import is_torch_available, is_vision_available from transformers.testing_utils import get_tests_dir, is_tool_test from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText if is_torch_available(): import torch if is_vision_available(): from PIL import Image _lowercase = ['''text''', '''image''', '''audio'''] def UpperCamelCase ( snake_case__): lowerCAmelCase_ : int = [] for input_type in input_types: if input_type == "text": inputs.append("Text input") elif input_type == "image": inputs.append( Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png").resize((5_12, 5_12))) elif input_type == "audio": inputs.append(torch.ones(30_00)) elif isinstance(snake_case__ , snake_case__): inputs.append(create_inputs(snake_case__)) else: raise ValueError(F'''Invalid type requested: {input_type}''') return inputs def UpperCamelCase ( snake_case__): lowerCAmelCase_ : List[Any] = [] for output in outputs: if isinstance(snake_case__ , (str, AgentText)): output_types.append("text") elif isinstance(snake_case__ , (Image.Image, AgentImage)): output_types.append("image") elif isinstance(snake_case__ , (torch.Tensor, AgentAudio)): output_types.append("audio") else: raise ValueError(F'''Invalid output: {output}''') return output_types @is_tool_test class __snake_case : """simple docstring""" def UpperCAmelCase_ ( self : int ) -> int: '''simple docstring''' self.assertTrue(hasattr(self.tool ,"inputs" ) ) self.assertTrue(hasattr(self.tool ,"outputs" ) ) lowerCAmelCase_ : List[Any] = self.tool.inputs for _input in inputs: if isinstance(_input ,lowerCAmelCase__ ): for __input in _input: self.assertTrue(__input in authorized_types ) else: self.assertTrue(_input in authorized_types ) lowerCAmelCase_ : Any = self.tool.outputs for _output in outputs: self.assertTrue(_output in authorized_types ) def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Any = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) # There is a single output if len(self.tool.outputs ) == 1: lowerCAmelCase_ : Optional[int] = [outputs] self.assertListEqual(output_types(lowerCAmelCase__ ) ,self.tool.outputs ) def UpperCAmelCase_ ( self : int ) -> Any: '''simple docstring''' self.assertTrue(hasattr(self.tool ,"description" ) ) self.assertTrue(hasattr(self.tool ,"default_checkpoint" ) ) self.assertTrue(self.tool.description.startswith("This is a tool that" ) ) def UpperCAmelCase_ ( self : List[Any] ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): lowerCAmelCase_ : str = [outputs] self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) ) for output, output_type in zip(lowerCAmelCase__ ,self.tool.outputs ): lowerCAmelCase_ : Tuple = AGENT_TYPE_MAPPING[output_type] self.assertTrue(isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) ) def UpperCAmelCase_ ( self : Any ) -> Tuple: '''simple docstring''' lowerCAmelCase_ : Tuple = create_inputs(self.tool.inputs ) lowerCAmelCase_ : List[Any] = [] for _input, input_type in zip(lowerCAmelCase__ ,self.tool.inputs ): if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] ) else: _inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) ) # Should not raise an error lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ ) if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): lowerCAmelCase_ : int = [outputs] self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
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import copy from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ..auto import CONFIG_MAPPING _lowercase = logging.get_logger(__name__) _lowercase = { '''microsoft/conditional-detr-resnet-50''': ( '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json''' ), } class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = 'conditional_detr' UpperCamelCase_ = ['past_key_values'] UpperCamelCase_ = { 'hidden_size': 'd_model', 'num_attention_heads': 'encoder_attention_heads', } def __init__( self : Union[str, Any] ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Dict=None ,lowerCAmelCase__ : Optional[int]=3 ,lowerCAmelCase__ : int=3_00 ,lowerCAmelCase__ : List[Any]=6 ,lowerCAmelCase__ : int=20_48 ,lowerCAmelCase__ : str=8 ,lowerCAmelCase__ : Tuple=6 ,lowerCAmelCase__ : str=20_48 ,lowerCAmelCase__ : str=8 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Optional[Any]=0.0 ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : str="relu" ,lowerCAmelCase__ : List[str]=2_56 ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : Optional[int]=0.0 ,lowerCAmelCase__ : Dict=0.0 ,lowerCAmelCase__ : str=0.02 ,lowerCAmelCase__ : List[str]=1.0 ,lowerCAmelCase__ : Any=False ,lowerCAmelCase__ : int="sine" ,lowerCAmelCase__ : int="resnet50" ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : Optional[int]=2 ,lowerCAmelCase__ : List[str]=5 ,lowerCAmelCase__ : Any=2 ,lowerCAmelCase__ : Dict=1 ,lowerCAmelCase__ : Any=1 ,lowerCAmelCase__ : Optional[int]=2 ,lowerCAmelCase__ : Tuple=5 ,lowerCAmelCase__ : Any=2 ,lowerCAmelCase__ : Union[str, Any]=0.25 ,**lowerCAmelCase__ : Optional[int] ,) -> Optional[int]: '''simple docstring''' if backbone_config is not None and use_timm_backbone: raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." ) if not use_timm_backbone: if backbone_config is None: logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." ) lowerCAmelCase_ : Dict = CONFIG_MAPPING["resnet"](out_features=["stage4"] ) elif isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ): lowerCAmelCase_ : Dict = backbone_config.get("model_type" ) lowerCAmelCase_ : Tuple = CONFIG_MAPPING[backbone_model_type] lowerCAmelCase_ : Tuple = config_class.from_dict(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = use_timm_backbone lowerCAmelCase_ : Optional[int] = backbone_config lowerCAmelCase_ : Union[str, Any] = num_channels lowerCAmelCase_ : int = num_queries lowerCAmelCase_ : Union[str, Any] = d_model lowerCAmelCase_ : Tuple = encoder_ffn_dim lowerCAmelCase_ : Union[str, Any] = encoder_layers lowerCAmelCase_ : List[Any] = encoder_attention_heads lowerCAmelCase_ : Optional[Any] = decoder_ffn_dim lowerCAmelCase_ : Optional[int] = decoder_layers lowerCAmelCase_ : Tuple = decoder_attention_heads lowerCAmelCase_ : Tuple = dropout lowerCAmelCase_ : List[Any] = attention_dropout lowerCAmelCase_ : int = activation_dropout lowerCAmelCase_ : Optional[int] = activation_function lowerCAmelCase_ : Tuple = init_std lowerCAmelCase_ : Optional[Any] = init_xavier_std lowerCAmelCase_ : List[Any] = encoder_layerdrop lowerCAmelCase_ : List[str] = decoder_layerdrop lowerCAmelCase_ : int = encoder_layers lowerCAmelCase_ : List[Any] = auxiliary_loss lowerCAmelCase_ : int = position_embedding_type lowerCAmelCase_ : Tuple = backbone lowerCAmelCase_ : Dict = use_pretrained_backbone lowerCAmelCase_ : str = dilation # Hungarian matcher lowerCAmelCase_ : List[str] = class_cost lowerCAmelCase_ : Union[str, Any] = bbox_cost lowerCAmelCase_ : Dict = giou_cost # Loss coefficients lowerCAmelCase_ : Tuple = mask_loss_coefficient lowerCAmelCase_ : str = dice_loss_coefficient lowerCAmelCase_ : Dict = cls_loss_coefficient lowerCAmelCase_ : str = bbox_loss_coefficient lowerCAmelCase_ : Optional[int] = giou_loss_coefficient lowerCAmelCase_ : Optional[Any] = focal_alpha super().