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"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor
class UpperCAmelCase_ ( unittest.TestCase ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_=7 , UpperCamelCase_=3 , UpperCamelCase_=30 , UpperCamelCase_=4_00 , UpperCamelCase_=True , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_=[0.5, 0.5, 0.5] , UpperCamelCase_=[0.5, 0.5, 0.5] , UpperCamelCase_=True , UpperCamelCase_=1 / 2_55 , UpperCamelCase_=True , ) -> Tuple:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__lowercase : Optional[int] = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
__lowercase : int = parent
__lowercase : Optional[int] = batch_size
__lowercase : int = num_channels
__lowercase : Optional[Any] = min_resolution
__lowercase : Union[str, Any] = max_resolution
__lowercase : str = do_resize
__lowercase : Dict = size
__lowercase : List[str] = do_normalize
__lowercase : Optional[int] = image_mean
__lowercase : List[Any] = image_std
__lowercase : Tuple = do_rescale
__lowercase : Tuple = rescale_factor
__lowercase : str = do_pad
def _lowerCamelCase ( self ) -> List[str]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_=False ) -> Any:
if not batched:
__lowercase : List[Any] = image_inputs[0]
if isinstance(UpperCamelCase__ , Image.Image ):
__lowercase : Tuple = image.size
else:
__lowercase : List[Any] = image.shape[1], image.shape[2]
if w < h:
__lowercase : Optional[int] = int(self.size['''shortest_edge'''] * h / w )
__lowercase : int = self.size["""shortest_edge"""]
elif w > h:
__lowercase : Any = self.size["""shortest_edge"""]
__lowercase : Dict = int(self.size['''shortest_edge'''] * w / h )
else:
__lowercase : Union[str, Any] = self.size["""shortest_edge"""]
__lowercase : Union[str, Any] = self.size["""shortest_edge"""]
else:
__lowercase : Optional[Any] = []
for image in image_inputs:
__lowercase : List[str] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__lowercase : Optional[Any] = max(UpperCamelCase__ , key=lambda UpperCamelCase_ : item[0] )[0]
__lowercase : Optional[int] = max(UpperCamelCase__ , key=lambda UpperCamelCase_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class UpperCAmelCase_ ( _lowercase , unittest.TestCase ):
UpperCamelCase =DetaImageProcessor if is_vision_available() else None
def _lowerCamelCase ( self ) -> int:
__lowercase : Optional[Any] = DetaImageProcessingTester(self )
@property
def _lowerCamelCase ( self ) -> Union[str, Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCamelCase ( self ) -> List[Any]:
__lowercase : str = 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__ , '''do_rescale''' ) )
self.assertTrue(hasattr(UpperCamelCase__ , '''do_pad''' ) )
self.assertTrue(hasattr(UpperCamelCase__ , '''size''' ) )
def _lowerCamelCase ( self ) -> Union[str, Any]:
__lowercase : str = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 13_33} )
self.assertEqual(image_processor.do_pad , UpperCamelCase__ )
def _lowerCamelCase ( self ) -> Dict:
pass
def _lowerCamelCase ( self ) -> Optional[Any]:
# Initialize image_processing
__lowercase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowercase : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , Image.Image )
# Test not batched input
__lowercase : int = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__lowercase : List[str] = self.image_processor_tester.get_expected_values(UpperCamelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowercase : int = self.image_processor_tester.get_expected_values(UpperCamelCase__ , batched=UpperCamelCase__ )
__lowercase : List[str] = image_processing(UpperCamelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _lowerCamelCase ( self ) -> List[str]:
# Initialize image_processing
__lowercase : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__lowercase : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , numpify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , np.ndarray )
# Test not batched input
__lowercase : Optional[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__lowercase : int = self.image_processor_tester.get_expected_values(UpperCamelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowercase : str = image_processing(UpperCamelCase__ , return_tensors='''pt''' ).pixel_values
__lowercase : Optional[Any] = self.image_processor_tester.get_expected_values(UpperCamelCase__ , batched=UpperCamelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _lowerCamelCase ( self ) -> Tuple:
# Initialize image_processing
__lowercase : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__lowercase : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , torchify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
# Test not batched input
__lowercase : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__lowercase : List[str] = self.image_processor_tester.get_expected_values(UpperCamelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowercase : List[Any] = image_processing(UpperCamelCase__ , return_tensors='''pt''' ).pixel_values
__lowercase : int = self.image_processor_tester.get_expected_values(UpperCamelCase__ , batched=UpperCamelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _lowerCamelCase ( self ) -> int:
# prepare image and target
__lowercase : str = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
__lowercase : Tuple = json.loads(f.read() )
__lowercase : Union[str, Any] = {"""image_id""": 3_97_69, """annotations""": target}
# encode them
__lowercase : List[Any] = DetaImageProcessor()
__lowercase : Union[str, Any] = image_processing(images=UpperCamelCase__ , annotations=UpperCamelCase__ , return_tensors='''pt''' )
# verify pixel values
__lowercase : Union[str, Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCamelCase__ )
__lowercase : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCamelCase__ , atol=1E-4 ) )
# verify area
__lowercase : Optional[int] = torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCamelCase__ ) )
# verify boxes
__lowercase : Optional[Any] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCamelCase__ )
__lowercase : List[Any] = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCamelCase__ , atol=1E-3 ) )
# verify image_id
__lowercase : str = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCamelCase__ ) )
# verify is_crowd
__lowercase : Tuple = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCamelCase__ ) )
# verify class_labels
__lowercase : str = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCamelCase__ ) )
# verify orig_size
__lowercase : Optional[Any] = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCamelCase__ ) )
# verify size
__lowercase : List[str] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCamelCase__ ) )
@slow
def _lowerCamelCase ( self ) -> Dict:
# prepare image, target and masks_path
__lowercase : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
__lowercase : List[Any] = json.loads(f.read() )
__lowercase : Dict = {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}
__lowercase : Optional[int] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
__lowercase : str = DetaImageProcessor(format='''coco_panoptic''' )
__lowercase : Union[str, Any] = image_processing(images=UpperCamelCase__ , annotations=UpperCamelCase__ , masks_path=UpperCamelCase__ , return_tensors='''pt''' )
# verify pixel values
__lowercase : List[str] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCamelCase__ )
__lowercase : int = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCamelCase__ , atol=1E-4 ) )
# verify area
__lowercase : Any = torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCamelCase__ ) )
# verify boxes
__lowercase : Optional[Any] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCamelCase__ )
__lowercase : Optional[int] = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCamelCase__ , atol=1E-3 ) )
# verify image_id
__lowercase : Tuple = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCamelCase__ ) )
# verify is_crowd
__lowercase : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCamelCase__ ) )
# verify class_labels
__lowercase : Tuple = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCamelCase__ ) )
# verify masks
__lowercase : List[str] = 82_28_73
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , UpperCamelCase__ )
# verify orig_size
__lowercase : Any = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCamelCase__ ) )
# verify size
__lowercase : Union[str, Any] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCamelCase__ ) )
| 249 |
'''simple docstring'''
class _lowercase :
def __init__( self: Tuple , UpperCamelCase__: list[int] ):
lowerCamelCase__ : Union[str, Any] = len(UpperCamelCase__ )
lowerCamelCase__ : Union[str, Any] = [0] * len_array
if len_array > 0:
lowerCamelCase__ : Union[str, Any] = array[0]
for i in range(1 , UpperCamelCase__ ):
lowerCamelCase__ : Tuple = self.prefix_sum[i - 1] + array[i]
def lowerCamelCase_ ( self: Tuple , UpperCamelCase__: int , UpperCamelCase__: int ):
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase__: int ):
lowerCamelCase__ : Dict = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(UpperCamelCase__ )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 41 | 0 |
"""simple docstring"""
from __future__ import annotations
def _a ( _snake_case ):
"""simple docstring"""
create_state_space_tree(UpperCamelCase__ , [] , 0 , [0 for i in range(len(UpperCamelCase__ ) )] )
def _a ( _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
if index == len(UpperCamelCase__ ):
print(UpperCamelCase__ )
return
for i in range(len(UpperCamelCase__ ) ):
if not index_used[i]:
current_sequence.append(sequence[i] )
UpperCAmelCase = True
create_state_space_tree(UpperCamelCase__ , UpperCamelCase__ , index + 1 , UpperCamelCase__ )
current_sequence.pop()
UpperCAmelCase = False
_UpperCamelCase = [3, 1, 2, 4]
generate_all_permutations(sequence)
_UpperCamelCase = ["""A""", """B""", """C"""]
generate_all_permutations(sequence_a)
| 363 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger("""transformers.models.speecht5""")
_UpperCamelCase = {
"""speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
"""speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
"""speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
"""speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
_UpperCamelCase = {
"""text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
"""text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
_UpperCamelCase = {
"""speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
"""speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
"""speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
"""speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
"""speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
_UpperCamelCase = {
"""speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
"""speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
"""speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
"""speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
"""speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
"""speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
"""speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
"""speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
"""speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
"""speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
"""speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
"""speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
_UpperCamelCase = {
"""text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
_UpperCamelCase = {
"""text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
_UpperCamelCase = {
"""encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
"""encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
"""encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
"""encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
"""encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
"""encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
"""encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
"""encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
"""encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
_UpperCamelCase = {
"""decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
"""decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
"""decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
"""decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
"""decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
"""decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
"""decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
"""decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
"""decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
"""decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
"""decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
"""decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
"""decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
_UpperCamelCase = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
_UpperCamelCase = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCamelCase = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCamelCase = []
_UpperCamelCase = [
"""encoder.version""",
"""encoder.layers.*.norm_k.weight""",
"""encoder.layers.*.norm_k.bias""",
"""decoder.version""",
"""decoder.layers.*.norm_k.weight""",
"""decoder.layers.*.norm_k.bias""",
"""decoder.pos_emb.pe_k""",
"""speech_encoder_prenet.embed_positions._float_tensor""",
"""text_decoder_prenet.embed_positions._float_tensor""",
]
_UpperCamelCase = IGNORE_KEYS + [
"""encoder.proj""",
"""text_encoder_prenet.*""",
"""speech_decoder_prenet.*""",
"""speech_decoder_postnet.*""",
]
_UpperCamelCase = IGNORE_KEYS + [
"""encoder.proj""",
"""speech_encoder_prenet.*""",
"""text_decoder_prenet.*""",
"""text_decoder_postnet.*""",
]
_UpperCamelCase = IGNORE_KEYS + [
"""encoder.proj""",
"""text_encoder_prenet.*""",
"""text_decoder_prenet.*""",
"""text_decoder_postnet.*""",
]
def _a ( _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
for attribute in key.split(""".""" ):
UpperCAmelCase = getattr(_snake_case , _snake_case )
if weight_type is not None:
UpperCAmelCase = getattr(_snake_case , _snake_case ).shape
else:
UpperCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
UpperCAmelCase = value
elif weight_type == "weight_g":
UpperCAmelCase = value
elif weight_type == "weight_v":
UpperCAmelCase = value
elif weight_type == "bias":
UpperCAmelCase = value
elif weight_type == "running_mean":
UpperCAmelCase = value
elif weight_type == "running_var":
UpperCAmelCase = value
elif weight_type == "num_batches_tracked":
UpperCAmelCase = value
else:
UpperCAmelCase = value
logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.''' )
def _a ( _snake_case , _snake_case ):
"""simple docstring"""
for key in ignore_keys:
if key.endswith(""".*""" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
UpperCAmelCase , UpperCAmelCase = key.split(""".*.""" )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def _a ( _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
UpperCAmelCase = []
if task == "s2t":
UpperCAmelCase = hf_model.speechta.encoder.prenet.feature_encoder
UpperCAmelCase = MAPPING_S2T
UpperCAmelCase = IGNORE_KEYS_S2T
elif task == "t2s":
UpperCAmelCase = None
UpperCAmelCase = MAPPING_T2S
UpperCAmelCase = IGNORE_KEYS_T2S
elif task == "s2s":
UpperCAmelCase = hf_model.speechta.encoder.prenet.feature_encoder
UpperCAmelCase = MAPPING_S2S
UpperCAmelCase = IGNORE_KEYS_S2S
else:
raise ValueError(F'''Unsupported task: {task}''' )
for name, value in fairseq_dict.items():
if should_ignore(_snake_case , _snake_case ):
logger.info(F'''{name} was ignored''' )
continue
UpperCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
_snake_case , _snake_case , _snake_case , _snake_case , hf_model.config.feat_extract_norm == """group""" , )
UpperCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
UpperCAmelCase , UpperCAmelCase = key.split(""".*.""" )
if prefix in name and suffix in name:
UpperCAmelCase = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
UpperCAmelCase = True
if "*" in mapped_key:
UpperCAmelCase = name.split(_snake_case )[0].split(""".""" )[-2]
UpperCAmelCase = mapped_key.replace("""*""" , _snake_case )
if "weight_g" in name:
UpperCAmelCase = """weight_g"""
elif "weight_v" in name:
UpperCAmelCase = """weight_v"""
elif "bias" in name:
UpperCAmelCase = """bias"""
elif "weight" in name:
UpperCAmelCase = """weight"""
elif "running_mean" in name:
UpperCAmelCase = """running_mean"""
elif "running_var" in name:
UpperCAmelCase = """running_var"""
elif "num_batches_tracked" in name:
UpperCAmelCase = """num_batches_tracked"""
else:
UpperCAmelCase = 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 _a ( _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
UpperCAmelCase = full_name.split("""conv_layers.""" )[-1]
UpperCAmelCase = name.split(""".""" )
UpperCAmelCase = int(items[0] )
UpperCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
UpperCAmelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
UpperCAmelCase = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
UpperCAmelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
UpperCAmelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(_snake_case )
@torch.no_grad()
def _a ( _snake_case , _snake_case , _snake_case , _snake_case=None , _snake_case=None , _snake_case=None , ):
"""simple docstring"""
if config_path is not None:
UpperCAmelCase = SpeechTaConfig.from_pretrained(_snake_case )
else:
UpperCAmelCase = SpeechTaConfig()
if task == "s2t":
UpperCAmelCase = config.max_text_positions
UpperCAmelCase = SpeechTaForSpeechToText(_snake_case )
elif task == "t2s":
UpperCAmelCase = 1876
UpperCAmelCase = 600
UpperCAmelCase = config.max_speech_positions
UpperCAmelCase = SpeechTaForTextToSpeech(_snake_case )
elif task == "s2s":
UpperCAmelCase = 1876
UpperCAmelCase = config.max_speech_positions
UpperCAmelCase = SpeechTaForSpeechToSpeech(_snake_case )
else:
raise ValueError(F'''Unknown task name: {task}''' )
if vocab_path:
UpperCAmelCase = SpeechTaTokenizer(_snake_case , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
UpperCAmelCase = AddedToken("""<mask>""" , lstrip=_snake_case , rstrip=_snake_case )
UpperCAmelCase = mask_token
tokenizer.add_special_tokens({"""mask_token""": mask_token} )
tokenizer.add_tokens(["""<ctc_blank>"""] )
UpperCAmelCase = SpeechTaFeatureExtractor()
UpperCAmelCase = SpeechTaProcessor(tokenizer=_snake_case , feature_extractor=_snake_case )
processor.save_pretrained(_snake_case )
UpperCAmelCase = torch.load(_snake_case )
recursively_load_weights(fairseq_checkpoint["""model"""] , _snake_case , _snake_case )
model.save_pretrained(_snake_case )
if repo_id:
print("""Pushing to the hub...""" )
processor.push_to_hub(_snake_case )
model.push_to_hub(_snake_case )
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""--task""",
default="""s2t""",
type=str,
help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
)
parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
_UpperCamelCase = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 234 | 0 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
lowercase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ):
super().__init__()
if hasattr(scheduler.config , '''steps_offset''' ) and scheduler.config.steps_offset != 1:
__a = (
f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
'''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
''' file'''
)
deprecate('''steps_offset!=1''' , '''1.0.0''' , _a , standard_warn=_a )
__a = dict(scheduler.config )
__a = 1
__a = FrozenDict(_a )
if hasattr(scheduler.config , '''skip_prk_steps''' ) and scheduler.config.skip_prk_steps is False:
__a = (
f'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
''' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'''
''' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'''
''' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'''
''' Hub, it would be very nice if you could open a Pull request for the'''
''' `scheduler/scheduler_config.json` file'''
)
deprecate('''skip_prk_steps not set''' , '''1.0.0''' , _a , standard_warn=_a )
__a = dict(scheduler.config )
__a = True
__a = FrozenDict(_a )
if safety_checker is None:
logger.warning(
f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .''' )
self.register_modules(
segmentation_model=_a , segmentation_processor=_a , vae=_a , text_encoder=_a , tokenizer=_a , unet=_a , scheduler=_a , safety_checker=_a , feature_extractor=_a , )
def __UpperCAmelCase ( self , _a = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
__a = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_a )
def __UpperCAmelCase ( self ):
self.enable_attention_slicing(_a )
def __UpperCAmelCase ( self ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
__a = torch.device('''cuda''' )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(_a , _a )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __UpperCAmelCase ( self ):
if self.device != torch.device('''meta''' ) or not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(_a , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
def __call__( self , _a , _a , _a , _a = 512 , _a = 512 , _a = 50 , _a = 7.5 , _a = None , _a = 1 , _a = 0.0 , _a = None , _a = None , _a = "pil" , _a = True , _a = None , _a = 1 , **_a , ):
__a = self.segmentation_processor(
text=[text] , images=[image] , padding='''max_length''' , return_tensors='''pt''' ).to(self.device )
__a = self.segmentation_model(**_a )
__a = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
__a = self.numpy_to_pil(_a )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
__a = StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=_a , image=_a , mask_image=_a , height=_a , width=_a , num_inference_steps=_a , guidance_scale=_a , negative_prompt=_a , num_images_per_prompt=_a , eta=_a , generator=_a , latents=_a , output_type=_a , return_dict=_a , callback=_a , callback_steps=_a , )
| 45 |
from heapq import heappop, heappush
import numpy as np
def _SCREAMING_SNAKE_CASE ( a , a , a , a , ) -> tuple[float | int, list[tuple[int, int]]]:
__A , __A : int = grid.shape
__A : Any = [-1, 1, 0, 0]
__A : Optional[Any] = [0, 0, -1, 1]
if allow_diagonal:
dx += [-1, -1, 1, 1]
dy += [-1, 1, -1, 1]
__A , __A : Optional[int] = [(0, source)], set()
__A : Any = np.full((rows, cols) , np.inf )
__A : Any = 0
__A : Any = np.empty((rows, cols) , dtype=a )
__A : Optional[Any] = None
while queue:
((__A) , (__A)) : List[str] = heappop(a )
if (x, y) in visited:
continue
visited.add((x, y) )
if (x, y) == destination:
__A : int = []
while (x, y) != source:
path.append((x, y) )
__A , __A : Optional[int] = predecessors[x, y]
path.append(a ) # add the source manually
path.reverse()
return matrix[destination], path
for i in range(len(a ) ):
__A , __A : Union[str, Any] = x + dx[i], y + dy[i]
if 0 <= nx < rows and 0 <= ny < cols:
__A : Optional[int] = grid[nx][ny]
if next_node == 1 and matrix[nx, ny] > dist + 1:
heappush(a , (dist + 1, (nx, ny)) )
__A : List[Any] = dist + 1
__A : Union[str, Any] = (x, y)
return np.inf, []
if __name__ == "__main__":
import doctest
doctest.testmod()
| 280 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase : Any = {
"""microsoft/trocr-base-handwritten""": (
"""https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"""
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : Any = '''trocr'''
_UpperCAmelCase : Optional[Any] = ['''past_key_values''']
_UpperCAmelCase : List[Any] = {
'''num_attention_heads''': '''decoder_attention_heads''',
'''hidden_size''': '''d_model''',
'''num_hidden_layers''': '''decoder_layers''',
}
def __init__( self : Any , lowerCAmelCase__ : Dict=5_0265 , lowerCAmelCase__ : Union[str, Any]=1024 , lowerCAmelCase__ : Union[str, Any]=12 , lowerCAmelCase__ : List[str]=16 , lowerCAmelCase__ : Optional[Any]=4096 , lowerCAmelCase__ : Any="gelu" , lowerCAmelCase__ : Optional[int]=512 , lowerCAmelCase__ : int=0.1 , lowerCAmelCase__ : str=0.0 , lowerCAmelCase__ : str=0.0 , lowerCAmelCase__ : List[Any]=2 , lowerCAmelCase__ : List[Any]=0.02 , lowerCAmelCase__ : List[Any]=0.0 , lowerCAmelCase__ : Optional[int]=True , lowerCAmelCase__ : Tuple=False , lowerCAmelCase__ : str=True , lowerCAmelCase__ : Optional[int]=True , lowerCAmelCase__ : Any=1 , lowerCAmelCase__ : int=0 , lowerCAmelCase__ : Union[str, Any]=2 , **lowerCAmelCase__ : List[str] , ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = vocab_size
SCREAMING_SNAKE_CASE_: int = d_model
SCREAMING_SNAKE_CASE_: str = decoder_layers
SCREAMING_SNAKE_CASE_: List[str] = decoder_attention_heads
SCREAMING_SNAKE_CASE_: List[Any] = decoder_ffn_dim
SCREAMING_SNAKE_CASE_: Tuple = activation_function
SCREAMING_SNAKE_CASE_: Dict = max_position_embeddings
SCREAMING_SNAKE_CASE_: Optional[Any] = dropout
SCREAMING_SNAKE_CASE_: List[str] = attention_dropout
SCREAMING_SNAKE_CASE_: Union[str, Any] = activation_dropout
SCREAMING_SNAKE_CASE_: int = init_std
SCREAMING_SNAKE_CASE_: str = decoder_layerdrop
SCREAMING_SNAKE_CASE_: int = use_cache
SCREAMING_SNAKE_CASE_: Dict = scale_embedding
SCREAMING_SNAKE_CASE_: List[Any] = use_learned_position_embeddings
SCREAMING_SNAKE_CASE_: Optional[int] = layernorm_embedding
super().__init__(
pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , decoder_start_token_id=lowerCAmelCase__ , **lowerCAmelCase__ , )
| 358 |
import doctest
from collections import deque
import numpy as np
class __lowercase :
"""simple docstring"""
def __init__( self : Union[str, Any]):
SCREAMING_SNAKE_CASE_: int = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE_: Optional[Any] = [1, 2, 3, 4]
def _SCREAMING_SNAKE_CASE ( self : Tuple):
SCREAMING_SNAKE_CASE_: Any = len(self.first_signal)
SCREAMING_SNAKE_CASE_: Dict = len(self.second_signal)
SCREAMING_SNAKE_CASE_: Union[str, Any] = max(lowerCAmelCase__ , lowerCAmelCase__)
# create a zero matrix of max_length x max_length
SCREAMING_SNAKE_CASE_: List[Any] = [[0] * max_length for i in range(lowerCAmelCase__)]
# fills the smaller signal with zeros to make both signals of same length
if length_first_signal < length_second_signal:
self.first_signal += [0] * (max_length - length_first_signal)
elif length_first_signal > length_second_signal:
self.second_signal += [0] * (max_length - length_second_signal)
for i in range(lowerCAmelCase__):
SCREAMING_SNAKE_CASE_: Tuple = deque(self.second_signal)
rotated_signal.rotate(lowerCAmelCase__)
for j, item in enumerate(lowerCAmelCase__):
matrix[i][j] += item
# multiply the matrix with the first signal
SCREAMING_SNAKE_CASE_: Optional[Any] = np.matmul(np.transpose(lowerCAmelCase__) , np.transpose(self.first_signal))
# rounding-off to two decimal places
return [round(lowerCAmelCase__ , 2) for i in final_signal]
if __name__ == "__main__":
doctest.testmod()
| 127 | 0 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : float) -> Optional[int]:
'''simple docstring'''
def brightness(_lowerCamelCase : int) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("level must be between -255.0 (black) and 255.0 (white)")
return img.point(_lowerCamelCase)
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
lowercase : str = change_brightness(img, 100)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 232 |
'''simple docstring'''
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
UpperCamelCase__ : Optional[int] = logging.get_logger(__name__)
UpperCamelCase__ : Optional[int] = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__ : 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''',
},
}
UpperCamelCase__ : List[Any] = {
'''facebook/bart-base''': 10_24,
'''facebook/bart-large''': 10_24,
'''facebook/bart-large-mnli''': 10_24,
'''facebook/bart-large-cnn''': 10_24,
'''facebook/bart-large-xsum''': 10_24,
'''yjernite/bart_eli5''': 10_24,
}
@lru_cache()
def lowerCAmelCase_ ( ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
__SCREAMING_SNAKE_CASE : int = bs[:]
__SCREAMING_SNAKE_CASE : Any = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_lowerCamelCase )
cs.append(2**8 + n )
n += 1
__SCREAMING_SNAKE_CASE : Dict = [chr(_lowerCamelCase ) for n in cs]
return dict(zip(_lowerCamelCase , _lowerCamelCase ) )
def lowerCAmelCase_ ( _lowerCamelCase: int ):
__SCREAMING_SNAKE_CASE : List[str] = set()
__SCREAMING_SNAKE_CASE : Optional[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__SCREAMING_SNAKE_CASE : Optional[int] = char
return pairs
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
_A : List[str] = VOCAB_FILES_NAMES
_A : Tuple = PRETRAINED_VOCAB_FILES_MAP
_A : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A : int = ['''input_ids''', '''attention_mask''']
def __init__( self : Tuple , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : Tuple="replace" , lowerCAmelCase__ : str="<s>" , lowerCAmelCase__ : Dict="</s>" , lowerCAmelCase__ : Union[str, Any]="</s>" , lowerCAmelCase__ : Any="<s>" , lowerCAmelCase__ : str="<unk>" , lowerCAmelCase__ : Tuple="<pad>" , lowerCAmelCase__ : Union[str, Any]="<mask>" , lowerCAmelCase__ : Dict=False , **lowerCAmelCase__ : Optional[int] , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else bos_token
__SCREAMING_SNAKE_CASE : Optional[Any] = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else eos_token
__SCREAMING_SNAKE_CASE : Tuple = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else sep_token
__SCREAMING_SNAKE_CASE : Tuple = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else cls_token
__SCREAMING_SNAKE_CASE : Dict = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else unk_token
__SCREAMING_SNAKE_CASE : List[Any] = 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
__SCREAMING_SNAKE_CASE : int = 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:
__SCREAMING_SNAKE_CASE : Dict = json.load(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[Any] = {v: k for k, v in self.encoder.items()}
__SCREAMING_SNAKE_CASE : Dict = errors # how to handle errors in decoding
__SCREAMING_SNAKE_CASE : Optional[int] = bytes_to_unicode()
__SCREAMING_SNAKE_CASE : Tuple = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase__ , encoding="""utf-8""" ) as merges_handle:
__SCREAMING_SNAKE_CASE : Tuple = merges_handle.read().split("""\n""" )[1:-1]
__SCREAMING_SNAKE_CASE : Optional[int] = [tuple(merge.split() ) for merge in bpe_merges]
__SCREAMING_SNAKE_CASE : str = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
__SCREAMING_SNAKE_CASE : Optional[int] = {}
__SCREAMING_SNAKE_CASE : Any = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__SCREAMING_SNAKE_CASE : Tuple = 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] ):
"""simple docstring"""
return len(self.encoder )
def UpperCamelCase__ ( self : List[str] ):
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCamelCase__ ( self : Any , lowerCAmelCase__ : Tuple ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
__SCREAMING_SNAKE_CASE : Optional[int] = tuple(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
__SCREAMING_SNAKE_CASE : Optional[int] = min(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = bigram
__SCREAMING_SNAKE_CASE : int = []
__SCREAMING_SNAKE_CASE : List[str] = 0
while i < len(lowerCAmelCase__ ):
try:
__SCREAMING_SNAKE_CASE : Any = word.index(lowerCAmelCase__ , lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__SCREAMING_SNAKE_CASE : Dict = 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
__SCREAMING_SNAKE_CASE : Optional[int] = tuple(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
__SCREAMING_SNAKE_CASE : Tuple = get_pairs(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[Any] = """ """.join(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = word
return word
def UpperCamelCase__ ( self : Optional[int] , lowerCAmelCase__ : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = []
for token in re.findall(self.pat , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = """""".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 : Dict , lowerCAmelCase__ : Tuple ):
"""simple docstring"""
return self.encoder.get(lowerCAmelCase__ , self.encoder.get(self.unk_token ) )
def UpperCamelCase__ ( self : Any , lowerCAmelCase__ : Tuple ):
"""simple docstring"""
return self.decoder.get(lowerCAmelCase__ )
def UpperCamelCase__ ( self : Dict , lowerCAmelCase__ : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = """""".join(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = 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 ):
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
__SCREAMING_SNAKE_CASE : str = os.path.join(
lowerCAmelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__SCREAMING_SNAKE_CASE : List[Any] = 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""" )
__SCREAMING_SNAKE_CASE : Optional[int] = 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!""" )
__SCREAMING_SNAKE_CASE : Optional[Any] = token_index
writer.write(""" """.join(lowerCAmelCase__ ) + """\n""" )
index += 1
return vocab_file, merge_file
def UpperCamelCase__ ( self : Optional[int] , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__SCREAMING_SNAKE_CASE : List[Any] = [self.cls_token_id]
__SCREAMING_SNAKE_CASE : Optional[int] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase__ ( self : List[Any] , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : bool = False ):
"""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 : Optional[Any] , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = [self.sep_token_id]
__SCREAMING_SNAKE_CASE : Dict = [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 : Dict , lowerCAmelCase__ : str , lowerCAmelCase__ : Dict=False , **lowerCAmelCase__ : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = 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()):
__SCREAMING_SNAKE_CASE : List[str] = """ """ + text
return (text, kwargs)
| 112 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( _UpperCamelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : Dict = LxmertTokenizer
__SCREAMING_SNAKE_CASE : Any = LxmertTokenizerFast
__SCREAMING_SNAKE_CASE : Optional[int] = True
__SCREAMING_SNAKE_CASE : int = True
def snake_case_ ( self : Optional[int] ):
super().setUp()
_UpperCAmelCase : Dict = [
"[UNK]",
"[CLS]",
"[SEP]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
_UpperCAmelCase : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def snake_case_ ( self : List[str] , A : Optional[Any] ):
_UpperCAmelCase : str = "UNwant\u00E9d,running"
_UpperCAmelCase : Optional[int] = "unwanted, running"
return input_text, output_text
def snake_case_ ( self : Optional[int] ):
_UpperCAmelCase : Optional[Any] = self.tokenizer_class(self.vocab_file )
_UpperCAmelCase : Union[str, Any] = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(A , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , [7, 4, 5, 1_0, 8, 9] )
def snake_case_ ( self : Any ):
if not self.test_rust_tokenizer:
return
_UpperCAmelCase : str = self.get_tokenizer()
_UpperCAmelCase : Any = self.get_rust_tokenizer()
_UpperCAmelCase : Tuple = "I was born in 92000, and this is falsé."
_UpperCAmelCase : List[str] = tokenizer.tokenize(A )
_UpperCAmelCase : Dict = rust_tokenizer.tokenize(A )
self.assertListEqual(A , A )
_UpperCAmelCase : List[str] = tokenizer.encode(A , add_special_tokens=A )
_UpperCAmelCase : Optional[int] = rust_tokenizer.encode(A , add_special_tokens=A )
self.assertListEqual(A , A )
_UpperCAmelCase : int = self.get_rust_tokenizer()
_UpperCAmelCase : Optional[Any] = tokenizer.encode(A )
_UpperCAmelCase : Dict = rust_tokenizer.encode(A )
self.assertListEqual(A , A )
| 202 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __snake_case ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[str]=7 ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = None
if token is not None:
_UpperCAmelCase : str = {"Accept": "application/vnd.github+json", "Authorization": f'Bearer {token}'}
# The id of a workflow (not of a workflow run)
_UpperCAmelCase : Any = "636036"
_UpperCAmelCase : Dict = f'https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += f'?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'
_UpperCAmelCase : Tuple = requests.get(SCREAMING_SNAKE_CASE__ , headers=SCREAMING_SNAKE_CASE__ ).json()
return result["workflow_runs"]
def __snake_case ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : int = get_daily_ci_runs(SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase : Optional[int] = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
_UpperCAmelCase : str = workflow_run["id"]
break
return workflow_run_id
def __snake_case ( SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase : List[str] = get_last_daily_ci_runs(SCREAMING_SNAKE_CASE__ )
if workflow_run_id is not None:
_UpperCAmelCase : Any = get_artifacts_links(worflow_run_id=SCREAMING_SNAKE_CASE__ , token=SCREAMING_SNAKE_CASE__ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
_UpperCAmelCase : List[str] = artifacts_links[artifact_name]
download_artifact(
artifact_name=SCREAMING_SNAKE_CASE__ , artifact_url=SCREAMING_SNAKE_CASE__ , output_dir=SCREAMING_SNAKE_CASE__ , token=SCREAMING_SNAKE_CASE__ )
def __snake_case ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
get_last_daily_ci_artifacts(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase : Any = {}
for artifact_name in artifact_names:
_UpperCAmelCase : Dict = os.path.join(SCREAMING_SNAKE_CASE__ , f'{artifact_name}.zip' )
if os.path.isfile(SCREAMING_SNAKE_CASE__ ):
_UpperCAmelCase : str = {}
with zipfile.ZipFile(SCREAMING_SNAKE_CASE__ ) as z:
for filename in z.namelist():
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
# read the file
with z.open(SCREAMING_SNAKE_CASE__ ) as f:
_UpperCAmelCase : List[str] = f.read().decode("UTF-8" )
return results
| 202 | 1 |
'''simple docstring'''
import baseaa
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> bytes:
return baseaa.baaencode(string.encode("""utf-8""" ) )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : bytes ) -> str:
return baseaa.baadecode(_UpperCAmelCase ).decode("""utf-8""" )
if __name__ == "__main__":
A__: int = '''Hello World!'''
A__: str = baseaa_encode(test)
print(encoded)
A__: Optional[Any] = baseaa_decode(encoded)
print(decoded)
| 276 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( snake_case__ ):
_lowercase : int = ['image_processor', 'tokenizer']
_lowercase : Union[str, Any] = 'LayoutLMv3ImageProcessor'
_lowercase : List[str] = ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')
def __init__( self : Any , UpperCAmelCase : Dict=None , UpperCAmelCase : Tuple=None , **UpperCAmelCase : Optional[Any] ) -> str:
__lowerCAmelCase: str = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , UpperCAmelCase , )
__lowerCAmelCase: List[Any] = kwargs.pop('feature_extractor' )
__lowerCAmelCase: Tuple = 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__(UpperCAmelCase , UpperCAmelCase )
def __call__( self : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , UpperCAmelCase : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , UpperCAmelCase : Union[List[List[int]], List[List[List[int]]]] = None , UpperCAmelCase : Optional[Union[List[int], List[List[int]]]] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , UpperCAmelCase : Union[bool, str, TruncationStrategy] = None , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : int = 0 , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = True , UpperCAmelCase : Optional[Union[str, TensorType]] = None , **UpperCAmelCase : Tuple , ) -> BatchEncoding:
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.' )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.' )
# first, apply the image processor
__lowerCAmelCase: str = self.image_processor(images=UpperCAmelCase , return_tensors=UpperCAmelCase )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(UpperCAmelCase , UpperCAmelCase ):
__lowerCAmelCase: Tuple = [text] # add batch dimension (as the image processor always adds a batch dimension)
__lowerCAmelCase: List[str] = features['words']
__lowerCAmelCase: List[Any] = self.tokenizer(
text=text if text is not None else features['words'] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['boxes'] , word_labels=UpperCAmelCase , add_special_tokens=UpperCAmelCase , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=UpperCAmelCase , stride=UpperCAmelCase , pad_to_multiple_of=UpperCAmelCase , return_token_type_ids=UpperCAmelCase , return_attention_mask=UpperCAmelCase , return_overflowing_tokens=UpperCAmelCase , return_special_tokens_mask=UpperCAmelCase , return_offsets_mapping=UpperCAmelCase , return_length=UpperCAmelCase , verbose=UpperCAmelCase , return_tensors=UpperCAmelCase , **UpperCAmelCase , )
# add pixel values
__lowerCAmelCase: Tuple = features.pop('pixel_values' )
if return_overflowing_tokens is True:
__lowerCAmelCase: int = self.get_overflowing_images(UpperCAmelCase , encoded_inputs['overflow_to_sample_mapping'] )
__lowerCAmelCase: str = images
return encoded_inputs
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[str, Any] ) -> List[str]:
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
__lowerCAmelCase: str = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(UpperCAmelCase ) != len(UpperCAmelCase ):
raise ValueError(
'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
F''' {len(UpperCAmelCase )} and {len(UpperCAmelCase )}''' )
return images_with_overflow
def UpperCAmelCase ( self : Optional[int] , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : Dict ) -> Union[str, Any]:
return self.tokenizer.batch_decode(*UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : Any , *UpperCAmelCase : Dict , **UpperCAmelCase : Any ) -> List[str]:
return self.tokenizer.decode(*UpperCAmelCase , **UpperCAmelCase )
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> str:
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def UpperCAmelCase ( self : str ) -> Union[str, Any]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , UpperCAmelCase , )
return self.image_processor_class
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , UpperCAmelCase , )
return self.image_processor
| 322 | 0 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def snake_case (__lowercase ) -> Dict:
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def snake_case () -> str:
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def snake_case () -> Any:
'''simple docstring'''
_snake_case : Tuple = "mock-s3-bucket"
_snake_case : Optional[int] = F"""s3://{mock_bucket}"""
_snake_case : Optional[Any] = extract_path_from_uri(__lowercase )
assert dataset_path.startswith("s3://" ) is False
_snake_case : Optional[int] = "./local/path"
_snake_case : List[Any] = extract_path_from_uri(__lowercase )
assert dataset_path == new_dataset_path
def snake_case (__lowercase ) -> Dict:
'''simple docstring'''
_snake_case : Optional[int] = is_remote_filesystem(__lowercase )
assert is_remote is True
_snake_case : int = fsspec.filesystem("file" )
_snake_case : List[Any] = is_remote_filesystem(__lowercase )
assert is_remote is False
@pytest.mark.parametrize("compression_fs_class" , __lowercase )
def snake_case (__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Dict:
'''simple docstring'''
_snake_case : int = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
_snake_case : Dict = input_paths[compression_fs_class.protocol]
if input_path is None:
_snake_case : List[Any] = F"""for '{compression_fs_class.protocol}' compression protocol, """
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__lowercase )
_snake_case : List[Any] = fsspec.filesystem(compression_fs_class.protocol , fo=__lowercase )
assert isinstance(__lowercase , __lowercase )
_snake_case : Dict = os.path.basename(__lowercase )
_snake_case : List[str] = expected_filename[: expected_filename.rindex("." )]
assert fs.glob("*" ) == [expected_filename]
with fs.open(__lowercase , "r" , encoding="utf-8" ) as f, open(__lowercase , encoding="utf-8" ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize("protocol" , ["zip", "gzip"] )
def snake_case (__lowercase , __lowercase , __lowercase ) -> Any:
'''simple docstring'''
_snake_case : List[Any] = {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
_snake_case : Optional[int] = compressed_file_paths[protocol]
_snake_case : Tuple = "dataset.jsonl"
_snake_case : Optional[int] = F"""{protocol}://{member_file_path}::{compressed_file_path}"""
_snake_case ,*_snake_case : Union[str, Any] = fsspec.get_fs_token_paths(__lowercase )
assert fs.isfile(__lowercase )
assert not fs.isfile("non_existing_" + member_file_path )
@pytest.mark.integration
def snake_case (__lowercase , __lowercase , __lowercase , __lowercase ) -> Any:
'''simple docstring'''
_snake_case : Dict = hf_api.dataset_info(__lowercase , token=__lowercase )
_snake_case : Union[str, Any] = HfFileSystem(repo_info=__lowercase , token=__lowercase )
assert sorted(hffs.glob("*" ) ) == [".gitattributes", "data"]
assert hffs.isdir("data" )
assert hffs.isfile(".gitattributes" ) and hffs.isfile("data/text_data.txt" )
with open(__lowercase ) as f:
assert hffs.open("data/text_data.txt" , "r" ).read() == f.read()
def snake_case () -> List[Any]:
'''simple docstring'''
_snake_case : Dict = "bz2"
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(__lowercase , __lowercase , clobber=__lowercase )
with pytest.warns(__lowercase ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(__lowercase ) == 1
assert (
str(warning_info[0].message )
== F"""A filesystem protocol was already set for {protocol} and will be overwritten."""
)
| 284 |
def snake_case (__lowercase ) -> bool:
'''simple docstring'''
_snake_case : Dict = 0
for ch in input_str:
_snake_case : int = ord(__lowercase )
_snake_case : List[Any] = pow(2 , __lowercase )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 284 | 1 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__SCREAMING_SNAKE_CASE : Dict = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__SCREAMING_SNAKE_CASE : int = " \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n"
class __A (unittest.TestCase):
'''simple docstring'''
def lowerCAmelCase ( self : Any ) ->Union[str, Any]:
"""simple docstring"""
snake_case_ = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , """schedulers/""" ) )
snake_case_ = self.diffusers_dir
shutil.copy(
os.path.join(lowerCAmelCase_ , """src/diffusers/schedulers/scheduling_ddpm.py""" ) , os.path.join(self.diffusers_dir , """schedulers/scheduling_ddpm.py""" ) , )
def lowerCAmelCase ( self : Union[str, Any] ) ->Dict:
"""simple docstring"""
snake_case_ = """src/diffusers"""
shutil.rmtree(self.diffusers_dir )
def lowerCAmelCase ( self : Union[str, Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Union[str, Any]=None ) ->Tuple:
"""simple docstring"""
snake_case_ = comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
snake_case_ = comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
snake_case_ = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
snake_case_ = black.format_str(lowerCAmelCase_ , mode=lowerCAmelCase_ )
snake_case_ = os.path.join(self.diffusers_dir , """new_code.py""" )
with open(lowerCAmelCase_ , """w""" , newline="""\n""" ) as f:
f.write(lowerCAmelCase_ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(lowerCAmelCase_ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=lowerCAmelCase_ )
with open(lowerCAmelCase_ , """r""" ) as f:
self.assertTrue(f.read() , lowerCAmelCase_ )
def lowerCAmelCase ( self : Optional[Any] ) ->Dict:
"""simple docstring"""
snake_case_ = check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput""" )
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
def lowerCAmelCase ( self : str ) ->Dict:
"""simple docstring"""
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , REFERENCE_CODE + """\n""" , )
# With no empty line at the end
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , lowerCAmelCase_ , )
# Copy consistency with rename
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , re.sub("""DDPM""" , """Test""" , lowerCAmelCase_ ) , )
# Copy consistency with a really long name
snake_case_ = """TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
self.check_copy_consistency(
F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , F"""{long_class_name}SchedulerOutput""" , re.sub("""Bert""" , lowerCAmelCase_ , lowerCAmelCase_ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , lowerCAmelCase_ , overwrite_result=re.sub("""DDPM""" , """Test""" , lowerCAmelCase_ ) , )
| 347 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 | 0 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCAmelCase_ ( _lowercase):
snake_case__ = ComputeEnvironment.AMAZON_SAGEMAKER
snake_case__ = True
snake_case__ = '''ml.p3.2xlarge'''
snake_case__ = '''accelerate_sagemaker_execution_role'''
snake_case__ = '''hf-sm'''
snake_case__ = '''us-east-1'''
snake_case__ = 1
snake_case__ = '''accelerate-sagemaker-1'''
snake_case__ = '''1.6'''
snake_case__ = '''4.4'''
snake_case__ = '''train.py'''
snake_case__ = [
'''--model_name_or_path''',
'''bert''',
'''--do_train''',
'''False''',
'''--epochs''',
'''3''',
'''--learning_rate''',
'''5e-5''',
'''--max_steps''',
'''50.5''',
]
snake_case__ = [
'''--model_name_or_path''',
'''bert''',
'''--do_train''',
'''--do_test''',
'''False''',
'''--do_predict''',
'''--epochs''',
'''3''',
'''--learning_rate''',
'''5e-5''',
'''--max_steps''',
'''50.5''',
]
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCamelCase ( self : List[str] ) -> List[Any]:
# If no defaults are changed, `to_kwargs` returns an empty dict.
_UpperCamelCase = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['''model_name_or_path'''] , __UpperCamelCase )
assert isinstance(converted_args['''do_train'''] , __UpperCamelCase )
assert isinstance(converted_args['''epochs'''] , __UpperCamelCase )
assert isinstance(converted_args['''learning_rate'''] , __UpperCamelCase )
assert isinstance(converted_args['''max_steps'''] , __UpperCamelCase )
with pytest.raises(__UpperCamelCase ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args )
| 360 |
"""simple docstring"""
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_decord,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class UpperCAmelCase_ ( unittest.TestCase):
snake_case__ = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def _UpperCamelCase ( self : Any , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : List[Any] ) -> Optional[Any]:
_UpperCamelCase = hf_hub_download(
repo_id='''nateraw/video-demo''' , filename='''archery.mp4''' , repo_type='''dataset''' )
_UpperCamelCase = VideoClassificationPipeline(model=__UpperCamelCase , image_processor=__UpperCamelCase , top_k=2 )
_UpperCamelCase = [
example_video_filepath,
'''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
]
return video_classifier, examples
def _UpperCamelCase ( self : Union[str, Any] , __UpperCamelCase : Tuple , __UpperCamelCase : List[Any] ) -> str:
for example in examples:
_UpperCamelCase = video_classifier(__UpperCamelCase )
self.assertEqual(
__UpperCamelCase , [
{'''score''': ANY(__UpperCamelCase ), '''label''': ANY(__UpperCamelCase )},
{'''score''': ANY(__UpperCamelCase ), '''label''': ANY(__UpperCamelCase )},
] , )
@require_torch
def _UpperCamelCase ( self : Tuple ) -> List[Any]:
_UpperCamelCase = '''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
_UpperCamelCase = VideoMAEFeatureExtractor(
size={'''shortest_edge''': 10} , crop_size={'''height''': 10, '''width''': 10} )
_UpperCamelCase = pipeline(
'''video-classification''' , model=__UpperCamelCase , feature_extractor=__UpperCamelCase , frame_sampling_rate=4 )
_UpperCamelCase = hf_hub_download(repo_id='''nateraw/video-demo''' , filename='''archery.mp4''' , repo_type='''dataset''' )
_UpperCamelCase = video_classifier(__UpperCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [{'''score''': 0.5_1_9_9, '''label''': '''LABEL_0'''}, {'''score''': 0.4_8_0_1, '''label''': '''LABEL_1'''}] , )
_UpperCamelCase = video_classifier(
[
video_file_path,
video_file_path,
] , top_k=2 , )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [
[{'''score''': 0.5_1_9_9, '''label''': '''LABEL_0'''}, {'''score''': 0.4_8_0_1, '''label''': '''LABEL_1'''}],
[{'''score''': 0.5_1_9_9, '''label''': '''LABEL_0'''}, {'''score''': 0.4_8_0_1, '''label''': '''LABEL_1'''}],
] , )
@require_tf
def _UpperCamelCase ( self : Tuple ) -> Union[str, Any]:
pass
| 54 | 0 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_UpperCAmelCase : Optional[Any] ="""src/diffusers"""
# Matches is_xxx_available()
_UpperCAmelCase : Dict =re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
_UpperCAmelCase : str =re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
_UpperCAmelCase : Dict ="""
{0} = None
"""
_UpperCAmelCase : List[str] ="""
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
"""
_UpperCAmelCase : Optional[int] ="""
def {0}(*args, **kwargs):
requires_backends({0}, {1})
"""
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[int]:
lowerCAmelCase_ : List[Any] = _re_backend.findall(lowerCAmelCase_ )
if len(lowerCAmelCase_ ) == 0:
return None
return "_and_".join(lowerCAmelCase_ )
def lowerCAmelCase ( )-> Optional[int]:
with open(os.path.join(lowerCAmelCase_ , '''__init__.py''' ) , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowerCAmelCase_ : Any = f.readlines()
# Get to the point we do the actual imports for type checking
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : Any = {}
# Go through the end of the file
while line_index < len(lowerCAmelCase_ ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
lowerCAmelCase_ : Optional[int] = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith('''else:''' ):
line_index += 1
line_index += 1
lowerCAmelCase_ : Any = []
# Until we unindent, add backend objects to the list
while line_index < len(lowerCAmelCase_ ) and len(lines[line_index] ) > 1:
lowerCAmelCase_ : Union[str, Any] = lines[line_index]
lowerCAmelCase_ : Optional[Any] = _re_single_line_import.search(lowerCAmelCase_ )
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
if len(lowerCAmelCase_ ) > 0:
lowerCAmelCase_ : List[Any] = objects
else:
line_index += 1
return backend_specific_objects
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Dict:
if name.isupper():
return DUMMY_CONSTANT.format(lowerCAmelCase_ )
elif name.islower():
return DUMMY_FUNCTION.format(lowerCAmelCase_ , lowerCAmelCase_ )
else:
return DUMMY_CLASS.format(lowerCAmelCase_ , lowerCAmelCase_ )
def lowerCAmelCase ( lowerCAmelCase_=None )-> Union[str, Any]:
if backend_specific_objects is None:
lowerCAmelCase_ : Optional[int] = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
lowerCAmelCase_ : Any = {}
for backend, objects in backend_specific_objects.items():
lowerCAmelCase_ : List[str] = '''[''' + ''', '''.join(f"""\"{b}\"""" for b in backend.split('''_and_''' ) ) + ''']'''
lowerCAmelCase_ : List[Any] = '''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(lowerCAmelCase_ , lowerCAmelCase_ ) for o in objects] )
lowerCAmelCase_ : int = dummy_file
return dummy_files
def lowerCAmelCase ( lowerCAmelCase_=False )-> Any:
lowerCAmelCase_ : Tuple = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
lowerCAmelCase_ : Union[str, Any] = {'''torch''': '''pt'''}
# Locate actual dummy modules and read their content.
lowerCAmelCase_ : Tuple = os.path.join(lowerCAmelCase_ , '''utils''' )
lowerCAmelCase_ : int = {
backend: os.path.join(lowerCAmelCase_ , f"""dummy_{short_names.get(lowerCAmelCase_ , lowerCAmelCase_ )}_objects.py""" )
for backend in dummy_files.keys()
}
lowerCAmelCase_ : Union[str, Any] = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(lowerCAmelCase_ ):
with open(lowerCAmelCase_ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowerCAmelCase_ : Union[str, Any] = f.read()
else:
lowerCAmelCase_ : List[str] = ''''''
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
f"""Updating diffusers.utils.dummy_{short_names.get(lowerCAmelCase_ , lowerCAmelCase_ )}_objects.py as the main """
'''__init__ has new objects.''' )
with open(dummy_file_paths[backend] , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
'''The main __init__ has objects that are not present in '''
f"""diffusers.utils.dummy_{short_names.get(lowerCAmelCase_ , lowerCAmelCase_ )}_objects.py. Run `make fix-copies` """
'''to fix this.''' )
if __name__ == "__main__":
_UpperCAmelCase : Dict =argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
_UpperCAmelCase : Union[str, Any] =parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 262 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 262 | 1 |
import os
# Precomputes a list of the 100 first triangular numbers
UpperCamelCase__ =[int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowerCamelCase__ ():
_SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.dirname(os.path.realpath(__lowerCamelCase ) )
_SCREAMING_SNAKE_CASE : List[str] = os.path.join(__lowerCamelCase, "words.txt" )
_SCREAMING_SNAKE_CASE : Optional[Any] = ""
with open(__lowerCamelCase ) as f:
_SCREAMING_SNAKE_CASE : List[str] = f.readline()
_SCREAMING_SNAKE_CASE : List[str] = [word.strip("\"" ) for word in words.strip("\r\n" ).split("," )]
_SCREAMING_SNAKE_CASE : Tuple = [
word
for word in [sum(ord(__lowerCamelCase ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(__lowerCamelCase )
if __name__ == "__main__":
print(solution())
| 325 |
from maths.prime_check import is_prime
def lowerCamelCase__ (__lowerCamelCase ):
if not isinstance(__lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[str] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__lowerCamelCase )
if is_prime(__lowerCamelCase ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 325 | 1 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
a_ = logging.get_logger(__name__)
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='AutoTokenizer'
lowerCamelCase__ =['tokenizer']
lowerCamelCase__ ={
'semantic_prompt': 1,
'coarse_prompt': 2,
'fine_prompt': 2,
}
def __init__( self : Any , a : int , a : Any=None ) -> List[Any]:
"""simple docstring"""
super().__init__(a )
SCREAMING_SNAKE_CASE : Tuple = speaker_embeddings
@classmethod
def __UpperCamelCase ( cls : Optional[int] , a : Optional[Any] , a : Any="speaker_embeddings_path.json" , **a : List[str] ) -> List[str]:
"""simple docstring"""
if speaker_embeddings_dict_path is not None:
SCREAMING_SNAKE_CASE : List[Any] = get_file_from_repo(
a , a , subfolder=kwargs.pop("subfolder" , a ) , cache_dir=kwargs.pop("cache_dir" , a ) , force_download=kwargs.pop("force_download" , a ) , proxies=kwargs.pop("proxies" , a ) , resume_download=kwargs.pop("resume_download" , a ) , local_files_only=kwargs.pop("local_files_only" , a ) , use_auth_token=kwargs.pop("use_auth_token" , a ) , revision=kwargs.pop("revision" , a ) , )
if speaker_embeddings_path is None:
logger.warning(
F"`{os.path.join(a , a )}` does not exists\n , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`." )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
else:
with open(a ) as speaker_embeddings_json:
SCREAMING_SNAKE_CASE : str = json.load(a )
else:
SCREAMING_SNAKE_CASE : List[str] = None
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoTokenizer.from_pretrained(a , **a )
return cls(tokenizer=a , speaker_embeddings=a )
def __UpperCamelCase ( self : Optional[Any] , a : Tuple , a : Tuple="speaker_embeddings_path.json" , a : Union[str, Any]="speaker_embeddings" , a : bool = False , **a : List[str] , ) -> List[Any]:
"""simple docstring"""
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(a , a , "v2" ) , exist_ok=a )
SCREAMING_SNAKE_CASE : Any = {}
SCREAMING_SNAKE_CASE : int = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
SCREAMING_SNAKE_CASE : Tuple = self._load_voice_preset(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["repo_or_path"] , a , F"{prompt_key}_{key}" ) , voice_preset[key] , allow_pickle=a , )
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(a , F"{prompt_key}_{key}.npy" )
SCREAMING_SNAKE_CASE : int = tmp_dict
with open(os.path.join(a , a ) , "w" ) as fp:
json.dump(a , a )
super().save_pretrained(a , a , **a )
def __UpperCamelCase ( self : List[str] , a : str = None , **a : str ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.speaker_embeddings[voice_preset]
SCREAMING_SNAKE_CASE : Any = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
F"Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]." )
SCREAMING_SNAKE_CASE : Optional[Any] = get_file_from_repo(
self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] , subfolder=kwargs.pop("subfolder" , a ) , cache_dir=kwargs.pop("cache_dir" , a ) , force_download=kwargs.pop("force_download" , a ) , proxies=kwargs.pop("proxies" , a ) , resume_download=kwargs.pop("resume_download" , a ) , local_files_only=kwargs.pop("local_files_only" , a ) , use_auth_token=kwargs.pop("use_auth_token" , a ) , revision=kwargs.pop("revision" , a ) , )
if path is None:
raise ValueError(
F"`{os.path.join(self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] )}` does not exists\n , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n embeddings." )
SCREAMING_SNAKE_CASE : Optional[int] = np.load(a )
return voice_preset_dict
def __UpperCamelCase ( self : Dict , a : Optional[dict] = None ) -> List[Any]:
"""simple docstring"""
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(F"Voice preset unrecognized, missing {key} as a key." )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray." )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray." )
def __call__( self : Union[str, Any] , a : Dict=None , a : Dict=None , a : Tuple="pt" , a : List[Any]=256 , a : Optional[Any]=False , a : Tuple=True , a : str=False , **a : List[str] , ) -> Tuple:
"""simple docstring"""
if voice_preset is not None and not isinstance(a , a ):
if (
isinstance(a , a )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
SCREAMING_SNAKE_CASE : str = self._load_voice_preset(a )
else:
if isinstance(a , a ) and not voice_preset.endswith(".npz" ):
SCREAMING_SNAKE_CASE : List[str] = voice_preset + ".npz"
SCREAMING_SNAKE_CASE : Dict = np.load(a )
if voice_preset is not None:
self._validate_voice_preset_dict(a , **a )
SCREAMING_SNAKE_CASE : Dict = BatchFeature(data=a , tensor_type=a )
SCREAMING_SNAKE_CASE : str = self.tokenizer(
a , return_tensors=a , padding="max_length" , max_length=a , return_attention_mask=a , return_token_type_ids=a , add_special_tokens=a , **a , )
if voice_preset is not None:
SCREAMING_SNAKE_CASE : Optional[int] = voice_preset
return encoded_text
| 76 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 301 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlenderbotConfig",
"BlenderbotOnnxConfig",
],
"tokenization_blenderbot": ["BlenderbotTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["BlenderbotTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotForCausalLM",
"BlenderbotForConditionalGeneration",
"BlenderbotModel",
"BlenderbotPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"TFBlenderbotForConditionalGeneration",
"TFBlenderbotModel",
"TFBlenderbotPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"FlaxBlenderbotForConditionalGeneration",
"FlaxBlenderbotModel",
"FlaxBlenderbotPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 371 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def _UpperCamelCase ( snake_case__ ) -> int:
__UpperCAmelCase : int = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
__UpperCAmelCase : int = [144, 192, 240]
__UpperCAmelCase : Optional[Any] = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
__UpperCAmelCase : Optional[Any] = [96, 120, 144]
__UpperCAmelCase : Tuple = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
__UpperCAmelCase : str = [64, 80, 96]
__UpperCAmelCase : Optional[Any] = [16, 16, 24, 48, 64, 80, 320]
__UpperCAmelCase : Tuple = 0.05
__UpperCAmelCase : Dict = 2.0
if mobilevit_name.startswith("deeplabv3_" ):
__UpperCAmelCase : str = 512
__UpperCAmelCase : Any = 16
__UpperCAmelCase : str = 21
__UpperCAmelCase : Union[str, Any] = "pascal-voc-id2label.json"
else:
__UpperCAmelCase : Optional[Any] = 1000
__UpperCAmelCase : int = "imagenet-1k-id2label.json"
__UpperCAmelCase : Dict = "huggingface/label-files"
__UpperCAmelCase : int = json.load(open(hf_hub_download(snake_case__, snake_case__, repo_type="dataset" ), "r" ) )
__UpperCAmelCase : Any = {int(snake_case__ ): v for k, v in idalabel.items()}
__UpperCAmelCase : int = idalabel
__UpperCAmelCase : List[str] = {v: k for k, v in idalabel.items()}
return config
def _UpperCamelCase ( snake_case__, snake_case__=False ) -> Tuple:
for i in range(1, 6 ):
if f'''layer_{i}.''' in name:
__UpperCAmelCase : Tuple = name.replace(f'''layer_{i}.''', f'''encoder.layer.{i - 1}.''' )
if "conv_1." in name:
__UpperCAmelCase : Dict = name.replace("conv_1.", "conv_stem." )
if ".block." in name:
__UpperCAmelCase : Optional[int] = name.replace(".block.", "." )
if "exp_1x1" in name:
__UpperCAmelCase : Tuple = name.replace("exp_1x1", "expand_1x1" )
if "red_1x1" in name:
__UpperCAmelCase : Optional[Any] = name.replace("red_1x1", "reduce_1x1" )
if ".local_rep.conv_3x3." in name:
__UpperCAmelCase : Optional[int] = name.replace(".local_rep.conv_3x3.", ".conv_kxk." )
if ".local_rep.conv_1x1." in name:
__UpperCAmelCase : Any = name.replace(".local_rep.conv_1x1.", ".conv_1x1." )
if ".norm." in name:
__UpperCAmelCase : Dict = name.replace(".norm.", ".normalization." )
if ".conv." in name:
__UpperCAmelCase : List[Any] = name.replace(".conv.", ".convolution." )
if ".conv_proj." in name:
__UpperCAmelCase : List[str] = name.replace(".conv_proj.", ".conv_projection." )
for i in range(0, 2 ):
for j in range(0, 4 ):
if f'''.{i}.{j}.''' in name:
__UpperCAmelCase : List[Any] = name.replace(f'''.{i}.{j}.''', f'''.{i}.layer.{j}.''' )
for i in range(2, 6 ):
for j in range(0, 4 ):
if f'''.{i}.{j}.''' in name:
__UpperCAmelCase : Any = name.replace(f'''.{i}.{j}.''', f'''.{i}.''' )
if "expand_1x1" in name:
__UpperCAmelCase : Optional[int] = name.replace("expand_1x1", "downsampling_layer.expand_1x1" )
if "conv_3x3" in name:
__UpperCAmelCase : List[Any] = name.replace("conv_3x3", "downsampling_layer.conv_3x3" )
if "reduce_1x1" in name:
__UpperCAmelCase : Dict = name.replace("reduce_1x1", "downsampling_layer.reduce_1x1" )
for i in range(2, 5 ):
if f'''.global_rep.{i}.weight''' in name:
__UpperCAmelCase : Any = name.replace(f'''.global_rep.{i}.weight''', ".layernorm.weight" )
if f'''.global_rep.{i}.bias''' in name:
__UpperCAmelCase : Optional[Any] = name.replace(f'''.global_rep.{i}.bias''', ".layernorm.bias" )
if ".global_rep." in name:
__UpperCAmelCase : Tuple = name.replace(".global_rep.", ".transformer." )
if ".pre_norm_mha.0." in name:
__UpperCAmelCase : Optional[Any] = name.replace(".pre_norm_mha.0.", ".layernorm_before." )
if ".pre_norm_mha.1.out_proj." in name:
__UpperCAmelCase : Tuple = name.replace(".pre_norm_mha.1.out_proj.", ".attention.output.dense." )
if ".pre_norm_ffn.0." in name:
__UpperCAmelCase : Optional[Any] = name.replace(".pre_norm_ffn.0.", ".layernorm_after." )
if ".pre_norm_ffn.1." in name:
__UpperCAmelCase : Dict = name.replace(".pre_norm_ffn.1.", ".intermediate.dense." )
if ".pre_norm_ffn.4." in name:
__UpperCAmelCase : int = name.replace(".pre_norm_ffn.4.", ".output.dense." )
if ".transformer." in name:
__UpperCAmelCase : Tuple = name.replace(".transformer.", ".transformer.layer." )
if ".aspp_layer." in name:
__UpperCAmelCase : Any = name.replace(".aspp_layer.", "." )
if ".aspp_pool." in name:
__UpperCAmelCase : Optional[Any] = name.replace(".aspp_pool.", "." )
if "seg_head." in name:
__UpperCAmelCase : Optional[int] = name.replace("seg_head.", "segmentation_head." )
if "segmentation_head.classifier.classifier." in name:
__UpperCAmelCase : str = name.replace("segmentation_head.classifier.classifier.", "segmentation_head.classifier." )
if "classifier.fc." in name:
__UpperCAmelCase : Optional[Any] = name.replace("classifier.fc.", "classifier." )
elif (not base_model) and ("segmentation_head." not in name):
__UpperCAmelCase : List[str] = "mobilevit." + name
return name
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__=False ) -> Union[str, Any]:
if base_model:
__UpperCAmelCase : Optional[int] = ""
else:
