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import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__a = logging.get_logger(__name__)
__a = {'vocab_file': 'spiece.model'}
__a = {
'vocab_file': {
'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
}
}
__a = {
'AI-Sweden/gpt-sw3-126m': 2_048,
'AI-Sweden/gpt-sw3-350m': 2_048,
'AI-Sweden/gpt-sw3-1.6b': 2_048,
'AI-Sweden/gpt-sw3-6.7b': 2_048,
'AI-Sweden/gpt-sw3-20b': 2_048,
}
class __a( _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 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,) -> None:
UpperCAmelCase_ : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase_ : int = kwargs.get('''name_or_path''' )
if name_or_path is None:
logger.warning(
'''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
''' you are testing the model, this can safely be ignored''' )
UpperCAmelCase_ : Optional[int] = '''None'''
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
UpperCAmelCase_ : Union[str, Any] = '''<|endoftext|>''' if eos_token is None else eos_token
UpperCAmelCase_ : Optional[int] = '''<unk>''' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
UpperCAmelCase_ : int = unk_token if pad_token is None else pad_token
UpperCAmelCase_ : List[Any] = eos_token if bos_token is None else bos_token
else:
UpperCAmelCase_ : Optional[Any] = '''<pad>''' if pad_token is None else pad_token
UpperCAmelCase_ : str = '''<s>''' if bos_token is None else bos_token
super().__init__(
do_lower_case=_SCREAMING_SNAKE_CASE ,remove_space=_SCREAMING_SNAKE_CASE ,keep_accents=_SCREAMING_SNAKE_CASE ,bos_token=_SCREAMING_SNAKE_CASE ,eos_token=_SCREAMING_SNAKE_CASE ,unk_token=_SCREAMING_SNAKE_CASE ,pad_token=_SCREAMING_SNAKE_CASE ,sp_model_kwargs=self.sp_model_kwargs ,**_SCREAMING_SNAKE_CASE ,)
UpperCAmelCase_ : Optional[int] = do_lower_case
UpperCAmelCase_ : Any = remove_space
UpperCAmelCase_ : Dict = keep_accents
UpperCAmelCase_ : Dict = vocab_file
UpperCAmelCase_ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_SCREAMING_SNAKE_CASE )
# Used for whitespace normalization in input texts
# fmt : off
UpperCAmelCase_ : Union[str, Any] = {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''''', ''''''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
UpperCAmelCase_ : Tuple = re.compile(
f'''[{"".join(map(_SCREAMING_SNAKE_CASE ,list(range(0 ,9 ) ) + list(range(11 ,32 ) ) + list(range(127 ,160 ) ) + [160, 173, 8_203] ) )}]''' )
def __getstate__( self ) -> Optional[int]:
UpperCAmelCase_ : Optional[Any] = self.__dict__.copy()
UpperCAmelCase_ : int = None
return state
def __setstate__( self ,_SCREAMING_SNAKE_CASE ) -> Dict:
UpperCAmelCase_ : int = d
# for backward compatibility
if not hasattr(self ,'''sp_model_kwargs''' ):
UpperCAmelCase_ : List[str] = {}
UpperCAmelCase_ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def a__ ( self ) -> int:
return len(self.sp_model )
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> str:
UpperCAmelCase_ : Optional[Any] = self.non_printing_characters_re.sub('''''' ,_SCREAMING_SNAKE_CASE )
# Normalize whitespaces
UpperCAmelCase_ : List[str] = ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text] )
# NFC Unicode normalization
UpperCAmelCase_ : Union[str, Any] = unicodedata.normalize('''NFC''' ,_SCREAMING_SNAKE_CASE )
return text
def a__ ( self ,_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> List[str]:
UpperCAmelCase_ : Optional[Any] = self.preprocess_text(_SCREAMING_SNAKE_CASE )
return self.sp_model.encode(_SCREAMING_SNAKE_CASE ,out_type=_SCREAMING_SNAKE_CASE )
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> int:
return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE )
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> str:
return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE )
@staticmethod
def a__ ( _SCREAMING_SNAKE_CASE ) -> str:
return out_string
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> str:
UpperCAmelCase_ : Optional[Any] = []
UpperCAmelCase_ : int = ''''''
UpperCAmelCase_ : Tuple = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE ) + token
UpperCAmelCase_ : str = True
UpperCAmelCase_ : int = []
else:
current_sub_tokens.append(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Any = False
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE )
return out_string
def a__ ( self ) -> Dict[str, int]:
UpperCAmelCase_ : int = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase_ : int = os.path.join(
_SCREAMING_SNAKE_CASE ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,_SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(_SCREAMING_SNAKE_CASE ,'''wb''' ) as fi:
UpperCAmelCase_ : Dict = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : str = self.preprocess_text(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Dict = self.sp_model.encode(_SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase_ : Any = [self.preprocess_text(_SCREAMING_SNAKE_CASE ) for t in text]
UpperCAmelCase_ : Optional[int] = self.sp_model.encode(_SCREAMING_SNAKE_CASE )
if return_tensors is True or return_tensors == "pt":
UpperCAmelCase_ : List[Any] = torch.tensor(_SCREAMING_SNAKE_CASE )
return token_ids
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> str:
return self.sp_model.decode(_SCREAMING_SNAKE_CASE )
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> List[int]:
UpperCAmelCase_ : str = [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
UpperCAmelCase_ : Any = (
f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(_SCREAMING_SNAKE_CASE ) + f'''{self.bos_token}Bot:'''
)
return self.encode(text=_SCREAMING_SNAKE_CASE ) | 30 |
from __future__ import annotations
import math
__a = '2020.9.26'
__a = 'xcodz-dot, cclaus, dhruvmanila'
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not all(isinstance(_lowercase , (float, int) ) for val in locals().values() ):
UpperCAmelCase_ : Optional[int] = f'''Input values must either be float or int: {list(locals().values() )}'''
raise TypeError(_lowercase )
UpperCAmelCase_ : Tuple = ((x * distance) / (z + distance)) * scale
UpperCAmelCase_ : str = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
raise TypeError('''Axis must be a str''' )
UpperCAmelCase_ : Optional[Any] = locals()
del input_variables["axis"]
if not all(isinstance(_lowercase , (float, int) ) for val in input_variables.values() ):
UpperCAmelCase_ : List[Any] = (
'''Input values except axis must either be float or int: '''
f'''{list(input_variables.values() )}'''
)
raise TypeError(_lowercase )
UpperCAmelCase_ : Dict = (angle % 360) / 450 * 180 / math.pi
if axis == "z":
UpperCAmelCase_ : Optional[int] = x * math.cos(_lowercase ) - y * math.sin(_lowercase )
UpperCAmelCase_ : List[Any] = y * math.cos(_lowercase ) + x * math.sin(_lowercase )
UpperCAmelCase_ : Optional[int] = z
elif axis == "x":
UpperCAmelCase_ : Any = y * math.cos(_lowercase ) - z * math.sin(_lowercase )
UpperCAmelCase_ : int = z * math.cos(_lowercase ) + y * math.sin(_lowercase )
UpperCAmelCase_ : Dict = x
elif axis == "y":
UpperCAmelCase_ : Union[str, Any] = x * math.cos(_lowercase ) - z * math.sin(_lowercase )
UpperCAmelCase_ : Optional[int] = z * math.cos(_lowercase ) + x * math.sin(_lowercase )
UpperCAmelCase_ : Optional[int] = 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, 10.0, 10.0) = }""")
print(F"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""") | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
snake_case__ : Tuple = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
"""processing_trocr""": ["""TrOCRProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : List[Any] = [
"""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
snake_case__ : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 702 |
snake_case__ : List[Any] = """Tobias Carryer"""
from time import time
class _a :
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=int(time() ) ) -> Tuple: # noqa: B008
UpperCamelCase_ = multiplier
UpperCamelCase_ = increment
UpperCamelCase_ = modulo
UpperCamelCase_ = seed
def _UpperCAmelCase ( self ) -> Any:
UpperCamelCase_ = (self.multiplier * self.seed + self.increment) % self.modulo
return self.seed
if __name__ == "__main__":
# Show the LCG in action.
snake_case__ : List[Any] = LinearCongruentialGenerator(1_6_6_4_5_2_5, 1_0_1_3_9_0_4_2_2_3, 2 << 3_1)
while True:
print(lcg.next_number())
| 618 | 0 |
"""simple docstring"""
import numpy
class lowercase__ :
"""simple docstring"""
def __init__( self , _A , _A ):
'''simple docstring'''
UpperCamelCase : Dict = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
UpperCamelCase : Optional[Any] = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
UpperCamelCase : Union[str, Any] = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
UpperCamelCase : Dict = numpy.random.rand(3 , 1 )
# Real output values provided.
UpperCamelCase : Dict = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
UpperCamelCase : Optional[Any] = numpy.zeros(output_array.shape )
def _a ( self ):
'''simple docstring'''
UpperCamelCase : Tuple = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
UpperCamelCase : Optional[int] = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
UpperCamelCase : Any = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def _a ( self ):
'''simple docstring'''
UpperCamelCase : Dict = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
UpperCamelCase : List[str] = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
UpperCamelCase : List[str] = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def _a ( self , _A , _A , _A ):
'''simple docstring'''
for iteration in range(1 , iterations + 1 ):
UpperCamelCase : int = self.feedforward()
self.back_propagation()
if give_loss:
UpperCamelCase : Dict = numpy.mean(numpy.square(output - self.feedforward() ) )
print(f"""Iteration {iteration} Loss: {loss}""" )
def _a ( self , _A ):
'''simple docstring'''
UpperCamelCase : List[Any] = input_arr
UpperCamelCase : int = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
UpperCamelCase : str = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
UpperCamelCase : int = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def UpperCamelCase (SCREAMING_SNAKE_CASE ):
return 1 / (1 + numpy.exp(-value ))
def UpperCamelCase (SCREAMING_SNAKE_CASE ):
return (value) * (1 - (value))
def UpperCamelCase ():
UpperCamelCase : str = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
UpperCamelCase : Optional[int] = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa )
# Calling neural network class.
UpperCamelCase : Dict = TwoHiddenLayerNeuralNetwork(
input_array=SCREAMING_SNAKE_CASE , output_array=SCREAMING_SNAKE_CASE )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=SCREAMING_SNAKE_CASE , iterations=10 , give_loss=SCREAMING_SNAKE_CASE )
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 102 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class lowerCAmelCase :
'''simple docstring'''
def __init__( self : str , __a : str = "cpu" , __a : str = "openai/clip-vit-large-patch14" ) -> None:
"""simple docstring"""
__lowercase : Tuple = device
__lowercase : Union[str, Any] = CLIPTokenizerFast.from_pretrained(__a )
__lowercase : int = [0.48145466, 0.4578275, 0.40821073]
__lowercase : Optional[Any] = [0.26862954, 0.26130258, 0.27577711]
__lowercase : Tuple = torchvision.transforms.Normalize(self.image_mean , self.image_std )
__lowercase : Optional[int] = torchvision.transforms.Resize(224 )
__lowercase : List[Any] = torchvision.transforms.CenterCrop(224 )
def lowerCAmelCase ( self : str , __a : Optional[int] ) -> Tuple:
"""simple docstring"""
__lowercase : Any = self.resize(__a )
__lowercase : Tuple = self.center_crop(__a )
__lowercase : Any = self.normalize(__a )
return images
def __call__( self : Any , __a : Optional[Any]=None , __a : List[Any]=None , **__a : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__lowercase : List[str] = self.tokenizer(text=__a , **__a )
__lowercase : List[str] = self.preprocess_img(__a )
__lowercase : Tuple = {key: value.to(self.device ) for (key, value) in encoding.items()}
return encoding
class lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] , __a : Tuple=10 , __a : Optional[int]=0.01 , __a : Optional[Any]=None , __a : Any=None , __a : List[str]=None , __a : Optional[Any]=None , __a : Optional[int]=None , __a : List[str]=None , __a : Optional[Any]=False , __a : int=True , __a : str="image" , __a : List[str]=True , __a : Tuple=False , __a : Optional[Any]=False , __a : Dict=False , ) -> None:
"""simple docstring"""
super().__init__()
__lowercase : int = None
__lowercase : List[Any] = device if device else get_device()
if vqgan:
__lowercase : Union[str, Any] = vqgan
else:
__lowercase : Dict = load_vqgan(self.device , conf_path=__a , ckpt_path=__a )
self.vqgan.eval()
if clip:
__lowercase : Any = clip
else:
__lowercase : List[str] = CLIPModel.from_pretrained("""openai/clip-vit-base-patch32""" )
self.clip.to(self.device )
__lowercase : Any = ProcessorGradientFlow(device=self.device )
__lowercase : List[Any] = iterations
__lowercase : List[Any] = lr
__lowercase : Union[str, Any] = log
__lowercase : List[Any] = make_grid
__lowercase : str = return_val
__lowercase : str = quantize
__lowercase : Dict = self.vqgan.decoder.z_shape
def lowerCAmelCase ( self : List[str] , __a : List[Any]=None , __a : int=None , __a : str=5 , __a : Union[str, Any]=True ) -> List[Any]:
"""simple docstring"""
__lowercase : Optional[Any] = []
if output_path is None:
__lowercase : Optional[int] = """./animation.gif"""
if input_path is None:
__lowercase : Any = self.save_path
__lowercase : Any = sorted(glob(input_path + """/*""" ) )
if not len(__a ):
raise ValueError(
"""No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
""" function?)""" )
if len(__a ) == 1:
print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)""" )
__lowercase : Any = total_duration / len(__a )
__lowercase : int = [frame_duration] * len(__a )
if extend_frames:
__lowercase : Optional[Any] = 1.5
__lowercase : Optional[Any] = 3
for file_name in paths:
if file_name.endswith(""".png""" ):
images.append(imageio.imread(__a ) )
imageio.mimsave(__a , __a , duration=__a )
print(F"gif saved to {output_path}" )
def lowerCAmelCase ( self : Dict , __a : int=None , __a : Dict=None ) -> Optional[int]:
"""simple docstring"""
if not (path or img):
raise ValueError("""Input either path or tensor""" )
if img is not None:
raise NotImplementedError
__lowercase : Dict = preprocess(Image.open(__a ) , target_image_size=256 ).to(self.device )
__lowercase : Optional[Any] = preprocess_vqgan(__a )
__lowercase , *__lowercase : Optional[int] = self.vqgan.encode(__a )
return z
def lowerCAmelCase ( self : str , __a : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase : List[str] = self.latent.detach().requires_grad_()
__lowercase : Union[str, Any] = base_latent + transform_vector
if self.quantize:
__lowercase , *__lowercase : List[Any] = self.vqgan.quantize(__a )
else:
__lowercase : Dict = trans_latent
return self.vqgan.decode(__a )
def lowerCAmelCase ( self : Tuple , __a : Optional[int] , __a : List[Any] , __a : int=None ) -> Optional[int]:
"""simple docstring"""
__lowercase : Dict = self.clip_preprocessor(text=__a , images=__a , return_tensors="""pt""" , padding=__a )
__lowercase : Optional[int] = self.clip(**__a )
__lowercase : str = clip_outputs.logits_per_image
if weights is not None:
__lowercase : Union[str, Any] = similarity_logits * weights
return similarity_logits.sum()
def lowerCAmelCase ( self : str , __a : str , __a : Dict , __a : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowercase : str = self._get_clip_similarity(pos_prompts["""prompts"""] , __a , weights=(1 / pos_prompts["""weights"""]) )
if neg_prompts:
__lowercase : Dict = self._get_clip_similarity(neg_prompts["""prompts"""] , __a , weights=neg_prompts["""weights"""] )
else:
__lowercase : int = torch.tensor([1] , device=self.device )
__lowercase : Optional[int] = -torch.log(__a ) + torch.log(__a )
return loss
def lowerCAmelCase ( self : Any , __a : Dict , __a : Optional[int] , __a : Any ) -> str:
"""simple docstring"""
__lowercase : str = torch.randn_like(self.latent , requires_grad=__a , device=self.device )
__lowercase : Union[str, Any] = torch.optim.Adam([vector] , lr=self.lr )
for i in range(self.iterations ):
optim.zero_grad()
__lowercase : Any = self._add_vector(__a )
__lowercase : Dict = loop_post_process(__a )
__lowercase : Union[str, Any] = self._get_CLIP_loss(__a , __a , __a )
print("""CLIP loss""" , __a )
if self.log:
wandb.log({"""CLIP Loss""": clip_loss} )
clip_loss.backward(retain_graph=__a )
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0] )
else:
yield vector
def lowerCAmelCase ( self : str , __a : str , __a : Any , __a : Optional[Any] ) -> Dict:
"""simple docstring"""
wandb.init(reinit=__a , project="""face-editor""" )
wandb.config.update({"""Positive Prompts""": positive_prompts} )
wandb.config.update({"""Negative Prompts""": negative_prompts} )
wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations} )
if image_path:
__lowercase : str = Image.open(__a )
__lowercase : Optional[int] = image.resize((256, 256) )
wandb.log("""Original Image""" , wandb.Image(__a ) )
def lowerCAmelCase ( self : Union[str, Any] , __a : Tuple ) -> List[Any]:
"""simple docstring"""
if not prompts:
return []
__lowercase : List[str] = []
__lowercase : Any = []
if isinstance(__a , __a ):
__lowercase : Union[str, Any] = [prompt.strip() for prompt in prompts.split("""|""" )]
for prompt in prompts:
if isinstance(__a , (tuple, list) ):
__lowercase : List[Any] = prompt[0]
__lowercase : Union[str, Any] = float(prompt[1] )
elif ":" in prompt:
__lowercase , __lowercase : Optional[int] = prompt.split(""":""" )
__lowercase : int = float(__a )
else:
__lowercase : Optional[int] = prompt
__lowercase : Any = 1.0
processed_prompts.append(__a )
weights.append(__a )
return {
"prompts": processed_prompts,
"weights": torch.tensor(__a , device=self.device ),
}
def lowerCAmelCase ( self : int , __a : Tuple , __a : int=None , __a : List[str]=None , __a : Optional[int]=True , __a : str=False , __a : List[Any]=True , __a : Optional[Any]=True , __a : Dict=None , ) -> str:
"""simple docstring"""
if image_path:
__lowercase : int = self._get_latent(__a )
else:
__lowercase : str = torch.randn(self.latent_dim , device=self.device )
if self.log:
self._init_logging(__a , __a , __a )
assert pos_prompts, "You must provide at least one positive prompt."
__lowercase : int = self.process_prompts(__a )
__lowercase : Dict = self.process_prompts(__a )
if save_final and save_path is None:
__lowercase : Tuple = os.path.join("""./outputs/""" , """_""".join(pos_prompts["""prompts"""] ) )
if not os.path.exists(__a ):
os.makedirs(__a )
else:
__lowercase : Any = save_path + """_""" + get_timestamp()
os.makedirs(__a )
__lowercase : Tuple = save_path
__lowercase : Tuple = self.vqgan.decode(self.latent )[0]
if show_intermediate:
print("""Original Image""" )
show_pil(custom_to_pil(__a ) )
__lowercase : List[Any] = loop_post_process(__a )
for iter, transformed_img in enumerate(self._optimize_CLIP(__a , __a , __a ) ):
if show_intermediate:
show_pil(__a )
if save_intermediate:
transformed_img.save(os.path.join(self.save_path , F"iter_{iter:03d}.png" ) )
if self.log:
wandb.log({"""Image""": wandb.Image(__a )} )
if show_final:
show_pil(__a )
if save_final:
transformed_img.save(os.path.join(self.save_path , F"iter_{iter:03d}_final.png" ) ) | 149 | 0 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self : str ):
"""simple docstring"""
_lowercase = inspect.getfile(accelerate.test_utils )
_lowercase = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_script.py"] )
_lowercase = os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def snake_case ( self : List[Any] ):
"""simple docstring"""
_lowercase = f"""
{self.test_dir}/xla_spawn.py
--num_cores 8
{self.test_file_path}
""".split()
_lowercase = [sys.executable] + distributed_args
execute_subprocess_async(__A , env=os.environ.copy() )
| 602 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
__magic_name__ : Optional[Any] = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
__magic_name__ : Tuple = '''
Args:
predictions: List of predicted texts.
references: List of reference texts.
regexes_to_ignore: List, defaults to None. Regex expressions of characters to
ignore when calculating the exact matches. Note: these regexes are removed
from the input data before the changes based on the options below (e.g. ignore_case,
ignore_punctuation, ignore_numbers) are applied.
ignore_case: Boolean, defaults to False. If true, turns everything
to lowercase so that capitalization differences are ignored.
ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
comparing predictions and references.
ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
comparing predictions and references.
Returns:
exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["the cat", "theater", "YELLING", "agent007"]
>>> preds = ["cat?", "theater", "yelling", "agent"]
>>> results = exact_match.compute(references=refs, predictions=preds)
>>> print(round(results["exact_match"], 1))
25.0
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["the cat", "theater", "YELLING", "agent007"]
>>> preds = ["cat?", "theater", "yelling", "agent"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
>>> print(round(results["exact_match"], 1))
50.0
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["the cat", "theater", "YELLING", "agent007"]
>>> preds = ["cat?", "theater", "yelling", "agent"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
>>> print(round(results["exact_match"], 1))
75.0
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["the cat", "theater", "YELLING", "agent007"]
>>> preds = ["cat?", "theater", "yelling", "agent"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
>>> print(round(results["exact_match"], 1))
100.0
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]
>>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]
>>> results = exact_match.compute(references=refs, predictions=preds)
>>> print(round(results["exact_match"], 1))
33.3
'''
__magic_name__ : Any = '''
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase__ ( datasets.Metric ):
"""simple docstring"""
def snake_case ( self : Union[str, Any] ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , reference_urls=[] , )
def snake_case ( self : List[Any] , __A : int , __A : List[Any] , __A : List[str]=None , __A : Tuple=False , __A : Dict=False , __A : Optional[int]=False , ):
"""simple docstring"""
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
_lowercase = np.array([re.sub(__A , "" , __A ) for x in predictions] )
_lowercase = np.array([re.sub(__A , "" , __A ) for x in references] )
else:
_lowercase = np.asarray(__A )
_lowercase = np.asarray(__A )
if ignore_case:
_lowercase = np.char.lower(__A )
_lowercase = np.char.lower(__A )
if ignore_punctuation:
_lowercase = string.punctuation.maketrans("" , "" , string.punctuation )
_lowercase = np.char.translate(__A , table=__A )
_lowercase = np.char.translate(__A , table=__A )
if ignore_numbers:
_lowercase = string.digits.maketrans("" , "" , string.digits )
_lowercase = np.char.translate(__A , table=__A )
_lowercase = np.char.translate(__A , table=__A )
_lowercase = predictions == references
return {"exact_match": np.mean(__A ) * 1_0_0}
| 602 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
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 (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __A :
'''simple docstring'''
def __init__( self : str ,_snake_case : Tuple ,_snake_case : List[str]=13 ,_snake_case : Optional[int]=32 ,_snake_case : Optional[Any]=2 ,_snake_case : Tuple=3 ,_snake_case : Any=16 ,_snake_case : List[str]=[32, 64, 128] ,_snake_case : Optional[Any]=[1, 2, 1] ,_snake_case : Optional[Any]=[2, 2, 4] ,_snake_case : List[str]=2 ,_snake_case : Dict=2.0 ,_snake_case : Union[str, Any]=True ,_snake_case : Optional[Any]=0.0 ,_snake_case : str=0.0 ,_snake_case : Dict=0.1 ,_snake_case : Any="gelu" ,_snake_case : List[str]=False ,_snake_case : str=True ,_snake_case : List[Any]=0.02 ,_snake_case : Dict=1e-5 ,_snake_case : List[Any]=True ,_snake_case : Dict=None ,_snake_case : Optional[Any]=True ,_snake_case : Optional[int]=10 ,_snake_case : Optional[int]=8 ,_snake_case : Optional[Any]=["stage1", "stage2"] ,_snake_case : Optional[int]=[1, 2] ,) -> List[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = parent
lowercase__ : str = batch_size
lowercase__ : Optional[Any] = image_size
lowercase__ : Optional[int] = patch_size
lowercase__ : Union[str, Any] = num_channels
lowercase__ : Optional[int] = embed_dim
lowercase__ : List[str] = hidden_sizes
lowercase__ : Dict = depths
lowercase__ : List[Any] = num_heads
lowercase__ : List[Any] = window_size
lowercase__ : Any = mlp_ratio
lowercase__ : Optional[int] = qkv_bias
lowercase__ : str = hidden_dropout_prob
lowercase__ : Any = attention_probs_dropout_prob
lowercase__ : Optional[Any] = drop_path_rate
lowercase__ : Dict = hidden_act
lowercase__ : Tuple = use_absolute_embeddings
lowercase__ : List[Any] = patch_norm
lowercase__ : Optional[Any] = layer_norm_eps
lowercase__ : Tuple = initializer_range
lowercase__ : str = is_training
lowercase__ : Optional[Any] = scope
lowercase__ : Tuple = use_labels
lowercase__ : List[Any] = type_sequence_label_size
lowercase__ : Any = encoder_stride
lowercase__ : Tuple = out_features
lowercase__ : str = out_indices
def UpperCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : int = None
if self.use_labels:
lowercase__ : List[Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowercase__ : Optional[int] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self : List[str] ) -> str:
"""simple docstring"""
return FocalNetConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,embed_dim=self.embed_dim ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,num_heads=self.num_heads ,window_size=self.window_size ,mlp_ratio=self.mlp_ratio ,qkv_bias=self.qkv_bias ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,drop_path_rate=self.drop_path_rate ,hidden_act=self.hidden_act ,use_absolute_embeddings=self.use_absolute_embeddings ,path_norm=self.patch_norm ,layer_norm_eps=self.layer_norm_eps ,initializer_range=self.initializer_range ,encoder_stride=self.encoder_stride ,out_features=self.out_features ,out_indices=self.out_indices ,)
def UpperCAmelCase ( self : str ,_snake_case : Tuple ,_snake_case : Tuple ,_snake_case : Optional[int] ) -> Dict:
"""simple docstring"""
lowercase__ : Dict = FocalNetModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Dict = model(_snake_case )
lowercase__ : Dict = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowercase__ : List[Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, expected_seq_len, expected_dim) )
def UpperCAmelCase ( self : List[str] ,_snake_case : int ,_snake_case : int ,_snake_case : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ : int = FocalNetBackbone(config=_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Tuple = model(_snake_case )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) ,len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,[self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ) ,len(config.out_features ) )
self.parent.assertListEqual(model.channels ,config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
lowercase__ : Tuple = None
lowercase__ : List[str] = FocalNetBackbone(config=_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : int = model(_snake_case )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) ,1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,[self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) ,1 )
self.parent.assertListEqual(model.channels ,[config.hidden_sizes[-1]] )
def UpperCAmelCase ( self : Any ,_snake_case : Union[str, Any] ,_snake_case : Any ,_snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
lowercase__ : Any = FocalNetForMaskedImageModeling(config=_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : str = model(_snake_case )
self.parent.assertEqual(
result.reconstruction.shape ,(self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowercase__ : Union[str, Any] = 1
lowercase__ : Dict = FocalNetForMaskedImageModeling(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Any = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase__ : Tuple = model(_snake_case )
self.parent.assertEqual(result.reconstruction.shape ,(self.batch_size, 1, self.image_size, self.image_size) )
def UpperCAmelCase ( self : int ,_snake_case : List[Any] ,_snake_case : List[str] ,_snake_case : Any ) -> Optional[int]:
"""simple docstring"""
lowercase__ : str = self.type_sequence_label_size
lowercase__ : List[Any] = FocalNetForImageClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Optional[Any] = model(_snake_case ,labels=_snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowercase__ : List[str] = 1
lowercase__ : Dict = FocalNetForImageClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase__ : Optional[int] = model(_snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = config_and_inputs
lowercase__ : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __A ( A_ ,A_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : List[str] = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
lowerCAmelCase : Dict = (
{"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase : Any = False
lowerCAmelCase : Optional[int] = False
lowerCAmelCase : List[str] = False
lowerCAmelCase : Union[str, Any] = False
lowerCAmelCase : Tuple = False
def UpperCAmelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ : Union[str, Any] = FocalNetModelTester(self )
lowercase__ : Dict = ConfigTester(self ,config_class=_snake_case ,embed_dim=37 ,has_text_modality=_snake_case )
def UpperCAmelCase ( self : Any ) -> int:
"""simple docstring"""
self.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()
def UpperCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
return
def UpperCAmelCase ( self : Any ) -> Dict:
"""simple docstring"""
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCAmelCase ( self : int ) -> Dict:
"""simple docstring"""
lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_snake_case )
def UpperCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_snake_case )
def UpperCAmelCase ( self : int ) -> str:
"""simple docstring"""
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_snake_case )
@unittest.skip(reason='''FocalNet does not use inputs_embeds''' )
def UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason='''FocalNet does not use feedforward chunking''' )
def UpperCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
pass
def UpperCAmelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
lowercase__ , lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
lowercase__ : Any = model_class(_snake_case )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
lowercase__ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_snake_case ,nn.Linear ) )
def UpperCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
lowercase__ , lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
lowercase__ : List[str] = model_class(_snake_case )
lowercase__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Tuple = [*signature.parameters.keys()]
lowercase__ : str = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,_snake_case )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : List[str] ,_snake_case : str ,_snake_case : Optional[Any] ,_snake_case : Tuple ) -> Any:
"""simple docstring"""
lowercase__ : int = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**self._prepare_for_class(_snake_case ,_snake_case ) )
lowercase__ : Tuple = outputs.hidden_states
lowercase__ : Union[str, Any] = getattr(
self.model_tester ,'''expected_num_hidden_layers''' ,len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_snake_case ) ,_snake_case )
# FocalNet has a different seq_length
lowercase__ : Union[str, Any] = (
config.patch_size
if isinstance(config.patch_size ,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : Tuple = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[num_patches, self.model_tester.embed_dim] ,)
lowercase__ : Union[str, Any] = outputs.reshaped_hidden_states
self.assertEqual(len(_snake_case ) ,_snake_case )
lowercase__ , lowercase__ , lowercase__ , lowercase__ : Dict = reshaped_hidden_states[0].shape
lowercase__ : Tuple = (
reshaped_hidden_states[0].view(_snake_case ,_snake_case ,height * width ).permute(0 ,2 ,1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) ,[num_patches, self.model_tester.embed_dim] ,)
def UpperCAmelCase ( self : Any ) -> Tuple:
"""simple docstring"""
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : int = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size ,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
lowercase__ : List[str] = True
self.check_hidden_states_output(_snake_case ,_snake_case ,_snake_case ,_snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Dict = True
self.check_hidden_states_output(_snake_case ,_snake_case ,_snake_case ,_snake_case )
def UpperCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
lowercase__ , lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : int = 3
lowercase__ : Tuple = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size ,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
lowercase__ : int = (
config.patch_size
if isinstance(config.patch_size ,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : int = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowercase__ : Dict = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
lowercase__ : Union[str, Any] = True
self.check_hidden_states_output(_snake_case ,_snake_case ,_snake_case ,(padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Union[str, Any] = True
self.check_hidden_states_output(_snake_case ,_snake_case ,_snake_case ,(padded_height, padded_width) )
@slow
def UpperCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : str = FocalNetModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def UpperCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[Any] = _config_zero_init(_snake_case )
for model_class in self.all_model_classes:
lowercase__ : int = model_class(config=_snake_case )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
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""" ,)
@require_vision
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny''' ) if is_vision_available() else None
@slow
def UpperCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
lowercase__ : List[str] = FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''' ).to(_snake_case )
lowercase__ : Tuple = self.default_image_processor
lowercase__ : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
lowercase__ : Tuple = image_processor(images=_snake_case ,return_tensors='''pt''' ).to(_snake_case )
# forward pass
with torch.no_grad():
lowercase__ : Dict = model(**_snake_case )
# verify the logits
lowercase__ : str = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape ,_snake_case )
lowercase__ : List[Any] = torch.tensor([0.2166, -0.4368, 0.2191] ).to(_snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,_snake_case ,atol=1e-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item() ,281 )
@require_torch
class __A ( A_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : str = (FocalNetBackbone,) if is_torch_available() else ()
lowerCAmelCase : List[Any] = FocalNetConfig
lowerCAmelCase : Union[str, Any] = False
def UpperCAmelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
lowercase__ : Any = FocalNetModelTester(self )
| 560 |
"""simple docstring"""
lowerCAmelCase_ = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4_186_800.00,
"electronvolt": 1.602176634E-19,
"britishthermalunit_it": 1_055.05_585,
"footpound": 1.3_5_5_8_1_8,
}
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> float:
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
lowercase__ : Optional[Any] = (
f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
f"""Valid values are: {", ".join(__lowerCamelCase )}"""
)
raise ValueError(__lowerCamelCase )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 560 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : List[Any] = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConfig",
"Blip2VisionConfig",
],
"processing_blip_2": ["Blip2Processor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : str = [
"BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Blip2Model",
"Blip2QFormerModel",
"Blip2PreTrainedModel",
"Blip2ForConditionalGeneration",
"Blip2VisionModel",
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 705 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def UpperCamelCase ( _a , _a=0.999 , _a="cosine" , ) -> Dict:
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(_a ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_a ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}" )
lowercase_ :Any = []
for i in range(_a ):
lowercase_ :List[str] = i / num_diffusion_timesteps
lowercase_ :str = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_a ) / alpha_bar_fn(_a ) , _a ) )
return torch.tensor(_a , dtype=torch.floataa )
class UpperCamelCase ( lowercase__ , lowercase__ ):
'''simple docstring'''
lowercase : Tuple =[e.name for e in KarrasDiffusionSchedulers]
lowercase : Tuple =2
@register_to_config
def __init__( self , UpperCamelCase_ = 1000 , UpperCamelCase_ = 0.0_0085 , UpperCamelCase_ = 0.012 , UpperCamelCase_ = "linear" , UpperCamelCase_ = None , UpperCamelCase_ = "epsilon" , UpperCamelCase_ = False , UpperCamelCase_ = False , UpperCamelCase_ = 1.0 , UpperCamelCase_ = "linspace" , UpperCamelCase_ = 0 , ):
if trained_betas is not None:
lowercase_ :int = torch.tensor(UpperCamelCase_ , dtype=torch.floataa )
elif beta_schedule == "linear":
lowercase_ :List[str] = torch.linspace(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowercase_ :int = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , UpperCamelCase_ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowercase_ :Optional[int] = betas_for_alpha_bar(UpperCamelCase_ , alpha_transform_type='''cosine''' )
elif beta_schedule == "exp":
lowercase_ :Dict = betas_for_alpha_bar(UpperCamelCase_ , alpha_transform_type='''exp''' )
else:
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}" )
lowercase_ :str = 1.0 - self.betas
lowercase_ :Optional[int] = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :str = use_karras_sigmas
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_=None ):
if schedule_timesteps is None:
lowercase_ :List[str] = self.timesteps
lowercase_ :int = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
lowercase_ :Dict = 1 if len(UpperCamelCase_ ) > 1 else 0
else:
lowercase_ :Any = timestep.cpu().item() if torch.is_tensor(UpperCamelCase_ ) else timestep
lowercase_ :Union[str, Any] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def UpperCamelCase ( self ):
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , ):
lowercase_ :List[str] = self.index_for_timestep(UpperCamelCase_ )
lowercase_ :Optional[Any] = self.sigmas[step_index]
lowercase_ :Union[str, Any] = sample / ((sigma**2 + 1) ** 0.5)
return sample
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , ):
lowercase_ :Optional[Any] = num_inference_steps
lowercase_ :Dict = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
lowercase_ :Union[str, Any] = np.linspace(0 , num_train_timesteps - 1 , UpperCamelCase_ , dtype=UpperCamelCase_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
lowercase_ :Any = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowercase_ :List[Any] = (np.arange(0 , UpperCamelCase_ ) * step_ratio).round()[::-1].copy().astype(UpperCamelCase_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
lowercase_ :Dict = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowercase_ :Dict = (np.arange(UpperCamelCase_ , 0 , -step_ratio )).round().copy().astype(UpperCamelCase_ )
timesteps -= 1
else:
raise ValueError(
f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." )
lowercase_ :Optional[int] = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
lowercase_ :Tuple = np.log(UpperCamelCase_ )
lowercase_ :Dict = np.interp(UpperCamelCase_ , np.arange(0 , len(UpperCamelCase_ ) ) , UpperCamelCase_ )
if self.config.use_karras_sigmas:
lowercase_ :int = self._convert_to_karras(in_sigmas=UpperCamelCase_ , num_inference_steps=self.num_inference_steps )
lowercase_ :Optional[int] = np.array([self._sigma_to_t(UpperCamelCase_ , UpperCamelCase_ ) for sigma in sigmas] )
lowercase_ :Tuple = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
lowercase_ :Optional[int] = torch.from_numpy(UpperCamelCase_ ).to(device=UpperCamelCase_ )
lowercase_ :Tuple = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )
lowercase_ :Any = torch.from_numpy(UpperCamelCase_ )
lowercase_ :List[str] = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )
if str(UpperCamelCase_ ).startswith('''mps''' ):
# mps does not support float64
lowercase_ :int = timesteps.to(UpperCamelCase_ , dtype=torch.floataa )
else:
lowercase_ :Optional[Any] = timesteps.to(device=UpperCamelCase_ )
# empty dt and derivative
lowercase_ :List[str] = None
lowercase_ :List[Any] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
lowercase_ :int = defaultdict(UpperCamelCase_ )
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ ):
# get log sigma
lowercase_ :Union[str, Any] = np.log(UpperCamelCase_ )
# get distribution
lowercase_ :Optional[Any] = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
lowercase_ :List[Any] = np.cumsum((dists >= 0) , axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
lowercase_ :str = low_idx + 1
lowercase_ :Any = log_sigmas[low_idx]
lowercase_ :int = log_sigmas[high_idx]
# interpolate sigmas
lowercase_ :Dict = (low - log_sigma) / (low - high)
lowercase_ :str = np.clip(UpperCamelCase_ , 0 , 1 )
# transform interpolation to time range
lowercase_ :Dict = (1 - w) * low_idx + w * high_idx
lowercase_ :int = t.reshape(sigma.shape )
return t
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ ):
lowercase_ :float = in_sigmas[-1].item()
lowercase_ :float = in_sigmas[0].item()
lowercase_ :int = 7.0 # 7.0 is the value used in the paper
lowercase_ :Optional[Any] = np.linspace(0 , 1 , UpperCamelCase_ )
lowercase_ :List[str] = sigma_min ** (1 / rho)
lowercase_ :List[Any] = sigma_max ** (1 / rho)
lowercase_ :Tuple = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
@property
def UpperCamelCase ( self ):
return self.dt is None
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = True , ):
lowercase_ :Any = self.index_for_timestep(UpperCamelCase_ )
# advance index counter by 1
lowercase_ :Any = timestep.cpu().item() if torch.is_tensor(UpperCamelCase_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
lowercase_ :Optional[int] = self.sigmas[step_index]
lowercase_ :List[Any] = self.sigmas[step_index + 1]
else:
# 2nd order / Heun's method
lowercase_ :Optional[int] = self.sigmas[step_index - 1]
lowercase_ :Dict = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
lowercase_ :List[Any] = 0
lowercase_ :List[str] = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
lowercase_ :Union[str, Any] = sigma_hat if self.state_in_first_order else sigma_next
lowercase_ :List[Any] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
lowercase_ :Dict = sigma_hat if self.state_in_first_order else sigma_next
lowercase_ :Optional[Any] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
lowercase_ :List[str] = model_output
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" )
if self.config.clip_sample:
lowercase_ :str = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
lowercase_ :Optional[int] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
lowercase_ :Optional[int] = sigma_next - sigma_hat
# store for 2nd order step
lowercase_ :str = derivative
lowercase_ :Union[str, Any] = dt
lowercase_ :Optional[int] = sample
else:
# 2. 2nd order / Heun's method
lowercase_ :str = (sample - pred_original_sample) / sigma_next
lowercase_ :List[str] = (self.prev_derivative + derivative) / 2
# 3. take prev timestep & sample
lowercase_ :Union[str, Any] = self.dt
lowercase_ :Any = self.sample
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
lowercase_ :List[Any] = None
lowercase_ :List[str] = None
lowercase_ :Dict = None
lowercase_ :int = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=UpperCamelCase_ )
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , ):
# Make sure sigmas and timesteps have the same device and dtype as original_samples
lowercase_ :List[str] = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase_ ):
# mps does not support float64
lowercase_ :Optional[Any] = self.timesteps.to(original_samples.device , dtype=torch.floataa )
lowercase_ :Tuple = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
lowercase_ :Union[str, Any] = self.timesteps.to(original_samples.device )
lowercase_ :int = timesteps.to(original_samples.device )
lowercase_ :int = [self.index_for_timestep(UpperCamelCase_ , UpperCamelCase_ ) for t in timesteps]
lowercase_ :Tuple = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
lowercase_ :List[str] = sigma.unsqueeze(-1 )
lowercase_ :List[str] = original_samples + noise * sigma
return noisy_samples
def __len__( self ):
return self.config.num_train_timesteps
| 441 | 0 |
"""simple docstring"""
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _lowerCamelCase ( UpperCAmelCase_ : Optional[int], UpperCAmelCase_ : Tuple, UpperCAmelCase_ : str, UpperCAmelCase_ : Any=1024 ) -> List[Any]:
"""simple docstring"""
A__ , A__ = [], []
A__ = list(zip(UpperCAmelCase_, UpperCAmelCase_ ) )
A__ , A__ = sorted_examples[0]
def is_too_big(UpperCAmelCase_ : str ):
return tok(UpperCAmelCase_, return_tensors="pt" ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
A__ = new_src + " " + src
A__ = new_tgt + " " + tgt
if is_too_big(UpperCAmelCase_ ) or is_too_big(UpperCAmelCase_ ): # cant fit, finalize example
finished_src.append(UpperCAmelCase_ )
finished_tgt.append(UpperCAmelCase_ )
A__ , A__ = src, tgt
else: # can fit, keep adding
A__ , A__ = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(UpperCAmelCase_ )
finished_tgt.append(UpperCAmelCase_ )
return finished_src, finished_tgt
def _lowerCamelCase ( UpperCAmelCase_ : Union[str, Any], UpperCAmelCase_ : Path, UpperCAmelCase_ : int, UpperCAmelCase_ : Dict ) -> str:
"""simple docstring"""
A__ = Path(UpperCAmelCase_ )
save_path.mkdir(exist_ok=UpperCAmelCase_ )
for split in ["train"]:
A__ , A__ = data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
A__ = [x.rstrip() for x in Path(UpperCAmelCase_ ).open().readlines()]
A__ = [x.rstrip() for x in Path(UpperCAmelCase_ ).open().readlines()]
A__ , A__ = pack_examples(UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ )
print(F"""packed {split} split from {len(UpperCAmelCase_ )} examples -> {len(UpperCAmelCase_ )}.""" )
Path(save_path / F"""{split}.source""" ).open("w" ).write("\n".join(UpperCAmelCase_ ) )
Path(save_path / F"""{split}.target""" ).open("w" ).write("\n".join(UpperCAmelCase_ ) )
for split in ["val", "test"]:
A__ , A__ = data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
shutil.copyfile(UpperCAmelCase_, save_path / F"""{split}.source""" )
shutil.copyfile(UpperCAmelCase_, save_path / F"""{split}.target""" )
def _lowerCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
A__ = argparse.ArgumentParser()
parser.add_argument("--tok_name", type=UpperCAmelCase_, help="like facebook/bart-large-cnn,t5-base, etc." )
parser.add_argument("--max_seq_len", type=UpperCAmelCase_, default=128 )
parser.add_argument("--data_dir", type=UpperCAmelCase_ )
parser.add_argument("--save_path", type=UpperCAmelCase_ )
A__ = parser.parse_args()
A__ = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(UpperCAmelCase_, Path(args.data_dir ), args.max_seq_len, args.save_path )
if __name__ == "__main__":
packer_cli()
| 104 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[int] = {
'google/vit-base-patch16-224': 'https://huggingface.co/vit-base-patch16-224/resolve/main/config.json',
# See all ViT models at https://huggingface.co/models?filter=vit
}
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : Union[str, Any] = """vit"""
def __init__( self , UpperCamelCase__=768 , UpperCamelCase__=12 , UpperCamelCase__=12 , UpperCamelCase__=3072 , UpperCamelCase__="gelu" , UpperCamelCase__=0.0 , UpperCamelCase__=0.0 , UpperCamelCase__=0.02 , UpperCamelCase__=1e-12 , UpperCamelCase__=224 , UpperCamelCase__=16 , UpperCamelCase__=3 , UpperCamelCase__=True , UpperCamelCase__=16 , **UpperCamelCase__ , ) -> Union[str, Any]:
super().__init__(**UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = hidden_size
lowerCamelCase : List[str] = num_hidden_layers
lowerCamelCase : Union[str, Any] = num_attention_heads
lowerCamelCase : int = intermediate_size
lowerCamelCase : Optional[Any] = hidden_act
lowerCamelCase : int = hidden_dropout_prob
lowerCamelCase : Optional[int] = attention_probs_dropout_prob
lowerCamelCase : List[str] = initializer_range
lowerCamelCase : Optional[int] = layer_norm_eps
lowerCamelCase : List[Any] = image_size
lowerCamelCase : Union[str, Any] = patch_size
lowerCamelCase : Tuple = num_channels
lowerCamelCase : Union[str, Any] = qkv_bias
lowerCamelCase : Union[str, Any] = encoder_stride
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : Any = version.parse("""1.11""" )
@property
def _lowercase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _lowercase ( self ) -> float:
return 1e-4
| 311 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase =logging.get_logger(__name__)
lowerCamelCase ={
"sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class _lowerCamelCase ( UpperCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = '''poolformer'''
def __init__( self , __SCREAMING_SNAKE_CASE=3 , __SCREAMING_SNAKE_CASE=1_6 , __SCREAMING_SNAKE_CASE=1_6 , __SCREAMING_SNAKE_CASE=3 , __SCREAMING_SNAKE_CASE=4.0 , __SCREAMING_SNAKE_CASE=[2, 2, 6, 2] , __SCREAMING_SNAKE_CASE=[6_4, 1_2_8, 3_2_0, 5_1_2] , __SCREAMING_SNAKE_CASE=[7, 3, 3, 3] , __SCREAMING_SNAKE_CASE=[4, 2, 2, 2] , __SCREAMING_SNAKE_CASE=[2, 1, 1, 1] , __SCREAMING_SNAKE_CASE=4 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=1e-5 , __SCREAMING_SNAKE_CASE=0.02 , **__SCREAMING_SNAKE_CASE , ) -> str:
"""simple docstring"""
UpperCamelCase__ : Optional[int] = num_channels
UpperCamelCase__ : List[Any] = patch_size
UpperCamelCase__ : Optional[Any] = stride
UpperCamelCase__ : Dict = padding
UpperCamelCase__ : Optional[int] = pool_size
UpperCamelCase__ : List[str] = hidden_sizes
UpperCamelCase__ : Tuple = mlp_ratio
UpperCamelCase__ : List[Any] = depths
UpperCamelCase__ : Any = patch_sizes
UpperCamelCase__ : Tuple = strides
UpperCamelCase__ : Dict = num_encoder_blocks
UpperCamelCase__ : Optional[Any] = drop_path_rate
UpperCamelCase__ : Any = hidden_act
UpperCamelCase__ : Dict = use_layer_scale
UpperCamelCase__ : Optional[int] = layer_scale_init_value
UpperCamelCase__ : List[str] = initializer_range
super().__init__(**__SCREAMING_SNAKE_CASE )
class _lowerCamelCase ( UpperCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = version.parse('''1.11''' )
@property
def __SCREAMING_SNAKE_CASE ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __SCREAMING_SNAKE_CASE ( self ) -> float:
"""simple docstring"""
return 2e-3
| 462 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase ={
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileBertConfig",
"MobileBertOnnxConfig",
],
"tokenization_mobilebert": ["MobileBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase =["MobileBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase =[
"MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MobileBertForMaskedLM",
"MobileBertForMultipleChoice",
"MobileBertForNextSentencePrediction",
"MobileBertForPreTraining",
"MobileBertForQuestionAnswering",
"MobileBertForSequenceClassification",
"MobileBertForTokenClassification",
"MobileBertLayer",
"MobileBertModel",
"MobileBertPreTrainedModel",
"load_tf_weights_in_mobilebert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase =[
"TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFMobileBertForMaskedLM",
"TFMobileBertForMultipleChoice",
"TFMobileBertForNextSentencePrediction",
"TFMobileBertForPreTraining",
"TFMobileBertForQuestionAnswering",
"TFMobileBertForSequenceClassification",
"TFMobileBertForTokenClassification",
"TFMobileBertMainLayer",
"TFMobileBertModel",
"TFMobileBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 462 | 1 |
"""simple docstring"""
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case : str ):
"""simple docstring"""
_lowerCamelCase : int = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
_lowerCamelCase : Union[str, Any] = 192
_lowerCamelCase : int = 768
_lowerCamelCase : Optional[Any] = 12
_lowerCamelCase : Optional[int] = 3
_lowerCamelCase : str = [800, 1333]
_lowerCamelCase : Dict = False
elif yolos_name == "yolos_s_dWr":
_lowerCamelCase : List[str] = 330
_lowerCamelCase : Tuple = 14
_lowerCamelCase : List[Any] = 6
_lowerCamelCase : Optional[int] = 1320
elif "yolos_s" in yolos_name:
_lowerCamelCase : int = 384
_lowerCamelCase : Optional[Any] = 1536
_lowerCamelCase : Union[str, Any] = 12
_lowerCamelCase : Any = 6
elif "yolos_b" in yolos_name:
_lowerCamelCase : List[Any] = [800, 1344]
_lowerCamelCase : Dict = 91
_lowerCamelCase : int = """huggingface/label-files"""
_lowerCamelCase : List[Any] = """coco-detection-id2label.json"""
_lowerCamelCase : Optional[Any] = json.load(open(hf_hub_download(__snake_case , __snake_case , repo_type="""dataset""" ) , """r""" ) )
_lowerCamelCase : Optional[Any] = {int(__snake_case ): v for k, v in idalabel.items()}
_lowerCamelCase : int = idalabel
_lowerCamelCase : Optional[Any] = {v: k for k, v in idalabel.items()}
return config
def _snake_case ( __snake_case : dict , __snake_case : YolosConfig , __snake_case : bool = False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowerCamelCase : int = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
_lowerCamelCase : Any = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Tuple = in_proj_weight[: config.hidden_size, :]
_lowerCamelCase : Optional[int] = in_proj_bias[: config.hidden_size]
_lowerCamelCase : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowerCamelCase : int = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_lowerCamelCase : int = in_proj_weight[-config.hidden_size :, :]
_lowerCamelCase : Dict = in_proj_bias[-config.hidden_size :]
def _snake_case ( __snake_case : str ):
"""simple docstring"""
if "backbone" in name:
_lowerCamelCase : Optional[Any] = name.replace("""backbone""" , """vit""" )
if "cls_token" in name:
_lowerCamelCase : str = name.replace("""cls_token""" , """embeddings.cls_token""" )
if "det_token" in name:
_lowerCamelCase : str = name.replace("""det_token""" , """embeddings.detection_tokens""" )
if "mid_pos_embed" in name:
_lowerCamelCase : Any = name.replace("""mid_pos_embed""" , """encoder.mid_position_embeddings""" )
if "pos_embed" in name:
_lowerCamelCase : Dict = name.replace("""pos_embed""" , """embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
_lowerCamelCase : Union[str, Any] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "blocks" in name:
_lowerCamelCase : int = name.replace("""blocks""" , """encoder.layer""" )
if "attn.proj" in name:
_lowerCamelCase : Any = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
_lowerCamelCase : Any = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
_lowerCamelCase : str = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
_lowerCamelCase : List[str] = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
_lowerCamelCase : List[Any] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
_lowerCamelCase : Optional[Any] = name.replace("""mlp.fc2""" , """output.dense""" )
if "class_embed" in name:
_lowerCamelCase : Any = name.replace("""class_embed""" , """class_labels_classifier""" )
if "bbox_embed" in name:
_lowerCamelCase : Optional[int] = name.replace("""bbox_embed""" , """bbox_predictor""" )
if "vit.norm" in name:
_lowerCamelCase : str = name.replace("""vit.norm""" , """vit.layernorm""" )
return name
def _snake_case ( __snake_case : dict , __snake_case : YolosForObjectDetection ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_lowerCamelCase : List[str] = orig_state_dict.pop(__snake_case )
if "qkv" in key:
_lowerCamelCase : Any = key.split(""".""" )
_lowerCamelCase : Dict = int(key_split[2] )
_lowerCamelCase : Optional[int] = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
_lowerCamelCase : Dict = val[:dim, :]
_lowerCamelCase : str = val[
dim : dim * 2, :
]
_lowerCamelCase : Union[str, Any] = val[-dim:, :]
else:
_lowerCamelCase : Optional[Any] = val[:dim]
_lowerCamelCase : Optional[Any] = val[dim : dim * 2]
_lowerCamelCase : List[str] = val[-dim:]
else:
_lowerCamelCase : int = val
return orig_state_dict
def _snake_case ( ):
"""simple docstring"""
_lowerCamelCase : List[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_lowerCamelCase : Any = Image.open(requests.get(__snake_case , stream=__snake_case ).raw )
return im
@torch.no_grad()
def _snake_case ( __snake_case : str , __snake_case : str , __snake_case : str , __snake_case : bool = False ):
"""simple docstring"""
_lowerCamelCase : str = get_yolos_config(__snake_case )
# load original state_dict
_lowerCamelCase : Any = torch.load(__snake_case , map_location="""cpu""" )["""model"""]
# load 🤗 model
_lowerCamelCase : Dict = YolosForObjectDetection(__snake_case )
model.eval()
_lowerCamelCase : Optional[Any] = convert_state_dict(__snake_case , __snake_case )
model.load_state_dict(__snake_case )
# Check outputs on an image, prepared by YolosImageProcessor
_lowerCamelCase : List[Any] = 800 if yolos_name != """yolos_ti""" else 512
_lowerCamelCase : str = YolosImageProcessor(format="""coco_detection""" , size=__snake_case )
_lowerCamelCase : List[str] = image_processor(images=prepare_img() , return_tensors="""pt""" )
_lowerCamelCase : Optional[Any] = model(**__snake_case )
_lowerCamelCase , _lowerCamelCase : List[Any] = outputs.logits, outputs.pred_boxes
_lowerCamelCase , _lowerCamelCase : Tuple = None, None
if yolos_name == "yolos_ti":
_lowerCamelCase : Union[str, Any] = torch.tensor(
[[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]] )
_lowerCamelCase : Any = torch.tensor(
[[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]] )
elif yolos_name == "yolos_s_200_pre":
_lowerCamelCase : List[str] = torch.tensor(
[[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] )
_lowerCamelCase : int = torch.tensor(
[[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] )
elif yolos_name == "yolos_s_300_pre":
_lowerCamelCase : Any = torch.tensor(
[[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]] )
_lowerCamelCase : List[str] = torch.tensor(
[[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]] )
elif yolos_name == "yolos_s_dWr":
_lowerCamelCase : Optional[int] = torch.tensor(
[[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]] )
_lowerCamelCase : Optional[int] = torch.tensor(
[[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]] )
elif yolos_name == "yolos_base":
_lowerCamelCase : Union[str, Any] = torch.tensor(
[[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]] )
_lowerCamelCase : List[Any] = torch.tensor(
[[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]] )
else:
raise ValueError(F'Unknown yolos_name: {yolos_name}' )
assert torch.allclose(logits[0, :3, :3] , __snake_case , atol=1E-4 )
assert torch.allclose(pred_boxes[0, :3, :3] , __snake_case , atol=1E-4 )
Path(__snake_case ).mkdir(exist_ok=__snake_case )
print(F'Saving model {yolos_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:
_lowerCamelCase : Any = {
"""yolos_ti""": """yolos-tiny""",
"""yolos_s_200_pre""": """yolos-small""",
"""yolos_s_300_pre""": """yolos-small-300""",
"""yolos_s_dWr""": """yolos-small-dwr""",
"""yolos_base""": """yolos-base""",
}
print("""Pushing to the hub...""" )
_lowerCamelCase : List[str] = model_mapping[yolos_name]
image_processor.push_to_hub(__snake_case , organization="""hustvl""" )
model.push_to_hub(__snake_case , organization="""hustvl""" )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--yolos_name""",
default="""yolos_s_200_pre""",
type=str,
help=(
"""Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"""
""" 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."""