__init__(is_encoder_decoder=lowerCAmelCase__ ,**lowerCAmelCase__ ) @property def UpperCAmelCase_ ( self : str ) -> int: '''simple docstring''' return self.encoder_attention_heads @property def UpperCAmelCase_ ( self : str ) -> int: '''simple docstring''' return self.d_model def UpperCAmelCase_ ( self : Optional[int] ) -> str: '''simple docstring''' lowerCAmelCase_ : int = copy.deepcopy(self.__dict__ ) if self.backbone_config is not None: lowerCAmelCase_ : Optional[Any] = self.backbone_config.to_dict() lowerCAmelCase_ : Any = self.__class__.model_type return output class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = version.parse('1.11' ) @property def UpperCAmelCase_ ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]: '''simple docstring''' return OrderedDict( [ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ("pixel_mask", {0: "batch"}), ] ) @property def UpperCAmelCase_ ( self : int ) -> float: '''simple docstring''' return 1e-5 @property def UpperCAmelCase_ ( self : Optional[int] ) -> int: '''simple docstring''' return 12
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import pytest _lowercase = '''__dummy_dataset1__''' _lowercase = ''' import json import os import datasets REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/" URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"} class __DummyDataset1__(datasets.GeneratorBasedBuilder): def _info(self): features = datasets.Features( { "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=[ "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", ] ) ), "langs": datasets.Sequence(datasets.Value("string")), "spans": datasets.Sequence(datasets.Value("string")), } ) return datasets.DatasetInfo(features=features) def _split_generators(self, dl_manager): dl_path = dl_manager.download(URLS) return [ datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}), datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}), ] def _generate_examples(self, filepath): with open(filepath, "r", encoding="utf-8") as f: for i, line in enumerate(f): yield i, json.loads(line) ''' @pytest.fixture def UpperCamelCase ( ): return DATASET_LOADING_SCRIPT_NAME @pytest.fixture def UpperCamelCase ( ): return DATASET_LOADING_SCRIPT_CODE @pytest.fixture def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : List[Any] = dataset_loading_script_name lowerCAmelCase_ : List[str] = tmp_path / "datasets" / script_name script_dir.mkdir(parents=snake_case__) lowerCAmelCase_ : List[Any] = script_dir / F'''{script_name}.py''' with open(snake_case__ , "w") as f: f.write(snake_case__) return str(snake_case__)
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0
def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : List[str] = "" for i in table: res += inp[i - 1] return res def UpperCamelCase ( snake_case__): return data[1:] + data[0] def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Tuple = "" for i in range(len(snake_case__)): if a[i] == b[i]: res += "0" else: res += "1" return res def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : int = int("0b" + data[0] + data[-1] , 2) lowerCAmelCase_ : Dict = int("0b" + data[1:3] , 2) return bin(s[row][col])[2:] def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Tuple = message[:4] lowerCAmelCase_ : Tuple = message[4:] lowerCAmelCase_ : List[str] = apply_table(snake_case__ , snake_case__) lowerCAmelCase_ : Optional[int] = xor(snake_case__ , snake_case__) lowerCAmelCase_ : Optional[int] = apply_sbox(snake_case__ , temp[:4]) # noqa: E741 lowerCAmelCase_ : Tuple = apply_sbox(snake_case__ , temp[4:]) lowerCAmelCase_ : Union[str, Any] = "0" * (2 - len(snake_case__)) + l # noqa: E741 lowerCAmelCase_ : Dict = "0" * (2 - len(snake_case__)) + r lowerCAmelCase_ : Dict = apply_table(l + r , snake_case__) lowerCAmelCase_ : List[str] = xor(snake_case__ , snake_case__) return temp + right if __name__ == "__main__": _lowercase = input('''Enter 10 bit key: ''') _lowercase = input('''Enter 8 bit message: ''') _lowercase = [6, 3, 7, 4, 8, 5, 10, 9] _lowercase = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6] _lowercase = [2, 4, 3, 1] _lowercase = [2, 6, 3, 1, 4, 8, 5, 7] _lowercase = [4, 1, 3, 5, 7, 2, 8, 6] _lowercase = [4, 1, 2, 3, 2, 3, 4, 1] _lowercase = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]] _lowercase = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]] # key generation _lowercase = apply_table(key, paa_table) _lowercase = temp[:5] _lowercase = temp[5:] _lowercase = left_shift(left) _lowercase = left_shift(right) _lowercase = apply_table(left + right, pa_table) _lowercase = left_shift(left) _lowercase = left_shift(right) _lowercase = left_shift(left) _lowercase = left_shift(right) _lowercase = apply_table(left + right, pa_table) # encryption _lowercase = apply_table(message, IP) _lowercase = function(expansion, sa, sa, keya, temp) _lowercase = temp[4:] + temp[:4] _lowercase = function(expansion, sa, sa, keya, temp) _lowercase = apply_table(temp, IP_inv) print('''Cipher text is:''', CT) # decryption _lowercase = apply_table(CT, IP) _lowercase = function(expansion, sa, sa, keya, temp) _lowercase = temp[4:] + temp[:4] _lowercase = function(expansion, sa, sa, keya, temp) _lowercase = apply_table(temp, IP_inv) print('''Plain text after decypting is:''', PT)
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import json import os import re import unittest from transformers import CodeGenTokenizer, CodeGenTokenizerFast from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class __snake_case ( snake_case__ , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = CodeGenTokenizer UpperCamelCase_ = CodeGenTokenizerFast UpperCamelCase_ = True UpperCamelCase_ = {'add_prefix_space': True} UpperCamelCase_ = False def UpperCAmelCase_ ( self : str ) -> Tuple: '''simple docstring''' super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt lowerCAmelCase_ : Optional[Any] = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "<unk>", "<|endoftext|>", ] lowerCAmelCase_ : int = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) ) lowerCAmelCase_ : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] lowerCAmelCase_ : List[Any] = {"unk_token": "<unk>"} lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase_ : Tuple = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] ) with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp: fp.write(json.dumps(lowerCAmelCase__ ) + "\n" ) with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp: fp.write("\n".join(lowerCAmelCase__ ) ) def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : str ) -> int: '''simple docstring''' kwargs.update(self.special_tokens_map ) return CodeGenTokenizer.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : Optional[Any] ) -> Tuple: '''simple docstring''' kwargs.update(self.special_tokens_map ) return CodeGenTokenizerFast.