__UpperCAmelCase : Tuple = "mobilevit."
for key in orig_state_dict.copy().keys():
__UpperCAmelCase : Optional[int] = orig_state_dict.pop(snake_case__ )
if key[:8] == "encoder.":
__UpperCAmelCase : str = key[8:]
if "qkv" in key:
__UpperCAmelCase : Tuple = key.split("." )
__UpperCAmelCase : List[Any] = int(key_split[0][6:] ) - 1
__UpperCAmelCase : Optional[Any] = int(key_split[3] )
__UpperCAmelCase : Tuple = model.get_submodule(f'''{model_prefix}encoder.layer.{layer_num}''' )
__UpperCAmelCase : List[str] = layer.transformer.layer[transformer_num].attention.attention.all_head_size
__UpperCAmelCase : Optional[Any] = (
f'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
)
if "weight" in key:
__UpperCAmelCase : Any = val[:dim, :]
__UpperCAmelCase : Any = val[dim : dim * 2, :]
__UpperCAmelCase : List[Any] = val[-dim:, :]
else:
__UpperCAmelCase : List[str] = val[:dim]
__UpperCAmelCase : Optional[Any] = val[dim : dim * 2]
__UpperCAmelCase : List[Any] = val[-dim:]
else:
__UpperCAmelCase : str = val
return orig_state_dict
def _UpperCamelCase ( ) -> Any:
__UpperCAmelCase : Tuple = "http://images.cocodataset.org/val2017/000000039769.jpg"
__UpperCAmelCase : List[str] = Image.open(requests.get(snake_case__, stream=snake_case__ ).raw )
return im
@torch.no_grad()
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__, snake_case__=False ) -> Optional[Any]:
__UpperCAmelCase : Tuple = get_mobilevit_config(snake_case__ )
# load original state_dict
__UpperCAmelCase : str = torch.load(snake_case__, map_location="cpu" )
# load 🤗 model
if mobilevit_name.startswith("deeplabv3_" ):
__UpperCAmelCase : Optional[int] = MobileViTForSemanticSegmentation(snake_case__ ).eval()
else:
__UpperCAmelCase : List[Any] = MobileViTForImageClassification(snake_case__ ).eval()
__UpperCAmelCase : Dict = convert_state_dict(snake_case__, snake_case__ )
model.load_state_dict(snake_case__ )
# Check outputs on an image, prepared by MobileViTImageProcessor
__UpperCAmelCase : Optional[Any] = MobileViTImageProcessor(crop_size=config.image_size, size=config.image_size + 32 )
__UpperCAmelCase : Any = image_processor(images=prepare_img(), return_tensors="pt" )
__UpperCAmelCase : Dict = model(**snake_case__ )
__UpperCAmelCase : Tuple = outputs.logits
if mobilevit_name.startswith("deeplabv3_" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
__UpperCAmelCase : int = torch.tensor(
[
[[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
[[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
[[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
__UpperCAmelCase : Tuple = torch.tensor(
[
[[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
[[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
[[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
__UpperCAmelCase : Any = torch.tensor(
[
[[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
[[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
[[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
] )
else:
raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}''' )
assert torch.allclose(logits[0, :3, :3, :3], snake_case__, atol=1e-4 )
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
__UpperCAmelCase : str = torch.tensor([-0.9866, 0.2392, -1.1241] )
elif mobilevit_name == "mobilevit_xs":
__UpperCAmelCase : Tuple = torch.tensor([-2.4761, -0.9399, -1.9587] )
elif mobilevit_name == "mobilevit_xxs":
__UpperCAmelCase : Union[str, Any] = torch.tensor([-1.9364, -1.2327, -0.4653] )
else:
raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}''' )
assert torch.allclose(logits[0, :3], snake_case__, atol=1e-4 )
Path(snake_case__ ).mkdir(exist_ok=snake_case__ )
print(f'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(snake_case__ )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(snake_case__ )
if push_to_hub:
__UpperCAmelCase : List[str] = {
"mobilevit_s": "mobilevit-small",
"mobilevit_xs": "mobilevit-x-small",
"mobilevit_xxs": "mobilevit-xx-small",
"deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
"deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
"deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
}
print("Pushing to the hub..." )
__UpperCAmelCase : int = model_mapping[mobilevit_name]
image_processor.push_to_hub(snake_case__, organization="apple" )
model.push_to_hub(snake_case__, organization="apple" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--mobilevit_name''',
default='''mobilevit_s''',
type=str,
help=(
'''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
_snake_case = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 342 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __magic_name__ ( lowerCAmelCase_):
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Optional[Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__a , """embed_dim""" ) )
self.parent.assertTrue(hasattr(__a , """num_heads""" ) )
class __magic_name__ :
def __init__( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : str=13 , lowercase_ : List[Any]=64 , lowercase_ : Dict=3 , lowercase_ : List[str]=[16, 48, 96] , lowercase_ : str=[1, 3, 6] , lowercase_ : Union[str, Any]=[1, 2, 10] , lowercase_ : str=[7, 3, 3] , lowercase_ : List[Any]=[4, 2, 2] , lowercase_ : int=[2, 1, 1] , lowercase_ : Tuple=[2, 2, 2] , lowercase_ : Tuple=[False, False, True] , lowercase_ : Tuple=[0.0, 0.0, 0.0] , lowercase_ : List[Any]=0.02 , lowercase_ : Optional[Any]=1E-12 , lowercase_ : int=True , lowercase_ : List[str]=True , lowercase_ : Any=2 , ):
lowercase_ : str = parent
lowercase_ : List[Any] = batch_size
lowercase_ : Optional[int] = image_size
lowercase_ : List[str] = patch_sizes
lowercase_ : str = patch_stride
lowercase_ : Any = patch_padding
lowercase_ : Dict = is_training
lowercase_ : Union[str, Any] = use_labels
lowercase_ : Dict = num_labels
lowercase_ : List[Any] = num_channels
lowercase_ : Any = embed_dim
lowercase_ : int = num_heads
lowercase_ : Optional[int] = stride_kv
lowercase_ : Dict = depth
lowercase_ : List[str] = cls_token
lowercase_ : List[Any] = attention_drop_rate
lowercase_ : Tuple = initializer_range
lowercase_ : int = layer_norm_eps
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase_ : Dict = None
if self.use_labels:
# create a random int32 tensor of given shape
lowercase_ : str = ids_tensor([self.batch_size] , self.num_labels )
lowercase_ : str = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : Tuple ):
lowercase_ : Optional[int] = TFCvtModel(config=__a )
lowercase_ : Dict = model(__a , training=__a )
lowercase_ : Any = (self.image_size, self.image_size)
lowercase_ : Dict = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
lowercase_ : Tuple = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
lowercase_ : str = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : List[str] ):
lowercase_ : List[Any] = self.num_labels
lowercase_ : Optional[int] = TFCvtForImageClassification(__a )
lowercase_ : Dict = model(__a , labels=__a , training=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Optional[Any] = self.prepare_config_and_inputs()
lowercase_ : Tuple = config_and_inputs
lowercase_ : str = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class __magic_name__ ( lowerCAmelCase_, lowerCAmelCase_, unittest.TestCase):
UpperCamelCase__ = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
UpperCamelCase__ = (
{'''feature-extraction''': TFCvtModel, '''image-classification''': TFCvtForImageClassification}
if is_tf_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : int = TFCvtModelTester(self )
lowercase_ : List[Any] = TFCvtConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason="""Cvt does not output attentions""" )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
pass
@unittest.skip(reason="""Cvt does not use inputs_embeds""" )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
pass
@unittest.skip(reason="""Cvt does not support input and output embeddings""" )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
def SCREAMING_SNAKE_CASE_ ( self : int ):
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
@slow
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
super().test_keras_fit()
@unittest.skip(reason="""Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8""" )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Any = tf.keras.mixed_precision.Policy("""mixed_float16""" )
tf.keras.mixed_precision.set_global_policy(__a )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy("""float32""" )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : Dict = model_class(__a )
lowercase_ : Optional[Any] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ : Optional[Any] = [*signature.parameters.keys()]
lowercase_ : Optional[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , __a )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
def check_hidden_states_output(lowercase_ : str , lowercase_ : Dict , lowercase_ : Optional[int] ):
lowercase_ : List[str] = model_class(__a )
lowercase_ : Union[str, Any] = model(**self._prepare_for_class(__a , __a ) )
lowercase_ : Any = outputs.hidden_states
lowercase_ : Union[str, Any] = len(self.model_tester.depth )
self.assertEqual(len(__a ) , __a )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : List[str] = True
check_hidden_states_output(__a , __a , __a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ : Optional[Any] = True
check_hidden_states_output(__a , __a , __a )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__a )
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : Optional[Any] = TFCvtModel.from_pretrained(__a )
self.assertIsNotNone(__a )
def lowerCamelCase ( ) -> Optional[Any]:
lowercase_ : List[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __magic_name__ ( unittest.TestCase):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : int = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowercase_ : Tuple = self.default_image_processor
lowercase_ : Any = prepare_img()
lowercase_ : int = image_processor(images=__a , return_tensors="""tf""" )
# forward pass
lowercase_ : Any = model(**__a )
# verify the logits
lowercase_ : Any = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , __a )
lowercase_ : Optional[Any] = tf.constant([0.92_85, 0.90_15, -0.31_50] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , __a , atol=1E-4 ) )
| 239 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__lowercase : Dict = logging.get_logger(__name__)
__lowercase : Optional[Any] = {
'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "umt5"
A_ = ["past_key_values"]
def __init__( self , __a=25_0112 , __a=512 , __a=64 , __a=1024 , __a=8 , __a=None , __a=6 , __a=32 , __a=128 , __a=0.1 , __a=1E-6 , __a=1.0 , __a="gated-gelu" , __a=True , __a=True , __a="T5Tokenizer" , __a=True , __a=0 , __a=1 , __a=0 , **__a , ):
'''simple docstring'''
super().__init__(
is_encoder_decoder=__a , tokenizer_class=__a , tie_word_embeddings=__a , pad_token_id=__a , eos_token_id=__a , decoder_start_token_id=__a , **__a , )
__a : Any = vocab_size
__a : Any = d_model
__a : str = d_kv
__a : Dict = d_ff
__a : Union[str, Any] = num_layers
__a : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__a : Optional[int] = num_heads
__a : Tuple = relative_attention_num_buckets
__a : Optional[Any] = relative_attention_max_distance
__a : Optional[int] = dropout_rate
__a : List[Any] = layer_norm_epsilon
__a : int = initializer_factor
__a : Union[str, Any] = feed_forward_proj
__a : Any = use_cache
__a : List[Any] = self.feed_forward_proj.split('-' )
__a : Dict = act_info[-1]
__a : Dict = act_info[0] == 'gated'
if len(__a ) > 1 and act_info[0] != "gated" or len(__a ) > 2:
raise ValueError(
f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'' )
if feed_forward_proj == "gated-gelu":
__a : Optional[int] = 'gelu_new'
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.d_model
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.num_heads
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.num_layers
class __UpperCamelCase ( lowerCAmelCase_ ):
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
__a : Dict = 'past_encoder_sequence + sequence'
__a : Tuple = {0: 'batch'}
__a : Tuple = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
__a : List[Any] = {0: 'batch', 1: 'decoder_sequence'}
__a : int = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(__a , direction='inputs' )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def __UpperCAmelCase ( self ):
'''simple docstring'''
return 13
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return 5E-4
| 27 | 0 |
'''simple docstring'''
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 lowerCamelCase__ ( __lowerCamelCase : List[Any]=None ):
'''simple docstring'''
if subparsers is not None:
_UpperCAmelCase : Optional[int] =subparsers.add_parser('env' )
else:
_UpperCAmelCase : List[Any] =argparse.ArgumentParser('Accelerate env command' )
parser.add_argument(
'--config_file' , default=_UpperCAmelCase , help='The config file to use for the default values in the launching script.' )
if subparsers is not None:
parser.set_defaults(func=_UpperCAmelCase )
return parser
def lowerCamelCase__ ( __lowerCamelCase : str ):
'''simple docstring'''
_UpperCAmelCase : Dict =torch.__version__
_UpperCAmelCase : str =torch.cuda.is_available()
_UpperCAmelCase : str =is_xpu_available()
_UpperCAmelCase : List[Any] =is_npu_available()
_UpperCAmelCase : Union[str, Any] ="Not found"
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(_UpperCAmelCase ):
_UpperCAmelCase : Union[str, Any] =load_config_from_file(args.config_file ).to_dict()
_UpperCAmelCase : List[str] ={
"`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(_UpperCAmelCase ),
"PyTorch NPU available": str(_UpperCAmelCase ),
"System RAM": f"{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB",
}
if pt_cuda_available:
_UpperCAmelCase : int =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:' )
_UpperCAmelCase : Tuple =(
"\n".join([f"\t- {prop}: {val}" for prop, val in accelerate_config.items()] )
if isinstance(_UpperCAmelCase , _UpperCAmelCase )
else f"\t{accelerate_config}"
)
print(_UpperCAmelCase )
_UpperCAmelCase : Any =accelerate_config
return info
def lowerCamelCase__ ( ):
'''simple docstring'''
_UpperCAmelCase : Tuple =env_command_parser()
_UpperCAmelCase : Dict =parser.parse_args()
env_command(_UpperCAmelCase )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 365 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : list[int] ):
'''simple docstring'''
if not nums:
return 0
_UpperCAmelCase : Tuple =nums[0]
_UpperCAmelCase : int =0
for num in nums[1:]:
_UpperCAmelCase , _UpperCAmelCase : Optional[int] =(
max_excluding + num,
max(__lowerCamelCase , __lowerCamelCase ),
)
return max(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 242 | 0 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowercase__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class __lowerCamelCase ( datasets.BuilderConfig ):
'''simple docstring'''
a_ : Optional[datasets.Features] = None
a_ : str = "utf-8"
a_ : Optional[str] = None
a_ : Optional[str] = None
a_ : bool = True # deprecated
a_ : Optional[int] = None # deprecated
a_ : int = 10 << 20 # 10MB
a_ : Optional[bool] = None
class __lowerCamelCase ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
a_ : int = JsonConfig
def lowerCamelCase ( self : List[Any] ):
if self.config.block_size is not None:
logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead" )
lowerCAmelCase_ : Union[str, Any] = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
"The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore." )
if self.config.newlines_in_values is not None:
raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported" )
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase ( self : int , a_ : Tuple ):
if not self.config.data_files:
raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
lowerCAmelCase_ : List[Any] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(__lowerCamelCase , (str, list, tuple) ):
lowerCAmelCase_ : Optional[int] = data_files
if isinstance(__lowerCamelCase , __lowerCamelCase ):
lowerCAmelCase_ : Optional[int] = [files]
lowerCAmelCase_ : Optional[Any] = [dl_manager.iter_files(__lowerCamelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
lowerCAmelCase_ : Tuple = []
for split_name, files in data_files.items():
if isinstance(__lowerCamelCase , __lowerCamelCase ):
lowerCAmelCase_ : Dict = [files]
lowerCAmelCase_ : List[Any] = [dl_manager.iter_files(__lowerCamelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=__lowerCamelCase , gen_kwargs={"files": files} ) )
return splits
def lowerCamelCase ( self : List[str] , a_ : pa.Table ):
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
lowerCAmelCase_ : List[str] = self.config.features.arrow_schema.field(__lowerCamelCase ).type
lowerCAmelCase_ : Union[str, Any] = pa_table.append_column(__lowerCamelCase , pa.array([None] * len(__lowerCamelCase ) , type=__lowerCamelCase ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
lowerCAmelCase_ : Any = table_cast(__lowerCamelCase , self.config.features.arrow_schema )
return pa_table
def lowerCamelCase ( self : Tuple , a_ : int ):
for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCamelCase ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(__lowerCamelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
lowerCAmelCase_ : Dict = json.load(__lowerCamelCase )
# We keep only the field we are interested in
lowerCAmelCase_ : int = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(__lowerCamelCase , (list, tuple) ):
lowerCAmelCase_ : Optional[int] = set().union(*[row.keys() for row in dataset] )
lowerCAmelCase_ : Any = {col: [row.get(__lowerCamelCase ) for row in dataset] for col in keys}
else:
lowerCAmelCase_ : Optional[int] = dataset
lowerCAmelCase_ : Optional[Any] = pa.Table.from_pydict(__lowerCamelCase )
yield file_idx, self._cast_table(__lowerCamelCase )
# If the file has one json object per line
else:
with open(__lowerCamelCase , "rb" ) as f:
lowerCAmelCase_ : List[str] = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
lowerCAmelCase_ : str = max(self.config.chunksize // 32 , 16 << 10 )
lowerCAmelCase_ : Optional[int] = (
self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
)
while True:
lowerCAmelCase_ : int = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(__lowerCamelCase )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
lowerCAmelCase_ : List[str] = batch.decode(self.config.encoding , errors=__lowerCamelCase ).encode("utf-8" )
try:
while True:
try:
lowerCAmelCase_ : int = paj.read_json(
io.BytesIO(__lowerCamelCase ) , read_options=paj.ReadOptions(block_size=__lowerCamelCase ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(__lowerCamelCase , pa.ArrowInvalid )
and "straddling" not in str(__lowerCamelCase )
or block_size > len(__lowerCamelCase )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f'''Batch of {len(__lowerCamelCase )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.''' )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
__lowerCamelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
lowerCAmelCase_ : Optional[Any] = json.load(__lowerCamelCase )
except json.JSONDecodeError:
logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCamelCase )}: {e}''' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(__lowerCamelCase , __lowerCamelCase ): # list is the only sequence type supported in JSON
try:
lowerCAmelCase_ : Tuple = set().union(*[row.keys() for row in dataset] )
lowerCAmelCase_ : Dict = {col: [row.get(__lowerCamelCase ) for row in dataset] for col in keys}
lowerCAmelCase_ : Any = pa.Table.from_pydict(__lowerCamelCase )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCamelCase )}: {e}''' )
raise ValueError(f'''Not able to read records in the JSON file at {file}.''' ) from None
yield file_idx, self._cast_table(__lowerCamelCase )
break
else:
logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCamelCase )}: {e}''' )
raise ValueError(
f'''Not able to read records in the JSON file at {file}. '''
f'''You should probably indicate the field of the JSON file containing your records. '''
f'''This JSON file contain the following fields: {str(list(dataset.keys() ) )}. '''
f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ''' ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(__lowerCamelCase )
batch_idx += 1
| 241 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
'''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
'''https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'''
),
# See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : Any = """trajectory_transformer"""
snake_case__ : Optional[Any] = ["""past_key_values"""]
snake_case__ : Tuple = {
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Union[str, Any] , __lowerCamelCase : Any=100 , __lowerCamelCase : str=5 , __lowerCamelCase : str=1 , __lowerCamelCase : Optional[int]=1 , __lowerCamelCase : int=249 , __lowerCamelCase : str=6 , __lowerCamelCase : Dict=17 , __lowerCamelCase : Optional[Any]=25 , __lowerCamelCase : List[str]=4 , __lowerCamelCase : str=4 , __lowerCamelCase : Tuple=128 , __lowerCamelCase : Dict=0.1 , __lowerCamelCase : str=0.1 , __lowerCamelCase : Any=0.1 , __lowerCamelCase : int=0.0006 , __lowerCamelCase : List[str]=512 , __lowerCamelCase : str=0.02 , __lowerCamelCase : Any=1E-12 , __lowerCamelCase : int=1 , __lowerCamelCase : Optional[Any]=True , __lowerCamelCase : Tuple=1 , __lowerCamelCase : int=50_256 , __lowerCamelCase : Union[str, Any]=50_256 , **__lowerCamelCase : Dict , ):
UpperCamelCase :Dict = vocab_size
UpperCamelCase :int = action_weight
UpperCamelCase :Tuple = reward_weight
UpperCamelCase :str = value_weight
UpperCamelCase :Tuple = max_position_embeddings
UpperCamelCase :Tuple = block_size
UpperCamelCase :Optional[int] = action_dim
UpperCamelCase :int = observation_dim
UpperCamelCase :List[str] = transition_dim
UpperCamelCase :List[Any] = learning_rate
UpperCamelCase :Optional[Any] = n_layer
UpperCamelCase :Any = n_head
UpperCamelCase :List[str] = n_embd
UpperCamelCase :Any = embd_pdrop
UpperCamelCase :str = attn_pdrop
UpperCamelCase :Union[str, Any] = resid_pdrop
UpperCamelCase :Optional[Any] = initializer_range
UpperCamelCase :List[Any] = layer_norm_eps
UpperCamelCase :Optional[int] = kaiming_initializer_range
UpperCamelCase :Tuple = use_cache
super().__init__(pad_token_id=__lowerCamelCase , bos_token_id=__lowerCamelCase , eos_token_id=__lowerCamelCase , **__lowerCamelCase )
| 38 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
UpperCAmelCase__ = logging.get_logger(__name__)
# General docstring
UpperCAmelCase__ = """RegNetConfig"""
# Base docstring
UpperCAmelCase__ = """facebook/regnet-y-040"""
UpperCAmelCase__ = [1, 1_0_8_8, 7, 7]
# Image classification docstring
UpperCAmelCase__ = """facebook/regnet-y-040"""
UpperCAmelCase__ = """tabby, tabby cat"""
UpperCAmelCase__ = [
"""facebook/regnet-y-040""",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class a ( tf.keras.layers.Layer ):
def __init__( self : Dict , __lowerCAmelCase : int , __lowerCAmelCase : int = 3 , __lowerCAmelCase : int = 1 , __lowerCAmelCase : int = 1 , __lowerCAmelCase : Optional[str] = "relu" , **__lowerCAmelCase : Optional[Any] , ):
super().__init__(**__lowerCAmelCase )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
_UpperCAmelCase = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
_UpperCAmelCase = tf.keras.layers.ConvaD(
filters=__lowerCAmelCase , kernel_size=__lowerCAmelCase , strides=__lowerCAmelCase , padding="""VALID""" , groups=__lowerCAmelCase , use_bias=__lowerCAmelCase , name="""convolution""" , )
_UpperCAmelCase = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name="""normalization""" )
_UpperCAmelCase = ACTaFN[activation] if activation is not None else tf.identity
def lowerCAmelCase_ ( self : Any , __lowerCAmelCase : Optional[int] ):
_UpperCAmelCase = self.convolution(self.padding(__lowerCAmelCase ) )
_UpperCAmelCase = self.normalization(__lowerCAmelCase )
_UpperCAmelCase = self.activation(__lowerCAmelCase )
return hidden_state
class a ( tf.keras.layers.Layer ):
def __init__( self : Optional[int] , __lowerCAmelCase : RegNetConfig , **__lowerCAmelCase : Dict ):
super().__init__(**__lowerCAmelCase )
_UpperCAmelCase = config.num_channels
_UpperCAmelCase = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name="""embedder""" , )
def lowerCAmelCase_ ( self : int , __lowerCAmelCase : Optional[int] ):
_UpperCAmelCase = shape_list(__lowerCAmelCase )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"""Make sure that the channel dimension of the pixel values match with the one set in the configuration.""" )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
_UpperCAmelCase = tf.transpose(__lowerCAmelCase , perm=(0, 2, 3, 1) )
_UpperCAmelCase = self.embedder(__lowerCAmelCase )
return hidden_state
class a ( tf.keras.layers.Layer ):
def __init__( self : str , __lowerCAmelCase : int , __lowerCAmelCase : int = 2 , **__lowerCAmelCase : List[str] ):
super().__init__(**__lowerCAmelCase )
_UpperCAmelCase = tf.keras.layers.ConvaD(
filters=__lowerCAmelCase , kernel_size=1 , strides=__lowerCAmelCase , use_bias=__lowerCAmelCase , name="""convolution""" )
_UpperCAmelCase = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name="""normalization""" )
def lowerCAmelCase_ ( self : Tuple , __lowerCAmelCase : tf.Tensor , __lowerCAmelCase : bool = False ):
return self.normalization(self.convolution(__lowerCAmelCase ) , training=__lowerCAmelCase )
class a ( tf.keras.layers.Layer ):
def __init__( self : Union[str, Any] , __lowerCAmelCase : int , __lowerCAmelCase : int , **__lowerCAmelCase : int ):
super().__init__(**__lowerCAmelCase )
_UpperCAmelCase = tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowerCAmelCase , name="""pooler""" )
_UpperCAmelCase = [
tf.keras.layers.ConvaD(filters=__lowerCAmelCase , kernel_size=1 , activation="""relu""" , name="""attention.0""" ),
tf.keras.layers.ConvaD(filters=__lowerCAmelCase , kernel_size=1 , activation="""sigmoid""" , name="""attention.2""" ),
]
def lowerCAmelCase_ ( self : Tuple , __lowerCAmelCase : Optional[int] ):
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
_UpperCAmelCase = self.pooler(__lowerCAmelCase )
for layer_module in self.attention:
_UpperCAmelCase = layer_module(__lowerCAmelCase )
_UpperCAmelCase = hidden_state * pooled
return hidden_state
class a ( tf.keras.layers.Layer ):
def __init__( self : Any , __lowerCAmelCase : RegNetConfig , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int = 1 , **__lowerCAmelCase : List[str] ):
super().__init__(**__lowerCAmelCase )
_UpperCAmelCase = in_channels != out_channels or stride != 1
_UpperCAmelCase = max(1 , out_channels // config.groups_width )
_UpperCAmelCase = (
TFRegNetShortCut(__lowerCAmelCase , stride=__lowerCAmelCase , name="""shortcut""" )
if should_apply_shortcut
else tf.keras.layers.Activation("""linear""" , name="""shortcut""" )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
_UpperCAmelCase = [
TFRegNetConvLayer(__lowerCAmelCase , kernel_size=1 , activation=config.hidden_act , name="""layer.0""" ),
TFRegNetConvLayer(
__lowerCAmelCase , stride=__lowerCAmelCase , groups=__lowerCAmelCase , activation=config.hidden_act , name="""layer.1""" ),
TFRegNetConvLayer(__lowerCAmelCase , kernel_size=1 , activation=__lowerCAmelCase , name="""layer.2""" ),
]
_UpperCAmelCase = ACTaFN[config.hidden_act]
def lowerCAmelCase_ ( self : Tuple , __lowerCAmelCase : Any ):
_UpperCAmelCase = hidden_state
for layer_module in self.layers:
_UpperCAmelCase = layer_module(__lowerCAmelCase )
_UpperCAmelCase = self.shortcut(__lowerCAmelCase )
hidden_state += residual
_UpperCAmelCase = self.activation(__lowerCAmelCase )
return hidden_state
class a ( tf.keras.layers.Layer ):
def __init__( self : Tuple , __lowerCAmelCase : RegNetConfig , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int = 1 , **__lowerCAmelCase : List[Any] ):
super().__init__(**__lowerCAmelCase )
_UpperCAmelCase = in_channels != out_channels or stride != 1
_UpperCAmelCase = max(1 , out_channels // config.groups_width )
_UpperCAmelCase = (
TFRegNetShortCut(__lowerCAmelCase , stride=__lowerCAmelCase , name="""shortcut""" )
if should_apply_shortcut
else tf.keras.layers.Activation("""linear""" , name="""shortcut""" )
)
_UpperCAmelCase = [
TFRegNetConvLayer(__lowerCAmelCase , kernel_size=1 , activation=config.hidden_act , name="""layer.0""" ),
TFRegNetConvLayer(
__lowerCAmelCase , stride=__lowerCAmelCase , groups=__lowerCAmelCase , activation=config.hidden_act , name="""layer.1""" ),
TFRegNetSELayer(__lowerCAmelCase , reduced_channels=int(round(in_channels / 4 ) ) , name="""layer.2""" ),
TFRegNetConvLayer(__lowerCAmelCase , kernel_size=1 , activation=__lowerCAmelCase , name="""layer.3""" ),
]
_UpperCAmelCase = ACTaFN[config.hidden_act]
def lowerCAmelCase_ ( self : Any , __lowerCAmelCase : Dict ):
_UpperCAmelCase = hidden_state
for layer_module in self.layers:
_UpperCAmelCase = layer_module(__lowerCAmelCase )
_UpperCAmelCase = self.shortcut(__lowerCAmelCase )
hidden_state += residual
_UpperCAmelCase = self.activation(__lowerCAmelCase )
return hidden_state
class a ( tf.keras.layers.Layer ):
def __init__( self : Any , __lowerCAmelCase : RegNetConfig , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int = 2 , __lowerCAmelCase : int = 2 , **__lowerCAmelCase : Dict ):
super().__init__(**__lowerCAmelCase )
_UpperCAmelCase = TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer
_UpperCAmelCase = [
# downsampling is done in the first layer with stride of 2
layer(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , stride=__lowerCAmelCase , name="""layers.0""" ),
*[layer(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def lowerCAmelCase_ ( self : int , __lowerCAmelCase : Optional[Any] ):
for layer_module in self.layers:
_UpperCAmelCase = layer_module(__lowerCAmelCase )
return hidden_state
class a ( tf.keras.layers.Layer ):
def __init__( self : str , __lowerCAmelCase : RegNetConfig , **__lowerCAmelCase : Optional[Any] ):
super().__init__(**__lowerCAmelCase )
_UpperCAmelCase = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
__lowerCAmelCase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name="""stages.0""" , ) )
_UpperCAmelCase = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(__lowerCAmelCase , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , depth=__lowerCAmelCase , name=f'''stages.{i+1}''' ) )
def lowerCAmelCase_ ( self : Tuple , __lowerCAmelCase : tf.Tensor , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = True ):
_UpperCAmelCase = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
_UpperCAmelCase = hidden_states + (hidden_state,)
_UpperCAmelCase = stage_module(__lowerCAmelCase )
if output_hidden_states:
_UpperCAmelCase = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=__lowerCAmelCase , hidden_states=__lowerCAmelCase )
@keras_serializable
class a ( tf.keras.layers.Layer ):
_snake_case : List[Any] = RegNetConfig
def __init__( self : Optional[int] , __lowerCAmelCase : Optional[int] , **__lowerCAmelCase : Optional[int] ):
super().__init__(**__lowerCAmelCase )
_UpperCAmelCase = config
_UpperCAmelCase = TFRegNetEmbeddings(__lowerCAmelCase , name="""embedder""" )
_UpperCAmelCase = TFRegNetEncoder(__lowerCAmelCase , name="""encoder""" )
_UpperCAmelCase = tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowerCAmelCase , name="""pooler""" )
@unpack_inputs
def lowerCAmelCase_ ( self : Any , __lowerCAmelCase : tf.Tensor , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : bool = False , ):
_UpperCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
_UpperCAmelCase = self.embedder(__lowerCAmelCase , training=__lowerCAmelCase )
_UpperCAmelCase = self.encoder(
__lowerCAmelCase , output_hidden_states=__lowerCAmelCase , return_dict=__lowerCAmelCase , training=__lowerCAmelCase )
_UpperCAmelCase = encoder_outputs[0]
_UpperCAmelCase = self.pooler(__lowerCAmelCase )
# Change to NCHW output format have uniformity in the modules
_UpperCAmelCase = tf.transpose(__lowerCAmelCase , perm=(0, 3, 1, 2) )
_UpperCAmelCase = tf.transpose(__lowerCAmelCase , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
_UpperCAmelCase = tuple([tf.transpose(__lowerCAmelCase , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__lowerCAmelCase , pooler_output=__lowerCAmelCase , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class a ( lowerCAmelCase_ ):
_snake_case : List[str] = RegNetConfig
_snake_case : Optional[Any] = 'regnet'
_snake_case : Union[str, Any] = 'pixel_values'
@property
def lowerCAmelCase_ ( self : Any ):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) , dtype=tf.floataa )}
UpperCAmelCase__ = r"""
Parameters:
This model is a Tensorflow
[tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
behavior.
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
UpperCAmelCase__ = r"""
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConveNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'The bare RegNet model outputting raw features without any specific head on top.' , lowerCAmelCase_ , )
class a ( lowerCAmelCase_ ):
def __init__( self : int , __lowerCAmelCase : RegNetConfig , *__lowerCAmelCase : Dict , **__lowerCAmelCase : List[str] ):
super().__init__(__lowerCAmelCase , *__lowerCAmelCase , **__lowerCAmelCase )
_UpperCAmelCase = TFRegNetMainLayer(__lowerCAmelCase , name="""regnet""" )
@unpack_inputs
@add_start_docstrings_to_model_forward(__lowerCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__lowerCAmelCase , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowerCAmelCase_ ( self : Union[str, Any] , __lowerCAmelCase : tf.Tensor , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Any=False , ):
_UpperCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
_UpperCAmelCase = self.regnet(
pixel_values=__lowerCAmelCase , output_hidden_states=__lowerCAmelCase , return_dict=__lowerCAmelCase , training=__lowerCAmelCase , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
'\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , lowerCAmelCase_ , )
class a ( lowerCAmelCase_ , lowerCAmelCase_ ):
def __init__( self : Dict , __lowerCAmelCase : RegNetConfig , *__lowerCAmelCase : List[Any] , **__lowerCAmelCase : List[Any] ):
super().__init__(__lowerCAmelCase , *__lowerCAmelCase , **__lowerCAmelCase )
_UpperCAmelCase = config.num_labels
_UpperCAmelCase = TFRegNetMainLayer(__lowerCAmelCase , name="""regnet""" )
# classification head
_UpperCAmelCase = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name="""classifier.1""" ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(__lowerCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__lowerCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowerCAmelCase_ ( self : str , __lowerCAmelCase : tf.Tensor = None , __lowerCAmelCase : tf.Tensor = None , __lowerCAmelCase : bool = None , __lowerCAmelCase : bool = None , __lowerCAmelCase : str=False , ):
_UpperCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
_UpperCAmelCase = self.regnet(
__lowerCAmelCase , output_hidden_states=__lowerCAmelCase , return_dict=__lowerCAmelCase , training=__lowerCAmelCase )
_UpperCAmelCase = outputs.pooler_output if return_dict else outputs[1]
_UpperCAmelCase = self.classifier[0](__lowerCAmelCase )
_UpperCAmelCase = self.classifier[1](__lowerCAmelCase )
_UpperCAmelCase = None if labels is None else self.hf_compute_loss(labels=__lowerCAmelCase , logits=__lowerCAmelCase )
if not return_dict:
_UpperCAmelCase = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=__lowerCAmelCase , logits=__lowerCAmelCase , hidden_states=outputs.hidden_states )
| 364 |
"""simple docstring"""
from itertools import product
def __UpperCAmelCase ( lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = sides_number
_UpperCAmelCase = max_face_number * dice_number
_UpperCAmelCase = [0] * (max_total + 1)
_UpperCAmelCase = 1
_UpperCAmelCase = range(lowercase ,max_face_number + 1 )
for dice_numbers in product(lowercase ,repeat=lowercase ):
_UpperCAmelCase = sum(lowercase )
totals_frequencies[total] += 1
return totals_frequencies
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = total_frequency_distribution(
sides_number=4 ,dice_number=9 )
_UpperCAmelCase = total_frequency_distribution(
sides_number=6 ,dice_number=6 )
_UpperCAmelCase = 0
_UpperCAmelCase = 9
_UpperCAmelCase = 4 * 9
_UpperCAmelCase = 6
for peter_total in range(lowercase ,max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
_UpperCAmelCase = (4**9) * (6**6)
_UpperCAmelCase = peter_wins_count / total_games_number
_UpperCAmelCase = round(lowercase ,ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F'''{solution() = }''')
| 30 | 0 |
from __future__ import annotations
import time
import numpy as np
__a : Optional[Any] = [8, 5, 9, 7]
__a : Optional[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__a : List[Any] = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class _UpperCamelCase :
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> None:
'''simple docstring'''
__lowercase = claim_vector
__lowercase = allocated_resources_table
__lowercase = maximum_claim_table
def _SCREAMING_SNAKE_CASE ( self ) -> list[int]:
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _SCREAMING_SNAKE_CASE ( self ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _SCREAMING_SNAKE_CASE ( self ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(lowerCAmelCase__ ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _SCREAMING_SNAKE_CASE ( self ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(lowerCAmelCase__ ): i for i in self.__need()}
def _SCREAMING_SNAKE_CASE ( self , **lowerCAmelCase__ ) -> None:
'''simple docstring'''
__lowercase = self.__need()
__lowercase = self.__allocated_resources_table
__lowercase = self.__available_resources()
__lowercase = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('''_''' * 50 + '''\n''' )
while need_list:
__lowercase = False
for each_need in need_list:
__lowercase = True
for index, need in enumerate(lowerCAmelCase__ ):
if need > available_resources[index]:
__lowercase = False
break
if execution:
__lowercase = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
__lowercase = original_need_index
print(F"Process {process_number + 1} is executing." )
# remove the process run from stack
need_list.remove(lowerCAmelCase__ )
# update available/freed resources stack
__lowercase = np.array(lowerCAmelCase__ ) + np.array(
alloc_resources_table[process_number] )
print(
'''Updated available resource stack for processes: '''
+ ''' '''.join([str(lowerCAmelCase__ ) for x in available_resources] ) )
break
if safe:
print('''The process is in a safe state.\n''' )
else:
print('''System in unsafe state. Aborting...\n''' )
break
def _SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
print(''' ''' * 9 + '''Allocated Resource Table''' )
for item in self.__allocated_resources_table:
print(
F"P{self.__allocated_resources_table.index(lowerCAmelCase__ ) + 1}"
+ ''' '''.join(F"{it:>8}" for it in item )
+ '''\n''' )
print(''' ''' * 9 + '''System Resource Table''' )
for item in self.__maximum_claim_table:
print(
F"P{self.__maximum_claim_table.index(lowerCAmelCase__ ) + 1}"
+ ''' '''.join(F"{it:>8}" for it in item )
+ '''\n''' )
print(
'''Current Usage by Active Processes: '''
+ ''' '''.join(str(lowerCAmelCase__ ) for x in self.__claim_vector ) )
print(
'''Initial Available Resources: '''
+ ''' '''.join(str(lowerCAmelCase__ ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 210 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class _UpperCamelCase ( _UpperCAmelCase ,unittest.TestCase ):
"""simple docstring"""
__a : List[Any] = BertJapaneseTokenizer
__a : Any = False
__a : Optional[int] = True
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
super().setUp()
__lowercase = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''こんにちは''',
'''こん''',
'''にちは''',
'''ばんは''',
'''##こん''',
'''##にちは''',
'''##ばんは''',
'''世界''',
'''##世界''',
'''、''',
'''##、''',
'''。''',
'''##。''',
]
__lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ ) -> str:
'''simple docstring'''
__lowercase = '''こんにちは、世界。 \nこんばんは、世界。'''
__lowercase = '''こんにちは 、 世界 。 こんばんは 、 世界 。'''
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ ) -> Dict:
'''simple docstring'''
__lowercase , __lowercase = self.get_input_output_texts(lowerCAmelCase__ )
__lowercase = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
__lowercase = tokenizer.decode(lowerCAmelCase__ , clean_up_tokenization_spaces=lowerCAmelCase__ )
return text, ids
def _SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
pass # TODO add if relevant
def _SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
pass # TODO add if relevant
def _SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
pass # TODO add if relevant
def _SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
__lowercase = self.tokenizer_class(self.vocab_file )
__lowercase = tokenizer.tokenize('''こんにちは、世界。\nこんばんは、世界。''' )
self.assertListEqual(lowerCAmelCase__ , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def _SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
__lowercase = self.tokenizer_class(self.vocab_file , word_tokenizer_type='''mecab''' )
self.assertIsNotNone(lowerCAmelCase__ )
__lowercase = '''こんにちは、世界。\nこんばんは、世界。'''
__lowercase = tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
__lowercase = os.path.join(self.tmpdirname , '''tokenizer.bin''' )
with open(lowerCAmelCase__ , '''wb''' ) as handle:
pickle.dump(lowerCAmelCase__ , lowerCAmelCase__ )
with open(lowerCAmelCase__ , '''rb''' ) as handle:
__lowercase = pickle.load(lowerCAmelCase__ )
__lowercase = tokenizer_new.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
__lowercase = MecabTokenizer(mecab_dic='''ipadic''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップルストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def _SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
try:
__lowercase = MecabTokenizer(mecab_dic='''unidic_lite''' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def _SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
try:
__lowercase = MecabTokenizer(mecab_dic='''unidic''' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
__lowercase = MecabTokenizer(do_lower_case=lowerCAmelCase__ , mecab_dic='''ipadic''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップルストア''', '''で''', '''iphone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def _SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
try:
__lowercase = MecabTokenizer(
do_lower_case=lowerCAmelCase__ , normalize_text=lowerCAmelCase__ , mecab_option='''-d /usr/local/lib/mecab/dic/jumandic''' )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''ｱｯﾌﾟﾙストア''', '''で''', '''iPhone''', '''８''', '''が''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
def _SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
__lowercase = MecabTokenizer(normalize_text=lowerCAmelCase__ , mecab_dic='''ipadic''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''ｱｯﾌﾟﾙストア''', '''で''', '''iPhone''', '''８''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', ''' ''', '''。'''] , )
@require_sudachi
def _SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
__lowercase = self.tokenizer_class(self.vocab_file , word_tokenizer_type='''sudachi''' )
self.assertIsNotNone(lowerCAmelCase__ )
__lowercase = '''こんにちは、世界。\nこんばんは、世界。'''
__lowercase = tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
__lowercase = os.path.join(self.tmpdirname , '''tokenizer.bin''' )
with open(lowerCAmelCase__ , '''wb''' ) as handle:
pickle.dump(lowerCAmelCase__ , lowerCAmelCase__ )
with open(lowerCAmelCase__ , '''rb''' ) as handle:
__lowercase = pickle.load(lowerCAmelCase__ )
__lowercase = tokenizer_new.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@require_sudachi
def _SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowercase = SudachiTokenizer(sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , [''' ''', '''\t''', '''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', ''' ''', '''が''', ''' ''', ''' ''', '''\n ''', '''発売''', '''さ''', '''れ''', '''た''', ''' ''', '''。''', ''' ''', ''' '''] , )
@require_sudachi
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
__lowercase = SudachiTokenizer(sudachi_dict_type='''core''' , sudachi_split_mode='''A''' )
self.assertListEqual(tokenizer.tokenize('''外国人参政権''' ) , ['''外国''', '''人''', '''参政''', '''権'''] )
@require_sudachi
def _SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
__lowercase = SudachiTokenizer(sudachi_dict_type='''core''' , sudachi_split_mode='''B''' )
self.assertListEqual(tokenizer.tokenize('''外国人参政権''' ) , ['''外国人''', '''参政権'''] )
@require_sudachi
def _SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
__lowercase = SudachiTokenizer(sudachi_dict_type='''core''' , sudachi_split_mode='''C''' )
self.assertListEqual(tokenizer.tokenize('''外国人参政権''' ) , ['''外国人参政権'''] )
@require_sudachi
def _SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
__lowercase = SudachiTokenizer(do_lower_case=lowerCAmelCase__ , sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , [''' ''', '''\t''', '''アップル''', '''ストア''', '''で''', '''iphone''', '''8''', ''' ''', '''が''', ''' ''', ''' ''', '''\n ''', '''発売''', '''さ''', '''れ''', '''た''', ''' ''', '''。''', ''' ''', ''' '''] , )
@require_sudachi
def _SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
__lowercase = SudachiTokenizer(normalize_text=lowerCAmelCase__ , sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , [''' ''', '''\t''', '''ｱｯﾌﾟﾙ''', '''ストア''', '''で''', '''iPhone''', '''８''', ''' ''', '''が''', ''' ''', ''' ''', '''\n ''', '''発売''', '''さ''', '''れ''', '''た''', '''\u3000''', '''。''', ''' ''', ''' '''] , )
@require_sudachi
def _SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
__lowercase = SudachiTokenizer(trim_whitespace=lowerCAmelCase__ , sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
@require_jumanpp
def _SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
__lowercase = self.tokenizer_class(self.vocab_file , word_tokenizer_type='''jumanpp''' )
self.assertIsNotNone(lowerCAmelCase__ )
__lowercase = '''こんにちは、世界。\nこんばんは、世界。'''
__lowercase = tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
__lowercase = os.path.join(self.tmpdirname , '''tokenizer.bin''' )
with open(lowerCAmelCase__ , '''wb''' ) as handle:
pickle.dump(lowerCAmelCase__ , lowerCAmelCase__ )
with open(lowerCAmelCase__ , '''rb''' ) as handle:
__lowercase = pickle.load(lowerCAmelCase__ )
__lowercase = tokenizer_new.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@require_jumanpp
def _SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
__lowercase = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''\u3000''', '''が''', '''\u3000''', '''\u3000''', '''\u3000''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
@require_jumanpp
def _SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
__lowercase = JumanppTokenizer(do_lower_case=lowerCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iphone''', '''8''', '''\u3000''', '''が''', '''\u3000''', '''\u3000''', '''\u3000''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
@require_jumanpp
def _SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
__lowercase = JumanppTokenizer(normalize_text=lowerCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''ｱ''', '''ｯ''', '''ﾌ''', '''ﾟ''', '''ﾙ''', '''ストア''', '''で''', '''iPhone''', '''８''', '''\u3000''', '''が''', '''\u3000''', '''\u3000''', '''\u3000''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
@require_jumanpp
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
__lowercase = JumanppTokenizer(trim_whitespace=lowerCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れた''', '''。'''] , )
@require_jumanpp
def _SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
__lowercase = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize('''ありがとうございますm(_ _)ｍ見つけるのが大変です。''' ) , ['''ありがとう''', '''ございます''', '''m(_ _)m''', '''見つける''', '''の''', '''が''', '''大変です''', '''。'''] , )
def _SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
__lowercase = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''こんにちは''', '''こん''', '''にちは''', '''ばんは''', '''##こん''', '''##にちは''', '''##ばんは''']
__lowercase = {}
for i, token in enumerate(lowerCAmelCase__ ):
__lowercase = i
__lowercase = WordpieceTokenizer(vocab=lowerCAmelCase__ , unk_token='''[UNK]''' )
self.assertListEqual(tokenizer.tokenize('''''' ) , [] )
self.assertListEqual(tokenizer.tokenize('''こんにちは''' ) , ['''こんにちは'''] )
self.assertListEqual(tokenizer.tokenize('''こんばんは''' ) , ['''こん''', '''##ばんは'''] )
self.assertListEqual(tokenizer.tokenize('''こんばんは こんばんにちは こんにちは''' ) , ['''こん''', '''##ばんは''', '''[UNK]''', '''こんにちは'''] )
def _SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
__lowercase = BertJapaneseTokenizer.from_pretrained('''nlp-waseda/roberta-base-japanese-with-auto-jumanpp''' )
__lowercase = tokenizer.subword_tokenizer
__lowercase = subword_tokenizer.tokenize('''国境 の 長い トンネル を 抜ける と 雪国 であった 。''' )
self.assertListEqual(lowerCAmelCase__ , ['''▁国境''', '''▁の''', '''▁長い''', '''▁トンネル''', '''▁を''', '''▁抜ける''', '''▁と''', '''▁雪''', '''国''', '''▁であった''', '''▁。'''] )
__lowercase = subword_tokenizer.tokenize('''こんばんは こんばん にち は こんにちは''' )
self.assertListEqual(lowerCAmelCase__ , ['''▁こん''', '''ばん''', '''は''', '''▁こん''', '''ばん''', '''▁に''', '''ち''', '''▁は''', '''▁こんにちは'''] )
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
__lowercase = self.tokenizer_class.from_pretrained('''cl-tohoku/bert-base-japanese''' )
__lowercase = tokenizer.encode('''ありがとう。''' , add_special_tokens=lowerCAmelCase__ )
__lowercase = tokenizer.encode('''どういたしまして。''' , add_special_tokens=lowerCAmelCase__ )
__lowercase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ )
__lowercase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ , lowerCAmelCase__ )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class _UpperCamelCase ( _UpperCAmelCase ,unittest.TestCase ):
"""simple docstring"""
__a : Union[str, Any] = BertJapaneseTokenizer
__a : Tuple = False
def _SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
super().setUp()
__lowercase = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''こ''', '''ん''', '''に''', '''ち''', '''は''', '''ば''', '''世''', '''界''', '''、''', '''。''']
__lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def _SCREAMING_SNAKE_CASE ( self , **lowerCAmelCase__ ) -> Dict:
'''simple docstring'''
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type='''character''' , **lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
__lowercase = '''こんにちは、世界。 \nこんばんは、世界。'''
__lowercase = '''こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。'''
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
pass # TODO add if relevant
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
pass # TODO add if relevant
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
pass # TODO add if relevant
def _SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowercase = self.tokenizer_class(self.vocab_file , subword_tokenizer_type='''character''' )
__lowercase = tokenizer.tokenize('''こんにちは、世界。 \nこんばんは、世界。''' )
self.assertListEqual(
lowerCAmelCase__ , ['''こ''', '''ん''', '''に''', '''ち''', '''は''', '''、''', '''世''', '''界''', '''。''', '''こ''', '''ん''', '''ば''', '''ん''', '''は''', '''、''', '''世''', '''界''', '''。'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def _SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
__lowercase = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''こ''', '''ん''', '''に''', '''ち''', '''は''', '''ば''', '''世''', '''界''', '''、''', '''。''']
__lowercase = {}
for i, token in enumerate(lowerCAmelCase__ ):
__lowercase = i
__lowercase = CharacterTokenizer(vocab=lowerCAmelCase__ , unk_token='''[UNK]''' )
self.assertListEqual(tokenizer.tokenize('''''' ) , [] )
self.assertListEqual(tokenizer.tokenize('''こんにちは''' ) , ['''こ''', '''ん''', '''に''', '''ち''', '''は'''] )
self.assertListEqual(tokenizer.tokenize('''こんにちほ''' ) , ['''こ''', '''ん''', '''に''', '''ち''', '''[UNK]'''] )
def _SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowercase = self.tokenizer_class.from_pretrained('''cl-tohoku/bert-base-japanese-char''' )
__lowercase = tokenizer.encode('''ありがとう。''' , add_special_tokens=lowerCAmelCase__ )
__lowercase = tokenizer.encode('''どういたしまして。''' , add_special_tokens=lowerCAmelCase__ )
__lowercase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ )
__lowercase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ , lowerCAmelCase__ )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class _UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
__lowercase = '''cl-tohoku/bert-base-japanese'''
__lowercase = AutoTokenizer.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
class _UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
__lowercase = '''cl-tohoku/bert-base-japanese'''
with self.assertLogs('''transformers''' , level='''WARNING''' ) as cm:
BertTokenizer.from_pretrained(lowerCAmelCase__ )
self.assertTrue(
cm.records[0].message.startswith(
'''The tokenizer class you load from this checkpoint is not the same type as the class this function'''
''' is called from.''' ) )
__lowercase = '''bert-base-cased'''
with self.assertLogs('''transformers''' , level='''WARNING''' ) as cm:
BertJapaneseTokenizer.from_pretrained(lowerCAmelCase__ )
self.assertTrue(
cm.records[0].message.startswith(
'''The tokenizer class you load from this checkpoint is not the same type as the class this function'''
''' is called from.''' ) )
| 210 | 1 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
lowercase =logging.getLogger()
def lowerCamelCase__ ( ):
'''simple docstring'''
_UpperCAmelCase : List[Any] =argparse.ArgumentParser()
parser.add_argument('-f' )
_UpperCAmelCase : Any =parser.parse_args()
return args.f
class __magic_name__ ( __lowercase ):
def lowerCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCAmelCase : Dict =logging.StreamHandler(sys.stdout)
logger.addHandler(snake_case_)
def lowerCAmelCase ( self , snake_case) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 , 'run_glue_deebert.py')
with patch.object(snake_case_ , 'argv' , snake_case_):
_UpperCAmelCase : Optional[int] =run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(snake_case_ , 0.6_66)
@slow
@require_torch_non_multi_gpu
def lowerCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase : List[Any] ='''
--model_type roberta
--model_name_or_path roberta-base
--task_name MRPC
--do_train
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--max_seq_length 128
--per_gpu_eval_batch_size=1
--per_gpu_train_batch_size=8
--learning_rate 2e-4
--num_train_epochs 3
--overwrite_output_dir
--seed 42
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--save_steps 0
--overwrite_cache
--eval_after_first_stage
'''.split()
self.run_and_check(snake_case_)
_UpperCAmelCase : Any ='''
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--eval_each_highway
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
'''.split()
self.run_and_check(snake_case_)
_UpperCAmelCase : Any ='''
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--early_exit_entropy 0.1
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
'''.split()
self.run_and_check(snake_case_)
| 352 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def lowerCamelCase__ ( __lowerCamelCase : Optional[int] ):
'''simple docstring'''
return choice(__lowerCamelCase )
def lowerCamelCase__ ( __lowerCamelCase : list[int] , __lowerCamelCase : int ):
'''simple docstring'''
_UpperCAmelCase : int =random_pivot(__lowerCamelCase )
# partition based on pivot
# linear time
_UpperCAmelCase : str =[e for e in lst if e < pivot]
_UpperCAmelCase : Dict =[e for e in lst if e > pivot]