),
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, help="""Path to the original state dict (.pth file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, 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."""
)
UpperCAmelCase = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 88 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.basicConfig(level=logging.DEBUG)
A = logging.getLogger()
A = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
os.makedirs(__UpperCamelCase , exist_ok=__UpperCamelCase )
snake_case_ = {'source': 'What is love ?', 'target': 'life'}
snake_case_ = {'train': 12, 'val': 2, 'test': 2}
for split in ["train", "test", "val"]:
for field in ["source", "target"]:
snake_case_ = '\n'.join([contents[field]] * n_lines[split] )
with open(os.path.join(__UpperCamelCase , f"""{split}.{field}""" ) , 'w' ) as f:
f.write(__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = "pytorch" ):
"""simple docstring"""
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = os.path.join(__UpperCamelCase , 'output' )
snake_case_ = os.path.join(__UpperCamelCase , 'data' )
self._create_dummy_data(data_dir=__UpperCamelCase )
snake_case_ = f"""
--data_dir {data_dir} \
--output_dir {output_dir} \
--model_name_or_path facebook/rag-sequence-base \
--model_type rag_sequence \
--do_train \
--do_predict \
--n_val -1 \
--val_check_interval 1.0 \
--train_batch_size 2 \
--eval_batch_size 1 \
--max_source_length 25 \
--max_target_length 25 \
--val_max_target_length 25 \
--test_max_target_length 25 \
--label_smoothing 0.1 \
--dropout 0.1 \
--attention_dropout 0.1 \
--weight_decay 0.001 \
--adam_epsilon 1e-08 \
--max_grad_norm 0.1 \
--lr_scheduler polynomial \
--learning_rate 3e-04 \
--num_train_epochs 1 \
--warmup_steps 4 \
--gradient_accumulation_steps 1 \
--distributed-port 8787 \
--use_dummy_dataset 1 \
--distributed_retriever {distributed_retriever} \
""".split()
if gpus > 0:
testargs.append(f"""--gpus={gpus}""" )
if is_apex_available():
testargs.append('--fp16' )
else:
testargs.append('--gpus=0' )
testargs.append('--distributed_backend=ddp_cpu' )
testargs.append('--num_processes=2' )
snake_case_ = [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
execute_subprocess_async(__UpperCamelCase , env=self.get_env() )
snake_case_ = os.path.join(__UpperCamelCase , 'metrics.json' )
with open(__UpperCamelCase ) as f:
snake_case_ = json.load(__UpperCamelCase )
return result
@require_torch_gpu
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self._run_finetune(gpus=1 )
self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 )
@require_torch_multi_gpu
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self._run_finetune(gpus=2 )
self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 )
@require_torch_gpu
@require_ray
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self._run_finetune(gpus=1 , distributed_retriever='ray' )
self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 )
@require_torch_multi_gpu
@require_ray
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self._run_finetune(gpus=1 , distributed_retriever='ray' )
self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 )
| 187 | 0 |
"""simple docstring"""
from math import factorial, radians
def lowercase (snake_case__ : float , snake_case__ : int = 18 , snake_case__ : int = 10 ) -> List[str]:
'''simple docstring'''
lowerCAmelCase = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
lowerCAmelCase = radians(__lowerCAmelCase )
lowerCAmelCase = angle_in_radians
lowerCAmelCase = 3
lowerCAmelCase = -1
for _ in range(__lowerCAmelCase ):
result += (b * (angle_in_radians**a)) / factorial(__lowerCAmelCase )
lowerCAmelCase = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
__import__('doctest').testmod()
| 709 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
a = logging.get_logger(__name__)
def lowercase (snake_case__ : bool , snake_case__ : bool ) -> Tuple:
'''simple docstring'''
def run_func(snake_case__ : Any ):
@wraps(snake_case__ )
def run_in_eager_mode(*snake_case__ : Optional[Any] , **snake_case__ : int ):
return func(*snake_case__ , **snake_case__ )
@wraps(snake_case__ )
@tf.function(experimental_compile=snake_case__ )
def run_in_graph_mode(*snake_case__ : int , **snake_case__ : Tuple ):
return func(*snake_case__ , **snake_case__ )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"""Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.""" )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def lowercase (snake_case__ : int , snake_case__ : int , snake_case__ : int ) -> ["tf.Tensor"]:
'''simple docstring'''
lowerCAmelCase = random.Random()
lowerCAmelCase = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(snake_case__ , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class SCREAMING_SNAKE_CASE__ ( _a ):
_a = 42
_a = 42
_a = "TensorFlow"
@property
def __lowercase ( self : Optional[int] ):
return tf.__version__
def __lowercase ( self : Optional[Any] , lowerCAmelCase : str , lowerCAmelCase : int , lowerCAmelCase : int ):
# initialize GPU on separate process
lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
lowerCAmelCase = self._prepare_inference_func(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
return self._measure_speed(_inference )
def __lowercase ( self : str , lowerCAmelCase : str , lowerCAmelCase : int , lowerCAmelCase : int ):
lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
lowerCAmelCase = self._prepare_train_func(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
return self._measure_speed(_train )
def __lowercase ( self : str , lowerCAmelCase : str , lowerCAmelCase : int , lowerCAmelCase : int ):
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCAmelCase )
lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
lowerCAmelCase = self._prepare_inference_func(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
return self._measure_memory(_inference )
def __lowercase ( self : int , lowerCAmelCase : str , lowerCAmelCase : int , lowerCAmelCase : int ):
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCAmelCase )
lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
lowerCAmelCase = self._prepare_train_func(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
return self._measure_memory(_train )
def __lowercase ( self : List[str] , lowerCAmelCase : str , lowerCAmelCase : int , lowerCAmelCase : int ):
lowerCAmelCase = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
lowerCAmelCase = (
hasattr(lowerCAmelCase , """architectures""" )
and isinstance(config.architectures , lowerCAmelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
lowerCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
lowerCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
lowerCAmelCase = getattr(lowerCAmelCase , lowerCAmelCase )
lowerCAmelCase = model_cls(lowerCAmelCase )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
lowerCAmelCase = TF_MODEL_MAPPING[config.__class__](lowerCAmelCase )
# encoder-decoder has vocab size saved differently
lowerCAmelCase = config.vocab_size if hasattr(lowerCAmelCase , """vocab_size""" ) else config.encoder.vocab_size
lowerCAmelCase = random_input_ids(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(lowerCAmelCase , decoder_input_ids=lowerCAmelCase , training=lowerCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(lowerCAmelCase , training=lowerCAmelCase )
lowerCAmelCase = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def __lowercase ( self : Optional[int] , lowerCAmelCase : str , lowerCAmelCase : int , lowerCAmelCase : int ):
lowerCAmelCase = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.""" )
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
lowerCAmelCase = (
hasattr(lowerCAmelCase , """architectures""" )
and isinstance(config.architectures , lowerCAmelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
lowerCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
lowerCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
lowerCAmelCase = getattr(lowerCAmelCase , lowerCAmelCase )
lowerCAmelCase = model_cls(lowerCAmelCase )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
lowerCAmelCase = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCAmelCase )
# encoder-decoder has vocab size saved differently
lowerCAmelCase = config.vocab_size if hasattr(lowerCAmelCase , """vocab_size""" ) else config.encoder.vocab_size
lowerCAmelCase = random_input_ids(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
lowerCAmelCase = model(lowerCAmelCase , decoder_input_ids=lowerCAmelCase , labels=lowerCAmelCase , training=lowerCAmelCase )[0]
lowerCAmelCase = tf.gradients(lowerCAmelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
lowerCAmelCase = model(lowerCAmelCase , labels=lowerCAmelCase , training=lowerCAmelCase )[0]
lowerCAmelCase = tf.gradients(lowerCAmelCase , model.trainable_variables )
return gradients
lowerCAmelCase = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def __lowercase ( self : Optional[int] , lowerCAmelCase : List[str] ):
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation""" )
timeit.repeat(lowerCAmelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
lowerCAmelCase = timeit.repeat(
lowerCAmelCase , repeat=self.args.repeat , number=10 , )
return min(lowerCAmelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
def __lowercase ( self : Optional[Any] , lowerCAmelCase : Callable[[], None] ):
logger.info(
"""Note that TensorFlow allocates more memory than """
"""it might need to speed up computation. """
"""The memory reported here corresponds to the memory """
"""reported by `nvidia-smi`, which can vary depending """
"""on total available memory on the GPU that is used.""" )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"""`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
""" consumption line by line.""" )
lowerCAmelCase = start_memory_tracing("""transformers""" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"""Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
""" with `args.memory=False`""" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"""py3nvml not installed, we won't log GPU memory usage. """
"""Install py3nvml (pip install py3nvml) to log information about GPU.""" )
lowerCAmelCase = """N/A"""
else:
logger.info(
"""Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
""" running on the same GPU.""" )
# init nvml
nvml.nvmlInit()
func()
lowerCAmelCase = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
lowerCAmelCase = nvml.nvmlDeviceGetMemoryInfo(lowerCAmelCase )
lowerCAmelCase = meminfo.used
lowerCAmelCase = Memory(lowerCAmelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"""When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
""" TensorFlow.""" )
lowerCAmelCase = None
else:
lowerCAmelCase = measure_peak_memory_cpu(lowerCAmelCase )
lowerCAmelCase = Memory(lowerCAmelCase ) if isinstance(lowerCAmelCase , lowerCAmelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
lowerCAmelCase = stop_memory_tracing(lowerCAmelCase )
if memory is None:
lowerCAmelCase = summary.total
else:
lowerCAmelCase = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
return "N/A", None
| 529 | 0 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
__A : int = "base_with_context"
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding'''] ) )
UpperCAmelCase = nn.Parameter(
torch.FloatTensor(weights['''Embed_0''']['''embedding'''] ) , requires_grad=lowerCAmelCase_ )
for lyr_num, lyr in enumerate(model.encoders ):
UpperCAmelCase = weights[F"""layers_{lyr_num}"""]
UpperCAmelCase = nn.Parameter(
torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale'''] ) )
UpperCAmelCase = ly_weight['''attention''']
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale'''] ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale'''] ) )
return model
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(
torch.FloatTensor(weights['''Embed_0''']['''embedding'''] ) , requires_grad=lowerCAmelCase_ )
for lyr_num, lyr in enumerate(model.encoders ):
UpperCAmelCase = weights[F"""layers_{lyr_num}"""]
UpperCAmelCase = ly_weight['''attention''']
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(
torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale'''] ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale'''] ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale'''] ) )
return model
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(
torch.FloatTensor(weights['''Embed_0''']['''embedding'''] ) , requires_grad=lowerCAmelCase_ )
UpperCAmelCase = nn.Parameter(
torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
UpperCAmelCase = weights[F"""layers_{lyr_num}"""]
UpperCAmelCase = nn.Parameter(
torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale'''] ) )
UpperCAmelCase = nn.Parameter(
torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T ) )
UpperCAmelCase = ly_weight['''self_attention''']
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )
UpperCAmelCase = ly_weight['''MultiHeadDotProductAttention_0''']
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(
torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale'''] ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale'''] ) )
UpperCAmelCase = nn.Parameter(
torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale'''] ) )
UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T ) )
return model
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Dict:
'''simple docstring'''
UpperCAmelCase = checkpoints.load_tax_checkpoint(args.checkpoint_path )
UpperCAmelCase = jnp.tree_util.tree_map(onp.array , lowerCAmelCase_ )
UpperCAmelCase = [
'''from __gin__ import dynamic_registration''',
'''from music_spectrogram_diffusion.models.diffusion import diffusion_utils''',
'''diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0''',
'''diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()''',
]
UpperCAmelCase = os.path.join(args.checkpoint_path , '''..''' , '''config.gin''' )
UpperCAmelCase = inference.parse_training_gin_file(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase = inference.InferenceModel(args.checkpoint_path , lowerCAmelCase_ )
UpperCAmelCase = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' , variance_type='''fixed_large''' )
UpperCAmelCase = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length['''inputs'''] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj='''gated-gelu''' , )
UpperCAmelCase = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length['''targets_context'''] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj='''gated-gelu''' , )
UpperCAmelCase = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length['''targets_context'''] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
UpperCAmelCase = load_notes_encoder(ta_checkpoint['''target''']['''token_encoder'''] , lowerCAmelCase_ )
UpperCAmelCase = load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''] , lowerCAmelCase_ )
UpperCAmelCase = load_decoder(ta_checkpoint['''target''']['''decoder'''] , lowerCAmelCase_ )
UpperCAmelCase = OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder''' )
UpperCAmelCase = SpectrogramDiffusionPipeline(
notes_encoder=lowerCAmelCase_ , continuous_encoder=lowerCAmelCase_ , decoder=lowerCAmelCase_ , scheduler=lowerCAmelCase_ , melgan=lowerCAmelCase_ , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
parser.add_argument("--output_path", default=None, type=str, required=True, help="Path to the converted model.")
parser.add_argument(
"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
)
parser.add_argument(
"--checkpoint_path",
default=F'{MODEL}/checkpoint_500000',
type=str,
required=False,
help="Path to the original jax model checkpoint.",
)
__A : Union[str, Any] = parser.parse_args()
main(args)
| 130 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __a ( ) -> int:
'''simple docstring'''
UpperCAmelCase_, UpperCAmelCase_= 9, 14 # noqa: F841
UpperCAmelCase_= [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
UpperCAmelCase_= defaultdict(lowerCAmelCase_ )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
UpperCAmelCase_= mst(lowerCAmelCase_ )
UpperCAmelCase_= [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
UpperCAmelCase_= tuple(answer[:2] )
UpperCAmelCase_= tuple(edge[::-1] )
assert edge in result or reverse in result
| 593 | 0 |
import torch
def UpperCAmelCase_ ( ) -> Optional[Any]:
if torch.cuda.is_available():
__lowercase : Dict = torch.cuda.device_count()
else:
__lowercase : Optional[Any] = 0
print(F'Successfully ran on {num_gpus} GPUs' )
if __name__ == "__main__":
main()
| 718 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
A__ : int = StableDiffusionSAGPipeline
A__ : Union[str, Any] = TEXT_TO_IMAGE_PARAMS
A__ : Dict = TEXT_TO_IMAGE_BATCH_PARAMS
A__ : List[str] = TEXT_TO_IMAGE_IMAGE_PARAMS
A__ : int = TEXT_TO_IMAGE_IMAGE_PARAMS
A__ : List[str] = False
def snake_case_ ( self : Optional[Any] ):
torch.manual_seed(0 )
__lowercase : Optional[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
__lowercase : int = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , clip_sample=_snake_case , set_alpha_to_one=_snake_case , )
torch.manual_seed(0 )
__lowercase : List[Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
__lowercase : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__lowercase : List[Any] = CLIPTextModel(_snake_case )
__lowercase : List[Any] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__lowercase : int = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def snake_case_ ( self : Tuple , _snake_case : List[str] , _snake_case : List[str]=0 ):
if str(_snake_case ).startswith('''mps''' ):
__lowercase : Optional[int] = torch.manual_seed(_snake_case )
else:
__lowercase : Tuple = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
__lowercase : Tuple = {
'''prompt''': '''.''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 1.0,
'''sag_scale''': 1.0,
'''output_type''': '''numpy''',
}
return inputs
def snake_case_ ( self : Any ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self : int ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self : Dict ):
__lowercase : Optional[int] = StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
__lowercase : Optional[int] = sag_pipe.to(_snake_case )
sag_pipe.set_progress_bar_config(disable=_snake_case )
__lowercase : List[str] = '''.'''
__lowercase : List[str] = torch.manual_seed(0 )
__lowercase : List[str] = sag_pipe(
[prompt] , generator=_snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' )
__lowercase : Optional[Any] = output.images
__lowercase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase : int = np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def snake_case_ ( self : Optional[int] ):
__lowercase : Dict = StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
__lowercase : Any = sag_pipe.to(_snake_case )
sag_pipe.set_progress_bar_config(disable=_snake_case )
__lowercase : List[str] = '''.'''
__lowercase : List[str] = torch.manual_seed(0 )
__lowercase : int = sag_pipe(
[prompt] , generator=_snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' )
__lowercase : str = output.images
__lowercase : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase : Any = np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def snake_case_ ( self : int ):
__lowercase : List[str] = StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
__lowercase : Tuple = sag_pipe.to(_snake_case )
sag_pipe.set_progress_bar_config(disable=_snake_case )
__lowercase : Union[str, Any] = '''.'''
__lowercase : Tuple = torch.manual_seed(0 )
__lowercase : Optional[int] = sag_pipe(
[prompt] , width=768 , height=512 , generator=_snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' , )
__lowercase : Dict = output.images
assert image.shape == (1, 512, 768, 3)
| 284 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase ={
"configuration_vision_encoder_decoder": ["VisionEncoderDecoderConfig", "VisionEncoderDecoderOnnxConfig"]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase =["VisionEncoderDecoderModel"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase =["TFVisionEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase =["FlaxVisionEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
UpperCAmelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 617 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 41 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class A_ ( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = KandinskyImgaImgPipeline
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
SCREAMING_SNAKE_CASE_ : Tuple = [
'''prompt''',
'''negative_prompt''',
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
]
SCREAMING_SNAKE_CASE_ : Any = [
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''negative_prompt''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
SCREAMING_SNAKE_CASE_ : Tuple = False
@property
def __UpperCAmelCase ( self : Any ) -> str:
return 32
@property
def __UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
return 32
@property
def __UpperCAmelCase ( self : List[Any] ) -> Optional[Any]:
return self.time_input_dim
@property
def __UpperCAmelCase ( self : Tuple ) -> List[Any]:
return self.time_input_dim * 4
@property
def __UpperCAmelCase ( self : List[str] ) -> Dict:
return 100
@property
def __UpperCAmelCase ( self : Tuple ) -> Dict:
_lowercase = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base' )
return tokenizer
@property
def __UpperCAmelCase ( self : List[Any] ) -> Any:
torch.manual_seed(0 )
_lowercase = MCLIPConfig(
numDims=self.cross_attention_dim ,transformerDimensions=self.text_embedder_hidden_size ,hidden_size=self.text_embedder_hidden_size ,intermediate_size=37 ,num_attention_heads=4 ,num_hidden_layers=5 ,vocab_size=1005 ,)
_lowercase = MultilingualCLIP(__A )
_lowercase = text_encoder.eval()
return text_encoder
@property
def __UpperCAmelCase ( self : Any ) -> Tuple:
torch.manual_seed(0 )
_lowercase = {
'in_channels': 4,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'text_image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'text_image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
_lowercase = UNetaDConditionModel(**__A )
return model
@property
def __UpperCAmelCase ( self : Optional[int] ) -> Tuple:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __UpperCAmelCase ( self : int ) -> Union[str, Any]:
torch.manual_seed(0 )
_lowercase = VQModel(**self.dummy_movq_kwargs )
return model
def __UpperCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
_lowercase = self.dummy_text_encoder
_lowercase = self.dummy_tokenizer
_lowercase = self.dummy_unet
_lowercase = self.dummy_movq
_lowercase = {
'num_train_timesteps': 1000,
'beta_schedule': 'linear',
'beta_start': 0.00085,
'beta_end': 0.012,
'clip_sample': False,
'set_alpha_to_one': False,
'steps_offset': 0,
'prediction_type': 'epsilon',
'thresholding': False,
}
_lowercase = DDIMScheduler(**__A )
_lowercase = {
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def __UpperCAmelCase ( self : Tuple ,__A : int ,__A : List[Any]=0 ) -> Dict:
_lowercase = floats_tensor((1, self.cross_attention_dim) ,rng=random.Random(__A ) ).to(__A )
_lowercase = floats_tensor((1, self.cross_attention_dim) ,rng=random.Random(seed + 1 ) ).to(__A )
# create init_image
_lowercase = floats_tensor((1, 3, 64, 64) ,rng=random.Random(__A ) ).to(__A )
_lowercase = image.cpu().permute(0 ,2 ,3 ,1 )[0]
_lowercase = Image.fromarray(np.uinta(__A ) ).convert('RGB' ).resize((256, 256) )
if str(__A ).startswith('mps' ):
_lowercase = torch.manual_seed(__A )
else:
_lowercase = torch.Generator(device=__A ).manual_seed(__A )
_lowercase = {
'prompt': 'horse',
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 10,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def __UpperCAmelCase ( self : List[Any] ) -> Tuple:
_lowercase = 'cpu'
_lowercase = self.get_dummy_components()
_lowercase = self.pipeline_class(**__A )
_lowercase = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
_lowercase = pipe(**self.get_dummy_inputs(__A ) )
_lowercase = output.images
_lowercase = pipe(
**self.get_dummy_inputs(__A ) ,return_dict=__A ,)[0]
_lowercase = image[0, -3:, -3:, -1]
_lowercase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowercase = np.array(
[0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class A_ ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self : str ) -> str:
_lowercase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_img2img_frog.npy' )
_lowercase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
_lowercase = 'A red cartoon frog, 4k'
_lowercase = KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' ,torch_dtype=torch.floataa )
pipe_prior.to(__A )
_lowercase = KandinskyImgaImgPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1' ,torch_dtype=torch.floataa )
_lowercase = pipeline.to(__A )
pipeline.set_progress_bar_config(disable=__A )
_lowercase = torch.Generator(device='cpu' ).manual_seed(0 )
_lowercase , _lowercase = pipe_prior(
__A ,generator=__A ,num_inference_steps=5 ,negative_prompt='' ,).to_tuple()
_lowercase = pipeline(
__A ,image=__A ,image_embeds=__A ,negative_image_embeds=__A ,generator=__A ,num_inference_steps=100 ,height=768 ,width=768 ,strength=0.2 ,output_type='np' ,)
_lowercase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__A ,__A ) | 535 |
def SCREAMING_SNAKE_CASE__ ( snake_case__ :List[str] ) -> Dict:
_lowercase = len(snake_case__ )
while cur > 1:
# Find the maximum number in arr
_lowercase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
_lowercase = arr[mi::-1] + arr[mi + 1 : len(snake_case__ )]
# Reverse whole list
_lowercase = arr[cur - 1 :: -1] + arr[cur : len(snake_case__ )]
cur -= 1
return arr
if __name__ == "__main__":
snake_case = input("""Enter numbers separated by a comma:\n""").strip()
snake_case = [int(item) for item in user_input.split(""",""")]
print(pancake_sort(unsorted)) | 535 | 1 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 0 ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = end or len(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = i
_SCREAMING_SNAKE_CASE = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
_SCREAMING_SNAKE_CASE = array[temp_index - 1]
temp_index -= 1
_SCREAMING_SNAKE_CASE = temp_index_value
return array
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> None: # Max Heap
"""simple docstring"""
_SCREAMING_SNAKE_CASE = index
_SCREAMING_SNAKE_CASE = 2 * index + 1 # Left Node
_SCREAMING_SNAKE_CASE = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
_SCREAMING_SNAKE_CASE = left_index
if right_index < heap_size and array[largest] < array[right_index]:
_SCREAMING_SNAKE_CASE = right_index
if largest != index:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = array[largest], array[index]
heapify(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(SCREAMING_SNAKE_CASE_ )
for i in range(n // 2 , -1 , -1 ):
heapify(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for i in range(n - 1 , 0 , -1 ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = array[0], array[i]
heapify(SCREAMING_SNAKE_CASE_ , 0 , SCREAMING_SNAKE_CASE_ )
return array
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = low
_SCREAMING_SNAKE_CASE = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = array[j], array[i]
i += 1
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return array
_SCREAMING_SNAKE_CASE = 2 * math.ceil(math.loga(len(SCREAMING_SNAKE_CASE_ ) ) )
_SCREAMING_SNAKE_CASE = 16
return intro_sort(SCREAMING_SNAKE_CASE_ , 0 , len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list:
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(SCREAMING_SNAKE_CASE_ )
max_depth -= 1
_SCREAMING_SNAKE_CASE = median_of_a(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , start + ((end - start) // 2) + 1 , end - 1 )
_SCREAMING_SNAKE_CASE = partition(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
intro_sort(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = p
return insertion_sort(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ : Tuple = input("Enter numbers separated by a comma : ").strip()
UpperCamelCase__ : List[Any] = [float(item) for item in user_input.split(",")]
print(sort(unsorted))
| 591 |
'''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 UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
_SCREAMING_SNAKE_CASE = 8
# DPR tok
_SCREAMING_SNAKE_CASE = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , """dpr_tokenizer""" )
os.makedirs(A__ , exist_ok=A__ )
_SCREAMING_SNAKE_CASE = os.path.join(A__ , 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
_SCREAMING_SNAKE_CASE = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
_SCREAMING_SNAKE_CASE = dict(zip(A__ , range(len(A__ ) ) ) )
_SCREAMING_SNAKE_CASE = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
_SCREAMING_SNAKE_CASE = {"""unk_token""": """<unk>"""}
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , """bart_tokenizer""" )
os.makedirs(A__ , exist_ok=A__ )
_SCREAMING_SNAKE_CASE = os.path.join(A__ , BART_VOCAB_FILES_NAMES["""vocab_file"""] )
_SCREAMING_SNAKE_CASE = os.path.join(A__ , BART_VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A__ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(A__ ) )
def UpperCamelCase ( self ) -> DPRQuestionEncoderTokenizer:
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , """dpr_tokenizer""" ) )
def UpperCamelCase ( self ) -> BartTokenizer:
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , """bart_tokenizer""" ) )
def UpperCamelCase ( self ) -> List[Any]:
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , """rag_tokenizer""" )
_SCREAMING_SNAKE_CASE = RagConfig(question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() )
_SCREAMING_SNAKE_CASE = RagTokenizer(question_encoder=self.get_dpr_tokenizer() , generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(A__ )
rag_tokenizer.save_pretrained(A__ )
_SCREAMING_SNAKE_CASE = RagTokenizer.from_pretrained(A__ , config=A__ )
self.assertIsInstance(new_rag_tokenizer.question_encoder , A__ )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() , rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator , A__ )
self.assertEqual(new_rag_tokenizer.generator.get_vocab() , rag_tokenizer.generator.get_vocab() )
@slow
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = RagTokenizer.from_pretrained("""facebook/rag-token-nq""" )
_SCREAMING_SNAKE_CASE = [
"""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""",
]
_SCREAMING_SNAKE_CASE = tokenizer(A__ )
self.assertIsNotNone(A__ )
@slow
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = RagTokenizer.from_pretrained("""facebook/rag-sequence-nq""" )
_SCREAMING_SNAKE_CASE = [
"""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""",
]
_SCREAMING_SNAKE_CASE = tokenizer(A__ )
self.assertIsNotNone(A__ )
| 591 | 1 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCamelCase_ :
def _snake_case ( self :Any , __A :Tuple ) -> Optional[Any]:
"""simple docstring"""
raise NotImplementedError()
def _snake_case ( self :Any ) -> Optional[Any]:
"""simple docstring"""
raise NotImplementedError()
class UpperCamelCase_ ( UpperCamelCase__ ):
def __init__( self :Optional[int] , __A :"AutoTokenizer" , __A :bool = False , **__A :Optional[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = tokenizer
SCREAMING_SNAKE_CASE__ = skip_prompt
SCREAMING_SNAKE_CASE__ = decode_kwargs
# variables used in the streaming process
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = True
def _snake_case ( self :Optional[Any] , __A :List[str] ) -> Union[str, Any]:
"""simple docstring"""
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError("""TextStreamer only supports batch size 1""" )
elif len(value.shape ) > 1:
SCREAMING_SNAKE_CASE__ = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
SCREAMING_SNAKE_CASE__ = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
SCREAMING_SNAKE_CASE__ = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith("""\n""" ):
SCREAMING_SNAKE_CASE__ = text[self.print_len :]
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = 0
# If the last token is a CJK character, we print the characters.
elif len(__A ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
SCREAMING_SNAKE_CASE__ = text[self.print_len :]
self.print_len += len(__A )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
SCREAMING_SNAKE_CASE__ = text[self.print_len : text.rfind(""" """ ) + 1]
self.print_len += len(__A )
self.on_finalized_text(__A )
def _snake_case ( self :Union[str, Any] ) -> int:
"""simple docstring"""
if len(self.token_cache ) > 0:
SCREAMING_SNAKE_CASE__ = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
SCREAMING_SNAKE_CASE__ = text[self.print_len :]
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = 0
else:
SCREAMING_SNAKE_CASE__ = """"""
SCREAMING_SNAKE_CASE__ = True
self.on_finalized_text(__A , stream_end=__A )
def _snake_case ( self :int , __A :str , __A :bool = False ) -> Union[str, Any]:
"""simple docstring"""
print(__A , flush=__A , end="""""" if not stream_end else None )
def _snake_case ( self :Optional[Any] , __A :Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if (
(cp >= 0X4E_00 and cp <= 0X9F_FF)
or (cp >= 0X34_00 and cp <= 0X4D_BF) #
or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF) #
or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F) #
or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F) #
or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF) #
or (cp >= 0XF9_00 and cp <= 0XFA_FF)
or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F) #
): #
return True
return False
class UpperCamelCase_ ( UpperCamelCase__ ):
def __init__( self :List[Any] , __A :"AutoTokenizer" , __A :bool = False , __A :Optional[float] = None , **__A :Optional[int] ) -> List[Any]:
"""simple docstring"""
super().__init__(__A , __A , **__A )
SCREAMING_SNAKE_CASE__ = Queue()
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = timeout
def _snake_case ( self :str , __A :str , __A :bool = False ) -> str:
"""simple docstring"""
self.text_queue.put(__A , timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal , timeout=self.timeout )
def __iter__( self :List[str] ) -> Tuple:
"""simple docstring"""
return self
def _snake_case ( self :Dict ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value | 59 |
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: list[list[float]] ):
SCREAMING_SNAKE_CASE__ = []
for data in source_data:
for i, el in enumerate(UpperCamelCase__ ):
if len(UpperCamelCase__ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(UpperCamelCase__ ) )
return data_lists
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: list[list[float]] , UpperCamelCase__: list[int] ):
SCREAMING_SNAKE_CASE__ = []
for dlist, weight in zip(UpperCamelCase__ , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ = min(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ = max(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
SCREAMING_SNAKE_CASE__ = f'''Invalid weight of {weight:f} provided'''
raise ValueError(UpperCamelCase__ )
score_lists.append(UpperCamelCase__ )
return score_lists
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: list[list[float]] ):
SCREAMING_SNAKE_CASE__ = [0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ = final_scores[j] + ele
return final_scores
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: list[list[float]] , UpperCamelCase__: list[int] ):
SCREAMING_SNAKE_CASE__ = get_data(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ = calculate_each_score(UpperCamelCase__ , UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ = generate_final_scores(UpperCamelCase__ )
# append scores to source data
for i, ele in enumerate(UpperCamelCase__ ):
source_data[i].append(UpperCamelCase__ )
return source_data | 59 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(_lowerCamelCase ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(_lowerCamelCase ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod() | 46 |
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class __UpperCamelCase (unittest.TestCase ):
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
'''simple docstring'''
self.assertEqual(len(_lowerCAmelCase ) , len(_lowerCAmelCase ) )
for a, b in zip(_lowerCAmelCase , _lowerCAmelCase ):
self.assertAlmostEqual(_lowerCAmelCase , _lowerCAmelCase , delta=_lowerCAmelCase )
def _a ( self ) -> Optional[int]:
'''simple docstring'''
lowercase = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(_lowerCAmelCase ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1E-2 )
def _a ( self ) -> int:
'''simple docstring'''
lowercase = None
ops.enable_eager_execution_internal()
lowercase = tf.config.list_physical_devices("""CPU""" )
if len(_lowerCAmelCase ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
lowercase = tf.config.list_logical_devices(device_type="""CPU""" )
lowercase = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
lowercase = GradientAccumulator()
lowercase = tf.Variable([4.0, 3.0] )
lowercase , lowercase = create_optimizer(5E-5 , 10 , 5 )
lowercase = tf.Variable([0.0, 0.0] , trainable=_lowerCAmelCase )
def accumulate_on_replica(_lowerCAmelCase ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(_lowerCAmelCase , _lowerCAmelCase ):
with strategy.scope():
lowercase = strategy.experimental_local_results(_lowerCAmelCase )
local_variables[0].assign(_lowerCAmelCase )
local_variables[1].assign(_lowerCAmelCase )
strategy.run(_lowerCAmelCase , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(_lowerCAmelCase )
def _check_local_values(_lowerCAmelCase , _lowerCAmelCase ):
lowercase = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , _lowerCAmelCase , tol=1E-2 )
self.assertListAlmostEqual(values[1].value() , _lowerCAmelCase , tol=1E-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 588 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'IBertForMaskedLM',
'IBertForMultipleChoice',
'IBertForQuestionAnswering',
'IBertForSequenceClassification',
'IBertForTokenClassification',
'IBertModel',
'IBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 220 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
A = 0
A = False
A = 3.0
class _a ( unittest.TestCase ):
"""simple docstring"""
def __a ( self ):
# If no defaults are changed, `to_kwargs` returns an empty dict.
self.assertDictEqual(MockClass().to_kwargs() ,{} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() ,{'a': 2} )
self.assertDictEqual(MockClass(a=2 ,b=__SCREAMING_SNAKE_CASE ).to_kwargs() ,{'a': 2, 'b': True} )
self.assertDictEqual(MockClass(a=2 ,c=2.25 ).to_kwargs() ,{'a': 2, 'c': 2.25} )
@require_cuda
def __a ( self ):