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : str ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Tuple = "lower newer" lowerCAmelCase_ : Tuple = "lower newer" return input_text, output_text def UpperCAmelCase_ ( self : Optional[Any] ) -> Any: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map ) lowerCAmelCase_ : Dict = "lower newer" lowerCAmelCase_ : Dict = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"] lowerCAmelCase_ : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = tokens + [tokenizer.unk_token] lowerCAmelCase_ : Union[str, Any] = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]: '''simple docstring''' if not self.test_rust_tokenizer: return lowerCAmelCase_ : Tuple = self.get_tokenizer() lowerCAmelCase_ : Optional[int] = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = "lower newer" # Testing tokenization lowerCAmelCase_ : Tuple = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = rust_tokenizer.tokenize(lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing conversion to ids without special tokens lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Any = rust_tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing conversion to ids with special tokens lowerCAmelCase_ : int = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) # Testing the unknown token lowerCAmelCase_ : Union[str, Any] = tokens + [rust_tokenizer.unk_token] lowerCAmelCase_ : List[str] = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[int] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Optional[Any] ) -> List[str]: '''simple docstring''' pass def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Any=15 ) -> str: '''simple docstring''' for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ): lowerCAmelCase_ : Any = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ ) # Simple input lowerCAmelCase_ : int = "This is a simple input" lowerCAmelCase_ : Dict = ["This is a simple input 1", "This is a simple input 2"] lowerCAmelCase_ : str = ("This is a simple input", "This is a pair") lowerCAmelCase_ : Optional[int] = [ ("This is a simple input 1", "This is a simple input 2"), ("This is a simple pair 1", "This is a simple pair 2"), ] # Simple input tests self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Simple input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Simple input self.assertRaises( lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,) # Pair input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Pair input self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ) # Pair input self.assertRaises( lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,) def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname ,pad_token="<pad>" ) # Simple input lowerCAmelCase_ : Dict = "This is a simple input" lowerCAmelCase_ : List[str] = ["This is a simple input looooooooong", "This is a simple input"] lowerCAmelCase_ : Any = ("This is a simple input", "This is a pair") lowerCAmelCase_ : List[str] = [ ("This is a simple input loooooong", "This is a simple input"), ("This is a simple pair loooooong", "This is a simple pair"), ] lowerCAmelCase_ : Dict = tokenizer.pad_token_id lowerCAmelCase_ : Union[str, Any] = tokenizer(lowerCAmelCase__ ,padding="max_length" ,max_length=30 ,return_tensors="np" ) lowerCAmelCase_ : Tuple = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" ) lowerCAmelCase_ : Any = tokenizer(*lowerCAmelCase__ ,padding="max_length" ,max_length=60 ,return_tensors="np" ) lowerCAmelCase_ : Optional[int] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" ) # s # test single string max_length padding self.assertEqual(out_s["input_ids"].shape[-1] ,30 ) self.assertTrue(pad_token_id in out_s["input_ids"] ) self.assertTrue(0 in out_s["attention_mask"] ) # s2 # test automatic padding self.assertEqual(out_sa["input_ids"].shape[-1] ,33 ) # long slice doesn't have padding self.assertFalse(pad_token_id in out_sa["input_ids"][0] ) self.assertFalse(0 in out_sa["attention_mask"][0] ) # short slice does have padding self.assertTrue(pad_token_id in out_sa["input_ids"][1] ) self.assertTrue(0 in out_sa["attention_mask"][1] ) # p # test single pair max_length padding self.assertEqual(out_p["input_ids"].shape[-1] ,60 ) self.assertTrue(pad_token_id in out_p["input_ids"] ) self.assertTrue(0 in out_p["attention_mask"] ) # p2 # test automatic padding pair self.assertEqual(out_pa["input_ids"].shape[-1] ,52 ) # long slice pair doesn't have padding self.assertFalse(pad_token_id in out_pa["input_ids"][0] ) self.assertFalse(0 in out_pa["attention_mask"][0] ) # short slice pair does have padding self.assertTrue(pad_token_id in out_pa["input_ids"][1] ) self.assertTrue(0 in out_pa["attention_mask"][1] ) def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any: '''simple docstring''' lowerCAmelCase_ : Any = "$$$" lowerCAmelCase_ : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname ,bos_token=lowerCAmelCase__ ,add_bos_token=lowerCAmelCase__ ) lowerCAmelCase_ : Dict = "This is a simple input" lowerCAmelCase_ : Union[str, Any] = ["This is a simple input 1", "This is a simple input 2"] lowerCAmelCase_ : int = tokenizer.bos_token_id lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ ) self.assertEqual(out_s.input_ids[0] ,lowerCAmelCase__ ) self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) ) lowerCAmelCase_ : List[str] = tokenizer.decode(out_s.input_ids ) lowerCAmelCase_ : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids ) self.assertEqual(decode_s.split()[0] ,lowerCAmelCase__ ) self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) ) @slow def UpperCAmelCase_ ( self : Any ) -> Optional[int]: '''simple docstring''' lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" ) lowerCAmelCase_ : str = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#" lowerCAmelCase_ : int = "\nif len_a > len_b: result = a\nelse: result = b" lowerCAmelCase_ : Dict = tokenizer.encode(lowerCAmelCase__ ) lowerCAmelCase_ : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"] lowerCAmelCase_ : Union[str, Any] = tokenizer.decode(lowerCAmelCase__ ,truncate_before_pattern=lowerCAmelCase__ ) self.assertEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]: '''simple docstring''' pass
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import fire from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer def UpperCamelCase ( snake_case__ , snake_case__ , **snake_case__): lowerCAmelCase_ : int = AutoConfig.from_pretrained(snake_case__ , **snake_case__) lowerCAmelCase_ : Any = AutoModelForSeqaSeqLM.from_config(snake_case__) model.save_pretrained(snake_case__) AutoTokenizer.from_pretrained(snake_case__).save_pretrained(snake_case__) return model if __name__ == "__main__": fire.Fire(save_randomly_initialized_version)