# if we get lucky, pivot might be the element we want.
# we can easily see this:
# small (elements smaller than k)
# + pivot (kth element)
# + big (elements larger than k)
if len(__lowerCamelCase ) == k - 1:
return pivot
# pivot is in elements bigger than k
elif len(__lowerCamelCase ) < k - 1:
return kth_number(__lowerCamelCase , k - len(__lowerCamelCase ) - 1 )
# pivot is in elements smaller than k
else:
return kth_number(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 242 | 0 |
"""simple docstring"""
def UpperCamelCase__ ( lowercase__ : str , lowercase__ : str = " " ):
snake_case : List[str] = []
snake_case : List[Any] = 0
for index, char in enumerate(lowercase__ ):
if char == separator:
split_words.append(string[last_index:index] )
snake_case : List[Any] = index + 1
elif index + 1 == len(lowercase__ ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 148 |
"""simple docstring"""
from typing import Any
class lowerCamelCase__ :
def __init__( self , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
snake_case : Tuple = data
snake_case : Union[str, Any] = None
class lowerCamelCase__ :
def __init__( self ):
"""simple docstring"""
snake_case : List[Any] = None
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : str = self.head
while temp is not None:
print(temp.data , end=" " )
snake_case : Optional[Any] = temp.next
print()
def lowerCamelCase_ ( self , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
snake_case : Union[str, Any] = Node(SCREAMING_SNAKE_CASE )
snake_case : List[Any] = self.head
snake_case : Optional[int] = new_node
def lowerCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if node_data_a == node_data_a:
return
else:
snake_case : int = self.head
while node_a is not None and node_a.data != node_data_a:
snake_case : Optional[Any] = node_a.next
snake_case : Tuple = self.head
while node_a is not None and node_a.data != node_data_a:
snake_case : Union[str, Any] = node_a.next
if node_a is None or node_a is None:
return
snake_case , snake_case : int = node_a.data, node_a.data
if __name__ == "__main__":
__A = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print("After swapping")
ll.print_list()
| 148 | 1 |
import requests
from bsa import BeautifulSoup
def __UpperCamelCase ( lowercase__ : str = "AAPL" ) -> str:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
lowerCAmelCase_ : Tuple = BeautifulSoup(requests.get(lowercase__ ).text , """html.parser""" )
lowerCAmelCase_ : int = """My(6px) Pos(r) smartphone_Mt(6px)"""
return soup.find("""div""" , class_=class_ ).find("""span""" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(f"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")
| 28 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger('transformers.models.speecht5')
def __UpperCamelCase ( lowercase__ : Optional[Any] , lowercase__ : Optional[Any] , lowercase__ : str ) -> List[str]:
'''simple docstring'''
hf_model.apply_weight_norm()
lowerCAmelCase_ : Dict = checkpoint["""input_conv.weight_g"""]
lowerCAmelCase_ : Any = checkpoint["""input_conv.weight_v"""]
lowerCAmelCase_ : Any = checkpoint["""input_conv.bias"""]
for i in range(len(config.upsample_rates ) ):
lowerCAmelCase_ : Tuple = checkpoint[f'upsamples.{i}.1.weight_g']
lowerCAmelCase_ : Any = checkpoint[f'upsamples.{i}.1.weight_v']
lowerCAmelCase_ : int = checkpoint[f'upsamples.{i}.1.bias']
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
lowerCAmelCase_ : Dict = checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
lowerCAmelCase_ : Dict = checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
lowerCAmelCase_ : Tuple = checkpoint[f'blocks.{i}.convs1.{j}.1.bias']
lowerCAmelCase_ : str = checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
lowerCAmelCase_ : Optional[Any] = checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
lowerCAmelCase_ : str = checkpoint[f'blocks.{i}.convs2.{j}.1.bias']
lowerCAmelCase_ : str = checkpoint["""output_conv.1.weight_g"""]
lowerCAmelCase_ : Dict = checkpoint["""output_conv.1.weight_v"""]
lowerCAmelCase_ : Optional[int] = checkpoint["""output_conv.1.bias"""]
hf_model.remove_weight_norm()
@torch.no_grad()
def __UpperCamelCase ( lowercase__ : str , lowercase__ : Tuple , lowercase__ : Dict , lowercase__ : List[Any]=None , lowercase__ : Union[str, Any]=None , ) -> List[Any]:
'''simple docstring'''
if config_path is not None:
lowerCAmelCase_ : Optional[Any] = SpeechTaHifiGanConfig.from_pretrained(lowercase__ )
else:
lowerCAmelCase_ : Any = SpeechTaHifiGanConfig()
lowerCAmelCase_ : str = SpeechTaHifiGan(lowercase__ )
lowerCAmelCase_ : Tuple = torch.load(lowercase__ )
load_weights(orig_checkpoint["""model"""]["""generator"""] , lowercase__ , lowercase__ )
lowerCAmelCase_ : Optional[int] = np.load(lowercase__ )
lowerCAmelCase_ : Any = stats[0].reshape(-1 )
lowerCAmelCase_ : List[str] = stats[1].reshape(-1 )
lowerCAmelCase_ : Optional[int] = torch.from_numpy(lowercase__ ).float()
lowerCAmelCase_ : Any = torch.from_numpy(lowercase__ ).float()
model.save_pretrained(lowercase__ )
if repo_id:
print("""Pushing to the hub...""" )
model.push_to_hub(lowercase__ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
__UpperCAmelCase = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 28 | 1 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __lowerCamelCase ( self : int):
'''simple docstring'''
__lowercase =get_activation('swish')
self.assertIsInstance(_lowerCAmelCase , nn.SiLU)
self.assertEqual(act(torch.tensor(-1_0_0 , dtype=torch.floataa)).item() , 0)
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(2_0 , dtype=torch.floataa)).item() , 2_0)
def __lowerCamelCase ( self : str):
'''simple docstring'''
__lowercase =get_activation('silu')
self.assertIsInstance(_lowerCAmelCase , nn.SiLU)
self.assertEqual(act(torch.tensor(-1_0_0 , dtype=torch.floataa)).item() , 0)
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(2_0 , dtype=torch.floataa)).item() , 2_0)
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__lowercase =get_activation('mish')
self.assertIsInstance(_lowerCAmelCase , nn.Mish)
self.assertEqual(act(torch.tensor(-2_0_0 , dtype=torch.floataa)).item() , 0)
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(2_0 , dtype=torch.floataa)).item() , 2_0)
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__lowercase =get_activation('gelu')
self.assertIsInstance(_lowerCAmelCase , nn.GELU)
self.assertEqual(act(torch.tensor(-1_0_0 , dtype=torch.floataa)).item() , 0)
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(2_0 , dtype=torch.floataa)).item() , 2_0)
| 166 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = """▁"""
lowerCamelCase = {"""vocab_file""": """spiece.model"""}
lowerCamelCase = {
"""vocab_file""": {
"""google/reformer-crime-and-punishment""": (
"""https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
)
}
}
lowerCamelCase = {
"""google/reformer-crime-and-punishment""": 52_4288,
}
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = ["""input_ids""", """attention_mask"""]
def __init__( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any]="</s>" , _lowerCAmelCase : Any="<unk>" , _lowerCAmelCase : int=[] , _lowerCAmelCase : Optional[Dict[str, Any]] = None , **_lowerCAmelCase : List[Any] , ):
'''simple docstring'''
__lowercase ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , additional_special_tokens=_lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCAmelCase , )
__lowercase =vocab_file
__lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(_lowerCAmelCase)
@property
def __lowerCamelCase ( self : int):
'''simple docstring'''
return self.sp_model.get_piece_size()
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__lowercase ={self.convert_ids_to_tokens(_lowerCAmelCase): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self : Any):
'''simple docstring'''
__lowercase =self.__dict__.copy()
__lowercase =None
return state
def __setstate__( self : Optional[int] , _lowerCAmelCase : Union[str, Any]):
'''simple docstring'''
__lowercase =d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs'):
__lowercase ={}
__lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def __lowerCamelCase ( self : List[str] , _lowerCAmelCase : str):
'''simple docstring'''
return self.sp_model.encode(_lowerCAmelCase , out_type=_lowerCAmelCase)
def __lowerCamelCase ( self : Optional[Any] , _lowerCAmelCase : List[Any]):
'''simple docstring'''
return self.sp_model.piece_to_id(_lowerCAmelCase)
def __lowerCamelCase ( self : List[Any] , _lowerCAmelCase : Optional[Any]):
'''simple docstring'''
if index < self.sp_model.get_piece_size():
__lowercase =self.sp_model.IdToPiece(_lowerCAmelCase)
return token
def __lowerCamelCase ( self : Any , _lowerCAmelCase : Optional[int]):
'''simple docstring'''
__lowercase =[]
__lowercase =''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowerCAmelCase) + token
__lowercase =[]
else:
current_sub_tokens.append(_lowerCAmelCase)
out_string += self.sp_model.decode(_lowerCAmelCase)
return out_string.strip()
def __lowerCamelCase ( self : int , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None):
'''simple docstring'''
if not os.path.isdir(_lowerCAmelCase):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
return
__lowercase =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) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , _lowerCAmelCase)
elif not os.path.isfile(self.vocab_file):
with open(_lowerCAmelCase , 'wb') as fi:
__lowercase =self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase)
return (out_vocab_file,)
| 166 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowerCAmelCase : Tuple = CTRLTokenizer
lowerCAmelCase : Any = False
lowerCAmelCase : Any = False
def __A ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
A__ = ["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
A__ = dict(zip(UpperCAmelCase__ , range(len(UpperCAmelCase__ ) ) ) )
A__ = ["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
A__ = {"unk_token": "<unk>"}
A__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
A__ = 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(UpperCAmelCase__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(UpperCAmelCase__ ) )
def __A ( self , **UpperCAmelCase__ ):
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def __A ( self , UpperCAmelCase__ ):
A__ = "adapt react readapt apt"
A__ = "adapt react readapt apt"
return input_text, output_text
def __A ( self ):
A__ = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
A__ = "adapt react readapt apt"
A__ = "adapt re@@ a@@ c@@ t re@@ adapt apt".split()
A__ = tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
A__ = tokens + [tokenizer.unk_token]
A__ = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , UpperCAmelCase__ )
| 350 |
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
UpperCAmelCase_ : List[Any] = [
"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 ( _A : List[Any] , _A : int=None )-> Optional[int]:
"""simple docstring"""
require_version(deps[pkg] , _A )
| 198 | 0 |
def __snake_case ( _UpperCAmelCase = 4000000 ):
__a = [0, 1]
__a = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
__a = 0
for j in range(len(_UpperCAmelCase ) - 1 ):
if fib[j] % 2 == 0:
total += fib[j]
return total
if __name__ == "__main__":
print(f'{solution() = }')
| 49 |
import functools
from typing import Any
def UpperCamelCase ( snake_case__ : str , snake_case__ : list[str] ) -> bool:
# Validation
if not isinstance(snake_case__ , snake_case__ ) or len(snake_case__ ) == 0:
raise ValueError('the string should be not empty string' )
if not isinstance(snake_case__ , snake_case__ ) or not all(
isinstance(snake_case__ , snake_case__ ) and len(snake_case__ ) > 0 for item in words ):
raise ValueError('the words should be a list of non-empty strings' )
# Build trie
UpperCamelCase : dict[str, Any] = {}
UpperCamelCase : List[str] = 'WORD_KEEPER'
for word in words:
UpperCamelCase : List[str] = trie
for c in word:
if c not in trie_node:
UpperCamelCase : int = {}
UpperCamelCase : str = trie_node[c]
UpperCamelCase : Tuple = True
UpperCamelCase : List[Any] = len(snake_case__ )
# Dynamic programming method
@functools.cache
def is_breakable(snake_case__ : int ) -> bool:
if index == len_string:
return True
UpperCamelCase : Dict = trie
for i in range(snake_case__ , snake_case__ ):
UpperCamelCase : List[Any] = trie_node.get(string[i] , snake_case__ )
if trie_node is None:
return False
if trie_node.get(snake_case__ , snake_case__ ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 119 | 0 |
'''simple docstring'''
import baseaa
def __magic_name__( lowerCamelCase):
return baseaa.baaencode(string.encode('''utf-8'''))
def __magic_name__( lowerCamelCase):
return baseaa.baadecode(lowerCamelCase).decode('''utf-8''')
if __name__ == "__main__":
_UpperCAmelCase : Tuple = "Hello World!"
_UpperCAmelCase : Union[str, Any] = baseaa_encode(test)
print(encoded)
_UpperCAmelCase : Tuple = baseaa_decode(encoded)
print(decoded)
| 353 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class a__ ( __A ):
"""simple docstring"""
def __init__(self , __lowercase , __lowercase=None , __lowercase=None , __lowercase=0 ):
__lowerCAmelCase = 1.0 if scale is None else scale
__lowerCAmelCase = 0.0 if loc is None else loc
super().__init__(__lowercase , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=__lowercase )] )
@property
def _snake_case (self ):
return self.base_dist.mean * self.scale + self.loc
@property
def _snake_case (self ):
return self.base_dist.variance * self.scale**2
@property
def _snake_case (self ):
return self.variance.sqrt()
class a__ ( nn.Module ):
"""simple docstring"""
def __init__(self , __lowercase , __lowercase , __lowercase , **__lowercase ):
super().__init__(**__lowercase )
__lowerCAmelCase = args_dim
__lowerCAmelCase = nn.ModuleList([nn.Linear(__lowercase , __lowercase ) for dim in args_dim.values()] )
__lowerCAmelCase = domain_map
def _snake_case (self , __lowercase ):
__lowerCAmelCase = [proj(__lowercase ) for proj in self.proj]
return self.domain_map(*__lowercase )
class a__ ( nn.Module ):
"""simple docstring"""
def __init__(self , __lowercase ):
super().__init__()
__lowerCAmelCase = function
def _snake_case (self , __lowercase , *__lowercase ):
return self.function(__lowercase , *__lowercase )
class a__ :
"""simple docstring"""
__UpperCamelCase : type
__UpperCamelCase : int
__UpperCamelCase : Dict[str, int]
def __init__(self , __lowercase = 1 ):
__lowerCAmelCase = dim
__lowerCAmelCase = {k: dim * self.args_dim[k] for k in self.args_dim}
def _snake_case (self , __lowercase ):
if self.dim == 1:
return self.distribution_class(*__lowercase )
else:
return Independent(self.distribution_class(*__lowercase ) , 1 )
def _snake_case (self , __lowercase , __lowercase = None , __lowercase = None , ):
__lowerCAmelCase = self._base_distribution(__lowercase )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(__lowercase , loc=__lowercase , scale=__lowercase , event_dim=self.event_dim )
@property
def _snake_case (self ):
return () if self.dim == 1 else (self.dim,)
@property
def _snake_case (self ):
return len(self.event_shape )
@property
def _snake_case (self ):
return 0.0
def _snake_case (self , __lowercase ):
return ParameterProjection(
in_features=__lowercase , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def _snake_case (self , *__lowercase ):
raise NotImplementedError()
@staticmethod
def _snake_case (__lowercase ):
return (x + torch.sqrt(torch.square(__lowercase ) + 4.0 )) / 2.0
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Dict[str, int] = {"df": 1, "loc": 1, "scale": 1}
__UpperCamelCase : type = StudentT
@classmethod
def _snake_case (cls , __lowercase , __lowercase , __lowercase ):
__lowerCAmelCase = cls.squareplus(__lowercase ).clamp_min(torch.finfo(scale.dtype ).eps )
__lowerCAmelCase = 2.0 + cls.squareplus(__lowercase )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Dict[str, int] = {"loc": 1, "scale": 1}
__UpperCamelCase : type = Normal
@classmethod
def _snake_case (cls , __lowercase , __lowercase ):
__lowerCAmelCase = cls.squareplus(__lowercase ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Dict[str, int] = {"total_count": 1, "logits": 1}
__UpperCamelCase : type = NegativeBinomial
@classmethod
def _snake_case (cls , __lowercase , __lowercase ):
__lowerCAmelCase = cls.squareplus(__lowercase )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def _snake_case (self , __lowercase ):
__lowerCAmelCase , __lowerCAmelCase = distr_args
if self.dim == 1:
return self.distribution_class(total_count=__lowercase , logits=__lowercase )
else:
return Independent(self.distribution_class(total_count=__lowercase , logits=__lowercase ) , 1 )
def _snake_case (self , __lowercase , __lowercase = None , __lowercase = None ):
__lowerCAmelCase , __lowerCAmelCase = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) )
| 9 | 0 |
"""simple docstring"""
from __future__ import annotations
__SCREAMING_SNAKE_CASE =[]
def lowercase__( __SCREAMING_SNAKE_CASE : list[list[int]] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ):
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if board[row][i] == 1:
return False
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(__SCREAMING_SNAKE_CASE , -1 , -1 ) , range(__SCREAMING_SNAKE_CASE , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(__SCREAMING_SNAKE_CASE , -1 , -1 ) , range(__SCREAMING_SNAKE_CASE , len(__SCREAMING_SNAKE_CASE ) ) ):
if board[i][j] == 1:
return False
return True
def lowercase__( __SCREAMING_SNAKE_CASE : list[list[int]] , __SCREAMING_SNAKE_CASE : int ):
if row >= len(__SCREAMING_SNAKE_CASE ):
solution.append(__SCREAMING_SNAKE_CASE )
printboard(__SCREAMING_SNAKE_CASE )
print()
return True
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if is_safe(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase_ : int = 1
solve(__SCREAMING_SNAKE_CASE , row + 1 )
lowercase_ : Dict = 0
return False
def lowercase__( __SCREAMING_SNAKE_CASE : list[list[int]] ):
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
for j in range(len(__SCREAMING_SNAKE_CASE ) ):
if board[i][j] == 1:
print('Q' , end=' ' )
else:
print('.' , end=' ' )
print()
# n=int(input("The no. of queens"))
__SCREAMING_SNAKE_CASE =8
__SCREAMING_SNAKE_CASE =[[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))
| 213 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class UpperCamelCase ( unittest.TestCase ):
def __init__( self ,__UpperCamelCase ,__UpperCamelCase=13 ,__UpperCamelCase=30 ,__UpperCamelCase=2 ,__UpperCamelCase=3 ,__UpperCamelCase=True ,__UpperCamelCase=True ,__UpperCamelCase=32 ,__UpperCamelCase=5 ,__UpperCamelCase=4 ,__UpperCamelCase=37 ,__UpperCamelCase="gelu" ,__UpperCamelCase=0.1 ,__UpperCamelCase=0.1 ,__UpperCamelCase=10 ,__UpperCamelCase=0.02 ,) -> Tuple:
'''simple docstring'''
lowercase_ : Tuple = parent
lowercase_ : Union[str, Any] = batch_size
lowercase_ : int = image_size
lowercase_ : Tuple = patch_size
lowercase_ : Optional[int] = num_channels
lowercase_ : Union[str, Any] = is_training
lowercase_ : Dict = use_labels
lowercase_ : Optional[int] = hidden_size
lowercase_ : List[str] = num_hidden_layers
lowercase_ : Optional[Any] = num_attention_heads
lowercase_ : Optional[int] = intermediate_size
lowercase_ : Tuple = hidden_act
lowercase_ : int = hidden_dropout_prob
lowercase_ : str = attention_probs_dropout_prob
lowercase_ : str = type_sequence_label_size
lowercase_ : Optional[int] = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ : str = (image_size // patch_size) ** 2
lowercase_ : Optional[int] = num_patches + 1
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
lowercase_ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase_ : List[Any] = ViTConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,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 ,is_decoder=__UpperCamelCase ,initializer_range=self.initializer_range ,)
return config, pixel_values
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ) -> Dict:
'''simple docstring'''
lowercase_ : List[Any] = FlaxViTModel(config=__UpperCamelCase )
lowercase_ : Dict = model(__UpperCamelCase )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
lowercase_ : Union[str, Any] = (self.image_size, self.image_size)
lowercase_ : List[Any] = (self.patch_size, self.patch_size)
lowercase_ : List[str] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, num_patches + 1, self.hidden_size) )
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ : List[Any] = self.type_sequence_label_size
lowercase_ : str = FlaxViTForImageClassification(config=__UpperCamelCase )
lowercase_ : Optional[Any] = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowercase_ : Union[str, Any] = 1
lowercase_ : Optional[int] = FlaxViTForImageClassification(__UpperCamelCase )
lowercase_ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase_ : str = model(__UpperCamelCase )
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
lowercase_ : Optional[int] = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) ,
) : List[Any] = config_and_inputs
lowercase_ : Tuple = {'pixel_values': pixel_values}
return config, inputs_dict
@require_flax
class UpperCamelCase ( lowercase_ , unittest.TestCase ):
lowercase = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def _UpperCAmelCase ( self ) -> None:
'''simple docstring'''
lowercase_ : Optional[Any] = FlaxViTModelTester(self )
lowercase_ : Union[str, Any] = ConfigTester(self ,config_class=__UpperCamelCase ,has_text_modality=__UpperCamelCase ,hidden_size=37 )
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
def _UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
lowercase_ , lowercase_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : Optional[Any] = model_class(__UpperCamelCase )
lowercase_ : Tuple = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ : Union[str, Any] = [*signature.parameters.keys()]
lowercase_ : str = ['pixel_values']
self.assertListEqual(arg_names[:1] ,__UpperCamelCase )
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ , lowercase_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowercase_ : Optional[Any] = self._prepare_for_class(__UpperCamelCase ,__UpperCamelCase )
lowercase_ : Dict = model_class(__UpperCamelCase )
@jax.jit
def model_jitted(__UpperCamelCase ,**__UpperCamelCase ):
return model(pixel_values=__UpperCamelCase ,**__UpperCamelCase )
with self.subTest('JIT Enabled' ):
lowercase_ : Optional[int] = model_jitted(**__UpperCamelCase ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
lowercase_ : List[str] = model_jitted(**__UpperCamelCase ).to_tuple()
self.assertEqual(len(__UpperCamelCase ) ,len(__UpperCamelCase ) )
for jitted_output, output in zip(__UpperCamelCase ,__UpperCamelCase ):
self.assertEqual(jitted_output.shape ,output.shape )
@slow
def _UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
lowercase_ : Optional[int] = model_class_name.from_pretrained('google/vit-base-patch16-224' )
lowercase_ : int = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(__UpperCamelCase )
| 213 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_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 torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class lowerCamelCase__ :
'''simple docstring'''
def __init__(self ,__lowerCamelCase ,__lowerCamelCase=13 ,__lowerCamelCase=2 ,__lowerCamelCase=24 ,__lowerCamelCase=16 ,__lowerCamelCase=True ,__lowerCamelCase=True ,__lowerCamelCase=32 ,__lowerCamelCase=5 ,__lowerCamelCase=4 ,__lowerCamelCase=37 ,__lowerCamelCase="gelu" ,__lowerCamelCase=0.1 ,__lowerCamelCase=0.1 ,__lowerCamelCase=10 ,__lowerCamelCase=0.02 ,__lowerCamelCase=None ,__lowerCamelCase=2 ,__lowerCamelCase=2 ,) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : Tuple = parent
lowerCAmelCase__ : Union[str, Any] = batch_size
lowerCAmelCase__ : Tuple = patch_size
lowerCAmelCase__ : str = max_length
lowerCAmelCase__ : Union[str, Any] = num_mel_bins
lowerCAmelCase__ : Union[str, Any] = is_training
lowerCAmelCase__ : Union[str, Any] = use_labels
lowerCAmelCase__ : List[str] = hidden_size
lowerCAmelCase__ : List[Any] = num_hidden_layers
lowerCAmelCase__ : Dict = num_attention_heads
lowerCAmelCase__ : int = intermediate_size
lowerCAmelCase__ : Optional[int] = hidden_act
lowerCAmelCase__ : Optional[int] = hidden_dropout_prob
lowerCAmelCase__ : Tuple = attention_probs_dropout_prob
lowerCAmelCase__ : Optional[int] = type_sequence_label_size
lowerCAmelCase__ : Any = initializer_range
lowerCAmelCase__ : Optional[int] = scope
lowerCAmelCase__ : Union[str, Any] = frequency_stride
lowerCAmelCase__ : Union[str, Any] = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
lowerCAmelCase__ : Dict = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
lowerCAmelCase__ : Tuple = (self.max_length - self.patch_size) // self.time_stride + 1
lowerCAmelCase__ : List[Any] = frequency_out_dimension * time_out_dimension
lowerCAmelCase__ : Dict = num_patches + 2
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
lowerCAmelCase__ : int = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
lowerCAmelCase__ : Tuple = None
if self.use_labels:
lowerCAmelCase__ : Union[str, Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowerCAmelCase__ : str = self.get_config()
return config, input_values, labels
def lowerCAmelCase__ (self ) -> List[str]:
"""simple docstring"""
return ASTConfig(
patch_size=self.patch_size ,max_length=self.max_length ,num_mel_bins=self.num_mel_bins ,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 ,is_decoder=__lowerCamelCase ,initializer_range=self.initializer_range ,frequency_stride=self.frequency_stride ,time_stride=self.time_stride ,)
def lowerCAmelCase__ (self ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = ASTModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
lowerCAmelCase__ : Any = model(__lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
lowerCAmelCase__ : List[str] = self.prepare_config_and_inputs()
(
lowerCAmelCase__
) : Dict = config_and_inputs
lowerCAmelCase__ : List[Any] = {'''input_values''': input_values}
return config, inputs_dict
@require_torch
class lowerCamelCase__ ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase):
'''simple docstring'''
snake_case_ =(
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
snake_case_ =(
{"""audio-classification""": ASTForAudioClassification, """feature-extraction""": ASTModel}
if is_torch_available()
else {}
)
snake_case_ =False
snake_case_ =False
snake_case_ =False
snake_case_ =False
def lowerCAmelCase__ (self ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ) -> Optional[Any]:
"""simple docstring"""
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
lowerCAmelCase__ : str = ASTModelTester(self )
lowerCAmelCase__ : Tuple = ConfigTester(self ,config_class=__lowerCamelCase ,has_text_modality=__lowerCamelCase ,hidden_size=37 )
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''AST does not use inputs_embeds''' )
def lowerCAmelCase__ (self ) -> List[str]:
"""simple docstring"""
pass
def lowerCAmelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ : Optional[Any] = model_class(__lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
lowerCAmelCase__ : Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowerCamelCase ,nn.Linear ) )
def lowerCAmelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ : Optional[Any] = model_class(__lowerCamelCase )
lowerCAmelCase__ : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ : Optional[Any] = [*signature.parameters.keys()]
lowerCAmelCase__ : Optional[int] = ['''input_values''']
self.assertListEqual(arg_names[:1] ,__lowerCamelCase )
def lowerCAmelCase__ (self ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
@slow
def lowerCAmelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ : Optional[int] = ASTModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
def lowerCAmelCase__ ( ):
'''simple docstring'''
lowerCAmelCase__ : int = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' ,filename='''sample_audio.flac''' ,repo_type='''dataset''')
lowerCAmelCase__ : int = torchaudio.load(lowerCamelCase_)
return audio, sampling_rate
@require_torch
@require_torchaudio
class lowerCamelCase__ ( unittest.TestCase):
'''simple docstring'''
@cached_property
def lowerCAmelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
return (
ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' )
if is_torchaudio_available()
else None
)
@slow
def lowerCAmelCase__ (self ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : int = self.default_feature_extractor
lowerCAmelCase__ : List[str] = ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' ).to(__lowerCamelCase )
lowerCAmelCase__ : List[str] = self.default_feature_extractor
lowerCAmelCase__ : int = prepare_audio()
lowerCAmelCase__ : Optional[int] = audio.squeeze().numpy()
lowerCAmelCase__ : List[Any] = feature_extractor(__lowerCamelCase ,sampling_rate=__lowerCamelCase ,return_tensors='''pt''' ).to(__lowerCamelCase )
# forward pass
with torch.no_grad():
lowerCAmelCase__ : str = model(**__lowerCamelCase )
# verify the logits
lowerCAmelCase__ : Union[str, Any] = torch.Size((1, 5_27) )
self.assertEqual(outputs.logits.shape ,__lowerCamelCase )
lowerCAmelCase__ : List[str] = torch.tensor([-0.8760, -7.0042, -8.6602] ).to(__lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,__lowerCamelCase ,atol=1e-4 ) )
| 353 |
from math import factorial
def lowerCAmelCase__ ( lowerCamelCase_ : int = 100):
'''simple docstring'''
return sum(map(lowerCamelCase_ ,str(factorial(lowerCamelCase_))))
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 94 | 0 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
snake_case : Optional[int] = logging.get_logger(__name__)
snake_case : List[Any] = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
snake_case : Any = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _snake_case :
SCREAMING_SNAKE_CASE__ = field(
default=_snake_case , metadata={'help': 'Model type selected in the list: ' + ', '.join(_snake_case )} )
SCREAMING_SNAKE_CASE__ = field(
default=_snake_case , metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'} )
SCREAMING_SNAKE_CASE__ = field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
SCREAMING_SNAKE_CASE__ = field(
default=128 , metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} , )
SCREAMING_SNAKE_CASE__ = field(
default=64 , metadata={
'help': (
'The maximum number of tokens for the question. Questions longer than this will '
'be truncated to this length.'
)
} , )
SCREAMING_SNAKE_CASE__ = field(
default=30 , metadata={
'help': (
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
)
} , )
SCREAMING_SNAKE_CASE__ = field(
default=_snake_case , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
SCREAMING_SNAKE_CASE__ = field(
default=_snake_case , metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'} )
SCREAMING_SNAKE_CASE__ = field(
default=0.0 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
SCREAMING_SNAKE_CASE__ = field(
default=20 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
SCREAMING_SNAKE_CASE__ = field(
default=0 , metadata={
'help': (
'language id of input for language-specific xlm models (see'
' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
)
} , )
SCREAMING_SNAKE_CASE__ = field(default=1 , metadata={'help': 'multiple threads for converting example to features'} )
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 'train'
SCREAMING_SNAKE_CASE__ = 'dev'
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = 42
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = Split.train , _lowerCamelCase = False , _lowerCamelCase = None , _lowerCamelCase = "pt" , ):
a :List[Any] = args
a :Any = is_language_sensitive
a :int = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_lowerCamelCase , _lowerCamelCase ):
try:
a :str = Split[mode]
except KeyError:
raise KeyError('''mode is not a valid split name''' )
a :Optional[int] = mode
# Load data features from cache or dataset file
a :Any = '''v2''' if args.version_2_with_negative else '''v1'''
a :Optional[Any] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
a :Optional[Any] = cached_features_file + '''.lock'''
with FileLock(_lowerCamelCase ):
if os.path.exists(_lowerCamelCase ) and not args.overwrite_cache:
a :List[Any] = time.time()
a :List[Any] = torch.load(_lowerCamelCase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
a :int = self.old_features['''features''']
a :Optional[Any] = self.old_features.get('''dataset''' , _lowerCamelCase )
a :int = self.old_features.get('''examples''' , _lowerCamelCase )
logger.info(
F'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
F'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
''' future run''' )
else:
if mode == Split.dev:
a :Tuple = self.processor.get_dev_examples(args.data_dir )
else:
a :Optional[Any] = self.processor.get_train_examples(args.data_dir )
a , a :Union[str, Any] = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_lowerCamelCase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_lowerCamelCase , )
a :Optional[Any] = time.time()
torch.save(
{'''features''': self.features, '''dataset''': self.dataset, '''examples''': self.examples} , _lowerCamelCase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self ):
return len(self.features )
def __getitem__( self , _lowerCamelCase ):
# Convert to Tensors and build dataset
a :List[Any] = self.features[i]
a :str = torch.tensor(feature.input_ids , dtype=torch.long )
a :Dict = torch.tensor(feature.attention_mask , dtype=torch.long )
a :List[Any] = torch.tensor(feature.token_type_ids , dtype=torch.long )
a :Dict = torch.tensor(feature.cls_index , dtype=torch.long )
a :Any = torch.tensor(feature.p_mask , dtype=torch.float )
a :Any = torch.tensor(feature.is_impossible , dtype=torch.float )
a :Union[str, Any] = {
'''input_ids''': input_ids,
'''attention_mask''': attention_mask,
'''token_type_ids''': token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({'''cls_index''': cls_index, '''p_mask''': p_mask} )
if self.args.version_2_with_negative:
inputs.update({'''is_impossible''': is_impossible} )
if self.is_language_sensitive:
inputs.update({'''langs''': (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
a :int = torch.tensor(feature.start_position , dtype=torch.long )
a :Dict = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({'''start_positions''': start_positions, '''end_positions''': end_positions} )
return inputs
| 94 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor
class _lowercase ( unittest.TestCase):
"""simple docstring"""
def __init__( self : Any , __lowerCamelCase : List[str] , __lowerCamelCase : List[Any]=7 , __lowerCamelCase : Dict=3 , __lowerCamelCase : Union[str, Any]=30 , __lowerCamelCase : Union[str, Any]=400 , __lowerCamelCase : Dict=True , __lowerCamelCase : Tuple=None , __lowerCamelCase : str=True , __lowerCamelCase : List[str]=[0.5, 0.5, 0.5] , __lowerCamelCase : str=[0.5, 0.5, 0.5] , __lowerCamelCase : Any=True , __lowerCamelCase : Union[str, Any]=1 / 255 , __lowerCamelCase : Optional[Any]=True , ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
lowerCamelCase__ : Dict = parent
lowerCamelCase__ : Optional[int] = batch_size
lowerCamelCase__ : str = num_channels
lowerCamelCase__ : Optional[Any] = min_resolution
lowerCamelCase__ : List[Any] = max_resolution
lowerCamelCase__ : int = do_resize
lowerCamelCase__ : Union[str, Any] = size
lowerCamelCase__ : Union[str, Any] = do_normalize
lowerCamelCase__ : int = image_mean
lowerCamelCase__ : Optional[int] = image_std
lowerCamelCase__ : List[Any] = do_rescale
lowerCamelCase__ : Optional[Any] = rescale_factor
lowerCamelCase__ : Any = do_pad
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def lowerCAmelCase ( self : List[Any] , __lowerCamelCase : int , __lowerCamelCase : List[str]=False ):
'''simple docstring'''
if not batched:
lowerCamelCase__ : Tuple = image_inputs[0]
if isinstance(__lowerCamelCase , Image.Image ):
lowerCamelCase__ , lowerCamelCase__ : Tuple = image.size
else:
lowerCamelCase__ , lowerCamelCase__ : List[str] = image.shape[1], image.shape[2]
if w < h:
lowerCamelCase__ : List[Any] = int(self.size["shortest_edge"] * h / w )
lowerCamelCase__ : Optional[Any] = self.size["shortest_edge"]
elif w > h:
lowerCamelCase__ : List[Any] = self.size["shortest_edge"]
lowerCamelCase__ : List[str] = int(self.size["shortest_edge"] * w / h )
else:
lowerCamelCase__ : Optional[int] = self.size["shortest_edge"]
lowerCamelCase__ : Union[str, Any] = self.size["shortest_edge"]
else:
lowerCamelCase__ : Dict = []
for image in image_inputs:
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowerCamelCase__ : str = max(__lowerCamelCase , key=lambda __lowerCamelCase : item[0] )[0]
lowerCamelCase__ : Optional[Any] = max(__lowerCamelCase , key=lambda __lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _lowercase ( lowercase__ , unittest.TestCase):
"""simple docstring"""
A__ = DetaImageProcessor if is_vision_available() else None
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
lowerCamelCase__ : str = DetaImageProcessingTester(self )
@property
def lowerCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase ( self : Dict ):
'''simple docstring'''
lowerCamelCase__ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowerCamelCase , "image_mean" ) )
self.assertTrue(hasattr(__lowerCamelCase , "image_std" ) )
self.assertTrue(hasattr(__lowerCamelCase , "do_normalize" ) )
self.assertTrue(hasattr(__lowerCamelCase , "do_resize" ) )
self.assertTrue(hasattr(__lowerCamelCase , "do_rescale" ) )
self.assertTrue(hasattr(__lowerCamelCase , "do_pad" ) )
self.assertTrue(hasattr(__lowerCamelCase , "size" ) )
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
lowerCamelCase__ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1333} )
self.assertEqual(image_processor.do_pad , __lowerCamelCase )
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
pass
def lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCamelCase__ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCamelCase , Image.Image )
# Test not batched input
lowerCamelCase__ : Optional[int] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = self.image_processor_tester.get_expected_values(__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCamelCase__ , lowerCamelCase__ : List[str] = self.image_processor_tester.get_expected_values(__lowerCamelCase , batched=__lowerCamelCase )
lowerCamelCase__ : List[Any] = image_processing(__lowerCamelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase ( self : Any ):
'''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_processor_tester , equal_resolution=__lowerCamelCase , numpify=__lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCamelCase , np.ndarray )
# Test not batched input
lowerCamelCase__ : Any = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowerCamelCase__ , lowerCamelCase__ : Any = self.image_processor_tester.get_expected_values(__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCamelCase__ : Optional[Any] = image_processing(__lowerCamelCase , return_tensors="pt" ).pixel_values
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = self.image_processor_tester.get_expected_values(__lowerCamelCase , batched=__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCamelCase__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCamelCase , torchify=__lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCamelCase , torch.Tensor )
# Test not batched input
lowerCamelCase__ : Dict = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowerCamelCase__ , lowerCamelCase__ : Any = self.image_processor_tester.get_expected_values(__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCamelCase__ : Union[str, Any] = image_processing(__lowerCamelCase , return_tensors="pt" ).pixel_values
lowerCamelCase__ , lowerCamelCase__ : Dict = self.image_processor_tester.get_expected_values(__lowerCamelCase , batched=__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def lowerCAmelCase ( self : Dict ):
'''simple docstring'''
lowerCamelCase__ : int = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
lowerCamelCase__ : Dict = json.loads(f.read() )
lowerCamelCase__ : Any = {"image_id": 39769, "annotations": target}
# encode them
lowerCamelCase__ : Union[str, Any] = DetaImageProcessor()
lowerCamelCase__ : List[str] = image_processing(images=__lowerCamelCase , annotations=__lowerCamelCase , return_tensors="pt" )
# verify pixel values
lowerCamelCase__ : List[str] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["pixel_values"].shape , __lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , __lowerCamelCase , atol=1E-4 ) )
# verify area
lowerCamelCase__ : Tuple = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , __lowerCamelCase ) )
# verify boxes
lowerCamelCase__ : Union[str, Any] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , __lowerCamelCase )
lowerCamelCase__ : List[Any] = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , __lowerCamelCase , atol=1E-3 ) )
# verify image_id
lowerCamelCase__ : Optional[Any] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , __lowerCamelCase ) )
# verify is_crowd
lowerCamelCase__ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , __lowerCamelCase ) )
# verify class_labels
lowerCamelCase__ : int = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , __lowerCamelCase ) )
# verify orig_size
lowerCamelCase__ : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , __lowerCamelCase ) )
# verify size
lowerCamelCase__ : Optional[int] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , __lowerCamelCase ) )
@slow
def lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
lowerCamelCase__ : Tuple = json.loads(f.read() )
lowerCamelCase__ : List[str] = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
lowerCamelCase__ : Union[str, Any] = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
lowerCamelCase__ : Tuple = DetaImageProcessor(format="coco_panoptic" )
lowerCamelCase__ : Dict = image_processing(images=__lowerCamelCase , annotations=__lowerCamelCase , masks_path=__lowerCamelCase , return_tensors="pt" )
# verify pixel values
lowerCamelCase__ : List[Any] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["pixel_values"].shape , __lowerCamelCase )
lowerCamelCase__ : Dict = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , __lowerCamelCase , atol=1E-4 ) )
# verify area
lowerCamelCase__ : List[Any] = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , __lowerCamelCase ) )
# verify boxes
lowerCamelCase__ : Any = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , __lowerCamelCase )
lowerCamelCase__ : int = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , __lowerCamelCase , atol=1E-3 ) )
# verify image_id
lowerCamelCase__ : Optional[int] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , __lowerCamelCase ) )
# verify is_crowd
lowerCamelCase__ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , __lowerCamelCase ) )
# verify class_labels
lowerCamelCase__ : Tuple = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , __lowerCamelCase ) )
# verify masks
lowerCamelCase__ : Union[str, Any] = 822873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , __lowerCamelCase )
# verify orig_size
lowerCamelCase__ : Optional[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , __lowerCamelCase ) )
# verify size
lowerCamelCase__ : Union[str, Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , __lowerCamelCase ) )
| 184 | 0 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
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 (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class __lowercase :
'''simple docstring'''
def __init__( self : Any , _a : str , _a : Dict=13 , _a : List[Any]=7 , _a : int=True , _a : Dict=True , _a : Optional[Any]=True , _a : List[str]=True , _a : Any=99 , _a : Dict=32 , _a : List[str]=2 , _a : Tuple=4 , _a : List[Any]=37 , _a : Any="gelu" , _a : List[str]=0.1 , _a : Any=0.1 , _a : Optional[Any]=512 , _a : Any=16 , _a : Union[str, Any]=2 , _a : Dict=0.02 , _a : Union[str, Any]=3 , _a : Tuple=4 , _a : int=None , ):
UpperCamelCase__ = parent
UpperCamelCase__ = 13
UpperCamelCase__ = 7
UpperCamelCase__ = True
UpperCamelCase__ = True
UpperCamelCase__ = True
UpperCamelCase__ = True
UpperCamelCase__ = 99
UpperCamelCase__ = 32
UpperCamelCase__ = 2
UpperCamelCase__ = 4
UpperCamelCase__ = 37
UpperCamelCase__ = """gelu"""
UpperCamelCase__ = 0.1
UpperCamelCase__ = 0.1
UpperCamelCase__ = 512
UpperCamelCase__ = 16
UpperCamelCase__ = 2
UpperCamelCase__ = 0.02
UpperCamelCase__ = 3
UpperCamelCase__ = 4
UpperCamelCase__ = None
def A_ ( self : Union[str, Any] ):
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase__ = None
if self.use_input_mask:
UpperCamelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase__ = None
if self.use_token_type_ids:
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = None
if self.use_labels:
UpperCamelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase__ = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase__ = RoFormerConfig(
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 , return_dict=__lowerCamelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A_ ( self : Optional[int] , _a : Optional[Any] , _a : Union[str, Any] , _a : Union[str, Any] , _a : str , _a : List[Any] , _a : List[Any] , _a : str ):
UpperCamelCase__ = TFRoFormerModel(config=__lowerCamelCase )
UpperCamelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCamelCase__ = [input_ids, input_mask]
UpperCamelCase__ = model(__lowerCamelCase )
UpperCamelCase__ = model(__lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A_ ( self : str , _a : Any , _a : int , _a : Optional[int] , _a : int , _a : List[Any] , _a : Tuple , _a : List[str] ):
UpperCamelCase__ = True
UpperCamelCase__ = TFRoFormerForCausalLM(config=__lowerCamelCase )
UpperCamelCase__ = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
UpperCamelCase__ = model(__lowerCamelCase )["""logits"""]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def A_ ( self : Tuple , _a : str , _a : Optional[int] , _a : Optional[int] , _a : Any , _a : List[str] , _a : Any , _a : int ):
UpperCamelCase__ = TFRoFormerForMaskedLM(config=__lowerCamelCase )
UpperCamelCase__ = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
UpperCamelCase__ = model(__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A_ ( self : Tuple , _a : Tuple , _a : Tuple , _a : Tuple , _a : Any , _a : Dict , _a : Dict , _a : Optional[Any] ):
UpperCamelCase__ = self.num_labels
UpperCamelCase__ = TFRoFormerForSequenceClassification(config=__lowerCamelCase )
UpperCamelCase__ = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
UpperCamelCase__ = model(__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A_ ( self : int , _a : Any , _a : List[str] , _a : int , _a : Optional[int] , _a : Union[str, Any] , _a : Optional[Any] , _a : Optional[int] ):
UpperCamelCase__ = self.num_choices
UpperCamelCase__ = TFRoFormerForMultipleChoice(config=__lowerCamelCase )
UpperCamelCase__ = tf.tile(tf.expand_dims(__lowerCamelCase , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase__ = tf.tile(tf.expand_dims(__lowerCamelCase , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase__ = tf.tile(tf.expand_dims(__lowerCamelCase , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase__ = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
UpperCamelCase__ = model(__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A_ ( self : Tuple , _a : Optional[int] , _a : Optional[int] , _a : Any , _a : Dict , _a : Dict , _a : int , _a : str ):
UpperCamelCase__ = self.num_labels
UpperCamelCase__ = TFRoFormerForTokenClassification(config=__lowerCamelCase )
UpperCamelCase__ = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
UpperCamelCase__ = model(__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A_ ( self : List[Any] , _a : str , _a : List[Any] , _a : int , _a : str , _a : Any , _a : Optional[int] , _a : str ):
UpperCamelCase__ = TFRoFormerForQuestionAnswering(config=__lowerCamelCase )
UpperCamelCase__ = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
UpperCamelCase__ = 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 A_ ( self : Dict ):
UpperCamelCase__ = self.prepare_config_and_inputs()
(
UpperCamelCase__
) = config_and_inputs
UpperCamelCase__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class __lowercase ( _a, _a, unittest.TestCase ):
'''simple docstring'''
_A : str = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
_A : Any = (
{
"""feature-extraction""": TFRoFormerModel,
"""fill-mask""": TFRoFormerForMaskedLM,
"""question-answering""": TFRoFormerForQuestionAnswering,
"""text-classification""": TFRoFormerForSequenceClassification,
"""text-generation""": TFRoFormerForCausalLM,
"""token-classification""": TFRoFormerForTokenClassification,
"""zero-shot""": TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
_A : List[Any] = False
_A : int = False
def A_ ( self : List[Any] , _a : Optional[Any] , _a : int , _a : Tuple , _a : Optional[Any] , _a : Union[str, Any] ):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def A_ ( self : Tuple ):
UpperCamelCase__ = TFRoFormerModelTester(self )
UpperCamelCase__ = ConfigTester(self , config_class=__lowerCamelCase , hidden_size=37 )
def A_ ( self : Tuple ):
self.config_tester.run_common_tests()
def A_ ( self : Optional[int] ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def A_ ( self : Any ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase )
def A_ ( self : Any ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*__lowerCamelCase )
def A_ ( self : Any ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase )
def A_ ( self : List[Any] ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase )
def A_ ( self : Dict ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase )
def A_ ( self : List[Any] ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase )
@slow
def A_ ( self : str ):
UpperCamelCase__ = TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''' )
self.assertIsNotNone(__lowerCamelCase )
@require_tf
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def A_ ( self : Any ):
UpperCamelCase__ = TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''' )
UpperCamelCase__ = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCamelCase__ = model(__lowerCamelCase )[0]
# TODO Replace vocab size
UpperCamelCase__ = 50_000
UpperCamelCase__ = [1, 6, vocab_size]
self.assertEqual(output.shape , __lowerCamelCase )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
UpperCamelCase__ = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __lowerCamelCase , atol=1E-4 )
@require_tf
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
_A : List[Any] = 1e-4
def A_ ( self : Optional[Any] ):
UpperCamelCase__ = tf.constant([[4, 10]] )
UpperCamelCase__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
UpperCamelCase__ = emba(input_ids.shape )
UpperCamelCase__ = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(__lowerCamelCase , __lowerCamelCase , atol=self.tolerance )
def A_ ( self : List[Any] ):
UpperCamelCase__ = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
UpperCamelCase__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512 , embedding_dim=512 )
emba([2, 16, 512] )
UpperCamelCase__ = emba.weight[:3, :5]
tf.debugging.assert_near(__lowerCamelCase , __lowerCamelCase , atol=self.tolerance )
@require_tf
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
_A : Any = 1e-4
def A_ ( self : Optional[int] ):
# 2,12,16,64
UpperCamelCase__ = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 100
UpperCamelCase__ = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 100
UpperCamelCase__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
UpperCamelCase__ = embed_positions([2, 16, 768] )[None, None, :, :]
UpperCamelCase__ = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
UpperCamelCase__ = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
UpperCamelCase__ = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , __lowerCamelCase , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , __lowerCamelCase , atol=self.tolerance )
| 367 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowercase = logging.get_logger(__name__)
class __lowercase ( A ):
'''simple docstring'''
def __init__( self : Any , *_a : Optional[Any] , **_a : Any ):
warnings.warn(
'''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use CLIPImageProcessor instead.''' , _a , )
super().__init__(*_a , **_a )
| 35 | 0 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class snake_case ( unittest.TestCase ):
def lowercase_ ( self : Union[str, Any])-> List[str]:
'''simple docstring'''
__lowerCAmelCase: int = 0
def lowercase_ ( self : int)-> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase: List[str] = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32")
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__)
def lowercase_ ( self : Tuple)-> Union[str, Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCAmelCase: int = Path(UpperCamelCase__) / "preprocessor_config.json"
__lowerCAmelCase: str = Path(UpperCamelCase__) / "config.json"
json.dump(
{"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"} , open(UpperCamelCase__ , "w") , )
json.dump({"model_type": "clip"} , open(UpperCamelCase__ , "w"))
__lowerCAmelCase: Optional[Any] = AutoImageProcessor.from_pretrained(UpperCamelCase__)
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__)
def lowercase_ ( self : Any)-> Optional[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCAmelCase: Any = Path(UpperCamelCase__) / "preprocessor_config.json"
__lowerCAmelCase: List[Any] = Path(UpperCamelCase__) / "config.json"
json.dump(
{"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"} , open(UpperCamelCase__ , "w") , )
json.dump({"model_type": "clip"} , open(UpperCamelCase__ , "w"))
__lowerCAmelCase: Optional[int] = AutoImageProcessor.from_pretrained(UpperCamelCase__)
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__)
def lowercase_ ( self : Any)-> Any:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCAmelCase: str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__lowerCAmelCase: Union[str, Any] = Path(UpperCamelCase__) / "preprocessor_config.json"
__lowerCAmelCase: Dict = Path(UpperCamelCase__) / "config.json"
json.dump(
{"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"} , open(UpperCamelCase__ , "w") , )
json.dump({"model_type": "clip"} , open(UpperCamelCase__ , "w"))
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__lowerCAmelCase: int = AutoImageProcessor.from_pretrained(UpperCamelCase__).to_dict()
config_dict.pop("image_processor_type")
__lowerCAmelCase: Any = CLIPImageProcessor(**UpperCamelCase__)
# save in new folder
model_config.save_pretrained(UpperCamelCase__)
config.save_pretrained(UpperCamelCase__)
__lowerCAmelCase: Any = AutoImageProcessor.from_pretrained(UpperCamelCase__)
# make sure private variable is not incorrectly saved
__lowerCAmelCase: Tuple = json.loads(config.to_json_string())
self.assertTrue("_processor_class" not in dict_as_saved)
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__)
def lowercase_ ( self : str)-> str:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCAmelCase: Any = Path(UpperCamelCase__) / "preprocessor_config.json"
json.dump(
{"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"} , open(UpperCamelCase__ , "w") , )
__lowerCAmelCase: Any = AutoImageProcessor.from_pretrained(UpperCamelCase__)
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__)
def lowercase_ ( self : Dict)-> Optional[int]:
'''simple docstring'''
with self.assertRaisesRegex(
UpperCamelCase__ , "clip-base is not a local folder and is not a valid model identifier"):
__lowerCAmelCase: Any = AutoImageProcessor.from_pretrained("clip-base")
def lowercase_ ( self : Dict)-> List[str]:
'''simple docstring'''
with self.assertRaisesRegex(
UpperCamelCase__ , R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"):
__lowerCAmelCase: Dict = AutoImageProcessor.from_pretrained(UpperCamelCase__ , revision="aaaaaa")
def lowercase_ ( self : int)-> Union[str, Any]:
'''simple docstring'''
with self.assertRaisesRegex(
UpperCamelCase__ , "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json." , ):
__lowerCAmelCase: List[str] = AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model")
def lowercase_ ( self : Union[str, Any])-> Optional[int]:
'''simple docstring'''
with self.assertRaises(UpperCamelCase__):
__lowerCAmelCase: List[str] = AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor")
# If remote code is disabled, we can't load this config.
with self.assertRaises(UpperCamelCase__):
__lowerCAmelCase: Dict = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor" , trust_remote_code=UpperCamelCase__)
__lowerCAmelCase: Optional[Any] = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor" , trust_remote_code=UpperCamelCase__)
self.assertEqual(image_processor.__class__.__name__ , "NewImageProcessor")
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(UpperCamelCase__)
__lowerCAmelCase: List[Any] = AutoImageProcessor.from_pretrained(UpperCamelCase__ , trust_remote_code=UpperCamelCase__)
self.assertEqual(reloaded_image_processor.__class__.__name__ , "NewImageProcessor")
def lowercase_ ( self : List[str])-> Union[str, Any]:
'''simple docstring'''
try:
AutoConfig.register("custom" , UpperCamelCase__)
AutoImageProcessor.register(UpperCamelCase__ , UpperCamelCase__)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(UpperCamelCase__):
AutoImageProcessor.register(UpperCamelCase__ , UpperCamelCase__)
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCAmelCase: List[str] = Path(UpperCamelCase__) / "preprocessor_config.json"
__lowerCAmelCase: Optional[Any] = Path(UpperCamelCase__) / "config.json"
json.dump(
{"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"} , open(UpperCamelCase__ , "w") , )
json.dump({"model_type": "clip"} , open(UpperCamelCase__ , "w"))
__lowerCAmelCase: Any = CustomImageProcessor.from_pretrained(UpperCamelCase__)
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(UpperCamelCase__)
__lowerCAmelCase: str = AutoImageProcessor.from_pretrained(UpperCamelCase__)
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def lowercase_ ( self : Optional[int])-> int:
'''simple docstring'''
class snake_case ( __snake_case ):
SCREAMING_SNAKE_CASE_ : List[Any] = True
try:
AutoConfig.register("custom" , UpperCamelCase__)
AutoImageProcessor.register(UpperCamelCase__ , UpperCamelCase__)
# If remote code is not set, the default is to use local
__lowerCAmelCase: List[str] = AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor")
self.assertEqual(image_processor.__class__.__name__ , "NewImageProcessor")
self.assertTrue(image_processor.is_local)