# If no defaults are changed, `to_kwargs` returns an empty dict.
SCREAMING_SNAKE_CASE : Union[str, Any] = GradScalerKwargs(init_scale=1024 ,growth_factor=2 )
AcceleratorState._reset_state()
SCREAMING_SNAKE_CASE : Optional[int] = Accelerator(mixed_precision='fp16' ,kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
SCREAMING_SNAKE_CASE : int = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale ,1024.0 )
self.assertEqual(scaler._growth_factor ,2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor ,0.5 )
self.assertEqual(scaler._growth_interval ,2000 )
self.assertEqual(scaler._enabled ,__SCREAMING_SNAKE_CASE )
@require_multi_gpu
def __a ( self ):
SCREAMING_SNAKE_CASE : Optional[int] = ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
execute_subprocess_async(__SCREAMING_SNAKE_CASE ,env=os.environ.copy() )
if __name__ == "__main__":
__UpperCAmelCase = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
__UpperCAmelCase = Accelerator(kwargs_handlers=[ddp_scaler])
__UpperCAmelCase = torch.nn.Linear(100, 200)
__UpperCAmelCase = accelerator.prepare(model)
# Check the values changed in kwargs
__UpperCAmelCase = ''
__UpperCAmelCase = model.bucket_bytes_cap // (1024 * 1024)
if observed_bucket_cap_map != 15:
error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 220 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = (DDPMScheduler,)
def SCREAMING_SNAKE_CASE ( self : List[Any] ,**lowercase__ : int ):
__lowercase = {
'''num_train_timesteps''': 1_0_0_0,
'''beta_start''': 0.0_0_0_1,
'''beta_end''': 0.0_2,
'''beta_schedule''': '''linear''',
'''variance_type''': '''fixed_small''',
'''clip_sample''': True,
}
config.update(**lowercase__ )
return config
def SCREAMING_SNAKE_CASE ( self : Any ):
for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] ,[0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=lowercase__ ,beta_end=lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowercase__ )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=lowercase__ )
def SCREAMING_SNAKE_CASE ( self : int ):
self.check_over_configs(thresholding=lowercase__ )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=lowercase__ ,prediction_type=lowercase__ ,sample_max_value=lowercase__ ,)
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
for t in [0, 5_0_0, 9_9_9]:
self.check_over_forward(time_step=lowercase__ )
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**lowercase__ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ) - 0.0_2 ) ) < 1e-5
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**lowercase__ )
__lowercase = len(lowercase__ )
__lowercase = self.dummy_model()
__lowercase = self.dummy_sample_deter
__lowercase = torch.manual_seed(0 )
for t in reversed(range(lowercase__ ) ):
# 1. predict noise residual
__lowercase = model(lowercase__ ,lowercase__ )
# 2. predict previous mean of sample x_t-1
__lowercase = scheduler.step(lowercase__ ,lowercase__ ,lowercase__ ,generator=lowercase__ ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
__lowercase = pred_prev_sample
__lowercase = torch.sum(torch.abs(lowercase__ ) )
__lowercase = torch.mean(torch.abs(lowercase__ ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config(prediction_type='''v_prediction''' )
__lowercase = scheduler_class(**lowercase__ )
__lowercase = len(lowercase__ )
__lowercase = self.dummy_model()
__lowercase = self.dummy_sample_deter
__lowercase = torch.manual_seed(0 )
for t in reversed(range(lowercase__ ) ):
# 1. predict noise residual
__lowercase = model(lowercase__ ,lowercase__ )
# 2. predict previous mean of sample x_t-1
__lowercase = scheduler.step(lowercase__ ,lowercase__ ,lowercase__ ,generator=lowercase__ ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
__lowercase = pred_prev_sample
__lowercase = torch.sum(torch.abs(lowercase__ ) )
__lowercase = torch.mean(torch.abs(lowercase__ ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**lowercase__ )
__lowercase = [1_0_0, 8_7, 5_0, 1, 0]
scheduler.set_timesteps(timesteps=lowercase__ )
__lowercase = scheduler.timesteps
for i, timestep in enumerate(lowercase__ ):
if i == len(lowercase__ ) - 1:
__lowercase = -1
else:
__lowercase = timesteps[i + 1]
__lowercase = scheduler.previous_timestep(lowercase__ )
__lowercase = prev_t.item()
self.assertEqual(lowercase__ ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : str ):
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**lowercase__ )
__lowercase = [1_0_0, 8_7, 5_0, 5_1, 0]
with self.assertRaises(lowercase__ ,msg='''`custom_timesteps` must be in descending order.''' ):
scheduler.set_timesteps(timesteps=lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**lowercase__ )
__lowercase = [1_0_0, 8_7, 5_0, 1, 0]
__lowercase = len(lowercase__ )
with self.assertRaises(lowercase__ ,msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.''' ):
scheduler.set_timesteps(num_inference_steps=lowercase__ ,timesteps=lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**lowercase__ )
__lowercase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
lowercase__ ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' ,):
scheduler.set_timesteps(timesteps=lowercase__ )
| 41 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def lowerCamelCase_( ) -> None:
'''simple docstring'''
print("Making key files..." )
make_key_files("rsa" , 1024 )
print("Key files generation successful." )
def lowerCamelCase_( _lowerCamelCase ) -> tuple[tuple[int, int], tuple[int, int]]:
'''simple docstring'''
print("Generating prime p..." )
_lowerCamelCase : List[str] = rabinMiller.generate_large_prime(_lowerCamelCase )
print("Generating prime q..." )
_lowerCamelCase : Tuple = rabinMiller.generate_large_prime(_lowerCamelCase )
_lowerCamelCase : Dict = p * q
print("Generating e that is relatively prime to (p - 1) * (q - 1)..." )
while True:
_lowerCamelCase : Tuple = random.randrange(2 ** (key_size - 1) , 2 ** (key_size) )
if cryptoMath.gcd(_lowerCamelCase , (p - 1) * (q - 1) ) == 1:
break
print("Calculating d that is mod inverse of e..." )
_lowerCamelCase : str = cryptoMath.find_mod_inverse(_lowerCamelCase , (p - 1) * (q - 1) )
_lowerCamelCase : Dict = (n, e)
_lowerCamelCase : Dict = (n, d)
return (public_key, private_key)
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> None:
'''simple docstring'''
if os.path.exists(F"""{name}_pubkey.txt""" ) or os.path.exists(F"""{name}_privkey.txt""" ):
print("\nWARNING:" )
print(
F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
"Use a different name or delete these files and re-run this program." )
sys.exit()
_lowerCamelCase, _lowerCamelCase : Dict = generate_key(_lowerCamelCase )
print(F"""\nWriting public key to file {name}_pubkey.txt...""" )
with open(F"""{name}_pubkey.txt""" , "w" ) as out_file:
out_file.write(F"""{key_size},{public_key[0]},{public_key[1]}""" )
print(F"""Writing private key to file {name}_privkey.txt...""" )
with open(F"""{name}_privkey.txt""" , "w" ) as out_file:
out_file.write(F"""{key_size},{private_key[0]},{private_key[1]}""" )
if __name__ == "__main__":
main() | 46 | 0 |
from __future__ import annotations
__UpperCAmelCase : Any = []
def lowercase_ ( __snake_case : list[list[int]] , __snake_case : int , __snake_case : int ) -> bool:
'''simple docstring'''
for i in range(len(__snake_case ) ):
if board[row][i] == 1:
return False
for i in range(len(__snake_case ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(__snake_case , -1 , -1 ) , range(__snake_case , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(__snake_case , -1 , -1 ) , range(__snake_case , len(__snake_case ) ) ):
if board[i][j] == 1:
return False
return True
def lowercase_ ( __snake_case : list[list[int]] , __snake_case : int ) -> bool:
'''simple docstring'''
if row >= len(__snake_case ):
solution.append(__snake_case )
printboard(__snake_case )
print()
return True
for i in range(len(__snake_case ) ):
if is_safe(__snake_case , __snake_case , __snake_case ):
snake_case__ :Union[str, Any] = 1
solve(__snake_case , row + 1 )
snake_case__ :int = 0
return False
def lowercase_ ( __snake_case : list[list[int]] ) -> None:
'''simple docstring'''
for i in range(len(__snake_case ) ):
for j in range(len(__snake_case ) ):
if board[i][j] == 1:
print("Q" , end=" " )
else:
print("." , end=" " )
print()
# n=int(input("The no. of queens"))
__UpperCAmelCase : Tuple = 8
__UpperCAmelCase : Optional[Any] = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution)) | 712 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__UpperCAmelCase : Optional[Any] = 1_6
__UpperCAmelCase : Optional[int] = 3_2
def lowercase_ ( __snake_case : Accelerator , __snake_case : int = 16 , __snake_case : str = "bert-base-cased" ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :int = AutoTokenizer.from_pretrained(__snake_case )
snake_case__ :Optional[int] = load_dataset("glue" , "mrpc" )
def tokenize_function(__snake_case : Tuple ):
# max_length=None => use the model max length (it's actually the default)
snake_case__ :Any = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=__snake_case , max_length=__snake_case )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
snake_case__ :List[Any] = datasets.map(
__snake_case , batched=__snake_case , remove_columns=["idx", "sentence1", "sentence2"] , load_from_cache_file=__snake_case )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case__ :Any = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(__snake_case : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__snake_case , padding="max_length" , max_length=1_28 , return_tensors="pt" )
return tokenizer.pad(__snake_case , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
snake_case__ :Any = DataLoader(
tokenized_datasets["train"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
snake_case__ :Tuple = DataLoader(
tokenized_datasets["validation"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
return train_dataloader, eval_dataloader
def lowercase_ ( __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : int , __snake_case : Optional[int] ) -> Tuple:
'''simple docstring'''
model.eval()
snake_case__ :Union[str, Any] = 0
for step, batch in enumerate(__snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case__ :List[Any] = model(**__snake_case )
snake_case__ :Any = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
snake_case__ , snake_case__ :Tuple = accelerator.gather(
(predictions, batch["labels"]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__snake_case ) - 1:
snake_case__ :List[str] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
snake_case__ :Optional[int] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__snake_case , references=__snake_case , )
snake_case__ :int = metric.compute()
return eval_metric["accuracy"]
def lowercase_ ( __snake_case : Union[str, Any] , __snake_case : Optional[Any] ) -> Any:
'''simple docstring'''
snake_case__ :Any = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case__ :Union[str, Any] = config["lr"]
snake_case__ :List[str] = int(config["num_epochs"] )
snake_case__ :Optional[Any] = int(config["seed"] )
snake_case__ :List[Any] = int(config["batch_size"] )
snake_case__ :List[Any] = args.model_name_or_path
set_seed(__snake_case )
snake_case__ , snake_case__ :List[Any] = get_dataloaders(__snake_case , __snake_case , __snake_case )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case__ :List[Any] = AutoModelForSequenceClassification.from_pretrained(__snake_case , return_dict=__snake_case )
# Instantiate optimizer
snake_case__ :int = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
snake_case__ :Tuple = optimizer_cls(params=model.parameters() , lr=__snake_case )
if accelerator.state.deepspeed_plugin is not None:
snake_case__ :List[str] = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
snake_case__ :Any = 1
snake_case__ :List[Any] = (len(__snake_case ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
snake_case__ :Optional[Any] = get_linear_schedule_with_warmup(
optimizer=__snake_case , num_warmup_steps=0 , num_training_steps=__snake_case , )
else:
snake_case__ :Any = DummyScheduler(__snake_case , total_num_steps=__snake_case , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ :int = accelerator.prepare(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
# We need to keep track of how many total steps we have iterated over
snake_case__ :Dict = 0
# We also need to keep track of the stating epoch so files are named properly
snake_case__ :Union[str, Any] = 0
snake_case__ :List[str] = evaluate.load("glue" , "mrpc" )
snake_case__ :Optional[Any] = num_epochs
if args.partial_train_epoch is not None:
snake_case__ :List[Any] = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
snake_case__ :Union[str, Any] = args.resume_from_checkpoint.split("epoch_" )[1]
snake_case__ :Dict = ""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
snake_case__ :str = int(__snake_case ) + 1
snake_case__ :List[Any] = evaluation_loop(__snake_case , __snake_case , __snake_case , __snake_case )
accelerator.print("resumed checkpoint performance:" , __snake_case )
accelerator.print("resumed checkpoint's scheduler's lr:" , lr_scheduler.get_lr()[0] )
accelerator.print("resumed optimizers's lr:" , optimizer.param_groups[0]["lr"] )
with open(os.path.join(args.output_dir , F'state_{starting_epoch-1}.json' ) , "r" ) as f:
snake_case__ :Tuple = json.load(__snake_case )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
snake_case__ :Optional[int] = {}
for epoch in range(__snake_case , __snake_case ):
model.train()
for step, batch in enumerate(__snake_case ):
snake_case__ :str = model(**__snake_case )
snake_case__ :List[str] = outputs.loss
snake_case__ :List[Any] = loss / gradient_accumulation_steps
accelerator.backward(__snake_case )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
snake_case__ :int = F'epoch_{epoch}'
snake_case__ :str = os.path.join(args.output_dir , __snake_case )
accelerator.save_state(__snake_case )
snake_case__ :Union[str, Any] = evaluation_loop(__snake_case , __snake_case , __snake_case , __snake_case )
snake_case__ :List[str] = accuracy
snake_case__ :List[str] = lr_scheduler.get_lr()[0]
snake_case__ :List[Any] = optimizer.param_groups[0]["lr"]
snake_case__ :Dict = epoch
snake_case__ :List[Any] = overall_step
accelerator.print(F'epoch {epoch}:' , __snake_case )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , F'state_{epoch}.json' ) , "w" ) as f:
json.dump(__snake_case , __snake_case )
def lowercase_ ( ) -> Any:
'''simple docstring'''
snake_case__ :List[Any] = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
parser.add_argument(
"--model_name_or_path" , type=__snake_case , default="bert-base-cased" , help="Path to pretrained model or model identifier from huggingface.co/models." , required=__snake_case , )
parser.add_argument(
"--output_dir" , type=__snake_case , default="." , help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." , )
parser.add_argument(
"--resume_from_checkpoint" , type=__snake_case , default=__snake_case , help="If the training should continue from a checkpoint folder." , )
parser.add_argument(
"--partial_train_epoch" , type=__snake_case , default=__snake_case , help="If passed, the training will stop after this number of epochs." , )
parser.add_argument(
"--num_epochs" , type=__snake_case , default=2 , help="Number of train epochs." , )
snake_case__ :Any = parser.parse_args()
snake_case__ :int = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
training_function(__snake_case , __snake_case )
if __name__ == "__main__":
main() | 57 | 0 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
snake_case__ : Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__ )
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def __init__( self , *_UpperCAmelCase , **_UpperCAmelCase ) -> Any:
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
requires_backends(self , 'vision' )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING )
def _UpperCAmelCase ( self , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None ) -> List[str]:
UpperCamelCase_ = {}
UpperCamelCase_ = {}
if prompt is not None:
UpperCamelCase_ = prompt
if generate_kwargs is not None:
UpperCamelCase_ = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
UpperCamelCase_ = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
'\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
' please use only one' )
UpperCamelCase_ = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self , _UpperCAmelCase , **_UpperCAmelCase ) -> int:
return super().__call__(_UpperCAmelCase , **_UpperCAmelCase )
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase=None ) -> str:
UpperCamelCase_ = load_image(_UpperCAmelCase )
if prompt is not None:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError(
f"""Received an invalid text input, got - {type(_UpperCAmelCase )} - but expected a single string. """
'Note also that one single text can be provided for conditional image to text generation.' )
UpperCamelCase_ = self.model.config.model_type
if model_type == "git":
UpperCamelCase_ = self.image_processor(images=_UpperCAmelCase , return_tensors=self.framework )
UpperCamelCase_ = self.tokenizer(text=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ).input_ids
UpperCamelCase_ = [self.tokenizer.cls_token_id] + input_ids
UpperCamelCase_ = torch.tensor(_UpperCAmelCase ).unsqueeze(0 )
model_inputs.update({'input_ids': input_ids} )
elif model_type == "pix2struct":
UpperCamelCase_ = self.image_processor(images=_UpperCAmelCase , header_text=_UpperCAmelCase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
UpperCamelCase_ = self.image_processor(images=_UpperCAmelCase , return_tensors=self.framework )
UpperCamelCase_ = self.tokenizer(_UpperCAmelCase , return_tensors=self.framework )
model_inputs.update(_UpperCAmelCase )
else:
raise ValueError(f"""Model type {model_type} does not support conditional text generation""" )
else:
UpperCamelCase_ = self.image_processor(images=_UpperCAmelCase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
UpperCamelCase_ = None
return model_inputs
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase=None ) -> Any:
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs['input_ids'] , _UpperCAmelCase )
and all(x is None for x in model_inputs['input_ids'] )
):
UpperCamelCase_ = None
if generate_kwargs is None:
UpperCamelCase_ = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
UpperCamelCase_ = model_inputs.pop(self.model.main_input_name )
UpperCamelCase_ = self.model.generate(_UpperCAmelCase , **_UpperCAmelCase , **_UpperCAmelCase )
return model_outputs
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> Any:
UpperCamelCase_ = []
for output_ids in model_outputs:
UpperCamelCase_ = {
'generated_text': self.tokenizer.decode(
_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase , )
}
records.append(_UpperCAmelCase )
return records
| 23 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
snake_case__ : Dict = 1_6
snake_case__ : List[str] = 3_2
def _snake_case (__lowercase , __lowercase = 16):
UpperCamelCase_ = AutoTokenizer.from_pretrained('bert-base-cased')
UpperCamelCase_ = load_dataset('glue' , 'mrpc')
def tokenize_function(__lowercase):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase_ = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=__lowercase , max_length=__lowercase)
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase_ = datasets.map(
__lowercase , batched=__lowercase , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase_ = tokenized_datasets.rename_column('label' , 'labels')
def collate_fn(__lowercase):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase_ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase_ = 16
elif accelerator.mixed_precision != "no":
UpperCamelCase_ = 8
else:
UpperCamelCase_ = None
return tokenizer.pad(
__lowercase , padding='longest' , max_length=__lowercase , pad_to_multiple_of=__lowercase , return_tensors='pt' , )
# Instantiate dataloaders.
UpperCamelCase_ = DataLoader(
tokenized_datasets['train'] , shuffle=__lowercase , collate_fn=__lowercase , batch_size=__lowercase)
UpperCamelCase_ = DataLoader(
tokenized_datasets['validation'] , shuffle=__lowercase , collate_fn=__lowercase , batch_size=__lowercase)
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
snake_case__ : List[str] = mocked_dataloaders # noqa: F811
def _snake_case (__lowercase , __lowercase):
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS' , __lowercase) == "1":
UpperCamelCase_ = 2
# New Code #
UpperCamelCase_ = int(args.gradient_accumulation_steps)
# Initialize accelerator
UpperCamelCase_ = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=__lowercase)
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`')
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase_ = config['lr']
UpperCamelCase_ = int(config['num_epochs'])
UpperCamelCase_ = int(config['seed'])
UpperCamelCase_ = int(config['batch_size'])
UpperCamelCase_ = evaluate.load('glue' , 'mrpc')
set_seed(__lowercase)
UpperCamelCase_ , UpperCamelCase_ = get_dataloaders(__lowercase , __lowercase)
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase_ = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=__lowercase)
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase_ = model.to(accelerator.device)
# Instantiate optimizer
UpperCamelCase_ = AdamW(params=model.parameters() , lr=__lowercase)
# Instantiate scheduler
UpperCamelCase_ = get_linear_schedule_with_warmup(
optimizer=__lowercase , num_warmup_steps=100 , num_training_steps=(len(__lowercase) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = accelerator.prepare(
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase)
# Now we train the model
for epoch in range(__lowercase):
model.train()
for step, batch in enumerate(__lowercase):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(__lowercase):
UpperCamelCase_ = model(**__lowercase)
UpperCamelCase_ = output.loss
accelerator.backward(__lowercase)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__lowercase):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
with torch.no_grad():
UpperCamelCase_ = model(**__lowercase)
UpperCamelCase_ = outputs.logits.argmax(dim=-1)
UpperCamelCase_ , UpperCamelCase_ = accelerator.gather_for_metrics((predictions, batch['labels']))
metric.add_batch(
predictions=__lowercase , references=__lowercase , )
UpperCamelCase_ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , __lowercase)
def _snake_case ():
UpperCamelCase_ = argparse.ArgumentParser(description='Simple example of training script.')
parser.add_argument(
'--mixed_precision' , type=__lowercase , default=__lowercase , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
# New Code #
parser.add_argument(
'--gradient_accumulation_steps' , type=__lowercase , default=1 , help='The number of minibatches to be ran before gradients are accumulated.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.')
UpperCamelCase_ = parser.parse_args()
UpperCamelCase_ = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(__lowercase , __lowercase)
if __name__ == "__main__":
main()
| 23 | 1 |
'''simple docstring'''
import re
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
if len(re.findall('[ATCG]' , snake_case_ ) ) != len(snake_case_ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 718 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
"""LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LukeForEntityClassification""",
"""LukeForEntityPairClassification""",
"""LukeForEntitySpanClassification""",
"""LukeForMultipleChoice""",
"""LukeForQuestionAnswering""",
"""LukeForSequenceClassification""",
"""LukeForTokenClassification""",
"""LukeForMaskedLM""",
"""LukeModel""",
"""LukePreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 69 | 0 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _UpperCamelCase ( A,unittest.TestCase ):
'''simple docstring'''
a_ : str = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
def _snake_case ( self : str , _lowerCamelCase : Optional[Any]=0 ):
'''simple docstring'''
__lowerCamelCase : str = np.random.RandomState(_lowerCamelCase )
__lowerCamelCase : str = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : Optional[Any] = self.get_dummy_inputs()
__lowerCamelCase : int = pipe(**_lowerCamelCase ).images
__lowerCamelCase : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__lowerCamelCase : Union[str, Any] = np.array([0.65_072, 0.58_492, 0.48_219, 0.55_521, 0.53_180, 0.55_939, 0.50_697, 0.39_800, 0.46_455] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowerCamelCase : Any = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowerCamelCase : Dict = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : List[str] = self.get_dummy_inputs()
__lowerCamelCase : Any = pipe(**_lowerCamelCase ).images
__lowerCamelCase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__lowerCamelCase : List[str] = np.array([0.65_863, 0.59_425, 0.49_326, 0.56_313, 0.53_875, 0.56_627, 0.51_065, 0.39_777, 0.46_330] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
__lowerCamelCase : Any = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowerCamelCase : Tuple = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : Any = self.get_dummy_inputs()
__lowerCamelCase : List[Any] = pipe(**_lowerCamelCase ).images
__lowerCamelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__lowerCamelCase : Optional[int] = np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowerCamelCase : List[str] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowerCamelCase : Any = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : List[str] = self.get_dummy_inputs()
__lowerCamelCase : Dict = pipe(**_lowerCamelCase ).images
__lowerCamelCase : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__lowerCamelCase : List[str] = np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self : Any ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowerCamelCase : Optional[Any] = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : Dict = self.get_dummy_inputs()
__lowerCamelCase : Union[str, Any] = pipe(**_lowerCamelCase ).images
__lowerCamelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__lowerCamelCase : Dict = np.array([0.53_817, 0.60_812, 0.47_384, 0.49_530, 0.51_894, 0.49_814, 0.47_984, 0.38_958, 0.44_271] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self : int ):
'''simple docstring'''
__lowerCamelCase : Any = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowerCamelCase : List[str] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : List[str] = self.get_dummy_inputs()
__lowerCamelCase : str = pipe(**_lowerCamelCase ).images
__lowerCamelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__lowerCamelCase : int = np.array([0.53_895, 0.60_808, 0.47_933, 0.49_608, 0.51_886, 0.49_950, 0.48_053, 0.38_957, 0.44_200] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self : Any ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : List[Any] = self.get_dummy_inputs()
__lowerCamelCase : int = 3 * [inputs["""prompt"""]]
# forward
__lowerCamelCase : Optional[Any] = pipe(**_lowerCamelCase )
__lowerCamelCase : List[Any] = output.images[0, -3:, -3:, -1]
__lowerCamelCase : str = self.get_dummy_inputs()
__lowerCamelCase : List[str] = 3 * [inputs.pop("""prompt""" )]
__lowerCamelCase : Optional[Any] = pipe.tokenizer(
_lowerCamelCase , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=_lowerCamelCase , return_tensors="""np""" , )
__lowerCamelCase : List[Any] = text_inputs["""input_ids"""]
__lowerCamelCase : List[Any] = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
__lowerCamelCase : Optional[int] = prompt_embeds
# forward
__lowerCamelCase : str = pipe(**_lowerCamelCase )
__lowerCamelCase : Any = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : List[str] = self.get_dummy_inputs()
__lowerCamelCase : List[Any] = 3 * ["""this is a negative prompt"""]
__lowerCamelCase : Optional[Any] = negative_prompt
__lowerCamelCase : int = 3 * [inputs["""prompt"""]]
# forward
__lowerCamelCase : Tuple = pipe(**_lowerCamelCase )
__lowerCamelCase : Optional[Any] = output.images[0, -3:, -3:, -1]
__lowerCamelCase : int = self.get_dummy_inputs()
__lowerCamelCase : int = 3 * [inputs.pop("""prompt""" )]
__lowerCamelCase : str = []
for p in [prompt, negative_prompt]:
__lowerCamelCase : Tuple = pipe.tokenizer(
_lowerCamelCase , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=_lowerCamelCase , return_tensors="""np""" , )
__lowerCamelCase : int = text_inputs["""input_ids"""]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
__lowerCamelCase , __lowerCamelCase : int = embeds
# forward
__lowerCamelCase : Tuple = pipe(**_lowerCamelCase )
__lowerCamelCase : Union[str, Any] = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def _snake_case ( self : Any ):
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _snake_case ( self : str ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = ort.SessionOptions()
__lowerCamelCase : Optional[Any] = False
return options
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = OnnxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=_lowerCamelCase , feature_extractor=_lowerCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : List[str] = """A painting of a squirrel eating a burger"""
np.random.seed(0 )
__lowerCamelCase : Tuple = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=1_0 , output_type="""np""" )
__lowerCamelCase : Dict = output.images
__lowerCamelCase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__lowerCamelCase : List[Any] = np.array([0.0_452, 0.0_390, 0.0_087, 0.0_350, 0.0_617, 0.0_364, 0.0_544, 0.0_523, 0.0_720] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowerCamelCase : Dict = DDIMScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
__lowerCamelCase : List[str] = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=_lowerCamelCase , safety_checker=_lowerCamelCase , feature_extractor=_lowerCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : Dict = """open neural network exchange"""
__lowerCamelCase : List[Any] = np.random.RandomState(0 )
__lowerCamelCase : Union[str, Any] = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=1_0 , generator=_lowerCamelCase , output_type="""np""" )
__lowerCamelCase : str = output.images
__lowerCamelCase : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__lowerCamelCase : List[Any] = np.array([0.2_867, 0.1_974, 0.1_481, 0.7_294, 0.7_251, 0.6_667, 0.4_194, 0.5_642, 0.6_486] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
__lowerCamelCase : Any = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
__lowerCamelCase : Optional[Any] = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=_lowerCamelCase , safety_checker=_lowerCamelCase , feature_extractor=_lowerCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : Optional[int] = """open neural network exchange"""
__lowerCamelCase : str = np.random.RandomState(0 )
__lowerCamelCase : Dict = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=1_0 , generator=_lowerCamelCase , output_type="""np""" )
__lowerCamelCase : List[Any] = output.images
__lowerCamelCase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__lowerCamelCase : List[str] = np.array([0.2_306, 0.1_959, 0.1_593, 0.6_549, 0.6_394, 0.5_408, 0.5_065, 0.6_010, 0.6_161] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowerCamelCase : int = 0
def test_callback_fn(_lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : np.ndarray ) -> None:
__lowerCamelCase : str = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 6_4, 6_4)
__lowerCamelCase : Tuple = latents[0, -3:, -3:, -1]
__lowerCamelCase : Tuple = np.array(
[-0.6_772, -0.3_835, -1.2_456, 0.1_905, -1.0_974, 0.6_967, -1.9_353, 0.0_178, 1.0_167] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3
elif step == 5:
assert latents.shape == (1, 4, 6_4, 6_4)
__lowerCamelCase : Optional[Any] = latents[0, -3:, -3:, -1]
__lowerCamelCase : str = np.array(
[-0.3_351, 0.2_241, -0.1_837, -0.2_325, -0.6_577, 0.3_393, -0.0_241, 0.5_899, 1.3_875] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : int = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=_lowerCamelCase , feature_extractor=_lowerCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : str = """Andromeda galaxy in a bottle"""
__lowerCamelCase : Optional[Any] = np.random.RandomState(0 )
pipe(
prompt=_lowerCamelCase , num_inference_steps=5 , guidance_scale=7.5 , generator=_lowerCamelCase , callback=_lowerCamelCase , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def _snake_case ( self : List[str] ):
'''simple docstring'''
__lowerCamelCase : Dict = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=_lowerCamelCase , feature_extractor=_lowerCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(_lowerCamelCase , _lowerCamelCase )
assert pipe.safety_checker is None
__lowerCamelCase : str = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCamelCase )
__lowerCamelCase : str = OnnxStableDiffusionPipeline.from_pretrained(_lowerCamelCase )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
__lowerCamelCase : Optional[int] = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
| 519 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__UpperCamelCase : Optional[Any] = 6_378_137.0
__UpperCamelCase : Any = 6_356_752.314_245
__UpperCamelCase : Optional[int] = 6378137
def _UpperCAmelCase ( UpperCAmelCase : float , UpperCAmelCase : float , UpperCAmelCase : float , UpperCAmelCase : float ):
"""simple docstring"""
__lowerCamelCase : List[Any] = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
__lowerCamelCase : Tuple = atan((1 - flattening) * tan(radians(UpperCAmelCase ) ) )
__lowerCamelCase : Dict = atan((1 - flattening) * tan(radians(UpperCAmelCase ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
__lowerCamelCase : Any = haversine_distance(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
__lowerCamelCase : Dict = (b_lata + b_lata) / 2
__lowerCamelCase : Union[str, Any] = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
__lowerCamelCase : str = (sin(UpperCAmelCase ) ** 2) * (cos(UpperCAmelCase ) ** 2)
__lowerCamelCase : List[Any] = cos(sigma / 2 ) ** 2
__lowerCamelCase : Dict = (sigma - sin(UpperCAmelCase )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
__lowerCamelCase : Tuple = (cos(UpperCAmelCase ) ** 2) * (sin(UpperCAmelCase ) ** 2)
__lowerCamelCase : List[str] = sin(sigma / 2 ) ** 2
__lowerCamelCase : List[str] = (sigma + sin(UpperCAmelCase )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 519 | 1 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase : List[Any] =get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class __snake_case ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
_snake_case = PegasusTokenizer
_snake_case = PegasusTokenizerFast
_snake_case = True
_snake_case = True
def _SCREAMING_SNAKE_CASE ( self : List[Any]) ->List[str]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCamelCase : List[Any] = PegasusTokenizer(_UpperCamelCase)
tokenizer.save_pretrained(self.tmpdirname)
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Any) ->Union[str, Any]:
"""simple docstring"""
return PegasusTokenizer.from_pretrained("""google/pegasus-large""")
def _SCREAMING_SNAKE_CASE ( self : Tuple , **_UpperCamelCase : Tuple) ->PegasusTokenizer:
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_UpperCamelCase)
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , _UpperCamelCase : List[Any]) ->Dict:
"""simple docstring"""
return ("This is a test", "This is a test")
def _SCREAMING_SNAKE_CASE ( self : List[Any]) ->Any:
"""simple docstring"""
_lowerCamelCase : Optional[int] = """</s>"""
_lowerCamelCase : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase) , _UpperCamelCase)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase) , _UpperCamelCase)
def _SCREAMING_SNAKE_CASE ( self : Tuple) ->int:
"""simple docstring"""
_lowerCamelCase : Dict = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , """<pad>""")
self.assertEqual(vocab_keys[1] , """</s>""")
self.assertEqual(vocab_keys[-1] , """v""")
self.assertEqual(len(_UpperCamelCase) , 1103)
def _SCREAMING_SNAKE_CASE ( self : List[str]) ->List[str]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1103)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]) ->Dict:
"""simple docstring"""
_lowerCamelCase : str = self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
_lowerCamelCase : Tuple = self.tokenizer_class.from_pretrained(self.tmpdirname)
_lowerCamelCase : Optional[int] = (
"""Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
""" </s> <pad> <pad> <pad>"""
)
_lowerCamelCase : List[Any] = rust_tokenizer([raw_input_str] , return_tensors=_UpperCamelCase , add_special_tokens=_UpperCamelCase).input_ids[0]
_lowerCamelCase : List[Any] = py_tokenizer([raw_input_str] , return_tensors=_UpperCamelCase , add_special_tokens=_UpperCamelCase).input_ids[0]
self.assertListEqual(_UpperCamelCase , _UpperCamelCase)
def _SCREAMING_SNAKE_CASE ( self : Any) ->Optional[Any]:
"""simple docstring"""
_lowerCamelCase : List[Any] = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
_lowerCamelCase : str = """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
_lowerCamelCase : Optional[int] = [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
_lowerCamelCase : List[Any] = tokenizer([raw_input_str] , return_tensors=_UpperCamelCase).input_ids[0]
self.assertListEqual(_UpperCamelCase , _UpperCamelCase)
def _SCREAMING_SNAKE_CASE ( self : Tuple) ->Any:
"""simple docstring"""
_lowerCamelCase : int = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
_lowerCamelCase : Any = """To ensure a smooth flow of bank resolutions."""
_lowerCamelCase : Optional[int] = [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
_lowerCamelCase : Any = tokenizer([raw_input_str] , return_tensors=_UpperCamelCase).input_ids[0]
self.assertListEqual(_UpperCamelCase , _UpperCamelCase)
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def _SCREAMING_SNAKE_CASE ( self : Dict) ->int:
"""simple docstring"""
_lowerCamelCase : Optional[int] = ["""This is going to be way too long.""" * 150, """short example"""]
_lowerCamelCase : Dict = ["""not super long but more than 5 tokens""", """tiny"""]
_lowerCamelCase : List[Any] = self._large_tokenizer(_UpperCamelCase , padding=_UpperCamelCase , truncation=_UpperCamelCase , return_tensors="""pt""")
_lowerCamelCase : int = self._large_tokenizer(
text_target=_UpperCamelCase , max_length=5 , padding=_UpperCamelCase , truncation=_UpperCamelCase , return_tensors="""pt""")
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(_UpperCamelCase) == 2 # input_ids, attention_mask.
@slow
def _SCREAMING_SNAKE_CASE ( self : Any) ->str:
"""simple docstring"""
_lowerCamelCase : List[str] = {"""input_ids""": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 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]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_UpperCamelCase , model_name="""google/bigbird-pegasus-large-arxiv""" , revision="""ba85d0851d708441f91440d509690f1ab6353415""" , )
@require_sentencepiece
@require_tokenizers
class __snake_case ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
_snake_case = PegasusTokenizer
_snake_case = PegasusTokenizerFast
_snake_case = True
_snake_case = True
def _SCREAMING_SNAKE_CASE ( self : Tuple) ->Tuple:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCamelCase : Union[str, Any] = PegasusTokenizer(_UpperCamelCase , offset=0 , mask_token_sent=_UpperCamelCase , mask_token="""[MASK]""")
tokenizer.save_pretrained(self.tmpdirname)
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Any) ->Optional[Any]:
"""simple docstring"""
return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv""")
def _SCREAMING_SNAKE_CASE ( self : List[str] , **_UpperCamelCase : List[Any]) ->PegasusTokenizer:
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_UpperCamelCase)
def _SCREAMING_SNAKE_CASE ( self : int , _UpperCamelCase : Dict) ->List[Any]:
"""simple docstring"""
return ("This is a test", "This is a test")
def _SCREAMING_SNAKE_CASE ( self : Dict) ->Dict:
"""simple docstring"""
_lowerCamelCase : Any = self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
_lowerCamelCase : List[str] = self.tokenizer_class.from_pretrained(self.tmpdirname)
_lowerCamelCase : int = (
"""Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
""" <pad> <pad> <pad>"""
)
_lowerCamelCase : Union[str, Any] = rust_tokenizer([raw_input_str] , return_tensors=_UpperCamelCase , add_special_tokens=_UpperCamelCase).input_ids[0]
_lowerCamelCase : str = py_tokenizer([raw_input_str] , return_tensors=_UpperCamelCase , add_special_tokens=_UpperCamelCase).input_ids[0]
self.assertListEqual(_UpperCamelCase , _UpperCamelCase)
@require_torch
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]) ->int:
"""simple docstring"""
_lowerCamelCase : List[Any] = ["""This is going to be way too long.""" * 1000, """short example"""]
_lowerCamelCase : Tuple = ["""not super long but more than 5 tokens""", """tiny"""]
_lowerCamelCase : Optional[int] = self._large_tokenizer(_UpperCamelCase , padding=_UpperCamelCase , truncation=_UpperCamelCase , return_tensors="""pt""")
_lowerCamelCase : List[str] = self._large_tokenizer(
text_target=_UpperCamelCase , max_length=5 , padding=_UpperCamelCase , truncation=_UpperCamelCase , return_tensors="""pt""")
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(_UpperCamelCase) == 2 # input_ids, attention_mask.