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from __future__ import annotations from random import random class __snake_case : """simple docstring""" def __init__( self : Optional[int] ,lowerCAmelCase__ : int | None = None ) -> int: '''simple docstring''' lowerCAmelCase_ : Dict = value lowerCAmelCase_ : Any = random() lowerCAmelCase_ : Node | None = None lowerCAmelCase_ : Node | None = None def __repr__( self : Any ) -> str: '''simple docstring''' from pprint import pformat if self.left is None and self.right is None: return f'''\'{self.value}: {self.prior:.5}\'''' else: return pformat( {f'''{self.value}: {self.prior:.5}''': (self.left, self.right)} ,indent=1 ) def __str__( self : str ) -> str: '''simple docstring''' lowerCAmelCase_ : List[Any] = str(self.value ) + " " lowerCAmelCase_ : List[Any] = str(self.left or "" ) lowerCAmelCase_ : Union[str, Any] = str(self.right or "" ) return value + left + right def UpperCamelCase ( snake_case__ , snake_case__): if root is None: # None tree is split into 2 Nones return None, None elif root.value is None: return None, None else: if value < root.value: lowerCAmelCase_ , lowerCAmelCase_ : Any = split(root.left , snake_case__) return left, root else: lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = split(root.right , snake_case__) return root, right def UpperCamelCase ( snake_case__ , snake_case__): if (not left) or (not right): # If one node is None, return the other return left or right elif left.prior < right.prior: lowerCAmelCase_ : Dict = merge(left.right , snake_case__) return left else: lowerCAmelCase_ : List[str] = merge(snake_case__ , right.left) return right def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : List[Any] = Node(snake_case__) lowerCAmelCase_ , lowerCAmelCase_ : Tuple = split(snake_case__ , snake_case__) return merge(merge(snake_case__ , snake_case__) , snake_case__) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ , lowerCAmelCase_ : List[str] = split(snake_case__ , value - 1) lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = split(snake_case__ , snake_case__) return merge(snake_case__ , snake_case__) def UpperCamelCase ( snake_case__): if not root: # None return else: inorder(root.left) print(root.value , end=",") inorder(root.right) def UpperCamelCase ( snake_case__ , snake_case__): for arg in args.split(): if arg[0] == "+": lowerCAmelCase_ : List[str] = insert(snake_case__ , int(arg[1:])) elif arg[0] == "-": lowerCAmelCase_ : Optional[int] = erase(snake_case__ , int(arg[1:])) else: print("Unknown command") return root def UpperCamelCase ( ): lowerCAmelCase_ : str = None print( "enter numbers to create a tree, + value to add value into treap, " "- value to erase all nodes with value. 'q' to quit. ") lowerCAmelCase_ : str = input() while args != "q": lowerCAmelCase_ : int = interact_treap(snake_case__ , snake_case__) print(snake_case__) lowerCAmelCase_ : str = input() print("good by!") if __name__ == "__main__": import doctest doctest.testmod() main()
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import pickle import shutil import tempfile import unittest from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from transformers.utils import cached_property from ...test_tokenization_common import TokenizerTesterMixin _lowercase = get_tests_dir('''fixtures/test_sentencepiece.model''') @require_sentencepiece @require_tokenizers class __snake_case ( snake_case__ , unittest.TestCase ): """simple docstring""" UpperCamelCase_ = XGLMTokenizer UpperCamelCase_ = XGLMTokenizerFast UpperCamelCase_ = True UpperCamelCase_ = True def UpperCAmelCase_ ( self : List[Any] ) -> Union[str, Any]: '''simple docstring''' super().setUp() # We have a SentencePiece fixture for testing lowerCAmelCase_ : Dict = XGLMTokenizer(lowerCAmelCase__ ,keep_accents=lowerCAmelCase__ ) tokenizer.save_pretrained(self.tmpdirname ) def UpperCAmelCase_ ( self : Optional[int] ) -> List[str]: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = "<pad>" lowerCAmelCase_ : List[str] = 1 self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__ ) ,lowerCAmelCase__ ) self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__ ) ,lowerCAmelCase__ ) def UpperCAmelCase_ ( self : str ) -> str: '''simple docstring''' lowerCAmelCase_ : Dict = list(self.get_tokenizer().get_vocab().keys() ) self.assertEqual(vocab_keys[0] ,"<s>" ) self.assertEqual(vocab_keys[1] ,"<pad>" ) self.assertEqual(len(lowerCAmelCase__ ) ,10_08 ) def UpperCAmelCase_ ( self : Any ) -> Tuple: '''simple docstring''' self.assertEqual(self.get_tokenizer().vocab_size ,10_08 ) def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[Any]: '''simple docstring''' lowerCAmelCase_ : str = XGLMTokenizer(lowerCAmelCase__ ,keep_accents=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[int] = tokenizer.tokenize("This is a test" ) self.assertListEqual(lowerCAmelCase__ ,["▁This", "▁is", "▁a", "▁t", "est"] ) self.assertListEqual( tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] ,) lowerCAmelCase_ : Union[str, Any] = tokenizer.tokenize("I was born in 92000, and this is falsé." ) self.assertListEqual( lowerCAmelCase__ ,[ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", ".", ] ,) lowerCAmelCase_ : Any = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) self.assertListEqual( lowerCAmelCase__ ,[ value + tokenizer.fairseq_offset for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4] ] ,) lowerCAmelCase_ : Optional[Any] = tokenizer.convert_ids_to_tokens(lowerCAmelCase__ ) self.assertListEqual( lowerCAmelCase__ ,[ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", ".", ] ,) @cached_property def UpperCAmelCase_ ( self : int ) -> List[Any]: '''simple docstring''' return XGLMTokenizer.from_pretrained("facebook/xglm-564M" ) def UpperCAmelCase_ ( self : Optional[int] ) -> Any: '''simple docstring''' with tempfile.NamedTemporaryFile() as f: shutil.copyfile(lowerCAmelCase__ ,f.name ) lowerCAmelCase_ : str = XGLMTokenizer(f.name ,keep_accents=lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = pickle.dumps(lowerCAmelCase__ ) pickle.loads(lowerCAmelCase__ ) def UpperCAmelCase_ ( self : List[str] ) -> List[str]: '''simple docstring''' if not self.test_rust_tokenizer: return lowerCAmelCase_ : int = self.get_tokenizer() lowerCAmelCase_ : List[Any] = self.get_rust_tokenizer() lowerCAmelCase_ : Tuple = "I was born in 92000, and this is falsé." lowerCAmelCase_ : Dict = tokenizer.tokenize(lowerCAmelCase__ ) lowerCAmelCase_ : Tuple = rust_tokenizer.tokenize(lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : List[Any] = tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = rust_tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = self.get_rust_tokenizer() lowerCAmelCase_ : Optional[int] = tokenizer.encode(lowerCAmelCase__ ) lowerCAmelCase_ : Optional[Any] = rust_tokenizer.encode(lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ ) @slow def UpperCAmelCase_ ( self : str ) -> int: '''simple docstring''' lowerCAmelCase_ : List[str] = "Hello World!" lowerCAmelCase_ : Tuple = [2, 3_12_27, 44_47, 35] self.assertListEqual(lowerCAmelCase__ ,self.big_tokenizer.encode(lowerCAmelCase__ ) ) @slow def UpperCAmelCase_ ( self : Optional[Any] ) -> int: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = ( "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will" " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth" ) # fmt: off lowerCAmelCase_ : Dict = [2, 10_18, 67, 11, 19_88, 26_17, 56_31, 2_78, 11, 34_07, 48, 7_16_30, 2_80_85, 4, 32_34, 1_57, 13, 6, 5, 6, 4, 35_26, 7_68, 15, 6_59, 57, 2_98, 39_83, 8_64, 1_29, 21, 6, 5, 1_36_75, 3_77, 6_52, 75_80, 1_03_41, 1_55, 28_17, 4_22, 16_66, 7, 16_74, 53, 1_13, 20_22_77, 1_78_92, 33, 60, 87, 4, 32_34, 1_57, 61, 26_67, 5_23_76, 19, 88, 23, 7_35] # fmt: on self.assertListEqual(lowerCAmelCase__ ,self.big_tokenizer.encode(lowerCAmelCase__ ) ) @slow