# If remote code is disabled, we load the local one.
__lowerCAmelCase: Dict = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor" , trust_remote_code=UpperCamelCase__)
self.assertEqual(image_processor.__class__.__name__ , "NewImageProcessor")
self.assertTrue(image_processor.is_local)
# If remote is enabled, we load from the Hub
__lowerCAmelCase: Tuple = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor" , trust_remote_code=UpperCamelCase__)
self.assertEqual(image_processor.__class__.__name__ , "NewImageProcessor")
self.assertTrue(not hasattr(UpperCamelCase__ , "is_local"))
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 217 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
class snake_case ( __snake_case ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = """switch_transformers"""
SCREAMING_SNAKE_CASE_ : Tuple = ["""past_key_values"""]
SCREAMING_SNAKE_CASE_ : Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self : List[str] , UpperCamelCase__ : List[str]=3_2_1_2_8 , UpperCamelCase__ : str=7_6_8 , UpperCamelCase__ : Union[str, Any]=6_4 , UpperCamelCase__ : Optional[int]=2_0_4_8 , UpperCamelCase__ : Dict=6_4 , UpperCamelCase__ : List[str]=1_2 , UpperCamelCase__ : Dict=3 , UpperCamelCase__ : Any=1_2 , UpperCamelCase__ : Dict=3 , UpperCamelCase__ : Dict=1_2 , UpperCamelCase__ : List[str]=8 , UpperCamelCase__ : Optional[Any]=False , UpperCamelCase__ : Optional[int]=0.01 , UpperCamelCase__ : Optional[int]="float32" , UpperCamelCase__ : Any=False , UpperCamelCase__ : Dict=3_2 , UpperCamelCase__ : Union[str, Any]=1_2_8 , UpperCamelCase__ : Union[str, Any]=0.1 , UpperCamelCase__ : Optional[int]=1e-6 , UpperCamelCase__ : Optional[Any]=0.001 , UpperCamelCase__ : Dict=0.001 , UpperCamelCase__ : int=1.0 , UpperCamelCase__ : str="relu" , UpperCamelCase__ : int=True , UpperCamelCase__ : Any=False , UpperCamelCase__ : str=True , UpperCamelCase__ : Tuple=0 , UpperCamelCase__ : str=1 , **UpperCamelCase__ : Tuple , )-> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase: int = vocab_size
__lowerCAmelCase: str = d_model
__lowerCAmelCase: str = d_kv
__lowerCAmelCase: str = d_ff
__lowerCAmelCase: List[str] = num_sparse_encoder_layers
__lowerCAmelCase: List[Any] = num_layers
__lowerCAmelCase: Optional[Any] = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__lowerCAmelCase: Tuple = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
__lowerCAmelCase: int = self.num_layers // self.num_sparse_encoder_layers
else:
__lowerCAmelCase: Union[str, Any] = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
__lowerCAmelCase: Dict = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
__lowerCAmelCase: Any = self.num_decoder_layers # HACK: this will create 0 sparse layers
__lowerCAmelCase: Dict = num_heads
__lowerCAmelCase: Dict = num_experts
__lowerCAmelCase: Any = expert_capacity
__lowerCAmelCase: List[Any] = router_bias
__lowerCAmelCase: int = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}")
__lowerCAmelCase: Dict = router_dtype
__lowerCAmelCase: Optional[Any] = router_ignore_padding_tokens
__lowerCAmelCase: Union[str, Any] = relative_attention_num_buckets
__lowerCAmelCase: str = relative_attention_max_distance
__lowerCAmelCase: Optional[int] = dropout_rate
__lowerCAmelCase: Optional[Any] = layer_norm_epsilon
__lowerCAmelCase: int = initializer_factor
__lowerCAmelCase: Tuple = feed_forward_proj
__lowerCAmelCase: int = use_cache
__lowerCAmelCase: int = add_router_probs
__lowerCAmelCase: Optional[Any] = router_z_loss_coef
__lowerCAmelCase: Dict = router_aux_loss_coef
__lowerCAmelCase: Union[str, Any] = self.feed_forward_proj.split("-")
__lowerCAmelCase: Tuple = act_info[-1]
__lowerCAmelCase: str = act_info[0] == "gated"
if len(UpperCamelCase__) > 1 and act_info[0] != "gated" or len(UpperCamelCase__) > 2:
raise ValueError(
f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
"Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
"'gated-gelu' or 'relu'")
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
__lowerCAmelCase: List[str] = "gelu_new"
super().__init__(
pad_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , is_encoder_decoder=UpperCamelCase__ , **UpperCamelCase__ , )
| 217 | 1 |
from math import sqrt
def UpperCAmelCase_ (_lowerCAmelCase : int = 1_00_00_00 ):
__UpperCamelCase : int = 0
__UpperCamelCase : int = 0
__UpperCamelCase : 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() = }""")
| 171 |
def UpperCAmelCase_ (_lowerCAmelCase : int = 1_00 ):
__UpperCamelCase : Tuple = n * (n + 1) * (2 * n + 1) / 6
__UpperCamelCase : List[str] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 171 | 1 |
import gc
import threading
import time
import psutil
import torch
class A_ :
def __init__( self : List[str]):
__lowerCamelCase : str = psutil.Process()
__lowerCamelCase : Tuple = False
def lowerCAmelCase ( self : List[Any]):
__lowerCamelCase : Union[str, Any] = -1
while True:
__lowerCamelCase : Any = max(self.process.memory_info().rss ,self.cpu_memory_peak)
# can't sleep or will not catch the peak right (this comment is here on purpose)
if not self.peak_monitoring:
break
def lowerCAmelCase ( self : Optional[Any]):
__lowerCamelCase : Optional[int] = True
__lowerCamelCase : str = threading.Thread(target=self.peak_monitor)
__lowerCamelCase : Optional[Any] = True
self.thread.start()
def lowerCAmelCase ( self : Tuple):
__lowerCamelCase : int = False
self.thread.join()
return self.cpu_memory_peak
a =PeakCPUMemory()
def SCREAMING_SNAKE_CASE__ ( ) -> List[str]:
__lowerCamelCase : List[Any] = {'time': time.time()}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
__lowerCamelCase : Tuple = psutil.Process().memory_info().rss
cpu_peak_tracker.start()
# GPU mem
for i in range(torch.cuda.device_count() ):
__lowerCamelCase : str = torch.cuda.memory_allocated(lowerCamelCase__ )
torch.cuda.reset_peak_memory_stats()
return measures
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> int:
__lowerCamelCase : Dict = {'time': time.time() - start_measures['time']}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
__lowerCamelCase : List[str] = (psutil.Process().memory_info().rss - start_measures['cpu']) / 2**2_0
__lowerCamelCase : str = (cpu_peak_tracker.stop() - start_measures['cpu']) / 2**2_0
# GPU mem
for i in range(torch.cuda.device_count() ):
__lowerCamelCase : int = (torch.cuda.memory_allocated(lowerCamelCase__ ) - start_measures[str(lowerCamelCase__ )]) / 2**2_0
__lowerCamelCase : Dict = (torch.cuda.max_memory_allocated(lowerCamelCase__ ) - start_measures[str(lowerCamelCase__ )]) / 2**2_0
return measures
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> Any:
print(F"{description}:" )
print(F"- Time: {measures['time']:.2f}s" )
for i in range(torch.cuda.device_count() ):
print(F"- GPU {i} allocated: {measures[str(lowerCamelCase__ )]:.2f}MiB" )
__lowerCamelCase : Union[str, Any] = measures[F"{i}-peak"]
print(F"- GPU {i} peak: {peak:.2f}MiB" )
print(F"- CPU RAM allocated: {measures['cpu']:.2f}MiB" )
print(F"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB" )
| 73 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a : int = logging.get_logger(__name__)
__a : str = {
"""ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}
class _UpperCamelCase ( _UpperCAmelCase ):
"""simple docstring"""
__a : Optional[int] = '''deta'''
__a : Optional[int] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=9_00 , lowerCAmelCase__=20_48 , lowerCAmelCase__=6 , lowerCAmelCase__=20_48 , lowerCAmelCase__=8 , lowerCAmelCase__=6 , lowerCAmelCase__=10_24 , lowerCAmelCase__=8 , lowerCAmelCase__=0.0 , lowerCAmelCase__=True , lowerCAmelCase__="relu" , lowerCAmelCase__=2_56 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.02 , lowerCAmelCase__=1.0 , lowerCAmelCase__=True , lowerCAmelCase__=False , lowerCAmelCase__="sine" , lowerCAmelCase__=5 , lowerCAmelCase__=4 , lowerCAmelCase__=4 , lowerCAmelCase__=True , lowerCAmelCase__=3_00 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=1 , lowerCAmelCase__=5 , lowerCAmelCase__=2 , lowerCAmelCase__=1 , lowerCAmelCase__=1 , lowerCAmelCase__=5 , lowerCAmelCase__=2 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.25 , **lowerCAmelCase__ , ) -> List[Any]:
'''simple docstring'''
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
__lowercase = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__lowercase = backbone_config.pop('''model_type''' )
__lowercase = CONFIG_MAPPING[backbone_model_type]
__lowercase = config_class.from_dict(lowerCAmelCase__ )
__lowercase = backbone_config
__lowercase = num_queries
__lowercase = max_position_embeddings
__lowercase = d_model
__lowercase = encoder_ffn_dim
__lowercase = encoder_layers
__lowercase = encoder_attention_heads
__lowercase = decoder_ffn_dim
__lowercase = decoder_layers
__lowercase = decoder_attention_heads
__lowercase = dropout
__lowercase = attention_dropout
__lowercase = activation_dropout
__lowercase = activation_function
__lowercase = init_std
__lowercase = init_xavier_std
__lowercase = encoder_layerdrop
__lowercase = auxiliary_loss
__lowercase = position_embedding_type
# deformable attributes
__lowercase = num_feature_levels
__lowercase = encoder_n_points
__lowercase = decoder_n_points
__lowercase = two_stage
__lowercase = two_stage_num_proposals
__lowercase = with_box_refine
__lowercase = 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
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = mask_loss_coefficient
__lowercase = dice_loss_coefficient
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = eos_coefficient
__lowercase = focal_alpha
super().__init__(is_encoder_decoder=lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def _SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def _SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
return self.d_model
def _SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
__lowercase = copy.deepcopy(self.__dict__ )
__lowercase = self.backbone_config.to_dict()
__lowercase = self.__class__.model_type
return output
| 210 | 0 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class _a :
def __init__( self : int, lowerCAmelCase__ : List[str], lowerCAmelCase__ : Tuple=1_4, lowerCAmelCase__ : int=7, lowerCAmelCase__ : Dict=True, lowerCAmelCase__ : List[str]=True, lowerCAmelCase__ : int=True, lowerCAmelCase__ : Union[str, Any]=True, lowerCAmelCase__ : Optional[Any]=True, lowerCAmelCase__ : str=9_9, lowerCAmelCase__ : int=3_2, lowerCAmelCase__ : List[str]=5, lowerCAmelCase__ : Union[str, Any]=4, lowerCAmelCase__ : Any=3_7, lowerCAmelCase__ : Union[str, Any]="gelu", lowerCAmelCase__ : str=0.1, lowerCAmelCase__ : int=0.1, lowerCAmelCase__ : Tuple=5_1_2, lowerCAmelCase__ : str=1_6, lowerCAmelCase__ : Union[str, Any]=2, lowerCAmelCase__ : Optional[int]=0.02, lowerCAmelCase__ : Dict=3, lowerCAmelCase__ : Optional[Any]=4, lowerCAmelCase__ : Any=None, ) -> str:
'''simple docstring'''
_UpperCamelCase : int = parent
_UpperCamelCase : Optional[int] = batch_size
_UpperCamelCase : List[Any] = seq_length
_UpperCamelCase : Optional[int] = is_training
_UpperCamelCase : Any = use_token_type_ids
_UpperCamelCase : List[str] = use_input_mask
_UpperCamelCase : Optional[int] = use_labels
_UpperCamelCase : Optional[Any] = use_mc_token_ids
_UpperCamelCase : Dict = vocab_size
_UpperCamelCase : Union[str, Any] = hidden_size
_UpperCamelCase : Optional[Any] = num_hidden_layers
_UpperCamelCase : Union[str, Any] = num_attention_heads
_UpperCamelCase : Tuple = intermediate_size
_UpperCamelCase : Dict = hidden_act
_UpperCamelCase : List[str] = hidden_dropout_prob
_UpperCamelCase : Tuple = attention_probs_dropout_prob
_UpperCamelCase : Optional[int] = max_position_embeddings
_UpperCamelCase : List[str] = type_vocab_size
_UpperCamelCase : Tuple = type_sequence_label_size
_UpperCamelCase : Union[str, Any] = initializer_range
_UpperCamelCase : Any = num_labels
_UpperCamelCase : Dict = num_choices
_UpperCamelCase : Tuple = scope
_UpperCamelCase : Union[str, Any] = self.vocab_size - 1
def snake_case ( self : List[Any] ) -> str:
'''simple docstring'''
_UpperCamelCase : int = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
_UpperCamelCase : List[Any] = None
if self.use_input_mask:
_UpperCamelCase : str = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCamelCase : str = None
if self.use_token_type_ids:
_UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
_UpperCamelCase : int = None
if self.use_mc_token_ids:
_UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.num_choices], self.seq_length )
_UpperCamelCase : int = None
_UpperCamelCase : Any = None
_UpperCamelCase : Tuple = None
if self.use_labels:
_UpperCamelCase : str = ids_tensor([self.batch_size], self.type_sequence_label_size )
_UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
_UpperCamelCase : Tuple = ids_tensor([self.batch_size], self.num_choices )
_UpperCamelCase : Optional[Any] = self.get_config()
_UpperCamelCase : Union[str, Any] = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def snake_case ( self : str ) -> Union[str, Any]:
'''simple docstring'''
return CTRLConfig(
vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, n_positions=self.max_position_embeddings, pad_token_id=self.pad_token_id, )
def snake_case ( self : Tuple, lowerCAmelCase__ : Optional[int], lowerCAmelCase__ : Optional[int], lowerCAmelCase__ : Optional[int], lowerCAmelCase__ : int, lowerCAmelCase__ : List[str], *lowerCAmelCase__ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = CTRLModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
model(lowerCAmelCase__, token_type_ids=lowerCAmelCase__, head_mask=lowerCAmelCase__ )
model(lowerCAmelCase__, token_type_ids=lowerCAmelCase__ )
_UpperCamelCase : Optional[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ), config.n_layer )
def snake_case ( self : int, lowerCAmelCase__ : List[str], lowerCAmelCase__ : List[str], lowerCAmelCase__ : Tuple, lowerCAmelCase__ : Any, lowerCAmelCase__ : Optional[int], *lowerCAmelCase__ : Optional[int] ) -> Dict:
'''simple docstring'''
_UpperCamelCase : Optional[Any] = CTRLLMHeadModel(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
_UpperCamelCase : List[Any] = model(lowerCAmelCase__, token_type_ids=lowerCAmelCase__, labels=lowerCAmelCase__ )
self.parent.assertEqual(result.loss.shape, () )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case ( self : int ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase : str = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) : Optional[int] = config_and_inputs
_UpperCamelCase : Union[str, Any] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def snake_case ( self : Optional[Any], lowerCAmelCase__ : int, lowerCAmelCase__ : Optional[int], lowerCAmelCase__ : Optional[Any], lowerCAmelCase__ : List[str], *lowerCAmelCase__ : str ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase : str = self.num_labels
_UpperCamelCase : int = CTRLForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
_UpperCamelCase : Any = ids_tensor([self.batch_size], self.type_sequence_label_size )
_UpperCamelCase : int = model(lowerCAmelCase__, token_type_ids=lowerCAmelCase__, labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
@require_torch
class _a ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
UpperCamelCase = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
UpperCamelCase = (CTRLLMHeadModel,) if is_torch_available() else ()
UpperCamelCase = (
{
'''feature-extraction''': CTRLModel,
'''text-classification''': CTRLForSequenceClassification,
'''text-generation''': CTRLLMHeadModel,
'''zero-shot''': CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase = True
UpperCamelCase = False
UpperCamelCase = False
def snake_case ( self : Tuple, lowerCAmelCase__ : List[str], lowerCAmelCase__ : Any, lowerCAmelCase__ : int, lowerCAmelCase__ : Optional[int], lowerCAmelCase__ : str ) -> List[str]:
'''simple docstring'''
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def snake_case ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase : Any = CTRLModelTester(self )
_UpperCamelCase : List[Any] = ConfigTester(self, config_class=lowerCAmelCase__, n_embd=3_7 )
def snake_case ( self : Dict ) -> Any:
'''simple docstring'''
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self : Dict ) -> List[str]:
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*lowerCAmelCase__ )
def snake_case ( self : Dict ) -> Any:
'''simple docstring'''
_UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase__ )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def snake_case ( self : int ) -> Optional[int]:
'''simple docstring'''
pass
@slow
def snake_case ( self : List[Any] ) -> str:
'''simple docstring'''
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase : Dict = CTRLModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def snake_case ( self : int ) -> Dict:
'''simple docstring'''
pass
@require_torch
class _a ( unittest.TestCase ):
def snake_case ( self : Dict ) -> str:
'''simple docstring'''
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def snake_case ( self : Tuple ) -> Tuple:
'''simple docstring'''
_UpperCamelCase : List[Any] = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(lowerCAmelCase__ )
_UpperCamelCase : str = torch.tensor(
[[1_1_8_5_9, 0, 1_6_1_1, 8]], dtype=torch.long, device=lowerCAmelCase__ ) # Legal the president is
_UpperCamelCase : List[Any] = [
1_1_8_5_9,
0,
1_6_1_1,
8,
5,
1_5_0,
2_6_4_4_9,
2,
1_9,
3_4_8,
4_6_9,
3,
2_5_9_5,
4_8,
2_0_7_4_0,
2_4_6_5_3_3,
2_4_6_5_3_3,
1_9,
3_0,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
_UpperCamelCase : Tuple = model.generate(lowerCAmelCase__, do_sample=lowerCAmelCase__ )
self.assertListEqual(output_ids[0].tolist(), lowerCAmelCase__ )
| 128 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 128 | 1 |
"""simple docstring"""
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
_UpperCamelCase : Tuple = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( __snake_case : Any , __snake_case : Optional[Any] , __snake_case : List[str] ):
'''simple docstring'''
return [
int(10_00 * (box[0] / width) ),
int(10_00 * (box[1] / height) ),
int(10_00 * (box[2] / width) ),
int(10_00 * (box[3] / height) ),
]
def _SCREAMING_SNAKE_CASE ( __snake_case : List[Any] , __snake_case : Dict , __snake_case : Any ):
'''simple docstring'''
lowercase = to_pil_image(_A )
lowercase , lowercase = pil_image.size
lowercase = pytesseract.image_to_data(_A , lang=_A , output_type='dict' , config=_A )
lowercase , lowercase , lowercase , lowercase , lowercase = data['text'], data['left'], data['top'], data['width'], data['height']
# filter empty words and corresponding coordinates
lowercase = [idx for idx, word in enumerate(_A ) if not word.strip()]
lowercase = [word for idx, word in enumerate(_A ) if idx not in irrelevant_indices]
lowercase = [coord for idx, coord in enumerate(_A ) if idx not in irrelevant_indices]
lowercase = [coord for idx, coord in enumerate(_A ) if idx not in irrelevant_indices]
lowercase = [coord for idx, coord in enumerate(_A ) if idx not in irrelevant_indices]
lowercase = [coord for idx, coord in enumerate(_A ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
lowercase = []
for x, y, w, h in zip(_A , _A , _A , _A ):
lowercase = [x, y, x + w, y + h]
actual_boxes.append(_A )
# finally, normalize the bounding boxes
lowercase = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(_A , _A , _A ) )
assert len(_A ) == len(_A ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class a ( A__ ):
UpperCAmelCase_ : Optional[int] =["pixel_values"]
def __init__( self , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = PILImageResampling.BILINEAR , _lowerCamelCase = True , _lowerCamelCase = 1 / 2_5_5 , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = "" , **_lowerCamelCase , ):
super().__init__(**__lowerCamelCase )
lowercase = size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
lowercase = get_size_dict(__lowerCamelCase )
lowercase = do_resize
lowercase = size
lowercase = resample
lowercase = do_rescale
lowercase = rescale_value
lowercase = do_normalize
lowercase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase = image_std if image_std is not None else IMAGENET_STANDARD_STD
lowercase = apply_ocr
lowercase = ocr_lang
lowercase = tesseract_config
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = PILImageResampling.BILINEAR , _lowerCamelCase = None , **_lowerCamelCase , ):
lowercase = get_size_dict(__lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
lowercase = (size['height'], size['width'])
return resize(__lowerCamelCase , size=__lowerCamelCase , resample=__lowerCamelCase , data_format=__lowerCamelCase , **__lowerCamelCase )
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , **_lowerCamelCase , ):
return rescale(__lowerCamelCase , scale=__lowerCamelCase , data_format=__lowerCamelCase , **__lowerCamelCase )
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , **_lowerCamelCase , ):
return normalize(__lowerCamelCase , mean=__lowerCamelCase , std=__lowerCamelCase , data_format=__lowerCamelCase , **__lowerCamelCase )
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase=None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = ChannelDimension.FIRST , **_lowerCamelCase , ):
lowercase = do_resize if do_resize is not None else self.do_resize
lowercase = size if size is not None else self.size
lowercase = get_size_dict(__lowerCamelCase )
lowercase = resample if resample is not None else self.resample
lowercase = do_rescale if do_rescale is not None else self.do_rescale
lowercase = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase = do_normalize if do_normalize is not None else self.do_normalize
lowercase = image_mean if image_mean is not None else self.image_mean
lowercase = image_std if image_std is not None else self.image_std
lowercase = apply_ocr if apply_ocr is not None else self.apply_ocr
lowercase = ocr_lang if ocr_lang is not None else self.ocr_lang
lowercase = tesseract_config if tesseract_config is not None else self.tesseract_config
lowercase = make_list_of_images(__lowerCamelCase )
if not valid_images(__lowerCamelCase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('If do_normalize is True, image_mean and image_std must be specified.' )
# All transformations expect numpy arrays.
lowercase = [to_numpy_array(__lowerCamelCase ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , 'pytesseract' )
lowercase = []
lowercase = []
for image in images:
lowercase , lowercase = apply_tesseract(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
words_batch.append(__lowerCamelCase )
boxes_batch.append(__lowerCamelCase )
if do_resize:
lowercase = [self.resize(image=__lowerCamelCase , size=__lowerCamelCase , resample=__lowerCamelCase ) for image in images]
if do_rescale:
lowercase = [self.rescale(image=__lowerCamelCase , scale=__lowerCamelCase ) for image in images]
if do_normalize:
lowercase = [self.normalize(image=__lowerCamelCase , mean=__lowerCamelCase , std=__lowerCamelCase ) for image in images]
lowercase = [to_channel_dimension_format(__lowerCamelCase , __lowerCamelCase ) for image in images]
lowercase = BatchFeature(data={'pixel_values': images} , tensor_type=__lowerCamelCase )
if apply_ocr:
lowercase = words_batch
lowercase = boxes_batch
return data
| 220 |
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( _A = "AAPL" ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
SCREAMING_SNAKE_CASE__ = BeautifulSoup(requests.get(_A ).text , '''html.parser''' )
SCREAMING_SNAKE_CASE__ = '''My(6px) Pos(r) smartphone_Mt(6px)'''
return soup.find('''div''' , class_=class_ ).find('''span''' ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F"Current {symbol:<4} stock price is {stock_price(symbol):>8}")
| 314 | 0 |
"""simple docstring"""
import math
import qiskit
def _A (__a = 1 , __a = 1 , __a = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(__a , __a )
or isinstance(__a , __a )
or isinstance(__a , __a )
):
raise TypeError('''inputs must be integers.''' )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('''inputs must be positive.''' )
if (
(math.floor(__a ) != input_a)
or (math.floor(__a ) != input_a)
or (math.floor(__a ) != carry_in)
):
raise ValueError('''inputs must be exact integers.''' )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('''inputs must be less or equal to 2.''' )
# build registers
SCREAMING_SNAKE_CASE_ : Tuple = qiskit.QuantumRegister(4 , '''qr''' )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = qiskit.ClassicalRegister(2 , '''cr''' )
# list the entries
SCREAMING_SNAKE_CASE_ : List[Any] = [input_a, input_a, carry_in]
SCREAMING_SNAKE_CASE_ : Tuple = qiskit.QuantumCircuit(__a , __a )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(__a ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(__a ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(__a ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , __a ) # measure the last two qbits
SCREAMING_SNAKE_CASE_ : int = qiskit.Aer.get_backend('''aer_simulator''' )
SCREAMING_SNAKE_CASE_ : Tuple = qiskit.execute(__a , __a , shots=10_00 )
return job.result().get_counts(__a )
if __name__ == "__main__":
print(f'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')
| 318 |
"""simple docstring"""
from collections import defaultdict
def _A (__a , __a ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = first_str.lower().strip()
SCREAMING_SNAKE_CASE_ : List[Any] = second_str.lower().strip()
# Remove whitespace
SCREAMING_SNAKE_CASE_ : Dict = first_str.replace(''' ''' , '''''' )
SCREAMING_SNAKE_CASE_ : Optional[Any] = second_str.replace(''' ''' , '''''' )
# Strings of different lengths are not anagrams
if len(__a ) != len(__a ):
return False
# Default values for count should be 0
SCREAMING_SNAKE_CASE_ : defaultdict[str, int] = defaultdict(__a )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(__a ) ):
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()
UpperCAmelCase_ : Any = input("""Enter the first string """).strip()
UpperCAmelCase_ : Optional[int] = input("""Enter the second string """).strip()
UpperCAmelCase_ : Union[str, Any] = check_anagrams(input_a, input_b)
print(f'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')
| 318 | 1 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : List[Any] = logging.get_logger(__name__)
a_ : Dict = {"vocab_file": "vocab.txt"}
a_ : str = {
"vocab_file": {
"openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
},
}
a_ : Optional[Any] = {
"openbmb/cpm-ant-10b": 1_0_2_4,
}
def _A (lowerCAmelCase__ :List[str] ) -> Optional[int]:
'''simple docstring'''
_a = collections.OrderedDict()
with open(lowerCAmelCase__ , 'r' , encoding='utf-8' ) as reader:
_a = reader.readlines()
for index, token in enumerate(lowerCAmelCase__ ):
_a = token.rstrip('\n' )
_a = index
return vocab
class a ( _SCREAMING_SNAKE_CASE ):
def __init__( self , __magic_name__ , __magic_name__="<unk>" , __magic_name__=2_00 ) -> Dict:
_a = vocab
_a = unk_token
_a = max_input_chars_per_word
def __UpperCAmelCase ( self , __magic_name__ ) -> List[Any]:
_a = list(__magic_name__ )
if len(__magic_name__ ) > self.max_input_chars_per_word:
return [self.unk_token]
_a = 0
_a = []
while start < len(__magic_name__ ):
_a = len(__magic_name__ )
_a = None
while start < end:
_a = ''.join(chars[start:end] )
if substr in self.vocab:
_a = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(__magic_name__ )
_a = end
return sub_tokens
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = VOCAB_FILES_NAMES
_lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase = ["""input_ids""", """attention_mask"""]
_lowerCAmelCase = False
def __init__( self , __magic_name__ , __magic_name__="<d>" , __magic_name__="</d>" , __magic_name__="<s>" , __magic_name__="</s>" , __magic_name__="<pad>" , __magic_name__="<unk>" , __magic_name__="</n>" , __magic_name__="</_>" , __magic_name__="left" , **__magic_name__ , ) -> Any:
requires_backends(self , ['jieba'] )
super().__init__(
bod_token=__magic_name__ , eod_token=__magic_name__ , bos_token=__magic_name__ , eos_token=__magic_name__ , pad_token=__magic_name__ , unk_token=__magic_name__ , line_token=__magic_name__ , space_token=__magic_name__ , padding_side=__magic_name__ , **__magic_name__ , )
_a = bod_token
_a = eod_token
_a = load_vocab(__magic_name__ )
_a = self.encoder[space_token]
_a = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
_a = collections.OrderedDict(sorted(self.encoder.items() , key=lambda __magic_name__ : x[1] ) )
_a = {v: k for k, v in self.encoder.items()}
_a = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def __UpperCAmelCase ( self ) -> Union[str, Any]:
return self.encoder[self.bod_token]
@property
def __UpperCAmelCase ( self ) -> List[str]:
return self.encoder[self.eod_token]
@property
def __UpperCAmelCase ( self ) -> int:
return self.encoder["\n"]
@property
def __UpperCAmelCase ( self ) -> int:
return len(self.encoder )
def __UpperCAmelCase ( self ) -> Optional[Any]:
return dict(self.encoder , **self.added_tokens_encoder )
def __UpperCAmelCase ( self , __magic_name__ ) -> int:
_a = []
for x in jieba.cut(__magic_name__ , cut_all=__magic_name__ ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(__magic_name__ ) )
return output_tokens
def __UpperCAmelCase ( self , __magic_name__ , **__magic_name__ ) -> int:
_a = [i for i in token_ids if i >= 0]
_a = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(__magic_name__ , **__magic_name__ )
def __UpperCAmelCase ( self , __magic_name__ ) -> Union[str, Any]:
return token in self.encoder
def __UpperCAmelCase ( self , __magic_name__ ) -> str:
return "".join(__magic_name__ )
def __UpperCAmelCase ( self , __magic_name__ ) -> List[Any]:
return self.encoder.get(__magic_name__ , self.encoder.get(self.unk_token ) )
def __UpperCAmelCase ( self , __magic_name__ ) -> Dict:
return self.decoder.get(__magic_name__ , self.unk_token )
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ = None ) -> Tuple[str]:
if os.path.isdir(__magic_name__ ):
_a = os.path.join(
__magic_name__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
else:
_a = (filename_prefix + '-' if filename_prefix else '') + save_directory
_a = 0
if " " in self.encoder:
_a = self.encoder[' ']
del self.encoder[" "]
if "\n" in self.encoder:
_a = self.encoder['\n']
del self.encoder["\n"]
_a = collections.OrderedDict(sorted(self.encoder.items() , key=lambda __magic_name__ : x[1] ) )
with open(__magic_name__ , 'w' , encoding='utf-8' ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
f'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
' Please check that the vocabulary is not corrupted!' )
_a = token_index
writer.write(token + '\n' )
index += 1
return (vocab_file,)
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ = None ) -> List[int]:
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ = None , __magic_name__ = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__magic_name__ , token_ids_a=__magic_name__ , already_has_special_tokens=__magic_name__ )
if token_ids_a is not None:
return [1] + ([0] * len(__magic_name__ )) + [1] + ([0] * len(__magic_name__ ))
return [1] + ([0] * len(__magic_name__ ))
| 168 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ : Union[str, Any] = {
"configuration_layoutlmv3": [
"LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP",
"LayoutLMv3Config",
"LayoutLMv3OnnxConfig",
],
"processing_layoutlmv3": ["LayoutLMv3Processor"],
"tokenization_layoutlmv3": ["LayoutLMv3Tokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Tuple = ["LayoutLMv3TokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : str = [
"LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
"LayoutLMv3ForQuestionAnswering",
"LayoutLMv3ForSequenceClassification",
"LayoutLMv3ForTokenClassification",
"LayoutLMv3Model",
"LayoutLMv3PreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Any = [
"TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLayoutLMv3ForQuestionAnswering",
"TFLayoutLMv3ForSequenceClassification",
"TFLayoutLMv3ForTokenClassification",
"TFLayoutLMv3Model",
"TFLayoutLMv3PreTrainedModel",
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Any = ["LayoutLMv3FeatureExtractor"]
a_ : List[str] = ["LayoutLMv3ImageProcessor"]
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
a_ : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 168 | 1 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
snake_case : Any = logging.get_logger(__name__)
class _snake_case ( _snake_case ):
def __init__( self , _lowerCamelCase ):
super().__init__()
a :str = nn.ModuleList(_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = False , _lowerCamelCase = True , ):
for i, (image, scale, controlnet) in enumerate(zip(_lowerCamelCase , _lowerCamelCase , self.nets ) ):
a , a :Any = controlnet(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , )
# merge samples
if i == 0:
a , a :str = down_samples, mid_sample
else:
a :Optional[int] = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(_lowerCamelCase , _lowerCamelCase )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = False , _lowerCamelCase = None , ):
a :Optional[Any] = 0
a :Optional[int] = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
_lowerCamelCase , is_main_process=_lowerCamelCase , save_function=_lowerCamelCase , safe_serialization=_lowerCamelCase , variant=_lowerCamelCase , )
idx += 1
a :Optional[Any] = model_path_to_save + F'''_{idx}'''
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , _lowerCamelCase , **_lowerCamelCase ):
a :List[str] = 0
a :str = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
a :Any = pretrained_model_path
while os.path.isdir(_lowerCamelCase ):
a :Dict = ControlNetModel.from_pretrained(_lowerCamelCase , **_lowerCamelCase )
controlnets.append(_lowerCamelCase )
idx += 1
a :List[str] = pretrained_model_path + F'''_{idx}'''
logger.info(F'''{len(_lowerCamelCase )} controlnets loaded from {pretrained_model_path}.''' )
if len(_lowerCamelCase ) == 0:
raise ValueError(
F'''No ControlNets found under {os.path.dirname(_lowerCamelCase )}. Expected at least {pretrained_model_path + '_0'}.''' )
return cls(_lowerCamelCase )
| 281 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : Union[str, Any] = [
'''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''VanForImageClassification''',
'''VanModel''',
'''VanPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
snake_case : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 281 | 1 |
import re
def UpperCamelCase( __UpperCamelCase : str ):
lowerCAmelCase_ : str = re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(__UpperCamelCase ,__UpperCamelCase ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator('''+918827897895'''))
| 103 |
from typing import Any
import numpy as np
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
return np.array_equal(snake_case_ , matrix.conjugate().T )
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = v.conjugate().T
_UpperCAmelCase = v_star.dot(snake_case_ )
assert isinstance(snake_case_ , np.ndarray )
return (v_star_dot.dot(snake_case_ )) / (v_star.dot(snake_case_ ))
def __SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
_UpperCAmelCase = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]] )
_UpperCAmelCase = np.array([[1], [2], [3]] )
assert is_hermitian(snake_case_ ), f"""{a} is not hermitian."""
print(rayleigh_quotient(snake_case_ , snake_case_ ) )
_UpperCAmelCase = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]] )
assert is_hermitian(snake_case_ ), f"""{a} is not hermitian."""
assert rayleigh_quotient(snake_case_ , snake_case_ ) == float(3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
tests()
| 133 | 0 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
'''stable diffusion controlnet''',
'''0.22.0''',
'''Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.''',
standard_warn=False,
stacklevel=3,
)
| 363 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def __lowercase ( __lowercase = 150_0000 ) -> int:
'''simple docstring'''
_A = defaultdict(__lowercase )
_A = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , __lowercase , 2 ):
if gcd(__lowercase , __lowercase ) > 1:
continue
_A = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(__lowercase , limit + 1 , __lowercase ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 174 | 0 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
_snake_case : Dict = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
_snake_case : int = subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('utf-8').split()
_snake_case : str = '|'.join(sys.argv[1:])
_snake_case : Union[str, Any] = re.compile(rF"""^({joined_dirs}).*?\.py$""")
_snake_case : List[Any] = [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')
| 292 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class _UpperCAmelCase :
UpperCamelCase = PegasusConfig
UpperCamelCase = {}
UpperCamelCase = '''gelu'''
def __init__( self :Union[str, Any] , __UpperCamelCase :Union[str, Any] , __UpperCamelCase :str=13 , __UpperCamelCase :List[Any]=7 , __UpperCamelCase :Union[str, Any]=True , __UpperCamelCase :List[Any]=False , __UpperCamelCase :Any=99 , __UpperCamelCase :Tuple=32 , __UpperCamelCase :Optional[int]=2 , __UpperCamelCase :Optional[Any]=4 , __UpperCamelCase :Tuple=37 , __UpperCamelCase :Optional[Any]=0.1 , __UpperCamelCase :Tuple=0.1 , __UpperCamelCase :Optional[int]=40 , __UpperCamelCase :Tuple=2 , __UpperCamelCase :Dict=1 , __UpperCamelCase :Any=0 , ):
A = parent
A = batch_size
A = seq_length
A = is_training
A = use_labels
A = vocab_size
A = hidden_size
A = num_hidden_layers
A = num_attention_heads
A = intermediate_size
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = max_position_embeddings
A = eos_token_id
A = pad_token_id
A = bos_token_id
def lowerCamelCase ( self :Tuple ):
A = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
A = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
A = tf.concat([input_ids, eos_tensor] , axis=1 )
A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A = 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 , )
A = prepare_pegasus_inputs_dict(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return config, inputs_dict
def lowerCamelCase ( self :str , __UpperCamelCase :str , __UpperCamelCase :Union[str, Any] ):
A = TFPegasusModel(config=__UpperCamelCase ).get_decoder()
A = inputs_dict["input_ids"]
A = input_ids[:1, :]
A = inputs_dict["attention_mask"][:1, :]
A = inputs_dict["head_mask"]
A = 1
# first forward pass
A = model(__UpperCamelCase , attention_mask=__UpperCamelCase , head_mask=__UpperCamelCase , use_cache=__UpperCamelCase )
A, A = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
A = ids_tensor((self.batch_size, 3) , config.vocab_size )
A = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
A = tf.concat([input_ids, next_tokens] , axis=-1 )
A = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
A = model(__UpperCamelCase , attention_mask=__UpperCamelCase )[0]
A = model(__UpperCamelCase , attention_mask=__UpperCamelCase , past_key_values=__UpperCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
A = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
A = output_from_no_past[:, -3:, random_slice_idx]
A = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__UpperCamelCase , __UpperCamelCase , rtol=1e-3 )
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase=None , UpperCamelCase=None , UpperCamelCase=None , UpperCamelCase=None , UpperCamelCase=None , ):
if attention_mask is None:
A = tf.cast(tf.math.not_equal(UpperCamelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
A = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
A = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
A = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
A = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class _UpperCAmelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
UpperCamelCase = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
UpperCamelCase = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
UpperCamelCase = (
{
'''conversational''': TFPegasusForConditionalGeneration,
'''feature-extraction''': TFPegasusModel,
'''summarization''': TFPegasusForConditionalGeneration,
'''text2text-generation''': TFPegasusForConditionalGeneration,
'''translation''': TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
UpperCamelCase = True
UpperCamelCase = False
UpperCamelCase = False
def lowerCamelCase ( self :int ):
A = TFPegasusModelTester(self )
A = ConfigTester(self , config_class=__UpperCamelCase )
def lowerCamelCase ( self :Dict ):
self.config_tester.run_common_tests()
def lowerCamelCase ( self :Any ):
A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase )
@require_sentencepiece
@require_tokenizers
@require_tf
class _UpperCAmelCase ( unittest.TestCase ):
UpperCamelCase = [
''' 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!" ''',
]
UpperCamelCase = [
'''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
''' reduce the risk of wildfires.''',
'''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
] # differs slightly from pytorch, likely due to numerical differences in linear layers
UpperCamelCase = '''google/pegasus-xsum'''
@cached_property
def lowerCamelCase ( self :Any ):
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def lowerCamelCase ( self :Dict ):
A = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def lowerCamelCase ( self :str , **__UpperCamelCase :str ):
A = self.translate_src_text(**__UpperCamelCase )
assert self.expected_text == generated_words
def lowerCamelCase ( self :Any , **__UpperCamelCase :List[str] ):
A = self.tokenizer(self.src_text , **__UpperCamelCase , padding=__UpperCamelCase , return_tensors="tf" )
A = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=__UpperCamelCase , )
A = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=__UpperCamelCase )
return generated_words
@slow
def lowerCamelCase ( self :Union[str, Any] ):
self._assert_generated_batch_equal_expected()
| 292 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _snake_case : list[int] ) ->list[int]: # This function is recursive
"""simple docstring"""
__snake_case : int = len(_snake_case )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
__snake_case : Optional[Any] = array[0]
__snake_case : Optional[Any] = False
__snake_case : List[Any] = 1
__snake_case : list[int] = []
while not is_found and i < array_length:
if array[i] < pivot:
__snake_case : Optional[Any] = True
__snake_case : List[str] = [element for element in array[i:] if element >= array[i]]
__snake_case : Dict = longest_subsequence(_snake_case )
if len(_snake_case ) > len(_snake_case ):
__snake_case : List[Any] = temp_array
else:
i += 1
__snake_case : Union[str, Any] = [element for element in array[1:] if element >= pivot]
__snake_case : str = [pivot, *longest_subsequence(_snake_case )]
if len(_snake_case ) > len(_snake_case ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
SCREAMING_SNAKE_CASE : int = datasets.utils.logging.get_logger(__name__)
@dataclass
class _UpperCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
lowerCamelCase__ =10000
lowerCamelCase__ =None
lowerCamelCase__ =None
class _UpperCAmelCase ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
lowerCamelCase__ =ParquetConfig
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
if not self.config.data_files:
raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
__snake_case : int = dl_manager.download_and_extract(self.config.data_files )
if isinstance(a_ , (str, list, tuple) ):
__snake_case : Union[str, Any] = data_files
if isinstance(a_ , a_ ):
__snake_case : Union[str, Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
__snake_case : List[Any] = [dl_manager.iter_files(a_ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
__snake_case : int = []
for split_name, files in data_files.items():
if isinstance(a_ , a_ ):
__snake_case : List[Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
__snake_case : int = [dl_manager.iter_files(a_ ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(a_ ):
with open(a_ , '''rb''' ) as f:
__snake_case : Any = datasets.Features.from_arrow_schema(pq.read_schema(a_ ) )
break
splits.append(datasets.SplitGenerator(name=a_ , gen_kwargs={'''files''': files} ) )
return splits
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
__snake_case : List[Any] = table_cast(a_ , self.info.features.arrow_schema )
return pa_table
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
__snake_case : List[Any] = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
f"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
for file_idx, file in enumerate(itertools.chain.from_iterable(a_ ) ):
with open(a_ , '''rb''' ) as f:
__snake_case : int = pq.ParquetFile(a_ )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
__snake_case : Dict = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield f"""{file_idx}_{batch_idx}""", self._cast_table(a_ )
except ValueError as e:
logger.error(f"""Failed to read file '{file}' with error {type(a_ )}: {e}""" )
raise
| 24 | 1 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCAmelCase__ : List[Any] = """\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
and Akul Arora
and Steven Basart
and Eric Tang
and Dawn Song
and Jacob Steinhardt},
journal={arXiv preprint arXiv:2103.03874},
year={2021}
}
"""
UpperCAmelCase__ : List[Any] = """\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.
"""
UpperCAmelCase__ : Dict = R"""
Calculates accuracy after canonicalizing inputs.
Args:
predictions: list of predictions to score. Each prediction
is a string that contains natural language and LaTex.
references: list of reference for each prediction. Each
reference is a string that contains natural language
and LaTex.
Returns:
accuracy: accuracy after canonicalizing inputs
(e.g., converting \"1/2\" to \"\\frac{1}{2}\")
Examples:
>>> metric = datasets.load_metric(\"competition_math\")
>>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])
>>> print(results)
{'accuracy': 1.0}
"""
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
"""simple docstring"""
def _lowercase ( self : List[str] ) ->Optional[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" ),
"""references""": datasets.Value("""string""" ),
} ) , homepage="""https://github.com/hendrycks/math""" , codebase_urls=["""https://github.com/hendrycks/math"""] , )
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[int] ) ->Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = 0.0
for i, j in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
n_correct += 1.0 if math_equivalence.is_equiv(UpperCAmelCase__ , UpperCAmelCase__ ) else 0.0
SCREAMING_SNAKE_CASE : Optional[Any] = n_correct / len(UpperCAmelCase__ )
return {
"accuracy": accuracy,
}
| 245 |
import inspect
import unittest
from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a__ :
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[Any]=1_3 , UpperCAmelCase__ : List[str]=[3_0, 3_0] , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : Dict=3 , UpperCAmelCase__ : str=True , UpperCAmelCase__ : Any=True , UpperCAmelCase__ : int=3_2 , UpperCAmelCase__ : List[str]=5 , UpperCAmelCase__ : Dict=4 , UpperCAmelCase__ : int=3_7 , UpperCAmelCase__ : List[str]="gelu" , UpperCAmelCase__ : Optional[Any]=0.1 , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : str=1_0 , UpperCAmelCase__ : Dict=0.02 , UpperCAmelCase__ : Any=3 , UpperCAmelCase__ : int=None , UpperCAmelCase__ : List[Any]=8 , UpperCAmelCase__ : Dict=1_0 , ) ->Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = parent
SCREAMING_SNAKE_CASE : int = batch_size
SCREAMING_SNAKE_CASE : str = image_size
SCREAMING_SNAKE_CASE : List[Any] = patch_size
SCREAMING_SNAKE_CASE : Any = num_channels
SCREAMING_SNAKE_CASE : str = is_training
SCREAMING_SNAKE_CASE : Dict = use_labels
SCREAMING_SNAKE_CASE : List[Any] = hidden_size
SCREAMING_SNAKE_CASE : Optional[int] = num_hidden_layers
SCREAMING_SNAKE_CASE : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Dict = intermediate_size
SCREAMING_SNAKE_CASE : int = hidden_act
SCREAMING_SNAKE_CASE : str = hidden_dropout_prob
SCREAMING_SNAKE_CASE : List[str] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : List[Any] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : str = num_labels
SCREAMING_SNAKE_CASE : Dict = scope
SCREAMING_SNAKE_CASE : Optional[Any] = n_targets
SCREAMING_SNAKE_CASE : Dict = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
SCREAMING_SNAKE_CASE : Tuple = (image_size[1] // patch_size) * (image_size[0] // patch_size)
SCREAMING_SNAKE_CASE : int = num_patches + 1 + self.num_detection_tokens
def _lowercase ( self : Tuple ) ->Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
SCREAMING_SNAKE_CASE : int = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
SCREAMING_SNAKE_CASE : str = []
for i in range(self.batch_size ):
SCREAMING_SNAKE_CASE : List[Any] = {}
SCREAMING_SNAKE_CASE : Any = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = torch.rand(self.n_targets , 4 , device=UpperCAmelCase__ )
labels.append(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_config()
return config, pixel_values, labels
def _lowercase ( self : Dict ) ->Optional[Any]:
"""simple docstring"""
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def _lowercase ( self : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Optional[Any] ) ->Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = YolosModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(UpperCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) )
def _lowercase ( self : str , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Any , UpperCAmelCase__ : Union[str, Any] ) ->int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = YolosForObjectDetection(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : List[Any] = model(pixel_values=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
SCREAMING_SNAKE_CASE : int = model(pixel_values=UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
def _lowercase ( self : Dict ) ->Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE : List[Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a__ ( UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Tuple =(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
UpperCAmelCase__ : Any =(
{"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
)
UpperCAmelCase__ : Tuple =False
UpperCAmelCase__ : int =False
UpperCAmelCase__ : Tuple =False
UpperCAmelCase__ : Optional[Any] =False
def _lowercase ( self : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Any=False ) ->int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = super()._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
SCREAMING_SNAKE_CASE : List[str] = []
for i in range(self.model_tester.batch_size ):
SCREAMING_SNAKE_CASE : Tuple = {}
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.ones(
size=(self.model_tester.n_targets,) , device=UpperCAmelCase__ , dtype=torch.long )
SCREAMING_SNAKE_CASE : str = torch.ones(
self.model_tester.n_targets , 4 , device=UpperCAmelCase__ , dtype=torch.float )
labels.append(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : str = labels
return inputs_dict
def _lowercase ( self : Dict ) ->Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = YolosModelTester(self )
SCREAMING_SNAKE_CASE : Optional[int] = ConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ , hidden_size=3_7 )
def _lowercase ( self : Union[str, Any] ) ->List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowercase ( self : List[Any] ) ->int:
"""simple docstring"""
pass
def _lowercase ( self : Optional[int] ) ->Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : List[str] = model_class(UpperCAmelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase__ , nn.Linear ) )
def _lowercase ( self : List[Any] ) ->int:
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Tuple = model_class(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE : Tuple = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE : str = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCAmelCase__ )
def _lowercase ( self : Union[str, Any] ) ->Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def _lowercase ( self : Optional[Any] ) ->Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE : Optional[Any] = True
# in YOLOS, the seq_len is different
SCREAMING_SNAKE_CASE : Any = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = False
SCREAMING_SNAKE_CASE : int = True
SCREAMING_SNAKE_CASE : Optional[Any] = model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : Union[str, Any] = model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.attentions
self.assertEqual(len(UpperCAmelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Tuple = model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : Dict = model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.attentions
self.assertEqual(len(UpperCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
SCREAMING_SNAKE_CASE : List[str] = len(UpperCAmelCase__ )
# Check attention is always last and order is fine
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : List[str] = model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[Any] = model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
SCREAMING_SNAKE_CASE : Optional[int] = 1
self.assertEqual(out_len + added_hidden_states , len(UpperCAmelCase__ ) )
SCREAMING_SNAKE_CASE : str = outputs.attentions
self.assertEqual(len(UpperCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def _lowercase ( self : Any ) ->str:
"""simple docstring"""
def check_hidden_states_output(UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : str ):
SCREAMING_SNAKE_CASE : List[Any] = model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[str] = model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
SCREAMING_SNAKE_CASE : Dict = outputs.hidden_states
SCREAMING_SNAKE_CASE : str = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(UpperCAmelCase__ ) , UpperCAmelCase__ )
# YOLOS has a different seq_length
SCREAMING_SNAKE_CASE : Tuple = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Any = True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE : Any = True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def _lowercase ( self : Any ) ->Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*UpperCAmelCase__ )
@slow
def _lowercase ( self : str ) ->List[Any]:
"""simple docstring"""
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : str = YolosModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def __lowercase ( ) -> List[Any]:
SCREAMING_SNAKE_CASE : Tuple = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class a__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowercase ( self : int ) ->Union[str, Any]:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""hustvl/yolos-small""" ) if is_vision_available() else None
@slow
def _lowercase ( self : List[Any] ) ->Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = YolosForObjectDetection.from_pretrained("""hustvl/yolos-small""" ).to(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = self.default_image_processor
SCREAMING_SNAKE_CASE : Optional[Any] = prepare_img()
SCREAMING_SNAKE_CASE : str = image_processor(images=UpperCAmelCase__ , return_tensors="""pt""" ).to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(inputs.pixel_values )
# verify outputs
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.Size((1, 1_0_0, 9_2) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]] , device=UpperCAmelCase__ , )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]] , device=UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , UpperCAmelCase__ , atol=1e-4 ) )
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , UpperCAmelCase__ , atol=1e-4 ) )
# verify postprocessing
SCREAMING_SNAKE_CASE : int = image_processor.post_process_object_detection(
UpperCAmelCase__ , threshold=0.3 , target_sizes=[image.size[::-1]] )[0]
SCREAMING_SNAKE_CASE : str = torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61] ).to(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : str = [7_5, 7_5, 1_7, 6_3, 1_7]
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95] ).to(UpperCAmelCase__ )
self.assertEqual(len(results["""scores"""] ) , 5 )
self.assertTrue(torch.allclose(results["""scores"""] , UpperCAmelCase__ , atol=1e-4 ) )
self.assertSequenceEqual(results["""labels"""].tolist() , UpperCAmelCase__ )
self.assertTrue(torch.allclose(results["""boxes"""][0, :] , UpperCAmelCase__ ) )
| 245 | 1 |
'''simple docstring'''
from __future__ import annotations
def __snake_case( _lowerCAmelCase ) -> int:
if not nums:
return 0
snake_case__ : List[str] = nums[0]
snake_case__ : Tuple = 0
for num in nums[1:]:
snake_case__ : Union[str, Any] = (
max_excluding + num,
max(_lowerCAmelCase , _lowerCAmelCase ),
)
return max(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 370 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__a = logging.get_logger(__name__)
__a = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
__a = {
"vocab_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
__a = {
"vocab_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
__a = {
"vocab_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
),
},
}
__a = {
"facebook/dpr-ctx_encoder-single-nq-base": 512,
"facebook/dpr-ctx_encoder-multiset-base": 512,
}
__a = {
"facebook/dpr-question_encoder-single-nq-base": 512,
"facebook/dpr-question_encoder-multiset-base": 512,
}
__a = {
"facebook/dpr-reader-single-nq-base": 512,
"facebook/dpr-reader-multiset-base": 512,
}
__a = {
"facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
__a = {
"facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
__a = {
"facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
"facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = VOCAB_FILES_NAMES
lowercase = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
lowercase = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = VOCAB_FILES_NAMES
lowercase = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
lowercase = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__a = collections.namedtuple(
"DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)
__a = collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])
__a = R"\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n ```\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n ```\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `'tf'`: Return TensorFlow `tf.constant` objects.\n - `'pt'`: Return PyTorch `torch.Tensor` objects.\n - `'np'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer's default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Returns:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n "
@add_start_docstrings(_a )
class UpperCAmelCase_ :
"""simple docstring"""
def __call__( self : str , snake_case_ : Optional[Any] , snake_case_ : Optional[str] = None , snake_case_ : Optional[str] = None , snake_case_ : Union[bool, str] = False , snake_case_ : Union[bool, str] = False , snake_case_ : Optional[int] = None , snake_case_ : Optional[Union[str, TensorType]] = None , snake_case_ : Optional[bool] = None , **snake_case_ : Union[str, Any] , ):
if titles is None and texts is None:
return super().__call__(
snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , return_tensors=snake_case_ , return_attention_mask=snake_case_ , **snake_case_ , )
elif titles is None or texts is None:
snake_case__ : int = titles if texts is None else texts
return super().__call__(
snake_case_ , snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , return_tensors=snake_case_ , return_attention_mask=snake_case_ , **snake_case_ , )
snake_case__ : List[str] = titles if not isinstance(snake_case_ , snake_case_ ) else [titles]
snake_case__ : Union[str, Any] = texts if not isinstance(snake_case_ , snake_case_ ) else [texts]
snake_case__ : Dict = len(snake_case_ )
snake_case__ : Union[str, Any] = questions if not isinstance(snake_case_ , snake_case_ ) else [questions] * n_passages
if len(snake_case_ ) != len(snake_case_ ):
raise ValueError(
f"There should be as many titles than texts but got {len(snake_case_ )} titles and {len(snake_case_ )} texts." )
snake_case__ : int = super().__call__(snake_case_ , snake_case_ , padding=snake_case_ , truncation=snake_case_ )["""input_ids"""]
snake_case__ : Any = super().__call__(snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ )["""input_ids"""]
snake_case__ : Dict = {
"""input_ids""": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(snake_case_ , snake_case_ )
]
}
if return_attention_mask is not False:
snake_case__ : List[Any] = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
snake_case__ : Union[str, Any] = attention_mask
return self.pad(snake_case_ , padding=snake_case_ , max_length=snake_case_ , return_tensors=snake_case_ )
def lowerCamelCase ( self : Optional[int] , snake_case_ : BatchEncoding , snake_case_ : DPRReaderOutput , snake_case_ : int = 16 , snake_case_ : int = 64 , snake_case_ : int = 4 , ):
snake_case__ : Optional[int] = reader_input["""input_ids"""]
snake_case__ , snake_case__ , snake_case__ : List[str] = reader_output[:3]
snake_case__ : Union[str, Any] = len(snake_case_ )
snake_case__ : Tuple = sorted(range(snake_case_ ) , reverse=snake_case_ , key=relevance_logits.__getitem__ )
snake_case__ : List[DPRReaderOutput] = []
for doc_id in sorted_docs:
snake_case__ : Union[str, Any] = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
snake_case__ : Optional[Any] = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
snake_case__ : int = sequence_ids.index(self.pad_token_id )
else:
snake_case__ : int = len(snake_case_ )
snake_case__ : Optional[int] = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=snake_case_ , top_spans=snake_case_ , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=snake_case_ , start_index=snake_case_ , end_index=snake_case_ , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(snake_case_ ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def lowerCamelCase ( self : str , snake_case_ : List[int] , snake_case_ : List[int] , snake_case_ : int , snake_case_ : int , ):
snake_case__ : List[str] = []
for start_index, start_score in enumerate(snake_case_ ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
snake_case__ : Any = sorted(snake_case_ , key=lambda snake_case_ : x[1] , reverse=snake_case_ )
snake_case__ : Optional[Any] = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]" )
snake_case__ : Union[str, Any] = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(f"Span is too long: {length} > {max_answer_length}" )
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(snake_case_ ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(_a )
class UpperCAmelCase_ ( _a , _a ):
"""simple docstring"""
lowercase = VOCAB_FILES_NAMES
lowercase = READER_PRETRAINED_VOCAB_FILES_MAP
lowercase = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = READER_PRETRAINED_INIT_CONFIGURATION
lowercase = ["input_ids", "attention_mask"]
| 43 | 0 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Tuple:
"""simple docstring"""
while b:
lowercase , lowercase : Any = b, a % b
return a
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Dict:
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(_UpperCamelCase, a % b )
def __lowercase ( ) ->List[Any]:
"""simple docstring"""
print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3, 5 )}""" )
print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5, 3 )}""" )
print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1, 3 )}""" )
print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3, 6 )}""" )
print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6, 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3, 5 )}""" )
print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5, 3 )}""" )
print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1, 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3, 6 )}""" )
print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6, 3 )}""" )
if __name__ == "__main__":
main()
| 337 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase__ : str = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : List[str] = [
'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RoCBertForCausalLM',
'RoCBertForMaskedLM',
'RoCBertForMultipleChoice',
'RoCBertForPreTraining',
'RoCBertForQuestionAnswering',
'RoCBertForSequenceClassification',
'RoCBertForTokenClassification',
'RoCBertLayer',
'RoCBertModel',
'RoCBertPreTrainedModel',
'load_tf_weights_in_roc_bert',
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
lowerCAmelCase__ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 98 | 0 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self, A="", A="train" ):
'''simple docstring'''
assert os.path.isdir(A )
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : Dict = os.listdir(A )
for story_filename in story_filenames_list:
if "summary" in story_filename:
continue
SCREAMING_SNAKE_CASE : str = os.path.join(A, A )
if not os.path.isfile(A ):
continue
self.documents.append(A )
def __len__( self ):
'''simple docstring'''
return len(self.documents )
def __getitem__( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.documents[idx]
SCREAMING_SNAKE_CASE : Optional[int] = document_path.split('/' )[-1]
with open(A, encoding='utf-8' ) as source:
SCREAMING_SNAKE_CASE : Optional[int] = source.read()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = process_story(A )
return document_name, story_lines, summary_lines
def lowercase__( __UpperCamelCase: Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = list(filter(lambda __UpperCamelCase : len(__UpperCamelCase ) != 0 ,[line.strip() for line in raw_story.split('\n' )] ) )
# for some unknown reason some lines miss a period, add it
SCREAMING_SNAKE_CASE : Dict = [_add_missing_period(__UpperCamelCase ) for line in nonempty_lines]
# gather article lines
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : List[str] = deque(__UpperCamelCase )
while True:
try:
SCREAMING_SNAKE_CASE : List[Any] = lines.popleft()
if element.startswith('@highlight' ):
break
story_lines.append(__UpperCamelCase )
except IndexError:
# if "@highlight" is absent from the file we pop
# all elements until there is None, raising an exception.
return story_lines, []
# gather summary lines
SCREAMING_SNAKE_CASE : Dict = list(filter(lambda __UpperCamelCase : not t.startswith('@highlight' ) ,__UpperCamelCase ) )
return story_lines, summary_lines
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
if line.startswith('@highlight' ):
return line
if line[-1] in END_TOKENS:
return line
return line + "."