def _SCREAMING_SNAKE_CASE ( self : Tuple) ->str:
"""simple docstring"""
_lowerCamelCase : Tuple = (
"""This is an example string that is used to test the original TF implementation against the HF"""
""" implementation"""
)
_lowerCamelCase : int = self._large_tokenizer(_UpperCamelCase).input_ids
self.assertListEqual(
_UpperCamelCase , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] , )
| 15 | import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = (EulerDiscreteScheduler,)
_snake_case = 10
def _SCREAMING_SNAKE_CASE ( self : Tuple , **_UpperCamelCase : Optional[Any]) ->Optional[Any]:
"""simple docstring"""
_lowerCamelCase : Optional[int] = {
"""num_train_timesteps""": 1100,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
}
config.update(**_UpperCamelCase)
return config
def _SCREAMING_SNAKE_CASE ( self : List[str]) ->List[str]:
"""simple docstring"""
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=_UpperCamelCase)
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->Dict:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] , [0.0_0_0_2, 0.0_0_2, 0.0_2]):
self.check_over_configs(beta_start=_UpperCamelCase , beta_end=_UpperCamelCase)
def _SCREAMING_SNAKE_CASE ( self : Any) ->Dict:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=_UpperCamelCase)
def _SCREAMING_SNAKE_CASE ( self : Optional[int]) ->Union[str, Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_UpperCamelCase)
def _SCREAMING_SNAKE_CASE ( self : List[Any]) ->Union[str, Any]:
"""simple docstring"""
_lowerCamelCase : List[Any] = self.scheduler_classes[0]
_lowerCamelCase : str = self.get_scheduler_config()
_lowerCamelCase : Any = scheduler_class(**_UpperCamelCase)
scheduler.set_timesteps(self.num_inference_steps)
_lowerCamelCase : str = torch.manual_seed(0)
_lowerCamelCase : str = self.dummy_model()
_lowerCamelCase : List[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
_lowerCamelCase : int = sample.to(_UpperCamelCase)
for i, t in enumerate(scheduler.timesteps):
_lowerCamelCase : Optional[int] = scheduler.scale_model_input(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : List[str] = model(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : str = scheduler.step(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , generator=_UpperCamelCase)
_lowerCamelCase : Dict = output.prev_sample
_lowerCamelCase : Any = torch.sum(torch.abs(_UpperCamelCase))
_lowerCamelCase : Any = torch.mean(torch.abs(_UpperCamelCase))
assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->Any:
"""simple docstring"""
_lowerCamelCase : int = self.scheduler_classes[0]
_lowerCamelCase : Optional[Any] = self.get_scheduler_config(prediction_type="""v_prediction""")
_lowerCamelCase : int = scheduler_class(**_UpperCamelCase)
scheduler.set_timesteps(self.num_inference_steps)
_lowerCamelCase : Any = torch.manual_seed(0)
_lowerCamelCase : int = self.dummy_model()
_lowerCamelCase : int = self.dummy_sample_deter * scheduler.init_noise_sigma
_lowerCamelCase : Dict = sample.to(_UpperCamelCase)
for i, t in enumerate(scheduler.timesteps):
_lowerCamelCase : Optional[int] = scheduler.scale_model_input(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : str = model(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : List[Any] = scheduler.step(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , generator=_UpperCamelCase)
_lowerCamelCase : Tuple = output.prev_sample
_lowerCamelCase : Union[str, Any] = torch.sum(torch.abs(_UpperCamelCase))
_lowerCamelCase : Optional[int] = torch.mean(torch.abs(_UpperCamelCase))
assert abs(result_sum.item() - 0.0_0_0_2) < 1E-2
assert abs(result_mean.item() - 2.2_6_7_6E-0_6) < 1E-3
def _SCREAMING_SNAKE_CASE ( self : List[Any]) ->List[Any]:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = self.scheduler_classes[0]
_lowerCamelCase : int = self.get_scheduler_config()
_lowerCamelCase : List[Any] = scheduler_class(**_UpperCamelCase)
scheduler.set_timesteps(self.num_inference_steps , device=_UpperCamelCase)
_lowerCamelCase : Optional[Any] = torch.manual_seed(0)
_lowerCamelCase : Tuple = self.dummy_model()
_lowerCamelCase : Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_lowerCamelCase : Tuple = sample.to(_UpperCamelCase)
for t in scheduler.timesteps:
_lowerCamelCase : List[Any] = scheduler.scale_model_input(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : List[str] = model(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : Any = scheduler.step(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , generator=_UpperCamelCase)
_lowerCamelCase : List[Any] = output.prev_sample
_lowerCamelCase : Any = torch.sum(torch.abs(_UpperCamelCase))
_lowerCamelCase : List[Any] = torch.mean(torch.abs(_UpperCamelCase))
assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3
def _SCREAMING_SNAKE_CASE ( self : int) ->Tuple:
"""simple docstring"""
_lowerCamelCase : List[str] = self.scheduler_classes[0]
_lowerCamelCase : Optional[int] = self.get_scheduler_config()
_lowerCamelCase : int = scheduler_class(**_UpperCamelCase , use_karras_sigmas=_UpperCamelCase)
scheduler.set_timesteps(self.num_inference_steps , device=_UpperCamelCase)
_lowerCamelCase : int = torch.manual_seed(0)
_lowerCamelCase : Tuple = self.dummy_model()
_lowerCamelCase : str = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_lowerCamelCase : Optional[int] = sample.to(_UpperCamelCase)
for t in scheduler.timesteps:
_lowerCamelCase : Tuple = scheduler.scale_model_input(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : Any = model(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : List[str] = scheduler.step(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , generator=_UpperCamelCase)
_lowerCamelCase : int = output.prev_sample
_lowerCamelCase : Tuple = torch.sum(torch.abs(_UpperCamelCase))
_lowerCamelCase : List[str] = torch.mean(torch.abs(_UpperCamelCase))
assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9) < 1E-2
assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3) < 1E-3
| 15 | 1 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_SCREAMING_SNAKE_CASE = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
_SCREAMING_SNAKE_CASE = "\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n"
_SCREAMING_SNAKE_CASE = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"
_SCREAMING_SNAKE_CASE = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n 'meteor': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric('meteor')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class lowerCAmelCase_ ( datasets.Metric ):
def _snake_case ( self ) -> Optional[int]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"] , reference_urls=[
"https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
"https://en.wikipedia.org/wiki/METEOR",
] , )
def _snake_case ( self , _lowerCAmelCase ) -> Optional[int]:
import nltk
nltk.download("wordnet" )
if NLTK_VERSION >= version.Version("3.6.5" ):
nltk.download("punkt" )
if NLTK_VERSION >= version.Version("3.6.6" ):
nltk.download("omw-1.4" )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=0.9 , _lowerCAmelCase=3 , _lowerCAmelCase=0.5 ) -> int:
if NLTK_VERSION >= version.Version("3.6.5" ):
_lowerCAmelCase = [
meteor_score.single_meteor_score(
word_tokenize(_lowerCAmelCase ) , word_tokenize(_lowerCAmelCase ) , alpha=_lowerCAmelCase , beta=_lowerCAmelCase , gamma=_lowerCAmelCase )
for ref, pred in zip(_lowerCAmelCase , _lowerCAmelCase )
]
else:
_lowerCAmelCase = [
meteor_score.single_meteor_score(_lowerCAmelCase , _lowerCAmelCase , alpha=_lowerCAmelCase , beta=_lowerCAmelCase , gamma=_lowerCAmelCase )
for ref, pred in zip(_lowerCAmelCase , _lowerCAmelCase )
]
return {"meteor": np.mean(_lowerCAmelCase )}
| 18 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
_SCREAMING_SNAKE_CASE = "\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results[\"exact_match\"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 33.3\n\n"
_SCREAMING_SNAKE_CASE = "\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class lowerCAmelCase_ ( datasets.Metric ):
def _snake_case ( self ) -> List[str]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , reference_urls=[] , )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=None , _lowerCAmelCase=False , _lowerCAmelCase=False , _lowerCAmelCase=False , ) -> str:
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
_lowerCAmelCase = np.array([re.sub(_lowerCAmelCase , "" , _lowerCAmelCase ) for x in predictions] )
_lowerCAmelCase = np.array([re.sub(_lowerCAmelCase , "" , _lowerCAmelCase ) for x in references] )
else:
_lowerCAmelCase = np.asarray(_lowerCAmelCase )
_lowerCAmelCase = np.asarray(_lowerCAmelCase )
if ignore_case:
_lowerCAmelCase = np.char.lower(_lowerCAmelCase )
_lowerCAmelCase = np.char.lower(_lowerCAmelCase )
if ignore_punctuation:
_lowerCAmelCase = string.punctuation.maketrans("" , "" , string.punctuation )
_lowerCAmelCase = np.char.translate(_lowerCAmelCase , table=_lowerCAmelCase )
_lowerCAmelCase = np.char.translate(_lowerCAmelCase , table=_lowerCAmelCase )
if ignore_numbers:
_lowerCAmelCase = string.digits.maketrans("" , "" , string.digits )
_lowerCAmelCase = np.char.translate(_lowerCAmelCase , table=_lowerCAmelCase )
_lowerCAmelCase = np.char.translate(_lowerCAmelCase , table=_lowerCAmelCase )
_lowerCAmelCase = predictions == references
return {"exact_match": np.mean(_lowerCAmelCase ) * 100}
| 18 | 1 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
return getitem, k
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return setitem, k, v
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
return delitem, k
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , *SCREAMING_SNAKE_CASE ):
try:
return fun(SCREAMING_SNAKE_CASE , *SCREAMING_SNAKE_CASE ), None
except Exception as e:
return None, e
UpperCamelCase = (
_set("""key_a""", """val_a"""),
_set("""key_b""", """val_b"""),
)
UpperCamelCase = [
_set("""key_a""", """val_a"""),
_set("""key_a""", """val_b"""),
]
UpperCamelCase = [
_set("""key_a""", """val_a"""),
_set("""key_b""", """val_b"""),
_del("""key_a"""),
_del("""key_b"""),
_set("""key_a""", """val_a"""),
_del("""key_a"""),
]
UpperCamelCase = [
_get("""key_a"""),
_del("""key_a"""),
_set("""key_a""", """val_a"""),
_del("""key_a"""),
_del("""key_a"""),
_get("""key_a"""),
]
UpperCamelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
UpperCamelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("""key_a""", """val_b"""),
]
@pytest.mark.parametrize(
'''operations''' , (
pytest.param(_add_items , id='''add items''' ),
pytest.param(_overwrite_items , id='''overwrite items''' ),
pytest.param(_delete_items , id='''delete items''' ),
pytest.param(_access_absent_items , id='''access absent items''' ),
pytest.param(_add_with_resize_up , id='''add with resize up''' ),
pytest.param(_add_with_resize_down , id='''add with resize down''' ),
) , )
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
A_ : Optional[Any] = HashMap(initial_block_size=4 )
A_ : Union[str, Any] = {}
for _, (fun, *args) in enumerate(SCREAMING_SNAKE_CASE ):
A_ : Union[str, Any] = _run_operation(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , *SCREAMING_SNAKE_CASE )
A_ : int = _run_operation(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , *SCREAMING_SNAKE_CASE )
assert my_res == py_res
assert str(SCREAMING_SNAKE_CASE ) == str(SCREAMING_SNAKE_CASE )
assert set(SCREAMING_SNAKE_CASE ) == set(SCREAMING_SNAKE_CASE )
assert len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE )
assert set(my.items() ) == set(py.items() )
def _SCREAMING_SNAKE_CASE ( ):
def is_public(SCREAMING_SNAKE_CASE ) -> bool:
return not name.startswith('''_''' )
A_ : Tuple = {name for name in dir({} ) if is_public(SCREAMING_SNAKE_CASE )}
A_ : Optional[Any] = {name for name in dir(HashMap() ) if is_public(SCREAMING_SNAKE_CASE )}
assert dict_public_names > hash_public_names
| 706 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase = [ord(letter) for letter in string.ascii_lowercase]
UpperCamelCase = {ord(char) for char in VALID_CHARS}
UpperCamelCase = ["the", "be", "to", "of", "and", "in", "that", "have"]
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : str = ""
A_ : int
A_ : int
A_ : int
for keychar, cipherchar in zip(cycle(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE ):
A_ : Tuple = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(SCREAMING_SNAKE_CASE )
return decoded
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
A_ : list[str] = []
for key in product(SCREAMING_SNAKE_CASE , repeat=3 ):
A_ : Tuple = try_key(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if encoded is not None:
possibles.append(SCREAMING_SNAKE_CASE )
return possibles
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return [possible for possible in possibles if common_word in possible.lower()]
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE = "p059_cipher.txt" ):
A_ : list[int]
A_ : list[str]
A_ : str
A_ : str
A_ : str = Path(SCREAMING_SNAKE_CASE ).parent.joinpath(SCREAMING_SNAKE_CASE ).read_text(encoding='''utf-8''' )
A_ : Optional[Any] = [int(SCREAMING_SNAKE_CASE ) for number in data.strip().split(''',''' )]
A_ : Optional[int] = filter_valid_chars(SCREAMING_SNAKE_CASE )
for common_word in COMMON_WORDS:
A_ : Optional[Any] = filter_common_word(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) == 1:
break
A_ : int = possibles[0]
return sum(ord(SCREAMING_SNAKE_CASE ) for char in decoded_text )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 152 | 0 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = (CMStochasticIterativeScheduler,)
SCREAMING_SNAKE_CASE : List[str] = 10
def UpperCamelCase ( self : Optional[int] , **UpperCamelCase__ : List[str] ):
A = {
"""num_train_timesteps""": 201,
"""sigma_min""": 0.002,
"""sigma_max""": 80.0,
}
config.update(**snake_case_ )
return config
def UpperCamelCase ( self : Tuple ):
A = 10
A = self.get_scheduler_config()
A = self.scheduler_classes[0](**snake_case_ )
scheduler.set_timesteps(snake_case_ )
A = scheduler.timesteps[0]
A = scheduler.timesteps[1]
A = self.dummy_sample
A = 0.1 * sample
A = scheduler.step(snake_case_ , snake_case_ , snake_case_ ).prev_sample
A = scheduler.step(snake_case_ , snake_case_ , snake_case_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def UpperCamelCase ( self : Optional[Any] ):
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=snake_case_ )
def UpperCamelCase ( self : Optional[Any] ):
for clip_denoised in [True, False]:
self.check_over_configs(clip_denoised=snake_case_ )
def UpperCamelCase ( self : Any ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**snake_case_ )
A = 1
scheduler.set_timesteps(snake_case_ )
A = scheduler.timesteps
A = torch.manual_seed(0 )
A = self.dummy_model()
A = self.dummy_sample_deter * scheduler.init_noise_sigma
for i, t in enumerate(snake_case_ ):
# 1. scale model input
A = scheduler.scale_model_input(snake_case_ , snake_case_ )
# 2. predict noise residual
A = model(snake_case_ , snake_case_ )
# 3. predict previous sample x_t-1
A = scheduler.step(snake_case_ , snake_case_ , snake_case_ , generator=snake_case_ ).prev_sample
A = pred_prev_sample
A = torch.sum(torch.abs(snake_case_ ) )
A = torch.mean(torch.abs(snake_case_ ) )
assert abs(result_sum.item() - 192.7_614 ) < 1e-2
assert abs(result_mean.item() - 0.2_510 ) < 1e-3
def UpperCamelCase ( self : str ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**snake_case_ )
A = [106, 0]
scheduler.set_timesteps(timesteps=snake_case_ )
A = scheduler.timesteps
A = torch.manual_seed(0 )
A = self.dummy_model()
A = self.dummy_sample_deter * scheduler.init_noise_sigma
for t in timesteps:
# 1. scale model input
A = scheduler.scale_model_input(snake_case_ , snake_case_ )
# 2. predict noise residual
A = model(snake_case_ , snake_case_ )
# 3. predict previous sample x_t-1
A = scheduler.step(snake_case_ , snake_case_ , snake_case_ , generator=snake_case_ ).prev_sample
A = pred_prev_sample
A = torch.sum(torch.abs(snake_case_ ) )
A = torch.mean(torch.abs(snake_case_ ) )
assert abs(result_sum.item() - 347.6_357 ) < 1e-2
assert abs(result_mean.item() - 0.4_527 ) < 1e-3
def UpperCamelCase ( self : str ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**snake_case_ )
A = [39, 30, 12, 15, 0]
with self.assertRaises(snake_case_ , msg='`timesteps` must be in descending order.' ):
scheduler.set_timesteps(timesteps=snake_case_ )
def UpperCamelCase ( self : str ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**snake_case_ )
A = [39, 30, 12, 1, 0]
A = len(snake_case_ )
with self.assertRaises(snake_case_ , msg='Can only pass one of `num_inference_steps` or `timesteps`.' ):
scheduler.set_timesteps(num_inference_steps=snake_case_ , timesteps=snake_case_ )
def UpperCamelCase ( self : int ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**snake_case_ )
A = [scheduler.config.num_train_timesteps]
with self.assertRaises(
snake_case_ , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}' , ):
scheduler.set_timesteps(timesteps=snake_case_ )
| 699 |
from __future__ import annotations
import time
lowerCamelCase_ : Union[str, Any] = list[tuple[int, int]]
lowerCamelCase_ : str = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
lowerCamelCase_ : Dict = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , snake_case_ : int , snake_case_ : int , snake_case_ : int , snake_case_ : int , snake_case_ : Node | None ):
UpperCamelCase_: str = pos_x
UpperCamelCase_: List[str] = pos_y
UpperCamelCase_: str = (pos_y, pos_x)
UpperCamelCase_: Any = goal_x
UpperCamelCase_: Optional[int] = goal_y
UpperCamelCase_: Union[str, Any] = parent
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : str , snake_case_ : tuple[int, int] , snake_case_ : tuple[int, int] ):
UpperCamelCase_: Optional[Any] = Node(start[1] , start[0] , goal[1] , goal[0] , snake_case_ )
UpperCamelCase_: List[str] = Node(goal[1] , goal[0] , goal[1] , goal[0] , snake_case_ )
UpperCamelCase_: Any = [self.start]
UpperCamelCase_: Dict = False
def lowerCAmelCase__ ( self : str ):
while self.node_queue:
UpperCamelCase_: int = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
UpperCamelCase_: List[str] = True
return self.retrace_path(snake_case_ )
UpperCamelCase_: List[str] = self.get_successors(snake_case_ )
for node in successors:
self.node_queue.append(snake_case_ )
if not self.reached:
return [self.start.pos]
return None
def lowerCAmelCase__ ( self : Dict , snake_case_ : Node ):
UpperCamelCase_: int = []
for action in delta:
UpperCamelCase_: Union[str, Any] = parent.pos_x + action[1]
UpperCamelCase_: str = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(snake_case_ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(snake_case_ , snake_case_ , self.target.pos_y , self.target.pos_x , snake_case_ ) )
return successors
def lowerCAmelCase__ ( self : int , snake_case_ : Node | None ):
UpperCamelCase_: int = node
UpperCamelCase_: Tuple = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
UpperCamelCase_: List[Any] = current_node.parent
path.reverse()
return path
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , snake_case_ : Optional[Any] , snake_case_ : Optional[Any] ):
UpperCamelCase_: Tuple = BreadthFirstSearch(snake_case_ , snake_case_ )
UpperCamelCase_: Dict = BreadthFirstSearch(snake_case_ , snake_case_ )
UpperCamelCase_: int = False
def lowerCAmelCase__ ( self : int ):
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
UpperCamelCase_: List[Any] = self.fwd_bfs.node_queue.pop(0 )
UpperCamelCase_: Optional[Any] = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
UpperCamelCase_: List[Any] = True
return self.retrace_bidirectional_path(
snake_case_ , snake_case_ )
UpperCamelCase_: Optional[Any] = current_bwd_node
UpperCamelCase_: List[Any] = current_fwd_node
UpperCamelCase_: List[str] = {
self.fwd_bfs: self.fwd_bfs.get_successors(snake_case_ ),
self.bwd_bfs: self.bwd_bfs.get_successors(snake_case_ ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(snake_case_ )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def lowerCAmelCase__ ( self : List[str] , snake_case_ : Node , snake_case_ : Node ):
UpperCamelCase_: List[str] = self.fwd_bfs.retrace_path(snake_case_ )
UpperCamelCase_: Tuple = self.bwd_bfs.retrace_path(snake_case_ )
bwd_path.pop()
bwd_path.reverse()
UpperCamelCase_: List[str] = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
lowerCamelCase_ : Tuple = (0, 0)
lowerCamelCase_ : Union[str, Any] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
lowerCamelCase_ : Optional[int] = time.time()
lowerCamelCase_ : Tuple = BreadthFirstSearch(init, goal)
lowerCamelCase_ : List[str] = bfs.search()
lowerCamelCase_ : Optional[int] = time.time() - start_bfs_time
print("""Unidirectional BFS computation time : """, bfs_time)
lowerCamelCase_ : Optional[int] = time.time()
lowerCamelCase_ : Optional[int] = BidirectionalBreadthFirstSearch(init, goal)
lowerCamelCase_ : str = bd_bfs.search()
lowerCamelCase_ : Tuple = time.time() - start_bd_bfs_time
print("""Bidirectional BFS computation time : """, bd_bfs_time)
| 548 | 0 |
def __lowerCamelCase (UpperCAmelCase__ : int ):
assert isinstance(UpperCAmelCase__ , UpperCAmelCase__ ), F"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
SCREAMING_SNAKE_CASE = F"The input value of [n={number}] has to be > 0"
raise ValueError(UpperCAmelCase__ )
else:
SCREAMING_SNAKE_CASE = sylvester(number - 1 )
SCREAMING_SNAKE_CASE = num - 1
SCREAMING_SNAKE_CASE = num
return lower * upper + 1
if __name__ == "__main__":
print(f"""The 8th number in Sylvester's sequence: {sylvester(8)}""")
| 647 | from __future__ import annotations
import math
def __lowerCamelCase (UpperCAmelCase__ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(UpperCAmelCase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
_lowerCamelCase : Tuple = [num for num in range(3, 10_00_01, 2) if not is_prime(num)]
def __lowerCamelCase (UpperCAmelCase__ : int ):
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError("n must be an integer" )
if n <= 0:
raise ValueError("n must be >= 0" )
SCREAMING_SNAKE_CASE = []
for num in range(len(UpperCAmelCase__ ) ):
SCREAMING_SNAKE_CASE = 0
while 2 * i * i <= odd_composites[num]:
SCREAMING_SNAKE_CASE = odd_composites[num] - 2 * i * i
if is_prime(UpperCAmelCase__ ):
break
i += 1
else:
list_nums.append(odd_composites[num] )
if len(UpperCAmelCase__ ) == n:
return list_nums
return []
def __lowerCamelCase ():
return compute_nums(1 )[0]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 647 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class snake_case__ ( UpperCamelCase_ ):
_lowerCAmelCase =['input_features', 'is_longer']
def __init__( self : int , _lowerCamelCase : Union[str, Any]=6_4 , _lowerCamelCase : Any=4_8_0_0_0 , _lowerCamelCase : Optional[int]=4_8_0 , _lowerCamelCase : Optional[Any]=1_0 , _lowerCamelCase : List[str]=1_0_2_4 , _lowerCamelCase : List[Any]=0.0 , _lowerCamelCase : Union[str, Any]=False , _lowerCamelCase : float = 0 , _lowerCamelCase : float = 1_4_0_0_0 , _lowerCamelCase : int = None , _lowerCamelCase : str = "fusion" , _lowerCamelCase : str = "repeatpad" , **_lowerCamelCase : Any , ):
super().__init__(
feature_size=_lowerCamelCase , sampling_rate=_lowerCamelCase , padding_value=_lowerCamelCase , return_attention_mask=_lowerCamelCase , **_lowerCamelCase , )
snake_case__ : Optional[int] = top_db
snake_case__ : Any = truncation
snake_case__ : List[Any] = padding
snake_case__ : str = fft_window_size
snake_case__ : Union[str, Any] = (fft_window_size >> 1) + 1
snake_case__ : List[str] = hop_length
snake_case__ : Union[str, Any] = max_length_s
snake_case__ : int = max_length_s * sampling_rate
snake_case__ : str = sampling_rate
snake_case__ : List[Any] = frequency_min
snake_case__ : Optional[Any] = frequency_max
snake_case__ : Optional[int] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=_lowerCamelCase , min_frequency=_lowerCamelCase , max_frequency=_lowerCamelCase , sampling_rate=_lowerCamelCase , norm=_lowerCamelCase , mel_scale='htk' , )
snake_case__ : List[Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=_lowerCamelCase , min_frequency=_lowerCamelCase , max_frequency=_lowerCamelCase , sampling_rate=_lowerCamelCase , norm='slaney' , mel_scale='slaney' , )
def UpperCAmelCase__ ( self : Optional[Any] ):
snake_case__ : Union[str, Any] = copy.deepcopy(self.__dict__ )
snake_case__ : str = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def UpperCAmelCase__ ( self : int , _lowerCamelCase : np.array , _lowerCamelCase : Optional[np.array] = None ):
snake_case__ : Optional[int] = spectrogram(
_lowerCamelCase , window_function(self.fft_window_size , 'hann' ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=_lowerCamelCase , log_mel='dB' , )
return log_mel_spectrogram.T
def UpperCAmelCase__ ( self : List[str] , _lowerCamelCase : int , _lowerCamelCase : List[str] , _lowerCamelCase : Tuple ):
snake_case__ : str = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
snake_case__ : Any = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
snake_case__ : List[Any] = [0]
# randomly choose index for each part
snake_case__ : Any = np.random.choice(ranges[0] )
snake_case__ : Optional[Any] = np.random.choice(ranges[1] )
snake_case__ : int = np.random.choice(ranges[2] )
snake_case__ : List[Any] = mel[idx_front : idx_front + chunk_frames, :]
snake_case__ : Union[str, Any] = mel[idx_middle : idx_middle + chunk_frames, :]
snake_case__ : Dict = mel[idx_back : idx_back + chunk_frames, :]
snake_case__ : str = torch.tensor(mel[None, None, :] )
snake_case__ : List[Any] = torch.nn.functional.interpolate(
_lowerCamelCase , size=[chunk_frames, 6_4] , mode='bilinear' , align_corners=_lowerCamelCase )
snake_case__ : Dict = mel_shrink[0][0].numpy()
snake_case__ : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def UpperCAmelCase__ ( self : Dict , _lowerCamelCase : np.array , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : List[str] ):
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
snake_case__ : str = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
snake_case__ : List[Any] = len(_lowerCamelCase ) - max_length
snake_case__ : Dict = np.random.randint(0 , overflow + 1 )
snake_case__ : Dict = waveform[idx : idx + max_length]
snake_case__ : Dict = self._np_extract_fbank_features(_lowerCamelCase , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
snake_case__ : Dict = self._np_extract_fbank_features(_lowerCamelCase , self.mel_filters )
snake_case__ : Optional[Any] = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
snake_case__ : Optional[int] = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
snake_case__ : Any = np.stack([mel, mel, mel, mel] , axis=0 )
snake_case__ : Union[str, Any] = False
else:
snake_case__ : Optional[int] = self._random_mel_fusion(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
snake_case__ : Tuple = True
else:
raise NotImplementedError(F'''data_truncating {truncation} not implemented''' )
else:
snake_case__ : List[Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
snake_case__ : Tuple = int(max_length / len(_lowerCamelCase ) )
snake_case__ : int = np.stack(np.tile(_lowerCamelCase , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
snake_case__ : int = int(max_length / len(_lowerCamelCase ) )
snake_case__ : List[str] = np.stack(np.tile(_lowerCamelCase , _lowerCamelCase ) )
snake_case__ : str = np.pad(_lowerCamelCase , (0, max_length - waveform.shape[0]) , mode='constant' , constant_values=0 )
if truncation == "fusion":
snake_case__ : List[str] = self._np_extract_fbank_features(_lowerCamelCase , self.mel_filters )
snake_case__ : Optional[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
snake_case__ : Union[str, Any] = self._np_extract_fbank_features(_lowerCamelCase , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : List[str] , _lowerCamelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _lowerCamelCase : str = None , _lowerCamelCase : Optional[str] = None , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : Optional[Union[str, TensorType]] = None , **_lowerCamelCase : Optional[int] , ):
snake_case__ : int = truncation if truncation is not None else self.truncation
snake_case__ : int = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
F''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
snake_case__ : int = 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}''' )
snake_case__ : str = is_batched_numpy or (
isinstance(_lowerCamelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
snake_case__ : Union[str, Any] = [np.asarray(_lowerCamelCase , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_lowerCamelCase , np.ndarray ):
snake_case__ : Optional[int] = np.asarray(_lowerCamelCase , dtype=np.floataa )
elif isinstance(_lowerCamelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
snake_case__ : str = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
snake_case__ : Union[str, Any] = [np.asarray(_lowerCamelCase )]
# convert to mel spectrogram, truncate and pad if needed.
snake_case__ : List[str] = [
self._get_input_mel(_lowerCamelCase , max_length if max_length else self.nb_max_samples , _lowerCamelCase , _lowerCamelCase )
for waveform in raw_speech
]
snake_case__ : Any = []
snake_case__ : Optional[int] = []
for mel, longer in padded_inputs:
input_mel.append(_lowerCamelCase )
is_longer.append(_lowerCamelCase )
if truncation == "fusion" and sum(_lowerCamelCase ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
snake_case__ : Dict = np.random.randint(0 , len(_lowerCamelCase ) )
snake_case__ : Union[str, Any] = True
if isinstance(input_mel[0] , _lowerCamelCase ):
snake_case__ : Tuple = [np.asarray(_lowerCamelCase , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
snake_case__ : Any = [[longer] for longer in is_longer]
snake_case__ : Union[str, Any] = {'input_features': input_mel, 'is_longer': is_longer}
snake_case__ : Any = BatchFeature(_lowerCamelCase )
if return_tensors is not None:
snake_case__ : Optional[int] = input_features.convert_to_tensors(_lowerCamelCase )
return input_features
| 170 |
# Copyright (c) 2021-, NVIDIA CORPORATION. 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.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def lowercase__( A , A , A=0 ):
# Format the message.
if name is None:
snake_case__ : Dict = None
else:
snake_case__ : Optional[int] = '.' * max(0 , spaces - 2 ) + '# {:' + str(5_0 - spaces ) + 's}'
snake_case__ : str = fmt.format(A )
# Print and recurse (if needed).
if isinstance(A , A ):
if msg is not None:
print(A )
for k in val.keys():
recursive_print(A , val[k] , spaces + 2 )
elif isinstance(A , torch.Tensor ):
print(A , ':' , val.size() )
else:
print(A , ':' , A )
def lowercase__( A , A , A , A , A ):
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
snake_case__ : Any = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
snake_case__ : Union[str, Any] = (num_heads, hidden_size, num_splits) + input_shape[1:]
snake_case__ : int = param.view(*A )
snake_case__ : Tuple = param.transpose(0 , 2 )
snake_case__ : Any = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
snake_case__ : Optional[Any] = (num_heads, num_splits, hidden_size) + input_shape[1:]
snake_case__ : Any = param.view(*A )
snake_case__ : Optional[int] = param.transpose(0 , 1 ).contiguous()
snake_case__ : List[Any] = param.view(*A )
return param
def lowercase__( A , A , A ):
# The converted output model.
snake_case__ : Optional[Any] = {}
# old versions did not store training args
snake_case__ : Any = input_state_dict.get('args' , A )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
snake_case__ : str = ds_args.padded_vocab_size
snake_case__ : Any = ds_args.max_position_embeddings
snake_case__ : Optional[int] = ds_args.hidden_size
snake_case__ : str = ds_args.num_layers
snake_case__ : List[Any] = ds_args.num_attention_heads
snake_case__ : int = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
snake_case__ : int = config.n_head
# The hidden_size per head.
snake_case__ : Any = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
snake_case__ : Optional[Any] = input_state_dict['checkpoint_version']
else:
snake_case__ : Tuple = 0.0
# The model.
snake_case__ : Dict = input_state_dict['model']
# The language model.
snake_case__ : int = model['language_model']
# The embeddings.
snake_case__ : Tuple = lm['embedding']
# The word embeddings.
snake_case__ : Tuple = embeddings['word_embeddings']['weight']
# Truncate the embedding table to vocab_size rows.
snake_case__ : int = word_embeddings[: config.vocab_size, :]
snake_case__ : List[str] = word_embeddings
# The position embeddings.
snake_case__ : Union[str, Any] = embeddings['position_embeddings']['weight']
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
snake_case__ : Any = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
f'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
snake_case__ : int = pos_embeddings
# The transformer.
snake_case__ : Optional[Any] = lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']
# The regex to extract layer names.
snake_case__ : Union[str, Any] = re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)' )
# The simple map of names for "automated" rules.
snake_case__ : Any = {
'attention.dense': '.attn.c_proj.',
'self_attention.dense': '.attn.c_proj.',
'mlp.dense_h_to_4h': '.mlp.c_fc.',
'mlp.dense_4h_to_h': '.mlp.c_proj.',
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
snake_case__ : Dict = layer_re.match(A )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
snake_case__ : Dict = int(m.group(1 ) )
# The name of the operation.
snake_case__ : List[Any] = m.group(2 )
# Is it a weight or a bias?
snake_case__ : Tuple = m.group(3 )
# The name of the layer.
snake_case__ : int = f'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith('layernorm' ):
snake_case__ : Union[str, Any] = 'ln_1' if op_name.startswith('input' ) else 'ln_2'
snake_case__ : List[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
snake_case__ : List[Any] = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , A , A )
snake_case__ : List[Any] = causal_mask
# Insert a "dummy" tensor for masked_bias.
snake_case__ : Optional[int] = torch.tensor(-1e4 , dtype=torch.floataa )
snake_case__ : int = masked_bias
snake_case__ : List[Any] = fix_query_key_value_ordering(A , A , 3 , A , A )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
snake_case__ : List[Any] = out_val.transpose(0 , 1 ).contiguous()
# Store.
snake_case__ : Optional[Any] = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
snake_case__ : int = fix_query_key_value_ordering(A , A , 3 , A , A )
# Store. No change of shape.
snake_case__ : List[Any] = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
snake_case__ : List[Any] = megatron_to_transformers[op_name]
snake_case__ : Union[str, Any] = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
snake_case__ : List[Any] = megatron_to_transformers[op_name]
snake_case__ : str = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
snake_case__ : Any = transformer['final_layernorm.weight']
snake_case__ : Optional[int] = transformer['final_layernorm.bias']
# For LM head, transformers' wants the matrix to weight embeddings.
snake_case__ : Optional[Any] = word_embeddings
# It should be done!
return output_state_dict
def lowercase__( ):
# Create the argument parser.
snake_case__ : str = argparse.ArgumentParser()
parser.add_argument('--print-checkpoint-structure' , action='store_true' )
parser.add_argument(
'path_to_checkpoint' , type=A , help='Path to the checkpoint file (.zip archive or direct .pt file)' , )
parser.add_argument(
'--config_file' , default='' , type=A , help='An optional config json file describing the pre-trained model.' , )
snake_case__ : Dict = parser.parse_args()
# Extract the basename.
snake_case__ : str = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(f'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith('.zip' ):
with zipfile.ZipFile(args.path_to_checkpoint , 'r' ) as checkpoint:
with checkpoint.open('release/mp_rank_00/model_optim_rng.pt' ) as pytorch_dict:
snake_case__ : Any = torch.load(A , map_location='cpu' )
else:
snake_case__ : Optional[int] = torch.load(args.path_to_checkpoint , map_location='cpu' )
snake_case__ : List[Any] = input_state_dict.get('args' , A )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
snake_case__ : Optional[int] = 'gelu_fast'
elif ds_args.openai_gelu:
snake_case__ : Optional[Any] = 'gelu_new'
else:
snake_case__ : str = 'gelu'
else:
# in the very early days this used to be "gelu_new"
snake_case__ : List[str] = 'gelu_new'
# Spell out all parameters in case the defaults change.
snake_case__ : str = GPTaConfig(
vocab_size=5_0_2_5_7 , n_positions=1_0_2_4 , n_embd=1_0_2_4 , n_layer=2_4 , n_head=1_6 , n_inner=4_0_9_6 , activation_function=A , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1e-5 , initializer_range=0.02 , summary_type='cls_index' , summary_use_proj=A , summary_activation=A , summary_proj_to_labels=A , summary_first_dropout=0.1 , scale_attn_weights=A , use_cache=A , bos_token_id=5_0_2_5_6 , eos_token_id=5_0_2_5_6 , )
else:
snake_case__ : str = GPTaConfig.from_json_file(args.config_file )
snake_case__ : Optional[Any] = ['GPT2LMHeadModel']
# Convert.
print('Converting' )
snake_case__ : Dict = convert_megatron_checkpoint(A , A , A )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(A , A )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
snake_case__ : Union[str, Any] = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
snake_case__ : Optional[int] = 'gpt2'
elif tokenizer_type == "PretrainedFromHF":
snake_case__ : Optional[Any] = ds_args.tokenizer_name_or_path
else:
raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
snake_case__ : str = 'gpt2'
snake_case__ : int = AutoTokenizer.from_pretrained(A )
snake_case__ : Optional[int] = type(A ).__name__
snake_case__ : Tuple = tokenizer_class
# Store the config to file.
print('Saving config' )
config.save_pretrained(A )
# Save tokenizer based on args
print(f'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(A )
# Store the state_dict to file.
snake_case__ : Optional[Any] = os.path.join(A , 'pytorch_model.bin' )
print(f'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(A , A )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 170 | 1 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__UpperCAmelCase = logging.get_logger(__name__)
def _snake_case ( ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :int = os.getenv("""SM_HP_MP_PARAMETERS""" , """{}""" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
lowerCAmelCase_ :Tuple = json.loads(__snake_case )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
lowerCAmelCase_ :Dict = os.getenv("""SM_FRAMEWORK_PARAMS""" , """{}""" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
lowerCAmelCase_ :List[str] = json.loads(__snake_case )
if not mpi_options.get("""sagemaker_mpi_enabled""" , __snake_case ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("""smdistributed""" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class _SCREAMING_SNAKE_CASE ( _A ):
UpperCAmelCase_ :List[Any] = field(
default="" , metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"} , )
def __lowerCAmelCase ( self ) -> Any:
super().__post_init__()
warnings.warn(
"""`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
"""`TrainingArguments` instead.""" , __A , )
@cached_property
def __lowerCAmelCase ( self ) -> "torch.device":
logger.info("""PyTorch: setting up devices""" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"""torch.distributed process group is initialized, but local_rank == -1. """
"""In order to use Torch DDP, launch your script with `python -m torch.distributed.launch""" )
if self.no_cuda:
lowerCAmelCase_ :int = torch.device("""cpu""" )
lowerCAmelCase_ :Any = 0
elif is_sagemaker_model_parallel_available():
lowerCAmelCase_ :str = smp.local_rank()
lowerCAmelCase_ :Dict = torch.device("""cuda""" , __A )
lowerCAmelCase_ :str = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="""smddp""" , timeout=self.ddp_timeout_delta )
lowerCAmelCase_ :str = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK""" ) )
lowerCAmelCase_ :Dict = torch.device("""cuda""" , self.local_rank )
lowerCAmelCase_ :Tuple = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
lowerCAmelCase_ :Dict = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
lowerCAmelCase_ :Tuple = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="""nccl""" , timeout=self.ddp_timeout_delta )
lowerCAmelCase_ :Optional[int] = torch.device("""cuda""" , self.local_rank )
lowerCAmelCase_ :Optional[Any] = 1
if device.type == "cuda":
torch.cuda.set_device(__A )
return device
@property
def __lowerCAmelCase ( self ) -> List[str]:
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def __lowerCAmelCase ( self ) -> Tuple:
return not is_sagemaker_model_parallel_available()
@property
def __lowerCAmelCase ( self ) -> Optional[int]:
return False
| 713 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Tuple = "bert-generation"
def __init__( self , __A=5_0358 , __A=1024 , __A=24 , __A=16 , __A=4096 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=0.0_2 , __A=1E-12 , __A=0 , __A=2 , __A=1 , __A="absolute" , __A=True , **__A , ) -> Union[str, Any]:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
lowerCAmelCase_ :List[Any] = vocab_size
lowerCAmelCase_ :int = hidden_size
lowerCAmelCase_ :Union[str, Any] = num_hidden_layers
lowerCAmelCase_ :str = num_attention_heads
lowerCAmelCase_ :str = hidden_act
lowerCAmelCase_ :List[str] = intermediate_size
lowerCAmelCase_ :Optional[Any] = hidden_dropout_prob
lowerCAmelCase_ :List[str] = attention_probs_dropout_prob
lowerCAmelCase_ :Dict = max_position_embeddings
lowerCAmelCase_ :int = initializer_range
lowerCAmelCase_ :Optional[int] = layer_norm_eps
lowerCAmelCase_ :str = position_embedding_type
lowerCAmelCase_ :Dict = use_cache
| 256 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowercase_( SCREAMING_SNAKE_CASE_ = "" ):
'''simple docstring'''
lowerCamelCase : Any = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
lowerCamelCase : Dict = BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE_ ).text , "html.parser" )
lowerCamelCase : Optional[int] = soup.find_all("td" , attrs="titleColumn" )
lowerCamelCase : Union[str, Any] = soup.find_all("td" , class_="ratingColumn imdbRating" )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
}
def lowercase_( SCREAMING_SNAKE_CASE_ = "IMDb_Top_250_Movies.csv" ):
'''simple docstring'''
lowerCamelCase : Optional[Any] = get_imdb_top_aaa_movies()
with open(SCREAMING_SNAKE_CASE_ , "w" , newline="" ) as out_file:
lowerCamelCase : int = csv.writer(SCREAMING_SNAKE_CASE_ )
writer.writerow(["Movie title", "IMDb rating"] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 340 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class UpperCAmelCase_ :
'''simple docstring'''
__A : Optional[int] = None
__A : Optional[jnp.ndarray] = None
__A : Optional[jnp.ndarray] = None # sigma(t_i)
@classmethod
def _snake_case ( cls ):
"""simple docstring"""
return cls()
@dataclass
class UpperCAmelCase_ ( UpperCamelCase ):
'''simple docstring'''
__A : jnp.ndarray
__A : jnp.ndarray
__A : KarrasVeSchedulerState
class UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase ):
'''simple docstring'''
@property
def _snake_case ( self ):
"""simple docstring"""
return True
@register_to_config
def __init__( self , __A = 0.02 , __A = 100 , __A = 1.007 , __A = 80 , __A = 0.05 , __A = 50 , ):
"""simple docstring"""
pass
def _snake_case ( self ):
"""simple docstring"""
return KarrasVeSchedulerState.create()
def _snake_case ( self , __A , __A , __A = () ):
"""simple docstring"""
lowerCamelCase : List[str] = jnp.arange(0 , __A )[::-1].copy()
lowerCamelCase : Tuple = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=__A , schedule=jnp.array(__A , dtype=jnp.floataa ) , timesteps=__A , )
def _snake_case ( self , __A , __A , __A , __A , ):
"""simple docstring"""
if self.config.s_min <= sigma <= self.config.s_max:
lowerCamelCase : List[str] = min(self.config.s_churn / state.num_inference_steps , 2**0.5 - 1 )
else:
lowerCamelCase : int = 0
# sample eps ~ N(0, S_noise^2 * I)
lowerCamelCase : Any = random.split(__A , num=1 )
lowerCamelCase : List[Any] = self.config.s_noise * random.normal(key=__A , shape=sample.shape )
lowerCamelCase : Dict = sigma + gamma * sigma
lowerCamelCase : Any = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def _snake_case ( self , __A , __A , __A , __A , __A , __A = True , ):
"""simple docstring"""
lowerCamelCase : Dict = sample_hat + sigma_hat * model_output
lowerCamelCase : str = (sample_hat - pred_original_sample) / sigma_hat
lowerCamelCase : Optional[Any] = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A , derivative=__A , state=__A )
def _snake_case ( self , __A , __A , __A , __A , __A , __A , __A , __A = True , ):
"""simple docstring"""
lowerCamelCase : List[Any] = sample_prev + sigma_prev * model_output
lowerCamelCase : List[str] = (sample_prev - pred_original_sample) / sigma_prev
lowerCamelCase : List[str] = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A , derivative=__A , state=__A )
def _snake_case ( self , __A , __A , __A , __A ):
"""simple docstring"""
raise NotImplementedError()
| 340 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class _lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] ="gpt_neox"
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : str=5_04_32 , SCREAMING_SNAKE_CASE__ : Tuple=61_44 , SCREAMING_SNAKE_CASE__ : List[Any]=44 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=64 , SCREAMING_SNAKE_CASE__ : str=2_45_76 , SCREAMING_SNAKE_CASE__ : Tuple="gelu" , SCREAMING_SNAKE_CASE__ : int=0.25 , SCREAMING_SNAKE_CASE__ : Optional[Any]=1_00_00 , SCREAMING_SNAKE_CASE__ : Any=0.0 , SCREAMING_SNAKE_CASE__ : List[Any]=0.0 , SCREAMING_SNAKE_CASE__ : Tuple=0.1 , SCREAMING_SNAKE_CASE__ : Tuple=20_48 , SCREAMING_SNAKE_CASE__ : Optional[Any]=0.02 , SCREAMING_SNAKE_CASE__ : int=1e-5 , SCREAMING_SNAKE_CASE__ : str=True , SCREAMING_SNAKE_CASE__ : Optional[Any]=0 , SCREAMING_SNAKE_CASE__ : List[str]=2 , SCREAMING_SNAKE_CASE__ : Any=False , SCREAMING_SNAKE_CASE__ : Dict=True , SCREAMING_SNAKE_CASE__ : str=None , **SCREAMING_SNAKE_CASE__ : List[str] , ):
"""simple docstring"""
super().__init__(bos_token_id=SCREAMING_SNAKE_CASE__ , eos_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
UpperCamelCase = vocab_size
UpperCamelCase = max_position_embeddings
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = rotary_pct
UpperCamelCase = rotary_emb_base
UpperCamelCase = attention_dropout
UpperCamelCase = hidden_dropout
UpperCamelCase = classifier_dropout
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = use_cache
UpperCamelCase = tie_word_embeddings
UpperCamelCase = use_parallel_residual
UpperCamelCase = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'The hidden size is not divisble by the number of attention heads! Make sure to update them!' )
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , SCREAMING_SNAKE_CASE__ ) or len(self.rope_scaling ) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F'got {self.rope_scaling}' )
UpperCamelCase = self.rope_scaling.get('type' , SCREAMING_SNAKE_CASE__ )
UpperCamelCase = self.rope_scaling.get('factor' , SCREAMING_SNAKE_CASE__ )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
if rope_scaling_factor is None or not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or rope_scaling_factor <= 1.0:
raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 712 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_snake_case = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=False ) -> Optional[int]:
try:
import torch # noqa: F401
except ImportError:
logger.error(
'Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'
' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
' instructions.' )
raise
if not is_sharded:
UpperCamelCase = os.path.abspath(_lowercase )
logger.info(F'Loading PyTorch weights from {pt_path}' )
UpperCamelCase = torch.load(_lowercase , map_location='cpu' )
logger.info(F'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.' )
UpperCamelCase = convert_pytorch_state_dict_to_flax(_lowercase , _lowercase )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
UpperCamelCase = convert_pytorch_sharded_state_dict_to_flax(_lowercase , _lowercase )
return flax_state_dict
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , ) -> (Tuple[str], np.ndarray):
def is_key_or_prefix_key_in_dict(_lowercase ) -> bool:
return len(set(_lowercase ) & {key, (model_prefix,) + key} ) > 0
# layer norm
UpperCamelCase = pt_tuple_key[:-1] + ('scale',)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowercase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
UpperCamelCase = pt_tuple_key[:-1] + ('mean',)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowercase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
UpperCamelCase = pt_tuple_key[:-1] + ('var',)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowercase ):
return renamed_pt_tuple_key, pt_tensor
# embedding
UpperCamelCase = pt_tuple_key[:-1] + ('embedding',)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowercase ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
UpperCamelCase = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_lowercase ):
UpperCamelCase = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
UpperCamelCase = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowercase ):
UpperCamelCase = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
UpperCamelCase = pt_tuple_key[:-1] + ('weight',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
UpperCamelCase = pt_tuple_key[:-1] + ('bias',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
UpperCamelCase = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
UpperCamelCase = pt_tuple_key[-2] + '_g'
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
UpperCamelCase = pt_tuple_key[-2] + '_v'
if name is not None:
UpperCamelCase = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def __lowerCamelCase ( _lowercase , _lowercase ) -> Optional[int]:
# convert pytorch tensor to numpy
UpperCamelCase = {k: v.numpy() for k, v in pt_state_dict.items()}
UpperCamelCase = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
UpperCamelCase = flax_model.params['params']
else:
UpperCamelCase = flax_model.params
UpperCamelCase = flatten_dict(_lowercase )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
UpperCamelCase = flatten_dict(flax_model.params['batch_stats'] )
random_flax_state_dict.update(_lowercase )
UpperCamelCase = {}
UpperCamelCase = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('.' )[0] for k in pt_state_dict.keys()}
)
UpperCamelCase = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('.' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
UpperCamelCase = tuple(pt_key.split('.' ) )
# remove base model prefix if necessary
UpperCamelCase = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
UpperCamelCase = pt_tuple_key[1:]
# Correctly rename weight parameters
UpperCamelCase , UpperCamelCase = rename_key_and_reshape_tensor(
_lowercase , _lowercase , _lowercase , _lowercase )
# add model prefix if necessary
UpperCamelCase = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
UpperCamelCase = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
UpperCamelCase = jnp.asarray(_lowercase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(_lowercase , _lowercase )
continue
# also add unexpected weight so that warning is thrown
UpperCamelCase = jnp.asarray(_lowercase )
else:
# also add unexpected weight so that warning is thrown
UpperCamelCase = jnp.asarray(_lowercase )
return unflatten_dict(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> Dict:
import torch
# Load the index
UpperCamelCase = {}
for shard_file in shard_filenames:
# load using msgpack utils
UpperCamelCase = torch.load(_lowercase )