def UpperCAmelCase_ ( self : Tuple ) -> str: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = { "input_ids": [[2, 10_88_25, 11_63, 15, 8_80_10, 4_73, 1_58_98, 1_57, 1_36_72, 18_57, 3_12, 8, 23_80_21, 11_63, 53, 1_36_72, 18_57, 3_12, 8, 5_32_83, 18_23_96, 8, 1_85_66, 16, 3_67_33, 41_01, 8, 2_30, 24_40_17, 12_25_53, 7, 15, 13_25_97, 4, 2_93, 1_25_11, 76_10, 4, 34_14, 13_25_97, 9, 4, 3_23_61, 3_62, 4, 7_34, 2_85_12, 3_25_69, 18, 4, 3_23_61, 2_60_96, 1_49_82, 73, 1_87_15, 2_14_33, 23_52_61, 15, 4_92, 1_24_27, 16, 53, 1_87_15, 2_14_33, 6_54_54, 15, 2_36_59, 5_63, 16, 2_78, 5_97, 28_43, 5_95, 79_31, 18_23_96, 6_41_86, 22, 8_86, 5_95, 13_29_81, 53, 2_55_40, 34_49, 4_39_82, 3_99_01, 59_51, 8_78, 3_30, 4, 2_76_94, 8_02_69, 3_12, 53, 65_17, 1_17_80, 6_11, 2_04_08, 5], [2, 6, 13_25_97, 67, 4_28_97, 33, 5_92, 8, 16_37_29, 2_55_40, 3_61, 13_69_97, 10_95_14, 17_32_30, 7, 5_01, 60, 10_29_13, 1_96, 56_31, 2_35, 6_32_43, 4_73, 6, 23_17_57, 74, 52_77, 79_05, 53, 30_95, 3_73_17, 22, 4_54, 18_38_74, 5], [2, 2_68, 3_12_98, 4_65_30, 6, 13_29_35, 4_38_31, 7, 5_97, 32, 24, 36_88, 98_65, 5]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] } # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=lowerCAmelCase__ ,model_name="facebook/xglm-564M" ,padding=lowerCAmelCase__ ,)
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_funnel import FunnelTokenizer _lowercase = logging.get_logger(__name__) _lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''} _lowercase = [ '''small''', '''small-base''', '''medium''', '''medium-base''', '''intermediate''', '''intermediate-base''', '''large''', '''large-base''', '''xlarge''', '''xlarge-base''', ] _lowercase = { '''vocab_file''': { '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''', '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''', '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''', '''funnel-transformer/medium-base''': ( '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt''' ), '''funnel-transformer/intermediate''': ( '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt''' ), '''funnel-transformer/intermediate-base''': ( '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt''' ), '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''', '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''', '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''', '''funnel-transformer/xlarge-base''': ( '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt''' ), }, '''tokenizer_file''': { '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''', '''funnel-transformer/small-base''': ( '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''', '''funnel-transformer/medium-base''': ( '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/intermediate''': ( '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json''' ), '''funnel-transformer/intermediate-base''': ( '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''', '''funnel-transformer/large-base''': ( '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json''' ), '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''', '''funnel-transformer/xlarge-base''': ( '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json''' ), }, } _lowercase = {f"funnel-transformer/{name}": 512 for name in _model_names} _lowercase = {f"funnel-transformer/{name}": {'''do_lower_case''': True} for name in _model_names} class __snake_case ( snake_case__ ): """simple docstring""" UpperCamelCase_ = VOCAB_FILES_NAMES UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ = PRETRAINED_INIT_CONFIGURATION UpperCamelCase_ = FunnelTokenizer UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ = 2 def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : List[str]="<unk>" ,lowerCAmelCase__ : int="<sep>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : List[str]="<cls>" ,lowerCAmelCase__ : Optional[int]="<mask>" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : List[Any]="##" ,**lowerCAmelCase__ : int ,) -> List[Any]: '''simple docstring''' super().__init__( lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,clean_text=lowerCAmelCase__ ,tokenize_chinese_chars=lowerCAmelCase__ ,strip_accents=lowerCAmelCase__ ,wordpieces_prefix=lowerCAmelCase__ ,**lowerCAmelCase__ ,) lowerCAmelCase_ : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() ) if ( normalizer_state.get("lowercase" ,lowerCAmelCase__ ) != do_lower_case or normalizer_state.get("strip_accents" ,lowerCAmelCase__ ) != strip_accents or normalizer_state.get("handle_chinese_chars" ,lowerCAmelCase__ ) != tokenize_chinese_chars ): lowerCAmelCase_ : Optional[int] = getattr(lowerCAmelCase__ ,normalizer_state.pop("type" ) ) lowerCAmelCase_ : List[Any] = do_lower_case lowerCAmelCase_ : List[str] = strip_accents lowerCAmelCase_ : Any = tokenize_chinese_chars lowerCAmelCase_ : List[Any] = normalizer_class(**lowerCAmelCase__ ) lowerCAmelCase_ : int = do_lower_case def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str=None ) -> List[Any]: '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id] if token_ids_a: output += token_ids_a + [self.sep_token_id] return output def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' lowerCAmelCase_ : str = [self.sep_token_id] lowerCAmelCase_ : Optional[Any] = [self.cls_token_id] if token_ids_a is None: return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ ) return tuple(lowerCAmelCase__ )
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from math import pow, sqrt def UpperCamelCase ( *snake_case__): lowerCAmelCase_ : str = len(snake_case__) > 0 and all(value > 0.0 for value in values) return result def UpperCamelCase ( snake_case__ , snake_case__): return ( round(sqrt(molar_mass_a / molar_mass_a) , 6) if validate(snake_case__ , snake_case__) else ValueError("Input Error: Molar mass values must greater than 0.") ) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): return ( round(effusion_rate * sqrt(molar_mass_a / molar_mass_a) , 6) if validate(snake_case__ , snake_case__ , snake_case__) else ValueError( "Input Error: Molar mass and effusion rate values must greater than 0.") ) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): return ( round(effusion_rate / sqrt(molar_mass_a / molar_mass_a) , 6) if validate(snake_case__ , snake_case__ , snake_case__) else ValueError( "Input Error: Molar mass and effusion rate values must greater than 0.") ) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): return ( round(molar_mass / pow(effusion_rate_a / effusion_rate_a , 2) , 6) if validate(snake_case__ , snake_case__ , snake_case__) else ValueError( "Input Error: Molar mass and effusion rate values must greater than 0.") ) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): return ( round(pow(effusion_rate_a / effusion_rate_a , 2) / molar_mass , 6) if validate(snake_case__ , snake_case__ , snake_case__) else ValueError( "Input Error: Molar mass and effusion rate values must greater than 0.") )