def lowercase__( __UpperCamelCase: List[str] ,__UpperCamelCase: Tuple ,__UpperCamelCase: List[str] ):
"""simple docstring"""
if len(__UpperCamelCase ) > block_size:
return sequence[:block_size]
else:
sequence.extend([pad_token_id] * (block_size - len(__UpperCamelCase )) )
return sequence
def lowercase__( __UpperCamelCase: Optional[Any] ,__UpperCamelCase: Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = torch.ones_like(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Dict = sequence == pad_token_id
SCREAMING_SNAKE_CASE : int = 0
return mask
def lowercase__( __UpperCamelCase: List[str] ,__UpperCamelCase: Dict ,__UpperCamelCase: Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = [tokenizer.encode(__UpperCamelCase ) for line in story_lines]
SCREAMING_SNAKE_CASE : Optional[int] = [token for sentence in story_lines_token_ids for token in sentence]
SCREAMING_SNAKE_CASE : Optional[int] = [tokenizer.encode(__UpperCamelCase ) for line in summary_lines]
SCREAMING_SNAKE_CASE : int = [token for sentence in summary_lines_token_ids for token in sentence]
return story_token_ids, summary_token_ids
def lowercase__( __UpperCamelCase: Any ,__UpperCamelCase: Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = []
for sequence in batch:
SCREAMING_SNAKE_CASE : Any = -1
SCREAMING_SNAKE_CASE : Optional[Any] = []
for s in sequence:
if s == separator_token_id:
sentence_num += 1
embeddings.append(sentence_num % 2 )
batch_embeddings.append(__UpperCamelCase )
return torch.tensor(__UpperCamelCase )
| 246 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : str = field(default='''text-classification''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
A : ClassVar[Features] = Features({'''text''': Value('''string''' )} )
A : ClassVar[Features] = Features({'''labels''': ClassLabel} )
A : str = "text"
A : str = "labels"
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
if self.label_column not in features:
raise ValueError(F"Column {self.label_column} is not present in features." )
if not isinstance(features[self.label_column], A ):
raise ValueError(F"Column {self.label_column} is not a ClassLabel." )
SCREAMING_SNAKE_CASE : Optional[Any] = copy.deepcopy(self )
SCREAMING_SNAKE_CASE : Optional[Any] = self.label_schema.copy()
SCREAMING_SNAKE_CASE : Optional[int] = features[self.label_column]
SCREAMING_SNAKE_CASE : Optional[Any] = label_schema
return task_template
@property
def UpperCamelCase_ ( self ):
'''simple docstring'''
return {
self.text_column: "text",
self.label_column: "labels",
}
| 246 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase ( UpperCamelCase__ ):
_a = ["image_processor", "tokenizer"]
_a = "LayoutLMv3ImageProcessor"
_a = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")
def __init__( self , _a=None , _a=None , **_a ) -> Tuple:
_A : 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.""" , _a , )
_A : Optional[int] = kwargs.pop("""feature_extractor""" )
_A : Union[str, 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__(_a , _a )
def __call__( self , _a , _a = None , _a = None , _a = None , _a = None , _a = True , _a = False , _a = None , _a = None , _a = 0 , _a = None , _a = None , _a = None , _a = False , _a = False , _a = False , _a = False , _a = True , _a = None , **_a , ) -> BatchEncoding:
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"""You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.""" )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"""You cannot provide word labels if you initialized the image processor with apply_ocr set to True.""" )
# first, apply the image processor
_A : Dict = self.image_processor(images=_a , return_tensors=_a )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(_a , _a ):
_A : Tuple = [text] # add batch dimension (as the image processor always adds a batch dimension)
_A : Any = features["""words"""]
_A : Optional[Any] = self.tokenizer(
text=text if text is not None else features["""words"""] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["""boxes"""] , word_labels=_a , add_special_tokens=_a , padding=_a , truncation=_a , max_length=_a , stride=_a , pad_to_multiple_of=_a , return_token_type_ids=_a , return_attention_mask=_a , return_overflowing_tokens=_a , return_special_tokens_mask=_a , return_offsets_mapping=_a , return_length=_a , verbose=_a , return_tensors=_a , **_a , )
# add pixel values
_A : Tuple = features.pop("""pixel_values""" )
if return_overflowing_tokens is True:
_A : Union[str, Any] = self.get_overflowing_images(_a , encoded_inputs["""overflow_to_sample_mapping"""] )
_A : Union[str, Any] = images
return encoded_inputs
def a__ ( self , _a , _a ) -> Optional[int]:
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
_A : List[str] = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(_a ) != len(_a ):
raise ValueError(
"""Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
F''' {len(_a )} and {len(_a )}''' )
return images_with_overflow
def a__ ( self , *_a , **_a ) -> Optional[int]:
return self.tokenizer.batch_decode(*_a , **_a )
def a__ ( self , *_a , **_a ) -> List[Any]:
return self.tokenizer.decode(*_a , **_a )
@property
def a__ ( self ) -> List[Any]:
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def a__ ( self ) -> Union[str, Any]:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , _a , )
return self.image_processor_class
@property
def a__ ( self ) -> int:
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , _a , )
return self.image_processor
| 26 |
from __future__ import annotations
import numpy as np
def lowerCAmelCase_ ( snake_case_ ):
return np.maximum(0,snake_case_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 26 | 1 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class A ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
@register_to_config
def __init__( self : str,lowercase_ : Tuple = 7_6_8,)-> Dict:
'''simple docstring'''
super().__init__()
A__ = nn.Parameter(torch.zeros(1,lowerCamelCase_ ) )
A__ = nn.Parameter(torch.ones(1,lowerCamelCase_ ) )
def snake_case__ ( self : Union[str, Any],lowercase_ : Union[str, Any] = None,lowercase_ : Any = None,)-> Union[str, Any]:
'''simple docstring'''
A__ = nn.Parameter(self.mean.to(lowerCamelCase_ ).to(lowerCamelCase_ ) )
A__ = nn.Parameter(self.std.to(lowerCamelCase_ ).to(lowerCamelCase_ ) )
return self
def snake_case__ ( self : Optional[Any],lowercase_ : Optional[Any] )-> int:
'''simple docstring'''
A__ = (embeds - self.mean) * 1.0 / self.std
return embeds
def snake_case__ ( self : int,lowercase_ : Optional[Any] )-> int:
'''simple docstring'''
A__ = (embeds * self.std) + self.mean
return embeds
| 361 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 1000 ) -> int:
'''simple docstring'''
A__ = 3
A__ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(f"""{solution() = }""")
| 282 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''huggingface/time-series-transformer-tourism-monthly''': (
'''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class A__ ( _snake_case ):
lowercase = "time_series_transformer"
lowercase = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__( self , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = "student_t" , UpperCamelCase__ = "nll" , UpperCamelCase__ = 1 , UpperCamelCase__ = [1, 2, 3, 4, 5, 6, 7] , UpperCamelCase__ = "mean" , UpperCamelCase__ = 0 , UpperCamelCase__ = 0 , UpperCamelCase__ = 0 , UpperCamelCase__ = 0 , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = 32 , UpperCamelCase__ = 32 , UpperCamelCase__ = 2 , UpperCamelCase__ = 2 , UpperCamelCase__ = 2 , UpperCamelCase__ = 2 , UpperCamelCase__ = True , UpperCamelCase__ = "gelu" , UpperCamelCase__ = 64 , UpperCamelCase__ = 0.1 , UpperCamelCase__ = 0.1 , UpperCamelCase__ = 0.1 , UpperCamelCase__ = 0.1 , UpperCamelCase__ = 0.1 , UpperCamelCase__ = 100 , UpperCamelCase__ = 0.02 , UpperCamelCase__=True , **UpperCamelCase__ , ) -> List[str]:
'''simple docstring'''
# time series specific configuration
A_ = prediction_length
A_ = context_length or prediction_length
A_ = distribution_output
A_ = loss
A_ = input_size
A_ = num_time_features
A_ = lags_sequence
A_ = scaling
A_ = num_dynamic_real_features
A_ = num_static_real_features
A_ = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(UpperCamelCase__ ) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""" )
A_ = cardinality
else:
A_ = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(UpperCamelCase__ ) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""" )
A_ = embedding_dimension
else:
A_ = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
A_ = num_parallel_samples
# Transformer architecture configuration
A_ = input_size * len(UpperCamelCase__ ) + self._number_of_features
A_ = d_model
A_ = encoder_attention_heads
A_ = decoder_attention_heads
A_ = encoder_ffn_dim
A_ = decoder_ffn_dim
A_ = encoder_layers
A_ = decoder_layers
A_ = dropout
A_ = attention_dropout
A_ = activation_dropout
A_ = encoder_layerdrop
A_ = decoder_layerdrop
A_ = activation_function
A_ = init_std
A_ = use_cache
super().__init__(is_encoder_decoder=UpperCamelCase__ , **UpperCamelCase__ )
@property
def snake_case_ ( self ) -> int:
'''simple docstring'''
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 162 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class A__ ( _snake_case , unittest.TestCase ):
lowercase = ShapEPipeline
lowercase = ["prompt"]
lowercase = ["prompt"]
lowercase = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
lowercase = False
@property
def snake_case_ ( self ) -> Optional[Any]:
'''simple docstring'''
return 32
@property
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
return 32
@property
def snake_case_ ( self ) -> Any:
'''simple docstring'''
return self.time_input_dim * 4
@property
def snake_case_ ( self ) -> Optional[Any]:
'''simple docstring'''
return 8
@property
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
A_ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def snake_case_ ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
A_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(UpperCamelCase__ )
@property
def snake_case_ ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
A_ = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 16,
"""embedding_dim""": self.time_input_dim,
"""num_embeddings""": 32,
"""embedding_proj_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""num_layers""": 1,
"""clip_embed_dim""": self.time_input_dim * 2,
"""additional_embeddings""": 0,
"""time_embed_act_fn""": """gelu""",
"""norm_in_type""": """layer""",
"""encoder_hid_proj_type""": None,
"""added_emb_type""": None,
}
A_ = PriorTransformer(**UpperCamelCase__ )
return model
@property
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
A_ = {
"""param_shapes""": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"""d_latent""": self.time_input_dim,
"""d_hidden""": self.renderer_dim,
"""n_output""": 12,
"""background""": (
0.1,
0.1,
0.1,
),
}
A_ = ShapERenderer(**UpperCamelCase__ )
return model
def snake_case_ ( self ) -> List[str]:
'''simple docstring'''
A_ = self.dummy_prior
A_ = self.dummy_text_encoder
A_ = self.dummy_tokenizer
A_ = self.dummy_renderer
A_ = HeunDiscreteScheduler(
beta_schedule="""exp""" , num_train_timesteps=1024 , prediction_type="""sample""" , use_karras_sigmas=UpperCamelCase__ , clip_sample=UpperCamelCase__ , clip_sample_range=1.0 , )
A_ = {
"""prior""": prior,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""renderer""": renderer,
"""scheduler""": scheduler,
}
return components
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__=0 ) -> List[str]:
'''simple docstring'''
if str(UpperCamelCase__ ).startswith("""mps""" ):
A_ = torch.manual_seed(UpperCamelCase__ )
else:
A_ = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
A_ = {
"""prompt""": """horse""",
"""generator""": generator,
"""num_inference_steps""": 1,
"""frame_size""": 32,
"""output_type""": """np""",
}
return inputs
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
A_ = """cpu"""
A_ = self.get_dummy_components()
A_ = self.pipeline_class(**UpperCamelCase__ )
A_ = pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
A_ = pipe(**self.get_dummy_inputs(UpperCamelCase__ ) )
A_ = output.images[0]
A_ = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
A_ = np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def snake_case_ ( self ) -> Dict:
'''simple docstring'''
A_ = torch_device == """cpu"""
A_ = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=UpperCamelCase__ , relax_max_difference=UpperCamelCase__ , )
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
A_ = self.get_dummy_components()
A_ = self.pipeline_class(**UpperCamelCase__ )
A_ = pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
A_ = 1
A_ = 2
A_ = self.get_dummy_inputs(UpperCamelCase__ )
for key in inputs.keys():
if key in self.batch_params:
A_ = batch_size * [inputs[key]]
A_ = pipe(**UpperCamelCase__ , num_images_per_prompt=UpperCamelCase__ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
def snake_case_ ( self ) -> Any:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self ) -> str:
'''simple docstring'''
A_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/shap_e/test_shap_e_np_out.npy""" )
A_ = ShapEPipeline.from_pretrained("""openai/shap-e""" )
A_ = pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
A_ = torch.Generator(device=UpperCamelCase__ ).manual_seed(0 )
A_ = pipe(
"""a shark""" , generator=UpperCamelCase__ , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(UpperCamelCase__ , UpperCamelCase__ )
| 162 | 1 |
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
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCase : str = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
# See all BART models at https://huggingface.co/models?filter=bart
_lowerCamelCase : Optional[Any] = {
"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",
},
}
_lowerCamelCase : Any = {
"facebook/bart-base": 1_0_2_4,
"facebook/bart-large": 1_0_2_4,
"facebook/bart-large-mnli": 1_0_2_4,
"facebook/bart-large-cnn": 1_0_2_4,
"facebook/bart-large-xsum": 1_0_2_4,
"yjernite/bart_eli5": 1_0_2_4,
}
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ["""input_ids""", """attention_mask"""]
UpperCamelCase = BartTokenizer
def __init__( self : int, __A : Dict=None, __A : int=None, __A : Optional[Any]=None, __A : List[str]="replace", __A : Any="<s>", __A : Dict="</s>", __A : List[Any]="</s>", __A : int="<s>", __A : Optional[Any]="<unk>", __A : int="<pad>", __A : str="<mask>", __A : List[Any]=False, __A : List[Any]=True, **__A : Any, ):
super().__init__(
__A, __A, tokenizer_file=__A, errors=__A, bos_token=__A, eos_token=__A, sep_token=__A, cls_token=__A, unk_token=__A, pad_token=__A, mask_token=__A, add_prefix_space=__A, trim_offsets=__A, **__A, )
UpperCAmelCase : Dict = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''', __A ) != add_prefix_space:
UpperCAmelCase : Optional[Any] = getattr(__A, pre_tok_state.pop('''type''' ) )
UpperCAmelCase : List[str] = add_prefix_space
UpperCAmelCase : Optional[Any] = pre_tok_class(**__A )
UpperCAmelCase : str = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
UpperCAmelCase : Optional[Any] = '''post_processor'''
UpperCAmelCase : str = getattr(self.backend_tokenizer, __A, __A )
if tokenizer_component_instance:
UpperCAmelCase : 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:
UpperCAmelCase : Optional[Any] = tuple(state['''sep'''] )
if "cls" in state:
UpperCAmelCase : str = tuple(state['''cls'''] )
UpperCAmelCase : Union[str, Any] = False
if state.get('''add_prefix_space''', __A ) != add_prefix_space:
UpperCAmelCase : Dict = add_prefix_space
UpperCAmelCase : List[str] = True
if state.get('''trim_offsets''', __A ) != trim_offsets:
UpperCAmelCase : Union[str, Any] = trim_offsets
UpperCAmelCase : Dict = True
if changes_to_apply:
UpperCAmelCase : str = getattr(__A, state.pop('''type''' ) )
UpperCAmelCase : Optional[int] = component_class(**__A )
setattr(self.backend_tokenizer, __A, __A )
@property
def __magic_name__ ( self : Union[str, Any] ):
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 __magic_name__ ( self : Optional[int], __A : Dict ):
UpperCAmelCase : Dict = AddedToken(__A, lstrip=__A, rstrip=__A ) if isinstance(__A, __A ) else value
UpperCAmelCase : Tuple = value
def __magic_name__ ( self : Tuple, *__A : int, **__A : Tuple ):
UpperCAmelCase : int = kwargs.get('''is_split_into_words''', __A )
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(*__A, **__A )
def __magic_name__ ( self : str, *__A : Dict, **__A : List[Any] ):
UpperCAmelCase : int = kwargs.get('''is_split_into_words''', __A )
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(*__A, **__A )
def __magic_name__ ( self : Union[str, Any], __A : str, __A : Optional[str] = None ):
UpperCAmelCase : int = self._tokenizer.model.save(__A, name=__A )
return tuple(__A )
def __magic_name__ ( self : List[Any], __A : Optional[int], __A : str=None ):
UpperCAmelCase : Tuple = [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 __magic_name__ ( self : Union[str, Any], __A : List[int], __A : Optional[List[int]] = None ):
UpperCAmelCase : int = [self.sep_token_id]
UpperCAmelCase : Dict = [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]
| 99 |
import logging
import os
from .state import PartialState
class __UpperCAmelCase ( logging.LoggerAdapter ):
@staticmethod
def __magic_name__ ( __A : str ):
UpperCAmelCase : Dict = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def __magic_name__ ( self : Union[str, Any], __A : Union[str, Any], __A : Union[str, Any], *__A : Optional[int], **__A : Tuple ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
UpperCAmelCase : List[str] = kwargs.pop('''main_process_only''', __A )
UpperCAmelCase : int = kwargs.pop('''in_order''', __A )
if self.isEnabledFor(__A ):
if self._should_log(__A ):
UpperCAmelCase , UpperCAmelCase : Dict = self.process(__A, __A )
self.logger.log(__A, __A, *__A, **__A )
elif in_order:
UpperCAmelCase : Dict = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
UpperCAmelCase , UpperCAmelCase : Optional[int] = self.process(__A, __A )
self.logger.log(__A, __A, *__A, **__A )
state.wait_for_everyone()
def a__ ( UpperCAmelCase : str , UpperCAmelCase : str = None ) -> Dict:
if log_level is None:
UpperCAmelCase : Union[str, Any] = os.environ.get('''ACCELERATE_LOG_LEVEL''' , UpperCAmelCase )
UpperCAmelCase : Tuple = logging.getLogger(UpperCAmelCase )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(UpperCAmelCase , {} )
| 99 | 1 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
lowerCamelCase : Optional[Any] = 50_000
lowerCamelCase : Dict = 5_000
lowerCamelCase , lowerCamelCase : Optional[Any] = os.path.split(__file__)
lowerCamelCase : Dict = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_duration
def _SCREAMING_SNAKE_CASE ( lowercase : datasets.Dataset , lowercase : Tuple ):
'''simple docstring'''
for i in range(lowercase ):
lowerCamelCase_ = dataset[i]
@get_duration
def _SCREAMING_SNAKE_CASE ( lowercase : datasets.Dataset , lowercase : Any , lowercase : Dict ):
'''simple docstring'''
for i in range(0 , len(lowercase ) , lowercase ):
lowerCamelCase_ = dataset[i : i + batch_size]
@get_duration
def _SCREAMING_SNAKE_CASE ( lowercase : datasets.Dataset , lowercase : Tuple , lowercase : List[str] ):
'''simple docstring'''
with dataset.formatted_as(type=lowercase ):
for i in range(lowercase ):
lowerCamelCase_ = dataset[i]
@get_duration
def _SCREAMING_SNAKE_CASE ( lowercase : datasets.Dataset , lowercase : Any , lowercase : Optional[Any] , lowercase : List[Any] ):
'''simple docstring'''
with dataset.formatted_as(type=lowercase ):
for i in range(0 , lowercase , lowercase ):
lowerCamelCase_ = dataset[i : i + batch_size]
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowerCamelCase_ = {'num examples': SPEED_TEST_N_EXAMPLES}
lowerCamelCase_ = [
(read, {'length': SMALL_TEST}),
(read, {'length': SPEED_TEST_N_EXAMPLES}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
(read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
(read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
(read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
(read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
]
lowerCamelCase_ = [
(read, {'length': SMALL_TEST}),
(read, {'length': SPEED_TEST_N_EXAMPLES}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
(read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print('generating dataset' )
lowerCamelCase_ = datasets.Features(
{'list': datasets.Sequence(datasets.Value('float32' ) ), 'numbers': datasets.Value('float32' )} )
lowerCamelCase_ = generate_example_dataset(
os.path.join(lowercase , 'dataset.arrow' ) , lowercase , num_examples=lowercase , seq_shapes={'list': (1_00,)} , )
print('first set of iterations' )
for func, kwargs in functions:
print(func.__name__ , str(lowercase ) )
lowerCamelCase_ = func(lowercase , **lowercase )
print('shuffling dataset' )
lowerCamelCase_ = dataset.shuffle()
print('Second set of iterations (after shuffling' )
for func, kwargs in functions_shuffled:
print('shuffled ' , func.__name__ , str(lowercase ) )
lowerCamelCase_ = func(
lowercase , **lowercase )
with open(lowercase , 'wb' ) as f:
f.write(json.dumps(lowercase ).encode('utf-8' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 204 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase : str = {
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : List[str] = [
"NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
"NezhaForNextSentencePrediction",
"NezhaForMaskedLM",
"NezhaForPreTraining",
"NezhaForMultipleChoice",
"NezhaForQuestionAnswering",
"NezhaForSequenceClassification",
"NezhaForTokenClassification",
"NezhaModel",
"NezhaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nezha import (
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
else:
import sys
lowerCamelCase : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 204 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( lowercase , lowercase , lowercase ) -> List[str]:
# Initialise PyTorch model
__lowerCAmelCase = BertConfig.from_json_file(lowercase )
print(f'Building PyTorch model from configuration: {config}' )
__lowerCAmelCase = BertForPreTraining(lowercase )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase , lowercase , lowercase )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , lowercase )
if __name__ == "__main__":
_a : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--bert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained BERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
_a : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 358 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def _lowerCAmelCase ( lowercase ) -> List[str]:
__lowerCAmelCase = np.max(lowercase , axis=-1 , keepdims=lowercase )
__lowerCAmelCase = np.exp(outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=lowercase )
class _UpperCAmelCase ( lowerCAmelCase_ ):
def lowerCamelCase__ ( self,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = {}
if "second_text" in kwargs:
__lowerCAmelCase = kwargs["""second_text"""]
return preprocess_kwargs, {}, {}
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
return self.tokenizer(__SCREAMING_SNAKE_CASE,text_pair=__SCREAMING_SNAKE_CASE,return_tensors=self.framework )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.model(**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = model_outputs.logits[0].numpy()
__lowerCAmelCase = softmax(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = np.argmax(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.model.config.idalabel[best_class]
__lowerCAmelCase = probabilities[best_class].item()
__lowerCAmelCase = logits.tolist()
return {"label": label, "score": score, "logits": logits}
| 46 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
a_ : Optional[int] = logging.get_logger(__name__)
a_ : Optional[Any] = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
"""self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
"""self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
"""self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
"""self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
"""self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
"""self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
"""self_attn.rotary_emb""": """encoder.embed_positions""",
"""self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
"""conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
"""conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
"""conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
"""conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
"""conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
"""ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
"""ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
"""ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
"""ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
"""ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
"""ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
"""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""",
}
a_ : Dict = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def __snake_case ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : List[Any] ):
for attribute in key.split("." ):
lowerCamelCase_ = getattr(UpperCAmelCase_ , UpperCAmelCase_ )
if weight_type is not None:
lowerCamelCase_ = getattr(UpperCAmelCase_ , UpperCAmelCase_ ).shape
else:
lowerCamelCase_ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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_ = value
elif weight_type == "weight_g":
lowerCamelCase_ = value
elif weight_type == "weight_v":
lowerCamelCase_ = value
elif weight_type == "bias":
lowerCamelCase_ = value
elif weight_type == "running_mean":
lowerCamelCase_ = value
elif weight_type == "running_var":
lowerCamelCase_ = value
elif weight_type == "num_batches_tracked":
lowerCamelCase_ = value
elif weight_type == "inv_freq":
lowerCamelCase_ = value
else:
lowerCamelCase_ = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def __snake_case ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Any ):
lowerCamelCase_ = []
lowerCamelCase_ = fairseq_model.state_dict()
lowerCamelCase_ = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
lowerCamelCase_ = False
if "conv_layers" in name:
load_conv_layer(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , hf_model.config.feat_extract_norm == "group" , )
lowerCamelCase_ = True
else:
for key, mapped_key in MAPPING.items():
lowerCamelCase_ = """wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
lowerCamelCase_ = True
if "*" in mapped_key:
lowerCamelCase_ = name.split(UpperCAmelCase_ )[0].split("." )[-2]
lowerCamelCase_ = mapped_key.replace("*" , UpperCAmelCase_ )
if "pos_bias_u" in name:
lowerCamelCase_ = None
elif "pos_bias_v" in name:
lowerCamelCase_ = None
elif "weight_g" in name:
lowerCamelCase_ = """weight_g"""
elif "weight_v" in name:
lowerCamelCase_ = """weight_v"""
elif "bias" in name:
lowerCamelCase_ = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowerCamelCase_ = """weight"""
elif "running_mean" in name:
lowerCamelCase_ = """running_mean"""
elif "inv_freq" in name:
lowerCamelCase_ = """inv_freq"""
elif "running_var" in name:
lowerCamelCase_ = """running_var"""
elif "num_batches_tracked" in name:
lowerCamelCase_ = """num_batches_tracked"""
else:
lowerCamelCase_ = None
set_recursively(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
continue
if not is_used:
unused_weights.append(UpperCAmelCase_ )
logger.warning(F'''Unused weights: {unused_weights}''' )
def __snake_case ( UpperCAmelCase_ : Tuple , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Tuple ):
lowerCamelCase_ = full_name.split("conv_layers." )[-1]
lowerCamelCase_ = name.split("." )
lowerCamelCase_ = int(items[0] )
lowerCamelCase_ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
lowerCamelCase_ = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
lowerCamelCase_ = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
lowerCamelCase_ = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
lowerCamelCase_ = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(UpperCAmelCase_ )
@torch.no_grad()
def __snake_case ( UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : int=None , UpperCAmelCase_ : str=None , UpperCAmelCase_ : Tuple=True ):
if config_path is not None:
lowerCamelCase_ = WavaVecaConformerConfig.from_pretrained(UpperCAmelCase_ , hidden_act="swish" )
else:
lowerCamelCase_ = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
lowerCamelCase_ = """rotary"""
if is_finetuned:
if dict_path:
lowerCamelCase_ = Dictionary.load(UpperCAmelCase_ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowerCamelCase_ = target_dict.pad_index
lowerCamelCase_ = target_dict.bos_index
lowerCamelCase_ = target_dict.eos_index
lowerCamelCase_ = len(target_dict.symbols )
lowerCamelCase_ = os.path.join(UpperCAmelCase_ , "vocab.json" )
if not os.path.isdir(UpperCAmelCase_ ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCAmelCase_ ) )
return
os.makedirs(UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
lowerCamelCase_ = target_dict.indices
# fairseq has the <pad> and <s> switched
lowerCamelCase_ = 0
lowerCamelCase_ = 1
with open(UpperCAmelCase_ , "w" , encoding="utf-8" ) as vocab_handle:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCamelCase_ = WavaVecaCTCTokenizer(
UpperCAmelCase_ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="|" , do_lower_case=UpperCAmelCase_ , )
lowerCamelCase_ = True if config.feat_extract_norm == """layer""" else False
lowerCamelCase_ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=UpperCAmelCase_ , return_attention_mask=UpperCAmelCase_ , )
lowerCamelCase_ = WavaVecaProcessor(feature_extractor=UpperCAmelCase_ , tokenizer=UpperCAmelCase_ )
processor.save_pretrained(UpperCAmelCase_ )
lowerCamelCase_ = WavaVecaConformerForCTC(UpperCAmelCase_ )
else:
lowerCamelCase_ = WavaVecaConformerForPreTraining(UpperCAmelCase_ )
if is_finetuned:
lowerCamelCase_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
lowerCamelCase_ = argparse.Namespace(task="audio_pretraining" )
lowerCamelCase_ = fairseq.tasks.setup_task(UpperCAmelCase_ )
lowerCamelCase_ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=UpperCAmelCase_ )
lowerCamelCase_ = model[0].eval()
recursively_load_weights(UpperCAmelCase_ , UpperCAmelCase_ , not is_finetuned )
hf_wavavec.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
a_ : Any = 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_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
a_ : Dict = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 55 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase=2 ,__UpperCAmelCase=True ,__UpperCAmelCase=False ,__UpperCAmelCase=10 ,__UpperCAmelCase=3 ,__UpperCAmelCase=32 * 4 ,__UpperCAmelCase=32 * 6 ,__UpperCAmelCase=4 ,__UpperCAmelCase=32 ,) -> str:
lowerCAmelCase__ : Optional[int] = parent
lowerCAmelCase__ : Optional[int] = batch_size
lowerCAmelCase__ : Optional[int] = is_training
lowerCAmelCase__ : Dict = use_auxiliary_loss
lowerCAmelCase__ : Union[str, Any] = num_queries
lowerCAmelCase__ : str = num_channels
lowerCAmelCase__ : List[str] = min_size
lowerCAmelCase__ : int = max_size
lowerCAmelCase__ : Optional[Any] = num_labels
lowerCAmelCase__ : List[Any] = mask_feature_size
def UpperCAmelCase_ ( self ) -> Tuple:
lowerCAmelCase__ : str = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__UpperCAmelCase )
lowerCAmelCase__ : str = torch.ones([self.batch_size, self.min_size, self.max_size] ,device=__UpperCAmelCase )
lowerCAmelCase__ : Any = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] ,device=__UpperCAmelCase ) > 0.5
).float()
lowerCAmelCase__ : Optional[int] = (torch.rand((self.batch_size, self.num_labels) ,device=__UpperCAmelCase ) > 0.5).long()
lowerCAmelCase__ : Any = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def UpperCAmelCase_ ( self ) -> Dict:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] ,) ,decoder_config=DetrConfig(
decoder_ffn_dim=128 ,num_queries=self.num_queries ,decoder_attention_heads=2 ,d_model=self.mask_feature_size ,) ,mask_feature_size=self.mask_feature_size ,fpn_feature_size=self.mask_feature_size ,num_channels=self.num_channels ,num_labels=self.num_labels ,)
def UpperCAmelCase_ ( self ) -> Optional[int]:
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : List[str] = self.prepare_config_and_inputs()
lowerCAmelCase__ : List[str] = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ) -> Any:
lowerCAmelCase__ : Optional[int] = output.encoder_hidden_states
lowerCAmelCase__ : Optional[int] = output.pixel_decoder_hidden_states
lowerCAmelCase__ : Dict = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__UpperCAmelCase ) ,len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__UpperCAmelCase ) ,len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__UpperCAmelCase ) ,config.decoder_config.decoder_layers )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase=False ) -> Optional[Any]:
with torch.no_grad():
lowerCAmelCase__ : int = MaskFormerModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : str = model(pixel_values=__UpperCAmelCase ,pixel_mask=__UpperCAmelCase )
lowerCAmelCase__ : int = model(__UpperCAmelCase ,output_hidden_states=__UpperCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape ,(self.batch_size, self.num_queries, self.mask_feature_size) ,)
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__UpperCAmelCase ,__UpperCAmelCase )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Optional[int]:
lowerCAmelCase__ : Dict = MaskFormerForInstanceSegmentation(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
def comm_check_on_output(__UpperCAmelCase ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape ,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) ,)
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape ,(self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
lowerCAmelCase__ : List[Any] = model(pixel_values=__UpperCAmelCase ,pixel_mask=__UpperCAmelCase )
lowerCAmelCase__ : Dict = model(__UpperCAmelCase )
comm_check_on_output(__UpperCAmelCase )
lowerCAmelCase__ : Optional[int] = model(
pixel_values=__UpperCAmelCase ,pixel_mask=__UpperCAmelCase ,mask_labels=__UpperCAmelCase ,class_labels=__UpperCAmelCase )
comm_check_on_output(__UpperCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape ,torch.Size([1] ) )
@require_torch
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
'''simple docstring'''
__lowercase : Optional[Any] = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
__lowercase : int = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
__lowercase : Union[str, Any] = False
__lowercase : Dict = False
__lowercase : Tuple = False
__lowercase : List[Any] = False
def UpperCAmelCase_ ( self ) -> Optional[int]:
lowerCAmelCase__ : str = MaskFormerModelTester(self )
lowerCAmelCase__ : List[Any] = ConfigTester(self ,config_class=__UpperCAmelCase ,has_text_modality=__UpperCAmelCase )
def UpperCAmelCase_ ( self ) -> List[str]:
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
lowerCAmelCase__ , lowerCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__UpperCAmelCase ,**__UpperCAmelCase ,output_hidden_states=__UpperCAmelCase )
def UpperCAmelCase_ ( self ) -> Optional[int]:
lowerCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__UpperCAmelCase )
@unittest.skip(reason="""MaskFormer does not use inputs_embeds""" )
def UpperCAmelCase_ ( self ) -> List[Any]:
pass
@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method""" )
def UpperCAmelCase_ ( self ) -> str:
pass
@unittest.skip(reason="""MaskFormer is not a generative model""" )
def UpperCAmelCase_ ( self ) -> Any:
pass
@unittest.skip(reason="""MaskFormer does not use token embeddings""" )
def UpperCAmelCase_ ( self ) -> List[str]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def UpperCAmelCase_ ( self ) -> List[str]:
pass
def UpperCAmelCase_ ( self ) -> Tuple:
lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ : str = model_class(__UpperCAmelCase )
lowerCAmelCase__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ : Dict = [*signature.parameters.keys()]
lowerCAmelCase__ : Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,__UpperCAmelCase )
@slow
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
lowerCAmelCase__ : List[str] = MaskFormerModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ : List[Any] = (self.model_tester.min_size,) * 2
lowerCAmelCase__ : Any = {
"""pixel_values""": torch.randn((2, 3, *size) ,device=__UpperCAmelCase ),
"""mask_labels""": torch.randn((2, 10, *size) ,device=__UpperCAmelCase ),
"""class_labels""": torch.zeros(2 ,10 ,device=__UpperCAmelCase ).long(),
}
lowerCAmelCase__ : Tuple = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = model(**__UpperCAmelCase )
self.assertTrue(outputs.loss is not None )
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__UpperCAmelCase ,**__UpperCAmelCase ,output_hidden_states=__UpperCAmelCase )
def UpperCAmelCase_ ( self ) -> Tuple:
lowerCAmelCase__ , lowerCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ : int = model_class(__UpperCAmelCase ).to(__UpperCAmelCase )
lowerCAmelCase__ : List[Any] = model(**__UpperCAmelCase ,output_attentions=__UpperCAmelCase )
self.assertTrue(outputs.attentions is not None )
def UpperCAmelCase_ ( self ) -> int:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
lowerCAmelCase__ : Dict = self.all_model_classes[1]
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase__ : List[Any] = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.train()
lowerCAmelCase__ : List[str] = model(__UpperCAmelCase ,mask_labels=__UpperCAmelCase ,class_labels=__UpperCAmelCase ).loss
loss.backward()
def UpperCAmelCase_ ( self ) -> List[str]:
# only MaskFormerForInstanceSegmentation has the loss
lowerCAmelCase__ : Tuple = self.all_model_classes[1]
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase__ : Union[str, Any] = True
lowerCAmelCase__ : Tuple = True
lowerCAmelCase__ : Optional[Any] = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.train()
lowerCAmelCase__ : Dict = model(__UpperCAmelCase ,mask_labels=__UpperCAmelCase ,class_labels=__UpperCAmelCase )
lowerCAmelCase__ : Optional[Any] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
lowerCAmelCase__ : str = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
lowerCAmelCase__ : Union[str, Any] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
lowerCAmelCase__ : List[Any] = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__UpperCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowerCAmelCase = 1e-4
def _SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class lowerCAmelCase_( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCAmelCase_ ( self ) -> List[Any]:
return (
MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco""" )
if is_vision_available()
else None
)
def UpperCAmelCase_ ( self ) -> Any:
lowerCAmelCase__ : Any = MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco""" ).to(__UpperCAmelCase )
lowerCAmelCase__ : str = self.default_image_processor
lowerCAmelCase__ : str = prepare_img()
lowerCAmelCase__ : Optional[int] = image_processor(__UpperCAmelCase ,return_tensors="""pt""" ).to(__UpperCAmelCase )
lowerCAmelCase__ : Dict = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__UpperCAmelCase ,(1, 3, 800, 1088) )
with torch.no_grad():
lowerCAmelCase__ : Union[str, Any] = model(**__UpperCAmelCase )
lowerCAmelCase__ : Optional[Any] = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(__UpperCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] ,__UpperCAmelCase ,atol=__UpperCAmelCase ) )
lowerCAmelCase__ : Dict = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(__UpperCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,__UpperCAmelCase ,atol=__UpperCAmelCase ) )
lowerCAmelCase__ : Optional[int] = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(__UpperCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,__UpperCAmelCase ,atol=__UpperCAmelCase ) )
def UpperCAmelCase_ ( self ) -> Optional[Any]:
lowerCAmelCase__ : List[Any] = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(__UpperCAmelCase )
.eval()
)
lowerCAmelCase__ : Optional[Any] = self.default_image_processor
lowerCAmelCase__ : List[str] = prepare_img()
lowerCAmelCase__ : str = image_processor(__UpperCAmelCase ,return_tensors="""pt""" ).to(__UpperCAmelCase )
lowerCAmelCase__ : Optional[int] = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__UpperCAmelCase ,(1, 3, 800, 1088) )
with torch.no_grad():
lowerCAmelCase__ : List[Any] = model(**__UpperCAmelCase )
# masks_queries_logits
lowerCAmelCase__ : Optional[int] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape ,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ,)
lowerCAmelCase__ : Optional[int] = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
lowerCAmelCase__ : Optional[int] = torch.tensor(__UpperCAmelCase ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,__UpperCAmelCase ,atol=__UpperCAmelCase ) )
# class_queries_logits
lowerCAmelCase__ : Tuple = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
lowerCAmelCase__ : Union[str, Any] = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,__UpperCAmelCase ,atol=__UpperCAmelCase ) )
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ : List[Any] = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff""" )
.to(__UpperCAmelCase )
.eval()
)
lowerCAmelCase__ : Optional[Any] = self.default_image_processor
lowerCAmelCase__ : int = prepare_img()
lowerCAmelCase__ : Optional[Any] = image_processor(__UpperCAmelCase ,return_tensors="""pt""" ).to(__UpperCAmelCase )
lowerCAmelCase__ : str = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__UpperCAmelCase ,(1, 3, 800, 1088) )
with torch.no_grad():
lowerCAmelCase__ : str = model(**__UpperCAmelCase )
# masks_queries_logits
lowerCAmelCase__ : Optional[Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape ,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ,)
lowerCAmelCase__ : int = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
lowerCAmelCase__ : List[str] = torch.tensor(__UpperCAmelCase ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,__UpperCAmelCase ,atol=__UpperCAmelCase ) )
# class_queries_logits
lowerCAmelCase__ : Optional[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
lowerCAmelCase__ : Tuple = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,__UpperCAmelCase ,atol=__UpperCAmelCase ) )
def UpperCAmelCase_ ( self ) -> Optional[Any]:
lowerCAmelCase__ : str = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(__UpperCAmelCase )
.eval()
)
lowerCAmelCase__ : Dict = self.default_image_processor
lowerCAmelCase__ : Union[str, Any] = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] ,segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] ,return_tensors="""pt""" ,)
lowerCAmelCase__ : Tuple = inputs["""pixel_values"""].to(__UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = [el.to(__UpperCAmelCase ) for el in inputs["""mask_labels"""]]
lowerCAmelCase__ : Union[str, Any] = [el.to(__UpperCAmelCase ) for el in inputs["""class_labels"""]]
with torch.no_grad():
lowerCAmelCase__ : Any = model(**__UpperCAmelCase )
self.assertTrue(outputs.loss is not None )
| 37 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowerCamelCase = ['''image_processor''', '''tokenizer''']
_lowerCamelCase = '''ChineseCLIPImageProcessor'''
_lowerCamelCase = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self ,lowerCamelCase_=None ,lowerCamelCase_=None ,**lowerCamelCase_ ) -> Optional[Any]:
A = 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_ ,)
A = kwargs.pop("""feature_extractor""" )
A = 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_ )
A = self.image_processor
def __call__( self ,lowerCamelCase_=None ,lowerCamelCase_=None ,lowerCamelCase_=None ,**lowerCamelCase_ ) -> str:
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:
A = self.tokenizer(lowerCamelCase_ ,return_tensors=lowerCamelCase_ ,**lowerCamelCase_ )
if images is not None:
A = self.image_processor(lowerCamelCase_ ,return_tensors=lowerCamelCase_ ,**lowerCamelCase_ )
if text is not None and images is not None:
A = 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 ,*lowerCamelCase_ ,**lowerCamelCase_ ) -> Any:
return self.tokenizer.batch_decode(*lowerCamelCase_ ,**lowerCamelCase_ )
def UpperCamelCase__ ( self ,*lowerCamelCase_ ,**lowerCamelCase_ ) -> int:
return self.tokenizer.decode(*lowerCamelCase_ ,**lowerCamelCase_ )
@property
def UpperCamelCase__ ( self ) -> Dict:
A = self.tokenizer.model_input_names
A = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCamelCase__ ( self ) -> Optional[Any]:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,lowerCamelCase_ ,)
return self.image_processor_class
| 371 |
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCAmelCase =argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--txt2img_unclip",
default="kakaobrain/karlo-v1-alpha",
type=str,
required=False,
help="The pretrained txt2img unclip.",
)
UpperCAmelCase =parser.parse_args()
UpperCAmelCase =UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
UpperCAmelCase =CLIPImageProcessor()
UpperCAmelCase =CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
UpperCAmelCase =UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 77 | 0 |
'''simple docstring'''
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 MobileViTImageProcessor
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Tuple , lowerCAmelCase__ : str , lowerCAmelCase__ : Tuple=7 , lowerCAmelCase__ : Optional[int]=3 , lowerCAmelCase__ : Dict=1_8 , lowerCAmelCase__ : List[Any]=3_0 , lowerCAmelCase__ : Tuple=4_0_0 , lowerCAmelCase__ : int=True , lowerCAmelCase__ : List[Any]=None , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : Union[str, Any]=None , lowerCAmelCase__ : Any=True , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = size if size is not None else {"shortest_edge": 2_0}
__SCREAMING_SNAKE_CASE : Optional[Any] = crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
__SCREAMING_SNAKE_CASE : Tuple = parent
__SCREAMING_SNAKE_CASE : Union[str, Any] = batch_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = num_channels
__SCREAMING_SNAKE_CASE : Tuple = image_size
__SCREAMING_SNAKE_CASE : List[str] = min_resolution
__SCREAMING_SNAKE_CASE : Union[str, Any] = max_resolution
__SCREAMING_SNAKE_CASE : int = do_resize
__SCREAMING_SNAKE_CASE : Optional[int] = size
__SCREAMING_SNAKE_CASE : int = do_center_crop
__SCREAMING_SNAKE_CASE : int = crop_size
__SCREAMING_SNAKE_CASE : List[Any] = do_flip_channel_order
def UpperCamelCase__ ( self : List[Any] ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class _UpperCamelCase ( __lowercase , unittest.TestCase ):
'''simple docstring'''
_A : Any = MobileViTImageProcessor if is_vision_available() else None
def UpperCamelCase__ ( self : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = MobileViTImageProcessingTester(self )
@property
def UpperCamelCase__ ( self : Dict ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase__ ( self : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , """do_resize""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """size""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """do_center_crop""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """center_crop""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """do_flip_channel_order""" ) )
def UpperCamelCase__ ( self : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 2_0} )
self.assertEqual(image_processor.crop_size , {"""height""": 1_8, """width""": 1_8} )
__SCREAMING_SNAKE_CASE : List[str] = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {"""shortest_edge""": 4_2} )
self.assertEqual(image_processor.crop_size , {"""height""": 8_4, """width""": 8_4} )
def UpperCamelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : Tuple = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : Tuple = image_processing(lowerCAmelCase__ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def UpperCamelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(lowerCAmelCase__ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def UpperCamelCase__ ( self : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(lowerCAmelCase__ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
| 112 |
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float) -> tuple[float, float]:
'''simple docstring'''
if not all(isinstance(_lowerCamelCase , (float, int)) for val in locals().values()):
__UpperCamelCase : str = F'Input values must either be float or int: {list(locals().values())}'
raise TypeError(_lowerCamelCase)
__UpperCamelCase : List[str] = ((x * distance) / (z + distance)) * scale
__UpperCamelCase : List[Any] = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : str , _lowerCamelCase : float) -> tuple[float, float, float]:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase):
raise TypeError("Axis must be a str")
__UpperCamelCase : str = locals()
del input_variables["axis"]
if not all(isinstance(_lowerCamelCase , (float, int)) for val in input_variables.values()):
__UpperCamelCase : Dict = (
"Input values except axis must either be float or int: "
F'{list(input_variables.values())}'
)
raise TypeError(_lowerCamelCase)
__UpperCamelCase : Optional[Any] = (angle % 360) / 450 * 180 / math.pi
if axis == "z":
__UpperCamelCase : Tuple = x * math.cos(_lowerCamelCase) - y * math.sin(_lowerCamelCase)
__UpperCamelCase : Union[str, Any] = y * math.cos(_lowerCamelCase) + x * math.sin(_lowerCamelCase)
__UpperCamelCase : Any = z
elif axis == "x":
__UpperCamelCase : Dict = y * math.cos(_lowerCamelCase) - z * math.sin(_lowerCamelCase)
__UpperCamelCase : Any = z * math.cos(_lowerCamelCase) + y * math.sin(_lowerCamelCase)
__UpperCamelCase : List[str] = x
elif axis == "y":
__UpperCamelCase : Any = x * math.cos(_lowerCamelCase) - z * math.sin(_lowerCamelCase)
__UpperCamelCase : Any = z * math.cos(_lowerCamelCase) + x * math.sin(_lowerCamelCase)
__UpperCamelCase : Dict = y
else:
raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'")
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{convert_to_ad(1.0, 2.0, 3.0, 1_0.0, 1_0.0) = }")
print(f"{rotate(1.0, 2.0, 3.0, 'y', 9_0.0) = }")
| 232 | 0 |
from math import factorial
lowerCAmelCase_ = {str(d): factorial(d) for d in range(10)}
def snake_case( __magic_name__ ) -> int:
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(__magic_name__ ) )
def snake_case( ) -> int:
'''simple docstring'''
lowercase : Optional[Any] = 7 * factorial(9 ) + 1
return sum(i for i in range(3 , __magic_name__ ) if sum_of_digit_factorial(__magic_name__ ) == i )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 116 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class _A ( tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__( self : str , _A : float , _A : Callable , _A : int , _A : float = 1.0 , _A : str = None , ) -> List[str]:
"""simple docstring"""
super().__init__()
lowercase : List[str] = initial_learning_rate
lowercase : List[str] = warmup_steps
lowercase : Tuple = power
lowercase : Any = decay_schedule_fn
lowercase : Union[str, Any] = name
def __call__( self : str , _A : Any ) -> Optional[int]:
"""simple docstring"""
with tf.name_scope(self.name or '''WarmUp''' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
lowercase : List[Any] = tf.cast(_A , tf.floataa )
lowercase : Union[str, Any] = tf.cast(self.warmup_steps , tf.floataa )
lowercase : List[str] = global_step_float / warmup_steps_float
lowercase : int = self.initial_learning_rate * tf.math.pow(_A , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=_A , )
def __a ( self : Dict ) -> List[str]:
"""simple docstring"""
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = 0.0 , __magic_name__ = 0.9 , __magic_name__ = 0.9_9_9 , __magic_name__ = 1e-8 , __magic_name__ = None , __magic_name__ = None , __magic_name__ = 0.0 , __magic_name__ = 1.0 , __magic_name__ = None , ) -> int:
'''simple docstring'''
lowercase : List[Any] = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=__magic_name__ , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=__magic_name__ , )
if num_warmup_steps:
lowercase : Optional[int] = WarmUp(
initial_learning_rate=__magic_name__ , decay_schedule_fn=__magic_name__ , warmup_steps=__magic_name__ , )
if weight_decay_rate > 0.0:
lowercase : Optional[Any] = AdamWeightDecay(
learning_rate=__magic_name__ , weight_decay_rate=__magic_name__ , beta_a=__magic_name__ , beta_a=__magic_name__ , epsilon=__magic_name__ , clipnorm=__magic_name__ , global_clipnorm=__magic_name__ , exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''] , include_in_weight_decay=__magic_name__ , )
else:
lowercase : str = tf.keras.optimizers.Adam(
learning_rate=__magic_name__ , beta_a=__magic_name__ , beta_a=__magic_name__ , epsilon=__magic_name__ , clipnorm=__magic_name__ , global_clipnorm=__magic_name__ , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class _A ( _lowerCamelCase ):
def __init__( self : Optional[Any] , _A : Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001 , _A : float = 0.9 , _A : float = 0.999 , _A : float = 1E-7 , _A : bool = False , _A : float = 0.0 , _A : Optional[List[str]] = None , _A : Optional[List[str]] = None , _A : str = "AdamWeightDecay" , **_A : Any , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(_A , _A , _A , _A , _A , _A , **_A )
lowercase : Tuple = weight_decay_rate
lowercase : List[str] = include_in_weight_decay
lowercase : Optional[Any] = exclude_from_weight_decay
@classmethod
def __a ( cls : Tuple , _A : Tuple ) -> List[str]:
"""simple docstring"""
lowercase : Optional[int] = {'''WarmUp''': WarmUp}
return super(_A , cls ).from_config(_A , custom_objects=_A )
def __a ( self : Dict , _A : Tuple , _A : Dict , _A : Tuple ) -> Tuple:
"""simple docstring"""
super(_A , self )._prepare_local(_A , _A , _A )
lowercase : List[Any] = tf.constant(
self.weight_decay_rate , name='''adam_weight_decay_rate''' )
def __a ( self : Tuple , _A : Optional[int] , _A : Union[str, Any] , _A : List[Any] ) -> Any:
"""simple docstring"""
lowercase : str = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate'''] , use_locking=self._use_locking , )
return tf.no_op()
def __a ( self : Union[str, Any] , _A : Any , _A : Optional[Any]=None , **_A : Tuple ) -> Any:
"""simple docstring"""
lowercase , lowercase : Tuple = list(zip(*_A ) )
return super(_A , self ).apply_gradients(zip(_A , _A ) , name=_A , **_A )
def __a ( self : List[Any] , _A : Optional[Any] , _A : str , _A : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
lowercase : Any = apply_state or {}
lowercase : str = apply_state.get((var_device, var_dtype) )
if coefficients is None:
lowercase : List[Any] = self._fallback_apply_state(_A , _A )
lowercase : List[Any] = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def __a ( self : Tuple , _A : Union[str, Any] , _A : Tuple , _A : str=None ) -> Optional[int]:
"""simple docstring"""
lowercase , lowercase : List[str] = self._get_lr(var.device , var.dtype.base_dtype , _A )
lowercase : Optional[Any] = self._decay_weights_op(_A , _A , _A )
with tf.control_dependencies([decay] ):
return super(_A , self )._resource_apply_dense(_A , _A , **_A )
def __a ( self : Optional[int] , _A : List[Any] , _A : Dict , _A : Union[str, Any] , _A : str=None ) -> Optional[int]:
"""simple docstring"""
lowercase , lowercase : Optional[int] = self._get_lr(var.device , var.dtype.base_dtype , _A )
lowercase : str = self._decay_weights_op(_A , _A , _A )
with tf.control_dependencies([decay] ):
return super(_A , self )._resource_apply_sparse(_A , _A , _A , **_A )
def __a ( self : List[Any] ) -> str:
"""simple docstring"""
lowercase : Optional[Any] = super().get_config()
config.update({'''weight_decay_rate''': self.weight_decay_rate} )
return config
def __a ( self : str , _A : Optional[int] ) -> Tuple:
"""simple docstring"""
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(_A , _A ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(_A , _A ) is not None:
return False
return True
class _A ( _lowerCamelCase ):
def __init__( self : List[Any] ) -> str:
"""simple docstring"""
lowercase : Optional[Any] = []
lowercase : str = None
@property
def __a ( self : Any ) -> int:
"""simple docstring"""
if self._accum_steps is None:
lowercase : Optional[Any] = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=_A , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def __a ( self : int ) -> List[Any]:
"""simple docstring"""
if not self._gradients:
raise ValueError('''The accumulator should be called first to initialize the gradients''' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self : str , _A : int ) -> str:
"""simple docstring"""
if not self._gradients:
lowercase : Optional[Any] = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(_A ) , trainable=_A , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(_A ) != len(self._gradients ):
raise ValueError(f"""Expected {len(self._gradients )} gradients, but got {len(_A )}""" )
for accum_gradient, gradient in zip(self._gradients , _A ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(_A )
self._accum_steps.assign_add(1 )
def __a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(_A ) )
| 116 | 1 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class SCREAMING_SNAKE_CASE__ :
def __init__( self , a , a=2 , a=32 , a=16 , a=3 , a=True , a=True , a=32 , a=4 , a=[0, 1, 2, 3] , a=4 , a=37 , a="gelu" , a=0.1 , a=0.1 , a=0.02 , a=3 , a=[1, 384, 24, 24] , a=True , a=None , ):
lowercase__ : Tuple = parent
lowercase__ : Optional[int] = batch_size
lowercase__ : Optional[int] = image_size
lowercase__ : str = patch_size
lowercase__ : List[Any] = num_channels
lowercase__ : Any = is_training
lowercase__ : str = use_labels
lowercase__ : int = hidden_size
lowercase__ : int = num_hidden_layers
lowercase__ : Tuple = backbone_out_indices
lowercase__ : Union[str, Any] = num_attention_heads
lowercase__ : List[Any] = intermediate_size
lowercase__ : List[Any] = hidden_act
lowercase__ : Dict = hidden_dropout_prob
lowercase__ : int = attention_probs_dropout_prob
lowercase__ : Any = initializer_range
lowercase__ : List[str] = num_labels
lowercase__ : Optional[Any] = backbone_featmap_shape
lowercase__ : int = scope
lowercase__ : Any = is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
lowercase__ : Dict = (image_size // patch_size) ** 2
lowercase__ : List[Any] = num_patches + 1
def snake_case_ ( self):
lowercase__ : Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
lowercase__ : Optional[Any] = None
if self.use_labels:
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels)
lowercase__ : List[str] = self.get_config()
return config, pixel_values, labels
def snake_case_ ( self):
lowercase__ : List[str] = {
'global_padding': 'same',
'layer_type': 'bottleneck',
'depths': [3, 4, 9],
'out_features': ['stage1', 'stage2', 'stage3'],
'embedding_dynamic_padding': True,
'hidden_sizes': [96, 192, 384, 768],
'num_groups': 2,
}
return DPTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , backbone_out_indices=self.backbone_out_indices , 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 , is_decoder=a , initializer_range=self.initializer_range , is_hybrid=self.is_hybrid , backbone_config=a , backbone_featmap_shape=self.backbone_featmap_shape , )
def snake_case_ ( self , a , a , a):
lowercase__ : int = DPTModel(config=a)
model.to(a)
model.eval()
lowercase__ : Any = model(a)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def snake_case_ ( self , a , a , a):
lowercase__ : str = self.num_labels
lowercase__ : int = DPTForDepthEstimation(a)
model.to(a)
model.eval()
lowercase__ : int = model(a)
self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size))
def snake_case_ ( self , a , a , a):
lowercase__ : str = self.num_labels
lowercase__ : Optional[int] = DPTForSemanticSegmentation(a)
model.to(a)
model.eval()
lowercase__ : Any = model(a , labels=a)
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size))
def snake_case_ ( self):
lowercase__ : Tuple = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Optional[int] = config_and_inputs
lowercase__ : int = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ (__snake_case , __snake_case , unittest.TestCase ):
__lowerCamelCase : Optional[Any] = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
__lowerCamelCase : List[Any] = (
{
"""depth-estimation""": DPTForDepthEstimation,
"""feature-extraction""": DPTModel,
"""image-segmentation""": DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__lowerCamelCase : Any = False
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : List[Any] = False
def snake_case_ ( self):
lowercase__ : List[str] = DPTModelTester(self)
lowercase__ : int = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=37)
def snake_case_ ( self):
self.config_tester.run_common_tests()
@unittest.skip(reason='DPT does not use inputs_embeds')
def snake_case_ ( self):
pass
def snake_case_ ( self):
lowercase__ , lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
lowercase__ : List[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear))
def snake_case_ ( self):
lowercase__ , lowercase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a)
lowercase__ : Any = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Any = [*signature.parameters.keys()]
lowercase__ : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , a)
def snake_case_ ( self):
lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a)
def snake_case_ ( self):
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*a)
def snake_case_ ( self):
lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*a)
def snake_case_ ( self):
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
lowercase__ , lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Optional[Any] = True
if model_class in get_values(a):
continue
lowercase__ : int = model_class(a)
model.to(a)
model.train()
lowercase__ : Tuple = self._prepare_for_class(a , a , return_labels=a)
lowercase__ : str = model(**a).loss
loss.backward()
def snake_case_ ( self):
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[str] = False
lowercase__ : List[Any] = True
if model_class in get_values(a) or not model_class.supports_gradient_checkpointing:
continue
lowercase__ : Optional[int] = model_class(a)
model.to(a)
model.gradient_checkpointing_enable()
model.train()
lowercase__ : Any = self._prepare_for_class(a , a , return_labels=a)
lowercase__ : Any = model(**a).loss
loss.backward()
def snake_case_ ( self):
lowercase__ , lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Optional[Any] = _config_zero_init(a)
for model_class in self.all_model_classes:
lowercase__ : List[Any] = model_class(config=a)
# Skip the check for the backbone
lowercase__ : Optional[int] = []
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
lowercase__ : Optional[int] = [f"""{name}.{key}""" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
def snake_case_ ( self):
pass
@slow
def snake_case_ ( self):
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
lowercase__ : Union[str, Any] = DPTModel.from_pretrained(a)
self.assertIsNotNone(a)
def snake_case_ ( self):
# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
lowercase__ , lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Optional[Any] = 'add'
with self.assertRaises(a):
lowercase__ : int = DPTForDepthEstimation(a)
def snake_case__ ( ):
'''simple docstring'''
lowercase__ : Tuple = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
@slow
class SCREAMING_SNAKE_CASE__ (unittest.TestCase ):
def snake_case_ ( self):
lowercase__ : Union[str, Any] = DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas')
lowercase__ : Optional[int] = DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas').to(a)
lowercase__ : Optional[int] = prepare_img()
lowercase__ : str = image_processor(images=a , return_tensors='pt').to(a)
# forward pass
with torch.no_grad():
lowercase__ : Dict = model(**a)
lowercase__ : Optional[Any] = outputs.predicted_depth
# verify the predicted depth
lowercase__ : List[Any] = torch.Size((1, 384, 384))
self.assertEqual(predicted_depth.shape , a)
lowercase__ : Optional[int] = torch.tensor(
[[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]).to(a)
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100 , a , atol=1e-4))
| 214 |
def snake_case__ ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError('String lengths must match!' )
lowercase__ : Union[str, Any] = 0
for chara, chara in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 214 | 1 |
from math import log
from scipy.constants import Boltzmann, physical_constants
lowercase : List[Any] = 3_0_0 # TEMPERATURE (unit = K)
def A_ ( A__ , A__ , A__ , ) -> float:
if donor_conc <= 0:
raise ValueError('Donor concentration should be positive' )
elif acceptor_conc <= 0:
raise ValueError('Acceptor concentration should be positive' )
elif intrinsic_conc <= 0:
raise ValueError('Intrinsic concentration should be positive' )
elif donor_conc <= intrinsic_conc:
raise ValueError(
'Donor concentration should be greater than intrinsic concentration' )
elif acceptor_conc <= intrinsic_conc:
raise ValueError(
'Acceptor concentration should be greater than intrinsic concentration' )
else:
return (
Boltzmann
* T
* log((donor_conc * acceptor_conc) / intrinsic_conc**2 )
/ physical_constants["electron volt"][0]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 364 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowercase : Optional[Any] = """"""
lowercase : int = """"""
lowercase : List[Any] = """"""
lowercase : Optional[int] = 1 # (0 is vertical, 1 is horizontal)
def A_ ( ) -> None:
a__ , a__ : str = get_dataset(A__ , A__ )
print('Processing...' )
a__ , a__ , a__ : Tuple = update_image_and_anno(A__ , A__ , A__ )
for index, image in enumerate(A__ ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
a__ : int = random_chars(32 )
a__ : Optional[Any] = paths[index].split(os.sep )[-1].rsplit('.' , 1 )[0]
a__ : Optional[int] = F'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
cva.imwrite(F'/{file_root}.jpg' , A__ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F'Success {index+1}/{len(A__ )} with {file_name}' )
a__ : List[str] = []
for anno in new_annos[index]:
a__ : Union[str, Any] = F'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
annos_list.append(A__ )
with open(F'/{file_root}.txt' , 'w' ) as outfile:
outfile.write('\n'.join(line for line in annos_list ) )
def A_ ( A__ , A__ ) -> tuple[list, list]:
a__ : int = []
a__ : int = []
for label_file in glob.glob(os.path.join(A__ , '*.txt' ) ):
a__ : Optional[Any] = label_file.split(os.sep )[-1].rsplit('.' , 1 )[0]
with open(A__ ) as in_file:
a__ : Tuple = in_file.readlines()
a__ : Dict = os.path.join(A__ , F'{label_name}.jpg' )
a__ : int = []
for obj_list in obj_lists:
a__ : Union[str, Any] = obj_list.rstrip('\n' ).split(' ' )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(A__ )
labels.append(A__ )
return img_paths, labels
def A_ ( A__ , A__ , A__ = 1 ) -> tuple[list, list, list]:
a__ : Optional[int] = []
a__ : Any = []
a__ : Dict = []
for idx in range(len(A__ ) ):
a__ : Optional[int] = []
a__ : Optional[Any] = img_list[idx]
path_list.append(A__ )
a__ : Union[str, Any] = anno_list[idx]
a__ : List[str] = cva.imread(A__ )
if flip_type == 1:
a__ : List[str] = cva.flip(A__ , A__ )
for bbox in img_annos:
a__ : Optional[Any] = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
a__ : Optional[Any] = cva.flip(A__ , A__ )
for bbox in img_annos:
a__ : Optional[int] = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(A__ )
new_imgs_list.append(A__ )
return new_imgs_list, new_annos_lists, path_list
def A_ ( A__ = 32 ) -> str:
assert number_char > 1, "The number of character should greater than 1"
a__ : Optional[int] = ascii_lowercase + digits
return "".join(random.choice(A__ ) for _ in range(A__ ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 225 | 0 |
from __future__ import annotations
def snake_case__ ( SCREAMING_SNAKE_CASE_ : list[int] ):
'''simple docstring'''
lowercase__ : Optional[Any] = len(UpperCAmelCase_ ) // 2
# choose the middle 3 elements
lowercase__ : Dict = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 214 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
snake_case : List[str] = logging.get_logger(__name__)
snake_case : int = {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 'blenderbot-small'
SCREAMING_SNAKE_CASE__ = ['past_key_values']
SCREAMING_SNAKE_CASE__ = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , _lowerCamelCase=5_0265 , _lowerCamelCase=512 , _lowerCamelCase=8 , _lowerCamelCase=2048 , _lowerCamelCase=16 , _lowerCamelCase=8 , _lowerCamelCase=2048 , _lowerCamelCase=16 , _lowerCamelCase=0.0 , _lowerCamelCase=0.0 , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase="gelu" , _lowerCamelCase=512 , _lowerCamelCase=0.1 , _lowerCamelCase=0.0 , _lowerCamelCase=0.0 , _lowerCamelCase=0.02 , _lowerCamelCase=1 , _lowerCamelCase=False , _lowerCamelCase=0 , _lowerCamelCase=1 , _lowerCamelCase=2 , _lowerCamelCase=2 , **_lowerCamelCase , ):
a :Dict = vocab_size
a :Optional[Any] = max_position_embeddings
a :str = d_model
a :Any = encoder_ffn_dim
a :Optional[int] = encoder_layers
a :List[str] = encoder_attention_heads
a :List[str] = decoder_ffn_dim
a :Optional[int] = decoder_layers
a :str = decoder_attention_heads
a :List[str] = dropout
a :Optional[int] = attention_dropout
a :Dict = activation_dropout
a :List[str] = activation_function
a :List[Any] = init_std
a :Optional[int] = encoder_layerdrop
a :Tuple = decoder_layerdrop
a :List[str] = use_cache
a :int = encoder_layers
a :Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , is_encoder_decoder=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , forced_eos_token_id=_lowerCamelCase , **_lowerCamelCase , )
class _snake_case ( _snake_case ):
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self.task in ["default", "seq2seq-lm"]:
a :Optional[Any] = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
a :Union[str, Any] = {0: '''batch'''}
a :Tuple = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
a :Optional[int] = {0: '''batch''', 1: '''decoder_sequence'''}
a :str = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(_lowerCamelCase , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
a :Optional[int] = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
a , a :str = self.num_layers
for i in range(_lowerCamelCase ):
a :List[Any] = {0: '''batch''', 2: '''past_sequence + sequence'''}
a :List[str] = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
a :Optional[int] = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self.task in ["default", "seq2seq-lm"]:
a :List[Any] = super().outputs
else:
a :Union[str, Any] = super(_lowerCamelCase , self ).outputs
if self.use_past:
a , a :int = self.num_layers
for i in range(_lowerCamelCase ):
a :int = {0: '''batch''', 2: '''past_sequence + sequence'''}
a :Optional[Any] = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = -1 , _lowerCamelCase = -1 , _lowerCamelCase = False , _lowerCamelCase = None , ):
a :Tuple = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# Generate decoder inputs
a :Dict = seq_length if not self.use_past else 1
a :Dict = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
a :List[Any] = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
a :List[str] = dict(**_lowerCamelCase , **_lowerCamelCase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
a , a :Optional[Any] = common_inputs['''input_ids'''].shape
a :Tuple = common_inputs['''decoder_input_ids'''].shape[1]
a , a :List[Any] = self.num_attention_heads
a :List[Any] = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
a :int = decoder_seq_length + 3
a :Union[str, Any] = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
a :Union[str, Any] = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCamelCase , _lowerCamelCase )] , dim=1 )
a :List[Any] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
a , a :Optional[int] = self.num_layers
a :str = min(_lowerCamelCase , _lowerCamelCase )
a :str = max(_lowerCamelCase , _lowerCamelCase ) - min_num_layers
a :Tuple = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(_lowerCamelCase ):
common_inputs["past_key_values"].append(
(
torch.zeros(_lowerCamelCase ),
torch.zeros(_lowerCamelCase ),
torch.zeros(_lowerCamelCase ),
torch.zeros(_lowerCamelCase ),
) )
# TODO: test this.
a :int = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(_lowerCamelCase , _lowerCamelCase ):
common_inputs["past_key_values"].append((torch.zeros(_lowerCamelCase ), torch.zeros(_lowerCamelCase )) )
return common_inputs
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = -1 , _lowerCamelCase = -1 , _lowerCamelCase = False , _lowerCamelCase = None , ):
a :Tuple = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
a , a :Dict = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
a :Optional[int] = seqlen + 2
a , a :Union[str, Any] = self.num_layers
a , a :Optional[Any] = self.num_attention_heads
a :str = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
a :Tuple = common_inputs['''attention_mask'''].dtype
a :Any = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(_lowerCamelCase , _lowerCamelCase , dtype=_lowerCamelCase )] , dim=1 )
a :Any = [
(torch.zeros(_lowerCamelCase ), torch.zeros(_lowerCamelCase )) for _ in range(_lowerCamelCase )
]
return common_inputs
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = -1 , _lowerCamelCase = -1 , _lowerCamelCase = False , _lowerCamelCase = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
a :Optional[Any] = compute_effective_axis_dimension(
_lowerCamelCase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
a :Optional[int] = tokenizer.num_special_tokens_to_add(_lowerCamelCase )
a :Tuple = compute_effective_axis_dimension(
_lowerCamelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=_lowerCamelCase )
# Generate dummy inputs according to compute batch and sequence
a :List[str] = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
a :Dict = dict(tokenizer(_lowerCamelCase , return_tensors=_lowerCamelCase ) )
return common_inputs
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = -1 , _lowerCamelCase = -1 , _lowerCamelCase = False , _lowerCamelCase = None , ):
if self.task in ["default", "seq2seq-lm"]:
a :Tuple = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
_lowerCamelCase , batch_size=_lowerCamelCase , seq_length=_lowerCamelCase , is_pair=_lowerCamelCase , framework=_lowerCamelCase )
elif self.task == "causal-lm":
a :Dict = self._generate_dummy_inputs_for_causal_lm(
_lowerCamelCase , batch_size=_lowerCamelCase , seq_length=_lowerCamelCase , is_pair=_lowerCamelCase , framework=_lowerCamelCase )
else:
a :Dict = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_lowerCamelCase , batch_size=_lowerCamelCase , seq_length=_lowerCamelCase , is_pair=_lowerCamelCase , framework=_lowerCamelCase )
return common_inputs
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if self.task in ["default", "seq2seq-lm"]:
a :Optional[int] = super()._flatten_past_key_values_(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
else:
a :Any = super(_lowerCamelCase , self )._flatten_past_key_values_(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
| 94 | 0 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCamelCase_ ( ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase_ : Tuple = {
'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
}
lowerCAmelCase_ : Dict = Dataset.from_dict(lowerCAmelCase__ )
return dataset
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self : str ):
lowerCAmelCase_ : List[str] = get_dataset()
lowerCAmelCase_ : str = make_duplicate_clusters(SCREAMING_SNAKE_CASE_ , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
lowerCAmelCase_ : str = get_dataset()
lowerCAmelCase_ : Union[str, Any] = deduplicate_dataset(SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 2 )
print(SCREAMING_SNAKE_CASE_ )
self.assertEqual(duplicate_clusters[0][0]['copies'] , 2 )
self.assertEqual(duplicate_clusters[0][0]['is_extreme'] , SCREAMING_SNAKE_CASE_ )
| 350 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowercase__ : Optional[int] = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
"""processing_trocr""": ["""TrOCRProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Dict = [
"""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__ : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 289 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _UpperCAmelCase ( snake_case__ ):
'''simple docstring'''
def __init__(self , a_ , a_ ):
'''simple docstring'''
__snake_case : Any = params
__snake_case : Dict = np.array(UpperCAmelCase_ )
__snake_case : List[Any] = np.array([len(UpperCAmelCase_ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__(self , a_ ):
'''simple docstring'''
return (self.token_ids[index], self.lengths[index])
def __len__(self ):
'''simple docstring'''
return len(self.lengths )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : List[Any] = self.params.max_model_input_size
__snake_case : List[Any] = self.lengths > max_len
logger.info(f"""Splitting {sum(UpperCAmelCase_ )} too long sequences.""" )
def divide_chunks(a_ , a_ ):
return [l[i : i + n] for i in range(0 , len(UpperCAmelCase_ ) , UpperCAmelCase_ )]
__snake_case : Dict = []
__snake_case : List[Any] = []
if self.params.mlm:
__snake_case , __snake_case : str = self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
else:
__snake_case , __snake_case : int = self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
__snake_case : Union[str, Any] = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
__snake_case : List[Any] = np.insert(UpperCAmelCase_ , 0 , UpperCAmelCase_ )
if sub_s[-1] != sep_id:
__snake_case : List[str] = np.insert(UpperCAmelCase_ , len(UpperCAmelCase_ ) , UpperCAmelCase_ )
assert len(UpperCAmelCase_ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(UpperCAmelCase_ )
new_tok_ids.extend(UpperCAmelCase_ )
new_lengths.extend([len(UpperCAmelCase_ ) for l in sub_seqs] )
__snake_case : Union[str, Any] = np.array(UpperCAmelCase_ )
__snake_case : Union[str, Any] = np.array(UpperCAmelCase_ )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : List[Any] = len(self )
__snake_case : List[Any] = self.lengths > 11
__snake_case : Optional[int] = self.token_ids[indices]
__snake_case : str = self.lengths[indices]
__snake_case : Union[str, Any] = len(self )
logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences.""" )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
if "unk_token" not in self.params.special_tok_ids:
return
else:
__snake_case : List[str] = self.params.special_tok_ids['''unk_token''']
__snake_case : str = len(self )
__snake_case : Any = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
__snake_case : List[str] = (unk_occs / self.lengths) < 0.5
__snake_case : Optional[Any] = self.token_ids[indices]
__snake_case : int = self.lengths[indices]
__snake_case : Optional[int] = len(self )
logger.info(f"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).""" )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
if not self.params.is_master:
return
logger.info(f"""{len(self )} sequences""" )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
__snake_case : List[str] = [t[0] for t in batch]
__snake_case : Dict = [t[1] for t in batch]
assert len(UpperCAmelCase_ ) == len(UpperCAmelCase_ )
# Max for paddings
__snake_case : List[str] = max(UpperCAmelCase_ )
# Pad token ids
if self.params.mlm:
__snake_case : Union[str, Any] = self.params.special_tok_ids['''pad_token''']
else:
__snake_case : Dict = self.params.special_tok_ids['''unk_token''']
__snake_case : int = [list(t.astype(UpperCAmelCase_ ) ) + [pad_idx] * (max_seq_len_ - len(UpperCAmelCase_ )) for t in token_ids]
assert len(tk_ ) == len(UpperCAmelCase_ )
assert all(len(UpperCAmelCase_ ) == max_seq_len_ for t in tk_ )
__snake_case : Tuple = torch.tensor(tk_ ) # (bs, max_seq_len_)
__snake_case : int = torch.tensor(UpperCAmelCase_ ) # (bs)
return tk_t, lg_t
| 102 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: List[Any] = ["""sentencepiece"""]
def __init__( self : int , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : List[str] ) ->List[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Optional[int] = ["""sentencepiece"""]
def __init__( self : Optional[int] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Tuple ) ->Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Any = ["""sentencepiece"""]
def __init__( self : Any , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : List[Any] ) ->List[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Dict = ["""sentencepiece"""]
def __init__( self : List[str] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : int ) ->Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: List[str] = ["""sentencepiece"""]
def __init__( self : Dict , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : int ) ->List[str]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : Tuple , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Dict ) ->Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : int , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Union[str, Any] ) ->Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Optional[Any] = ["""sentencepiece"""]
def __init__( self : List[str] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Optional[Any] ) ->Union[str, Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Optional[int] = ["""sentencepiece"""]
def __init__( self : Optional[Any] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : List[Any] ) ->Tuple:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Any = ["""sentencepiece"""]
def __init__( self : List[Any] , *UpperCAmelCase_ : str , **UpperCAmelCase_ : int ) ->List[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Any = ["""sentencepiece"""]
def __init__( self : int , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : List[Any] ) ->Tuple:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : Optional[int] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Union[str, Any] ) ->Union[str, Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Tuple = ["""sentencepiece"""]
def __init__( self : Dict , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : List[Any] ) ->Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Union[str, Any] = ["""sentencepiece"""]
def __init__( self : List[Any] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : List[Any] ) ->Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : int , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : List[str] ) ->Union[str, Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Tuple = ["""sentencepiece"""]
def __init__( self : int , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Optional[Any] ) ->str:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: List[Any] = ["""sentencepiece"""]
def __init__( self : Dict , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Optional[Any] ) ->int:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Union[str, Any] = ["""sentencepiece"""]
def __init__( self : Optional[Any] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Optional[int] ) ->Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Any = ["""sentencepiece"""]
def __init__( self : Optional[Any] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Dict ) ->Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Any = ["""sentencepiece"""]
def __init__( self : List[Any] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : List[str] ) ->List[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : Optional[Any] ) ->List[str]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : List[Any] ) ->Union[str, Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: List[Any] = ["""sentencepiece"""]
def __init__( self : Any , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Optional[Any] ) ->List[str]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Dict = ["""sentencepiece"""]
def __init__( self : int , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Union[str, Any] ) ->List[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: List[Any] = ["""sentencepiece"""]
def __init__( self : List[Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Optional[int] ) ->Optional[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Union[str, Any] = ["""sentencepiece"""]
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : str ) ->Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Optional[int] = ["""sentencepiece"""]
def __init__( self : Tuple , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Optional[int] ) ->Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Dict = ["""sentencepiece"""]
def __init__( self : Optional[int] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : List[str] ) ->Optional[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : Dict , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Optional[int] ) ->Any:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: List[str] = ["""sentencepiece"""]
def __init__( self : List[str] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Any = ["""sentencepiece"""]
def __init__( self : List[str] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Optional[int] ) ->str:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
| 347 | 0 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
a ="""src/transformers"""
a ="""docs/source/en"""
a ="""."""