UpperCamelCase = {k: v.numpy() for k, v in pt_state_dict.items()}
UpperCamelCase = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
UpperCamelCase = flax_model.params['params']
UpperCamelCase = flatten_dict(_lowercase )
random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats'] ) )
else:
UpperCamelCase = flax_model.params
UpperCamelCase = flatten_dict(_lowercase )
UpperCamelCase = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('.' )[0] for k in pt_state_dict.keys()}
)
UpperCamelCase = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('.' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
UpperCamelCase = tuple(pt_key.split('.' ) )
# remove base model prefix if necessary
UpperCamelCase = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
UpperCamelCase = pt_tuple_key[1:]
# Correctly rename weight parameters
UpperCamelCase , UpperCamelCase = rename_key_and_reshape_tensor(
_lowercase , _lowercase , _lowercase , _lowercase )
# add model prefix if necessary
UpperCamelCase = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
UpperCamelCase = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
UpperCamelCase = jnp.asarray(_lowercase )
continue
if "var" in flax_key[-1]:
UpperCamelCase = jnp.asarray(_lowercase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(_lowercase , _lowercase )
continue
# also add unexpected weight so that warning is thrown
UpperCamelCase = jnp.asarray(_lowercase )
else:
# also add unexpected weight so that warning is thrown
UpperCamelCase = jnp.asarray(_lowercase )
return unflatten_dict(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
UpperCamelCase = os.path.abspath(_lowercase )
logger.info(F'Loading Flax weights from {flax_checkpoint_path}' )
# import correct flax class
UpperCamelCase = getattr(_lowercase , 'Flax' + model.__class__.__name__ )
# load flax weight dict
with open(_lowercase , 'rb' ) as state_f:
try:
UpperCamelCase = from_bytes(_lowercase , state_f.read() )
except UnpicklingError:
raise EnvironmentError(F'Unable to convert {flax_checkpoint_path} to Flax deserializable object. ' )
return load_flax_weights_in_pytorch_model(_lowercase , _lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> List[Any]:
try:
import torch # noqa: F401
except ImportError:
logger.error(
'Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'
' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
' instructions.' )
raise
# check if we have bf16 weights
UpperCamelCase = flatten_dict(jax.tree_util.tree_map(lambda _lowercase : x.dtype == jnp.bfloataa , _lowercase ) ).values()
if any(_lowercase ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
'before loading those in PyTorch model.' )
UpperCamelCase = jax.tree_util.tree_map(
lambda _lowercase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , _lowercase )
UpperCamelCase = flatten_dict(_lowercase )
UpperCamelCase = pt_model.state_dict()
UpperCamelCase = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split('.' )[0] for k in pt_model_dict.keys()}
)
UpperCamelCase = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split('.' )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
UpperCamelCase = []
UpperCamelCase = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
UpperCamelCase = flax_key_tuple[0] == pt_model.base_model_prefix
UpperCamelCase = '.'.join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
UpperCamelCase = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
UpperCamelCase = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_lowercase ) not in pt_model_dict:
# conv layer
UpperCamelCase = flax_key_tuple[:-1] + ('weight',)
UpperCamelCase = jnp.transpose(_lowercase , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(_lowercase ) not in pt_model_dict:
# linear layer
UpperCamelCase = flax_key_tuple[:-1] + ('weight',)
UpperCamelCase = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
UpperCamelCase = flax_key_tuple[:-1] + ('weight',)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
UpperCamelCase = flax_key_tuple[:-1] + ('running_mean',)
elif "var" in flax_key_tuple[-1]:
UpperCamelCase = flax_key_tuple[:-1] + ('running_var',)
if "batch_stats" in flax_state:
UpperCamelCase = '.'.join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
UpperCamelCase = '.'.join(_lowercase )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
UpperCamelCase = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
UpperCamelCase = key.split('.' )
UpperCamelCase = None
if key_components[-3::2] == ["parametrizations", "original0"]:
UpperCamelCase = key_components[-2] + '_g'
elif key_components[-3::2] == ["parametrizations", "original1"]:
UpperCamelCase = key_components[-2] + '_v'
if name is not None:
UpperCamelCase = key_components[:-3] + [name]
UpperCamelCase = '.'.join(_lowercase )
UpperCamelCase = key
if flax_key in special_pt_names:
UpperCamelCase = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
else:
# add weight to pytorch dict
UpperCamelCase = np.asarray(_lowercase ) if not isinstance(_lowercase , np.ndarray ) else flax_tensor
UpperCamelCase = torch.from_numpy(_lowercase )
# remove from missing keys
missing_keys.remove(_lowercase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(_lowercase )
pt_model.load_state_dict(_lowercase )
# re-transform missing_keys to list
UpperCamelCase = list(_lowercase )
if len(_lowercase ) > 0:
logger.warning(
'Some weights of the Flax model were not used when initializing the PyTorch model'
F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
' FlaxBertForSequenceClassification model).' )
else:
logger.warning(F'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n' )
if len(_lowercase ) > 0:
logger.warning(
F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
' use it for predictions and inference.' )
else:
logger.warning(
F'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
'If your task is similar to the task the model of the checkpoint was trained on, '
F'you can already use {pt_model.__class__.__name__} for predictions without further training.' )
return pt_model
| 170 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _UpperCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : str = StableDiffusionXLImgaImgPipeline
_lowerCAmelCase : Union[str, Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
_lowerCAmelCase : Union[str, Any] = PipelineTesterMixin.required_optional_params - {"""latents"""}
_lowerCAmelCase : int = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_lowerCAmelCase : List[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
_lowerCAmelCase : Optional[int] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def _snake_case ( self : Optional[Any] ):
torch.manual_seed(0 )
snake_case_ : List[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , attention_head_dim=(2, 4) , use_linear_projection=lowercase_ , addition_embed_type='''text_time''' , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
snake_case_ : int = EulerDiscreteScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , steps_offset=1 , beta_schedule='''scaled_linear''' , timestep_spacing='''leading''' , )
torch.manual_seed(0 )
snake_case_ : Union[str, Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
snake_case_ : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=32 , )
snake_case_ : Optional[Any] = CLIPTextModel(lowercase_ )
snake_case_ : int = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=lowercase_ )
snake_case_ : Tuple = CLIPTextModelWithProjection(lowercase_ )
snake_case_ : str = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=lowercase_ )
snake_case_ : str = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''text_encoder_2''': text_encoder_a,
'''tokenizer_2''': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def _snake_case ( self : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : Tuple=0 ):
snake_case_ : Tuple = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
snake_case_ : str = image / 2 + 0.5
if str(lowercase_ ).startswith('''mps''' ):
snake_case_ : Union[str, Any] = torch.manual_seed(lowercase_ )
else:
snake_case_ : Any = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
snake_case_ : Optional[int] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 5.0,
'''output_type''': '''numpy''',
'''strength''': 0.75,
}
return inputs
def _snake_case ( self : Optional[Any] ):
snake_case_ : int = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case_ : List[str] = self.get_dummy_components()
snake_case_ : List[Any] = StableDiffusionXLImgaImgPipeline(**lowercase_ )
snake_case_ : List[Any] = sd_pipe.to(lowercase_ )
sd_pipe.set_progress_bar_config(disable=lowercase_ )
snake_case_ : List[Any] = self.get_dummy_inputs(lowercase_ )
snake_case_ : List[str] = sd_pipe(**lowercase_ ).images
snake_case_ : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case_ : Dict = np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self : Any ):
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def _snake_case ( self : str ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def _snake_case ( self : Dict ):
pass
def _snake_case ( self : Optional[int] ):
snake_case_ : Union[str, Any] = self.get_dummy_components()
snake_case_ : Tuple = StableDiffusionXLImgaImgPipeline(**lowercase_ )
snake_case_ : Union[str, Any] = sd_pipe.to(lowercase_ )
snake_case_ : Union[str, Any] = sd_pipe.to(lowercase_ )
sd_pipe.set_progress_bar_config(disable=lowercase_ )
# forward without prompt embeds
snake_case_ : List[str] = self.get_dummy_inputs(lowercase_ )
snake_case_ : Tuple = 3 * ['''this is a negative prompt''']
snake_case_ : Any = negative_prompt
snake_case_ : Tuple = 3 * [inputs['''prompt''']]
snake_case_ : Dict = sd_pipe(**lowercase_ )
snake_case_ : Tuple = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
snake_case_ : Tuple = self.get_dummy_inputs(lowercase_ )
snake_case_ : str = 3 * ['''this is a negative prompt''']
snake_case_ : Optional[Any] = 3 * [inputs.pop('''prompt''' )]
(
(
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
),
) : str = sd_pipe.encode_prompt(lowercase_ , negative_prompt=lowercase_ )
snake_case_ : List[str] = sd_pipe(
**lowercase_ , prompt_embeds=lowercase_ , negative_prompt_embeds=lowercase_ , pooled_prompt_embeds=lowercase_ , negative_pooled_prompt_embeds=lowercase_ , )
snake_case_ : Union[str, Any] = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Optional[int] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self : Any , lowercase_ : List[str] , lowercase_ : int="cpu" , lowercase_ : int=torch.floataa , lowercase_ : Optional[int]=0 ):
snake_case_ : Dict = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
snake_case_ : Optional[int] = np.random.RandomState(lowercase_ ).standard_normal((1, 4, 64, 64) )
snake_case_ : List[Any] = torch.from_numpy(lowercase_ ).to(device=lowercase_ , dtype=lowercase_ )
snake_case_ : List[str] = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def _snake_case ( self : str ):
snake_case_ : Optional[int] = DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''' )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
snake_case_ : Union[str, Any] = self.get_inputs(lowercase_ )
snake_case_ : int = pipe(**lowercase_ ).images
snake_case_ : int = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
snake_case_ : Optional[Any] = np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 123 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
lowercase__ : str = random.Random()
def __lowercase ( _a , _a=1.0 , _a=None , _a=None ):
if rng is None:
snake_case_ : Tuple = global_rng
snake_case_ : str = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class _UpperCAmelCase ( unittest.TestCase):
def __init__( self : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : Tuple=7 , lowercase_ : Union[str, Any]=400 , lowercase_ : Tuple=2000 , lowercase_ : str=24 , lowercase_ : Any=24 , lowercase_ : str=0.0 , lowercase_ : str=16000 , lowercase_ : Any=True , lowercase_ : Tuple=True , ):
snake_case_ : Any = parent
snake_case_ : Dict = batch_size
snake_case_ : Tuple = min_seq_length
snake_case_ : List[str] = max_seq_length
snake_case_ : int = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
snake_case_ : Optional[Any] = feature_size
snake_case_ : Union[str, Any] = num_mel_bins
snake_case_ : List[str] = padding_value
snake_case_ : List[str] = sampling_rate
snake_case_ : str = return_attention_mask
snake_case_ : str = do_normalize
def _snake_case ( self : Any ):
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def _snake_case ( self : Tuple , lowercase_ : Tuple=False , lowercase_ : Optional[int]=False ):
def _flatten(lowercase_ : Union[str, Any] ):
return list(itertools.chain(*lowercase_ ) )
if equal_length:
snake_case_ : Optional[Any] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
snake_case_ : Optional[int] = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
snake_case_ : Dict = [np.asarray(lowercase_ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class _UpperCAmelCase ( lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : Any = SpeechaTextFeatureExtractor if is_speech_available() else None
def _snake_case ( self : str ):
snake_case_ : Union[str, Any] = SpeechaTextFeatureExtractionTester(self )
def _snake_case ( self : List[str] , lowercase_ : Union[str, Any] ):
self.assertTrue(np.all(np.mean(lowercase_ , axis=0 ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(lowercase_ , axis=0 ) - 1 ) < 1E-3 ) )
def _snake_case ( self : Optional[Any] ):
# Tests that all call wrap to encode_plus and batch_encode_plus
snake_case_ : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
snake_case_ : Any = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
snake_case_ : Any = [np.asarray(lowercase_ ) for speech_input in speech_inputs]
# Test feature size
snake_case_ : Tuple = feature_extractor(lowercase_ , padding=lowercase_ , return_tensors='''np''' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
snake_case_ : List[str] = feature_extractor(speech_inputs[0] , return_tensors='''np''' ).input_features
snake_case_ : Optional[Any] = feature_extractor(np_speech_inputs[0] , return_tensors='''np''' ).input_features
self.assertTrue(np.allclose(lowercase_ , lowercase_ , atol=1E-3 ) )
# Test batched
snake_case_ : List[Any] = feature_extractor(lowercase_ , return_tensors='''np''' ).input_features
snake_case_ : List[Any] = feature_extractor(lowercase_ , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(lowercase_ , lowercase_ ):
self.assertTrue(np.allclose(lowercase_ , lowercase_ , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
snake_case_ : Union[str, Any] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
snake_case_ : Dict = np.asarray(lowercase_ )
snake_case_ : Optional[int] = feature_extractor(lowercase_ , return_tensors='''np''' ).input_features
snake_case_ : Optional[int] = feature_extractor(lowercase_ , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(lowercase_ , lowercase_ ):
self.assertTrue(np.allclose(lowercase_ , lowercase_ , atol=1E-3 ) )
def _snake_case ( self : Optional[Any] ):
snake_case_ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
snake_case_ : int = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
snake_case_ : int = ['''longest''', '''max_length''', '''do_not_pad''']
snake_case_ : List[Any] = [None, 16, None]
for max_length, padding in zip(lowercase_ , lowercase_ ):
snake_case_ : Union[str, Any] = feature_extractor(
lowercase_ , padding=lowercase_ , max_length=lowercase_ , return_attention_mask=lowercase_ )
snake_case_ : Optional[int] = inputs.input_features
snake_case_ : List[Any] = inputs.attention_mask
snake_case_ : Any = [np.sum(lowercase_ ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def _snake_case ( self : List[Any] ):
snake_case_ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
snake_case_ : Union[str, Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
snake_case_ : List[Any] = ['''longest''', '''max_length''', '''do_not_pad''']
snake_case_ : Dict = [None, 16, None]
for max_length, padding in zip(lowercase_ , lowercase_ ):
snake_case_ : Dict = feature_extractor(
lowercase_ , max_length=lowercase_ , padding=lowercase_ , return_tensors='''np''' , return_attention_mask=lowercase_ )
snake_case_ : Any = inputs.input_features
snake_case_ : Optional[Any] = inputs.attention_mask
snake_case_ : List[str] = [np.sum(lowercase_ ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def _snake_case ( self : Optional[Any] ):
snake_case_ : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
snake_case_ : Any = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
snake_case_ : Dict = feature_extractor(
lowercase_ , padding='''max_length''' , max_length=4 , truncation=lowercase_ , return_tensors='''np''' , return_attention_mask=lowercase_ , )
snake_case_ : int = inputs.input_features
snake_case_ : Any = inputs.attention_mask
snake_case_ : List[Any] = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def _snake_case ( self : int ):
snake_case_ : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
snake_case_ : Optional[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
snake_case_ : List[str] = feature_extractor(
lowercase_ , padding='''longest''' , max_length=4 , truncation=lowercase_ , return_tensors='''np''' , return_attention_mask=lowercase_ , )
snake_case_ : Union[str, Any] = inputs.input_features
snake_case_ : Any = inputs.attention_mask
snake_case_ : Optional[int] = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 4, 24) )
snake_case_ : Union[str, Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
snake_case_ : str = feature_extractor(
lowercase_ , padding='''longest''' , max_length=16 , truncation=lowercase_ , return_tensors='''np''' , return_attention_mask=lowercase_ , )
snake_case_ : Optional[int] = inputs.input_features
snake_case_ : Optional[int] = inputs.attention_mask
snake_case_ : Union[str, Any] = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 6, 24) )
def _snake_case ( self : Tuple ):
import torch
snake_case_ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
snake_case_ : Union[str, Any] = np.random.rand(100 , 32 ).astype(np.floataa )
snake_case_ : Optional[Any] = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
snake_case_ : str = feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''np''' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
snake_case_ : List[str] = feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''pt''' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def _snake_case ( self : List[str] , lowercase_ : List[str] ):
from datasets import load_dataset
snake_case_ : str = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
snake_case_ : int = ds.sort('''id''' ).select(range(lowercase_ ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def _snake_case ( self : str ):
# fmt: off
snake_case_ : List[Any] = np.array([
-1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
-1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
-1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
] )
# fmt: on
snake_case_ : Tuple = self._load_datasamples(1 )
snake_case_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
snake_case_ : Tuple = feature_extractor(lowercase_ , return_tensors='''pt''' ).input_features
self.assertEquals(input_features.shape , (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30] , lowercase_ , atol=1E-4 ) )
| 123 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
@require_tf
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __lowercase ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
_lowercase : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' )
_lowercase : str = AutoTokenizer.from_pretrained('''google/mt5-small''' )
_lowercase : Union[str, Any] = tokenizer('''Hello there''' , return_tensors='''tf''' ).input_ids
_lowercase : Any = tokenizer('''Hi I am''' , return_tensors='''tf''' ).input_ids
_lowercase : Optional[int] = model(UpperCamelCase_ , labels=UpperCamelCase_ ).loss
_lowercase : Optional[Any] = -tf.math.reduce_mean(UpperCamelCase_ ).numpy()
_lowercase : List[str] = -21.228_168
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2e-4 )
| 704 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCamelCase__ = 3
def _SCREAMING_SNAKE_CASE( snake_case_ : int ) ->int:
'''simple docstring'''
print('''Generating primitive root of p''' )
while True:
_lowercase : Optional[int] = random.randrange(3 , snake_case_ )
if pow(snake_case_ , 2 , snake_case_ ) == 1:
continue
if pow(snake_case_ , snake_case_ , snake_case_ ) == 1:
continue
return g
def _SCREAMING_SNAKE_CASE( snake_case_ : int ) ->tuple[tuple[int, int, int, int], tuple[int, int]]:
'''simple docstring'''
print('''Generating prime p...''' )
_lowercase : str = rabin_miller.generate_large_prime(snake_case_ ) # select large prime number.
_lowercase : Union[str, Any] = primitive_root(snake_case_ ) # one primitive root on modulo p.
_lowercase : Optional[int] = random.randrange(3 , snake_case_ ) # private_key -> have to be greater than 2 for safety.
_lowercase : int = cryptomath.find_mod_inverse(pow(snake_case_ , snake_case_ , snake_case_ ) , snake_case_ )
_lowercase : Any = (key_size, e_a, e_a, p)
_lowercase : str = (key_size, d)
return public_key, private_key
def _SCREAMING_SNAKE_CASE( snake_case_ : str , snake_case_ : int ) ->None:
'''simple docstring'''
if os.path.exists(F"{name}_pubkey.txt" ) or os.path.exists(F"{name}_privkey.txt" ):
print('''\nWARNING:''' )
print(
F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
'''Use a different name or delete these files and re-run this program.''' )
sys.exit()
_lowercase , _lowercase : Dict = generate_key(snake_case_ )
print(F"\nWriting public key to file {name}_pubkey.txt..." )
with open(F"{name}_pubkey.txt" , '''w''' ) as fo:
fo.write(F"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}" )
print(F"Writing private key to file {name}_privkey.txt..." )
with open(F"{name}_privkey.txt" , '''w''' ) as fo:
fo.write(F"{private_key[0]},{private_key[1]}" )
def _SCREAMING_SNAKE_CASE( ) ->None:
'''simple docstring'''
print('''Making key files...''' )
make_key_files('''elgamal''' , 20_48 )
print('''Key files generation successful''' )
if __name__ == "__main__":
main()
| 411 | 0 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCamelCase__ = HfApi()
UpperCamelCase__ = {}
# fmt: off
UpperCamelCase__ = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
UpperCamelCase__ = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
UpperCamelCase__ = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
UpperCamelCase__ = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
UpperCamelCase__ = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
UpperCamelCase__ = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
UpperCamelCase__ = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
UpperCamelCase__ = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
UpperCamelCase__ = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
UpperCamelCase__ = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
UpperCamelCase__ = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
UpperCamelCase__ = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
UpperCamelCase__ = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
UpperCamelCase__ = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
UpperCamelCase__ = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
UpperCamelCase__ = api.list_models(filter='diffusers')
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
UpperCamelCase__ = '/home/patrick/google_checkpoints/' + mod.modelId.split('/')[-1]
print(F"""Started running {mod.modelId}!!!""")
if mod.modelId.startswith('CompVis'):
UpperCamelCase__ = UNetaDModel.from_pretrained(local_checkpoint, subfolder='unet')
else:
UpperCamelCase__ = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
UpperCamelCase__ = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
UpperCamelCase__ = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
UpperCamelCase__ = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results['_'.join('_'.join(mod.modelId.split('/')).split('-'))], atol=1e-3
)
print(F"""{mod.modelId} has passed successfully!!!""") | 322 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class UpperCAmelCase__ ( A_ ):
'''simple docstring'''
def __init__( self : int , *UpperCamelCase : Optional[Any] , **UpperCamelCase : int ):
"""simple docstring"""
warnings.warn(
'''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use CLIPImageProcessor instead.''' , UpperCamelCase , )
super().__init__(*UpperCamelCase , **UpperCamelCase ) | 322 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/config.json''',
'''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/config.json'''
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class _SCREAMING_SNAKE_CASE( _SCREAMING_SNAKE_CASE ):
A_ : Union[str, Any] = 'fnet'
def __init__( self : Dict , UpperCamelCase_ : str=3_20_00 , UpperCamelCase_ : Tuple=7_68 , UpperCamelCase_ : int=12 , UpperCamelCase_ : Union[str, Any]=30_72 , UpperCamelCase_ : List[Any]="gelu_new" , UpperCamelCase_ : List[str]=0.1 , UpperCamelCase_ : str=5_12 , UpperCamelCase_ : Union[str, Any]=4 , UpperCamelCase_ : Any=0.02 , UpperCamelCase_ : List[str]=1e-12 , UpperCamelCase_ : Union[str, Any]=False , UpperCamelCase_ : Any=5_12 , UpperCamelCase_ : int=3 , UpperCamelCase_ : str=1 , UpperCamelCase_ : List[str]=2 , **UpperCamelCase_ : Optional[int] , ) -> Optional[Any]:
super().__init__(pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :int = vocab_size
SCREAMING_SNAKE_CASE__ :str = max_position_embeddings
SCREAMING_SNAKE_CASE__ :Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ :List[str] = num_hidden_layers
SCREAMING_SNAKE_CASE__ :Optional[int] = intermediate_size
SCREAMING_SNAKE_CASE__ :Tuple = hidden_act
SCREAMING_SNAKE_CASE__ :Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ :Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE__ :str = type_vocab_size
SCREAMING_SNAKE_CASE__ :Dict = layer_norm_eps
SCREAMING_SNAKE_CASE__ :Dict = use_tpu_fourier_optimizations
SCREAMING_SNAKE_CASE__ :Any = tpu_short_seq_length
| 702 | '''simple docstring'''
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
UpperCamelCase_ = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE( _SCREAMING_SNAKE_CASE ):
def __init__( self : Any , UpperCamelCase_ : Union[List[ControlNetModel], Tuple[ControlNetModel]] ) -> Optional[Any]:
super().__init__()
SCREAMING_SNAKE_CASE__ :Any = nn.ModuleList(UpperCamelCase_ )
def __lowerCamelCase ( self : Union[str, Any] , UpperCamelCase_ : torch.FloatTensor , UpperCamelCase_ : Union[torch.Tensor, float, int] , UpperCamelCase_ : torch.Tensor , UpperCamelCase_ : List[torch.tensor] , UpperCamelCase_ : List[float] , UpperCamelCase_ : Optional[torch.Tensor] = None , UpperCamelCase_ : Optional[torch.Tensor] = None , UpperCamelCase_ : Optional[torch.Tensor] = None , UpperCamelCase_ : Optional[Dict[str, Any]] = None , UpperCamelCase_ : bool = False , UpperCamelCase_ : bool = True , ) -> Union[ControlNetOutput, Tuple]:
for i, (image, scale, controlnet) in enumerate(zip(UpperCamelCase_ , UpperCamelCase_ , self.nets ) ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ :Any = controlnet(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , )
# merge samples
if i == 0:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ :Dict = down_samples, mid_sample
else:
SCREAMING_SNAKE_CASE__ :int = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(UpperCamelCase_ , UpperCamelCase_ )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def __lowerCamelCase ( self : List[str] , UpperCamelCase_ : Union[str, os.PathLike] , UpperCamelCase_ : bool = True , UpperCamelCase_ : Callable = None , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[str] = None , ) -> List[Any]:
SCREAMING_SNAKE_CASE__ :Any = 0
SCREAMING_SNAKE_CASE__ :List[str] = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
UpperCamelCase_ , is_main_process=UpperCamelCase_ , save_function=UpperCamelCase_ , safe_serialization=UpperCamelCase_ , variant=UpperCamelCase_ , )
idx += 1
SCREAMING_SNAKE_CASE__ :str = model_path_to_save + f'''_{idx}'''
@classmethod
def __lowerCamelCase ( cls : str , UpperCamelCase_ : Optional[Union[str, os.PathLike]] , **UpperCamelCase_ : Dict ) -> Dict:
SCREAMING_SNAKE_CASE__ :Optional[int] = 0
SCREAMING_SNAKE_CASE__ :Tuple = []
# 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`, ...
SCREAMING_SNAKE_CASE__ :Dict = pretrained_model_path
while os.path.isdir(UpperCamelCase_ ):
SCREAMING_SNAKE_CASE__ :Optional[Any] = ControlNetModel.from_pretrained(UpperCamelCase_ , **UpperCamelCase_ )
controlnets.append(UpperCamelCase_ )
idx += 1
SCREAMING_SNAKE_CASE__ :List[Any] = pretrained_model_path + f'''_{idx}'''
logger.info(f'''{len(UpperCamelCase_ )} controlnets loaded from {pretrained_model_path}.''' )
if len(UpperCamelCase_ ) == 0:
raise ValueError(
f'''No ControlNets found under {os.path.dirname(UpperCamelCase_ )}. Expected at least {pretrained_model_path + "_0"}.''' )
return cls(UpperCamelCase_ )
| 320 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
lowercase__ = logging.get_logger(__name__)
def _snake_case ( lowercase__ ):
if isinstance(lowercase__ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowercase__ , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowercase__ ):
return [[videos]]
raise ValueError(f'''Could not make batched video from {videos}''' )
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = ["""pixel_values"""]
def __init__( self , lowercase = True , lowercase = None , lowercase = PILImageResampling.BILINEAR , lowercase = True , lowercase = None , lowercase = True , lowercase = 1 / 255 , lowercase = True , lowercase = True , lowercase = None , lowercase = None , **lowercase , ):
super().__init__(**lowercase )
_lowerCamelCase : Union[str, Any] = size if size is not None else {'shortest_edge': 256}
_lowerCamelCase : str = get_size_dict(lowercase , default_to_square=lowercase )
_lowerCamelCase : Any = crop_size if crop_size is not None else {'height': 224, 'width': 224}
_lowerCamelCase : Dict = get_size_dict(lowercase , param_name='crop_size' )
_lowerCamelCase : int = do_resize
_lowerCamelCase : str = size
_lowerCamelCase : Optional[Any] = do_center_crop
_lowerCamelCase : List[Any] = crop_size
_lowerCamelCase : str = resample
_lowerCamelCase : str = do_rescale
_lowerCamelCase : Optional[int] = rescale_factor
_lowerCamelCase : Optional[int] = offset
_lowerCamelCase : Optional[Any] = do_normalize
_lowerCamelCase : Union[str, Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowerCamelCase : str = image_std if image_std is not None else IMAGENET_STANDARD_STD
def A_ ( self , lowercase , lowercase , lowercase = PILImageResampling.BILINEAR , lowercase = None , **lowercase , ):
_lowerCamelCase : List[str] = get_size_dict(lowercase , default_to_square=lowercase )
if "shortest_edge" in size:
_lowerCamelCase : Union[str, Any] = get_resize_output_image_size(lowercase , size['shortest_edge'] , default_to_square=lowercase )
elif "height" in size and "width" in size:
_lowerCamelCase : Union[str, Any] = (size['height'], size['width'])
else:
raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
return resize(lowercase , size=lowercase , resample=lowercase , data_format=lowercase , **lowercase )
def A_ ( self , lowercase , lowercase , lowercase = None , **lowercase , ):
_lowerCamelCase : Dict = get_size_dict(lowercase )
if "height" not in size or "width" not in size:
raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
return center_crop(lowercase , size=(size['height'], size['width']) , data_format=lowercase , **lowercase )
def A_ ( self , lowercase , lowercase , lowercase = True , lowercase = None , **lowercase , ):
_lowerCamelCase : Tuple = image.astype(np.floataa )
if offset:
_lowerCamelCase : Optional[Any] = image - (scale / 2)
return rescale(lowercase , scale=lowercase , data_format=lowercase , **lowercase )
def A_ ( self , lowercase , lowercase , lowercase , lowercase = None , **lowercase , ):
return normalize(lowercase , mean=lowercase , std=lowercase , data_format=lowercase , **lowercase )
def A_ ( self , lowercase , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = ChannelDimension.FIRST , ):
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop 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('Image mean and std must be specified if do_normalize is True.' )
if offset and not do_rescale:
raise ValueError('For offset, do_rescale must also be set to True.' )
# All transformations expect numpy arrays.
_lowerCamelCase : List[Any] = to_numpy_array(lowercase )
if do_resize:
_lowerCamelCase : int = self.resize(image=lowercase , size=lowercase , resample=lowercase )
if do_center_crop:
_lowerCamelCase : List[str] = self.center_crop(lowercase , size=lowercase )
if do_rescale:
_lowerCamelCase : int = self.rescale(image=lowercase , scale=lowercase , offset=lowercase )
if do_normalize:
_lowerCamelCase : Union[str, Any] = self.normalize(image=lowercase , mean=lowercase , std=lowercase )
_lowerCamelCase : Optional[int] = to_channel_dimension_format(lowercase , lowercase )
return image
def A_ ( self , lowercase , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = ChannelDimension.FIRST , **lowercase , ):
_lowerCamelCase : Tuple = do_resize if do_resize is not None else self.do_resize
_lowerCamelCase : Any = resample if resample is not None else self.resample
_lowerCamelCase : Union[str, Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowerCamelCase : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
_lowerCamelCase : Optional[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCamelCase : List[str] = offset if offset is not None else self.offset
_lowerCamelCase : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
_lowerCamelCase : Optional[Any] = image_mean if image_mean is not None else self.image_mean
_lowerCamelCase : str = image_std if image_std is not None else self.image_std
_lowerCamelCase : str = size if size is not None else self.size
_lowerCamelCase : Dict = get_size_dict(lowercase , default_to_square=lowercase )
_lowerCamelCase : Dict = crop_size if crop_size is not None else self.crop_size
_lowerCamelCase : str = get_size_dict(lowercase , param_name='crop_size' )
if not valid_images(lowercase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
_lowerCamelCase : Optional[Any] = make_batched(lowercase )
_lowerCamelCase : List[str] = [
[
self._preprocess_image(
image=lowercase , do_resize=lowercase , size=lowercase , resample=lowercase , do_center_crop=lowercase , crop_size=lowercase , do_rescale=lowercase , rescale_factor=lowercase , offset=lowercase , do_normalize=lowercase , image_mean=lowercase , image_std=lowercase , data_format=lowercase , )
for img in video
]
for video in videos
]
_lowerCamelCase : Union[str, Any] = {'pixel_values': videos}
return BatchFeature(data=lowercase , tensor_type=lowercase ) | 630 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def A_ ( self ):
torch.manual_seed(0 )
_lowerCamelCase : Union[str, Any] = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return model
@property
def A_ ( self ):
torch.manual_seed(0 )
_lowerCamelCase : Dict = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') , cross_attention_dim=10 , )
return model
@property
def A_ ( self ):
torch.manual_seed(0 )
_lowerCamelCase : List[str] = AutoencoderKL(
sample_size=(128, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , )
_lowerCamelCase : int = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return vqvae, unet
@slow
def A_ ( self ):
_lowerCamelCase : str = 'cpu' # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : Optional[int] = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
_lowerCamelCase : List[str] = DDPMScheduler()
_lowerCamelCase : List[Any] = AudioDiffusionPipeline(vqvae=lowercase , unet=self.dummy_unet , mel=lowercase , scheduler=lowercase )
_lowerCamelCase : List[Any] = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : Union[str, Any] = torch.Generator(device=lowercase ).manual_seed(42 )
_lowerCamelCase : Union[str, Any] = pipe(generator=lowercase , steps=4 )
_lowerCamelCase : Optional[Any] = output.audios[0]
_lowerCamelCase : int = output.images[0]
_lowerCamelCase : Dict = torch.Generator(device=lowercase ).manual_seed(42 )
_lowerCamelCase : Dict = pipe(generator=lowercase , steps=4 , return_dict=lowercase )
_lowerCamelCase : List[str] = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
_lowerCamelCase : Optional[Any] = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
_lowerCamelCase : Dict = np.frombuffer(image_from_tuple.tobytes() , dtype='uint8' )[:10]
_lowerCamelCase : List[str] = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
_lowerCamelCase : List[Any] = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] , )
_lowerCamelCase : Dict = DDIMScheduler()
_lowerCamelCase : Tuple = self.dummy_vqvae_and_unet
_lowerCamelCase : List[str] = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=lowercase , scheduler=lowercase )
_lowerCamelCase : Tuple = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
np.random.seed(0 )
_lowerCamelCase : Any = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
_lowerCamelCase : Optional[int] = torch.Generator(device=lowercase ).manual_seed(42 )
_lowerCamelCase : Dict = pipe(raw_audio=lowercase , generator=lowercase , start_step=5 , steps=10 )
_lowerCamelCase : str = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
_lowerCamelCase : Dict = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
_lowerCamelCase : Union[str, Any] = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
_lowerCamelCase : Dict = self.dummy_unet_condition
_lowerCamelCase : Optional[int] = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=lowercase , mel=lowercase , scheduler=lowercase )
_lowerCamelCase : Dict = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
np.random.seed(0 )
_lowerCamelCase : Optional[int] = torch.rand((1, 1, 10) )
_lowerCamelCase : Optional[Any] = pipe(generator=lowercase , encoding=lowercase )
_lowerCamelCase : Dict = output.images[0]
_lowerCamelCase : Tuple = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
_lowerCamelCase : List[str] = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self ):
_lowerCamelCase : Optional[Any] = torch_device
_lowerCamelCase : Dict = DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256' )
_lowerCamelCase : List[str] = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : int = torch.Generator(device=lowercase ).manual_seed(42 )
_lowerCamelCase : Tuple = pipe(generator=lowercase )
_lowerCamelCase : Dict = output.audios[0]
_lowerCamelCase : Dict = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
_lowerCamelCase : Optional[int] = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
_lowerCamelCase : int = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0 | 630 | 1 |
def snake_case_ ( lowercase__ : int ):
'''simple docstring'''
_lowerCAmelCase =[1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =0, 0, 0
_lowerCAmelCase =ugly_nums[ia] * 2
_lowerCAmelCase =ugly_nums[ia] * 3
_lowerCAmelCase =ugly_nums[ia] * 5
for _ in range(1 , lowercase__ ):
_lowerCAmelCase =min(lowercase__ , lowercase__ , lowercase__ )
ugly_nums.append(lowercase__ )
if next_num == next_a:
ia += 1
_lowerCAmelCase =ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
_lowerCAmelCase =ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
_lowerCAmelCase =ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(F'{ugly_numbers(200) = }')
| 712 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
# TODO Update this
__SCREAMING_SNAKE_CASE : int = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __lowerCamelCase ( lowerCamelCase_ ):
"""simple docstring"""
a_: Any = """esm"""
def __init__( self : Dict , lowerCamelCase_ : Any=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : Dict=None , lowerCamelCase_ : Tuple=768 , lowerCamelCase_ : List[str]=12 , lowerCamelCase_ : List[Any]=12 , lowerCamelCase_ : Optional[Any]=3072 , lowerCamelCase_ : str=0.1 , lowerCamelCase_ : int=0.1 , lowerCamelCase_ : List[Any]=1026 , lowerCamelCase_ : List[str]=0.02 , lowerCamelCase_ : str=1e-12 , lowerCamelCase_ : int="absolute" , lowerCamelCase_ : Dict=True , lowerCamelCase_ : Optional[int]=None , lowerCamelCase_ : Any=False , lowerCamelCase_ : Dict=False , lowerCamelCase_ : Any=None , lowerCamelCase_ : Union[str, Any]=None , **lowerCamelCase_ : Union[str, Any] , ):
super().__init__(pad_token_id=lowerCamelCase_ , mask_token_id=lowerCamelCase_ , **lowerCamelCase_ )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =use_cache
_lowerCAmelCase =emb_layer_norm_before
_lowerCAmelCase =token_dropout
_lowerCAmelCase =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("""No esmfold_config supplied for folding model, using default values.""" )
_lowerCAmelCase =EsmFoldConfig()
elif isinstance(lowerCamelCase_ , lowerCamelCase_ ):
_lowerCAmelCase =EsmFoldConfig(**lowerCamelCase_ )
_lowerCAmelCase =esmfold_config
if vocab_list is None:
logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!""" )
_lowerCAmelCase =get_default_vocab_list()
else:
_lowerCAmelCase =vocab_list
else:
_lowerCAmelCase =None
_lowerCAmelCase =None
if self.esmfold_config is not None and getattr(self.esmfold_config , """use_esm_attn_map""" , lowerCamelCase_ ):
raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!""" )
def lowerCAmelCase__ ( self : Tuple ):
_lowerCAmelCase =super().to_dict()
if isinstance(self.esmfold_config , lowerCamelCase_ ):
_lowerCAmelCase =self.esmfold_config.to_dict()
return output
@dataclass
class __lowerCamelCase :
"""simple docstring"""
a_: str = None
a_: bool = True
a_: bool = False
a_: bool = False
a_: bool = False
a_: float = 0
a_: bool = True
a_: bool = False
a_: int = 1_28
a_: "TrunkConfig" = None
def lowerCAmelCase__ ( self : str ):
if self.trunk is None:
_lowerCAmelCase =TrunkConfig()
elif isinstance(self.trunk , lowerCamelCase_ ):
_lowerCAmelCase =TrunkConfig(**self.trunk )
def lowerCAmelCase__ ( self : str ):
_lowerCAmelCase =asdict(self )
_lowerCAmelCase =self.trunk.to_dict()
return output
@dataclass
class __lowerCamelCase :
"""simple docstring"""
a_: int = 48
a_: int = 10_24
a_: int = 1_28
a_: int = 32
a_: int = 32
a_: int = 32
a_: float = 0
a_: float = 0
a_: bool = False
a_: int = 4
a_: Optional[int] = 1_28
a_: "StructureModuleConfig" = None
def lowerCAmelCase__ ( self : Optional[Any] ):
if self.structure_module is None:
_lowerCAmelCase =StructureModuleConfig()
elif isinstance(self.structure_module , lowerCamelCase_ ):
_lowerCAmelCase =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F"`max_recycles` should be positive, got {self.max_recycles}." )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"""`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
F" {self.sequence_state_dim} and {self.sequence_state_dim}." )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"""`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
F" {self.pairwise_state_dim} and {self.pairwise_state_dim}." )
_lowerCAmelCase =self.sequence_state_dim // self.sequence_head_width
_lowerCAmelCase =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"""`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
F" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}." )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"""`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
F" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}." )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}." )
if self.dropout >= 0.4:
raise ValueError(F"`dropout` should not be greater than 0.4, got {self.dropout}." )
def lowerCAmelCase__ ( self : Any ):
_lowerCAmelCase =asdict(self )
_lowerCAmelCase =self.structure_module.to_dict()
return output
@dataclass
class __lowerCamelCase :
"""simple docstring"""
a_: int = 3_84
a_: int = 1_28
a_: int = 16
a_: int = 1_28
a_: int = 12
a_: int = 4
a_: int = 8
a_: float = 0.1
a_: int = 8
a_: int = 1
a_: int = 2
a_: int = 7
a_: int = 10
a_: float = 1e-8
a_: float = 1e5
def lowerCAmelCase__ ( self : int ):
return asdict(self )
def snake_case_ ( ):
'''simple docstring'''
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 149 | 0 |
import argparse
import struct
import unittest
class snake_case_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _UpperCamelCase : bytes ) ->None:
snake_case_ = data
# Initialize hash values
snake_case_ = [
0x6_a_0_9_e_6_6_7,
0xb_b_6_7_a_e_8_5,
0x3_c_6_e_f_3_7_2,
0xa_5_4_f_f_5_3_a,
0x5_1_0_e_5_2_7_f,
0x9_b_0_5_6_8_8_c,
0x1_f_8_3_d_9_a_b,
0x5_b_e_0_c_d_1_9,
]
# Initialize round constants
snake_case_ = [
0x4_2_8_a_2_f_9_8,
0x7_1_3_7_4_4_9_1,
0xb_5_c_0_f_b_c_f,
0xe_9_b_5_d_b_a_5,
0x3_9_5_6_c_2_5_b,
0x5_9_f_1_1_1_f_1,
0x9_2_3_f_8_2_a_4,
0xa_b_1_c_5_e_d_5,
0xd_8_0_7_a_a_9_8,
0x1_2_8_3_5_b_0_1,
0x2_4_3_1_8_5_b_e,
0x5_5_0_c_7_d_c_3,
0x7_2_b_e_5_d_7_4,
0x8_0_d_e_b_1_f_e,
0x9_b_d_c_0_6_a_7,
0xc_1_9_b_f_1_7_4,
0xe_4_9_b_6_9_c_1,
0xe_f_b_e_4_7_8_6,
0x0_f_c_1_9_d_c_6,
0x2_4_0_c_a_1_c_c,
0x2_d_e_9_2_c_6_f,
0x4_a_7_4_8_4_a_a,
0x5_c_b_0_a_9_d_c,
0x7_6_f_9_8_8_d_a,
0x9_8_3_e_5_1_5_2,
0xa_8_3_1_c_6_6_d,
0xb_0_0_3_2_7_c_8,
0xb_f_5_9_7_f_c_7,
0xc_6_e_0_0_b_f_3,
0xd_5_a_7_9_1_4_7,
0x0_6_c_a_6_3_5_1,
0x1_4_2_9_2_9_6_7,
0x2_7_b_7_0_a_8_5,
0x2_e_1_b_2_1_3_8,
0x4_d_2_c_6_d_f_c,
0x5_3_3_8_0_d_1_3,
0x6_5_0_a_7_3_5_4,
0x7_6_6_a_0_a_b_b,
0x8_1_c_2_c_9_2_e,
0x9_2_7_2_2_c_8_5,
0xa_2_b_f_e_8_a_1,
0xa_8_1_a_6_6_4_b,
0xc_2_4_b_8_b_7_0,
0xc_7_6_c_5_1_a_3,
0xd_1_9_2_e_8_1_9,
0xd_6_9_9_0_6_2_4,
0xf_4_0_e_3_5_8_5,
0x1_0_6_a_a_0_7_0,
0x1_9_a_4_c_1_1_6,
0x1_e_3_7_6_c_0_8,
0x2_7_4_8_7_7_4_c,
0x3_4_b_0_b_c_b_5,
0x3_9_1_c_0_c_b_3,
0x4_e_d_8_a_a_4_a,
0x5_b_9_c_c_a_4_f,
0x6_8_2_e_6_f_f_3,
0x7_4_8_f_8_2_e_e,
0x7_8_a_5_6_3_6_f,
0x8_4_c_8_7_8_1_4,
0x8_c_c_7_0_2_0_8,
0x9_0_b_e_f_f_f_a,
0xa_4_5_0_6_c_e_b,
0xb_e_f_9_a_3_f_7,
0xc_6_7_1_7_8_f_2,
]
snake_case_ = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def snake_case__( _UpperCamelCase : bytes ) ->bytes:
snake_case_ = B'''\x80''' + (B'''\x00''' * (6_3 - (len(_UpperCamelCase ) + 8) % 6_4))
snake_case_ = struct.pack('''>Q''' , (len(_UpperCamelCase ) * 8) )
return data + padding + big_endian_integer
def snake_case__( self : Optional[Any] ) ->None:
# Convert into blocks of 64 bytes
snake_case_ = [
self.preprocessed_data[x : x + 6_4]
for x in range(0 , len(self.preprocessed_data ) , 6_4 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
snake_case_ = list(struct.unpack('''>16L''' , _UpperCamelCase ) )
# add 48 0-ed integers
words += [0] * 4_8
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ = self.hashes
for index in range(0 , 6_4 ):
if index > 1_5:
# modify the zero-ed indexes at the end of the array
snake_case_ = (
self.ror(words[index - 1_5] , 7 )
^ self.ror(words[index - 1_5] , 1_8 )
^ (words[index - 1_5] >> 3)
)
snake_case_ = (
self.ror(words[index - 2] , 1_7 )
^ self.ror(words[index - 2] , 1_9 )
^ (words[index - 2] >> 1_0)
)
snake_case_ = (
words[index - 1_6] + sa + words[index - 7] + sa
) % 0x1_0_0_0_0_0_0_0_0
# Compression
snake_case_ = self.ror(_UpperCamelCase , 6 ) ^ self.ror(_UpperCamelCase , 1_1 ) ^ self.ror(_UpperCamelCase , 2_5 )
snake_case_ = (e & f) ^ ((~e & 0xf_f_f_f_f_f_f_f) & g)
snake_case_ = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_0_0_0_0_0_0_0_0
snake_case_ = self.ror(_UpperCamelCase , 2 ) ^ self.ror(_UpperCamelCase , 1_3 ) ^ self.ror(_UpperCamelCase , 2_2 )
snake_case_ = (a & b) ^ (a & c) ^ (b & c)
snake_case_ = (sa + maj) % 0x1_0_0_0_0_0_0_0_0
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ = (
g,
f,
e,
((d + tempa) % 0x1_0_0_0_0_0_0_0_0),
c,
b,
a,
((tempa + tempa) % 0x1_0_0_0_0_0_0_0_0),
)
snake_case_ = [a, b, c, d, e, f, g, h]
# Modify final values
snake_case_ = [
((element + mutated_hash_values[index]) % 0x1_0_0_0_0_0_0_0_0)
for index, element in enumerate(self.hashes )
]
snake_case_ = ''''''.join([hex(_UpperCamelCase )[2:].zfill(8 ) for value in self.hashes] )
def snake_case__( self : Union[str, Any] , _UpperCamelCase : int , _UpperCamelCase : int ) ->int:
return 0xf_f_f_f_f_f_f_f & (value << (3_2 - rotations)) | (value >> rotations)
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self : Any ) ->None:
import hashlib
snake_case_ = bytes('''Test String''' , '''utf-8''' )
self.assertEqual(SHAaaa(_UpperCamelCase ).hash , hashlib.shaaaa(_UpperCamelCase ).hexdigest() )
def __SCREAMING_SNAKE_CASE ():
import doctest
doctest.testmod()
snake_case_ = argparse.ArgumentParser()
parser.add_argument(
'''-s''' , '''--string''' , dest='''input_string''' , default='''Hello World!! Welcome to Cryptography''' , help='''Hash the string''' , )
parser.add_argument(
'''-f''' , '''--file''' , dest='''input_file''' , help='''Hash contents of a file''' )
snake_case_ = parser.parse_args()
snake_case_ = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , '''rb''' ) as f:
snake_case_ = f.read()
else:
snake_case_ = bytes(SCREAMING_SNAKE_CASE__ , '''utf-8''' )
print(SHAaaa(SCREAMING_SNAKE_CASE__ ).hash )
if __name__ == "__main__":
main() | 39 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
enable_full_determinism()
class a__( snake_case__ , snake_case__ , unittest.TestCase ):
a_ : Dict = UNetaDModel
a_ : List[Any] = '''sample'''
@property
def _lowercase ( self ) -> Tuple:
snake_case__ =4
snake_case__ =3
snake_case__ =(32, 32)
snake_case__ =floats_tensor((batch_size, num_channels) + sizes ).to(_UpperCAmelCase )
snake_case__ =torch.tensor([10] ).to(_UpperCAmelCase )
return {"sample": noise, "timestep": time_step}
@property
def _lowercase ( self ) -> Optional[int]:
return (3, 32, 32)
@property
def _lowercase ( self ) -> Optional[int]:
return (3, 32, 32)
def _lowercase ( self ) -> Union[str, Any]:
snake_case__ ={
'block_out_channels': (32, 64),
'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
'attention_head_dim': 3,
'out_channels': 3,
'in_channels': 3,
'layers_per_block': 2,
'sample_size': 32,
}
snake_case__ =self.dummy_input
return init_dict, inputs_dict
class a__( snake_case__ , snake_case__ , unittest.TestCase ):
a_ : Union[str, Any] = UNetaDModel
a_ : Optional[Any] = '''sample'''
@property
def _lowercase ( self ) -> Union[str, Any]:
snake_case__ =4
snake_case__ =4
snake_case__ =(32, 32)
snake_case__ =floats_tensor((batch_size, num_channels) + sizes ).to(_UpperCAmelCase )
snake_case__ =torch.tensor([10] ).to(_UpperCAmelCase )
return {"sample": noise, "timestep": time_step}
@property
def _lowercase ( self ) -> Optional[int]:
return (4, 32, 32)
@property
def _lowercase ( self ) -> Dict:
return (4, 32, 32)
def _lowercase ( self ) -> str:
snake_case__ ={
'sample_size': 32,
'in_channels': 4,
'out_channels': 4,
'layers_per_block': 2,
'block_out_channels': (32, 64),
'attention_head_dim': 32,
'down_block_types': ('DownBlock2D', 'DownBlock2D'),
'up_block_types': ('UpBlock2D', 'UpBlock2D'),
}
snake_case__ =self.dummy_input
return init_dict, inputs_dict
def _lowercase ( self ) -> Dict:
snake_case__ , snake_case__ =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update' , output_loading_info=_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
self.assertEqual(len(loading_info['missing_keys'] ) , 0 )
model.to(_UpperCAmelCase )
snake_case__ =model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != 'cuda' , 'This test is supposed to run on GPU' )
def _lowercase ( self ) -> Optional[Any]:
snake_case__ , snake_case__ =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update' , output_loading_info=_UpperCAmelCase )
model.to(_UpperCAmelCase )
snake_case__ =model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != 'cuda' , 'This test is supposed to run on GPU' )
def _lowercase ( self ) -> Optional[Any]:
# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
snake_case__ , snake_case__ =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update' , output_loading_info=_UpperCAmelCase )
model_accelerate.to(_UpperCAmelCase )