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# tests directory-specific settings - this file is run automatically # by pytest before any tests are run import doctest import sys import warnings from os.path import abspath, dirname, join import _pytest from transformers.testing_utils import HfDoctestModule, HfDocTestParser # allow having multiple repository checkouts and not needing to remember to rerun # 'pip install -e .[dev]' when switching between checkouts and running tests. _lowercase = abspath(join(dirname(__file__), '''src''')) sys.path.insert(1, git_repo_path) # silence FutureWarning warnings in tests since often we can't act on them until # they become normal warnings - i.e. the tests still need to test the current functionality warnings.simplefilter(action='''ignore''', category=FutureWarning) def UpperCamelCase ( snake_case__): config.addinivalue_line( "markers" , "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested") config.addinivalue_line( "markers" , "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested") config.addinivalue_line("markers" , "is_pipeline_test: mark test to run only when pipelines are tested") config.addinivalue_line("markers" , "is_staging_test: mark test to run only in the staging environment") config.addinivalue_line("markers" , "accelerate_tests: mark test that require accelerate") config.addinivalue_line("markers" , "tool_tests: mark the tool tests that are run on their specific schedule") def UpperCamelCase ( snake_case__): from transformers.testing_utils import pytest_addoption_shared pytest_addoption_shared(snake_case__) def UpperCamelCase ( snake_case__): from transformers.testing_utils import pytest_terminal_summary_main lowerCAmelCase_ : int = terminalreporter.config.getoption("--make-reports") if make_reports: pytest_terminal_summary_main(snake_case__ , id=snake_case__) def UpperCamelCase ( snake_case__ , snake_case__): # If no tests are collected, pytest exists with code 5, which makes the CI fail. if exitstatus == 5: lowerCAmelCase_ : List[Any] = 0 # Doctest custom flag to ignore output. _lowercase = doctest.register_optionflag('''IGNORE_RESULT''') _lowercase = doctest.OutputChecker class __snake_case ( snake_case__ ): """simple docstring""" def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Any: '''simple docstring''' if IGNORE_RESULT & optionflags: return True return OutputChecker.check_output(self ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ) _lowercase = CustomOutputChecker _lowercase = HfDoctestModule _lowercase = HfDocTestParser
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def UpperCamelCase ( snake_case__): lowerCAmelCase_ : list[list[float]] = [] for data in source_data: for i, el in enumerate(snake_case__): if len(snake_case__) < i + 1: data_lists.append([]) data_lists[i].append(float(snake_case__)) return data_lists def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : list[list[float]] = [] for dlist, weight in zip(snake_case__ , snake_case__): lowerCAmelCase_ : Dict = min(snake_case__) lowerCAmelCase_ : str = max(snake_case__) lowerCAmelCase_ : list[float] = [] # for weight 0 score is 1 - actual score if weight == 0: for item in dlist: try: score.append(1 - ((item - mind) / (maxd - mind))) except ZeroDivisionError: score.append(1) elif weight == 1: for item in dlist: try: score.append((item - mind) / (maxd - mind)) except ZeroDivisionError: score.append(0) # weight not 0 or 1 else: lowerCAmelCase_ : List[Any] = F'''Invalid weight of {weight:f} provided''' raise ValueError(snake_case__) score_lists.append(snake_case__) return score_lists def UpperCamelCase ( snake_case__): lowerCAmelCase_ : list[float] = [0 for i in range(len(score_lists[0]))] for slist in score_lists: for j, ele in enumerate(snake_case__): lowerCAmelCase_ : Optional[Any] = final_scores[j] + ele return final_scores def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : int = get_data(snake_case__) lowerCAmelCase_ : int = calculate_each_score(snake_case__ , snake_case__) lowerCAmelCase_ : Optional[Any] = generate_final_scores(snake_case__) # append scores to source data for i, ele in enumerate(snake_case__): source_data[i].append(snake_case__) return source_data
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from __future__ import annotations from collections.abc import Sequence from typing import Literal def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = list(snake_case__) lowerCAmelCase_ : Tuple = list(snake_case__) lowerCAmelCase_ : List[str] = 0 for i in range(len(snake_case__)): if lista[i] != lista[i]: count += 1 lowerCAmelCase_ : Dict = "_" if count > 1: return False else: return "".join(snake_case__) def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Union[str, Any] = [] while True: lowerCAmelCase_ : Tuple = ["$"] * len(snake_case__) lowerCAmelCase_ : Tuple = [] for i in range(len(snake_case__)): for j in range(i + 1 , len(snake_case__)): lowerCAmelCase_ : Optional[int] = compare_string(binary[i] , binary[j]) if k is False: lowerCAmelCase_ : str = "*" lowerCAmelCase_ : Tuple = "*" temp.append("X") for i in range(len(snake_case__)): if checka[i] == "$": pi.append(binary[i]) if len(snake_case__) == 0: return pi lowerCAmelCase_ : List[Any] = list(set(snake_case__)) def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[int] = [] for minterm in minterms: lowerCAmelCase_ : Dict = "" for _ in range(snake_case__): lowerCAmelCase_ : Dict = str(minterm % 2) + string minterm //= 2 temp.append(snake_case__) return temp def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = list(snake_case__) lowerCAmelCase_ : Dict = list(snake_case__) lowerCAmelCase_ : Dict = 0 for i in range(len(snake_case__)): if lista[i] != lista[i]: count_n += 1 return count_n == count def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : Optional[Any] = [] lowerCAmelCase_ : Dict = [0] * len(snake_case__) for i in range(len(chart[0])): lowerCAmelCase_ : List[Any] = 0 lowerCAmelCase_ : int = -1 for j in range(len(snake_case__)): if chart[j][i] == 1: count += 1 lowerCAmelCase_ : Optional[int] = j if count == 1: lowerCAmelCase_ : Union[str, Any] = 1 for i in range(len(snake_case__)): if select[i] == 1: for j in range(len(chart[0])): if chart[i][j] == 1: for k in range(len(snake_case__)): lowerCAmelCase_ : Tuple = 0 temp.append(prime_implicants[i]) while True: lowerCAmelCase_ : Optional[Any] = 0 lowerCAmelCase_ : Dict = -1 lowerCAmelCase_ : Tuple = 0 for i in range(len(snake_case__)): lowerCAmelCase_ : Dict = chart[i].count(1) if count_n > max_n: lowerCAmelCase_ : Optional[int] = count_n lowerCAmelCase_ : Optional[Any] = i if max_n == 0: return temp temp.append(prime_implicants[rem]) for i in range(len(chart[0])): if chart[rem][i] == 1: for j in range(len(snake_case__)): lowerCAmelCase_ : Any = 0 def UpperCamelCase ( snake_case__ , snake_case__): lowerCAmelCase_ : str = [[0 for x in range(len(snake_case__))] for x in range(len(snake_case__))] for i in range(len(snake_case__)): lowerCAmelCase_ : Optional[Any] = prime_implicants[i].count("_") for j in range(len(snake_case__)): if is_for_table(prime_implicants[i] , binary[j] , snake_case__): lowerCAmelCase_ : Dict = 1 return chart def UpperCamelCase ( ): lowerCAmelCase_ : Optional[Any] = int(input("Enter the no. of variables\n")) lowerCAmelCase_ : Tuple = [ float(snake_case__) for x in input( "Enter the decimal representation of Minterms 'Spaces Separated'\n").split() ] lowerCAmelCase_ : Any = decimal_to_binary(snake_case__ , snake_case__) lowerCAmelCase_ : Dict = check(snake_case__) print("Prime Implicants are:") print(snake_case__) lowerCAmelCase_ : int = prime_implicant_chart(snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = selection(snake_case__ , snake_case__) print("Essential Prime Implicants are:") print(snake_case__) if __name__ == "__main__": import doctest doctest.testmod() main()