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Optional[int]:
with open(lowerCamelCase__ , 'r' , encoding='utf-8' , newline='\n' ) as f:
__lowerCamelCase : Any = f.readlines()
# Find the start prompt.
__lowerCamelCase : List[str] = 0
while not lines[start_index].startswith(lowerCamelCase__ ):
start_index += 1
start_index += 1
__lowerCamelCase : int = start_index
while not lines[end_index].startswith(lowerCamelCase__ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
a ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
a =re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
a =re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
a =re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# This is to make sure the transformers module imported is the one in the repo.
a =direct_transformers_import(TRANSFORMERS_PATH)
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> List[str]:
__lowerCamelCase : int = re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)' , lowerCamelCase__ )
return [m.group(0 ) for m in matches]
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
__lowerCamelCase : int = 2 if text == '✅' or text == '❌' else len(lowerCamelCase__ )
__lowerCamelCase : Union[str, Any] = (width - text_length) // 2
__lowerCamelCase : List[Any] = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def SCREAMING_SNAKE_CASE__ ( ) -> str:
__lowerCamelCase : Union[str, Any] = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
__lowerCamelCase : List[str] = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
__lowerCamelCase : Dict = {name: config.replace('Config' , '' ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
__lowerCamelCase : Union[str, Any] = collections.defaultdict(lowerCamelCase__ )
__lowerCamelCase : Union[str, Any] = collections.defaultdict(lowerCamelCase__ )
__lowerCamelCase : Union[str, Any] = collections.defaultdict(lowerCamelCase__ )
__lowerCamelCase : List[str] = collections.defaultdict(lowerCamelCase__ )
__lowerCamelCase : Union[str, Any] = collections.defaultdict(lowerCamelCase__ )
# Let's lookup through all transformers object (once).
for attr_name in dir(lowerCamelCase__ ):
__lowerCamelCase : List[Any] = None
if attr_name.endswith('Tokenizer' ):
__lowerCamelCase : Dict = slow_tokenizers
__lowerCamelCase : List[Any] = attr_name[:-9]
elif attr_name.endswith('TokenizerFast' ):
__lowerCamelCase : Union[str, Any] = fast_tokenizers
__lowerCamelCase : str = attr_name[:-1_3]
elif _re_tf_models.match(lowerCamelCase__ ) is not None:
__lowerCamelCase : List[str] = tf_models
__lowerCamelCase : Optional[int] = _re_tf_models.match(lowerCamelCase__ ).groups()[0]
elif _re_flax_models.match(lowerCamelCase__ ) is not None:
__lowerCamelCase : List[Any] = flax_models
__lowerCamelCase : Optional[Any] = _re_flax_models.match(lowerCamelCase__ ).groups()[0]
elif _re_pt_models.match(lowerCamelCase__ ) is not None:
__lowerCamelCase : Optional[int] = pt_models
__lowerCamelCase : Any = _re_pt_models.match(lowerCamelCase__ ).groups()[0]
if lookup_dict is not None:
while len(lowerCamelCase__ ) > 0:
if attr_name in model_name_to_prefix.values():
__lowerCamelCase : List[Any] = True
break
# Try again after removing the last word in the name
__lowerCamelCase : str = ''.join(camel_case_split(lowerCamelCase__ )[:-1] )
# Let's build that table!
__lowerCamelCase : str = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
__lowerCamelCase : Union[str, Any] = ['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
__lowerCamelCase : List[Any] = [len(lowerCamelCase__ ) + 2 for c in columns]
__lowerCamelCase : int = max([len(lowerCamelCase__ ) for name in model_names] ) + 2
# Build the table per se
__lowerCamelCase : Union[str, Any] = '|' + '|'.join([_center_text(lowerCamelCase__ , lowerCamelCase__ ) for c, w in zip(lowerCamelCase__ , lowerCamelCase__ )] ) + '|\n'
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths] ) + "|\n"
__lowerCamelCase : List[str] = {True: '✅', False: '❌'}
for name in model_names:
__lowerCamelCase : Optional[int] = model_name_to_prefix[name]
__lowerCamelCase : Any = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(lowerCamelCase__ , lowerCamelCase__ ) for l, w in zip(lowerCamelCase__ , lowerCamelCase__ )] ) + "|\n"
return table
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__=False ) -> Any:
__lowerCamelCase : List[str] = _find_text_in_file(
filename=os.path.join(lowerCamelCase__ , 'index.md' ) , start_prompt='<!--This table is updated automatically from the auto modules' , end_prompt='<!-- End table-->' , )
__lowerCamelCase : List[Any] = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(lowerCamelCase__ , 'index.md' ) , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
'The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.' )
if __name__ == "__main__":
a =argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
a =parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 358 |
# Copyright 2021 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
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
a =[
"""EAGER""",
"""AOT_EAGER""",
"""INDUCTOR""",
"""NVFUSER""",
"""AOT_NVFUSER""",
"""AOT_CUDAGRAPHS""",
"""OFI""",
"""FX2TRT""",
"""ONNXRT""",
"""IPEX""",
]
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None ) -> Optional[int]:
__lowerCamelCase : int = True
while ask_again:
__lowerCamelCase : Dict = input(lowerCamelCase__ )
try:
if default is not None and len(lowerCamelCase__ ) == 0:
return default
return convert_value(lowerCamelCase__ ) if convert_value is not None else result
except Exception:
if error_message is not None:
print(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__=[] , lowerCamelCase__=None , lowerCamelCase__=0 ) -> str:
__lowerCamelCase : Union[str, Any] = BulletMenu(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : Tuple = menu.run(default_choice=lowerCamelCase__ )
return convert_value(lowerCamelCase__ ) if convert_value is not None else result
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Dict:
__lowerCamelCase : List[str] = int(lowerCamelCase__ )
return ComputeEnvironment(['LOCAL_MACHINE', 'AMAZON_SAGEMAKER'][value] )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Optional[int]:
__lowerCamelCase : Union[str, Any] = int(lowerCamelCase__ )
return DistributedType(['NO', 'MULTI_CPU', 'MULTI_XPU', 'MULTI_GPU', 'MULTI_NPU', 'TPU'][value] )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Union[str, Any]:
__lowerCamelCase : Optional[Any] = int(lowerCamelCase__ )
return DynamoBackend(DYNAMO_BACKENDS[value] ).value
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> int:
__lowerCamelCase : Union[str, Any] = int(lowerCamelCase__ )
return PrecisionType(['no', 'fp16', 'bf16', 'fp8'][value] )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> str:
__lowerCamelCase : Optional[Any] = int(lowerCamelCase__ )
return SageMakerDistributedType(['NO', 'DATA_PARALLEL', 'MODEL_PARALLEL'][value] )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Optional[Any]:
return {"yes": True, "no": False}[value.lower()]
class A_ ( argparse.RawDescriptionHelpFormatter ):
def lowerCAmelCase ( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : str ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : str):
__lowerCamelCase : int = super()._format_usage(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Union[str, Any] = usage.replace('<command> [<args>] ' ,'')
return usage
| 113 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : Dict = {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : int = ["""GLPNFeatureExtractor"""]
UpperCAmelCase_ : Union[str, Any] = ["""GLPNImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[int] = [
"""GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GLPNForDepthEstimation""",
"""GLPNLayer""",
"""GLPNModel""",
"""GLPNPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_glpn import GLPNFeatureExtractor
from .image_processing_glpn import GLPNImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_glpn import (
GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
GLPNForDepthEstimation,
GLPNLayer,
GLPNModel,
GLPNPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 91 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase = "cpu" ,lowercase = None ) -> None:
snake_case : int = torch.load(lowercase ,map_location=lowercase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(lowercase ,torch.Tensor ):
raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin""" )
snake_case : Dict = v.half()
if save_path is None: # overwrite src_path
snake_case : Optional[Any] = src_path
torch.save(lowercase ,lowercase )
if __name__ == "__main__":
fire.Fire(convert)
| 124 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class a__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _snake_case (self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__lowerCAmelCase = AutoConfig.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = TFAutoModel.from_pretrained(__lowercase , from_pt=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = AutoModel.from_pretrained(__lowercase , from_tf=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
@slow
def _snake_case (self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__lowerCAmelCase = AutoConfig.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = TFAutoModelForPreTraining.from_pretrained(__lowercase , from_pt=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = AutoModelForPreTraining.from_pretrained(__lowercase , from_tf=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
@slow
def _snake_case (self ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase = AutoConfig.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = TFAutoModelForCausalLM.from_pretrained(__lowercase , from_pt=__lowercase )
__lowerCAmelCase , __lowerCAmelCase = TFAutoModelForCausalLM.from_pretrained(
__lowercase , output_loading_info=__lowercase , from_pt=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = AutoModelForCausalLM.from_pretrained(__lowercase , from_tf=__lowercase )
__lowerCAmelCase , __lowerCAmelCase = AutoModelForCausalLM.from_pretrained(
__lowercase , output_loading_info=__lowercase , from_tf=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
@slow
def _snake_case (self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase = AutoConfig.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = TFAutoModelWithLMHead.from_pretrained(__lowercase , from_pt=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = AutoModelWithLMHead.from_pretrained(__lowercase , from_tf=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
@slow
def _snake_case (self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase = AutoConfig.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = TFAutoModelForMaskedLM.from_pretrained(__lowercase , from_pt=__lowercase )
__lowerCAmelCase , __lowerCAmelCase = TFAutoModelForMaskedLM.from_pretrained(
__lowercase , output_loading_info=__lowercase , from_pt=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = AutoModelForMaskedLM.from_pretrained(__lowercase , from_tf=__lowercase )
__lowerCAmelCase , __lowerCAmelCase = AutoModelForMaskedLM.from_pretrained(
__lowercase , output_loading_info=__lowercase , from_tf=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
@slow
def _snake_case (self ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase = AutoConfig.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = TFAutoModelForSeqaSeqLM.from_pretrained(__lowercase , from_pt=__lowercase )
__lowerCAmelCase , __lowerCAmelCase = TFAutoModelForSeqaSeqLM.from_pretrained(
__lowercase , output_loading_info=__lowercase , from_pt=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(__lowercase , from_tf=__lowercase )
__lowerCAmelCase , __lowerCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(
__lowercase , output_loading_info=__lowercase , from_tf=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
@slow
def _snake_case (self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__lowerCAmelCase = AutoConfig.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = TFAutoModelForSequenceClassification.from_pretrained(__lowercase , from_pt=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained(__lowercase , from_tf=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
@slow
def _snake_case (self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__lowerCAmelCase = AutoConfig.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = TFAutoModelForQuestionAnswering.from_pretrained(__lowercase , from_pt=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = AutoModelForQuestionAnswering.from_pretrained(__lowercase , from_tf=__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
def _snake_case (self ):
__lowerCAmelCase = TFAutoModelWithLMHead.from_pretrained(__lowercase , from_pt=__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__lowercase ) , 1_44_10 )
__lowerCAmelCase = AutoModelWithLMHead.from_pretrained(__lowercase , from_tf=__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__lowercase ) , 1_44_10 )
def _snake_case (self ):
__lowerCAmelCase = TFAutoModelWithLMHead.from_pretrained(__lowercase , from_pt=__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__lowercase ) , 1_44_10 )
__lowerCAmelCase = AutoModelWithLMHead.from_pretrained(__lowercase , from_tf=__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__lowercase ) , 1_44_10 )
| 358 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class a__ ( __A , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : str = DebertaTokenizer
__UpperCamelCase : str = True
__UpperCamelCase : Any = DebertaTokenizerFast
def _snake_case (self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__lowerCAmelCase = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''[UNK]''',
]
__lowerCAmelCase = dict(zip(__lowercase , range(len(__lowercase ) ) ) )
__lowerCAmelCase = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
__lowerCAmelCase = {'''unk_token''': '''[UNK]'''}
__lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
__lowerCAmelCase = 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(__lowercase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__lowercase ) )
def _snake_case (self , **__lowercase ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__lowercase )
def _snake_case (self , __lowercase ):
__lowerCAmelCase = '''lower newer'''
__lowerCAmelCase = '''lower newer'''
return input_text, output_text
def _snake_case (self ):
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = '''lower newer'''
__lowerCAmelCase = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
__lowerCAmelCase = tokenizer.tokenize(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
__lowerCAmelCase = tokens + [tokenizer.unk_token]
__lowerCAmelCase = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase ) , __lowercase )
def _snake_case (self ):
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = tokenizer('''Hello''' , '''World''' )
__lowerCAmelCase = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
self.assertListEqual(tokd['''token_type_ids'''] , __lowercase )
@slow
def _snake_case (self ):
__lowerCAmelCase = self.tokenizer_class.from_pretrained('''microsoft/deberta-base''' )
__lowerCAmelCase = tokenizer.encode('''sequence builders''' , add_special_tokens=__lowercase )
__lowerCAmelCase = tokenizer.encode('''multi-sequence build''' , add_special_tokens=__lowercase )
__lowerCAmelCase = tokenizer.encode(
'''sequence builders''' , add_special_tokens=__lowercase , add_prefix_space=__lowercase )
__lowerCAmelCase = tokenizer.encode(
'''sequence builders''' , '''multi-sequence build''' , add_special_tokens=__lowercase , add_prefix_space=__lowercase )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(__lowercase )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(__lowercase , __lowercase )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
@slow
def _snake_case (self ):
__lowerCAmelCase = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class )
for tokenizer_class in tokenizer_classes:
__lowerCAmelCase = tokenizer_class.from_pretrained('''microsoft/deberta-base''' )
__lowerCAmelCase = [
'''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
'''ALBERT incorporates two parameter reduction techniques''',
'''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
''' vocabulary embedding.''',
]
__lowerCAmelCase = tokenizer(__lowercase , padding=__lowercase )
__lowerCAmelCase = [tokenizer.decode(__lowercase , skip_special_tokens=__lowercase ) for seq in encoding['''input_ids''']]
# fmt: off
__lowerCAmelCase = {
'''input_ids''': [
[1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
],
'''token_type_ids''': [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
],
'''attention_mask''': [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[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]
]
}
# fmt: on
__lowerCAmelCase = [
'''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
'''ALBERT incorporates two parameter reduction techniques''',
'''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
''' vocabulary embedding.''',
]
self.assertDictEqual(encoding.data , __lowercase )
for expected, decoded in zip(__lowercase , __lowercase ):
self.assertEqual(__lowercase , __lowercase )
| 9 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A__ :
lowerCAmelCase__ : int
lowerCAmelCase__ : Node | None = None
lowerCAmelCase__ : Node | None = None
def __SCREAMING_SNAKE_CASE ( ) -> List[str]:
__lowercase = Node(1 )
__lowercase = Node(2 )
__lowercase = Node(3 )
__lowercase = Node(4 )
__lowercase = Node(5 )
return tree
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Node | None ) -> Tuple:
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Node | None ) -> List[Any]:
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Node | None ) -> int:
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Node | None ) -> List[str]:
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Node | None ) -> List[Any]:
__lowercase = []
if root is None:
return output
__lowercase = deque([root] )
while process_queue:
__lowercase = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Node | None , SCREAMING_SNAKE_CASE : int ) -> Optional[int]:
__lowercase = []
def populate_output(SCREAMING_SNAKE_CASE : Node | None , SCREAMING_SNAKE_CASE : int ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(_A , _A )
return output
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Node | None , SCREAMING_SNAKE_CASE : int ) -> List[Any]:
__lowercase = []
def populate_output(SCREAMING_SNAKE_CASE : Node | None , SCREAMING_SNAKE_CASE : int ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(_A , _A )
return output
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Node | None ) -> List[Any]:
if root is None:
return []
__lowercase = []
__lowercase = 0
__lowercase = height(_A )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(_A , _A ) )
__lowercase = 1
else:
output.append(get_nodes_from_right_to_left(_A , _A ) )
__lowercase = 0
return output
def __SCREAMING_SNAKE_CASE ( ) -> List[Any]: # Main function for testing.
__lowercase = make_tree()
print(F"""In-order Traversal: {inorder(_A )}""" )
print(F"""Pre-order Traversal: {preorder(_A )}""" )
print(F"""Post-order Traversal: {postorder(_A )}""" , '\n' )
print(F"""Height of Tree: {height(_A )}""" , '\n' )
print('Complete Level Order Traversal: ' )
print(level_order(_A ) , '\n' )
print('Level-wise order Traversal: ' )
for level in range(1 , height(_A ) + 1 ):
print(F"""Level {level}:""" , get_nodes_from_left_to_right(_A , level=_A ) )
print('\nZigZag order Traversal: ' )
print(zigzag(_A ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 325 |
import os
import string
import sys
lowerCamelCase = 1 << 8
lowerCamelCase = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
'''right''': 67 + ARROW_KEY_FLAG,
'''left''': 68 + ARROW_KEY_FLAG,
'''mod_int''': 91,
'''undefined''': sys.maxsize,
'''interrupt''': 3,
'''insert''': 50,
'''delete''': 51,
'''pg_up''': 53,
'''pg_down''': 54,
}
lowerCamelCase = KEYMAP['''up''']
lowerCamelCase = KEYMAP['''left''']
if sys.platform == "win32":
lowerCamelCase = []
lowerCamelCase = {
b'''\xe0H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
b'''\x00H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
b'''\xe0P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
b'''\x00P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
b'''\xe0M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
b'''\x00M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
b'''\xe0K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
b'''\x00K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
}
for i in range(10):
lowerCamelCase = ord(str(i))
def UpperCAmelCase__ ( ):
'''simple docstring'''
if os.name == "nt":
import msvcrt
a__ ='''mbcs'''
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(_A ) == 0:
# Read the keystroke
a__ =msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
a__ =ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
a__ =chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['''mod_int'''] ) )
WIN_CH_BUFFER.append(_A )
if ord(_A ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(1_26 ) )
a__ =chr(KEYMAP['''esc'''] )
except KeyError:
a__ =cha[1]
else:
a__ =ch.decode(_A )
else:
a__ =WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
a__ =sys.stdin.fileno()
a__ =termios.tcgetattr(_A )
try:
tty.setraw(_A )
a__ =sys.stdin.read(1 )
finally:
termios.tcsetattr(_A , termios.TCSADRAIN , _A )
return ch
def UpperCAmelCase__ ( ):
'''simple docstring'''
a__ =get_raw_chars()
if ord(_A ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(_A ) == KEYMAP["esc"]:
a__ =get_raw_chars()
if ord(_A ) == KEYMAP["mod_int"]:
a__ =get_raw_chars()
if ord(_A ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(_A ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(_A ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 188 | 0 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
lowerCAmelCase: Optional[int] = 'naver-clova-ix/donut-base'
class a__( unittest.TestCase ):
def lowercase_ ( self : Union[str, Any] ):
a : Dict = DonutProcessor.from_pretrained(__snake_case )
def lowercase_ ( self : Any ):
a : Dict = {
'name': 'John Doe',
'age': '99',
'city': 'Atlanta',
'state': 'GA',
'zip': '30301',
'phone': '123-4567',
'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
}
a : str = (
'<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
'<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
'<s_nicknames><s_nickname>Johnny</s_nickname>'
'<sep/><s_nickname>JD</s_nickname></s_nicknames>'
)
a : int = self.processor.tokenajson(__snake_case )
self.assertDictEqual(__snake_case , __snake_case )
| 357 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a__:
def __init__( self : Optional[int] ):
a : int = ''
a : List[str] = ''
a : int = []
a : Optional[Any] = 0
a : Optional[Any] = 2_56
a : int = 0
a : Optional[int] = 0
a : str = 0
a : int = 0
def lowercase_ ( self : List[str] , __snake_case : int ):
a : Optional[Any] = cva.imread(__snake_case , 0 )
a : int = copy.deepcopy(self.img )
a , a , a : Optional[int] = plt.hist(self.img.ravel() , 2_56 , [0, 2_56] , label='x' )
a : str = np.sum(__snake_case )
for i in range(len(__snake_case ) ):
a : List[str] = x[i] / self.k
self.sk += prk
a : List[Any] = (self.L - 1) * self.sk
if self.rem != 0:
a : Union[str, Any] = int(last % last )
a : int = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__snake_case )
a : int = int(np.ma.count(self.img ) / self.img[1].size )
a : Dict = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
a : Tuple = self.img[j][i]
if num != self.last_list[num]:
a : Union[str, Any] = self.last_list[num]
cva.imwrite('output_data/output.jpg' , self.img )
def lowercase_ ( self : Union[str, Any] ):
plt.hist(self.img.ravel() , 2_56 , [0, 2_56] )
def lowercase_ ( self : Any ):
cva.imshow('Output-Image' , self.img )
cva.imshow('Input-Image' , self.original_image )
cva.waitKey(50_00 )
cva.destroyAllWindows()
if __name__ == "__main__":
lowerCAmelCase: Dict = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
lowerCAmelCase: Optional[Any] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 96 | 0 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
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
_lowerCamelCase : str = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class __UpperCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = XLMRobertaTokenizer
__lowerCAmelCase = XLMRobertaTokenizerFast
__lowerCAmelCase = True
__lowerCAmelCase = True
def A (self : int ):
super().setUp()
# We have a SentencePiece fixture for testing
A = XLMRobertaTokenizer(__lowerCAmelCase , keep_accents=__lowerCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def A (self : Dict ):
A = """<pad>"""
A = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase ) , __lowerCAmelCase )
def A (self : Optional[int] ):
A = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """<mask>""" )
self.assertEqual(len(__lowerCAmelCase ) , 1002 )
def A (self : Dict ):
self.assertEqual(self.get_tokenizer().vocab_size , 1002 )
def A (self : str ):
A = XLMRobertaTokenizer(__lowerCAmelCase , keep_accents=__lowerCAmelCase )
A = 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 [285, 46, 10, 170, 382]] , )
A = 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""",
""".""",
] , )
A = tokenizer.convert_tokens_to_ids(__lowerCAmelCase )
self.assertListEqual(
__lowerCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
A = 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>""",
""".""",
] , )
def A (self : Dict ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
A = (self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
A = self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase )
A = self.tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase )
A = tempfile.mkdtemp()
A = tokenizer_r.save_pretrained(__lowerCAmelCase )
A = tokenizer_p.save_pretrained(__lowerCAmelCase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
A = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(__lowerCAmelCase , __lowerCAmelCase )
# Checks everything loads correctly in the same way
A = tokenizer_r.from_pretrained(__lowerCAmelCase )
A = tokenizer_p.from_pretrained(__lowerCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowerCAmelCase , __lowerCAmelCase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__lowerCAmelCase )
# Save tokenizer rust, legacy_format=True
A = tempfile.mkdtemp()
A = tokenizer_r.save_pretrained(__lowerCAmelCase , legacy_format=__lowerCAmelCase )
A = tokenizer_p.save_pretrained(__lowerCAmelCase )
# Checks it save with the same files
self.assertSequenceEqual(__lowerCAmelCase , __lowerCAmelCase )
# Checks everything loads correctly in the same way
A = tokenizer_r.from_pretrained(__lowerCAmelCase )
A = tokenizer_p.from_pretrained(__lowerCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowerCAmelCase , __lowerCAmelCase ) )
shutil.rmtree(__lowerCAmelCase )
# Save tokenizer rust, legacy_format=False
A = tempfile.mkdtemp()
A = tokenizer_r.save_pretrained(__lowerCAmelCase , legacy_format=__lowerCAmelCase )
A = tokenizer_p.save_pretrained(__lowerCAmelCase )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
A = tokenizer_r.from_pretrained(__lowerCAmelCase )
A = tokenizer_p.from_pretrained(__lowerCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowerCAmelCase , __lowerCAmelCase ) )
shutil.rmtree(__lowerCAmelCase )
@cached_property
def A (self : Optional[Any] ):
return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base""" )
def A (self : Union[str, Any] ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(__lowerCAmelCase , f.name )
A = XLMRobertaTokenizer(f.name , keep_accents=__lowerCAmelCase )
A = pickle.dumps(__lowerCAmelCase )
pickle.loads(__lowerCAmelCase )
def A (self : List[Any] ):
if not self.test_rust_tokenizer:
return
A = self.get_tokenizer()
A = self.get_rust_tokenizer()
A = """I was born in 92000, and this is falsé."""
A = tokenizer.tokenize(__lowerCAmelCase )
A = rust_tokenizer.tokenize(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
A = tokenizer.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase )
A = rust_tokenizer.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
A = self.get_rust_tokenizer()
A = tokenizer.encode(__lowerCAmelCase )
A = rust_tokenizer.encode(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
@slow
def A (self : int ):
A = """Hello World!"""
A = [0, 3_5378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__lowerCAmelCase , self.big_tokenizer.encode(__lowerCAmelCase ) )
@slow
def A (self : Dict ):
A = (
"""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"""
)
A = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
17_9459,
12_4850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
1_0114,
711,
152,
20,
6,
5,
2_2376,
642,
1221,
1_5190,
3_4153,
450,
5608,
959,
1119,
5_7702,
136,
186,
47,
1098,
2_9367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
5_0901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__lowerCAmelCase , self.big_tokenizer.encode(__lowerCAmelCase ) )
@slow
def A (self : List[str] ):
A = {"""input_ids""": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 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], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 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]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowerCAmelCase , model_name="""xlm-roberta-base""" , revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3""" , )
| 258 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
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 a__( lowerCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = ['''input_features''']
def __init__( self , __lowerCAmelCase=80 , __lowerCAmelCase=16000 , __lowerCAmelCase=160 , __lowerCAmelCase=30 , __lowerCAmelCase=400 , __lowerCAmelCase=0.0 , __lowerCAmelCase=False , **__lowerCAmelCase , ):
"""simple docstring"""
super().__init__(
feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , **__lowerCAmelCase , )
lowerCAmelCase = n_fft
lowerCAmelCase = hop_length
lowerCAmelCase = chunk_length
lowerCAmelCase = chunk_length * sampling_rate
lowerCAmelCase = self.n_samples // hop_length
lowerCAmelCase = sampling_rate
lowerCAmelCase = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCAmelCase , min_frequency=0.0 , max_frequency=8000.0 , sampling_rate=__lowerCAmelCase , norm="""slaney""" , mel_scale="""slaney""" , )
def a_ ( self , __lowerCAmelCase):
"""simple docstring"""
lowerCAmelCase = spectrogram(
__lowerCAmelCase , window_function(self.n_fft , """hann""") , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel="""log10""" , )
lowerCAmelCase = log_spec[:, :-1]
lowerCAmelCase = np.maximum(__lowerCAmelCase , log_spec.max() - 8.0)
lowerCAmelCase = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def a_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 0.0):
"""simple docstring"""
if attention_mask is not None:
lowerCAmelCase = np.array(__lowerCAmelCase , np.intaa)
lowerCAmelCase = []
for vector, length in zip(__lowerCAmelCase , attention_mask.sum(-1)):
lowerCAmelCase = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7)
if length < normed_slice.shape[0]:
lowerCAmelCase = padding_value
normed_input_values.append(__lowerCAmelCase)
else:
lowerCAmelCase = [(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]
return normed_input_values
def __call__( self , __lowerCAmelCase , __lowerCAmelCase = True , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = "max_length" , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , **__lowerCAmelCase , ):
"""simple docstring"""
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 = 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 = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple)) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)))
)
if is_batched:
lowerCAmelCase = [np.asarray([speech] , dtype=np.floataa).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray):
lowerCAmelCase = np.asarray(__lowerCAmelCase , dtype=np.floataa)
elif isinstance(__lowerCAmelCase , np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
lowerCAmelCase = raw_speech.astype(np.floataa)
# always return batch
if not is_batched:
lowerCAmelCase = [np.asarray([raw_speech]).T]
lowerCAmelCase = BatchFeature({"""input_features""": raw_speech})
# convert into correct format for padding
lowerCAmelCase = self.pad(
__lowerCAmelCase , padding=__lowerCAmelCase , max_length=max_length if max_length else self.n_samples , truncation=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
lowerCAmelCase = self.zero_mean_unit_var_norm(
padded_inputs["""input_features"""] , attention_mask=padded_inputs["""attention_mask"""] , padding_value=self.padding_value , )
lowerCAmelCase = np.stack(padded_inputs["""input_features"""] , axis=0)
# make sure list is in array format
lowerCAmelCase = padded_inputs.get("""input_features""").transpose(2 , 0 , 1)
lowerCAmelCase = [self._np_extract_fbank_features(__lowerCAmelCase) for waveform in input_features[0]]
if isinstance(input_features[0] , __lowerCAmelCase):
lowerCAmelCase = [np.asarray(__lowerCAmelCase , dtype=np.floataa) for feature in input_features]
else:
lowerCAmelCase = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
lowerCAmelCase = padded_inputs["""attention_mask"""][:, :: self.hop_length]
if return_tensors is not None:
lowerCAmelCase = padded_inputs.convert_to_tensors(__lowerCAmelCase)
return padded_inputs
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = copy.deepcopy(self.__dict__)
lowerCAmelCase = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 272 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
lowercase : Dict = ["image_processor", "tokenizer"]
lowercase : List[str] = "FlavaImageProcessor"
lowercase : Dict = ("BertTokenizer", "BertTokenizerFast")
def __init__( self :List[Any] ,_UpperCamelCase :int=None ,_UpperCamelCase :int=None ,**_UpperCamelCase :Any ):
snake_case_ : Dict = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" ,__UpperCAmelCase ,)
snake_case_ : List[Any] = kwargs.pop("""feature_extractor""" )
snake_case_ : 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__(__UpperCAmelCase ,__UpperCAmelCase )
snake_case_ : Dict = self.image_processor
def __call__( self :List[str] ,_UpperCamelCase :Optional[int] = None ,_UpperCamelCase :int = None ,_UpperCamelCase :Union[str, Any] = True ,_UpperCamelCase :Optional[int] = False ,_UpperCamelCase :Tuple = False ,_UpperCamelCase :List[str] = None ,_UpperCamelCase :List[str] = 0 ,_UpperCamelCase :List[str] = None ,_UpperCamelCase :int = None ,_UpperCamelCase :Dict = None ,_UpperCamelCase :Union[str, Any] = None ,_UpperCamelCase :int = None ,_UpperCamelCase :Any = False ,_UpperCamelCase :Union[str, Any] = False ,_UpperCamelCase :int = False ,_UpperCamelCase :List[str] = False ,_UpperCamelCase :Optional[int] = True ,_UpperCamelCase :List[Any] = None ,**_UpperCamelCase :List[Any] ,):
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:
snake_case_ : Optional[Any] = self.tokenizer(
text=__UpperCAmelCase ,add_special_tokens=__UpperCAmelCase ,padding=__UpperCAmelCase ,truncation=__UpperCAmelCase ,max_length=__UpperCAmelCase ,stride=__UpperCAmelCase ,pad_to_multiple_of=__UpperCAmelCase ,return_token_type_ids=__UpperCAmelCase ,return_attention_mask=__UpperCAmelCase ,return_overflowing_tokens=__UpperCAmelCase ,return_special_tokens_mask=__UpperCAmelCase ,return_offsets_mapping=__UpperCAmelCase ,return_length=__UpperCAmelCase ,verbose=__UpperCAmelCase ,return_tensors=__UpperCAmelCase ,**__UpperCAmelCase ,)
if images is not None:
snake_case_ : List[Any] = self.image_processor(
__UpperCAmelCase ,return_image_mask=__UpperCAmelCase ,return_codebook_pixels=__UpperCAmelCase ,return_tensors=__UpperCAmelCase ,**__UpperCAmelCase ,)
if text is not None and images is not None:
encoding.update(__UpperCAmelCase )
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCAmelCase ) ,tensor_type=__UpperCAmelCase )
def a__ ( self :str ,*_UpperCamelCase :Optional[int] ,**_UpperCamelCase :Union[str, Any] ):
return self.tokenizer.batch_decode(*__UpperCAmelCase ,**__UpperCAmelCase )
def a__ ( self :Optional[Any] ,*_UpperCamelCase :Optional[int] ,**_UpperCamelCase :int ):
return self.tokenizer.decode(*__UpperCAmelCase ,**__UpperCAmelCase )
@property
def a__ ( self :Optional[int] ):
snake_case_ : Tuple = self.tokenizer.model_input_names
snake_case_ : Union[str, Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def a__ ( self :Union[str, Any] ):
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,__UpperCAmelCase ,)
return self.image_processor_class
@property
def a__ ( self :int ):
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,__UpperCAmelCase ,)
return self.image_processor
| 365 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : Optional[int] = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
def __init__( self :List[str] ,*_UpperCamelCase :str ,**_UpperCamelCase :Optional[int] ):
warnings.warn(
"""The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use MobileViTImageProcessor instead.""" ,_UpperCamelCase ,)
super().__init__(*_UpperCamelCase ,**_UpperCamelCase )
| 8 | 0 |
from copy import deepcopy
class __snake_case :
def __init__( self : List[str] , _snake_case : list[int] | None = None , _snake_case : int | None = None):
"""simple docstring"""
if arr is None and size is not None:
UpperCAmelCase_ = size
UpperCAmelCase_ = [0] * size
elif arr is not None:
self.init(_snake_case)
else:
raise ValueError('''Either arr or size must be specified''')
def lowerCamelCase ( self : Tuple , _snake_case : list[int]):
"""simple docstring"""
UpperCAmelCase_ = len(_snake_case)
UpperCAmelCase_ = deepcopy(_snake_case)
for i in range(1 , self.size):
UpperCAmelCase_ = self.next_(_snake_case)
if j < self.size:
self.tree[j] += self.tree[i]
def lowerCamelCase ( self : List[Any]):
"""simple docstring"""
UpperCAmelCase_ = self.tree[:]
for i in range(self.size - 1 , 0 , -1):
UpperCAmelCase_ = self.next_(_snake_case)
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def lowerCamelCase ( _snake_case : int):
"""simple docstring"""
return index + (index & (-index))
@staticmethod
def lowerCamelCase ( _snake_case : int):
"""simple docstring"""
return index - (index & (-index))
def lowerCamelCase ( self : Tuple , _snake_case : int , _snake_case : int):
"""simple docstring"""
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
UpperCAmelCase_ = self.next_(_snake_case)
def lowerCamelCase ( self : Tuple , _snake_case : int , _snake_case : int):
"""simple docstring"""
self.add(_snake_case , value - self.get(_snake_case))
def lowerCamelCase ( self : str , _snake_case : int):
"""simple docstring"""
if right == 0:
return 0
UpperCAmelCase_ = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
UpperCAmelCase_ = self.prev(_snake_case)
return result
def lowerCamelCase ( self : Dict , _snake_case : int , _snake_case : int):
"""simple docstring"""
return self.prefix(_snake_case) - self.prefix(_snake_case)
def lowerCamelCase ( self : Optional[int] , _snake_case : int):
"""simple docstring"""
return self.query(_snake_case , index + 1)
def lowerCamelCase ( self : Tuple , _snake_case : int):
"""simple docstring"""
value -= self.tree[0]
if value < 0:
return -1
UpperCAmelCase_ = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
UpperCAmelCase_ = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| 51 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowercase__ ( ctypes.Structure ):
'''simple docstring'''
A_ : Optional[Any] = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def snake_case_ ( ):
"""simple docstring"""
if os.name == "nt":
_SCREAMING_SNAKE_CASE : Tuple = CursorInfo()
_SCREAMING_SNAKE_CASE : Tuple = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__ , ctypes.byref(SCREAMING_SNAKE_CASE__ ) )
_SCREAMING_SNAKE_CASE : Optional[Any] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__ , ctypes.byref(SCREAMING_SNAKE_CASE__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def snake_case_ ( ):
"""simple docstring"""
if os.name == "nt":
_SCREAMING_SNAKE_CASE : int = CursorInfo()
_SCREAMING_SNAKE_CASE : List[str] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__ , ctypes.byref(SCREAMING_SNAKE_CASE__ ) )
_SCREAMING_SNAKE_CASE : Tuple = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__ , ctypes.byref(SCREAMING_SNAKE_CASE__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def snake_case_ ( ):
"""simple docstring"""
try:
hide_cursor()
yield
finally:
show_cursor()
| 200 | 0 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__snake_case : str = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
__snake_case : Optional[int] = parser.parse_args()
__snake_case : Tuple = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
__snake_case : Union[str, Any] = CLIPImageProcessor()
__snake_case : Union[str, Any] = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
__snake_case : Any = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 354 |
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
__snake_case = (CMStochasticIterativeScheduler,)
__snake_case = 10
def lowercase__ ( self : List[str] , **lowerCAmelCase_ : Dict ) -> Dict:
'''simple docstring'''
A__ : int ={
"""num_train_timesteps""": 2_01,
"""sigma_min""": 0.002,
"""sigma_max""": 80.0,
}
config.update(**lowerCAmelCase_ )
return config
def lowercase__ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
A__ : Dict =10
A__ : str =self.get_scheduler_config()
A__ : Any =self.scheduler_classes[0](**lowerCAmelCase_ )
scheduler.set_timesteps(lowerCAmelCase_ )
A__ : List[Any] =scheduler.timesteps[0]
A__ : Union[str, Any] =scheduler.timesteps[1]
A__ : Optional[int] =self.dummy_sample
A__ : Union[str, Any] =0.1 * sample
A__ : Dict =scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ).prev_sample
A__ : int =scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowercase__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase_ )
def lowercase__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
for clip_denoised in [True, False]:
self.check_over_configs(clip_denoised=lowerCAmelCase_ )
def lowercase__ ( self : List[Any] ) -> Dict:
'''simple docstring'''
A__ : Union[str, Any] =self.scheduler_classes[0]
A__ : Dict =self.get_scheduler_config()
A__ : Any =scheduler_class(**lowerCAmelCase_ )
A__ : int =1
scheduler.set_timesteps(lowerCAmelCase_ )
A__ : Any =scheduler.timesteps
A__ : Optional[int] =torch.manual_seed(0 )
A__ : List[Any] =self.dummy_model()
A__ : Optional[int] =self.dummy_sample_deter * scheduler.init_noise_sigma
for i, t in enumerate(lowerCAmelCase_ ):
# 1. scale model input
A__ : Optional[Any] =scheduler.scale_model_input(lowerCAmelCase_ , lowerCAmelCase_ )
# 2. predict noise residual
A__ : Union[str, Any] =model(lowerCAmelCase_ , lowerCAmelCase_ )
# 3. predict previous sample x_t-1
A__ : Tuple =scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , generator=lowerCAmelCase_ ).prev_sample
A__ : Dict =pred_prev_sample
A__ : Union[str, Any] =torch.sum(torch.abs(lowerCAmelCase_ ) )
A__ : Optional[Any] =torch.mean(torch.abs(lowerCAmelCase_ ) )
assert abs(result_sum.item() - 192.7614 ) < 1e-2
assert abs(result_mean.item() - 0.2510 ) < 1e-3
def lowercase__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
A__ : Dict =self.scheduler_classes[0]
A__ : Dict =self.get_scheduler_config()
A__ : Tuple =scheduler_class(**lowerCAmelCase_ )
A__ : Tuple =[1_06, 0]
scheduler.set_timesteps(timesteps=lowerCAmelCase_ )
A__ : List[Any] =scheduler.timesteps
A__ : Optional[Any] =torch.manual_seed(0 )
A__ : int =self.dummy_model()
A__ : int =self.dummy_sample_deter * scheduler.init_noise_sigma
for t in timesteps:
# 1. scale model input
A__ : Any =scheduler.scale_model_input(lowerCAmelCase_ , lowerCAmelCase_ )
# 2. predict noise residual
A__ : List[str] =model(lowerCAmelCase_ , lowerCAmelCase_ )
# 3. predict previous sample x_t-1
A__ : Dict =scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , generator=lowerCAmelCase_ ).prev_sample
A__ : Union[str, Any] =pred_prev_sample
A__ : Union[str, Any] =torch.sum(torch.abs(lowerCAmelCase_ ) )
A__ : List[Any] =torch.mean(torch.abs(lowerCAmelCase_ ) )
assert abs(result_sum.item() - 347.6357 ) < 1e-2
assert abs(result_mean.item() - 0.4527 ) < 1e-3
def lowercase__ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
A__ : Optional[Any] =self.scheduler_classes[0]
A__ : Union[str, Any] =self.get_scheduler_config()
A__ : List[Any] =scheduler_class(**lowerCAmelCase_ )
A__ : Tuple =[39, 30, 12, 15, 0]
with self.assertRaises(lowerCAmelCase_ , msg="""`timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=lowerCAmelCase_ )
def lowercase__ ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
A__ : Union[str, Any] =self.scheduler_classes[0]
A__ : List[str] =self.get_scheduler_config()
A__ : Tuple =scheduler_class(**lowerCAmelCase_ )
A__ : Dict =[39, 30, 12, 1, 0]
A__ : int =len(lowerCAmelCase_ )
with self.assertRaises(lowerCAmelCase_ , msg="""Can only pass one of `num_inference_steps` or `timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_ , timesteps=lowerCAmelCase_ )
def lowercase__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
A__ : Optional[Any] =self.scheduler_classes[0]
A__ : Any =self.get_scheduler_config()
A__ : Optional[int] =scheduler_class(**lowerCAmelCase_ )
A__ : List[str] =[scheduler.config.num_train_timesteps]
with self.assertRaises(
lowerCAmelCase_ , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=lowerCAmelCase_ )
| 136 | 0 |
def lowerCAmelCase_ ( __A ) -> str:
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
'''configuration_layoutlmv3''': [
'''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LayoutLMv3Config''',
'''LayoutLMv3OnnxConfig''',
],
'''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
'''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ['''LayoutLMv3TokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LayoutLMv3ForQuestionAnswering''',
'''LayoutLMv3ForSequenceClassification''',
'''LayoutLMv3ForTokenClassification''',
'''LayoutLMv3Model''',
'''LayoutLMv3PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLayoutLMv3ForQuestionAnswering''',
'''TFLayoutLMv3ForSequenceClassification''',
'''TFLayoutLMv3ForTokenClassification''',
'''TFLayoutLMv3Model''',
'''TFLayoutLMv3PreTrainedModel''',
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ['''LayoutLMv3FeatureExtractor''']
__A = ['''LayoutLMv3ImageProcessor''']
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
__A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 135 | 0 |
"""simple docstring"""
from __future__ import annotations
def _A ( lowercase ):
"""simple docstring"""
a =[True] * limit
a =False
a =False
a =True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
a =i * 2
while index < limit:
a =False
a =index + i
a =[2]
for i in range(3 , lowercase , 2 ):
if is_prime[i]:
primes.append(lowercase )
return primes
def _A ( lowercase = 1_00_00_00 ):
"""simple docstring"""
a =prime_sieve(lowercase )
a =0
a =0
for i in range(len(lowercase ) ):
for j in range(i + length , len(lowercase ) ):
a =sum(primes[i:j] )
if sol >= ceiling:
break
if sol in primes:
a =j - i
a =sol
return largest
if __name__ == "__main__":
print(F'{solution() = }')
| 215 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
lowerCamelCase_ : str = ["""bert-base-uncased""", """bert-base-cased"""]
lowerCamelCase_ : List[str] = """hf-internal-testing/tiny-bert-tf-only"""
if is_tf_available():
class __A ( tf.keras.Model ):
"""simple docstring"""
def __init__( self , __A ) -> Dict:
super().__init__()
a =tokenizer
a =AutoConfig.from_pretrained(__A )
a =TFAutoModel.from_config(__A )
def SCREAMING_SNAKE_CASE ( self , __A ) -> int:
a =self.tokenizer(__A )
a =self.bert(**__A )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class __A ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self ) -> str:
super().setUp()
a =[
BertTokenizer.from_pretrained(__A ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
a =[TFBertTokenizer.from_pretrained(__A ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(__A , use_fast_bert_tokenizer=__A )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
a =[
'''This is a straightforward English test sentence.''',
'''This one has some weird characters\rto\nsee\r\nif those\u00E9break things.''',
'''Now we\'re going to add some Chinese: 一 二 三 一二三''',
'''And some much more rare Chinese: 齉 堃 齉堃''',
'''Je vais aussi écrire en français pour tester les accents''',
'''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
]
a =list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
a =tokenizer(__A , return_tensors='''tf''' , padding='''longest''' )
a =tf_tokenizer(__A )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> str:
for tf_tokenizer in self.tf_tokenizers:
a =tf_tokenizer(self.paired_sentences )
a =tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
for tf_tokenizer in self.tf_tokenizers:
a =tf.function(__A )
for test_inputs in (self.test_sentences, self.paired_sentences):
a =tf.constant(__A )
a =compiled_tokenizer(__A )
a =tf_tokenizer(__A )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
for tf_tokenizer in self.tf_tokenizers:
a =ModelToSave(tokenizer=__A )
a =tf.convert_to_tensor(self.test_sentences )
a =model(__A ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
a =Path(__A ) / '''saved.model'''
model.save(__A )
a =tf.keras.models.load_model(__A )
a =loaded_model(__A )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1E-5 )
| 215 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = """encoder-decoder"""
SCREAMING_SNAKE_CASE_ : Tuple = True
def __init__( self : Optional[int] , **__lowerCamelCase : Tuple ) -> List[str]:
super().__init__(**__lowerCamelCase )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
a = kwargs.pop("encoder" )
a = encoder_config.pop("model_type" )
a = kwargs.pop("decoder" )
a = decoder_config.pop("model_type" )
from ..auto.configuration_auto import AutoConfig
a = AutoConfig.for_model(__lowerCamelCase , **__lowerCamelCase )
a = AutoConfig.for_model(__lowerCamelCase , **__lowerCamelCase )
a = True
@classmethod
def __UpperCAmelCase ( cls : List[Any] , __lowerCamelCase : PretrainedConfig , __lowerCamelCase : PretrainedConfig , **__lowerCamelCase : Union[str, Any] ) -> PretrainedConfig:
logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
a = True
a = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__lowerCamelCase )
def __UpperCAmelCase ( self : List[Any] ) -> Dict:
a = copy.deepcopy(self.__dict__ )
a = self.encoder.to_dict()
a = self.decoder.to_dict()
a = self.__class__.model_type
return output
| 107 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
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
__lowerCAmelCase : str = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class snake_case__ (_UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = XLMRobertaTokenizer
SCREAMING_SNAKE_CASE_ : int = XLMRobertaTokenizerFast
SCREAMING_SNAKE_CASE_ : str = True
SCREAMING_SNAKE_CASE_ : Optional[Any] = True
def __UpperCAmelCase ( self : int ) -> List[str]:
super().setUp()
# We have a SentencePiece fixture for testing
a = XLMRobertaTokenizer(__lowerCamelCase , keep_accents=__lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCAmelCase ( self : List[str] ) -> Any:
a = "<pad>"
a = 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 : Dict ) -> Optional[Any]:
a = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(__lowerCamelCase ) , 10_02 )
def __UpperCAmelCase ( self : List[Any] ) -> Any:
self.assertEqual(self.get_tokenizer().vocab_size , 10_02 )
def __UpperCAmelCase ( self : Dict ) -> List[str]:
a = XLMRobertaTokenizer(__lowerCamelCase , keep_accents=__lowerCamelCase )
a = 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]] , )
a = 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",
".",
] , )
a = 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]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
a = 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>",
".",
] , )
def __UpperCAmelCase ( self : Optional[Any] ) -> List[str]:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
a = (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
a = self.rust_tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
a = self.tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
a = tempfile.mkdtemp()
a = tokenizer_r.save_pretrained(__lowerCamelCase )
a = tokenizer_p.save_pretrained(__lowerCamelCase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
a = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(__lowerCamelCase , __lowerCamelCase )
# Checks everything loads correctly in the same way
a = tokenizer_r.from_pretrained(__lowerCamelCase )
a = tokenizer_p.from_pretrained(__lowerCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowerCamelCase , __lowerCamelCase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__lowerCamelCase )
# Save tokenizer rust, legacy_format=True
a = tempfile.mkdtemp()
a = tokenizer_r.save_pretrained(__lowerCamelCase , legacy_format=__lowerCamelCase )
a = tokenizer_p.save_pretrained(__lowerCamelCase )
# Checks it save with the same files
self.assertSequenceEqual(__lowerCamelCase , __lowerCamelCase )
# Checks everything loads correctly in the same way
a = tokenizer_r.from_pretrained(__lowerCamelCase )
a = tokenizer_p.from_pretrained(__lowerCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowerCamelCase , __lowerCamelCase ) )
shutil.rmtree(__lowerCamelCase )
# Save tokenizer rust, legacy_format=False
a = tempfile.mkdtemp()
a = tokenizer_r.save_pretrained(__lowerCamelCase , legacy_format=__lowerCamelCase )
a = tokenizer_p.save_pretrained(__lowerCamelCase )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
a = tokenizer_r.from_pretrained(__lowerCamelCase )
a = tokenizer_p.from_pretrained(__lowerCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowerCamelCase , __lowerCamelCase ) )
shutil.rmtree(__lowerCamelCase )
@cached_property
def __UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base" )
def __UpperCAmelCase ( self : List[Any] ) -> List[Any]:
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(__lowerCamelCase , f.name )
a = XLMRobertaTokenizer(f.name , keep_accents=__lowerCamelCase )
a = pickle.dumps(__lowerCamelCase )
pickle.loads(__lowerCamelCase )
def __UpperCAmelCase ( self : int ) -> str:
if not self.test_rust_tokenizer:
return
a = self.get_tokenizer()
a = self.get_rust_tokenizer()
a = "I was born in 92000, and this is falsé."
a = tokenizer.tokenize(__lowerCamelCase )
a = rust_tokenizer.tokenize(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
a = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
a = rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
a = self.get_rust_tokenizer()
a = tokenizer.encode(__lowerCamelCase )
a = rust_tokenizer.encode(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
@slow
def __UpperCAmelCase ( self : Dict ) -> Any:
a = "Hello World!"
a = [0, 3_53_78, 66_61, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__lowerCamelCase , self.big_tokenizer.encode(__lowerCamelCase ) )
@slow
def __UpperCAmelCase ( self : Tuple ) -> int:
a = (
"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"
)
a = [
0,
32_93,
83,
10,
45_52,
49_89,
79_86,
6_78,
10,
59_15,
1_11,
17_94_59,
12_48_50,
4,
60_44,
2_37,
12,
6,
5,
6,
4,
67_80,
7_05,
15,
13_88,
44,
3_78,
1_01_14,
7_11,
1_52,
20,
6,
5,
2_23_76,
6_42,
12_21,
1_51_90,
3_41_53,
4_50,
56_08,
9_59,
11_19,
5_77_02,
1_36,
1_86,
47,
10_98,
2_93_67,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
60_44,
2_37,
62_84,
5_09_01,
5_28,
31,
90,
34,
9_27,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__lowerCamelCase , self.big_tokenizer.encode(__lowerCamelCase ) )
@slow
def __UpperCAmelCase ( self : Optional[Any] ) -> List[Any]:
# fmt: off
a = {"input_ids": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowerCamelCase , model_name="xlm-roberta-base" , revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3" , )
| 107 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a__ ( __snake_case ):
@staticmethod
@abstractmethod
def __SCREAMING_SNAKE_CASE ( UpperCAmelCase ) -> Any:
raise NotImplementedError()
@abstractmethod
def __SCREAMING_SNAKE_CASE ( self ) -> int:
raise NotImplementedError()
| 370 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class a__ ( __snake_case ):
def __init__( self , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=True , UpperCAmelCase=None , **UpperCAmelCase ) -> List[Any]:
__a = parent
__a = config_class
__a = has_text_modality
__a = kwargs
__a = common_properties
def __SCREAMING_SNAKE_CASE ( self ) -> List[str]:
__a = self.config_class(**self.inputs_dict )
__a = (
['hidden_size', 'num_attention_heads', 'num_hidden_layers']
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(['vocab_size'] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(UpperCAmelCase , UpperCAmelCase ) , msg=f'''`{prop}` does not exist''' )
# Test that config has the common properties as setter
for idx, name in enumerate(UpperCAmelCase ):
try:
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
self.parent.assertEqual(
getattr(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase , msg=f'''`{name} value {idx} expected, but was {getattr(UpperCAmelCase , UpperCAmelCase )}''' )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(UpperCAmelCase ):
try:
__a = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase , msg=f'''`{name} value {idx} expected, but was {getattr(UpperCAmelCase , UpperCAmelCase )}''' )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def __SCREAMING_SNAKE_CASE ( self ) -> Tuple:
__a = self.config_class(**self.inputs_dict )
__a = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self ) -> Tuple:
__a = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
__a = os.path.join(UpperCAmelCase , 'config.json' )
config_first.to_json_file(UpperCAmelCase )
__a = self.config_class.from_json_file(UpperCAmelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def __SCREAMING_SNAKE_CASE ( self ) -> Tuple:
__a = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(UpperCAmelCase )
__a = self.config_class.from_pretrained(UpperCAmelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def __SCREAMING_SNAKE_CASE ( self ) -> str:
__a = self.config_class(**self.inputs_dict )
__a = 'test'
with tempfile.TemporaryDirectory() as tmpdirname:
__a = os.path.join(UpperCAmelCase , UpperCAmelCase )
config_first.save_pretrained(UpperCAmelCase )
__a = self.config_class.from_pretrained(UpperCAmelCase , subfolder=UpperCAmelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
__a = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
__a = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def __SCREAMING_SNAKE_CASE ( self ) -> List[str]:
if self.config_class.is_composition:
return
__a = self.config_class()
self.parent.assertIsNotNone(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
__a = copy.deepcopy(UpperCAmelCase )
__a = self.config_class(**UpperCAmelCase )
__a = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa) )
elif getattr(UpperCAmelCase , UpperCAmelCase ) != value:
wrong_values.append((key, getattr(UpperCAmelCase , UpperCAmelCase ), value) )
if len(UpperCAmelCase ) > 0:
__a = '\n'.join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values] )
raise ValueError(f'''The following keys were not properly set in the config:\n{errors}''' )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 197 | 0 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowerCamelCase ( __lowerCamelCase : Dict , __lowerCamelCase : bool = True , __lowerCamelCase : float = math.inf , __lowerCamelCase : float = -math.inf , __lowerCamelCase : float = math.inf , __lowerCamelCase : float = -math.inf , __lowerCamelCase : bool = False , __lowerCamelCase : float = 100 , __lowerCamelCase : float = 0.01 , __lowerCamelCase : float = 1 , ) ->Any:
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = search_prob
_SCREAMING_SNAKE_CASE = start_temperate
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = None
while not search_end:
_SCREAMING_SNAKE_CASE = current_state.score()
if best_state is None or current_score > best_state.score():
_SCREAMING_SNAKE_CASE = current_state
scores.append(__lowerCamelCase )
iterations += 1
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
_SCREAMING_SNAKE_CASE = random.randint(0 , len(__lowerCamelCase ) - 1 ) # picking a random neighbor
_SCREAMING_SNAKE_CASE = neighbors.pop(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
_SCREAMING_SNAKE_CASE = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
_SCREAMING_SNAKE_CASE = picked_neighbor
else:
_SCREAMING_SNAKE_CASE = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
_SCREAMING_SNAKE_CASE = picked_neighbor
_SCREAMING_SNAKE_CASE = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
_SCREAMING_SNAKE_CASE = True
else:
_SCREAMING_SNAKE_CASE = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(__lowerCamelCase ) , __lowerCamelCase )
plt.xlabel("""Iterations""" )
plt.ylabel("""Function values""" )
plt.show()
return best_state
if __name__ == "__main__":
def lowerCamelCase ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : List[str] ) ->Optional[Any]:
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
lowercase_ = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
lowercase_ = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"""The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
# starting the problem with initial coordinates (12, 47)
lowercase_ = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
lowercase_ = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"""The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
def lowerCamelCase ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : int ) ->int:
return (3 * x**2) - (6 * y)
lowercase_ = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
lowercase_ = simulated_annealing(prob, find_max=False, visualization=True)
print(
"""The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
f"""{local_min.score()}"""
)
lowercase_ = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
lowercase_ = simulated_annealing(prob, find_max=True, visualization=True)
print(
"""The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
f"""{local_min.score()}"""
)
| 58 |
def __UpperCAmelCase ( __a : int ,__a : list[int] ,__a : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(__a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(__a )
def __UpperCAmelCase ( __a : int ,__a : list[int] ,__a : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations_with_dp_array(
__a : int ,__a : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
_a : Union[str, Any] = sum(
count_of_possible_combinations_with_dp_array(target - item ,__a )
for item in array )
_a : Optional[int] = answer
return answer
_a : int = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(__a ,__a )
def __UpperCAmelCase ( __a : int ,__a : list[int] ,__a : int ) -> int:
"""simple docstring"""
_a : str = [0] * (target + 1)
_a : Optional[Any] = 1
for i in range(1 ,target + 1 ):
for j in range(__a ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
a__ = 3
a__ = 5
a__ = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 235 | 0 |
"""simple docstring"""
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 lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , ) -> int:
'''simple docstring'''
_lowerCamelCase : List[str] = {}
if train_file is not None:
_lowerCamelCase : Tuple = [train_file]
if eval_file is not None:
_lowerCamelCase : int = [eval_file]
if test_file is not None:
_lowerCamelCase : List[str] = [test_file]
_lowerCamelCase : Union[str, Any] = datasets.load_dataset("csv" , data_files=_lowerCamelCase )
_lowerCamelCase : List[Any] = list(ds[list(files.keys() )[0]].features.keys() )
_lowerCamelCase : str = features_name.pop(_lowerCamelCase )
_lowerCamelCase : List[Any] = list(set(ds[list(files.keys() )[0]][label_name] ) )
_lowerCamelCase : Union[str, Any] = {label: i for i, label in enumerate(_lowerCamelCase )}
_lowerCamelCase : List[Any] = tokenizer.model_input_names
_lowerCamelCase : List[str] = {}
if len(_lowerCamelCase ) == 1:
for k in files.keys():
_lowerCamelCase : Optional[int] = ds[k].map(
lambda _lowerCamelCase : tokenizer.batch_encode_plus(
example[features_name[0]] , truncation=_lowerCamelCase , max_length=_lowerCamelCase , padding="max_length" ) , batched=_lowerCamelCase , )
elif len(_lowerCamelCase ) == 2:
for k in files.keys():
_lowerCamelCase : Union[str, Any] = ds[k].map(
lambda _lowerCamelCase : tokenizer.batch_encode_plus(
(example[features_name[0]], example[features_name[1]]) , truncation=_lowerCamelCase , max_length=_lowerCamelCase , padding="max_length" , ) , batched=_lowerCamelCase , )
def gen_train():
for ex in transformed_ds[datasets.Split.TRAIN]:
_lowerCamelCase : List[Any] = {k: v for k, v in ex.items() if k in input_names}
_lowerCamelCase : Tuple = labelaid[ex[label_name]]
yield (d, label)
def gen_val():
for ex in transformed_ds[datasets.Split.VALIDATION]:
_lowerCamelCase : List[Any] = {k: v for k, v in ex.items() if k in input_names}
_lowerCamelCase : str = labelaid[ex[label_name]]
yield (d, label)
def gen_test():
for ex in transformed_ds[datasets.Split.TEST]:
_lowerCamelCase : Tuple = {k: v for k, v in ex.items() if k in input_names}
_lowerCamelCase : int = labelaid[ex[label_name]]
yield (d, label)
_lowerCamelCase : Dict = (
tf.data.Dataset.from_generator(
_lowerCamelCase , ({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 : int = (
tf.data.Dataset.from_generator(
_lowerCamelCase , ({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 : str = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )
_lowerCamelCase : Any = (
tf.data.Dataset.from_generator(
_lowerCamelCase , ({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 : str = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )
return train_ds, val_ds, test_ds, labelaid
_lowerCAmelCase : List[Any] = logging.getLogger(__name__)
@dataclass
class A_ :
lowerCAmelCase__ = field(metadata={'help': 'Which column contains the label'} )
lowerCAmelCase__ = field(default=_a , metadata={'help': 'The path of the training file'} )
lowerCAmelCase__ = field(default=_a , metadata={'help': 'The path of the development file'} )
lowerCAmelCase__ = field(default=_a , metadata={'help': 'The path of the test file'} )
lowerCAmelCase__ = 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.'