model_accelerate.eval()
snake_case__ =torch.randn(
1 , model_accelerate.config.in_channels , model_accelerate.config.sample_size , model_accelerate.config.sample_size , generator=torch.manual_seed(0 ) , )
snake_case__ =noise.to(_UpperCAmelCase )
snake_case__ =torch.tensor([10] * noise.shape[0] ).to(_UpperCAmelCase )
snake_case__ =model_accelerate(_UpperCAmelCase , _UpperCAmelCase )['sample']
# two models don't need to stay in the device at the same time
del model_accelerate
torch.cuda.empty_cache()
gc.collect()
snake_case__ , snake_case__ =UNetaDModel.from_pretrained(
'fusing/unet-ldm-dummy-update' , output_loading_info=_UpperCAmelCase , low_cpu_mem_usage=_UpperCAmelCase )
model_normal_load.to(_UpperCAmelCase )
model_normal_load.eval()
snake_case__ =model_normal_load(_UpperCAmelCase , _UpperCAmelCase )['sample']
assert torch_all_close(_UpperCAmelCase , _UpperCAmelCase , rtol=1E-3 )
def _lowercase ( self ) -> Optional[Any]:
snake_case__ =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update' )
model.eval()
model.to(_UpperCAmelCase )
snake_case__ =torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
snake_case__ =noise.to(_UpperCAmelCase )
snake_case__ =torch.tensor([10] * noise.shape[0] ).to(_UpperCAmelCase )
with torch.no_grad():
snake_case__ =model(_UpperCAmelCase , _UpperCAmelCase ).sample
snake_case__ =output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
snake_case__ =torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800] )
# fmt: on
self.assertTrue(torch_all_close(_UpperCAmelCase , _UpperCAmelCase , rtol=1E-3 ) )
class a__( snake_case__ , snake_case__ , unittest.TestCase ):
a_ : List[str] = UNetaDModel
a_ : Optional[int] = '''sample'''
@property
def _lowercase ( self , _UpperCAmelCase=(32, 32) ) -> Tuple:
snake_case__ =4
snake_case__ =3
snake_case__ =floats_tensor((batch_size, num_channels) + sizes ).to(_UpperCAmelCase )
snake_case__ =torch.tensor(batch_size * [10] ).to(dtype=torch.intaa , device=_UpperCAmelCase )
return {"sample": noise, "timestep": time_step}
@property
def _lowercase ( self ) -> Union[str, Any]:
return (3, 32, 32)
@property
def _lowercase ( self ) -> Optional[Any]:
return (3, 32, 32)
def _lowercase ( self ) -> str:
snake_case__ ={
'block_out_channels': [32, 64, 64, 64],
'in_channels': 3,
'layers_per_block': 1,
'out_channels': 3,
'time_embedding_type': 'fourier',
'norm_eps': 1E-6,
'mid_block_scale_factor': math.sqrt(2.0 ),
'norm_num_groups': None,
'down_block_types': [
'SkipDownBlock2D',
'AttnSkipDownBlock2D',
'SkipDownBlock2D',
'SkipDownBlock2D',
],
'up_block_types': [
'SkipUpBlock2D',
'SkipUpBlock2D',
'AttnSkipUpBlock2D',
'SkipUpBlock2D',
],
}
snake_case__ =self.dummy_input
return init_dict, inputs_dict
@slow
def _lowercase ( self ) -> List[Any]:
snake_case__ , snake_case__ =UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256' , output_loading_info=_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
self.assertEqual(len(loading_info['missing_keys'] ) , 0 )
model.to(_UpperCAmelCase )
snake_case__ =self.dummy_input
snake_case__ =floats_tensor((4, 3) + (256, 256) ).to(_UpperCAmelCase )
snake_case__ =noise
snake_case__ =model(**_UpperCAmelCase )
assert image is not None, "Make sure output is not None"
@slow
def _lowercase ( self ) -> Union[str, Any]:
snake_case__ =UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256' )
model.to(_UpperCAmelCase )
snake_case__ =4
snake_case__ =3
snake_case__ =(256, 256)
snake_case__ =torch.ones((batch_size, num_channels) + sizes ).to(_UpperCAmelCase )
snake_case__ =torch.tensor(batch_size * [1E-4] ).to(_UpperCAmelCase )
with torch.no_grad():
snake_case__ =model(_UpperCAmelCase , _UpperCAmelCase ).sample
snake_case__ =output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
snake_case__ =torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608] )
# fmt: on
self.assertTrue(torch_all_close(_UpperCAmelCase , _UpperCAmelCase , rtol=1E-2 ) )
def _lowercase ( self ) -> List[Any]:
snake_case__ =UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update' )
model.to(_UpperCAmelCase )
snake_case__ =4
snake_case__ =3
snake_case__ =(32, 32)
snake_case__ =torch.ones((batch_size, num_channels) + sizes ).to(_UpperCAmelCase )
snake_case__ =torch.tensor(batch_size * [1E-4] ).to(_UpperCAmelCase )
with torch.no_grad():
snake_case__ =model(_UpperCAmelCase , _UpperCAmelCase ).sample
snake_case__ =output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
snake_case__ =torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256] )
# fmt: on
self.assertTrue(torch_all_close(_UpperCAmelCase , _UpperCAmelCase , rtol=1E-2 ) )
def _lowercase ( self ) -> Optional[Any]:
# not required for this model
pass
| 538 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : str = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class __UpperCamelCase (A__ ,A__ ):
lowerCamelCase__ : Optional[int] = 'focalnet'
def __init__( self : List[Any] , __UpperCAmelCase : Any=2_2_4 , __UpperCAmelCase : List[Any]=4 , __UpperCAmelCase : Union[str, Any]=3 , __UpperCAmelCase : Optional[Any]=9_6 , __UpperCAmelCase : Any=False , __UpperCAmelCase : int=[1_9_2, 3_8_4, 7_6_8, 7_6_8] , __UpperCAmelCase : Optional[Any]=[2, 2, 6, 2] , __UpperCAmelCase : Dict=[2, 2, 2, 2] , __UpperCAmelCase : Dict=[3, 3, 3, 3] , __UpperCAmelCase : Dict="gelu" , __UpperCAmelCase : List[Any]=4.0 , __UpperCAmelCase : int=0.0 , __UpperCAmelCase : Any=0.1 , __UpperCAmelCase : Dict=False , __UpperCAmelCase : Any=1e-4 , __UpperCAmelCase : List[Any]=False , __UpperCAmelCase : str=False , __UpperCAmelCase : Any=False , __UpperCAmelCase : Dict=0.02 , __UpperCAmelCase : Dict=1e-5 , __UpperCAmelCase : str=3_2 , __UpperCAmelCase : Tuple=None , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : List[Any] , ) -> List[Any]:
super().__init__(**__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = image_size
SCREAMING_SNAKE_CASE__ = patch_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = embed_dim
SCREAMING_SNAKE_CASE__ = use_conv_embed
SCREAMING_SNAKE_CASE__ = hidden_sizes
SCREAMING_SNAKE_CASE__ = depths
SCREAMING_SNAKE_CASE__ = focal_levels
SCREAMING_SNAKE_CASE__ = focal_windows
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = mlp_ratio
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = drop_path_rate
SCREAMING_SNAKE_CASE__ = use_layerscale
SCREAMING_SNAKE_CASE__ = layerscale_value
SCREAMING_SNAKE_CASE__ = use_post_layernorm
SCREAMING_SNAKE_CASE__ = use_post_layernorm_in_modulation
SCREAMING_SNAKE_CASE__ = normalize_modulator
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = layer_norm_eps
SCREAMING_SNAKE_CASE__ = encoder_stride
SCREAMING_SNAKE_CASE__ = ["""stem"""] + [F"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_aligned_output_features_output_indices(
out_features=__UpperCAmelCase , out_indices=__UpperCAmelCase , stage_names=self.stage_names )
| 719 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Tuple = "▁"
A_ : int = {
"vocab_file": "vocab.json",
"spm_file": "sentencepiece.bpe.model",
"tokenizer_config_file": "tokenizer_config.json",
}
A_ : Dict = {
"vocab_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
},
"spm_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_config_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
},
}
A_ : Dict = {
"facebook/m2m100_418M": 1_024,
}
# fmt: off
A_ : Any = {
"m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
"wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}
class lowerCamelCase (A__ ):
lowerCamelCase__ : Optional[int] = VOCAB_FILES_NAMES
lowerCamelCase__ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ : Tuple = ['input_ids', 'attention_mask']
lowerCamelCase__ : List[int] = []
lowerCamelCase__ : List[int] = []
def __init__( self : List[str] , __UpperCAmelCase : int , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Optional[Any]=None , __UpperCAmelCase : int="<s>" , __UpperCAmelCase : Optional[int]="</s>" , __UpperCAmelCase : Any="</s>" , __UpperCAmelCase : Any="<pad>" , __UpperCAmelCase : str="<unk>" , __UpperCAmelCase : Dict="m2m100" , __UpperCAmelCase : Optional[Dict[str, Any]] = None , __UpperCAmelCase : str=8 , **__UpperCAmelCase : str , ) -> None:
SCREAMING_SNAKE_CASE__ = {} if sp_model_kwargs is None else sp_model_kwargs
SCREAMING_SNAKE_CASE__ = language_codes
SCREAMING_SNAKE_CASE__ = FAIRSEQ_LANGUAGE_CODES[language_codes]
SCREAMING_SNAKE_CASE__ = {lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}
SCREAMING_SNAKE_CASE__ = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(__UpperCAmelCase )
for lang_code in fairseq_language_code
if self.get_lang_token(__UpperCAmelCase ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=__UpperCAmelCase , tgt_lang=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , language_codes=__UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=__UpperCAmelCase , **__UpperCAmelCase , )
SCREAMING_SNAKE_CASE__ = vocab_file
SCREAMING_SNAKE_CASE__ = load_json(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE__ = spm_file
SCREAMING_SNAKE_CASE__ = load_spm(__UpperCAmelCase , self.sp_model_kwargs )
SCREAMING_SNAKE_CASE__ = len(self.encoder )
SCREAMING_SNAKE_CASE__ = {
self.get_lang_token(__UpperCAmelCase ): self.encoder_size + i for i, lang_code in enumerate(__UpperCAmelCase )
}
SCREAMING_SNAKE_CASE__ = {lang_code: self.encoder_size + i for i, lang_code in enumerate(__UpperCAmelCase )}
SCREAMING_SNAKE_CASE__ = {v: k for k, v in self.lang_token_to_id.items()}
SCREAMING_SNAKE_CASE__ = src_lang if src_lang is not None else """en"""
SCREAMING_SNAKE_CASE__ = tgt_lang
SCREAMING_SNAKE_CASE__ = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
SCREAMING_SNAKE_CASE__ = num_madeup_words
@property
def SCREAMING_SNAKE_CASE ( self : int ) -> int:
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def SCREAMING_SNAKE_CASE ( self : Any ) -> str:
return self._src_lang
@src_lang.setter
def SCREAMING_SNAKE_CASE ( self : str , __UpperCAmelCase : str ) -> None:
SCREAMING_SNAKE_CASE__ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def SCREAMING_SNAKE_CASE ( self : int , __UpperCAmelCase : str ) -> List[str]:
return self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : str , __UpperCAmelCase : Tuple ) -> Tuple:
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(__UpperCAmelCase , self.encoder[self.unk_token] )
def SCREAMING_SNAKE_CASE ( self : Dict , __UpperCAmelCase : int ) -> str:
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(__UpperCAmelCase , self.unk_token )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __UpperCAmelCase : Optional[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = """"""
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(__UpperCAmelCase ) + token
SCREAMING_SNAKE_CASE__ = []
else:
current_sub_tokens.append(__UpperCAmelCase )
out_string += self.sp_model.decode(__UpperCAmelCase )
return out_string.strip()
def SCREAMING_SNAKE_CASE ( self : int , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None , __UpperCAmelCase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCAmelCase , token_ids_a=__UpperCAmelCase , already_has_special_tokens=__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = [1] * len(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(__UpperCAmelCase )) + suffix_ones
return prefix_ones + ([0] * len(__UpperCAmelCase )) + ([0] * len(__UpperCAmelCase )) + suffix_ones
def SCREAMING_SNAKE_CASE ( self : List[str] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Dict:
SCREAMING_SNAKE_CASE__ = {self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Union[str, Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ = self.__dict__.copy()
SCREAMING_SNAKE_CASE__ = None
return state
def __setstate__( self : Union[str, Any] , __UpperCAmelCase : Dict ) -> None:
SCREAMING_SNAKE_CASE__ = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE__ = {}
SCREAMING_SNAKE_CASE__ = load_spm(self.spm_file , self.sp_model_kwargs )
def SCREAMING_SNAKE_CASE ( self : Tuple , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
SCREAMING_SNAKE_CASE__ = Path(__UpperCAmelCase )
if not save_dir.is_dir():
raise OSError(F"""{save_directory} should be a directory""" )
SCREAMING_SNAKE_CASE__ = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
)
SCREAMING_SNAKE_CASE__ = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
)
save_json(self.encoder , __UpperCAmelCase )
if os.path.abspath(self.spm_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , __UpperCAmelCase )
elif not os.path.isfile(self.spm_file ):
with open(__UpperCAmelCase , """wb""" ) as fi:
SCREAMING_SNAKE_CASE__ = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (str(__UpperCAmelCase ), str(__UpperCAmelCase ))
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : str = "en" , __UpperCAmelCase : Optional[List[str]] = None , __UpperCAmelCase : str = "ro" , **__UpperCAmelCase : str , ) -> BatchEncoding:
SCREAMING_SNAKE_CASE__ = src_lang
SCREAMING_SNAKE_CASE__ = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(__UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Dict , __UpperCAmelCase : int , __UpperCAmelCase : Optional[str] , __UpperCAmelCase : Optional[str] , **__UpperCAmelCase : Tuple ) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ = src_lang
SCREAMING_SNAKE_CASE__ = self(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , **__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = self.get_lang_id(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = tgt_lang_id
return inputs
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> int:
self.set_src_lang_special_tokens(self.src_lang )
def SCREAMING_SNAKE_CASE ( self : str ) -> str:
self.set_tgt_lang_special_tokens(self.tgt_lang )
def SCREAMING_SNAKE_CASE ( self : List[Any] , __UpperCAmelCase : str ) -> None:
SCREAMING_SNAKE_CASE__ = self.get_lang_token(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = self.lang_token_to_id[lang_token]
SCREAMING_SNAKE_CASE__ = [self.cur_lang_id]
SCREAMING_SNAKE_CASE__ = [self.eos_token_id]
def SCREAMING_SNAKE_CASE ( self : Dict , __UpperCAmelCase : str ) -> None:
SCREAMING_SNAKE_CASE__ = self.get_lang_token(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = self.lang_token_to_id[lang_token]
SCREAMING_SNAKE_CASE__ = [self.cur_lang_id]
SCREAMING_SNAKE_CASE__ = [self.eos_token_id]
def SCREAMING_SNAKE_CASE ( self : Dict , __UpperCAmelCase : str ) -> str:
return self.lang_code_to_token[lang]
def SCREAMING_SNAKE_CASE ( self : int , __UpperCAmelCase : str ) -> int:
SCREAMING_SNAKE_CASE__ = self.get_lang_token(__UpperCAmelCase )
return self.lang_token_to_id[lang_token]
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = sentencepiece.SentencePieceProcessor(**snake_case__ )
spm.Load(str(snake_case__ ) )
return spm
def A ( snake_case__ ):
'''simple docstring'''
with open(snake_case__ , """r""" ) as f:
return json.load(snake_case__ )
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
with open(snake_case__ , """w""" ) as f:
json.dump(snake_case__ , snake_case__ , indent=2 )
| 616 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCAmelCase : int ={
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLIPConfig",
"ChineseCLIPOnnxConfig",
"ChineseCLIPTextConfig",
"ChineseCLIPVisionConfig",
],
"processing_chinese_clip": ["ChineseCLIPProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : str =["ChineseCLIPFeatureExtractor"]
__lowerCAmelCase : str =["ChineseCLIPImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Any =[
"CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ChineseCLIPModel",
"ChineseCLIPPreTrainedModel",
"ChineseCLIPTextModel",
"ChineseCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_chinese_clip import (
CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
ChineseCLIPConfig,
ChineseCLIPOnnxConfig,
ChineseCLIPTextConfig,
ChineseCLIPVisionConfig,
)
from .processing_chinese_clip import ChineseCLIPProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_chinese_clip import (
CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
ChineseCLIPModel,
ChineseCLIPPreTrainedModel,
ChineseCLIPTextModel,
ChineseCLIPVisionModel,
)
else:
import sys
__lowerCAmelCase : Dict =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 440 | import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_ :
__lowerCAmelCase = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__lowerCAmelCase = field(
default="""NER""" , metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""} )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__lowerCAmelCase = field(default=__lowerCAmelCase , 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=__lowerCAmelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
@dataclass
class SCREAMING_SNAKE_CASE_ :
__lowerCAmelCase = field(
metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""} )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""} , )
__lowerCAmelCase = 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."""
)
} , )
__lowerCAmelCase = field(
default=__lowerCAmelCase , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def lowercase( ) -> Optional[Any]:
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase , UpperCamelCase , UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase , UpperCamelCase , UpperCamelCase = 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.""" )
UpperCamelCase = import_module("""tasks""" )
try:
UpperCamelCase = getattr(UpperCamelCase_ , model_args.task_type )
UpperCamelCase = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , UpperCamelCase_ )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
UpperCamelCase = token_classification_task.get_labels(data_args.labels )
UpperCamelCase = dict(enumerate(UpperCamelCase_ ) )
UpperCamelCase = len(UpperCamelCase_ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCamelCase_ , idalabel=UpperCamelCase_ , labelaid={label: i for i, label in enumerate(UpperCamelCase_ )} , cache_dir=model_args.cache_dir , )
UpperCamelCase = 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 , use_fast=model_args.use_fast , )
UpperCamelCase = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=UpperCamelCase_ , cache_dir=model_args.cache_dir , )
# Get datasets
UpperCamelCase = (
TokenClassificationDataset(
token_classification_task=UpperCamelCase_ , data_dir=data_args.data_dir , tokenizer=UpperCamelCase_ , labels=UpperCamelCase_ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
UpperCamelCase = (
TokenClassificationDataset(
token_classification_task=UpperCamelCase_ , data_dir=data_args.data_dir , tokenizer=UpperCamelCase_ , labels=UpperCamelCase_ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(UpperCamelCase_ , UpperCamelCase_ ) -> Tuple[List[int], List[int]]:
UpperCamelCase = np.argmax(UpperCamelCase_ , axis=2 )
UpperCamelCase , UpperCamelCase = preds.shape
UpperCamelCase = [[] for _ in range(UpperCamelCase_ )]
UpperCamelCase = [[] for _ in range(UpperCamelCase_ )]
for i in range(UpperCamelCase_ ):
for j in range(UpperCamelCase_ ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(UpperCamelCase_ ) -> Dict:
UpperCamelCase , UpperCamelCase = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(UpperCamelCase_ , UpperCamelCase_ ),
"precision": precision_score(UpperCamelCase_ , UpperCamelCase_ ),
"recall": recall_score(UpperCamelCase_ , UpperCamelCase_ ),
"f1": fa_score(UpperCamelCase_ , UpperCamelCase_ ),
}
# Data collator
UpperCamelCase = DataCollatorWithPadding(UpperCamelCase_ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
UpperCamelCase = Trainer(
model=UpperCamelCase_ , args=UpperCamelCase_ , train_dataset=UpperCamelCase_ , eval_dataset=UpperCamelCase_ , compute_metrics=UpperCamelCase_ , data_collator=UpperCamelCase_ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
UpperCamelCase = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
UpperCamelCase = trainer.evaluate()
UpperCamelCase = os.path.join(training_args.output_dir , """eval_results.txt""" )
if trainer.is_world_process_zero():
with open(UpperCamelCase_ , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in result.items():
logger.info(""" %s = %s""" , UpperCamelCase_ , UpperCamelCase_ )
writer.write("""%s = %s\n""" % (key, value) )
results.update(UpperCamelCase_ )
# Predict
if training_args.do_predict:
UpperCamelCase = TokenClassificationDataset(
token_classification_task=UpperCamelCase_ , data_dir=data_args.data_dir , tokenizer=UpperCamelCase_ , labels=UpperCamelCase_ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
UpperCamelCase , UpperCamelCase , UpperCamelCase = trainer.predict(UpperCamelCase_ )
UpperCamelCase , UpperCamelCase = align_predictions(UpperCamelCase_ , UpperCamelCase_ )
UpperCamelCase = os.path.join(training_args.output_dir , """test_results.txt""" )
if trainer.is_world_process_zero():
with open(UpperCamelCase_ , """w""" ) as writer:
for key, value in metrics.items():
logger.info(""" %s = %s""" , UpperCamelCase_ , UpperCamelCase_ )
writer.write("""%s = %s\n""" % (key, value) )
# Save predictions
UpperCamelCase = os.path.join(training_args.output_dir , """test_predictions.txt""" )
if trainer.is_world_process_zero():
with open(UpperCamelCase_ , """w""" ) as writer:
with open(os.path.join(data_args.data_dir , """test.txt""" ) , """r""" ) as f:
token_classification_task.write_predictions_to_file(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
return results
def lowercase( UpperCamelCase_ ) -> Dict:
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 537 | 0 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( A__ ):
'''simple docstring'''
def __init__( self :List[Any] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] ) -> Tuple:
super().__init__()
self.register_modules(unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ )
@torch.no_grad()
def __call__( self :List[str] , lowerCAmelCase__ :int = 1 , lowerCAmelCase__ :Optional[torch.Generator] = None , lowerCAmelCase__ :int = 50 , lowerCAmelCase__ :Optional[str] = "pil" , lowerCAmelCase__ :bool = True , **lowerCAmelCase__ :Any , ) -> Union[ImagePipelineOutput, Tuple]:
__SCREAMING_SNAKE_CASE : int = torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=lowerCAmelCase__ , )
__SCREAMING_SNAKE_CASE : int = image.to(self.device )
# set step values
self.scheduler.set_timesteps(lowerCAmelCase__ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
__SCREAMING_SNAKE_CASE : Any = self.unet(lowerCAmelCase__ , lowerCAmelCase__ ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
__SCREAMING_SNAKE_CASE : int = (image / 2 + 0.5).clamp(0 , 1 )
__SCREAMING_SNAKE_CASE : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__SCREAMING_SNAKE_CASE : Optional[int] = self.numpy_to_pil(lowerCAmelCase__ )
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=lowerCAmelCase__ ), "This is a local test"
| 260 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = (EulerDiscreteScheduler,)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 10
def __magic_name__( self :Dict , **lowerCAmelCase__ :Any ) -> int:
__SCREAMING_SNAKE_CASE : List[str] = {
'''num_train_timesteps''': 1_100,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
}
config.update(**lowerCAmelCase__ )
return config
def __magic_name__( self :str ) -> Optional[Any]:
for timesteps in [10, 50, 100, 1_000]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def __magic_name__( self :str ) -> List[str]:
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase__ , beta_end=lowerCAmelCase__ )
def __magic_name__( self :Dict ) -> Any:
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def __magic_name__( self :List[Any] ) -> List[str]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def __magic_name__( self :Dict ) -> int:
__SCREAMING_SNAKE_CASE : Dict = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE : List[str] = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE : Dict = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : List[Any] = self.dummy_model()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
__SCREAMING_SNAKE_CASE : Any = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
__SCREAMING_SNAKE_CASE : List[Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = model(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = output.prev_sample
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1E-2
assert abs(result_mean.item() - 0.0131 ) < 1E-3
def __magic_name__( self :Union[str, Any] ) -> int:
__SCREAMING_SNAKE_CASE : Tuple = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE : int = self.get_scheduler_config(prediction_type='''v_prediction''' )
__SCREAMING_SNAKE_CASE : Optional[int] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
__SCREAMING_SNAKE_CASE : Dict = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : Optional[Any] = self.dummy_model()
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
__SCREAMING_SNAKE_CASE : Dict = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
__SCREAMING_SNAKE_CASE : str = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[Any] = model(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = output.prev_sample
__SCREAMING_SNAKE_CASE : Tuple = torch.sum(torch.abs(lowerCAmelCase__ ) )
__SCREAMING_SNAKE_CASE : Tuple = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 0.0002 ) < 1E-2
assert abs(result_mean.item() - 2.2_6_7_6E-0_6 ) < 1E-3
def __magic_name__( self :Optional[int] ) -> List[str]:
__SCREAMING_SNAKE_CASE : Any = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE : List[str] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : int = self.dummy_model()
__SCREAMING_SNAKE_CASE : int = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
__SCREAMING_SNAKE_CASE : Optional[Any] = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
__SCREAMING_SNAKE_CASE : Optional[Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = model(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = output.prev_sample
__SCREAMING_SNAKE_CASE : Dict = torch.sum(torch.abs(lowerCAmelCase__ ) )
__SCREAMING_SNAKE_CASE : int = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1E-2
assert abs(result_mean.item() - 0.0131 ) < 1E-3
def __magic_name__( self :List[Any] ) -> int:
__SCREAMING_SNAKE_CASE : str = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE : Tuple = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE : Any = scheduler_class(**lowerCAmelCase__ , use_karras_sigmas=lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : Optional[int] = self.dummy_model()
__SCREAMING_SNAKE_CASE : List[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
__SCREAMING_SNAKE_CASE : List[str] = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
__SCREAMING_SNAKE_CASE : Any = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : int = model(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = output.prev_sample
__SCREAMING_SNAKE_CASE : Any = torch.sum(torch.abs(lowerCAmelCase__ ) )
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 124.52_2994_9951_1719 ) < 1E-2
assert abs(result_mean.item() - 0.1_6213_9326_3339_9963 ) < 1E-3
| 260 | 1 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
_a: Dict = get_logger(__name__)
_a: int = r"""\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary.\n\n Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n search or log softmax for each vocabulary token when using beam search\n kwargs (`Dict[str, Any]`, *optional*):\n Additional logits processor specific kwargs.\n\n Return:\n `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"""
class __UpperCamelCase :
@add_start_docstrings(__lowerCAmelCase )
def __call__( self : List[Any] , lowerCAmelCase : str , lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
raise NotImplementedError(
F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )
class __UpperCamelCase :
@add_start_docstrings(__lowerCAmelCase )
def __call__( self : Dict , lowerCAmelCase : Any , lowerCAmelCase : int ):
'''simple docstring'''
raise NotImplementedError(
F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )
class __UpperCamelCase ( SCREAMING_SNAKE_CASE__ ):
@add_start_docstrings(__lowerCAmelCase )
def __call__( self : List[str] , lowerCAmelCase : List[Any] , lowerCAmelCase : Any , lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
for processor in self:
UpperCAmelCase_ = inspect.signature(processor.__call__ ).parameters
if len(__lowerCAmelCase ) > 3:
if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
raise ValueError(
F"Make sure that all the required parameters: {list(function_args.keys() )} for "
F"{processor.__class__} are passed to the logits processor." )
UpperCAmelCase_ = processor(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
else:
UpperCAmelCase_ = processor(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return scores
class __UpperCamelCase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : Dict , lowerCAmelCase : Tuple ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not (temperature > 0):
raise ValueError(F"`temperature` has to be a strictly positive float, but is {temperature}" )
UpperCAmelCase_ = temperature
def __call__( self : int , lowerCAmelCase : Any , lowerCAmelCase : List[Any] , lowerCAmelCase : Any ):
'''simple docstring'''
UpperCAmelCase_ = scores / self.temperature
return scores
class __UpperCamelCase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : int , lowerCAmelCase : List[Any] , lowerCAmelCase : Union[str, Any] = -float("Inf" ) , lowerCAmelCase : List[Any] = 1 ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or (top_p < 0 or top_p > 1.0):
raise ValueError(F"`top_p` has to be a float > 0 and < 1, but is {top_p}" )
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or (min_tokens_to_keep < 1):
raise ValueError(F"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}" )
UpperCAmelCase_ = top_p
UpperCAmelCase_ = filter_value
UpperCAmelCase_ = min_tokens_to_keep
def __call__( self : int , lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : Dict ):
'''simple docstring'''
UpperCAmelCase_ = lax.top_k(__lowerCAmelCase , scores.shape[-1] )
UpperCAmelCase_ = jnp.full_like(__lowerCAmelCase , self.filter_value )
UpperCAmelCase_ = jax.nn.softmax(__lowerCAmelCase , axis=-1 ).cumsum(axis=-1 )
UpperCAmelCase_ = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
UpperCAmelCase_ = jnp.roll(__lowerCAmelCase , 1 )
score_mask |= score_mask.at[:, 0].set(__lowerCAmelCase )
# min tokens to keep
UpperCAmelCase_ = score_mask.at[:, : self.min_tokens_to_keep].set(__lowerCAmelCase )
UpperCAmelCase_ = jnp.where(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase_ = jax.lax.sort_key_val(__lowerCAmelCase , __lowerCAmelCase )[-1]
return next_scores
class __UpperCamelCase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : Union[str, Any] , lowerCAmelCase : str , lowerCAmelCase : int = -float("Inf" ) , lowerCAmelCase : Optional[int] = 1 ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or top_k <= 0:
raise ValueError(F"`top_k` has to be a strictly positive integer, but is {top_k}" )
UpperCAmelCase_ = max(__lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase_ = filter_value
def __call__( self : Optional[Any] , lowerCAmelCase : List[Any] , lowerCAmelCase : Tuple , lowerCAmelCase : str ):
'''simple docstring'''
UpperCAmelCase_ = scores.shape
UpperCAmelCase_ = jnp.full(batch_size * vocab_size , self.filter_value )
UpperCAmelCase_ = min(self.top_k , scores.shape[-1] ) # Safety check
UpperCAmelCase_ = lax.top_k(__lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase_ = jnp.broadcast_to((jnp.arange(__lowerCAmelCase ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
UpperCAmelCase_ = topk_scores.flatten()
UpperCAmelCase_ = topk_indices.flatten() + shift
UpperCAmelCase_ = next_scores_flat.at[topk_indices_flat].set(__lowerCAmelCase )
UpperCAmelCase_ = next_scores_flat.reshape(__lowerCAmelCase , __lowerCAmelCase )
return next_scores
class __UpperCamelCase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : Dict , lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase_ = bos_token_id
def __call__( self : List[Any] , lowerCAmelCase : List[Any] , lowerCAmelCase : Dict , lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase_ = jnp.full(scores.shape , -float("inf" ) )
UpperCAmelCase_ = 1 - jnp.bool_(cur_len - 1 )
UpperCAmelCase_ = jnp.where(__lowerCAmelCase , new_scores.at[:, self.bos_token_id].set(0 ) , __lowerCAmelCase )
return scores
class __UpperCamelCase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : Optional[Any] , lowerCAmelCase : Dict , lowerCAmelCase : Dict ):
'''simple docstring'''
UpperCAmelCase_ = max_length
UpperCAmelCase_ = eos_token_id
def __call__( self : Tuple , lowerCAmelCase : List[Any] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase_ = jnp.full(scores.shape , -float("inf" ) )
UpperCAmelCase_ = 1 - jnp.bool_(cur_len - self.max_length + 1 )
UpperCAmelCase_ = jnp.where(__lowerCAmelCase , new_scores.at[:, self.eos_token_id].set(0 ) , __lowerCAmelCase )
return scores
class __UpperCamelCase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : List[str] , lowerCAmelCase : Any , lowerCAmelCase : Any ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or min_length < 0:
raise ValueError(F"`min_length` has to be a positive integer, but is {min_length}" )
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or eos_token_id < 0:
raise ValueError(F"`eos_token_id` has to be a positive integer, but is {eos_token_id}" )
UpperCAmelCase_ = min_length
UpperCAmelCase_ = eos_token_id
def __call__( self : Any , lowerCAmelCase : Optional[Any] , lowerCAmelCase : List[str] , lowerCAmelCase : str ):
'''simple docstring'''
UpperCAmelCase_ = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
UpperCAmelCase_ = jnp.where(__lowerCAmelCase , scores.at[:, self.eos_token_id].set(-float("inf" ) ) , __lowerCAmelCase )
return scores
class __UpperCamelCase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : Union[str, Any] , lowerCAmelCase : Dict , lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ = list(__lowerCAmelCase )
UpperCAmelCase_ = begin_index
def __call__( self : Optional[Any] , lowerCAmelCase : List[str] , lowerCAmelCase : int , lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ = 1 - jnp.bool_(cur_len - self.begin_index )
UpperCAmelCase_ = jnp.where(__lowerCAmelCase , scores.at[:, self.begin_suppress_tokens].set(-float("inf" ) ) , __lowerCAmelCase )
return scores
class __UpperCamelCase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : Optional[Any] , lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase_ = list(__lowerCAmelCase )
def __call__( self : Dict , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : List[Any] , lowerCAmelCase : List[str] ):
'''simple docstring'''
UpperCAmelCase_ = scores.at[..., self.suppress_tokens].set(-float("inf" ) )
return scores
class __UpperCamelCase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : int , lowerCAmelCase : Tuple ):
'''simple docstring'''
UpperCAmelCase_ = dict(__lowerCAmelCase )
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
UpperCAmelCase_ = jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1
for index, token in force_token_map.items():
if token is not None:
UpperCAmelCase_ = force_token_array.at[index].set(__lowerCAmelCase )
UpperCAmelCase_ = jnp.intaa(__lowerCAmelCase )
def __call__( self : Any , lowerCAmelCase : Any , lowerCAmelCase : Dict , lowerCAmelCase : Dict ):
'''simple docstring'''
def _force_token(lowerCAmelCase : List[Any] ):
UpperCAmelCase_ = scores.shape[0]
UpperCAmelCase_ = self.force_token_array[generation_idx]
UpperCAmelCase_ = jnp.ones_like(__lowerCAmelCase , dtype=scores.dtype ) * -float("inf" )
UpperCAmelCase_ = jnp.zeros((batch_size, 1) , dtype=scores.dtype )
UpperCAmelCase_ = lax.dynamic_update_slice(__lowerCAmelCase , __lowerCAmelCase , (0, current_token) )
return new_scores
UpperCAmelCase_ = lax.cond(
cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond(
self.force_token_array[cur_len] >= 0 , lambda: _force_token(__lowerCAmelCase ) , lambda: scores , ) , )
return scores
class __UpperCamelCase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : Union[str, Any] , lowerCAmelCase : Any , lowerCAmelCase : Dict , lowerCAmelCase : Tuple ):
'''simple docstring'''
UpperCAmelCase_ = generate_config.eos_token_id
UpperCAmelCase_ = generate_config.no_timestamps_token_id
UpperCAmelCase_ = generate_config.no_timestamps_token_id + 1
UpperCAmelCase_ = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(__lowerCAmelCase , "max_initial_timestamp_index" ):
UpperCAmelCase_ = generate_config.max_initial_timestamp_index
else:
UpperCAmelCase_ = model_config.vocab_size
if self.max_initial_timestamp_index is None:
UpperCAmelCase_ = model_config.vocab_size
def __call__( self : int , lowerCAmelCase : Dict , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Tuple ):
'''simple docstring'''
UpperCAmelCase_ = scores.at[:, self.no_timestamps_token_id].set(-float("inf" ) )
def handle_pairs(lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Dict ):
UpperCAmelCase_ = jnp.where((cur_len - self.begin_index) >= 1 , __lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase_ = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , __lowerCAmelCase , )
UpperCAmelCase_ = jnp.where((cur_len - self.begin_index) < 2 , __lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase_ = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , __lowerCAmelCase , __lowerCAmelCase , )
return jnp.where(
__lowerCAmelCase , jnp.where(
penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float("inf" ) ) , scores_k.at[: self.eos_token_id].set(-float("inf" ) ) , ) , __lowerCAmelCase , )
UpperCAmelCase_ = jax.vmap(__lowerCAmelCase )(__lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase_ = jnp.where(cur_len == self.begin_index , __lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase_ = jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , __lowerCAmelCase , )
UpperCAmelCase_ = self.timestamp_begin + self.max_initial_timestamp_index
UpperCAmelCase_ = jnp.where(
__lowerCAmelCase , scores.at[:, last_allowed + 1 :].set(-float("inf" ) ) , __lowerCAmelCase , )
# if sum of probability over timestamps is above any other token, sample timestamp
UpperCAmelCase_ = jax.nn.log_softmax(__lowerCAmelCase , axis=-1 )
def handle_cumulative_probs(lowerCAmelCase : Dict , lowerCAmelCase : List[Any] ):
UpperCAmelCase_ = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
UpperCAmelCase_ = jnp.max(logprobs_k[: self.timestamp_begin] )
return jnp.where(
timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float("inf" ) ) , __lowerCAmelCase , )
UpperCAmelCase_ = jax.vmap(__lowerCAmelCase )(__lowerCAmelCase , __lowerCAmelCase )
return scores | 162 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCamelCase =logging.get_logger(__name__)
class A ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
__a : List[Any] = ['''input_features''', '''attention_mask''']
def __init__( self , __lowerCAmelCase=80 , __lowerCAmelCase=1_60_00 , __lowerCAmelCase=80 , __lowerCAmelCase=0.0 , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=True , **__lowerCAmelCase , ):
super().__init__(feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , **__lowerCAmelCase )
UpperCamelCase_ : int = num_mel_bins
UpperCamelCase_ : Union[str, Any] = do_ceptral_normalize
UpperCamelCase_ : Any = normalize_means
UpperCamelCase_ : int = normalize_vars
UpperCamelCase_ : int = True
def _UpperCAmelCase ( self , __lowerCAmelCase , ):
UpperCamelCase_ : Optional[Any] = waveform * (2**15) # Kaldi compliance: 16-bit signed integers
UpperCamelCase_ : Optional[int] = torch.from_numpy(__lowerCAmelCase ).unsqueeze(0 )
UpperCamelCase_ : Tuple = ta_kaldi.fbank(__lowerCAmelCase , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def _UpperCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = True , __lowerCAmelCase = True , __lowerCAmelCase = 0.0 , ):
# make sure we normalize float32 arrays
if normalize_means:
UpperCamelCase_ : int = x[:input_length].mean(axis=0 )
UpperCamelCase_ : str = np.subtract(__lowerCAmelCase , __lowerCAmelCase )
if normalize_vars:
UpperCamelCase_ : Optional[int] = x[:input_length].std(axis=0 )
UpperCamelCase_ : Optional[Any] = np.divide(__lowerCAmelCase , __lowerCAmelCase )
if input_length < x.shape[0]:
UpperCamelCase_ : Any = padding_value
# make sure array is in float32
UpperCamelCase_ : str = x.astype(np.floataa )
return x
def _UpperCAmelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None ):
UpperCamelCase_ : List[str] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(__lowerCAmelCase , __lowerCAmelCase , self.normalize_means , self.normalize_vars , self.padding_value )
for x, n in zip(__lowerCAmelCase , __lowerCAmelCase )
]
def __call__( self , __lowerCAmelCase , __lowerCAmelCase = False , __lowerCAmelCase = None , __lowerCAmelCase = False , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , **__lowerCAmelCase , ):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
F" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
F" {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.""" )
UpperCamelCase_ : Optional[int] = 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}" )
UpperCamelCase_ : List[str] = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
UpperCamelCase_ : str = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
UpperCamelCase_ : Optional[Any] = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCamelCase_ : int = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCamelCase_ : Tuple = [raw_speech]
# extract fbank features
UpperCamelCase_ : Any = [self._extract_fbank_features(__lowerCAmelCase ) for waveform in raw_speech]
# convert into correct format for padding
UpperCamelCase_ : Tuple = BatchFeature({"""input_features""": features} )
UpperCamelCase_ : Tuple = self.pad(
__lowerCAmelCase , padding=__lowerCAmelCase , max_length=__lowerCAmelCase , truncation=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , **__lowerCAmelCase , )
# make sure list is in array format
UpperCamelCase_ : str = padded_inputs.get("""input_features""" )
if isinstance(input_features[0] , __lowerCAmelCase ):
UpperCamelCase_ : Any = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in input_features]
UpperCamelCase_ : Union[str, Any] = padded_inputs.get("""attention_mask""" )
if attention_mask is not None:
UpperCamelCase_ : Union[str, Any] = [np.asarray(__lowerCAmelCase , dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
UpperCamelCase_ : List[str] = (
np.array(__lowerCAmelCase , dtype=np.intaa )
if self._get_padding_strategies(__lowerCAmelCase , max_length=__lowerCAmelCase ) is not PaddingStrategy.DO_NOT_PAD
else None
)
UpperCamelCase_ : Optional[int] = self.normalize(
padded_inputs["""input_features"""] , attention_mask=__lowerCAmelCase )
if return_tensors is not None:
UpperCamelCase_ : Optional[Any] = padded_inputs.convert_to_tensors(__lowerCAmelCase )
return padded_inputs
| 208 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class __lowercase (__lowerCamelCase ):
_lowerCamelCase = 42
class __lowercase (__lowerCamelCase , __lowerCamelCase ):
@register_to_config
def __init__( self : List[Any] , UpperCAmelCase_ : int = 3 , UpperCAmelCase_ : int = 3 , UpperCAmelCase_ : Tuple[str] = ("DownEncoderBlock2D",) , UpperCAmelCase_ : Tuple[str] = ("UpDecoderBlock2D",) , UpperCAmelCase_ : Tuple[int] = (64,) , UpperCAmelCase_ : int = 1 , UpperCAmelCase_ : str = "silu" , UpperCAmelCase_ : int = 3 , UpperCAmelCase_ : int = 32 , UpperCAmelCase_ : int = 256 , UpperCAmelCase_ : int = 32 , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : float = 0.1_82_15 , UpperCAmelCase_ : str = "group" , ):
super().__init__()
# pass init params to Encoder
UpperCamelCase__ : List[Any] = Encoder(
in_channels=UpperCAmelCase_ , out_channels=UpperCAmelCase_ , down_block_types=UpperCAmelCase_ , block_out_channels=UpperCAmelCase_ , layers_per_block=UpperCAmelCase_ , act_fn=UpperCAmelCase_ , norm_num_groups=UpperCAmelCase_ , double_z=UpperCAmelCase_ , )
UpperCamelCase__ : Tuple = vq_embed_dim if vq_embed_dim is not None else latent_channels
UpperCamelCase__ : str = nn.Convad(UpperCAmelCase_ , UpperCAmelCase_ , 1)
UpperCamelCase__ : Tuple = VectorQuantizer(UpperCAmelCase_ , UpperCAmelCase_ , beta=0.25 , remap=UpperCAmelCase_ , sane_index_shape=UpperCAmelCase_)
UpperCamelCase__ : Any = nn.Convad(UpperCAmelCase_ , UpperCAmelCase_ , 1)
# pass init params to Decoder
UpperCamelCase__ : List[str] = Decoder(
in_channels=UpperCAmelCase_ , out_channels=UpperCAmelCase_ , up_block_types=UpperCAmelCase_ , block_out_channels=UpperCAmelCase_ , layers_per_block=UpperCAmelCase_ , act_fn=UpperCAmelCase_ , norm_num_groups=UpperCAmelCase_ , norm_type=UpperCAmelCase_ , )
@apply_forward_hook
def __UpperCamelCase ( self : Optional[Any] , UpperCAmelCase_ : torch.FloatTensor , UpperCAmelCase_ : bool = True):
UpperCamelCase__ : Dict = self.encoder(UpperCAmelCase_)
UpperCamelCase__ : Dict = self.quant_conv(UpperCAmelCase_)
if not return_dict:
return (h,)
return VQEncoderOutput(latents=UpperCAmelCase_)
@apply_forward_hook
def __UpperCamelCase ( self : Union[str, Any] , UpperCAmelCase_ : torch.FloatTensor , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = True):
# also go through quantization layer
if not force_not_quantize:
UpperCamelCase__ : List[str] = self.quantize(UpperCAmelCase_)
else:
UpperCamelCase__ : Tuple = h
UpperCamelCase__ : Union[str, Any] = self.post_quant_conv(UpperCAmelCase_)
UpperCamelCase__ : Tuple = self.decoder(UpperCAmelCase_ , quant if self.config.norm_type == 'spatial' else None)
if not return_dict:
return (dec,)
return DecoderOutput(sample=UpperCAmelCase_)
def __UpperCamelCase ( self : Optional[int] , UpperCAmelCase_ : torch.FloatTensor , UpperCAmelCase_ : bool = True):
UpperCamelCase__ : str = sample
UpperCamelCase__ : int = self.encode(UpperCAmelCase_).latents
UpperCamelCase__ : Union[str, Any] = self.decode(UpperCAmelCase_).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=UpperCAmelCase_)
| 700 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def __UpperCAmelCase ( lowerCamelCase_ , lowerCamelCase_ = "cpu" , lowerCamelCase_ = None) -> None:
UpperCamelCase__ : List[Any] = torch.load(lowerCamelCase_ , map_location=lowerCamelCase_)
for k, v in tqdm(state_dict.items()):
if not isinstance(lowerCamelCase_ , torch.Tensor):
raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin')
UpperCamelCase__ : int = v.half()
if save_path is None: # overwrite src_path
UpperCamelCase__ : List[Any] = src_path
torch.save(lowerCamelCase_ , lowerCamelCase_)
if __name__ == "__main__":
fire.Fire(convert)
| 6 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
__UpperCAmelCase = get_logger()
__UpperCAmelCase = None
class lowerCAmelCase_ ( TensorFormatter[Mapping, "jax.Array", Mapping] ):
def __init__( self, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
super().__init__(features=SCREAMING_SNAKE_CASE_ )
import jax
from jaxlib.xla_client import Device
if isinstance(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
raise ValueError(
F"""Expected {device} to be a `str` not {type(SCREAMING_SNAKE_CASE_ )}, as `jaxlib.xla_extension.Device` """
'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
'the device with `str()` to get its string identifier that will be internally mapped '
'to the actual `jaxlib.xla_extension.Device`.' )
UpperCamelCase : Union[str, Any] = device if isinstance(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
UpperCamelCase : int = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
F"""Device with string identifier {self.device} not listed among the available """
F"""devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default """
F"""device: {str(jax.devices()[0] )}.""" )
UpperCamelCase : Dict = str(jax.devices()[0] )
UpperCamelCase : Dict = jnp_array_kwargs
@staticmethod
def snake_case_ ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
import jax
return {str(SCREAMING_SNAKE_CASE_ ): device for device in jax.devices()}
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
import jax
import jax.numpy as jnp
if isinstance(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) and column:
if all(
isinstance(SCREAMING_SNAKE_CASE_, jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(SCREAMING_SNAKE_CASE_, axis=0 )
return column
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> int:
import jax
import jax.numpy as jnp
if isinstance(SCREAMING_SNAKE_CASE_, (str, bytes, type(SCREAMING_SNAKE_CASE_ )) ):
return value
elif isinstance(SCREAMING_SNAKE_CASE_, (np.character, np.ndarray) ) and np.issubdtype(value.dtype, np.character ):
return value.tolist()
UpperCamelCase : Optional[Any] = {}
if isinstance(SCREAMING_SNAKE_CASE_, (np.number, np.ndarray) ) and np.issubdtype(value.dtype, np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
UpperCamelCase : List[Any] = {'dtype': jnp.intaa}
else:
UpperCamelCase : Optional[int] = {'dtype': jnp.intaa}
elif isinstance(SCREAMING_SNAKE_CASE_, (np.number, np.ndarray) ) and np.issubdtype(value.dtype, np.floating ):
UpperCamelCase : Union[str, Any] = {'dtype': jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(SCREAMING_SNAKE_CASE_, PIL.Image.Image ):
UpperCamelCase : int = np.asarray(SCREAMING_SNAKE_CASE_ )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
UpperCamelCase : Dict = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(SCREAMING_SNAKE_CASE_, **{**default_dtype, **self.jnp_array_kwargs} )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Dict:
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(SCREAMING_SNAKE_CASE_, torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(SCREAMING_SNAKE_CASE_, '__array__' ) and not isinstance(SCREAMING_SNAKE_CASE_, jax.Array ):
UpperCamelCase : Any = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(SCREAMING_SNAKE_CASE_, np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE_ ) for substruct in data_struct] )
elif isinstance(SCREAMING_SNAKE_CASE_, (list, tuple) ):
return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE_ ) for substruct in data_struct] )
return self._tensorize(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Any:
return map_nested(self._recursive_tensorize, SCREAMING_SNAKE_CASE_, map_list=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Mapping:
UpperCamelCase : List[Any] = self.numpy_arrow_extractor().extract_row(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.python_features_decoder.decode_row(SCREAMING_SNAKE_CASE_ )
return self.recursive_tensorize(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> "jax.Array":
UpperCamelCase : str = self.numpy_arrow_extractor().extract_column(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self.python_features_decoder.decode_column(SCREAMING_SNAKE_CASE_, pa_table.column_names[0] )
UpperCamelCase : Tuple = self.recursive_tensorize(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self._consolidate(SCREAMING_SNAKE_CASE_ )
return column
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Mapping:
UpperCamelCase : List[Any] = self.numpy_arrow_extractor().extract_batch(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = self.python_features_decoder.decode_batch(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.recursive_tensorize(SCREAMING_SNAKE_CASE_ )
for column_name in batch:
UpperCamelCase : List[Any] = self._consolidate(batch[column_name] )
return batch
| 40 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def __A ( __lowerCamelCase ) -> List[str]:
return 1.0 / (1.0 + np.exp(-_outputs ))
def __A ( __lowerCamelCase ) -> List[str]:
a = np.max(_outputs , axis=-1 , keepdims=__lowerCamelCase )
a = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=__lowerCamelCase )
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = '''sigmoid'''
UpperCamelCase__ = '''softmax'''
UpperCamelCase__ = '''none'''
@add_end_docstrings(
__magic_name__ , r'''
return_all_scores (`bool`, *optional*, defaults to `False`):
Whether to return all prediction scores or just the one of the predicted class.