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_lowercase = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)] def UpperCamelCase ( snake_case__): lowerCAmelCase_ : Optional[int] = 0 while number: # Increased Speed Slightly by checking every 5 digits together. sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00] number //= 10_00_00 return sum_of_digits_squared # There are 2 Chains made, # One ends with 89 with the chain member 58 being the one which when declared first, # there will be the least number of iterations for all the members to be checked. # The other one ends with 1 and has only one element 1. # So 58 and 1 are chosen to be declared at the starting. # Changed dictionary to an array to quicken the solution _lowercase = [None] * 10000000 _lowercase = True _lowercase = False def UpperCamelCase ( snake_case__): if CHAINS[number - 1] is not None: return CHAINS[number - 1] # type: ignore lowerCAmelCase_ : Dict = chain(next_number(snake_case__)) lowerCAmelCase_ : Optional[Any] = number_chain while number < 10_00_00_00: lowerCAmelCase_ : List[Any] = number_chain number *= 10 return number_chain def UpperCamelCase ( snake_case__ = 10_00_00_00): for i in range(1 , snake_case__): if CHAINS[i] is None: chain(i + 1) return CHAINS[:number].count(snake_case__) if __name__ == "__main__": import doctest doctest.testmod() print(f"{solution() = }")
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import logging import os from typing import Dict, List, Optional, Union import torch import torch.nn as nn from accelerate.utils.imports import ( is_abit_bnb_available, is_abit_bnb_available, is_bnb_available, ) from ..big_modeling import dispatch_model, init_empty_weights from .dataclasses import BnbQuantizationConfig from .modeling import ( find_tied_parameters, get_balanced_memory, infer_auto_device_map, load_checkpoint_in_model, offload_weight, set_module_tensor_to_device, ) if is_bnb_available(): import bitsandbytes as bnb from copy import deepcopy _lowercase = logging.getLogger(__name__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = False , ): lowerCAmelCase_ : List[Any] = bnb_quantization_config.load_in_abit lowerCAmelCase_ : Optional[Any] = bnb_quantization_config.load_in_abit if load_in_abit and not is_abit_bnb_available(): raise ImportError( "You have a version of `bitsandbytes` that is not compatible with 8bit quantization," " make sure you have the latest version of `bitsandbytes` installed.") if load_in_abit and not is_abit_bnb_available(): raise ValueError( "You have a version of `bitsandbytes` that is not compatible with 4bit quantization," "make sure you have the latest version of `bitsandbytes` installed.") lowerCAmelCase_ : List[str] = [] # custom device map if isinstance(snake_case__ , snake_case__) and len(device_map.keys()) > 1: lowerCAmelCase_ : Union[str, Any] = [key for key, value in device_map.items() if value in ["disk", "cpu"]] # We keep some modules such as the lm_head in their original dtype for numerical stability reasons if bnb_quantization_config.skip_modules is None: lowerCAmelCase_ : Union[str, Any] = get_keys_to_not_convert(snake_case__) # add cpu modules to skip modules only for 4-bit modules if load_in_abit: bnb_quantization_config.skip_modules.extend(snake_case__) lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules # We add the modules we want to keep in full precision if bnb_quantization_config.keep_in_fpaa_modules is None: lowerCAmelCase_ : Optional[int] = [] lowerCAmelCase_ : int = bnb_quantization_config.keep_in_fpaa_modules modules_to_not_convert.extend(snake_case__) # compatibility with peft lowerCAmelCase_ : Optional[int] = load_in_abit lowerCAmelCase_ : List[str] = load_in_abit lowerCAmelCase_ : Optional[int] = get_parameter_device(snake_case__) if model_device.type != "meta": # quantization of an already loaded model logger.warning( "It is not recommended to quantize a loaded model. " "The model should be instantiated under the `init_empty_weights` context manager.") lowerCAmelCase_ : Union[str, Any] = replace_with_bnb_layers(snake_case__ , snake_case__ , modules_to_not_convert=snake_case__) # convert param to the right dtype lowerCAmelCase_ : Any = bnb_quantization_config.torch_dtype for name, param in model.state_dict().items(): if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules): param.to(torch.floataa) if param.dtype != torch.floataa: lowerCAmelCase_ : Optional[int] = name.replace(".weight" , "").replace(".bias" , "") lowerCAmelCase_ : Optional[int] = getattr(snake_case__ , snake_case__ , snake_case__) if param is not None: param.to(torch.floataa) elif torch.is_floating_point(snake_case__): param.to(snake_case__) if model_device.type == "cuda": # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda model.cuda(torch.cuda.current_device()) torch.cuda.empty_cache() elif torch.cuda.is_available(): model.to(torch.cuda.current_device()) else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info( F'''The model device type is {model_device.type}. However, cuda is needed for quantization.''' "We move the model to cuda.") return model elif weights_location is None: raise RuntimeError( F'''`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} ''') else: with init_empty_weights(): lowerCAmelCase_ : str = replace_with_bnb_layers( snake_case__ , snake_case__ , modules_to_not_convert=snake_case__) lowerCAmelCase_ : Optional[int] = get_quantized_model_device_map( snake_case__ , snake_case__ , snake_case__ , max_memory=snake_case__ , no_split_module_classes=snake_case__ , ) if offload_state_dict is None and device_map is not None and "disk" in device_map.values(): lowerCAmelCase_ : Optional[Any] = True lowerCAmelCase_ : Optional[int] = any(x in list(device_map.values()) for x in ["cpu", "disk"]) load_checkpoint_in_model( snake_case__ , snake_case__ , snake_case__ , dtype=bnb_quantization_config.torch_dtype , offload_folder=snake_case__ , offload_state_dict=snake_case__ , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , ) return dispatch_model(snake_case__ , device_map=snake_case__ , offload_dir=snake_case__) def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=None): if device_map is None: if torch.cuda.is_available(): lowerCAmelCase_ : Any = {"": torch.cuda.current_device()} else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info("The device_map was not initialized." "Setting device_map to `{'':torch.cuda.current_device()}`.") if isinstance(snake_case__ , snake_case__): if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]: raise ValueError( "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or " "'sequential'.") lowerCAmelCase_ : Dict = {} special_dtypes.update( { name: bnb_quantization_config.torch_dtype