)
} , )
lowerCAmelCase__ = field(
default=_a , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
@dataclass
class A_ :
lowerCAmelCase__ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCAmelCase__ = field(
default=_a , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCAmelCase__ = field(
default=_a , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCAmelCase__ = 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.
lowerCAmelCase__ = field(
default=_a , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
def lowerCamelCase_( ) -> Any:
'''simple docstring'''
_lowerCamelCase : str = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Optional[int] = 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 : str = 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 : Optional[int] = get_tfds(
train_file=data_args.train_file , eval_file=data_args.dev_file , test_file=data_args.test_file , tokenizer=_lowerCamelCase , label_column_id=data_args.label_column_id , max_seq_length=data_args.max_seq_length , )
_lowerCamelCase : int = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=len(_lowerCamelCase ) , labelaid=_lowerCamelCase , 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 : str = TFAutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_pt=bool(".bin" in model_args.model_name_or_path ) , config=_lowerCamelCase , cache_dir=model_args.cache_dir , )
def compute_metrics(_lowerCamelCase ) -> Dict:
_lowerCamelCase : Dict = np.argmax(p.predictions , axis=1 )
return {"acc": (preds == p.label_ids).mean()}
# Initialize our Trainer
_lowerCamelCase : Any = TFTrainer(
model=_lowerCamelCase , args=_lowerCamelCase , train_dataset=_lowerCamelCase , eval_dataset=_lowerCamelCase , compute_metrics=_lowerCamelCase , )
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_lowerCamelCase : List[Any] = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_lowerCamelCase : int = trainer.evaluate()
_lowerCamelCase : List[Any] = os.path.join(training_args.output_dir , "eval_results.txt" )
with open(_lowerCamelCase , "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(_lowerCamelCase )
return results
if __name__ == "__main__":
main()
| 340 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def lowerCamelCase_( _lowerCamelCase ) -> Optional[int]:
'''simple docstring'''
_lowerCamelCase : Optional[Any] = np.inf
def set_batch_size(_lowerCamelCase ) -> None:
nonlocal batch_size
if isinstance(_lowerCamelCase , _lowerCamelCase ):
_lowerCamelCase : Optional[int] = min(_lowerCamelCase , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
_lowerCamelCase : Union[str, Any] = min(_lowerCamelCase , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(_lowerCamelCase , _lowerCamelCase ) and feature.dtype == "binary":
_lowerCamelCase : List[str] = min(_lowerCamelCase , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(_lowerCamelCase , _lowerCamelCase )
return None if batch_size is np.inf else batch_size
class A_ ( _a ):
def __init__( self: Optional[int] ,__lowerCAmelCase: NestedDataStructureLike[PathLike] ,__lowerCAmelCase: Optional[NamedSplit] = None ,__lowerCAmelCase: Optional[Features] = None ,__lowerCAmelCase: str = None ,__lowerCAmelCase: bool = False ,__lowerCAmelCase: bool = False ,__lowerCAmelCase: Optional[int] = None ,**__lowerCAmelCase: int ,):
'''simple docstring'''
super().__init__(
__lowerCAmelCase ,split=__lowerCAmelCase ,features=__lowerCAmelCase ,cache_dir=__lowerCAmelCase ,keep_in_memory=__lowerCAmelCase ,streaming=__lowerCAmelCase ,num_proc=__lowerCAmelCase ,**__lowerCAmelCase ,)
_lowerCamelCase : Tuple = path_or_paths if isinstance(__lowerCAmelCase ,__lowerCAmelCase ) else {self.split: path_or_paths}
_lowerCamelCase : Any = _PACKAGED_DATASETS_MODULES["parquet"][1]
_lowerCamelCase : int = Parquet(
cache_dir=__lowerCAmelCase ,data_files=__lowerCAmelCase ,features=__lowerCAmelCase ,hash=__lowerCAmelCase ,**__lowerCAmelCase ,)
def _lowercase ( self: Optional[int] ):
'''simple docstring'''
if self.streaming:
_lowerCamelCase : List[Any] = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
_lowerCamelCase : Tuple = None
_lowerCamelCase : Optional[int] = None
_lowerCamelCase : List[str] = None
_lowerCamelCase : str = None
self.builder.download_and_prepare(
download_config=__lowerCAmelCase ,download_mode=__lowerCAmelCase ,verification_mode=__lowerCAmelCase ,base_path=__lowerCAmelCase ,num_proc=self.num_proc ,)
_lowerCamelCase : Any = self.builder.as_dataset(
split=self.split ,verification_mode=__lowerCAmelCase ,in_memory=self.keep_in_memory )
return dataset
class A_ :
def __init__( self: str ,__lowerCAmelCase: Dataset ,__lowerCAmelCase: Union[PathLike, BinaryIO] ,__lowerCAmelCase: Optional[int] = None ,**__lowerCAmelCase: List[Any] ,):
'''simple docstring'''
_lowerCamelCase : Any = dataset
_lowerCamelCase : Any = path_or_buf
_lowerCamelCase : Any = batch_size or get_writer_batch_size(dataset.features )
_lowerCamelCase : List[str] = parquet_writer_kwargs
def _lowercase ( self: Tuple ):
'''simple docstring'''
_lowerCamelCase : Tuple = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf ,(str, bytes, os.PathLike) ):
with open(self.path_or_buf ,"wb+" ) as buffer:
_lowerCamelCase : str = self._write(file_obj=__lowerCAmelCase ,batch_size=__lowerCAmelCase ,**self.parquet_writer_kwargs )
else:
_lowerCamelCase : Optional[int] = self._write(file_obj=self.path_or_buf ,batch_size=__lowerCAmelCase ,**self.parquet_writer_kwargs )
return written
def _lowercase ( self: Optional[Any] ,__lowerCAmelCase: BinaryIO ,__lowerCAmelCase: int ,**__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
_lowerCamelCase : List[str] = 0
_lowerCamelCase : Optional[int] = parquet_writer_kwargs.pop("path_or_buf" ,__lowerCAmelCase )
_lowerCamelCase : List[str] = self.dataset.features.arrow_schema
_lowerCamelCase : str = pq.ParquetWriter(__lowerCAmelCase ,schema=__lowerCAmelCase ,**__lowerCAmelCase )
for offset in logging.tqdm(
range(0 ,len(self.dataset ) ,__lowerCAmelCase ) ,unit="ba" ,disable=not logging.is_progress_bar_enabled() ,desc="Creating parquet from Arrow format" ,):
_lowerCamelCase : List[str] = query_table(
table=self.dataset._data ,key=slice(__lowerCAmelCase ,offset + batch_size ) ,indices=self.dataset._indices if self.dataset._indices is not None else None ,)
writer.write_table(__lowerCAmelCase )
written += batch.nbytes
writer.close()
return written
| 340 | 1 |
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
__a: List[str] = get_logger(__name__)
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , __lowerCAmelCase = None ) -> Union[str, Any]:
lowercase__ : Optional[int] = (
os.path.join(__lowerCAmelCase , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
lowercase__ : Optional[Any] = Extractor
def _lowerCAmelCase( self , __lowerCAmelCase ) -> str:
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
lowercase__ : Dict = os.path.abspath(__lowerCAmelCase )
return os.path.join(self.extract_dir , hash_url_to_filename(__lowerCAmelCase ) )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase ) -> bool:
return force_extract or (
not os.path.isfile(__lowerCAmelCase ) and not (os.path.isdir(__lowerCAmelCase ) and os.listdir(__lowerCAmelCase ))
)
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase = False ) -> str:
lowercase__ : List[Any] = self.extractor.infer_extractor_format(__lowerCAmelCase )
if not extractor_format:
return input_path
lowercase__ : Optional[Any] = self._get_output_path(__lowerCAmelCase )
if self._do_extract(__lowerCAmelCase , __lowerCAmelCase ):
self.extractor.extract(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return output_path
class UpperCAmelCase ( a__ ):
'''simple docstring'''
@classmethod
@abstractmethod
def _lowerCAmelCase( cls , __lowerCAmelCase , **__lowerCAmelCase ) -> bool:
...
@staticmethod
@abstractmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase ) -> None:
...
class UpperCAmelCase ( a__ , a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
with open(__lowerCAmelCase , '''rb''' ) as f:
return f.read(__lowerCAmelCase )
@classmethod
def _lowerCAmelCase( cls , __lowerCAmelCase , __lowerCAmelCase = b"" ) -> bool:
if not magic_number:
lowercase__ : Optional[int] = max(len(__lowerCAmelCase ) for cls_magic_number in cls.magic_numbers )
try:
lowercase__ : Optional[Any] = cls.read_magic_number(__lowerCAmelCase , __lowerCAmelCase )
except OSError:
return False
return any(magic_number.startswith(__lowerCAmelCase ) for cls_magic_number in cls.magic_numbers )
class UpperCAmelCase ( a__ ):
'''simple docstring'''
@classmethod
def _lowerCAmelCase( cls , __lowerCAmelCase , **__lowerCAmelCase ) -> bool:
return tarfile.is_tarfile(__lowerCAmelCase )
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
def resolved(__lowerCAmelCase ) -> str:
return os.path.realpath(os.path.abspath(__lowerCAmelCase ) )
def badpath(__lowerCAmelCase , __lowerCAmelCase ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) ).startswith(__lowerCAmelCase )
def badlink(__lowerCAmelCase , __lowerCAmelCase ) -> bool:
# Links are interpreted relative to the directory containing the link
lowercase__ : List[Any] = resolved(os.path.join(__lowerCAmelCase , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=__lowerCAmelCase )
lowercase__ : List[str] = resolved(__lowerCAmelCase )
for finfo in members:
if badpath(finfo.name , __lowerCAmelCase ):
logger.error(F"""Extraction of {finfo.name} is blocked (illegal path)""" )
elif finfo.issym() and badlink(__lowerCAmelCase , __lowerCAmelCase ):
logger.error(F"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}""" )
elif finfo.islnk() and badlink(__lowerCAmelCase , __lowerCAmelCase ):
logger.error(F"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}""" )
else:
yield finfo
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase ) -> None:
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
lowercase__ : List[str] = tarfile.open(__lowerCAmelCase )
tar_file.extractall(__lowerCAmelCase , members=TarExtractor.safemembers(__lowerCAmelCase , __lowerCAmelCase ) )
tar_file.close()
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = [b"\x1F\x8B"]
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase ) -> None:
with gzip.open(__lowerCAmelCase , '''rb''' ) as gzip_file:
with open(__lowerCAmelCase , '''wb''' ) as extracted_file:
shutil.copyfileobj(__lowerCAmelCase , __lowerCAmelCase )
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = [
b"PK\x03\x04",
b"PK\x05\x06", # empty archive
b"PK\x07\x08", # spanned archive
]
@classmethod
def _lowerCAmelCase( cls , __lowerCAmelCase , __lowerCAmelCase = b"" ) -> bool:
if super().is_extractable(__lowerCAmelCase , magic_number=__lowerCAmelCase ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(__lowerCAmelCase , '''rb''' ) as fp:
lowercase__ : Any = _EndRecData(__lowerCAmelCase )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
lowercase__ : Dict = fp.read(__lowerCAmelCase ) # CD is where we expect it to be
if len(__lowerCAmelCase ) == sizeCentralDir:
lowercase__ : Any = struct.unpack(__lowerCAmelCase , __lowerCAmelCase ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase ) -> None:
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
with zipfile.ZipFile(__lowerCAmelCase , '''r''' ) as zip_file:
zip_file.extractall(__lowerCAmelCase )
zip_file.close()
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = [b"\xFD\x37\x7A\x58\x5A\x00"]
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase ) -> None:
with lzma.open(__lowerCAmelCase ) as compressed_file:
with open(__lowerCAmelCase , '''wb''' ) as extracted_file:
shutil.copyfileobj(__lowerCAmelCase , __lowerCAmelCase )
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = [b"Rar!\x1a\x07\x00", b"Rar!\x1a\x07\x01\x00"] # RAR_ID # RAR5_ID
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase ) -> None:
if not config.RARFILE_AVAILABLE:
raise ImportError('''Please pip install rarfile''' )
import rarfile
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
lowercase__ : Dict = rarfile.RarFile(__lowerCAmelCase )
rf.extractall(__lowerCAmelCase )
rf.close()
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = [b"\x28\xb5\x2F\xFD"]
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase ) -> None:
if not config.ZSTANDARD_AVAILABLE:
raise ImportError('''Please pip install zstandard''' )
import zstandard as zstd
lowercase__ : List[str] = zstd.ZstdDecompressor()
with open(__lowerCAmelCase , '''rb''' ) as ifh, open(__lowerCAmelCase , '''wb''' ) as ofh:
dctx.copy_stream(__lowerCAmelCase , __lowerCAmelCase )
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = [b"\x42\x5A\x68"]
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase ) -> None:
with bza.open(__lowerCAmelCase , '''rb''' ) as compressed_file:
with open(__lowerCAmelCase , '''wb''' ) as extracted_file:
shutil.copyfileobj(__lowerCAmelCase , __lowerCAmelCase )
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = [b"\x37\x7A\xBC\xAF\x27\x1C"]
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase ) -> None:
if not config.PY7ZR_AVAILABLE:
raise ImportError('''Please pip install py7zr''' )
import pyazr
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
with pyazr.SevenZipFile(__lowerCAmelCase , '''r''' ) as archive:
archive.extractall(__lowerCAmelCase )
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = [b"\x04\x22\x4D\x18"]
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase ) -> None:
if not config.LZ4_AVAILABLE:
raise ImportError('''Please pip install lz4''' )
import lza.frame
with lza.frame.open(__lowerCAmelCase , '''rb''' ) as compressed_file:
with open(__lowerCAmelCase , '''wb''' ) as extracted_file:
shutil.copyfileobj(__lowerCAmelCase , __lowerCAmelCase )
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def _lowerCAmelCase( cls ) -> Optional[int]:
return max(
len(__lowerCAmelCase )
for extractor in cls.extractors.values()
if issubclass(__lowerCAmelCase , __lowerCAmelCase )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
try:
return MagicNumberBaseExtractor.read_magic_number(__lowerCAmelCase , magic_number_length=__lowerCAmelCase )
except OSError:
return b""
@classmethod
def _lowerCAmelCase( cls , __lowerCAmelCase , __lowerCAmelCase = False ) -> bool:
warnings.warn(
'''Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
'''Use \'infer_extractor_format\' instead.''' , category=__lowerCAmelCase , )
lowercase__ : int = cls.infer_extractor_format(__lowerCAmelCase )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def _lowerCAmelCase( cls , __lowerCAmelCase ) -> str: # <Added version="2.4.0"/>
lowercase__ : Optional[Any] = cls._get_magic_number_max_length()
lowercase__ : Tuple = cls._read_magic_number(__lowerCAmelCase , __lowerCAmelCase )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(__lowerCAmelCase , magic_number=__lowerCAmelCase ):
return extractor_format
@classmethod
def _lowerCAmelCase( cls , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = "deprecated" , ) -> None:
os.makedirs(os.path.dirname(__lowerCAmelCase ) , exist_ok=__lowerCAmelCase )
# Prevent parallel extractions
lowercase__ : Dict = str(Path(__lowerCAmelCase ).with_suffix('''.lock''' ) )
with FileLock(__lowerCAmelCase ):
shutil.rmtree(__lowerCAmelCase , ignore_errors=__lowerCAmelCase )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(__lowerCAmelCase , __lowerCAmelCase ): # passed as positional arg
warnings.warn(
'''Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
'''Use \'extractor_format\' instead.''' , category=__lowerCAmelCase , )
lowercase__ : Dict = extractor if extractor != '''deprecated''' else extractor_format
else:
lowercase__ : Any = cls.extractors[extractor_format]
return extractor.extract(__lowerCAmelCase , __lowerCAmelCase )
else:
warnings.warn(
'''Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '''
'''exception in 3.0.0.''' , category=__lowerCAmelCase , )
for extractor in cls.extractors.values():
if extractor.is_extractable(__lowerCAmelCase ):
return extractor.extract(__lowerCAmelCase , __lowerCAmelCase )
| 198 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
__a: Tuple = None
__a: Tuple = logging.get_logger(__name__)
__a: Optional[Any] = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
__a: Optional[Any] = {
"""vocab_file""": {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
"""t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
"""t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
"""t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
"""t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
},
"""tokenizer_file""": {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
"""t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
"""t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
"""t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
"""t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
},
}
# TODO(PVP) - this should be removed in Transformers v5
__a: Tuple = {
"""t5-small""": 5_12,
"""t5-base""": 5_12,
"""t5-large""": 5_12,
"""t5-3b""": 5_12,
"""t5-11b""": 5_12,
}
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = ["input_ids", "attention_mask"]
SCREAMING_SNAKE_CASE = TaTokenizer
SCREAMING_SNAKE_CASE = []
def __init__( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase="</s>" , __lowerCAmelCase="<unk>" , __lowerCAmelCase="<pad>" , __lowerCAmelCase=100 , __lowerCAmelCase=None , **__lowerCAmelCase , ) -> Union[str, Any]:
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
lowercase__ : Union[str, Any] = [F"""<extra_id_{i}>""" for i in range(__lowerCAmelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
lowercase__ : Dict = len(set(filter(lambda __lowerCAmelCase : bool('''extra_id_''' in str(__lowerCAmelCase ) ) , __lowerCAmelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
super().__init__(
__lowerCAmelCase , tokenizer_file=__lowerCAmelCase , eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , extra_ids=__lowerCAmelCase , additional_special_tokens=__lowerCAmelCase , **__lowerCAmelCase , )
lowercase__ : Union[str, Any] = vocab_file
lowercase__ : Optional[int] = False if not self.vocab_file else True
lowercase__ : Any = extra_ids
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
lowercase__ : Any = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
F""" {pretrained_model_name_or_path} automatically truncating your input to"""
F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , __lowerCAmelCase , )
return max_model_length
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase = None ) -> Tuple[str]:
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
lowercase__ : List[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 )
logger.info(F"""Copy vocab file to {out_vocab_file}""" )
return (out_vocab_file,)
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase = None ) -> List[int]:
lowercase__ : Any = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
lowercase__ : Dict = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase = None ) -> List[int]:
lowercase__ : Optional[int] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def _lowerCAmelCase( self ) -> List[Any]:
return list(
set(filter(lambda __lowerCAmelCase : bool(re.search(r'''<extra_id_\d+>''' , __lowerCAmelCase ) ) is not None , self.additional_special_tokens ) ) )
def _lowerCAmelCase( self ) -> Tuple:
return [self.convert_tokens_to_ids(__lowerCAmelCase ) for token in self.get_sentinel_tokens()]
| 198 | 1 |
def lowercase_ ( A__ ) -> List[Any]:
"""simple docstring"""
snake_case = len(A__ )
for i in range(length - 1 ):
snake_case = i
for k in range(i + 1 , A__ ):
if collection[k] < collection[least]:
snake_case = k
if least != i:
snake_case , snake_case = (collection[i], collection[least])
return collection
if __name__ == "__main__":
_A = input("Enter numbers separated by a comma:\n").strip()
_A = [int(item) for item in user_input.split(",")]
print(selection_sort(unsorted))
| 137 |
def lowercase_ ( A__ = 1000 ) -> int:
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 137 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCAmelCase : Tuple = {
"xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
"xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
"xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
"xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
"xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
"xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
"xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
"xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
"xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
"xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}
class __snake_case ( lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase__ = """xlm"""
lowerCAmelCase__ = {
"""hidden_size""": """emb_dim""",
"""num_attention_heads""": """n_heads""",
"""num_hidden_layers""": """n_layers""",
"""n_words""": """vocab_size""", # For backward compatibility
}
def __init__( self : Optional[Any] , A : Optional[Any]=30_145 , A : Optional[int]=2_048 , A : List[Any]=12 , A : List[str]=16 , A : Dict=0.1 , A : int=0.1 , A : Union[str, Any]=True , A : Optional[int]=False , A : Any=False , A : Tuple=False , A : Union[str, Any]=1 , A : Any=True , A : Tuple=512 , A : Dict=2_048**-0.5 , A : Union[str, Any]=1E-12 , A : List[Any]=0.02 , A : List[str]=0 , A : int=1 , A : Optional[Any]=2 , A : Optional[int]=3 , A : Union[str, Any]=5 , A : List[str]=True , A : Union[str, Any]="first" , A : Optional[int]=True , A : int=None , A : List[Any]=True , A : str=0.1 , A : List[Any]=5 , A : Optional[Any]=5 , A : Optional[Any]=0 , A : List[Any]=0 , A : List[str]=2 , A : Optional[Any]=0 , **A : Any , ):
__snake_case: int = vocab_size
__snake_case: Optional[int] = emb_dim
__snake_case: str = n_layers
__snake_case: Union[str, Any] = n_heads
__snake_case: int = dropout
__snake_case: Optional[Any] = attention_dropout
__snake_case: Tuple = gelu_activation
__snake_case: str = sinusoidal_embeddings
__snake_case: List[Any] = causal
__snake_case: Union[str, Any] = asm
__snake_case: Optional[Any] = n_langs
__snake_case: Union[str, Any] = use_lang_emb
__snake_case: Optional[Any] = layer_norm_eps
__snake_case: Dict = bos_index
__snake_case: Optional[int] = eos_index
__snake_case: Optional[int] = pad_index
__snake_case: List[str] = unk_index
__snake_case: List[str] = mask_index
__snake_case: Any = is_encoder
__snake_case: Tuple = max_position_embeddings
__snake_case: Optional[int] = embed_init_std
__snake_case: Any = init_std
__snake_case: Dict = summary_type
__snake_case: Tuple = summary_use_proj
__snake_case: int = summary_activation
__snake_case: Union[str, Any] = summary_proj_to_labels
__snake_case: Tuple = summary_first_dropout
__snake_case: Optional[Any] = start_n_top
__snake_case: List[Any] = end_n_top
__snake_case: Tuple = mask_token_id
__snake_case: int = lang_id
if "n_words" in kwargs:
__snake_case: Tuple = kwargs["""n_words"""]
super().__init__(pad_token_id=A , bos_token_id=A , **A )
class __snake_case ( lowerCamelCase_ ):
'''simple docstring'''
@property
def UpperCAmelCase__ ( self : Dict ):
if self.task == "multiple-choice":
__snake_case: int = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__snake_case: Union[str, Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 111 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'vit_mae'
def __init__( self : Union[str, Any] ,snake_case : Any=768 ,snake_case : List[str]=12 ,snake_case : Optional[int]=12 ,snake_case : int=3072 ,snake_case : List[Any]="gelu" ,snake_case : str=0.0 ,snake_case : str=0.0 ,snake_case : Optional[Any]=0.02 ,snake_case : Dict=1e-12 ,snake_case : List[str]=224 ,snake_case : Any=16 ,snake_case : Any=3 ,snake_case : Tuple=True ,snake_case : List[Any]=16 ,snake_case : List[str]=512 ,snake_case : List[Any]=8 ,snake_case : Dict=2048 ,snake_case : Union[str, Any]=0.75 ,snake_case : Union[str, Any]=False ,**snake_case : Optional[int] ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =qkv_bias
SCREAMING_SNAKE_CASE =decoder_num_attention_heads
SCREAMING_SNAKE_CASE =decoder_hidden_size
SCREAMING_SNAKE_CASE =decoder_num_hidden_layers
SCREAMING_SNAKE_CASE =decoder_intermediate_size
SCREAMING_SNAKE_CASE =mask_ratio
SCREAMING_SNAKE_CASE =norm_pix_loss
| 334 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
__magic_name__ = logging.get_logger(__name__)
class lowercase ( A__ ):
'''simple docstring'''
def __init__( self , _snake_case , _snake_case , _snake_case , **_snake_case ) -> str:
"""simple docstring"""
UpperCAmelCase = feature_size
UpperCAmelCase = sampling_rate
UpperCAmelCase = padding_value
UpperCAmelCase = kwargs.pop('''padding_side''' , '''right''' )
UpperCAmelCase = kwargs.pop('''return_attention_mask''' , _snake_case )
super().__init__(**_snake_case )
def snake_case_ ( self , _snake_case , _snake_case = True , _snake_case = None , _snake_case = False , _snake_case = None , _snake_case = None , _snake_case = None , ) -> BatchFeature:
"""simple docstring"""
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(_snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
UpperCAmelCase = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
'''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
f""" to this method that includes {self.model_input_names[0]}, but you provided"""
f""" {list(processed_features.keys() )}""" )
UpperCAmelCase = processed_features[self.model_input_names[0]]
UpperCAmelCase = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(_snake_case ) == 0:
if return_attention_mask:
UpperCAmelCase = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
UpperCAmelCase = required_input[0]
if isinstance(_snake_case , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
UpperCAmelCase = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(_snake_case ):
UpperCAmelCase = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(_snake_case ):
UpperCAmelCase = '''tf'''
elif is_torch_tensor(_snake_case ):
UpperCAmelCase = '''pt'''
elif isinstance(_snake_case , (int, float, list, tuple, np.ndarray) ):
UpperCAmelCase = '''np'''
else:
raise ValueError(
f"""type of {first_element} unknown: {type(_snake_case )}. """
'''Should be one of a python, numpy, pytorch or tensorflow object.''' )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
UpperCAmelCase = to_numpy(_snake_case )
else:
UpperCAmelCase = [to_numpy(_snake_case ) for v in value]
# Convert padding_strategy in PaddingStrategy
UpperCAmelCase = self._get_padding_strategies(padding=_snake_case , max_length=_snake_case )
UpperCAmelCase = processed_features[self.model_input_names[0]]
UpperCAmelCase = len(_snake_case )
if not all(len(_snake_case ) == batch_size for v in processed_features.values() ):
raise ValueError('''Some items in the output dictionary have a different batch size than others.''' )
UpperCAmelCase = []
for i in range(_snake_case ):
UpperCAmelCase = {k: v[i] for k, v in processed_features.items()}
# truncation
UpperCAmelCase = self._truncate(
_snake_case , max_length=_snake_case , pad_to_multiple_of=_snake_case , truncation=_snake_case , )
truncated_inputs.append(_snake_case )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
UpperCAmelCase = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
UpperCAmelCase = PaddingStrategy.MAX_LENGTH
UpperCAmelCase = {}
for i in range(_snake_case ):
# padding
UpperCAmelCase = self._pad(
truncated_inputs[i] , max_length=_snake_case , padding_strategy=_snake_case , pad_to_multiple_of=_snake_case , return_attention_mask=_snake_case , )
for key, value in outputs.items():
if key not in batch_outputs:
UpperCAmelCase = []
if value.dtype is np.dtype(np.floataa ):
UpperCAmelCase = value.astype(np.floataa )
batch_outputs[key].append(_snake_case )
return BatchFeature(_snake_case , tensor_type=_snake_case )
def snake_case_ ( self , _snake_case , _snake_case = None , _snake_case = PaddingStrategy.DO_NOT_PAD , _snake_case = None , _snake_case = None , ) -> dict:
"""simple docstring"""
UpperCAmelCase = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
UpperCAmelCase = len(_snake_case )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
UpperCAmelCase = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
UpperCAmelCase = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_snake_case ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
UpperCAmelCase = np.ones(len(_snake_case ) , dtype=np.intaa )
if needs_to_be_padded:
UpperCAmelCase = max_length - len(_snake_case )
if self.padding_side == "right":
if return_attention_mask:
UpperCAmelCase = np.pad(
processed_features['''attention_mask'''] , (0, difference) )
UpperCAmelCase = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
UpperCAmelCase = np.pad(
_snake_case , _snake_case , '''constant''' , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
UpperCAmelCase = np.pad(
processed_features['''attention_mask'''] , (difference, 0) )
UpperCAmelCase = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
UpperCAmelCase = np.pad(
_snake_case , _snake_case , '''constant''' , constant_values=self.padding_value )
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return processed_features
def snake_case_ ( self , _snake_case , _snake_case = None , _snake_case = None , _snake_case = None , ) -> Optional[Any]:
"""simple docstring"""
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.''' )
UpperCAmelCase = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
UpperCAmelCase = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
UpperCAmelCase = len(_snake_case ) > max_length
if needs_to_be_truncated:
UpperCAmelCase = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
UpperCAmelCase = processed_features['''attention_mask'''][:max_length]
return processed_features
def snake_case_ ( self , _snake_case=False , _snake_case=None ) -> Union[str, Any]:
"""simple docstring"""
# Get padding strategy
if padding is not False:
if padding is True:
UpperCAmelCase = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(_snake_case , _snake_case ):
UpperCAmelCase = PaddingStrategy(_snake_case )
elif isinstance(_snake_case , _snake_case ):
UpperCAmelCase = padding
else:
UpperCAmelCase = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined""" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
'''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.''' )
return padding_strategy
| 355 |
import operator as op
def _lowerCAmelCase ( A__: List[str] ):
'''simple docstring'''
UpperCAmelCase = []
UpperCAmelCase = lambda A__ , A__ : int(x / y ) # noqa: E731 integer division operation
UpperCAmelCase = {
'''^''': op.pow,
'''*''': op.mul,
'''/''': div,
'''+''': op.add,
'''-''': op.sub,
} # operators & their respective operation
# print table header
print('''Symbol'''.center(8 ) , '''Action'''.center(12 ) , '''Stack''' , sep=''' | ''' )
print('''-''' * (30 + len(A__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(A__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('''push(''' + x + ''')''').ljust(12 ) , ''','''.join(A__ ) , sep=''' | ''' )
else:
UpperCAmelCase = stack.pop() # pop stack
# output in tabular format
print(''''''.rjust(8 ) , ('''pop(''' + b + ''')''').ljust(12 ) , ''','''.join(A__ ) , sep=''' | ''' )
UpperCAmelCase = stack.pop() # pop stack
# output in tabular format
print(''''''.rjust(8 ) , ('''pop(''' + a + ''')''').ljust(12 ) , ''','''.join(A__ ) , sep=''' | ''' )
stack.append(
str(opr[x](int(A__ ) , int(A__ ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('''push(''' + a + x + b + ''')''').ljust(12 ) , ''','''.join(A__ ) , sep=''' | ''' , )
return int(stack[0] )
if __name__ == "__main__":
__magic_name__ = input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
print("\n\tResult = ", solve(Postfix))
| 152 | 0 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a ( snake_case__: Dict , snake_case__: str , snake_case__: List[str] ):
'''simple docstring'''
lowercase_ = 1.5
lowercase_ = int(factor * num_class_images )
lowercase_ = ClipClient(
url='''https://knn.laion.ai/knn-service''' , indice_name='''laion_400m''' , num_images=snake_case__ , aesthetic_weight=0.1 )
os.makedirs(F'''{class_data_dir}/images''' , exist_ok=snake_case__ )
if len(list(Path(F'''{class_data_dir}/images''' ).iterdir() ) ) >= num_class_images:
return
while True:
lowercase_ = client.query(text=snake_case__ )
if len(snake_case__ ) >= factor * num_class_images or num_images > 1e4:
break
else:
lowercase_ = int(factor * num_images )
lowercase_ = ClipClient(
url='''https://knn.laion.ai/knn-service''' , indice_name='''laion_400m''' , num_images=snake_case__ , aesthetic_weight=0.1 , )
lowercase_ = 0
lowercase_ = 0
lowercase_ = tqdm(desc='''downloading real regularization images''' , total=snake_case__ )
with open(F'''{class_data_dir}/caption.txt''' , '''w''' ) as fa, open(F'''{class_data_dir}/urls.txt''' , '''w''' ) as fa, open(
F'''{class_data_dir}/images.txt''' , '''w''' ) as fa:
while total < num_class_images:
lowercase_ = class_images[count]
count += 1
try:
lowercase_ = requests.get(images['''url'''] )
if img.status_code == 200:
lowercase_ = Image.open(BytesIO(img.content ) )
with open(F'''{class_data_dir}/images/{total}.jpg''' , '''wb''' ) as f:
f.write(img.content )
fa.write(images['''caption'''] + '''\n''' )
fa.write(images['''url'''] + '''\n''' )
fa.write(F'''{class_data_dir}/images/{total}.jpg''' + '''\n''' )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def a ( ):
'''simple docstring'''
lowercase_ = argparse.ArgumentParser('''''' , add_help=snake_case__ )
parser.add_argument('''--class_prompt''' , help='''text prompt to retrieve images''' , required=snake_case__ , type=snake_case__ )
parser.add_argument('''--class_data_dir''' , help='''path to save images''' , required=snake_case__ , type=snake_case__ )
parser.add_argument('''--num_class_images''' , help='''number of images to download''' , default=200 , type=snake_case__ )
return parser.parse_args()
if __name__ == "__main__":
__a = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 30 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = torch.nn.Linear(10 , 10 )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.optim.SGD(model.parameters() , 0.1 )
SCREAMING_SNAKE_CASE__ : int = Accelerator()
SCREAMING_SNAKE_CASE__ : List[Any] = accelerator.prepare(SCREAMING_SNAKE_CASE__ )
try:
pickle.loads(pickle.dumps(SCREAMING_SNAKE_CASE__ ) )
except Exception as e:
self.fail(F'''Accelerated optimizer pickling failed with {e}''' )
AcceleratorState._reset_state()
| 25 | 0 |
'''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
__SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Tuple = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
# See all MVP models at https://huggingface.co/models?filter=mvp
__SCREAMING_SNAKE_CASE :str = {
"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",
},
}
__SCREAMING_SNAKE_CASE :List[str] = {
"RUCAIBox/mvp": 1024,
}
class A_ ( UpperCamelCase_ ):
_lowerCamelCase : Tuple = VOCAB_FILES_NAMES
_lowerCamelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : Optional[int] = ["""input_ids""", """attention_mask"""]
_lowerCamelCase : int = MvpTokenizer
def __init__( self : Optional[int] , snake_case_ : Union[str, Any]=None , snake_case_ : List[Any]=None , snake_case_ : Optional[int]=None , snake_case_ : Optional[Any]="replace" , snake_case_ : List[str]="<s>" , snake_case_ : int="</s>" , snake_case_ : List[Any]="</s>" , snake_case_ : Any="<s>" , snake_case_ : str="<unk>" , snake_case_ : Optional[int]="<pad>" , snake_case_ : Union[str, Any]="<mask>" , snake_case_ : Union[str, Any]=False , snake_case_ : Optional[int]=True , **snake_case_ : Dict , ):
super().__init__(
_a , _a , tokenizer_file=_a , errors=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , add_prefix_space=_a , trim_offsets=_a , **_a , )
_UpperCAmelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , _a ) != add_prefix_space:
_UpperCAmelCase = getattr(_a , pre_tok_state.pop("type" ) )
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = pre_tok_class(**_a )
_UpperCAmelCase = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
_UpperCAmelCase = """post_processor"""
_UpperCAmelCase = getattr(self.backend_tokenizer , _a , _a )
if tokenizer_component_instance:
_UpperCAmelCase = 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:
_UpperCAmelCase = tuple(state["sep"] )
if "cls" in state:
_UpperCAmelCase = tuple(state["cls"] )
_UpperCAmelCase = False
if state.get("add_prefix_space" , _a ) != add_prefix_space:
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = True
if state.get("trim_offsets" , _a ) != trim_offsets:
_UpperCAmelCase = trim_offsets
_UpperCAmelCase = True
if changes_to_apply:
_UpperCAmelCase = getattr(_a , state.pop("type" ) )
_UpperCAmelCase = component_class(**_a )
setattr(self.backend_tokenizer , _a , _a )
@property
def lowercase ( self : List[Any] ):
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 lowercase ( self : List[str] , snake_case_ : int ):
_UpperCAmelCase = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else value
_UpperCAmelCase = value
def lowercase ( self : Union[str, Any] , *snake_case_ : Any , **snake_case_ : Dict ):
_UpperCAmelCase = kwargs.get("is_split_into_words" , _a )
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(*_a , **_a )
def lowercase ( self : List[str] , *snake_case_ : Any , **snake_case_ : Any ):
_UpperCAmelCase = kwargs.get("is_split_into_words" , _a )
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(*_a , **_a )
def lowercase ( self : Union[str, Any] , snake_case_ : int , snake_case_ : List[str] = None ):
_UpperCAmelCase = self._tokenizer.model.save(_a , name=_a )
return tuple(_a )
def lowercase ( self : List[str] , snake_case_ : str , snake_case_ : Union[str, Any]=None ):
_UpperCAmelCase = [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 lowercase ( self : int , snake_case_ : str , snake_case_ : Tuple = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [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]
| 362 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
'''simple docstring'''
_UpperCAmelCase = set()
# Replace all the whitespace in our sentence
_UpperCAmelCase = input_str.replace(" " , "" )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(__lowercase ) == 26
def UpperCAmelCase_ ( __lowercase : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
'''simple docstring'''
_UpperCAmelCase = [False] * 26
for char in input_str:
if char.islower():
_UpperCAmelCase = True
elif char.isupper():
_UpperCAmelCase = True
return all(__lowercase )
def UpperCAmelCase_ ( __lowercase : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
'''simple docstring'''
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def UpperCAmelCase_ ( ) -> None:
'''simple docstring'''
from timeit import timeit
_UpperCAmelCase = "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
print(timeit("is_pangram()" , setup=__lowercase ) )
print(timeit("is_pangram_faster()" , setup=__lowercase ) )
print(timeit("is_pangram_fastest()" , setup=__lowercase ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 156 | 0 |
lowercase : Union[str, Any] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( A__ , A__ , A__ ) -> list[str]:
a__ : List[str] = set()
# keep track of all the paths to be checked
a__ : Union[str, Any] = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
a__ : Tuple = queue.pop(0 )
# get the last node from the path
a__ : Optional[int] = path[-1]
if node not in explored:
a__ : List[str] = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
a__ : Optional[Any] = list(A__ )
new_path.append(A__ )
queue.append(A__ )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(A__ )
# in case there's no path between the 2 nodes
return []
def A_ ( A__ , A__ , A__ ) -> int:
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
a__ : Tuple = [start]
a__ : Union[str, Any] = set(A__ )
# Keep tab on distances from `start` node.
a__ : Optional[Any] = {start: 0, target: -1}
while queue:
a__ : str = queue.pop(0 )
if node == target:
a__ : List[Any] = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(A__ )
queue.append(A__ )
a__ : int = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, """G""", """D""")) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, """G""", """D""")) # returns 4
| 99 |
def lowerCAmelCase_ ( __UpperCAmelCase: int ) -> int:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
UpperCamelCase__ : Optional[Any] = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 201 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Tuple = {
'configuration_distilbert': [
'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'DistilBertConfig',
'DistilBertOnnxConfig',
],
'tokenization_distilbert': ['DistilBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : str = ['DistilBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Any = [
'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DistilBertForMaskedLM',
'DistilBertForMultipleChoice',
'DistilBertForQuestionAnswering',
'DistilBertForSequenceClassification',
'DistilBertForTokenClassification',
'DistilBertModel',
'DistilBertPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDistilBertForMaskedLM',
'TFDistilBertForMultipleChoice',
'TFDistilBertForQuestionAnswering',
'TFDistilBertForSequenceClassification',
'TFDistilBertForTokenClassification',
'TFDistilBertMainLayer',
'TFDistilBertModel',
'TFDistilBertPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'FlaxDistilBertForMaskedLM',
'FlaxDistilBertForMultipleChoice',
'FlaxDistilBertForQuestionAnswering',
'FlaxDistilBertForSequenceClassification',
'FlaxDistilBertForTokenClassification',
'FlaxDistilBertModel',
'FlaxDistilBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
__lowercase : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 352 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = None
A_ = None
A_ = None
A_ = None
class __UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self , __a=1 , __a=0 , __a=2 , __a=512 , __a="cls" , __a=False , __a=True , **__a , ):
'''simple docstring'''
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
__a : Any = project_dim
__a : Optional[Any] = pooler_fn
__a : int = learn_encoder
__a : str = use_attention_mask
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = [r"pooler", r"logit_scale"]
A_ = [r"position_ids", r"predictions.decoder.bias"]
A_ = "roberta"
A_ = RobertaSeriesConfig
def __init__( self , __a ):
'''simple docstring'''
super().__init__(__a )
__a : Optional[Any] = XLMRobertaModel(__a )
__a : str = nn.Linear(config.hidden_size , config.project_dim )
__a : Optional[int] = getattr(__a , 'has_pre_transformation' , __a )
if self.has_pre_transformation:
__a : int = nn.Linear(config.hidden_size , config.project_dim )
__a : List[str] = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def __UpperCAmelCase ( self , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , ):
'''simple docstring'''
__a : Optional[Any] = return_dict if return_dict is not None else self.config.use_return_dict
__a : Tuple = self.base_model(
input_ids=__a , attention_mask=__a , token_type_ids=__a , position_ids=__a , head_mask=__a , inputs_embeds=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , output_attentions=__a , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=__a , )
if self.has_pre_transformation:
__a : Optional[Any] = outputs['hidden_states'][-2]
__a : Optional[int] = self.pre_LN(__a )
__a : Union[str, Any] = self.transformation_pre(__a )
return TransformationModelOutput(
projection_state=__a , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
__a : Optional[Any] = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=__a , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 294 | 0 |
import os
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
__lowerCAmelCase : str =logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] ={'vocab_file': 'sentencepiece.bpe.model'}
__lowerCAmelCase : Dict ={
'vocab_file': {
'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez-orangesum-title': (
'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
),
},
}
__lowerCAmelCase : List[str] ={
'moussaKam/mbarthez': 1_0_2_4,
'moussaKam/barthez': 1_0_2_4,
'moussaKam/barthez-orangesum-title': 1_0_2_4,
}
__lowerCAmelCase : Any ='▁'
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ : Tuple = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ : List[str] = ['''input_ids''', '''attention_mask''']
def __init__( self :Tuple , lowerCAmelCase__ :int , lowerCAmelCase__ :List[str]="<s>" , lowerCAmelCase__ :Optional[int]="</s>" , lowerCAmelCase__ :List[Any]="</s>" , lowerCAmelCase__ :List[str]="<s>" , lowerCAmelCase__ :str="<unk>" , lowerCAmelCase__ :List[str]="<pad>" , lowerCAmelCase__ :str="<mask>" , lowerCAmelCase__ :Optional[Dict[str, Any]] = None , **lowerCAmelCase__ :Optional[int] , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
__SCREAMING_SNAKE_CASE : Dict = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else mask_token
__SCREAMING_SNAKE_CASE : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase__ , )
__SCREAMING_SNAKE_CASE : Optional[Any] = vocab_file
__SCREAMING_SNAKE_CASE : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCAmelCase__ ) )
__SCREAMING_SNAKE_CASE : List[str] = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
__SCREAMING_SNAKE_CASE : str = len(self.sp_model ) - 1
__SCREAMING_SNAKE_CASE : List[Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __magic_name__( self :int , lowerCAmelCase__ :List[int] , lowerCAmelCase__ :Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__SCREAMING_SNAKE_CASE : str = [self.cls_token_id]
__SCREAMING_SNAKE_CASE : Optional[int] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __magic_name__( self :List[str] , lowerCAmelCase__ :List[int] , lowerCAmelCase__ :Optional[List[int]] = None , lowerCAmelCase__ :bool = False ) -> List[int]:
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 __magic_name__( self :Tuple , lowerCAmelCase__ :List[int] , lowerCAmelCase__ :Optional[List[int]] = None ) -> List[int]:
__SCREAMING_SNAKE_CASE : List[str] = [self.sep_token_id]
__SCREAMING_SNAKE_CASE : 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]
@property
def __magic_name__( self :int ) -> Optional[int]:
return len(self.sp_model )
def __magic_name__( self :Dict ) -> Any:
__SCREAMING_SNAKE_CASE : Optional[Any] = {self.convert_ids_to_tokens(lowerCAmelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __magic_name__( self :Any , lowerCAmelCase__ :str ) -> List[str]:
return self.sp_model.encode(lowerCAmelCase__ , out_type=lowerCAmelCase__ )
def __magic_name__( self :Union[str, Any] , lowerCAmelCase__ :Optional[Any] ) -> List[Any]:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.sp_model.PieceToId(lowerCAmelCase__ )
return spm_id if spm_id else self.unk_token_id
def __magic_name__( self :str , lowerCAmelCase__ :Any ) -> List[Any]:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(lowerCAmelCase__ )
def __magic_name__( self :Any , lowerCAmelCase__ :Tuple ) -> Tuple:
__SCREAMING_SNAKE_CASE : int = []
__SCREAMING_SNAKE_CASE : Any = ''''''
__SCREAMING_SNAKE_CASE : 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(lowerCAmelCase__ ) + token
__SCREAMING_SNAKE_CASE : Any = True
__SCREAMING_SNAKE_CASE : str = []
else:
current_sub_tokens.append(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[Any] = False
out_string += self.sp_model.decode(lowerCAmelCase__ )
return out_string.strip()
def __getstate__( self :str ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE : int = self.__dict__.copy()
__SCREAMING_SNAKE_CASE : Optional[int] = None
return state
def __setstate__( self :int , lowerCAmelCase__ :Optional[int] ) -> str:
__SCREAMING_SNAKE_CASE : Optional[int] = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = {}
__SCREAMING_SNAKE_CASE : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __magic_name__( self :Any , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__SCREAMING_SNAKE_CASE : 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__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCAmelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCAmelCase__ , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE : Tuple = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase__ )
return (out_vocab_file,)
| 9 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase : Optional[int] =logging.get_logger(__name__)
__lowerCAmelCase : Optional[Any] ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
__lowerCAmelCase : List[str] ={
'tokenizer_file': {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
},
}
__lowerCAmelCase : Optional[int] ={
'gpt-neox-20b': 2_0_4_8,
}
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ : Dict = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ : Optional[Any] = ['''input_ids''', '''attention_mask''']
def __init__( self :int , lowerCAmelCase__ :Any=None , lowerCAmelCase__ :Optional[Any]=None , lowerCAmelCase__ :List[Any]=None , lowerCAmelCase__ :str="<|endoftext|>" , lowerCAmelCase__ :str="<|endoftext|>" , lowerCAmelCase__ :Dict="<|endoftext|>" , lowerCAmelCase__ :Union[str, Any]=False , **lowerCAmelCase__ :List[str] , ) -> Any:
super().__init__(
lowerCAmelCase__ , lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , **lowerCAmelCase__ , )
__SCREAMING_SNAKE_CASE : List[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , lowerCAmelCase__ ) != add_prefix_space:
__SCREAMING_SNAKE_CASE : List[str] = getattr(lowerCAmelCase__ , pre_tok_state.pop('''type''' ) )
__SCREAMING_SNAKE_CASE : str = add_prefix_space
__SCREAMING_SNAKE_CASE : Any = pre_tok_class(**lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = add_prefix_space
def __magic_name__( self :Union[str, Any] , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[str] = None ) -> Tuple[str]:
__SCREAMING_SNAKE_CASE : List[str] = self._tokenizer.model.save(lowerCAmelCase__ , name=lowerCAmelCase__ )
return tuple(lowerCAmelCase__ )
def __magic_name__( self :Optional[Any] , lowerCAmelCase__ :"Conversation" ) -> List[int]:
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ ) + [self.eos_token_id] )
if len(lowerCAmelCase__ ) > self.model_max_length:
__SCREAMING_SNAKE_CASE : List[str] = input_ids[-self.model_max_length :]
return input_ids
| 9 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class _a ( lowerCAmelCase):
"""simple docstring"""
def lowercase__ ( self : Any )->int:
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = 8
# DPR tok
_UpperCAmelCase = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_UpperCAmelCase = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(__UpperCamelCase , exist_ok=__UpperCamelCase )
_UpperCAmelCase = os.path.join(__UpperCamelCase , DPR_VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
# BART tok
_UpperCAmelCase = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
_UpperCAmelCase = dict(zip(__UpperCamelCase , range(len(__UpperCamelCase ) ) ) )
_UpperCAmelCase = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
_UpperCAmelCase = {'''unk_token''': '''<unk>'''}
_UpperCAmelCase = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(__UpperCamelCase , exist_ok=__UpperCamelCase )
_UpperCAmelCase = os.path.join(__UpperCamelCase , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
_UpperCAmelCase = os.path.join(__UpperCamelCase , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__UpperCamelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__UpperCamelCase ) )
def lowercase__ ( self : Tuple )->DPRQuestionEncoderTokenizer:
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def lowercase__ ( self : Dict )->BartTokenizer:
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def lowercase__ ( self : Tuple )->List[Any]:
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def lowercase__ ( self : Union[str, Any] )->int:
_UpperCAmelCase = os.path.join(self.tmpdirname , '''rag_tokenizer''' )
_UpperCAmelCase = RagConfig(question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() )
_UpperCAmelCase = RagTokenizer(question_encoder=self.get_dpr_tokenizer() , generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(__UpperCamelCase )
rag_tokenizer.save_pretrained(__UpperCamelCase )
_UpperCAmelCase = RagTokenizer.from_pretrained(__UpperCamelCase , config=__UpperCamelCase )
self.assertIsInstance(new_rag_tokenizer.question_encoder , __UpperCamelCase )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() , rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator , __UpperCamelCase )
self.assertEqual(new_rag_tokenizer.generator.get_vocab() , rag_tokenizer.generator.get_vocab() )
@slow
def lowercase__ ( self : Dict )->Optional[int]:
_UpperCAmelCase = RagTokenizer.from_pretrained('''facebook/rag-token-nq''' )
_UpperCAmelCase = [
'''who got the first nobel prize in physics''',
'''when is the next deadpool movie being released''',
'''which mode is used for short wave broadcast service''',
'''who is the owner of reading football club''',
'''when is the next scandal episode coming out''',
'''when is the last time the philadelphia won the superbowl''',
'''what is the most current adobe flash player version''',
'''how many episodes are there in dragon ball z''',
'''what is the first step in the evolution of the eye''',
'''where is gall bladder situated in human body''',
'''what is the main mineral in lithium batteries''',
'''who is the president of usa right now''',
'''where do the greasers live in the outsiders''',
'''panda is a national animal of which country''',
'''what is the name of manchester united stadium''',
]
_UpperCAmelCase = tokenizer(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
@slow
def lowercase__ ( self : str )->List[Any]:
_UpperCAmelCase = RagTokenizer.from_pretrained('''facebook/rag-sequence-nq''' )
_UpperCAmelCase = [
'''who got the first nobel prize in physics''',
'''when is the next deadpool movie being released''',
'''which mode is used for short wave broadcast service''',
'''who is the owner of reading football club''',
'''when is the next scandal episode coming out''',
'''when is the last time the philadelphia won the superbowl''',
'''what is the most current adobe flash player version''',
'''how many episodes are there in dragon ball z''',
'''what is the first step in the evolution of the eye''',
'''where is gall bladder situated in human body''',
'''what is the main mineral in lithium batteries''',
'''who is the president of usa right now''',
'''where do the greasers live in the outsiders''',
'''panda is a national animal of which country''',
'''what is the name of manchester united stadium''',
]
_UpperCAmelCase = tokenizer(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
| 326 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
with open(_SCREAMING_SNAKE_CASE ) as metadata_file:
_UpperCAmelCase = json.load(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = LukeConfig(use_entity_aware_attention=_SCREAMING_SNAKE_CASE , **metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
_UpperCAmelCase = torch.load(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )['''module''']
# Load the entity vocab file
_UpperCAmelCase = load_original_entity_vocab(_SCREAMING_SNAKE_CASE )
# add an entry for [MASK2]
_UpperCAmelCase = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
_UpperCAmelCase = XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
_UpperCAmelCase = AddedToken('''<ent>''' , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = AddedToken('''<ent2>''' , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
with open(os.path.join(_SCREAMING_SNAKE_CASE , '''tokenizer_config.json''' ) , '''r''' ) as f:
_UpperCAmelCase = json.load(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = '''MLukeTokenizer'''
with open(os.path.join(_SCREAMING_SNAKE_CASE , '''tokenizer_config.json''' ) , '''w''' ) as f:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
with open(os.path.join(_SCREAMING_SNAKE_CASE , MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ) , '''w''' ) as f:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
# Initialize the embeddings of the special tokens
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(['''@'''] )[0]
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(['''#'''] )[0]
_UpperCAmelCase = state_dict['''embeddings.word_embeddings.weight''']
_UpperCAmelCase = word_emb[ent_init_index].unsqueeze(0 )
_UpperCAmelCase = word_emb[enta_init_index].unsqueeze(0 )
_UpperCAmelCase = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
_UpperCAmelCase = state_dict[bias_name]
_UpperCAmelCase = decoder_bias[ent_init_index].unsqueeze(0 )
_UpperCAmelCase = decoder_bias[enta_init_index].unsqueeze(0 )
_UpperCAmelCase = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
_UpperCAmelCase = f'encoder.layer.{layer_index}.attention.self.'