function_to_apply (`str`, *optional*, defaults to `"default"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
- `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.
- `"sigmoid"`: Applies the sigmoid function on the output.
- `"softmax"`: Applies the softmax function on the output.
- `"none"`: Does not apply any function on the output.
''' , )
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = False
UpperCamelCase__ = ClassificationFunction.NONE
def __init__( self :List[str] , **__magic_name__ :List[Any] ):
'''simple docstring'''
super().__init__(**__magic_name__ )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == """tf"""
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def lowerCamelCase__ ( self :Any , __magic_name__ :int=None , __magic_name__ :Any=None , __magic_name__ :Union[str, Any]="" , **__magic_name__ :Tuple ):
'''simple docstring'''
a = tokenizer_kwargs
a = {}
if hasattr(self.model.config , """return_all_scores""" ) and return_all_scores is None:
a = self.model.config.return_all_scores
if isinstance(__magic_name__ , __magic_name__ ) or top_k is None:
a = top_k
a = False
elif return_all_scores is not None:
warnings.warn(
"""`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of"""
""" `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.""" , __magic_name__ , )
if return_all_scores:
a = None
else:
a = 1
if isinstance(__magic_name__ , __magic_name__ ):
a = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
a = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self :Dict , *__magic_name__ :Optional[int] , **__magic_name__ :Optional[Any] ):
'''simple docstring'''
a = super().__call__(*__magic_name__ , **__magic_name__ )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
a = """top_k""" not in kwargs
if isinstance(args[0] , __magic_name__ ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def lowerCamelCase__ ( self :Union[str, Any] , __magic_name__ :Optional[Any] , **__magic_name__ :Optional[Any] ):
'''simple docstring'''
a = self.framework
if isinstance(__magic_name__ , __magic_name__ ):
return self.tokenizer(**__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ )
elif isinstance(__magic_name__ , __magic_name__ ) and len(__magic_name__ ) == 1 and isinstance(inputs[0] , __magic_name__ ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=__magic_name__ , **__magic_name__ )
elif isinstance(__magic_name__ , __magic_name__ ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
"""The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"""
""" dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair.""" )
return self.tokenizer(__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ )
def lowerCamelCase__ ( self :List[str] , __magic_name__ :Tuple ):
'''simple docstring'''
return self.model(**__magic_name__ )
def lowerCamelCase__ ( self :Dict , __magic_name__ :Union[str, Any] , __magic_name__ :int=None , __magic_name__ :Union[str, Any]=1 , __magic_name__ :Tuple=True ):
'''simple docstring'''
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
a = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
a = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , """function_to_apply""" ) and function_to_apply is None:
a = self.model.config.function_to_apply
else:
a = ClassificationFunction.NONE
a = model_outputs["""logits"""][0]
a = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
a = sigmoid(__magic_name__ )
elif function_to_apply == ClassificationFunction.SOFTMAX:
a = softmax(__magic_name__ )
elif function_to_apply == ClassificationFunction.NONE:
a = outputs
else:
raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}' )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
a = [
{"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(__magic_name__ )
]
if not _legacy:
dict_scores.sort(key=lambda __magic_name__ : x["score"] , reverse=__magic_name__ )
if top_k is not None:
a = dict_scores[:top_k]
return dict_scores
| 468 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
),
'''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
),
'''distilbert-base-uncased-finetuned-sst-2-english''': (
'''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
),
}
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = "distilbert"
SCREAMING_SNAKE_CASE_ : List[str] = {
"hidden_size": "dim",
"num_attention_heads": "n_heads",
"num_hidden_layers": "n_layers",
}
def __init__( self : int , A : str=3_05_22 , A : Union[str, Any]=5_12 , A : Tuple=False , A : str=6 , A : Union[str, Any]=12 , A : Any=7_68 , A : List[Any]=4 * 7_68 , A : Any=0.1 , A : Optional[Any]=0.1 , A : Optional[int]="gelu" , A : int=0.02 , A : Tuple=0.1 , A : List[Any]=0.2 , A : int=0 , **A : Optional[Any] , ) -> Optional[int]:
lowercase_ : List[Any] = vocab_size
lowercase_ : Union[str, Any] = max_position_embeddings
lowercase_ : Dict = sinusoidal_pos_embds
lowercase_ : str = n_layers
lowercase_ : Optional[int] = n_heads
lowercase_ : int = dim
lowercase_ : Dict = hidden_dim
lowercase_ : Dict = dropout
lowercase_ : List[Any] = attention_dropout
lowercase_ : Dict = activation
lowercase_ : List[str] = initializer_range
lowercase_ : Dict = qa_dropout
lowercase_ : Optional[int] = seq_classif_dropout
super().__init__(**A , pad_token_id=A )
class _UpperCAmelCase ( _A ):
@property
def A ( self : List[Any] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowercase_ : Dict = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowercase_ : List[str] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 702 |
"""simple docstring"""
from math import ceil, sqrt
def lowercase ( __snake_case : int = 1_0_0_0_0_0_0 ):
lowercase_ : Tuple = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowercase_ : int = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
lowercase_ : int = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F"""{solution() = }""")
| 141 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowerCamelCase : Any =KandinskyInpaintPipeline
lowerCamelCase : Tuple =["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
lowerCamelCase : List[Any] =[
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
lowerCamelCase : str =[
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
lowerCamelCase : Any =False
@property
def __a ( self ) -> str:
return 32
@property
def __a ( self ) -> List[Any]:
return 32
@property
def __a ( self ) -> List[str]:
return self.time_input_dim
@property
def __a ( self ) -> Union[str, Any]:
return self.time_input_dim * 4
@property
def __a ( self ) -> Any:
return 100
@property
def __a ( self ) -> Tuple:
a : Optional[int] = XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
return tokenizer
@property
def __a ( self ) -> Optional[int]:
torch.manual_seed(0 )
a : Union[str, Any] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
a : Optional[Any] = MultilingualCLIP(SCREAMING_SNAKE_CASE__ )
a : List[Any] = text_encoder.eval()
return text_encoder
@property
def __a ( self ) -> int:
torch.manual_seed(0 )
a : Any = {
'''in_channels''': 9,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''text_image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''text_image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
a : Dict = UNetaDConditionModel(**SCREAMING_SNAKE_CASE__ )
return model
@property
def __a ( self ) -> str:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __a ( self ) -> Optional[Any]:
torch.manual_seed(0 )
a : List[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __a ( self ) -> Any:
a : int = self.dummy_text_encoder
a : str = self.dummy_tokenizer
a : Dict = self.dummy_unet
a : Dict = self.dummy_movq
a : Optional[Any] = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule="linear" , beta_start=0.00_085 , beta_end=0.012 , clip_sample=SCREAMING_SNAKE_CASE__ , set_alpha_to_one=SCREAMING_SNAKE_CASE__ , steps_offset=1 , prediction_type="epsilon" , thresholding=SCREAMING_SNAKE_CASE__ , )
a : int = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__=0 ) -> Any:
a : List[Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(SCREAMING_SNAKE_CASE__ ) ).to(SCREAMING_SNAKE_CASE__ )
a : int = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(SCREAMING_SNAKE_CASE__ )
# create init_image
a : Optional[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(SCREAMING_SNAKE_CASE__ ) ).to(SCREAMING_SNAKE_CASE__ )
a : Any = image.cpu().permute(0 , 2 , 3 , 1 )[0]
a : List[Any] = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__ ) ).convert("RGB" ).resize((256, 256) )
# create mask
a : Tuple = np.ones((64, 64) , dtype=np.floataa )
a : List[str] = 0
if str(SCREAMING_SNAKE_CASE__ ).startswith("mps" ):
a : Dict = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
a : Any = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
a : Any = {
'''prompt''': '''horse''',
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def __a ( self ) -> int:
a : int = '''cpu'''
a : Union[str, Any] = self.get_dummy_components()
a : Tuple = self.pipeline_class(**SCREAMING_SNAKE_CASE__ )
a : Union[str, Any] = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
a : int = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ ) )
a : Optional[int] = output.images
a : Optional[int] = pipe(
**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ ) , return_dict=SCREAMING_SNAKE_CASE__ , )[0]
a : Optional[int] = image[0, -3:, -3:, -1]
a : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
print(f"""image.shape {image.shape}""" )
assert image.shape == (1, 64, 64, 3)
a : Any = np.array(
[0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
def __a ( self ) -> List[str]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def __a ( self ) -> str:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self ) -> List[str]:
a : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy" )
a : str = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
a : str = np.ones((768, 768) , dtype=np.floataa )
a : List[Any] = 0
a : Optional[Any] = '''a hat'''
a : Tuple = KandinskyPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1-prior" , torch_dtype=torch.floataa )
pipe_prior.to(SCREAMING_SNAKE_CASE__ )
a : Union[str, Any] = KandinskyInpaintPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1-inpaint" , torch_dtype=torch.floataa )
a : List[Any] = pipeline.to(SCREAMING_SNAKE_CASE__ )
pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
a : Dict = torch.Generator(device="cpu" ).manual_seed(0 )
a : Any = pipe_prior(
SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
a : Union[str, Any] = pipeline(
SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , mask_image=SCREAMING_SNAKE_CASE__ , image_embeds=SCREAMING_SNAKE_CASE__ , negative_image_embeds=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=100 , height=768 , width=768 , output_type="np" , )
a : Optional[Any] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
| 633 |
"""simple docstring"""
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def A_ ( snake_case__ ) -> str:
return 1 / (1 + np.exp(-z ))
def A_ ( snake_case__ , snake_case__ ) -> str:
return (-y * np.log(snake_case__ ) - (1 - y) * np.log(1 - h )).mean()
def A_ ( snake_case__ , snake_case__ , snake_case__ ) -> Union[str, Any]:
_UpperCamelCase :Tuple = np.dot(snake_case__ , snake_case__ )
return np.sum(y * scores - np.log(1 + np.exp(snake_case__ ) ) )
def A_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__=7_00_00 ) -> Optional[int]:
_UpperCamelCase :Union[str, Any] = np.zeros(x.shape[1] )
for iterations in range(snake_case__ ):
_UpperCamelCase :Optional[int] = np.dot(snake_case__ , snake_case__ )
_UpperCamelCase :Tuple = sigmoid_function(snake_case__ )
_UpperCamelCase :Optional[Any] = np.dot(x.T , h - y ) / y.size
_UpperCamelCase :int = theta - alpha * gradient # updating the weights
_UpperCamelCase :Union[str, Any] = np.dot(snake_case__ , snake_case__ )
_UpperCamelCase :Dict = sigmoid_function(snake_case__ )
_UpperCamelCase :Optional[int] = cost_function(snake_case__ , snake_case__ )
if iterations % 1_00 == 0:
print(f"loss: {j} \t" ) # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
UpperCamelCase__ :Union[str, Any] = datasets.load_iris()
UpperCamelCase__ :Dict = iris.data[:, :2]
UpperCamelCase__ :Any = (iris.target != 0) * 1
UpperCamelCase__ :Tuple = 0.1
UpperCamelCase__ :List[str] = logistic_reg(alpha, x, y, max_iterations=70_000)
print("""theta: """, theta) # printing the theta i.e our weights vector
def A_ ( snake_case__ ) -> Optional[Any]:
return sigmoid_function(
np.dot(snake_case__ , snake_case__ ) ) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
((UpperCamelCase__) , (UpperCamelCase__)) :Union[str, Any] = (x[:, 0].min(), x[:, 0].max())
((UpperCamelCase__) , (UpperCamelCase__)) :Tuple = (x[:, 1].min(), x[:, 1].max())
((UpperCamelCase__) , (UpperCamelCase__)) :Tuple = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
UpperCamelCase__ :List[Any] = np.c_[xxa.ravel(), xxa.ravel()]
UpperCamelCase__ :List[Any] = predict_prob(grid).reshape(xxa.shape)
plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")
plt.legend()
plt.show()
| 355 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class __A :
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : Tuple ,_snake_case : Dict=13 ,_snake_case : Optional[int]=7 ,_snake_case : List[str]=True ,_snake_case : Optional[Any]=True ,_snake_case : str=False ,_snake_case : Optional[int]=True ,_snake_case : int=99 ,_snake_case : int=32 ,_snake_case : str=5 ,_snake_case : Any=4 ,_snake_case : str=37 ,_snake_case : str="gelu" ,_snake_case : Optional[Any]=0.1 ,_snake_case : Union[str, Any]=0.1 ,_snake_case : str=512 ,_snake_case : Dict=16 ,_snake_case : Dict=2 ,_snake_case : Tuple=0.02 ,_snake_case : int=3 ,_snake_case : Optional[int]=4 ,_snake_case : int=None ,) -> Tuple:
"""simple docstring"""
lowercase__ : Optional[Any] = parent
lowercase__ : List[str] = batch_size
lowercase__ : str = seq_length
lowercase__ : Tuple = is_training
lowercase__ : List[str] = use_input_mask
lowercase__ : Optional[Any] = use_token_type_ids
lowercase__ : str = use_labels
lowercase__ : Any = vocab_size
lowercase__ : str = hidden_size
lowercase__ : int = num_hidden_layers
lowercase__ : int = num_attention_heads
lowercase__ : Optional[int] = intermediate_size
lowercase__ : Dict = hidden_act
lowercase__ : Optional[int] = hidden_dropout_prob
lowercase__ : Optional[int] = attention_probs_dropout_prob
lowercase__ : List[str] = max_position_embeddings
lowercase__ : Union[str, Any] = type_vocab_size
lowercase__ : Any = type_sequence_label_size
lowercase__ : List[str] = initializer_range
lowercase__ : Tuple = num_labels
lowercase__ : int = num_choices
lowercase__ : Optional[int] = scope
def UpperCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
lowercase__ : int = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowercase__ : Optional[int] = None
if self.use_input_mask:
lowercase__ : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : Tuple = None
if self.use_token_type_ids:
lowercase__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
lowercase__ : Optional[Any] = None
lowercase__ : Any = None
lowercase__ : List[Any] = None
if self.use_labels:
lowercase__ : List[str] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowercase__ : str = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
lowercase__ : List[Any] = ids_tensor([self.batch_size] ,self.num_choices )
lowercase__ : str = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
return OpenLlamaConfig(
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 ,is_decoder=_snake_case ,initializer_range=self.initializer_range ,use_stable_embedding=_snake_case ,)
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : List[Any] ,_snake_case : Tuple ,_snake_case : int ,_snake_case : Optional[Any] ,_snake_case : Optional[int] ,_snake_case : Tuple ,_snake_case : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Optional[int] = OpenLlamaModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Optional[Any] = model(_snake_case ,attention_mask=_snake_case )
lowercase__ : Dict = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : List[str] ,_snake_case : Optional[int] ,_snake_case : Tuple ,_snake_case : int ,_snake_case : Tuple ,_snake_case : Union[str, Any] ,_snake_case : str ,_snake_case : List[str] ,_snake_case : List[str] ,_snake_case : Optional[Any] ,) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = True
lowercase__ : Tuple = OpenLlamaModel(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Dict = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,encoder_attention_mask=_snake_case ,)
lowercase__ : str = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,)
lowercase__ : Optional[Any] = model(_snake_case ,attention_mask=_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : List[Any] ,_snake_case : Optional[Any] ,_snake_case : Union[str, Any] ,_snake_case : List[str] ,_snake_case : List[Any] ,_snake_case : Any ,_snake_case : List[Any] ,_snake_case : List[str] ,_snake_case : List[Any] ,_snake_case : Union[str, Any] ,) -> Dict:
"""simple docstring"""
lowercase__ : int = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : List[str] = model(_snake_case ,attention_mask=_snake_case ,labels=_snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[int] ,_snake_case : Dict ,_snake_case : Union[str, Any] ,_snake_case : Tuple ,_snake_case : str ,_snake_case : int ,_snake_case : List[Any] ,_snake_case : Optional[Any] ,_snake_case : Union[str, Any] ,) -> int:
"""simple docstring"""
lowercase__ : List[Any] = True
lowercase__ : Tuple = True
lowercase__ : Optional[int] = OpenLlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
# first forward pass
lowercase__ : List[str] = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,encoder_attention_mask=_snake_case ,use_cache=_snake_case ,)
lowercase__ : Union[str, Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowercase__ : List[Any] = ids_tensor((self.batch_size, 3) ,config.vocab_size )
lowercase__ : Tuple = ids_tensor((self.batch_size, 3) ,vocab_size=2 )
# append to next input_ids and
lowercase__ : str = torch.cat([input_ids, next_tokens] ,dim=-1 )
lowercase__ : Tuple = torch.cat([input_mask, next_mask] ,dim=-1 )
lowercase__ : Tuple = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,encoder_attention_mask=_snake_case ,output_hidden_states=_snake_case ,)['''hidden_states'''][0]
lowercase__ : Union[str, Any] = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,encoder_attention_mask=_snake_case ,past_key_values=_snake_case ,output_hidden_states=_snake_case ,)['''hidden_states'''][0]
# select random slice
lowercase__ : Optional[int] = ids_tensor((1,) ,output_from_past.shape[-1] ).item()
lowercase__ : List[str] = output_from_no_past[:, -3:, random_slice_idx].detach()
lowercase__ : int = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_snake_case ,_snake_case ,atol=1e-3 ) )
def UpperCAmelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) : str = config_and_inputs
lowercase__ : Dict = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A ( A_ ,A_ ,A_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Optional[Any] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
lowerCAmelCase : List[str] = (OpenLlamaForCausalLM,) if is_torch_available() else ()
lowerCAmelCase : int = (
{
"feature-extraction": OpenLlamaModel,
"text-classification": OpenLlamaForSequenceClassification,
"text-generation": OpenLlamaForCausalLM,
"zero-shot": OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase : Union[str, Any] = False
lowerCAmelCase : Any = False
def UpperCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
lowercase__ : Tuple = OpenLlamaModelTester(self )
lowercase__ : Tuple = ConfigTester(self ,config_class=_snake_case ,hidden_size=37 )
def UpperCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCAmelCase ( self : Any ) -> Dict:
"""simple docstring"""
lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase__ : int = type
self.model_tester.create_and_check_model(*_snake_case )
def UpperCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ , lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Optional[Any] = 3
lowercase__ : List[str] = input_dict['''input_ids''']
lowercase__ : Optional[Any] = input_ids.ne(1 ).to(_snake_case )
lowercase__ : Optional[int] = ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size )
lowercase__ : Dict = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Any = model(_snake_case ,attention_mask=_snake_case ,labels=_snake_case )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Optional[int] = 3
lowercase__ : str = '''single_label_classification'''
lowercase__ : Optional[Any] = input_dict['''input_ids''']
lowercase__ : Any = input_ids.ne(1 ).to(_snake_case )
lowercase__ : List[Any] = ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size )
lowercase__ : Any = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Union[str, Any] = model(_snake_case ,attention_mask=_snake_case ,labels=_snake_case )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCAmelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
lowercase__ , lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Optional[int] = 3
lowercase__ : Tuple = '''multi_label_classification'''
lowercase__ : str = input_dict['''input_ids''']
lowercase__ : Union[str, Any] = input_ids.ne(1 ).to(_snake_case )
lowercase__ : List[Any] = ids_tensor(
[self.model_tester.batch_size, config.num_labels] ,self.model_tester.type_sequence_label_size ).to(torch.float )
lowercase__ : Optional[Any] = OpenLlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : List[str] = model(_snake_case ,attention_mask=_snake_case ,labels=_snake_case )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test''' )
def UpperCAmelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Optional[int] = ids_tensor([1, 10] ,config.vocab_size )
lowercase__ : int = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] ,config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase__ : Optional[int] = OpenLlamaModel(_snake_case )
original_model.to(_snake_case )
original_model.eval()
lowercase__ : List[Any] = original_model(_snake_case ).last_hidden_state
lowercase__ : Optional[Any] = original_model(_snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase__ : Optional[int] = {'''type''': scaling_type, '''factor''': 10.0}
lowercase__ : Optional[int] = OpenLlamaModel(_snake_case )
scaled_model.to(_snake_case )
scaled_model.eval()
lowercase__ : Optional[int] = scaled_model(_snake_case ).last_hidden_state
lowercase__ : Optional[Any] = scaled_model(_snake_case ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(_snake_case ,_snake_case ,atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(_snake_case ,_snake_case ,atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(_snake_case ,_snake_case ,atol=1e-5 ) )
| 122 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _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 ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __A :
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : Any ,_snake_case : str=13 ,_snake_case : int=64 ,_snake_case : Dict=2 ,_snake_case : int=3 ,_snake_case : Optional[Any]=True ,_snake_case : List[str]=True ,_snake_case : Dict=32 ,_snake_case : int=5 ,_snake_case : Any=4 ,_snake_case : Optional[int]=37 ,_snake_case : Dict="gelu" ,_snake_case : Union[str, Any]=0.1 ,_snake_case : List[Any]=0.1 ,_snake_case : int=10 ,_snake_case : Any=0.02 ,_snake_case : List[str]=[1, 16, 4, 4] ,_snake_case : str=None ,) -> List[str]:
"""simple docstring"""
lowercase__ : Optional[int] = parent
lowercase__ : Tuple = batch_size
lowercase__ : Union[str, Any] = image_size
lowercase__ : Dict = patch_size
lowercase__ : Dict = num_channels
lowercase__ : str = is_training
lowercase__ : Optional[int] = use_labels
lowercase__ : Dict = hidden_size
lowercase__ : List[Any] = num_hidden_layers
lowercase__ : List[str] = num_attention_heads
lowercase__ : List[str] = intermediate_size
lowercase__ : List[Any] = hidden_act
lowercase__ : Tuple = hidden_dropout_prob
lowercase__ : Union[str, Any] = attention_probs_dropout_prob
lowercase__ : str = type_sequence_label_size
lowercase__ : Tuple = initializer_range
lowercase__ : Union[str, Any] = scope
lowercase__ : Optional[Any] = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
lowercase__ : List[str] = (self.image_size // 32) ** 2
lowercase__ : List[str] = num_patches + 1
def UpperCAmelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : int = None
if self.use_labels:
lowercase__ : Dict = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowercase__ : List[str] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self : int ) -> Tuple:
"""simple docstring"""
lowercase__ : str = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 16, 32],
'''num_groups''': 2,
}
return ViTHybridConfig(
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=_snake_case ,initializer_range=self.initializer_range ,backbone_featmap_shape=self.backbone_featmap_shape ,backbone_config=_snake_case ,)
def UpperCAmelCase ( self : int ,_snake_case : Dict ,_snake_case : str ,_snake_case : Optional[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ : int = ViTHybridModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : str = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Dict ,_snake_case : Dict ,_snake_case : Dict ) -> List[str]:
"""simple docstring"""
lowercase__ : List[str] = self.type_sequence_label_size
lowercase__ : str = ViTHybridForImageClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : List[str] = model(_snake_case ,labels=_snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Any = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : List[Any] = config_and_inputs
lowercase__ : Optional[int] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __A ( A_ ,A_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Optional[Any] = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
lowerCAmelCase : Optional[int] = (
{"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase : Optional[Any] = False
lowerCAmelCase : Any = False
lowerCAmelCase : Optional[int] = False
def UpperCAmelCase ( self : int ) -> Tuple:
"""simple docstring"""
lowercase__ : str = ViTHybridModelTester(self )
lowercase__ : int = ConfigTester(self ,config_class=_snake_case ,has_text_modality=_snake_case ,hidden_size=37 )
def UpperCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCAmelCase ( self : Tuple ) -> Any:
"""simple docstring"""
pass
def UpperCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Union[str, Any] = model_class(_snake_case )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
lowercase__ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_snake_case ,nn.Linear ) )
def UpperCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ , lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Any = model_class(_snake_case )
lowercase__ : List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : str = [*signature.parameters.keys()]
lowercase__ : int = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,_snake_case )
def UpperCAmelCase ( self : Any ) -> str:
"""simple docstring"""
lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_snake_case )
def UpperCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
lowercase__ , lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Optional[Any] = _config_zero_init(_snake_case )
for model_class in self.all_model_classes:
lowercase__ : Dict = model_class(config=_snake_case )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
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""" ,)
@slow
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : Optional[int] = ViTHybridModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def __UpperCAmelCase ( ) -> Dict:
lowercase__ : 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 UpperCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
lowercase__ : List[str] = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
_snake_case )
lowercase__ : Union[str, Any] = self.default_image_processor
lowercase__ : Any = prepare_img()
lowercase__ : Optional[Any] = image_processor(images=_snake_case ,return_tensors='''pt''' ).to(_snake_case )
# forward pass
with torch.no_grad():
lowercase__ : Optional[int] = model(**_snake_case )
# verify the logits
lowercase__ : Any = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape ,_snake_case )
lowercase__ : str = torch.tensor([-1.9090, -0.4993, -0.2389] ).to(_snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,_snake_case ,atol=1e-4 ) )
@slow
@require_accelerate
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Dict = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''' )
lowercase__ : Dict = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' ,device_map='''auto''' )
lowercase__ : Optional[int] = prepare_img()
lowercase__ : List[str] = image_processor(images=_snake_case ,return_tensors='''pt''' )
lowercase__ : Union[str, Any] = model(**_snake_case )
lowercase__ : List[str] = outputs.logits
# model predicts one of the 1000 ImageNet classes
lowercase__ : List[str] = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] ,'''tabby, tabby cat''' )
| 122 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_distilbert''': [
'''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''DistilBertConfig''',
'''DistilBertOnnxConfig''',
],
'''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = ['''DistilBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DistilBertForMaskedLM''',
'''DistilBertForMultipleChoice''',
'''DistilBertForQuestionAnswering''',
'''DistilBertForSequenceClassification''',
'''DistilBertForTokenClassification''',
'''DistilBertModel''',
'''DistilBertPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'''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:
__lowerCamelCase = [
'''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
__lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 467 |
'''simple docstring'''
class UpperCAmelCase :
def __init__( self : Union[str, Any] ):
UpperCAmelCase__ :dict[str, TrieNode] = {} # Mapping from char to TrieNode
UpperCAmelCase__ :Union[str, Any] = False
def __SCREAMING_SNAKE_CASE ( self : Optional[int] , __lowerCamelCase : list[str] ):
for word in words:
self.insert(__lowerCamelCase )
def __SCREAMING_SNAKE_CASE ( self : Dict , __lowerCamelCase : str ):
UpperCAmelCase__ :Tuple = self
for char in word:
if char not in curr.nodes:
UpperCAmelCase__ :Optional[int] = TrieNode()
UpperCAmelCase__ :Dict = curr.nodes[char]
UpperCAmelCase__ :Optional[int] = True
def __SCREAMING_SNAKE_CASE ( self : Any , __lowerCamelCase : str ):
UpperCAmelCase__ :str = self
for char in word:
if char not in curr.nodes:
return False
UpperCAmelCase__ :Dict = curr.nodes[char]
return curr.is_leaf
def __SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCamelCase : str ):
def _delete(__lowerCamelCase : TrieNode , __lowerCamelCase : str , __lowerCamelCase : int ) -> bool:
if index == len(__lowerCamelCase ):
# If word does not exist
if not curr.is_leaf:
return False
UpperCAmelCase__ :int = False
return len(curr.nodes ) == 0
UpperCAmelCase__ :str = word[index]
UpperCAmelCase__ :Optional[Any] = curr.nodes.get(__lowerCamelCase )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
UpperCAmelCase__ :Any = _delete(__lowerCamelCase , __lowerCamelCase , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , __lowerCamelCase , 0 )
def a__ ( UpperCamelCase_ : TrieNode, UpperCamelCase_ : str ):
if node.is_leaf:
print(UpperCamelCase_, end=''' ''' )
for key, value in node.nodes.items():
print_words(UpperCamelCase_, word + key )
def a__ ( ):
UpperCAmelCase__ :Union[str, Any] = '''banana bananas bandana band apple all beast'''.split()
UpperCAmelCase__ :Union[str, Any] = TrieNode()
root.insert_many(UpperCamelCase_ )
# print_words(root, "")
assert all(root.find(UpperCamelCase_ ) for word in words )
assert root.find('''banana''' )
assert not root.find('''bandanas''' )
assert not root.find('''apps''' )
assert root.find('''apple''' )
assert root.find('''all''' )
root.delete('''all''' )
assert not root.find('''all''' )
root.delete('''banana''' )
assert not root.find('''banana''' )
assert root.find('''bananas''' )
return True
def a__ ( UpperCamelCase_ : str, UpperCamelCase_ : bool ):
print(str(UpperCamelCase_ ), '''works!''' if passes else '''doesn\'t work :(''' )
def a__ ( ):
assert test_trie()
def a__ ( ):
print_results('''Testing trie functionality''', test_trie() )
if __name__ == "__main__":
main()
| 467 | 1 |
from __future__ import annotations
def _lowerCamelCase ( _a ):
"""simple docstring"""
_lowerCamelCase = 0.00
_lowerCamelCase = 0
for resistor in resistors:
if resistor <= 0:
_lowerCamelCase = F'''Resistor at index {index} has a negative or zero value!'''
raise ValueError(_a )
first_sum += 1 / float(_a )
index += 1
return 1 / first_sum
def _lowerCamelCase ( _a ):
"""simple docstring"""
_lowerCamelCase = 0.00
_lowerCamelCase = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
_lowerCamelCase = F'''Resistor at index {index} has a negative value!'''
raise ValueError(_a )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 297 |
from maths.prime_factors import prime_factors
def _lowerCamelCase ( _a ):
"""simple docstring"""
if not isinstance(_a , _a ):
_lowerCamelCase = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_a )
if number < 1:
raise ValueError('''Input must be a positive integer''' )
return -1 if len(prime_factors(_a ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 297 | 1 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a__ = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''PatchingSpec''',
],
'''convert''': ['''export''', '''validate_model_outputs'''],
'''features''': ['''FeaturesManager'''],
'''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
a__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 14 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowercase__( A ):
if "model" in orig_key:
snake_case__ : Any = orig_key.replace('model.' , '' )
if "norm1" in orig_key:
snake_case__ : Optional[int] = orig_key.replace('norm1' , 'attention.output.LayerNorm' )
if "norm2" in orig_key:
snake_case__ : Tuple = orig_key.replace('norm2' , 'output.LayerNorm' )
if "norm" in orig_key:
snake_case__ : List[Any] = orig_key.replace('norm' , 'LayerNorm' )
if "transformer" in orig_key:
snake_case__ : Tuple = orig_key.split('.' )[0].split('_' )[-1]
snake_case__ : Optional[Any] = orig_key.replace(f'''transformer_{layer_num}''' , f'''encoder.layer.{layer_num}''' )
if "mha.attn" in orig_key:
snake_case__ : Union[str, Any] = orig_key.replace('mha.attn' , 'attention.self' )
if "mha" in orig_key:
snake_case__ : Optional[Any] = orig_key.replace('mha' , 'attention' )
if "W_q" in orig_key:
snake_case__ : Optional[int] = orig_key.replace('W_q' , 'self.query' )
if "W_k" in orig_key:
snake_case__ : List[Any] = orig_key.replace('W_k' , 'self.key' )
if "W_v" in orig_key:
snake_case__ : str = orig_key.replace('W_v' , 'self.value' )
if "ff1" in orig_key:
snake_case__ : int = orig_key.replace('ff1' , 'intermediate.dense' )
if "ff2" in orig_key:
snake_case__ : str = orig_key.replace('ff2' , 'output.dense' )
if "ff" in orig_key:
snake_case__ : Union[str, Any] = orig_key.replace('ff' , 'output.dense' )
if "mlm_class" in orig_key:
snake_case__ : int = orig_key.replace('mlm.mlm_class' , 'cls.predictions.decoder' )
if "mlm" in orig_key:
snake_case__ : Optional[int] = orig_key.replace('mlm' , 'cls.predictions.transform' )
if "cls" not in orig_key:
snake_case__ : Optional[int] = 'yoso.' + orig_key
return orig_key
def lowercase__( A , A ):
for key in orig_state_dict.copy().keys():
snake_case__ : Optional[Any] = orig_state_dict.pop(A )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
snake_case__ : Optional[Any] = val
snake_case__ : Tuple = orig_state_dict['cls.predictions.decoder.bias']
snake_case__ : Optional[Any] = torch.arange(A ).expand((1, -1) ) + 2
return orig_state_dict
def lowercase__( A , A , A ):
snake_case__ : Tuple = torch.load(A , map_location='cpu' )['model_state_dict']
snake_case__ : Union[str, Any] = YosoConfig.from_json_file(A )
snake_case__ : Optional[int] = YosoForMaskedLM(A )
snake_case__ : str = convert_checkpoint_helper(config.max_position_embeddings , A )
print(model.load_state_dict(A ) )
model.eval()
model.save_pretrained(A )
print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
if __name__ == "__main__":
lowerCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The json file for YOSO model config.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
lowerCamelCase : Optional[Any] = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 170 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case =logging.get_logger(__name__)
__snake_case ={
"""microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}
class UpperCAmelCase_ ( __lowercase , __lowercase ):
lowerCamelCase : Optional[int] = '''focalnet'''
def __init__( self : Any , UpperCAmelCase__ : Optional[Any]=2_2_4 , UpperCAmelCase__ : Union[str, Any]=4 , UpperCAmelCase__ : str=3 , UpperCAmelCase__ : Dict=9_6 , UpperCAmelCase__ : Dict=False , UpperCAmelCase__ : Dict=[1_9_2, 3_8_4, 7_6_8, 7_6_8] , UpperCAmelCase__ : Any=[2, 2, 6, 2] , UpperCAmelCase__ : int=[2, 2, 2, 2] , UpperCAmelCase__ : Union[str, Any]=[3, 3, 3, 3] , UpperCAmelCase__ : Dict="gelu" , UpperCAmelCase__ : Optional[Any]=4.0 , UpperCAmelCase__ : int=0.0 , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : Union[str, Any]=False , UpperCAmelCase__ : Any=1E-4 , UpperCAmelCase__ : Dict=False , UpperCAmelCase__ : List[Any]=False , UpperCAmelCase__ : List[Any]=False , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : Dict=1E-5 , UpperCAmelCase__ : Optional[int]=3_2 , UpperCAmelCase__ : Tuple=None , UpperCAmelCase__ : Optional[Any]=None , **UpperCAmelCase__ : Any , ) -> Any:
super().__init__(**UpperCAmelCase__ )
lowerCAmelCase = image_size
lowerCAmelCase = patch_size
lowerCAmelCase = num_channels
lowerCAmelCase = embed_dim
lowerCAmelCase = use_conv_embed
lowerCAmelCase = hidden_sizes
lowerCAmelCase = depths
lowerCAmelCase = focal_levels
lowerCAmelCase = focal_windows
lowerCAmelCase = hidden_act
lowerCAmelCase = mlp_ratio
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = drop_path_rate
lowerCAmelCase = use_layerscale
lowerCAmelCase = layerscale_value
lowerCAmelCase = use_post_layernorm
lowerCAmelCase = use_post_layernorm_in_modulation
lowerCAmelCase = normalize_modulator
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = encoder_stride
lowerCAmelCase = ['stem'] + [F'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
lowerCAmelCase , lowerCAmelCase = get_aligned_output_features_output_indices(
out_features=UpperCAmelCase__ , out_indices=UpperCAmelCase__ , stage_names=self.stage_names )
| 513 |
'''simple docstring'''
def a_ ( lowerCamelCase : Tuple , lowerCamelCase : Tuple ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
lowerCAmelCase = (boundary[1] - boundary[0]) / steps
lowerCAmelCase = boundary[0]
lowerCAmelCase = boundary[1]
lowerCAmelCase = make_points(lowerCamelCase , lowerCamelCase , lowerCamelCase )
lowerCAmelCase = 0.0
y += (h / 2.0) * f(lowerCamelCase )
for i in x_i:
# print(i)
y += h * f(lowerCamelCase )
y += (h / 2.0) * f(lowerCamelCase )
return y
def a_ ( lowerCamelCase : Dict , lowerCamelCase : Union[str, Any] , lowerCamelCase : Any ):
lowerCAmelCase = a + h
while x < (b - h):
yield x
lowerCAmelCase = x + h
def a_ ( lowerCamelCase : Optional[Any] ): # enter your function here
lowerCAmelCase = (x - 0) * (x - 0)
return y
def a_ ( ):
lowerCAmelCase = 0.0 # Lower bound of integration
lowerCAmelCase = 1.0 # Upper bound of integration
lowerCAmelCase = 10.0 # define number of steps or resolution
lowerCAmelCase = [a, b] # define boundary of integration
lowerCAmelCase = method_a(lowerCamelCase , lowerCamelCase )
print(f'''y = {y}''' )
if __name__ == "__main__":
main()
| 513 | 1 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : str="attention" ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
SCREAMING_SNAKE_CASE_ : str = params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
SCREAMING_SNAKE_CASE_ : Optional[int] = params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
SCREAMING_SNAKE_CASE_ : Union[str, Any] = params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
return k, o, q, v
def __UpperCAmelCase ( lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Any , lowerCamelCase_ : Tuple=False ) -> Dict:
"""simple docstring"""
if split_mlp_wi:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
SCREAMING_SNAKE_CASE_ : int = params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
SCREAMING_SNAKE_CASE_ : Any = (wi_a, wi_a)
else:
SCREAMING_SNAKE_CASE_ : int = params[F'{prefix}/layers_{i}/mlp/wi/kernel']
SCREAMING_SNAKE_CASE_ : str = params[F'{prefix}/layers_{i}/mlp/wo/kernel']
return wi, wo
def __UpperCAmelCase ( lowerCamelCase_ : Any , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Dict ) -> Any:
"""simple docstring"""
return params[F'{prefix}/layers_{i}/{layer_name}/scale']
def __UpperCAmelCase ( lowerCamelCase_ : dict , *, lowerCamelCase_ : int , lowerCamelCase_ : bool ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = traverse_util.flatten_dict(variables['target'] )
SCREAMING_SNAKE_CASE_ : str = {'/'.join(lowerCamelCase_ ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
SCREAMING_SNAKE_CASE_ : Tuple = 'encoder/layers_0/mlp/wi_0/kernel' in old
print('Split MLP:' , lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : int = collections.OrderedDict()