for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.skip_modules) }) special_dtypes.update( { name: torch.floataa for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules) }) lowerCAmelCase_ : List[str] = {} lowerCAmelCase_ : Union[str, Any] = special_dtypes lowerCAmelCase_ : Union[str, Any] = no_split_module_classes lowerCAmelCase_ : Any = bnb_quantization_config.target_dtype # get max_memory for each device. if device_map != "sequential": lowerCAmelCase_ : Tuple = get_balanced_memory( snake_case__ , low_zero=(device_map == "balanced_low_0") , max_memory=snake_case__ , **snake_case__ , ) lowerCAmelCase_ : Tuple = max_memory lowerCAmelCase_ : Optional[Any] = infer_auto_device_map(snake_case__ , **snake_case__) if isinstance(snake_case__ , snake_case__): # check if don't have any quantized module on the cpu lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules lowerCAmelCase_ : List[Any] = { key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert } for device in ["cpu", "disk"]: if device in device_map_without_some_modules.values(): if bnb_quantization_config.load_in_abit: raise ValueError( "\n Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n these modules in `torch_dtype`, you need to pass a custom `device_map` to\n `load_and_quantize_model`. Check\n https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n for more details.\n ") else: logger.info( "Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit") del device_map_without_some_modules return device_map def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None): if modules_to_not_convert is None: lowerCAmelCase_ : List[str] = [] lowerCAmelCase_ , lowerCAmelCase_ : Tuple = _replace_with_bnb_layers( snake_case__ , snake_case__ , snake_case__ , snake_case__) if not has_been_replaced: logger.warning( "You are loading your model in 8bit or 4bit but no linear modules were found in your model." " this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers." " Please double check your model architecture, or submit an issue on github if you think this is" " a bug.") return model def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , ): lowerCAmelCase_ : str = False for name, module in model.named_children(): if current_key_name is None: lowerCAmelCase_ : Optional[int] = [] current_key_name.append(snake_case__) if isinstance(snake_case__ , nn.Linear) and name not in modules_to_not_convert: # Check if the current key is not in the `modules_to_not_convert` lowerCAmelCase_ : Optional[int] = ".".join(snake_case__) lowerCAmelCase_ : List[str] = True for key in modules_to_not_convert: if ( (key in current_key_name_str) and (key + "." in current_key_name_str) ) or key == current_key_name_str: lowerCAmelCase_ : List[Any] = False break if proceed: # Load bnb module with empty weight and replace ``nn.Linear` module if bnb_quantization_config.load_in_abit: lowerCAmelCase_ : Tuple = bnb.nn.LinearabitLt( module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=snake_case__ , threshold=bnb_quantization_config.llm_inta_threshold , ) elif bnb_quantization_config.load_in_abit: lowerCAmelCase_ : Dict = bnb.nn.Linearabit( module.in_features , module.out_features , module.bias is not None , bnb_quantization_config.bnb_abit_compute_dtype , compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant , quant_type=bnb_quantization_config.bnb_abit_quant_type , ) else: raise ValueError("load_in_8bit and load_in_4bit can't be both False") lowerCAmelCase_ : List[str] = module.weight.data if module.bias is not None: lowerCAmelCase_ : Any = module.bias.data bnb_module.requires_grad_(snake_case__) setattr(snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : List[str] = True if len(list(module.children())) > 0: lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = _replace_with_bnb_layers( snake_case__ , snake_case__ , snake_case__ , snake_case__) lowerCAmelCase_ : Optional[int] = has_been_replaced | _has_been_replaced # Remove the last key for recursion current_key_name.pop(-1) return model, has_been_replaced def UpperCamelCase ( snake_case__): # Create a copy of the model with init_empty_weights(): lowerCAmelCase_ : List[Any] = deepcopy(snake_case__) # this has 0 cost since it is done inside `init_empty_weights` context manager` lowerCAmelCase_ : Dict = find_tied_parameters(snake_case__) # For compatibility with Accelerate < 0.18 if isinstance(snake_case__ , snake_case__): lowerCAmelCase_ : List[str] = sum(list(tied_params.values()) , []) + list(tied_params.keys()) else: lowerCAmelCase_ : Optional[Any] = sum(snake_case__ , []) lowerCAmelCase_ : List[Any] = len(snake_case__) > 0 # Check if it is a base model lowerCAmelCase_ : List[str] = False if hasattr(snake_case__ , "base_model_prefix"): lowerCAmelCase_ : Tuple = not hasattr(snake_case__ , model.base_model_prefix) # Ignore this for base models (BertModel, GPT2Model, etc.) if (not has_tied_params) and is_base_model: return [] # otherwise they have an attached head lowerCAmelCase_ : Union[str, Any] = list(model.named_children()) lowerCAmelCase_ : Optional[int] = [list_modules[-1][0]] # add last module together with tied weights lowerCAmelCase_ : Any = set(snake_case__) - set(snake_case__) lowerCAmelCase_ : Tuple = list(set(snake_case__)) + list(snake_case__) # remove ".weight" from the keys lowerCAmelCase_ : List[str] = [".weight", ".bias"] lowerCAmelCase_ : Tuple = [] for name in list_untouched: for name_to_remove in names_to_remove: if name_to_remove in name: lowerCAmelCase_ : str = name.replace(snake_case__ , "") filtered_module_names.append(snake_case__) return filtered_module_names def UpperCamelCase ( snake_case__): for m in model.modules(): if isinstance(snake_case__ , bnb.nn.Linearabit): return True return False def UpperCamelCase ( snake_case__): return next(parameter.parameters()).device def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__): # if it is not quantized, we quantize and offload the quantized weights and the SCB stats if fpaa_statistics is None: set_module_tensor_to_device(snake_case__ , snake_case__ , 0 , dtype=snake_case__ , value=snake_case__) lowerCAmelCase_ : str = param_name lowerCAmelCase_ : Tuple = model if "." in tensor_name: lowerCAmelCase_ : Dict = tensor_name.split(".") for split in splits[:-1]: lowerCAmelCase_ : Any = getattr(snake_case__ , snake_case__) if new_module is None: raise ValueError(F'''{module} has no attribute {split}.''') lowerCAmelCase_ : Union[str, Any] = new_module lowerCAmelCase_ : Any = splits[-1] # offload weights lowerCAmelCase_ : List[Any] = False offload_weight(module._parameters[tensor_name] , snake_case__ , snake_case__ , index=snake_case__) if hasattr(module._parameters[tensor_name] , "SCB"): offload_weight( module._parameters[tensor_name].SCB , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__ , ) else: offload_weight(snake_case__ , snake_case__ , snake_case__ , index=snake_case__) offload_weight(snake_case__ , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__) set_module_tensor_to_device(snake_case__ , snake_case__ , "meta" , dtype=snake_case__ , value=torch.empty(*param.size()))
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