_UpperCAmelCase = state_dict[prefix + matrix_name]
_UpperCAmelCase = state_dict[prefix + matrix_name]
_UpperCAmelCase = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
_UpperCAmelCase = state_dict['''entity_embeddings.entity_embeddings.weight''']
_UpperCAmelCase = entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
_UpperCAmelCase = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
_UpperCAmelCase = state_dict['''entity_predictions.bias''']
_UpperCAmelCase = entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
_UpperCAmelCase = torch.cat([entity_prediction_bias, entity_mask_bias] )
_UpperCAmelCase = LukeForMaskedLM(config=_SCREAMING_SNAKE_CASE ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
_UpperCAmelCase = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
_UpperCAmelCase = state_dict[key]
else:
_UpperCAmelCase = state_dict[key]
_UpperCAmelCase , _UpperCAmelCase = model.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
if set(_SCREAMING_SNAKE_CASE ) != {"luke.embeddings.position_ids"}:
raise ValueError(f'Unexpected unexpected_keys: {unexpected_keys}' )
if set(_SCREAMING_SNAKE_CASE ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(f'Unexpected missing_keys: {missing_keys}' )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
_UpperCAmelCase = MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE , task='''entity_classification''' )
_UpperCAmelCase = '''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
_UpperCAmelCase = (0, 9)
_UpperCAmelCase = tokenizer(_SCREAMING_SNAKE_CASE , entity_spans=[span] , return_tensors='''pt''' )
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_UpperCAmelCase = torch.Size((1, 33, 768) )
_UpperCAmelCase = torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_UpperCAmelCase = torch.Size((1, 1, 768) )
_UpperCAmelCase = torch.tensor([[-0.1482, 0.0609, 0.0322]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
f' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 ):
raise ValueError
# Verify masked word/entity prediction
_UpperCAmelCase = MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = '''Tokyo is the capital of <mask>.'''
_UpperCAmelCase = (24, 30)
_UpperCAmelCase = tokenizer(_SCREAMING_SNAKE_CASE , entity_spans=[span] , return_tensors='''pt''' )
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = encoding['''input_ids'''][0].tolist()
_UpperCAmelCase = input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
_UpperCAmelCase = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.entity_logits[0][0].argmax().item()
_UpperCAmelCase = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(_SCREAMING_SNAKE_CASE ) )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
_UpperCAmelCase = ['''[MASK]''', '''[PAD]''', '''[UNK]''']
_UpperCAmelCase = [json.loads(_SCREAMING_SNAKE_CASE ) for line in open(_SCREAMING_SNAKE_CASE )]
_UpperCAmelCase = {}
for entry in data:
_UpperCAmelCase = entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
_UpperCAmelCase = entity_id
break
_UpperCAmelCase = f'{language}:{entity_name}'
_UpperCAmelCase = entity_id
return new_mapping
if __name__ == "__main__":
__A : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
parser.add_argument(
"--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
)
parser.add_argument(
"--entity_vocab_path",
default=None,
type=str,
help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
)
parser.add_argument(
"--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
)
__A : List[str] = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 326 | 1 |
'''simple docstring'''
import requests
def __lowercase ( __lowercase , __lowercase ) -> None:
'''simple docstring'''
_A = {"Content-Type": "application/json"}
_A = requests.post(__lowercase , json={"text": message_body} , headers=__lowercase )
if response.status_code != 200:
_A = (
"Request to slack returned an error "
F'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(__lowercase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 79 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = PegasusConfig
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = '''gelu'''
def __init__( self , lowercase , lowercase=1_3 , lowercase=7 , lowercase=True , lowercase=False , lowercase=9_9 , lowercase=3_2 , lowercase=2 , lowercase=4 , lowercase=3_7 , lowercase=0.1 , lowercase=0.1 , lowercase=4_0 , lowercase=2 , lowercase=1 , lowercase=0 , ) -> Any:
__UpperCamelCase = parent
__UpperCamelCase = batch_size
__UpperCamelCase = seq_length
__UpperCamelCase = is_training
__UpperCamelCase = use_labels
__UpperCamelCase = vocab_size
__UpperCamelCase = hidden_size
__UpperCamelCase = num_hidden_layers
__UpperCamelCase = num_attention_heads
__UpperCamelCase = intermediate_size
__UpperCamelCase = hidden_dropout_prob
__UpperCamelCase = attention_probs_dropout_prob
__UpperCamelCase = max_position_embeddings
__UpperCamelCase = eos_token_id
__UpperCamelCase = pad_token_id
__UpperCamelCase = bos_token_id
def __lowerCamelCase ( self ) -> Dict:
__UpperCamelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__UpperCamelCase = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__UpperCamelCase = tf.concat([input_ids, eos_tensor] , axis=1 )
__UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase = 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 , )
__UpperCamelCase = prepare_pegasus_inputs_dict(lowercase , lowercase , lowercase )
return config, inputs_dict
def __lowerCamelCase ( self , lowercase , lowercase ) -> Union[str, Any]:
__UpperCamelCase = TFPegasusModel(config=lowercase ).get_decoder()
__UpperCamelCase = inputs_dict["""input_ids"""]
__UpperCamelCase = input_ids[:1, :]
__UpperCamelCase = inputs_dict["""attention_mask"""][:1, :]
__UpperCamelCase = inputs_dict["""head_mask"""]
__UpperCamelCase = 1
# first forward pass
__UpperCamelCase = model(lowercase , attention_mask=lowercase , head_mask=lowercase , use_cache=lowercase )
__UpperCamelCase , __UpperCamelCase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__UpperCamelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
__UpperCamelCase = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__UpperCamelCase = tf.concat([input_ids, next_tokens] , axis=-1 )
__UpperCamelCase = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__UpperCamelCase = model(lowercase , attention_mask=lowercase )[0]
__UpperCamelCase = model(lowercase , attention_mask=lowercase , past_key_values=lowercase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__UpperCamelCase = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__UpperCamelCase = output_from_no_past[:, -3:, random_slice_idx]
__UpperCamelCase = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowercase , lowercase , rtol=1E-3 )
def _lowercase ( __A ,__A ,__A ,__A=None ,__A=None ,__A=None ,__A=None ,__A=None ,):
'''simple docstring'''
if attention_mask is None:
__UpperCamelCase = tf.cast(tf.math.not_equal(__A ,config.pad_token_id ) ,tf.inta )
if decoder_attention_mask is None:
__UpperCamelCase = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape ,dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,config.pad_token_id ) ,tf.inta ),
] ,axis=-1 ,)
if head_mask is None:
__UpperCamelCase = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__UpperCamelCase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__UpperCamelCase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCAmelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase):
__SCREAMING_SNAKE_CASE = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
__SCREAMING_SNAKE_CASE = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
__SCREAMING_SNAKE_CASE = (
{
'''conversational''': TFPegasusForConditionalGeneration,
'''feature-extraction''': TFPegasusModel,
'''summarization''': TFPegasusForConditionalGeneration,
'''text2text-generation''': TFPegasusForConditionalGeneration,
'''translation''': TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
def __lowerCamelCase ( self ) -> str:
__UpperCamelCase = TFPegasusModelTester(self )
__UpperCamelCase = ConfigTester(self , config_class=lowercase )
def __lowerCamelCase ( self ) -> str:
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ) -> Tuple:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowercase )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCAmelCase__ ( unittest.TestCase):
__SCREAMING_SNAKE_CASE = [
''' 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!" ''',
]
__SCREAMING_SNAKE_CASE = [
'''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
''' reduce the risk of wildfires.''',
'''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
] # differs slightly from pytorch, likely due to numerical differences in linear layers
__SCREAMING_SNAKE_CASE = '''google/pegasus-xsum'''
@cached_property
def __lowerCamelCase ( self ) -> int:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def __lowerCamelCase ( self ) -> str:
__UpperCamelCase = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def __lowerCamelCase ( self , **lowercase ) -> Optional[int]:
__UpperCamelCase = self.translate_src_text(**lowercase )
assert self.expected_text == generated_words
def __lowerCamelCase ( self , **lowercase ) -> Optional[Any]:
__UpperCamelCase = self.tokenizer(self.src_text , **lowercase , padding=lowercase , return_tensors="""tf""" )
__UpperCamelCase = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=lowercase , )
__UpperCamelCase = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=lowercase )
return generated_words
@slow
def __lowerCamelCase ( self ) -> Dict:
self._assert_generated_batch_equal_expected()
| 349 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : int = logging.get_logger(__name__)
lowercase__ : Union[str, Any] = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class UpperCamelCase__ ( lowerCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = "vit_msn"
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Dict=7_6_8 , SCREAMING_SNAKE_CASE_ : Any=1_2 , SCREAMING_SNAKE_CASE_ : Tuple=1_2 , SCREAMING_SNAKE_CASE_ : Dict=3_0_7_2 , SCREAMING_SNAKE_CASE_ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE_ : int=0.0 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.0 , SCREAMING_SNAKE_CASE_ : Optional[int]=0.02 , SCREAMING_SNAKE_CASE_ : Tuple=1E-06 , SCREAMING_SNAKE_CASE_ : str=2_2_4 , SCREAMING_SNAKE_CASE_ : Optional[Any]=1_6 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=3 , SCREAMING_SNAKE_CASE_ : Tuple=True , **SCREAMING_SNAKE_CASE_ : Optional[Any] , ):
super().__init__(**a__ )
lowerCAmelCase_ : List[str] = hidden_size
lowerCAmelCase_ : int = num_hidden_layers
lowerCAmelCase_ : Optional[int] = num_attention_heads
lowerCAmelCase_ : List[Any] = intermediate_size
lowerCAmelCase_ : Optional[Any] = hidden_act
lowerCAmelCase_ : Tuple = hidden_dropout_prob
lowerCAmelCase_ : List[str] = attention_probs_dropout_prob
lowerCAmelCase_ : int = initializer_range
lowerCAmelCase_ : Dict = layer_norm_eps
lowerCAmelCase_ : List[str] = image_size
lowerCAmelCase_ : List[Any] = patch_size
lowerCAmelCase_ : List[str] = num_channels
lowerCAmelCase_ : Union[str, Any] = qkv_bias
| 364 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCamelCase__ :
"""simple docstring"""
pass
| 289 | 0 |
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_big_bird import BigBirdTokenizer
else:
__snake_case = None
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case = {
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
},
'''tokenizer_file''': {
'''google/bigbird-roberta-base''': (
'''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
),
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
),
},
}
__snake_case = {
'''google/bigbird-roberta-base''': 40_96,
'''google/bigbird-roberta-large''': 40_96,
'''google/bigbird-base-trivia-itc''': 40_96,
}
__snake_case = '''▁'''
class __snake_case ( A__ ):
__lowerCamelCase : Tuple = VOCAB_FILES_NAMES
__lowerCamelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Optional[int] = BigBirdTokenizer
__lowerCamelCase : List[Any] = ["""input_ids""", """attention_mask"""]
__lowerCamelCase : Union[str, Any] = []
def __init__( self , snake_case__=None , snake_case__=None , snake_case__="<unk>" , snake_case__="<s>" , snake_case__="</s>" , snake_case__="<pad>" , snake_case__="[SEP]" , snake_case__="[MASK]" , snake_case__="[CLS]" , **snake_case__ , ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Dict =AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else bos_token
UpperCAmelCase : int =AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else eos_token
UpperCAmelCase : Dict =AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else unk_token
UpperCAmelCase : str =AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else pad_token
UpperCAmelCase : int =AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else cls_token
UpperCAmelCase : List[str] =AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase : List[Any] =AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else mask_token
super().__init__(
_A , tokenizer_file=_A , bos_token=_A , eos_token=_A , unk_token=_A , sep_token=_A , pad_token=_A , cls_token=_A , mask_token=_A , **_A , )
UpperCAmelCase : Optional[int] =vocab_file
UpperCAmelCase : Union[str, Any] =False if not self.vocab_file else True
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> int:
'''simple docstring'''
UpperCAmelCase : List[Any] =[self.sep_token_id]
UpperCAmelCase : Optional[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 , snake_case__ , snake_case__ = None , snake_case__ = False ) -> str:
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(_A )) + [1]
return [1] + ([0] * len(_A )) + [1] + ([0] * len(_A )) + [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Optional[int] =[self.sep_token_id]
UpperCAmelCase : List[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 ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[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(_A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : List[str] =os.path.join(
_A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_A ):
copyfile(self.vocab_file , _A )
return (out_vocab_file,)
| 348 |
from functools import lru_cache
@lru_cache
def _snake_case ( lowerCAmelCase : int ):
"""simple docstring"""
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 18 | 0 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
snake_case : List[str] = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': operator.gt,
}
def __lowercase ( __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any ):
if got_ver is None or want_ver is None:
raise ValueError(
F'Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'
F' reinstalling {pkg}.' )
if not ops[op](version.parse(__lowerCAmelCase ) , version.parse(__lowerCAmelCase ) ):
raise ImportError(
F'{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}' )
def __lowercase ( __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ):
a__ = F'\n{hint}' if hint is not None else ''
# non-versioned check
if re.match(R'^[\w_\-\d]+$' , __lowerCAmelCase ):
a__ , a__ , a__ = requirement, None, None
else:
a__ = re.findall(R'^([^!=<>\s]+)([\s!=<>]{1,2}.+)' , __lowerCAmelCase )
if not match:
raise ValueError(
'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
F' got {requirement}' )
a__ , a__ = match[0]
a__ = want_full.split(',' ) # there could be multiple requirements
a__ = {}
for w in want_range:
a__ = re.findall(R'^([\s!=<>]{1,2})(.+)' , __lowerCAmelCase )
if not match:
raise ValueError(
'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
F' but got {requirement}' )
a__ , a__ = match[0]
a__ = want_ver
if op not in ops:
raise ValueError(F'{requirement}: need one of {list(ops.keys() )}, but got {op}' )
# special case
if pkg == "python":
a__ = '.'.join([str(__lowerCAmelCase ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return
# check if any version is installed
try:
a__ = importlib.metadata.version(__lowerCAmelCase )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
F'The \'{requirement}\' distribution was not found and is required by this application. {hint}' )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def __lowercase ( __lowerCAmelCase : List[str] ):
a__ = 'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
return require_version(__lowerCAmelCase , __lowerCAmelCase )
| 109 |
from __future__ import annotations
def __lowercase ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str , __lowerCAmelCase : List[str] ): # noqa: E741
while r - l > 1:
a__ = (l + r) // 2
if v[m] >= key:
a__ = m
else:
a__ = m # noqa: E741
return r
def __lowercase ( __lowerCAmelCase : list[int] ):
if len(__lowerCAmelCase ) == 0:
return 0
a__ = [0] * len(__lowerCAmelCase )
a__ = 1
a__ = v[0]
for i in range(1 , len(__lowerCAmelCase ) ):
if v[i] < tail[0]:
a__ = v[i]
elif v[i] > tail[length - 1]:
a__ = v[i]
length += 1
else:
a__ = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 109 | 1 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
A__ = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}
class a ( __snake_case ):
__lowerCAmelCase : Union[str, Any] = "ernie_m"
__lowerCAmelCase : Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self :str ,__lowercase :int = 2_5_0_0_0_2 ,__lowercase :int = 7_6_8 ,__lowercase :int = 1_2 ,__lowercase :int = 1_2 ,__lowercase :int = 3_0_7_2 ,__lowercase :str = "gelu" ,__lowercase :float = 0.1 ,__lowercase :float = 0.1 ,__lowercase :int = 5_1_4 ,__lowercase :float = 0.02 ,__lowercase :int = 1 ,__lowercase :float = 1e-0_5 ,__lowercase :Any=None ,__lowercase :List[Any]=False ,__lowercase :Tuple=0.0 ,**__lowercase :Optional[int] ,):
super().__init__(pad_token_id=lowerCamelCase_ ,**lowerCamelCase_ )
snake_case__ : Optional[Any] = vocab_size
snake_case__ : Any = hidden_size
snake_case__ : Optional[Any] = num_hidden_layers
snake_case__ : Union[str, Any] = num_attention_heads
snake_case__ : List[Any] = intermediate_size
snake_case__ : List[Any] = hidden_act
snake_case__ : Any = hidden_dropout_prob
snake_case__ : List[Any] = attention_probs_dropout_prob
snake_case__ : str = max_position_embeddings
snake_case__ : Union[str, Any] = initializer_range
snake_case__ : Union[str, Any] = layer_norm_eps
snake_case__ : List[Any] = classifier_dropout
snake_case__ : str = is_decoder
snake_case__ : List[str] = act_dropout
| 230 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}
class _snake_case ( __snake_case ):
'''simple docstring'''
A__ : Optional[Any] = "layoutlmv3"
def __init__( self: str ,lowerCamelCase_: Any=50265 ,lowerCamelCase_: int=768 ,lowerCamelCase_: Any=12 ,lowerCamelCase_: Any=12 ,lowerCamelCase_: List[Any]=3072 ,lowerCamelCase_: str="gelu" ,lowerCamelCase_: List[str]=0.1 ,lowerCamelCase_: Any=0.1 ,lowerCamelCase_: Tuple=512 ,lowerCamelCase_: Union[str, Any]=2 ,lowerCamelCase_: Dict=0.0_2 ,lowerCamelCase_: List[str]=1e-5 ,lowerCamelCase_: int=1 ,lowerCamelCase_: int=0 ,lowerCamelCase_: List[str]=2 ,lowerCamelCase_: Dict=1024 ,lowerCamelCase_: Tuple=128 ,lowerCamelCase_: Tuple=128 ,lowerCamelCase_: Dict=True ,lowerCamelCase_: Union[str, Any]=32 ,lowerCamelCase_: Union[str, Any]=128 ,lowerCamelCase_: Tuple=64 ,lowerCamelCase_: Tuple=256 ,lowerCamelCase_: List[str]=True ,lowerCamelCase_: Optional[int]=True ,lowerCamelCase_: Any=True ,lowerCamelCase_: Dict=224 ,lowerCamelCase_: Optional[int]=3 ,lowerCamelCase_: Optional[int]=16 ,lowerCamelCase_: Dict=None ,**lowerCamelCase_: str ,) -> List[Any]:
super().__init__(
vocab_size=lowerCamelCase_ ,hidden_size=lowerCamelCase_ ,num_hidden_layers=lowerCamelCase_ ,num_attention_heads=lowerCamelCase_ ,intermediate_size=lowerCamelCase_ ,hidden_act=lowerCamelCase_ ,hidden_dropout_prob=lowerCamelCase_ ,attention_probs_dropout_prob=lowerCamelCase_ ,max_position_embeddings=lowerCamelCase_ ,type_vocab_size=lowerCamelCase_ ,initializer_range=lowerCamelCase_ ,layer_norm_eps=lowerCamelCase_ ,pad_token_id=lowerCamelCase_ ,bos_token_id=lowerCamelCase_ ,eos_token_id=lowerCamelCase_ ,**lowerCamelCase_ ,)
UpperCAmelCase_ : List[Any] = max_ad_position_embeddings
UpperCAmelCase_ : Optional[int] = coordinate_size
UpperCAmelCase_ : Optional[int] = shape_size
UpperCAmelCase_ : Optional[Any] = has_relative_attention_bias
UpperCAmelCase_ : Optional[int] = rel_pos_bins
UpperCAmelCase_ : Union[str, Any] = max_rel_pos
UpperCAmelCase_ : Dict = has_spatial_attention_bias
UpperCAmelCase_ : Optional[int] = rel_ad_pos_bins
UpperCAmelCase_ : Tuple = max_rel_ad_pos
UpperCAmelCase_ : Union[str, Any] = text_embed
UpperCAmelCase_ : Optional[Any] = visual_embed
UpperCAmelCase_ : List[str] = input_size
UpperCAmelCase_ : str = num_channels
UpperCAmelCase_ : Optional[int] = patch_size
UpperCAmelCase_ : Tuple = classifier_dropout
class _snake_case ( __snake_case ):
'''simple docstring'''
A__ : Optional[Any] = version.parse("1.12" )
@property
def A__ ( self: Dict ) -> Mapping[str, Mapping[int, str]]:
# The order of inputs is different for question answering and sequence classification
if self.task in ["question-answering", "sequence-classification"]:
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
("""bbox""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
else:
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""bbox""", {0: """batch""", 1: """sequence"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
] )
@property
def A__ ( self: Any ) -> float:
return 1e-5
@property
def A__ ( self: int ) -> int:
return 12
def A__ ( self: List[str] ,lowerCamelCase_: "ProcessorMixin" ,lowerCamelCase_: int = -1 ,lowerCamelCase_: int = -1 ,lowerCamelCase_: bool = False ,lowerCamelCase_: Optional["TensorType"] = None ,lowerCamelCase_: int = 3 ,lowerCamelCase_: int = 40 ,lowerCamelCase_: int = 40 ,) -> Mapping[str, Any]:
setattr(processor.image_processor ,"""apply_ocr""" ,lowerCamelCase_ )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
UpperCAmelCase_ : List[str] = compute_effective_axis_dimension(
lowerCamelCase_ ,fixed_dimension=OnnxConfig.default_fixed_batch ,num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCAmelCase_ : int = processor.tokenizer.num_special_tokens_to_add(lowerCamelCase_ )
UpperCAmelCase_ : int = compute_effective_axis_dimension(
lowerCamelCase_ ,fixed_dimension=OnnxConfig.default_fixed_sequence ,num_token_to_add=lowerCamelCase_ )
# Generate dummy inputs according to compute batch and sequence
UpperCAmelCase_ : Optional[int] = [[""" """.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
UpperCAmelCase_ : List[Any] = [[[48, 84, 73, 128]]] * batch_size
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
UpperCAmelCase_ : Any = self._generate_dummy_images(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ )
UpperCAmelCase_ : Optional[Any] = dict(
processor(
lowerCamelCase_ ,text=lowerCamelCase_ ,boxes=lowerCamelCase_ ,return_tensors=lowerCamelCase_ ,) )
return inputs
| 345 | 0 |
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
lowercase : Optional[int] =["""flax""", """transformers"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def UpperCamelCase ( cls , *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def UpperCamelCase ( cls , *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(cls , ['''flax''', '''transformers'''] )
class UpperCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
lowercase : List[Any] =["""flax""", """transformers"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def UpperCamelCase ( cls , *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def UpperCamelCase ( cls , *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(cls , ['''flax''', '''transformers'''] )
class UpperCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
lowercase : Any =["""flax""", """transformers"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def UpperCamelCase ( cls , *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def UpperCamelCase ( cls , *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(cls , ['''flax''', '''transformers'''] )
class UpperCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
lowercase : Dict =["""flax""", """transformers"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def UpperCamelCase ( cls , *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def UpperCamelCase ( cls , *UpperCamelCase_ , **UpperCamelCase_ ):
requires_backends(cls , ['''flax''', '''transformers'''] )
| 365 |
from __future__ import annotations
from random import random
class UpperCamelCase :
'''simple docstring'''
def __init__( self , UpperCamelCase_ = None ):
lowercase_ :Tuple = value
lowercase_ :Tuple = random()
lowercase_ :Node | None = None
lowercase_ :Node | None = None
def __repr__( self ):
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 ):
lowercase_ :Optional[int] = str(self.value ) + ''' '''
lowercase_ :List[str] = str(self.left or '''''' )
lowercase_ :List[Any] = str(self.right or '''''' )
return value + left + right
def UpperCamelCase ( _a , _a ) -> tuple[Node | None, Node | None]:
'''simple docstring'''
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:
lowercase_ , lowercase_ :List[Any] = split(root.left , _a )
return left, root
else:
lowercase_ , lowercase_ :Tuple = split(root.right , _a )
return root, right
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
lowercase_ :Tuple = merge(left.right , _a )
return left
else:
lowercase_ :Optional[int] = merge(_a , right.left )
return right
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
lowercase_ :str = Node(_a )
lowercase_ , lowercase_ :Dict = split(_a , _a )
return merge(merge(_a , _a ) , _a )
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
lowercase_ , lowercase_ :List[str] = split(_a , value - 1 )
lowercase_ , lowercase_ :Tuple = split(_a , _a )
return merge(_a , _a )
def UpperCamelCase ( _a ) -> None:
'''simple docstring'''
if not root: # None
return
else:
inorder(root.left )
print(root.value , end=''',''' )
inorder(root.right )
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
for arg in args.split():
if arg[0] == "+":
lowercase_ :Any = insert(_a , int(arg[1:] ) )
elif arg[0] == "-":
lowercase_ :Optional[int] = erase(_a , int(arg[1:] ) )
else:
print('''Unknown command''' )
return root
def UpperCamelCase ( ) -> None:
'''simple docstring'''
lowercase_ :List[Any] = None
print(
'''enter numbers to create a tree, + value to add value into treap, '''
'''- value to erase all nodes with value. \'q\' to quit. ''' )
lowercase_ :Optional[Any] = input()
while args != "q":
lowercase_ :Union[str, Any] = interact_treap(_a , _a )
print(_a )
lowercase_ :str = input()
print('''good by!''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 252 | 0 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
UpperCAmelCase__ = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def _a ( a :Matrix , a :int , a :int , a :int ) -> bool:
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def _a ( a :Matrix ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def _a ( a :Matrix ) -> Matrix | None:
if location := find_empty_location(a ):
a , a = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(a , a , a , a ):
a = digit
if sudoku(a ) is not None:
return grid
a = 0
return None
def _a ( a :Matrix ) -> None:
for row in grid:
for cell in row:
print(a , end=''' ''' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
UpperCAmelCase__ = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| 0 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__a = pd.read_csv("sample_data.csv", header=None)
__a = df.shape[:1][0]
# If you're using some other dataset input the target column
__a = df.iloc[:, 1:2]
__a = actual_data.values.reshape(len_data, 1)
__a = MinMaxScaler().fit_transform(actual_data)
__a = 10
__a = 5
__a = 20
__a = len_data - periods * look_back
__a = actual_data[:division]
__a = actual_data[division - look_back :]
__a , __a = [], []
__a , __a = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
__a = np.array(train_x)
__a = np.array(test_x)
__a = np.array([list(i.ravel()) for i in train_y])
__a = np.array([list(i.ravel()) for i in test_y])
__a = Sequential()
model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(1_28, 1)))
model.add(Dense(forward_days))
model.compile(loss="mean_squared_error", optimizer="adam")
__a = model.fit(
x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
)
__a = model.predict(x_test)
| 66 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( snake_case : int , snake_case : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
a : List[str] = str(bin(snake_case ) )[2:] # remove the leading "0b"
a : Any = str(bin(snake_case ) )[2:]
a : Optional[Any] = max(len(snake_case ) , len(snake_case ) )
return "0b" + "".join(
str(int('1' in (char_a, char_b) ) )
for char_a, char_b in zip(a_binary.zfill(snake_case ) , b_binary.zfill(snake_case ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 345 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase : List[str] = {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Any = ["""LayoutXLMTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[int] = ["""LayoutXLMTokenizerFast"""]
if TYPE_CHECKING:
from .processing_layoutxlm import LayoutXLMProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm import LayoutXLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
else:
import sys
UpperCamelCase : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 345 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Union[str, Any] = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class UpperCamelCase__ ( a_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = "vit_msn"
def __init__( self , snake_case=7_6_8 , snake_case=1_2 , snake_case=1_2 , snake_case=3_0_7_2 , snake_case="gelu" , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1e-06 , snake_case=2_2_4 , snake_case=1_6 , snake_case=3 , snake_case=True , **snake_case , ):
'''simple docstring'''
super().__init__(**_lowerCamelCase )
UpperCAmelCase : Optional[Any] = hidden_size
UpperCAmelCase : str = num_hidden_layers
UpperCAmelCase : int = num_attention_heads
UpperCAmelCase : str = intermediate_size
UpperCAmelCase : Dict = hidden_act
UpperCAmelCase : Union[str, Any] = hidden_dropout_prob
UpperCAmelCase : List[Any] = attention_probs_dropout_prob
UpperCAmelCase : Union[str, Any] = initializer_range
UpperCAmelCase : List[Any] = layer_norm_eps
UpperCAmelCase : Union[str, Any] = image_size
UpperCAmelCase : Any = patch_size
UpperCAmelCase : List[Any] = num_channels
UpperCAmelCase : Optional[int] = qkv_bias
| 311 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
lowercase = parser.add_subparsers(help='diffusers-cli command helpers' )
# Register commands
EnvironmentCommand.register_subcommand(__snake_case )
# Let's go
lowercase = parser.parse_args()
if not hasattr(__snake_case , 'func' ):
parser.print_help()
exit(1 )
# Run
lowercase = args.func(__snake_case )
service.run()
if __name__ == "__main__":
main()
| 220 | 0 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
UpperCAmelCase = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.linear_1.weight'),
('time_embed.0.bias', 'time_embedding.linear_1.bias'),
('time_embed.2.weight', 'time_embedding.linear_2.weight'),
('time_embed.2.bias', 'time_embedding.linear_2.bias'),
('input_blocks.0.0.weight', 'conv_in.weight'),
('input_blocks.0.0.bias', 'conv_in.bias'),
('out.0.weight', 'conv_norm_out.weight'),
('out.0.bias', 'conv_norm_out.bias'),
('out.2.weight', 'conv_out.weight'),
('out.2.bias', 'conv_out.bias'),
]
UpperCAmelCase = [
# (stable-diffusion, HF Diffusers)
('in_layers.0', 'norm1'),
('in_layers.2', 'conv1'),
('out_layers.0', 'norm2'),
('out_layers.3', 'conv2'),
('emb_layers.1', 'time_emb_proj'),
('skip_connection', 'conv_shortcut'),
]
UpperCAmelCase = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
UpperCAmelCase = F'''down_blocks.{i}.resnets.{j}.'''
UpperCAmelCase = F'''input_blocks.{3*i + j + 1}.0.'''
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
UpperCAmelCase = F'''down_blocks.{i}.attentions.{j}.'''
UpperCAmelCase = F'''input_blocks.{3*i + j + 1}.1.'''
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
UpperCAmelCase = F'''up_blocks.{i}.resnets.{j}.'''
UpperCAmelCase = F'''output_blocks.{3*i + j}.0.'''
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
UpperCAmelCase = F'''up_blocks.{i}.attentions.{j}.'''
UpperCAmelCase = F'''output_blocks.{3*i + j}.1.'''
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
UpperCAmelCase = F'''down_blocks.{i}.downsamplers.0.conv.'''
UpperCAmelCase = F'''input_blocks.{3*(i+1)}.0.op.'''
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
UpperCAmelCase = F'''up_blocks.{i}.upsamplers.0.'''
UpperCAmelCase = F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
UpperCAmelCase = 'mid_block.attentions.0.'
UpperCAmelCase = 'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
UpperCAmelCase = F'''mid_block.resnets.{j}.'''
UpperCAmelCase = F'''middle_block.{2*j}.'''
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def _snake_case ( _SCREAMING_SNAKE_CASE : Optional[int] ) -> Tuple:
"""simple docstring"""
lowerCAmelCase = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
lowerCAmelCase = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
lowerCAmelCase = v.replace(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
lowerCAmelCase = v.replace(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = v
lowerCAmelCase = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
UpperCAmelCase = [
# (stable-diffusion, HF Diffusers)
('nin_shortcut', 'conv_shortcut'),
('norm_out', 'conv_norm_out'),
('mid.attn_1.', 'mid_block.attentions.0.'),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
UpperCAmelCase = F'''encoder.down_blocks.{i}.resnets.{j}.'''
UpperCAmelCase = F'''encoder.down.{i}.block.{j}.'''
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
UpperCAmelCase = F'''down_blocks.{i}.downsamplers.0.'''
UpperCAmelCase = F'''down.{i}.downsample.'''
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
UpperCAmelCase = F'''up_blocks.{i}.upsamplers.0.'''
UpperCAmelCase = F'''up.{3-i}.upsample.'''
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
UpperCAmelCase = F'''decoder.up_blocks.{i}.resnets.{j}.'''
UpperCAmelCase = F'''decoder.up.{3-i}.block.{j}.'''
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
UpperCAmelCase = F'''mid_block.resnets.{i}.'''
UpperCAmelCase = F'''mid.block_{i+1}.'''
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
UpperCAmelCase = [
# (stable-diffusion, HF Diffusers)
('norm.', 'group_norm.'),
('q.', 'query.'),
('k.', 'key.'),
('v.', 'value.'),
('proj_out.', 'proj_attn.'),
]
def _snake_case ( _SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[Any]:
"""simple docstring"""
return w.reshape(*w.shape , 1 , 1 )
def _snake_case ( _SCREAMING_SNAKE_CASE : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowerCAmelCase = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
lowerCAmelCase = v.replace(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
lowerCAmelCase = v.replace(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = v
lowerCAmelCase = {v: vae_state_dict[k] for k, v in mapping.items()}
lowerCAmelCase = ["""q""", """k""", """v""", """proj_out"""]
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if f'mid.attn_1.{weight_name}.weight' in k:
print(f'Reshaping {k} for SD format' )
lowerCAmelCase = reshape_weight_for_sd(_SCREAMING_SNAKE_CASE )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
UpperCAmelCase = [
# (stable-diffusion, HF Diffusers)
('resblocks.', 'text_model.encoder.layers.'),
('ln_1', 'layer_norm1'),
('ln_2', 'layer_norm2'),
('.c_fc.', '.fc1.'),
('.c_proj.', '.fc2.'),
('.attn', '.self_attn'),
('ln_final.', 'transformer.text_model.final_layer_norm.'),
('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
UpperCAmelCase = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
UpperCAmelCase = re.compile('|'.join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
UpperCAmelCase = {'q': 0, 'k': 1, 'v': 2}
def _snake_case ( _SCREAMING_SNAKE_CASE : int ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase = {}
lowerCAmelCase = {}
lowerCAmelCase = {}
for k, v in text_enc_dict.items():
if (
k.endswith(""".self_attn.q_proj.weight""" )
or k.endswith(""".self_attn.k_proj.weight""" )
or k.endswith(""".self_attn.v_proj.weight""" )
):
lowerCAmelCase = k[: -len(""".q_proj.weight""" )]
lowerCAmelCase = k[-len("""q_proj.weight""" )]
if k_pre not in capture_qkv_weight:
lowerCAmelCase = [None, None, None]
lowerCAmelCase = v
continue
if (
k.endswith(""".self_attn.q_proj.bias""" )
or k.endswith(""".self_attn.k_proj.bias""" )
or k.endswith(""".self_attn.v_proj.bias""" )
):
lowerCAmelCase = k[: -len(""".q_proj.bias""" )]
lowerCAmelCase = k[-len("""q_proj.bias""" )]
if k_pre not in capture_qkv_bias:
lowerCAmelCase = [None, None, None]
lowerCAmelCase = v
continue
lowerCAmelCase = textenc_pattern.sub(lambda _SCREAMING_SNAKE_CASE : protected[re.escape(m.group(0 ) )] , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing""" )
lowerCAmelCase = textenc_pattern.sub(lambda _SCREAMING_SNAKE_CASE : protected[re.escape(m.group(0 ) )] , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = torch.cat(_SCREAMING_SNAKE_CASE )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing""" )
lowerCAmelCase = textenc_pattern.sub(lambda _SCREAMING_SNAKE_CASE : protected[re.escape(m.group(0 ) )] , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = torch.cat(_SCREAMING_SNAKE_CASE )
return new_state_dict
def _snake_case ( _SCREAMING_SNAKE_CASE : List[Any] ) -> Tuple:
"""simple docstring"""
return text_enc_dict
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
)
UpperCAmelCase = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
UpperCAmelCase = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
UpperCAmelCase = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
UpperCAmelCase = osp.join(args.model_path, 'text_encoder', 'model.safetensors')
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
UpperCAmelCase = load_file(unet_path, device='cpu')
else:
UpperCAmelCase = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
UpperCAmelCase = torch.load(unet_path, map_location='cpu')
if osp.exists(vae_path):
UpperCAmelCase = load_file(vae_path, device='cpu')
else:
UpperCAmelCase = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
UpperCAmelCase = torch.load(vae_path, map_location='cpu')
if osp.exists(text_enc_path):
UpperCAmelCase = load_file(text_enc_path, device='cpu')
else:
UpperCAmelCase = osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
UpperCAmelCase = torch.load(text_enc_path, map_location='cpu')
# Convert the UNet model
UpperCAmelCase = convert_unet_state_dict(unet_state_dict)
UpperCAmelCase = {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
UpperCAmelCase = convert_vae_state_dict(vae_state_dict)
UpperCAmelCase = {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
UpperCAmelCase = 'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
UpperCAmelCase = {'transformer.' + k: v for k, v in text_enc_dict.items()}
UpperCAmelCase = convert_text_enc_state_dict_vaa(text_enc_dict)
UpperCAmelCase = {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
else:
UpperCAmelCase = convert_text_enc_state_dict(text_enc_dict)
UpperCAmelCase = {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
UpperCAmelCase = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
UpperCAmelCase = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
UpperCAmelCase = {'state_dict': state_dict}
torch.save(state_dict, args.checkpoint_path)
| 365 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
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 (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class __snake_case:
'''simple docstring'''
def __init__( self , A_ , A_=13 , A_=7 , A_=True , A_=True , A_=True , A_=True , A_=99 , A_=32 , A_=2 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=16 , A_=2 , A_=0.0_2 , A_=3 , A_=4 , A_=None , ) -> Dict:
lowerCAmelCase = parent
lowerCAmelCase = 13
lowerCAmelCase = 7
lowerCAmelCase = True
lowerCAmelCase = True
lowerCAmelCase = True
lowerCAmelCase = True
lowerCAmelCase = 99
lowerCAmelCase = 384
lowerCAmelCase = 2
lowerCAmelCase = 4
lowerCAmelCase = 37
lowerCAmelCase = """gelu"""
lowerCAmelCase = 0.1
lowerCAmelCase = 0.1
lowerCAmelCase = 512
lowerCAmelCase = 16
lowerCAmelCase = 2
lowerCAmelCase = 0.0_2
lowerCAmelCase = 3
lowerCAmelCase = 4
lowerCAmelCase = 128
lowerCAmelCase = 2
lowerCAmelCase = 9
lowerCAmelCase = 1
lowerCAmelCase = None
def __snake_case ( self ) -> Optional[int]:
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase = None
if self.use_input_mask:
lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase = None
if self.use_token_type_ids:
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase = None
lowerCAmelCase = None
lowerCAmelCase = None
if self.use_labels:
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase = ConvBertConfig(
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 , return_dict=A_ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __snake_case ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ) -> int:
lowerCAmelCase = TFConvBertModel(config=A_ )
lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
lowerCAmelCase = [input_ids, input_mask]
lowerCAmelCase = model(A_ )
lowerCAmelCase = model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __snake_case ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ) -> List[Any]:
lowerCAmelCase = TFConvBertForMaskedLM(config=A_ )
lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
lowerCAmelCase = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __snake_case ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ) -> Optional[int]:
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFConvBertForSequenceClassification(config=A_ )
lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
lowerCAmelCase = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __snake_case ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ) -> Any:
lowerCAmelCase = self.num_choices
lowerCAmelCase = TFConvBertForMultipleChoice(config=A_ )
lowerCAmelCase = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) )
lowerCAmelCase = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) )
lowerCAmelCase = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) )
lowerCAmelCase = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
lowerCAmelCase = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __snake_case ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ) -> Union[str, Any]:
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFConvBertForTokenClassification(config=A_ )
lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
lowerCAmelCase = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __snake_case ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ) -> Optional[int]:
lowerCAmelCase = TFConvBertForQuestionAnswering(config=A_ )
lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
lowerCAmelCase = model(A_ )
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 __snake_case ( self ) -> Any:
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
), (
lowerCAmelCase
), (
lowerCAmelCase
), (
lowerCAmelCase
), (
lowerCAmelCase
), (
lowerCAmelCase
), (
lowerCAmelCase
),
) = config_and_inputs
lowerCAmelCase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class __snake_case( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
UpperCAmelCase : Union[str, Any] = (
{
"feature-extraction": TFConvBertModel,
"fill-mask": TFConvBertForMaskedLM,
"question-answering": TFConvBertForQuestionAnswering,
"text-classification": TFConvBertForSequenceClassification,
"token-classification": TFConvBertForTokenClassification,
"zero-shot": TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCAmelCase : Union[str, Any] = False
UpperCAmelCase : Optional[int] = False
UpperCAmelCase : Dict = False
def __snake_case ( self ) -> Optional[int]:
lowerCAmelCase = TFConvBertModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=A_ , hidden_size=37 )
def __snake_case ( self ) -> Tuple:
self.config_tester.run_common_tests()
def __snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def __snake_case ( self ) -> Tuple:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A_ )
def __snake_case ( self ) -> Optional[int]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A_ )
def __snake_case ( self ) -> List[str]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A_ )
def __snake_case ( self ) -> str:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A_ )
def __snake_case ( self ) -> Tuple:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A_ )
@slow
def __snake_case ( self ) -> Any:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = True
lowerCAmelCase = True
if hasattr(A_ , """use_cache""" ):
lowerCAmelCase = True
lowerCAmelCase = getattr(self.model_tester , """encoder_seq_length""" , self.model_tester.seq_length )
lowerCAmelCase = getattr(self.model_tester , """key_length""" , A_ )
for model_class in self.all_model_classes:
lowerCAmelCase = self._prepare_for_class(A_ , A_ )
lowerCAmelCase = model_class(A_ )
lowerCAmelCase = len(model(A_ ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(A_ , saved_model=A_ )
lowerCAmelCase = os.path.join(A_ , """saved_model""" , """1""" )
lowerCAmelCase = tf.keras.models.load_model(A_ )
lowerCAmelCase = model(A_ )
if self.is_encoder_decoder:
lowerCAmelCase = outputs["""encoder_hidden_states"""]
lowerCAmelCase = outputs["""encoder_attentions"""]
else:
lowerCAmelCase = outputs["""hidden_states"""]
lowerCAmelCase = outputs["""attentions"""]
self.assertEqual(len(A_ ) , A_ )
lowerCAmelCase = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(A_ ) , A_ )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(A_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def __snake_case ( self ) -> Optional[Any]:
lowerCAmelCase = TFConvBertModel.from_pretrained("""YituTech/conv-bert-base""" )
self.assertIsNotNone(A_ )
def __snake_case ( self ) -> str:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = True
lowerCAmelCase = getattr(self.model_tester , """decoder_seq_length""" , self.model_tester.seq_length )
lowerCAmelCase = getattr(self.model_tester , """encoder_seq_length""" , self.model_tester.seq_length )
lowerCAmelCase = getattr(self.model_tester , """key_length""" , A_ )
lowerCAmelCase = getattr(self.model_tester , """key_length""" , A_ )
def check_decoder_attentions_output(A_ ):
lowerCAmelCase = len(A_ )
self.assertEqual(out_len % 2 , 0 )
lowerCAmelCase = outputs.decoder_attentions
self.assertEqual(len(A_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(A_ ):
lowerCAmelCase = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(A_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = model_class(A_ )
lowerCAmelCase = model(self._prepare_for_class(A_ , A_ ) )
lowerCAmelCase = len(A_ )
self.assertEqual(config.output_hidden_states , A_ )
check_encoder_attentions_output(A_ )
if self.is_encoder_decoder:
lowerCAmelCase = model_class(A_ )
lowerCAmelCase = model(self._prepare_for_class(A_ , A_ ) )
self.assertEqual(config.output_hidden_states , A_ )
check_decoder_attentions_output(A_ )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
lowerCAmelCase = True
lowerCAmelCase = model_class(A_ )
lowerCAmelCase = model(self._prepare_for_class(A_ , A_ ) )
self.assertEqual(config.output_hidden_states , A_ )
check_encoder_attentions_output(A_ )
# Check attention is always last and order is fine
lowerCAmelCase = True
lowerCAmelCase = True
lowerCAmelCase = model_class(A_ )
lowerCAmelCase = model(self._prepare_for_class(A_ , A_ ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(A_ ) )
self.assertEqual(model.config.output_hidden_states , A_ )
check_encoder_attentions_output(A_ )
@require_tf
class __snake_case( unittest.TestCase ):
'''simple docstring'''
@slow
def __snake_case ( self ) -> Any:
lowerCAmelCase = TFConvBertModel.from_pretrained("""YituTech/conv-bert-base""" )
lowerCAmelCase = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowerCAmelCase = model(A_ )[0]
lowerCAmelCase = [1, 6, 768]
self.assertEqual(output.shape , A_ )
lowerCAmelCase = tf.constant(
[
[
[-0.0_3_4_7_5_4_9_3, -0.4_6_8_6_0_3_4, -0.3_0_6_3_8_8_3_2],
[0.2_2_6_3_7_2_4_8, -0.2_6_9_8_8_6_4_6, -0.7_4_2_3_4_2_4],
[0.1_0_3_2_4_8_6_8, -0.4_5_0_1_3_5_0_8, -0.5_8_2_8_0_7_8_4],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , A_ , atol=1e-4 )
| 187 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
lowerCAmelCase: List[str] = logging.get_logger(__name__)
class a__( UpperCamelCase_ ):
def __init__( self : str , *__snake_case : Optional[int] , **__snake_case : List[Any] ):
warnings.warn(
'The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use DeformableDetrImageProcessor instead.' , __SCREAMING_SNAKE_CASE , )
super().__init__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
| 297 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
class lowercase_ :
"""simple docstring"""
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=13 , __SCREAMING_SNAKE_CASE=7 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=19 , __SCREAMING_SNAKE_CASE=32 , __SCREAMING_SNAKE_CASE=5 , __SCREAMING_SNAKE_CASE=4 , __SCREAMING_SNAKE_CASE=37 , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=512 , __SCREAMING_SNAKE_CASE=16 , __SCREAMING_SNAKE_CASE=2 , __SCREAMING_SNAKE_CASE=0.0_2 , __SCREAMING_SNAKE_CASE=3 , __SCREAMING_SNAKE_CASE=4 , __SCREAMING_SNAKE_CASE=None , ) ->Union[str, Any]:
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = seq_length
lowerCAmelCase = is_training
lowerCAmelCase = use_input_mask
lowerCAmelCase = use_token_type_ids
lowerCAmelCase = use_labels
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = type_vocab_size
lowerCAmelCase = type_sequence_label_size
lowerCAmelCase = initializer_range
lowerCAmelCase = num_labels
lowerCAmelCase = num_choices
lowerCAmelCase = scope
def SCREAMING_SNAKE_CASE_ ( self ) ->Any:
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase = None
if self.use_input_mask:
lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase = None
lowerCAmelCase = None
lowerCAmelCase = None
if self.use_labels:
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[int]:
lowerCAmelCase = EsmConfig(
vocab_size=33 , hidden_size=self.hidden_size , pad_token_id=1 , 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 , is_folding_model=__SCREAMING_SNAKE_CASE , esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False} , )
return config
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ->Tuple:
lowerCAmelCase = EsmForProteinFolding(config=__SCREAMING_SNAKE_CASE ).float()
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
lowerCAmelCase = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE )
lowerCAmelCase = model(__SCREAMING_SNAKE_CASE )
lowerCAmelCase = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.positions.shape , (8, self.batch_size, self.seq_length, 14, 3) )
self.parent.assertEqual(result.angles.shape , (8, self.batch_size, self.seq_length, 7, 2) )
def SCREAMING_SNAKE_CASE_ ( self ) ->int:
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class lowercase_ ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = False
UpperCAmelCase_ : Dict = (EsmForProteinFolding,) if is_torch_available() else ()
UpperCAmelCase_ : List[Any] = ()
UpperCAmelCase_ : Tuple = {} if is_torch_available() else {}
UpperCAmelCase_ : List[str] = False
def SCREAMING_SNAKE_CASE_ ( self ) ->Dict:
lowerCAmelCase = EsmFoldModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self ) ->Any:
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[int]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
@unittest.skip('''Does not support attention outputs''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->Tuple:
pass
@unittest.skip
def SCREAMING_SNAKE_CASE_ ( self ) ->Any:
pass
@unittest.skip('''Esm does not support embedding resizing''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->List[str]:
pass
@unittest.skip('''Esm does not support embedding resizing''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
pass
@unittest.skip('''ESMFold does not support passing input embeds!''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->Dict:
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->List[str]:
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[int]:
pass
@unittest.skip('''ESMFold does not output hidden states in the normal way.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->Tuple:
pass
@unittest.skip('''ESMfold does not output hidden states in the normal way.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->Dict:
pass
@unittest.skip('''ESMFold only has one output format.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->List[str]:
pass
@unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->Dict:
pass
@unittest.skip('''ESMFold does not support input chunking.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->Union[str, Any]:
pass
@unittest.skip('''ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[Any]:
pass
@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
pass
@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->Any:
pass
@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
pass
@unittest.skip('''ESMFold doesn\'t support data parallel.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[int]:
pass
@require_torch
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
@slow
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
lowerCAmelCase = EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1''' ).float()
model.eval()
lowerCAmelCase = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowerCAmelCase = model(__SCREAMING_SNAKE_CASE )['''positions''']
lowerCAmelCase = torch.tensor([2.5_8_2_8, 0.7_9_9_3, -1_0.9_3_3_4] , dtype=torch.floataa )
self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0] , __SCREAMING_SNAKE_CASE , atol=1e-4 ) )
| 338 | 0 |
'''simple docstring'''
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class UpperCamelCase__( unittest.TestCase ):
def a__( self : List[str] )-> Union[str, Any]:
"""simple docstring"""
debug_launcher(test_script.main )
def a__( self : List[Any] )-> Optional[int]:
"""simple docstring"""
debug_launcher(test_ops.main )
| 353 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase__:
def __init__( self : Any , lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : float = 0 )-> None:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase = row, column
UpperCAmelCase = [[default_value for c in range(lowerCAmelCase )] for r in range(lowerCAmelCase )]
def __str__( self : int )-> str:
"""simple docstring"""
UpperCAmelCase = F"""Matrix consist of {self.row} rows and {self.column} columns\n"""
# Make string identifier
UpperCAmelCase = 0
for row_vector in self.array:
for obj in row_vector:
UpperCAmelCase = max(lowerCAmelCase , len(str(lowerCAmelCase ) ) )
UpperCAmelCase = F"""%{max_element_length}s"""
# Make string and return
def single_line(lowerCAmelCase : list[float] ) -> str:
nonlocal string_format_identifier
UpperCAmelCase = '''['''
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
line += "]"
return line
s += "\n".join(single_line(lowerCAmelCase ) for row_vector in self.array )
return s
def __repr__( self : Tuple )-> str:
"""simple docstring"""
return str(self )
def a__( self : str , lowerCAmelCase : tuple[int, int] )-> bool:
"""simple docstring"""
if not (isinstance(lowerCAmelCase , (list, tuple) ) and len(lowerCAmelCase ) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__( self : int , lowerCAmelCase : tuple[int, int] )-> Any:
"""simple docstring"""
assert self.validate_indicies(lowerCAmelCase )
return self.array[loc[0]][loc[1]]
def __setitem__( self : List[str] , lowerCAmelCase : tuple[int, int] , lowerCAmelCase : float )-> None:
"""simple docstring"""
assert self.validate_indicies(lowerCAmelCase )
UpperCAmelCase = value
def __add__( self : int , lowerCAmelCase : Matrix )-> Matrix:
"""simple docstring"""
assert isinstance(lowerCAmelCase , lowerCAmelCase )
assert self.row == another.row and self.column == another.column
# Add
UpperCAmelCase = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
UpperCAmelCase = self[r, c] + another[r, c]
return result
def __neg__( self : Dict )-> Matrix:
"""simple docstring"""
UpperCAmelCase = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
UpperCAmelCase = -self[r, c]
return result
def __sub__( self : Union[str, Any] , lowerCAmelCase : Matrix )-> Matrix:
"""simple docstring"""
return self + (-another)
def __mul__( self : Union[str, Any] , lowerCAmelCase : int | float | Matrix )-> Matrix:
"""simple docstring"""
if isinstance(lowerCAmelCase , (int, float) ): # Scalar multiplication
UpperCAmelCase = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
UpperCAmelCase = self[r, c] * another
return result
elif isinstance(lowerCAmelCase , lowerCAmelCase ): # Matrix multiplication
assert self.column == another.row
UpperCAmelCase = Matrix(self.row , another.column )
for r in range(self.row ):
for c in range(another.column ):
for i in range(self.column ):
result[r, c] += self[r, i] * another[i, c]
return result
else:
UpperCAmelCase = F"""Unsupported type given for another ({type(lowerCAmelCase )})"""
raise TypeError(lowerCAmelCase )
def a__( self : Optional[Any] )-> Matrix:
"""simple docstring"""
UpperCAmelCase = Matrix(self.column , self.row )
for r in range(self.row ):
for c in range(self.column ):
UpperCAmelCase = self[r, c]
return result
def a__( self : Tuple , lowerCAmelCase : Matrix , lowerCAmelCase : Matrix )-> Any:
"""simple docstring"""
assert isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase )
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
UpperCAmelCase = v.transpose()
UpperCAmelCase = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase = Matrix(3 , 3 , 0 )
for i in range(3 ):
UpperCAmelCase = 1
print(f"""a^(-1) is {ainv}""" )
# u, v
UpperCAmelCase = Matrix(3 , 1 , 0 )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 1, 2, -3
UpperCAmelCase = Matrix(3 , 1 , 0 )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 4, -2, 5
print(f"""u is {u}""" )
print(f"""v is {v}""" )
print(f"""uv^T is {u * v.transpose()}""" )
# Sherman Morrison
print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(A , A )}""" )
def lowerCamelCase__ ( ):
'''simple docstring'''
import doctest
doctest.testmod()
testa()
| 91 | 0 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def UpperCAmelCase__ (snake_case__ : List[str] , snake_case__ : Tuple , snake_case__ : int , snake_case__ : List[str]=None ):
"""simple docstring"""
_snake_case : List[Any] = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
_snake_case , _snake_case : Dict = True, True
_snake_case : str = dfs(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
return path
def UpperCAmelCase__ (snake_case__ : Union[str, Any] , snake_case__ : Optional[int] ):
"""simple docstring"""
_snake_case : List[str] = 0
_snake_case : List[str] = -1
for i in range(snake_case__ ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
_snake_case : int = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def UpperCAmelCase__ (snake_case__ : Optional[int] , snake_case__ : List[Any] ):
"""simple docstring"""
_snake_case : Tuple = [[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
_snake_case , _snake_case : Dict = check_circuit_or_path(snake_case__ , snake_case__ )
if check == 3:
print("""graph is not Eulerian""" )
print("""no path""" )
return
_snake_case : int = 1
if check == 2:
_snake_case : Optional[int] = odd_node
print("""graph has a Euler path""" )
if check == 1:
print("""graph has a Euler cycle""" )
_snake_case : Optional[int] = dfs(snake_case__ , snake_case__ , snake_case__ )
print(snake_case__ )
def UpperCAmelCase__ ():
"""simple docstring"""
_snake_case : List[str] = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
_snake_case : Dict = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
_snake_case : Optional[Any] = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
_snake_case : List[str] = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
_snake_case : List[str] = {
1: [],
2: []
# all degree is zero
}
_snake_case : List[Any] = 10
check_euler(snake_case__ , snake_case__ )
check_euler(snake_case__ , snake_case__ )
check_euler(snake_case__ , snake_case__ )
check_euler(snake_case__ , snake_case__ )
check_euler(snake_case__ , snake_case__ )
if __name__ == "__main__":
main()
| 64 |
"""simple docstring"""
from math import factorial
A_ = {str(d): factorial(d) for d in range(10)}
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(snake_case__ ) )
def UpperCAmelCase__ ():
"""simple docstring"""
_snake_case : List[str] = 7 * factorial(9 ) + 1
return sum(i for i in range(3 , snake_case__ ) if sum_of_digit_factorial(snake_case__ ) == i )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 64 | 1 |
# 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 re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class a (_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : List[Any] = "naver-clova-ix/donut-base-finetuned-docvqa"
__UpperCAmelCase : str = (
"This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
"should be the document containing the information, as well as a `question` that is the question about the "
"document. It returns a text that contains the answer to the question."
)
__UpperCAmelCase : Union[str, Any] = "document_qa"
__UpperCAmelCase : str = AutoProcessor
__UpperCAmelCase : str = VisionEncoderDecoderModel
__UpperCAmelCase : str = ["image", "text"]
__UpperCAmelCase : Optional[int] = ["text"]
def __init__( self : Union[str, Any] , *lowerCamelCase : Tuple , **lowerCamelCase : List[str] ) -> Optional[int]:
if not is_vision_available():
raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool." )
super().__init__(*lowerCamelCase , **lowerCamelCase )
def __snake_case ( self : Any , lowerCamelCase : "Image" , lowerCamelCase : str ) -> Optional[Any]:
__snake_case : Any = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
__snake_case : Optional[Any] = task_prompt.replace("{user_input}" , lowerCamelCase )
__snake_case : Optional[int] = self.pre_processor.tokenizer(
lowerCamelCase , add_special_tokens=lowerCamelCase , return_tensors="pt" ).input_ids
__snake_case : Tuple = self.pre_processor(lowerCamelCase , return_tensors="pt" ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def __snake_case ( self : int , lowerCamelCase : List[str] ) -> List[str]:
return self.model.generate(
inputs["pixel_values"].to(self.device ) , decoder_input_ids=inputs["decoder_input_ids"].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=lowerCamelCase , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=lowerCamelCase , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=lowerCamelCase , ).sequences
def __snake_case ( self : Optional[int] , lowerCamelCase : Dict ) -> List[Any]:
__snake_case : Optional[Any] = self.pre_processor.batch_decode(lowerCamelCase )[0]
__snake_case : str = sequence.replace(self.pre_processor.tokenizer.eos_token , "" )
__snake_case : Union[str, Any] = sequence.replace(self.pre_processor.tokenizer.pad_token , "" )
__snake_case : str = re.sub(R"<.*?>" , "" , lowerCamelCase , count=1 ).strip() # remove first task start token
__snake_case : Dict = self.pre_processor.tokenajson(lowerCamelCase )
return sequence["answer"]
| 134 |
from __future__ import annotations
_snake_case : Union[str, Any] = []
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
for i in range(len(__lowerCamelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(__lowerCamelCase ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(__lowerCamelCase , -1 , -1 ) , range(__lowerCamelCase , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(__lowerCamelCase , -1 , -1 ) , range(__lowerCamelCase , len(__lowerCamelCase ) ) ):
if board[i][j] == 1:
return False
return True
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
if row >= len(__lowerCamelCase ):
solution.append(__lowerCamelCase )
printboard(__lowerCamelCase )
print()
return True
for i in range(len(__lowerCamelCase ) ):
if is_safe(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
__snake_case : Optional[Any] = 1
solve(__lowerCamelCase , row + 1 )
__snake_case : Union[str, Any] = 0
return False
def lowerCAmelCase_ ( __lowerCamelCase ):
for i in range(len(__lowerCamelCase ) ):
for j in range(len(__lowerCamelCase ) ):
if board[i][j] == 1:
print("Q" , end=" " )
else:
print("." , end=" " )
print()
# n=int(input("The no. of queens"))
_snake_case : List[str] = 8
_snake_case : Optional[int] = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))
| 134 | 1 |
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