# Shared embeddings.
SCREAMING_SNAKE_CASE_ : Union[str, Any] = old['token_embedder/embedding']
# Encoder.
for i in range(lowerCamelCase_ ):
# Block i, layer 0 (Self Attention).
SCREAMING_SNAKE_CASE_ : Optional[Any] = tax_layer_norm_lookup(lowerCamelCase_ , lowerCamelCase_ , 'encoder' , 'pre_attention_layer_norm' )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = tax_attention_lookup(lowerCamelCase_ , lowerCamelCase_ , 'encoder' , 'attention' )
SCREAMING_SNAKE_CASE_ : List[str] = layer_norm
SCREAMING_SNAKE_CASE_ : List[Any] = k.T
SCREAMING_SNAKE_CASE_ : Any = o.T
SCREAMING_SNAKE_CASE_ : List[str] = q.T
SCREAMING_SNAKE_CASE_ : List[Any] = v.T
# Block i, layer 1 (MLP).
SCREAMING_SNAKE_CASE_ : Dict = tax_layer_norm_lookup(lowerCamelCase_ , lowerCamelCase_ , 'encoder' , 'pre_mlp_layer_norm' )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Union[str, Any] = tax_mlp_lookup(lowerCamelCase_ , lowerCamelCase_ , 'encoder' , lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] = layer_norm
if split_mlp_wi:
SCREAMING_SNAKE_CASE_ : Optional[Any] = wi[0].T
SCREAMING_SNAKE_CASE_ : str = wi[1].T
else:
SCREAMING_SNAKE_CASE_ : List[str] = wi.T
SCREAMING_SNAKE_CASE_ : Tuple = wo.T
SCREAMING_SNAKE_CASE_ : str = old[
'encoder/relpos_bias/rel_embedding'
].T
SCREAMING_SNAKE_CASE_ : Any = old['encoder/encoder_norm/scale']
if not is_encoder_only:
# Decoder.
for i in range(lowerCamelCase_ ):
# Block i, layer 0 (Self Attention).
SCREAMING_SNAKE_CASE_ : str = tax_layer_norm_lookup(lowerCamelCase_ , lowerCamelCase_ , 'decoder' , 'pre_self_attention_layer_norm' )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = tax_attention_lookup(lowerCamelCase_ , lowerCamelCase_ , 'decoder' , 'self_attention' )
SCREAMING_SNAKE_CASE_ : int = layer_norm
SCREAMING_SNAKE_CASE_ : Any = k.T
SCREAMING_SNAKE_CASE_ : Tuple = o.T
SCREAMING_SNAKE_CASE_ : Optional[Any] = q.T
SCREAMING_SNAKE_CASE_ : Any = v.T
# Block i, layer 1 (Cross Attention).
SCREAMING_SNAKE_CASE_ : Dict = tax_layer_norm_lookup(lowerCamelCase_ , lowerCamelCase_ , 'decoder' , 'pre_cross_attention_layer_norm' )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[Any] = tax_attention_lookup(lowerCamelCase_ , lowerCamelCase_ , 'decoder' , 'encoder_decoder_attention' )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = layer_norm
SCREAMING_SNAKE_CASE_ : str = k.T
SCREAMING_SNAKE_CASE_ : List[str] = o.T
SCREAMING_SNAKE_CASE_ : Union[str, Any] = q.T
SCREAMING_SNAKE_CASE_ : List[str] = v.T
# Block i, layer 2 (MLP).
SCREAMING_SNAKE_CASE_ : str = tax_layer_norm_lookup(lowerCamelCase_ , lowerCamelCase_ , 'decoder' , 'pre_mlp_layer_norm' )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[Any] = tax_mlp_lookup(lowerCamelCase_ , lowerCamelCase_ , 'decoder' , lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[Any] = layer_norm
if split_mlp_wi:
SCREAMING_SNAKE_CASE_ : Optional[int] = wi[0].T
SCREAMING_SNAKE_CASE_ : Dict = wi[1].T
else:
SCREAMING_SNAKE_CASE_ : Optional[Any] = wi.T
SCREAMING_SNAKE_CASE_ : Any = wo.T
SCREAMING_SNAKE_CASE_ : Tuple = old['decoder/decoder_norm/scale']
SCREAMING_SNAKE_CASE_ : Optional[int] = old[
'decoder/relpos_bias/rel_embedding'
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
SCREAMING_SNAKE_CASE_ : Any = old['decoder/logits_dense/kernel'].T
return new
def __UpperCAmelCase ( lowerCamelCase_ : Optional[int] , lowerCamelCase_ : bool ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
SCREAMING_SNAKE_CASE_ : Optional[int] = state_dict['shared.weight']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
SCREAMING_SNAKE_CASE_ : str = state_dict['shared.weight']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('Using shared word embeddings as lm_head.' )
SCREAMING_SNAKE_CASE_ : Dict = state_dict['shared.weight']
return state_dict
def __UpperCAmelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : int , lowerCamelCase_ : str ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = checkpoints.load_tax_checkpoint(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Dict = convert_tax_to_pytorch(lowerCamelCase_ , num_layers=config.num_layers , is_encoder_only=lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : int = make_state_dict(lowerCamelCase_ , lowerCamelCase_ )
model.load_state_dict(lowerCamelCase_ , strict=lowerCamelCase_ )
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : List[Any] , lowerCamelCase_ : List[str] , lowerCamelCase_ : bool = False ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = TaConfig.from_json_file(lowerCamelCase_ )
print(F'Building PyTorch model from configuration: {config}' )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
SCREAMING_SNAKE_CASE_ : Optional[Any] = TaEncoderModel(lowerCamelCase_ )
else:
SCREAMING_SNAKE_CASE_ : Tuple = TaForConditionalGeneration(lowerCamelCase_ )
# Load weights from tf checkpoint
load_tax_weights_in_ta(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(lowerCamelCase_ )
# Verify that we can load the checkpoint.
model.from_pretrained(lowerCamelCase_ )
print('Done' )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
UpperCamelCase__ : Dict = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 105 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class _a ( A__ ):
"""simple docstring"""
snake_case ="""EncodecFeatureExtractor"""
snake_case =("""T5Tokenizer""", """T5TokenizerFast""")
def __init__( self , _snake_case , _snake_case ):
super().__init__(_snake_case , _snake_case )
_UpperCAmelCase =self.feature_extractor
_UpperCAmelCase =False
def SCREAMING_SNAKE_CASE ( self , _snake_case=None , _snake_case=None , _snake_case=True ):
return self.tokenizer.get_decoder_prompt_ids(task=_snake_case , language=_snake_case , no_timestamps=_snake_case )
def __call__( self , *_snake_case , **_snake_case ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*_snake_case , **_snake_case )
_UpperCAmelCase =kwargs.pop("audio" , _snake_case )
_UpperCAmelCase =kwargs.pop("sampling_rate" , _snake_case )
_UpperCAmelCase =kwargs.pop("text" , _snake_case )
if len(_snake_case ) > 0:
_UpperCAmelCase =args[0]
_UpperCAmelCase =args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if text is not None:
_UpperCAmelCase =self.tokenizer(_snake_case , **_snake_case )
if audio is not None:
_UpperCAmelCase =self.feature_extractor(_snake_case , *_snake_case , sampling_rate=_snake_case , **_snake_case )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
_UpperCAmelCase =audio_inputs["input_values"]
if "padding_mask" in audio_inputs:
_UpperCAmelCase =audio_inputs["padding_mask"]
return inputs
def SCREAMING_SNAKE_CASE ( self , *_snake_case , **_snake_case ):
_UpperCAmelCase =kwargs.pop("audio" , _snake_case )
_UpperCAmelCase =kwargs.pop("padding_mask" , _snake_case )
if len(_snake_case ) > 0:
_UpperCAmelCase =args[0]
_UpperCAmelCase =args[1:]
if audio_values is not None:
return self._decode_audio(_snake_case , padding_mask=_snake_case )
else:
return self.tokenizer.batch_decode(*_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE ( self , *_snake_case , **_snake_case ):
return self.tokenizer.decode(*_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE ( self , _snake_case , _snake_case = None ):
_UpperCAmelCase =to_numpy(_snake_case )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase =audio_values.shape
if padding_mask is None:
return list(_snake_case )
_UpperCAmelCase =to_numpy(_snake_case )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
_UpperCAmelCase =seq_len - padding_mask.shape[-1]
_UpperCAmelCase =1 - self.feature_extractor.padding_value
_UpperCAmelCase =np.pad(_snake_case , ((0, 0), (0, difference)) , "constant" , constant_values=_snake_case )
_UpperCAmelCase =audio_values.tolist()
for i in range(_snake_case ):
_UpperCAmelCase =np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
_UpperCAmelCase =sliced_audio.reshape(_snake_case , -1 )
return audio_values
| 408 | 0 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a :Union[str, Any] = 10
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
for i in range(__lowerCAmelCase , __lowerCAmelCase ):
if array[i] == target:
return i
return -1
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase )
while left <= right:
if right - left < precision:
return lin_search(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = (left + right) // 3 + 1
SCREAMING_SNAKE_CASE__ : int = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
SCREAMING_SNAKE_CASE__ : List[Any] = one_third - 1
elif array[two_third] < target:
SCREAMING_SNAKE_CASE__ : str = two_third + 1
else:
SCREAMING_SNAKE_CASE__ : List[Any] = one_third + 1
SCREAMING_SNAKE_CASE__ : Optional[int] = two_third - 1
else:
return -1
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
if left < right:
if right - left < precision:
return lin_search(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Any = (left + right) // 3 + 1
SCREAMING_SNAKE_CASE__ : Optional[int] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(__lowerCAmelCase , one_third - 1 , __lowerCAmelCase , __lowerCAmelCase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , __lowerCAmelCase , __lowerCAmelCase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
a :Union[str, Any] = input("Enter numbers separated by comma:\n").strip()
a :Tuple = [int(item.strip()) for item in user_input.split(",")]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
a :Union[str, Any] = int(input("Enter the number to be found in the list:\n").strip())
a :Dict = ite_ternary_search(collection, target)
a :str = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(f'Iterative search: {target} found at positions: {resulta}')
print(f'Recursive search: {target} found at positions: {resulta}')
else:
print("Not found")
| 709 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """t5"""
_SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = d_model
SCREAMING_SNAKE_CASE__ : int = d_kv
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers
SCREAMING_SNAKE_CASE__ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
SCREAMING_SNAKE_CASE__ : Tuple = num_heads
SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor
SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj
SCREAMING_SNAKE_CASE__ : str = use_cache
SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" )
SCREAMING_SNAKE_CASE__ : Dict = act_info[-1]
SCREAMING_SNAKE_CASE__ : str = 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'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new"""
super().__init__(
pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , )
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""}
SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""}
SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_a , direction="""inputs""" )
return common_inputs
@property
def _a ( self ) -> int:
"""simple docstring"""
return 13
| 12 | 0 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class snake_case_ :
'''simple docstring'''
__UpperCamelCase = None
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = True
__UpperCamelCase = None
__UpperCamelCase = 1
__UpperCamelCase = None
__UpperCamelCase = False
__UpperCamelCase = None
__UpperCamelCase = None
def UpperCAmelCase ( self : int ) -> "DownloadConfig":
'''simple docstring'''
return self.__class__(**{k: copy.deepcopy(__lowerCamelCase ) for k, v in self.__dict__.items()} )
| 375 |
def SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , snake_case ) -> int:
# Return True if there is node that has not iterated.
__lowercase = [False] * len(snake_case )
__lowercase = []
queue.append(snake_case )
__lowercase = True
while queue:
__lowercase = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(snake_case )
__lowercase = True
__lowercase = u
return visited[t]
def SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> Dict:
# This array is filled by BFS and to store path
__lowercase = [-1] * (len(snake_case ))
__lowercase = 0
while bfs(snake_case , snake_case , snake_case , snake_case ):
__lowercase = float('Inf' )
__lowercase = sink
while s != source:
# Find the minimum value in select path
__lowercase = min(snake_case , graph[parent[s]][s] )
__lowercase = parent[s]
max_flow += path_flow
__lowercase = sink
while v != source:
__lowercase = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
__lowercase = parent[v]
return max_flow
SCREAMING_SNAKE_CASE_ : Any = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ : Optional[Any] = 0, 5
print(ford_fulkerson(graph, source, sink))
| 375 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""facebook/deit-base-distilled-patch16-224""": (
"""https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
),
# See all DeiT models at https://huggingface.co/models?filter=deit
}
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : Any = 'deit'
def __init__( self : Any , a : Union[str, Any]=768 , a : Optional[Any]=12 , a : Union[str, Any]=12 , a : Optional[int]=3_072 , a : Optional[int]="gelu" , a : Optional[Any]=0.0 , a : List[Any]=0.0 , a : int=0.02 , a : List[str]=1E-1_2 , a : Optional[int]=224 , a : Tuple=16 , a : List[Any]=3 , a : List[str]=True , a : Any=16 , **a : Union[str, Any] , )-> int:
"""simple docstring"""
super().__init__(**a )
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = qkv_bias
lowercase__ = encoder_stride
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : List[Any] = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE_ ( self : int )-> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def SCREAMING_SNAKE_CASE_ ( self : Any )-> float:
"""simple docstring"""
return 1E-4
| 45 |
from PIL import Image
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Image:
def brightness(_SCREAMING_SNAKE_CASE ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' )
return img.point(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
# Load image
with Image.open("""image_data/lena.jpg""") as img:
# Change brightness to 100
lowercase_ = change_brightness(img, 100)
brigt_img.save("""image_data/lena_brightness.png""", format="""png""")
| 45 | 1 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 339 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase__ ( _UpperCAmelCase, unittest.TestCase ):
a_ =LongformerTokenizer
a_ =True
a_ =LongformerTokenizerFast
a_ =True
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
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(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) )
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(__UpperCAmelCase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(__UpperCAmelCase ) )
def UpperCAmelCase ( self , **__UpperCAmelCase )-> Tuple:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def UpperCAmelCase ( self , **__UpperCAmelCase )-> List[str]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def UpperCAmelCase ( self , __UpperCAmelCase )-> List[str]:
'''simple docstring'''
lowerCAmelCase__ = "lower newer"
lowerCAmelCase__ = "lower newer"
return input_text, output_text
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
lowerCAmelCase__ = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowerCAmelCase__ = "lower newer"
lowerCAmelCase__ = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
lowerCAmelCase__ = tokenizer.tokenize(__UpperCAmelCase ) # , add_prefix_space=True)
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = tokens + [tokenizer.unk_token]
lowerCAmelCase__ = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
lowerCAmelCase__ = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" , add_special_tokens=__UpperCAmelCase ) , [0, 31414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" , add_special_tokens=__UpperCAmelCase ) , [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2] , )
@slow
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.tokenizer_class.from_pretrained("allenai/longformer-base-4096" )
lowerCAmelCase__ = tokenizer.encode("sequence builders" , add_special_tokens=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer.encode("multi-sequence build" , add_special_tokens=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer.encode(
"sequence builders" , add_special_tokens=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase )
lowerCAmelCase__ = tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase , __UpperCAmelCase )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def UpperCAmelCase ( self )-> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = "Encode this sequence."
lowerCAmelCase__ = tokenizer.byte_encoder[" ".encode("utf-8" )[0]]
# Testing encoder arguments
lowerCAmelCase__ = tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
lowerCAmelCase__ = tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(__UpperCAmelCase , __UpperCAmelCase )
# Testing spaces after special tokens
lowerCAmelCase__ = "<mask>"
tokenizer.add_special_tokens(
{"mask_token": AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase )} ) # mask token has a left space
lowerCAmelCase__ = tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
lowerCAmelCase__ = "Encode <mask> sequence"
lowerCAmelCase__ = "Encode <mask>sequence"
lowerCAmelCase__ = tokenizer.encode(__UpperCAmelCase )
lowerCAmelCase__ = encoded.index(__UpperCAmelCase )
lowerCAmelCase__ = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = tokenizer.encode(__UpperCAmelCase )
lowerCAmelCase__ = encoded.index(__UpperCAmelCase )
lowerCAmelCase__ = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCAmelCase ( self )-> Union[str, Any]:
'''simple docstring'''
pass
def UpperCAmelCase ( self )-> Optional[int]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
lowerCAmelCase__ = "A, <mask> AllenNLP sentence."
lowerCAmelCase__ = tokenizer_r.encode_plus(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_p.encode_plus(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
lowerCAmelCase__ = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
lowerCAmelCase__ = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(
__UpperCAmelCase , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
__UpperCAmelCase , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , trim_offsets=__UpperCAmelCase )
lowerCAmelCase__ = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
lowerCAmelCase__ = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] , __UpperCAmelCase )
self.assertEqual(post_processor_state["add_prefix_space"] , __UpperCAmelCase )
self.assertEqual(post_processor_state["trim_offsets"] , __UpperCAmelCase )
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
lowerCAmelCase__ = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
lowerCAmelCase__ = F"{text_of_1_token} {text_of_1_token}"
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
__UpperCAmelCase , use_fast=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , trim_offsets=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_r(__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__UpperCAmelCase ) + 1, len(__UpperCAmelCase ) + 1 + len(__UpperCAmelCase )) , )
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
__UpperCAmelCase , use_fast=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , trim_offsets=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_r(__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__UpperCAmelCase ) + 1, len(__UpperCAmelCase ) + 1 + len(__UpperCAmelCase )) , )
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
__UpperCAmelCase , use_fast=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , trim_offsets=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_r(__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__UpperCAmelCase ), len(__UpperCAmelCase ) + 1 + len(__UpperCAmelCase )) , )
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
__UpperCAmelCase , use_fast=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , trim_offsets=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_r(__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__UpperCAmelCase ), len(__UpperCAmelCase ) + 1 + len(__UpperCAmelCase )) , )
lowerCAmelCase__ = F" {text}"
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
__UpperCAmelCase , use_fast=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , trim_offsets=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_r(__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(__UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__UpperCAmelCase ) + 1, 1 + len(__UpperCAmelCase ) + 1 + len(__UpperCAmelCase )) , )
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
__UpperCAmelCase , use_fast=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , trim_offsets=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_r(__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(__UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__UpperCAmelCase ), 1 + len(__UpperCAmelCase ) + 1 + len(__UpperCAmelCase )) , )
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(
__UpperCAmelCase , use_fast=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , trim_offsets=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_r(__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(__UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__UpperCAmelCase ), 1 + len(__UpperCAmelCase ) + 1 + len(__UpperCAmelCase )) , )
| 339 | 1 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
__A =open # noqa: we just need to have a builtin inside this module to test it properly | 113 |
'''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)
__A =logging.getLogger()
def _UpperCamelCase ( ):
UpperCAmelCase__ : List[Any] = argparse.ArgumentParser()
parser.add_argument("""-f""" )
UpperCAmelCase__ : Optional[Any] = parser.parse_args()
return args.f
class _snake_case ( a__ ):
def snake_case__ ( self):
UpperCAmelCase__ : Tuple = logging.StreamHandler(sys.stdout)
logger.addHandler(_lowerCamelCase)
def snake_case__ ( self , _lowerCamelCase):
UpperCAmelCase__ : 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(_lowerCamelCase , """argv""" , _lowerCamelCase):
UpperCAmelCase__ : Union[str, Any] = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(_lowerCamelCase , 0.666)
@slow
@require_torch_non_multi_gpu
def snake_case__ ( self):
UpperCAmelCase__ : List[str] = """
--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(_lowerCamelCase)
UpperCAmelCase__ : Dict = """
--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(_lowerCamelCase)
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(_lowerCamelCase) | 113 | 1 |
def lowerCAmelCase_ ( _lowercase : Tuple , _lowercase : Union[str, Any]) -> float:
"""simple docstring"""
_validate_point(a__)
_validate_point(a__)
if len(a__) != len(a__):
raise ValueError("""Both points must be in the same n-dimensional space""")
return float(sum(abs(a - b) for a, b in zip(a__ , a__)))
def lowerCAmelCase_ ( _lowercase : Union[str, Any]) -> None:
"""simple docstring"""
if point:
if isinstance(a__ , a__):
for item in point:
if not isinstance(a__ , (int, float)):
a__ : Optional[Any] = (
"""Expected a list of numbers as input, found """
F'''{type(a__).__name__}'''
)
raise TypeError(a__)
else:
a__ : Optional[int] = F'''Expected a list of numbers as input, found {type(a__).__name__}'''
raise TypeError(a__)
else:
raise ValueError("""Missing an input""")
def lowerCAmelCase_ ( _lowercase : Optional[int] , _lowercase : str) -> float:
"""simple docstring"""
_validate_point(a__)
_validate_point(a__)
if len(a__) != len(a__):
raise ValueError("""Both points must be in the same n-dimensional space""")
return float(sum(abs(x - y) for x, y in zip(a__ , a__)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 136 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class __A( unittest.TestCase ):
def __init__( self , _snake_case ) -> Dict:
'''simple docstring'''
__a = parent
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
return {}
def __lowerCAmelCase ( ) -> Dict:
__a = '''<HTML>
<HEAD>
<TITLE>sample document</TITLE>
</HEAD>
<BODY BGCOLOR="FFFFFF">
<HR>
<a href="http://google.com">Goog</a>
<H1>This is one header</H1>
<H2>This is a another Header</H2>
<P>Travel from
<P>
<B>SFO to JFK</B>
<BR>
<B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>
<HR>
<div style="color:#0000FF">
<h3>Traveler <b> name </b> is
<p> John Doe </p>
</div>'''
__a = '''
<!DOCTYPE html>
<html>
<body>
<h1>My First Heading</h1>
<p>My first paragraph.</p>
</body>
</html>
'''
return [html_string_a, html_string_a]
@require_bsa
class __A( a , unittest.TestCase ):
snake_case_ = MarkupLMFeatureExtractor if is_bsa_available() else None
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = MarkupLMFeatureExtractionTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
return self.feature_extract_tester.prepare_feat_extract_dict()
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = self.feature_extraction_class()
# Test not batched input
__a = get_html_strings()[0]
__a = feature_extractor(_snake_case )
# fmt: off
__a = [['''sample document''', '''Goog''', '''This is one header''', '''This is a another Header''', '''Travel from''', '''SFO to JFK''', '''on May 2, 2015 at 2:00 pm. For details go to confirm.com''', '''Traveler''', '''name''', '''is''', '''John Doe''']]
__a = [['''/html/head/title''', '''/html/body/a''', '''/html/body/h1''', '''/html/body/h2''', '''/html/body/p''', '''/html/body/p/p/b[1]''', '''/html/body/p/p/b[2]/i''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/b''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/p''']]
# fmt: on
self.assertEqual(encoding.nodes , _snake_case )
self.assertEqual(encoding.xpaths , _snake_case )
# Test batched
__a = get_html_strings()
__a = feature_extractor(_snake_case )
# fmt: off
__a = expected_nodes + [['''My First Heading''', '''My first paragraph.''']]
__a = expected_xpaths + [['''/html/body/h1''', '''/html/body/p''']]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , _snake_case )
self.assertEqual(encoding.xpaths , _snake_case ) | 219 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase = {}
UpperCAmelCase = 2
while True:
UpperCAmelCase = factor_map.pop(A , A )
if factor:
UpperCAmelCase = factor + prime
while x in factor_map:
x += factor
UpperCAmelCase = factor
else:
UpperCAmelCase = prime
yield prime
prime += 1
def lowerCamelCase__ ( A : float = 1E10 ):
'''simple docstring'''
UpperCAmelCase = sieve()
UpperCAmelCase = 1
while True:
UpperCAmelCase = next(A )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(A )
n += 2
if __name__ == "__main__":
print(solution())
| 711 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : int = {
"""configuration_xmod""": [
"""XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XmodConfig""",
"""XmodOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : str = [
"""XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XmodForCausalLM""",
"""XmodForMaskedLM""",
"""XmodForMultipleChoice""",
"""XmodForQuestionAnswering""",
"""XmodForSequenceClassification""",
"""XmodForTokenClassification""",
"""XmodModel""",
"""XmodPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
_lowercase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 50 | 0 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def a (lowerCAmelCase__ = "isbn/0140328726" ):
__a = olid.strip().strip("""/""" ) # Remove leading/trailing whitespace & slashes
if new_olid.count("""/""" ) != 1:
__a = f'''{olid} is not a valid Open Library olid'''
raise ValueError(lowerCAmelCase__ )
return requests.get(f'''https://openlibrary.org/{new_olid}.json''' ).json()
def a (lowerCAmelCase__ ):
__a = {
"""title""": """Title""",
"""publish_date""": """Publish date""",
"""authors""": """Authors""",
"""number_of_pages""": """Number of pages:""",
"""first_sentence""": """First sentence""",
"""isbn_10""": """ISBN (10)""",
"""isbn_13""": """ISBN (13)""",
}
__a = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
__a = [
get_openlibrary_data(author["""key"""] )["""name"""] for author in data["""Authors"""]
]
__a = data["""First sentence"""]["""value"""]
for key, value in data.items():
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__a = """, """.join(lowerCAmelCase__ )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
SCREAMING_SNAKE_CASE = input('\nEnter the ISBN code to search (or \'quit\' to stop): ').strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (1_0, 1_3) or not isbn.isdigit():
print(f'''Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.''')
continue
print(f'''\nSearching Open Library for ISBN: {isbn}...\n''')
try:
SCREAMING_SNAKE_CASE = summarize_book(get_openlibrary_data(f'''isbn/{isbn}'''))
print('\n'.join(f'''{key}: {value}''' for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(f'''Sorry, there are no results for ISBN: {isbn}.''')
| 99 |
import warnings
from ..trainer import Trainer
from ..utils import logging
_SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
class A ( lowerCamelCase_ ):
'''simple docstring'''
def __init__( self : List[str] , _UpperCamelCase : int=None , **_UpperCamelCase : Optional[int]):
warnings.warn(
"`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
"instead." , _UpperCamelCase , )
super().__init__(args=_UpperCamelCase , **_UpperCamelCase)
| 226 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Any = dataset
snake_case : str = process
snake_case : Dict = params
def __len__( self ):
'''simple docstring'''
return len(self.dataset )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : List[Any] = self.dataset[i]
snake_case : int = self.process(SCREAMING_SNAKE_CASE_ ,**self.params )
return processed
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=None ):
'''simple docstring'''
snake_case : List[str] = loader
snake_case : Union[str, Any] = infer
snake_case : List[Any] = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
snake_case : Tuple = None
snake_case : Tuple = loader_batch_size
# Internal bookkeeping
snake_case : List[str] = None
snake_case : Dict = None
def __len__( self ):
'''simple docstring'''
return len(self.loader )
def __iter__( self ):
'''simple docstring'''
snake_case : Optional[Any] = iter(self.loader )
return self
def snake_case_ ( self ):
'''simple docstring'''
if isinstance(self._loader_batch_data ,torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
snake_case : Optional[Any] = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
snake_case : str = {}
for k, element in self._loader_batch_data.items():
if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
# Convert ModelOutput to tuple first
snake_case : List[str] = element.to_tuple()
if isinstance(element[0] ,torch.Tensor ):
snake_case : List[Any] = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] ,np.ndarray ):
snake_case : Tuple = tuple(np.expand_dims(el[self._loader_batch_index] ,0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] ,torch.Tensor ):
snake_case : Optional[Any] = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] ,np.ndarray ):
snake_case : Dict = tuple(np.expand_dims(el[self._loader_batch_index] ,0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
snake_case : str = None
elif isinstance(element[self._loader_batch_index] ,torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
snake_case : Dict = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] ,np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
snake_case : Tuple = np.expand_dims(element[self._loader_batch_index] ,0 )
else:
# This is typically a list, so no need to `unsqueeze`.
snake_case : str = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
snake_case : int = self._loader_batch_data.__class__(SCREAMING_SNAKE_CASE_ )
self._loader_batch_index += 1
return result
def snake_case_ ( self ):
'''simple docstring'''
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
snake_case : int = next(self.iterator )
snake_case : Optional[int] = self.infer(SCREAMING_SNAKE_CASE_ ,**self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(SCREAMING_SNAKE_CASE_ ,torch.Tensor ):
snake_case : int = processed
else:
snake_case : Optional[Any] = list(processed.keys() )[0]
snake_case : List[str] = processed[key]
if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : Tuple = len(SCREAMING_SNAKE_CASE_ )
else:
snake_case : Any = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
snake_case : str = observed_batch_size
# Setting internal index to unwrap the batch
snake_case : str = processed
snake_case : Tuple = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=None ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def __iter__( self ):
'''simple docstring'''
snake_case : Any = iter(self.loader )
snake_case : Optional[Any] = None
return self
def snake_case_ ( self ):
'''simple docstring'''
if self.subiterator is None:
snake_case : Union[str, Any] = self.infer(next(self.iterator ) ,**self.params )
try:
# Try to return next item
snake_case : Any = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
snake_case : List[str] = self.infer(next(self.iterator ) ,**self.params )
snake_case : Any = next(self.subiterator )
return processed
class _A ( snake_case ):
'''simple docstring'''
def __iter__( self ):
'''simple docstring'''
snake_case : str = iter(self.loader )
return self
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[int] = False
snake_case : str = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
snake_case : Any = self.loader_batch_item()
snake_case : str = item.pop("""is_last""" )
accumulator.append(SCREAMING_SNAKE_CASE_ )
if is_last:
return accumulator
while not is_last:
snake_case : Union[str, Any] = self.infer(next(self.iterator ) ,**self.params )
if self.loader_batch_size is not None:
if isinstance(SCREAMING_SNAKE_CASE_ ,torch.Tensor ):
snake_case : Optional[int] = processed
else:
snake_case : Any = list(processed.keys() )[0]
snake_case : str = processed[key]
if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
snake_case : Union[str, Any] = len(SCREAMING_SNAKE_CASE_ )
else:
snake_case : Dict = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
snake_case : int = observed_batch_size
snake_case : List[Any] = processed
snake_case : Union[str, Any] = 0
while self._loader_batch_index < self.loader_batch_size:
snake_case : Union[str, Any] = self.loader_batch_item()
snake_case : str = item.pop("""is_last""" )
accumulator.append(SCREAMING_SNAKE_CASE_ )
if is_last:
return accumulator
else:
snake_case : Dict = processed
snake_case : Union[str, Any] = item.pop("""is_last""" )
accumulator.append(SCREAMING_SNAKE_CASE_ )
return accumulator
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Any = dataset
snake_case : Optional[Any] = key
def __len__( self ):
'''simple docstring'''
return len(self.dataset )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return self.dataset[i][self.key]
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : int = dataset
snake_case : Any = keya
snake_case : Any = keya
def __len__( self ):
'''simple docstring'''
return len(self.dataset )
def __getitem__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 700 |
def lowercase ( __A : Union[str, Any] ) -> int:
'''simple docstring'''
snake_case : Dict = [0] * len(__A )
snake_case : int = []
snake_case : Optional[Any] = [1] * len(__A )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__A ) ):
if indegree[i] == 0:
queue.append(__A )
while queue:
snake_case : Any = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
snake_case : Any = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__A )
print(max(__A ) )
# Adjacency list of Graph
__lowercase : Any = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 315 | 0 |
'''simple docstring'''
from math import pi, sqrt
def _lowercase ( lowerCamelCase__ : float ):
if num <= 0:
raise ValueError("math domain error" )
if num > 1_71.5:
raise OverflowError("math range error" )
elif num - int(lowerCamelCase__ ) not in (0, 0.5):
raise NotImplementedError("num must be an integer or a half-integer" )
elif num == 0.5:
return sqrt(lowerCamelCase__ )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def _lowercase ( ):
assert gamma(0.5 ) == sqrt(lowerCamelCase__ )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
__snake_case : Union[str, Any] = 1.0
while num:
__snake_case : Optional[Any] = float(input("Gamma of: "))
print(f'''gamma({num}) = {gamma(num)}''')
print("\nEnter 0 to exit...")
| 131 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def __lowerCAmelCase ( self ) -> List[Any]:
_a = 1
_a = 3
_a = (3_2, 3_2)
_a = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(snake_case_ )
return image
@property
def __lowerCAmelCase ( self ) -> int:
torch.manual_seed(0 )
_a = UNetaDConditionModel(
block_out_channels=(3_2, 3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=7 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=3_2 , attention_head_dim=8 , use_linear_projection=snake_case_ , only_cross_attention=(True, True, False) , num_class_embeds=1_0_0 , )
return model
@property
def __lowerCAmelCase ( self ) -> List[Any]:
torch.manual_seed(0 )
_a = AutoencoderKL(
block_out_channels=[3_2, 3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
return model
@property
def __lowerCAmelCase ( self ) -> Optional[int]:
torch.manual_seed(0 )
_a = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , hidden_act="gelu" , projection_dim=5_1_2 , )
return CLIPTextModel(snake_case_ )
def __lowerCAmelCase ( self ) -> Optional[int]:
_a = "cpu" # ensure determinism for the device-dependent torch.Generator
_a = self.dummy_cond_unet_upscale
_a = DDPMScheduler()
_a = DDIMScheduler(prediction_type="v_prediction" )
_a = self.dummy_vae
_a = self.dummy_text_encoder
_a = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_a = self.dummy_image.cpu().permute(0 , 2 , 3 , 1 )[0]
_a = Image.fromarray(np.uinta(snake_case_ ) ).convert("RGB" ).resize((6_4, 6_4) )
# make sure here that pndm scheduler skips prk
_a = StableDiffusionUpscalePipeline(
unet=snake_case_ , low_res_scheduler=snake_case_ , scheduler=snake_case_ , vae=snake_case_ , text_encoder=snake_case_ , tokenizer=snake_case_ , max_noise_level=3_5_0 , )
_a = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_a = "A painting of a squirrel eating a burger"
_a = torch.Generator(device=snake_case_ ).manual_seed(0 )
_a = sd_pipe(
[prompt] , image=snake_case_ , generator=snake_case_ , guidance_scale=6.0 , noise_level=2_0 , num_inference_steps=2 , output_type="np" , )
_a = output.images
_a = torch.Generator(device=snake_case_ ).manual_seed(0 )
_a = sd_pipe(
[prompt] , image=snake_case_ , generator=snake_case_ , guidance_scale=6.0 , noise_level=2_0 , num_inference_steps=2 , output_type="np" , return_dict=snake_case_ , )[0]
_a = image[0, -3:, -3:, -1]
_a = image_from_tuple[0, -3:, -3:, -1]
_a = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
_a = np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ) -> int:
_a = "cpu" # ensure determinism for the device-dependent torch.Generator
_a = self.dummy_cond_unet_upscale
_a = DDPMScheduler()
_a = DDIMScheduler(prediction_type="v_prediction" )
_a = self.dummy_vae
_a = self.dummy_text_encoder
_a = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_a = self.dummy_image.cpu().permute(0 , 2 , 3 , 1 )[0]
_a = Image.fromarray(np.uinta(snake_case_ ) ).convert("RGB" ).resize((6_4, 6_4) )
# make sure here that pndm scheduler skips prk
_a = StableDiffusionUpscalePipeline(
unet=snake_case_ , low_res_scheduler=snake_case_ , scheduler=snake_case_ , vae=snake_case_ , text_encoder=snake_case_ , tokenizer=snake_case_ , max_noise_level=3_5_0 , )
_a = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_a = "A painting of a squirrel eating a burger"
_a = sd_pipe(
2 * [prompt] , image=2 * [low_res_image] , guidance_scale=6.0 , noise_level=2_0 , num_inference_steps=2 , output_type="np" , )
_a = output.images
assert image.shape[0] == 2
_a = torch.Generator(device=snake_case_ ).manual_seed(0 )
_a = sd_pipe(
[prompt] , image=snake_case_ , generator=snake_case_ , num_images_per_prompt=2 , guidance_scale=6.0 , noise_level=2_0 , num_inference_steps=2 , output_type="np" , )
_a = output.images
assert image.shape[0] == 2
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU" )
def __lowerCAmelCase ( self ) -> Any:
_a = self.dummy_cond_unet_upscale
_a = DDPMScheduler()
_a = DDIMScheduler(prediction_type="v_prediction" )
_a = self.dummy_vae
_a = self.dummy_text_encoder
_a = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_a = self.dummy_image.cpu().permute(0 , 2 , 3 , 1 )[0]
_a = Image.fromarray(np.uinta(snake_case_ ) ).convert("RGB" ).resize((6_4, 6_4) )
# put models in fp16, except vae as it overflows in fp16
_a = unet.half()
_a = text_encoder.half()
# make sure here that pndm scheduler skips prk
_a = StableDiffusionUpscalePipeline(
unet=snake_case_ , low_res_scheduler=snake_case_ , scheduler=snake_case_ , vae=snake_case_ , text_encoder=snake_case_ , tokenizer=snake_case_ , max_noise_level=3_5_0 , )
_a = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_a = "A painting of a squirrel eating a burger"
_a = torch.manual_seed(0 )
_a = sd_pipe(
[prompt] , image=snake_case_ , generator=snake_case_ , num_inference_steps=2 , output_type="np" , ).images
_a = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
@slow
@require_torch_gpu
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self ) -> Optional[int]:
_a = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png" )
_a = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
"/upsampled_cat.npy" )
_a = "stabilityai/stable-diffusion-x4-upscaler"
_a = StableDiffusionUpscalePipeline.from_pretrained(snake_case_ )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing()
_a = "a cat sitting on a park bench"
_a = torch.manual_seed(0 )
_a = pipe(
prompt=snake_case_ , image=snake_case_ , generator=snake_case_ , output_type="np" , )
_a = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert np.abs(expected_image - image ).max() < 1E-3
def __lowerCAmelCase ( self ) -> Optional[Any]:
_a = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png" )
_a = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
"/upsampled_cat_fp16.npy" )
_a = "stabilityai/stable-diffusion-x4-upscaler"
_a = StableDiffusionUpscalePipeline.from_pretrained(
snake_case_ , torch_dtype=torch.floataa , )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing()
_a = "a cat sitting on a park bench"
_a = torch.manual_seed(0 )
_a = pipe(
prompt=snake_case_ , image=snake_case_ , generator=snake_case_ , output_type="np" , )
_a = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert np.abs(expected_image - image ).max() < 5E-1
def __lowerCAmelCase ( self ) -> Optional[Any]:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_a = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png" )
_a = "stabilityai/stable-diffusion-x4-upscaler"
_a = StableDiffusionUpscalePipeline.from_pretrained(
snake_case_ , torch_dtype=torch.floataa , )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
_a = "a cat sitting on a park bench"
_a = torch.manual_seed(0 )
_a = pipe(
prompt=snake_case_ , image=snake_case_ , generator=snake_case_ , num_inference_steps=5 , output_type="np" , )
_a = torch.cuda.max_memory_allocated()
# make sure that less than 2.9 GB is allocated
assert mem_bytes < 2.9 * 1_0**9
| 131 | 1 |
'''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
__A = datasets.utils.logging.get_logger(__name__)
@dataclass
class a_ ( datasets.BuilderConfig ):
_snake_case = None
_snake_case = "utf-8"
_snake_case = None
_snake_case = None
_snake_case = True # deprecated
_snake_case = None # deprecated
_snake_case = 10 << 20 # 10MB
_snake_case = None
class a_ ( datasets.ArrowBasedBuilder ):
_snake_case = JsonConfig
def SCREAMING_SNAKE_CASE__ (self) -> List[Any]:
"""simple docstring"""
if self.config.block_size is not None:
logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead')
__snake_case : Tuple = 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 SCREAMING_SNAKE_CASE__ (self , __a) -> List[str]:
"""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 : Optional[int] = dl_manager.download_and_extract(self.config.data_files)
if isinstance(__a , (str, list, tuple)):
__snake_case : Tuple = data_files
if isinstance(__a , __a):
__snake_case : Tuple = [files]
__snake_case : Optional[int] = [dl_manager.iter_files(__a) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'files': files})]
__snake_case : Dict = []
for split_name, files in data_files.items():
if isinstance(__a , __a):
__snake_case : List[str] = [files]
__snake_case : Any = [dl_manager.iter_files(__a) for file in files]
splits.append(datasets.SplitGenerator(name=__a , gen_kwargs={'files': files}))
return splits
def SCREAMING_SNAKE_CASE__ (self , __a) -> pa.Table:
"""simple docstring"""
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features) - set(pa_table.column_names):
__snake_case : Any = self.config.features.arrow_schema.field(__a).type
__snake_case : Any = pa_table.append_column(__a , pa.array([None] * len(__a) , type=__a))
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
__snake_case : Optional[Any] = table_cast(__a , self.config.features.arrow_schema)
return pa_table
def SCREAMING_SNAKE_CASE__ (self , __a) -> Tuple:
"""simple docstring"""
for file_idx, file in enumerate(itertools.chain.from_iterable(__a)):
# 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(__a , encoding=self.config.encoding , errors=self.config.encoding_errors) as f:
__snake_case : Union[str, Any] = json.load(__a)
# We keep only the field we are interested in
__snake_case : Tuple = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(__a , (list, tuple)):
__snake_case : Optional[Any] = set().union(*[row.keys() for row in dataset])
__snake_case : List[str] = {col: [row.get(__a) for row in dataset] for col in keys}
else:
__snake_case : Tuple = dataset
__snake_case : Optional[int] = pa.Table.from_pydict(__a)
yield file_idx, self._cast_table(__a)
# If the file has one json object per line
else:
with open(__a , 'rb') as f:
__snake_case : Any = 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
__snake_case : List[Any] = max(self.config.chunksize // 3_2 , 1_6 << 1_0)
__snake_case : Union[str, Any] = (
self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
)
while True:
__snake_case : str = f.read(self.config.chunksize)
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(__a)
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
__snake_case : int = batch.decode(self.config.encoding , errors=__a).encode('utf-8')
try:
while True:
try:
__snake_case : Tuple = paj.read_json(
io.BytesIO(__a) , read_options=paj.ReadOptions(block_size=__a))
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(__a , pa.ArrowInvalid)
and "straddling" not in str(__a)
or block_size > len(__a)
):
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(__a)} 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(
__a , encoding=self.config.encoding , errors=self.config.encoding_errors) as f:
__snake_case : str = json.load(__a)
except json.JSONDecodeError:
logger.error(F"""Failed to read file '{file}' with error {type(__a)}: {e}""")
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(__a , __a): # list is the only sequence type supported in JSON
try:
__snake_case : List[Any] = set().union(*[row.keys() for row in dataset])
__snake_case : Union[str, Any] = {col: [row.get(__a) for row in dataset] for col in keys}
__snake_case : List[Any] = pa.Table.from_pydict(__a)
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(F"""Failed to read file '{file}' with error {type(__a)}: {e}""")
raise ValueError(F"""Not able to read records in the JSON file at {file}.""") from None
yield file_idx, self._cast_table(__a)
break
else:
logger.error(F"""Failed to read file '{file}' with error {type(__a)}: {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(__a)
batch_idx += 1 | 61 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a_ :
def __init__(self , __a = None) -> None:
"""simple docstring"""
if components is None:
__snake_case : List[str] = []
__snake_case : Optional[int] = list(__a)
def __len__(self) -> int:
"""simple docstring"""
return len(self.__components)
def __str__(self) -> str:
"""simple docstring"""
return "(" + ",".join(map(__a , self.__components)) + ")"
def __add__(self , __a) -> Vector:
"""simple docstring"""
__snake_case : Optional[Any] = len(self)
if size == len(__a):
__snake_case : Optional[int] = [self.__components[i] + other.component(__a) for i in range(__a)]
return Vector(__a)
else:
raise Exception('must have the same size')
def __sub__(self , __a) -> Vector:
"""simple docstring"""
__snake_case : Optional[Any] = len(self)
if size == len(__a):
__snake_case : Optional[int] = [self.__components[i] - other.component(__a) for i in range(__a)]
return Vector(__a)
else: # error case
raise Exception('must have the same size')
@overload
def __mul__(self , __a) -> Vector:
"""simple docstring"""
...
@overload
def __mul__(self , __a) -> float:
"""simple docstring"""
...
def __mul__(self , __a) -> float | Vector:
"""simple docstring"""
if isinstance(__a , (float, int)):
__snake_case : str = [c * other for c in self.__components]
return Vector(__a)
elif isinstance(__a , __a) and len(self) == len(__a):
__snake_case : List[Any] = len(self)
__snake_case : Dict = [self.__components[i] * other.component(__a) for i in range(__a)]
return sum(__a)
else: # error case
raise Exception('invalid operand!')
def SCREAMING_SNAKE_CASE__ (self) -> Vector:
"""simple docstring"""
return Vector(self.__components)
def SCREAMING_SNAKE_CASE__ (self , __a) -> float:
"""simple docstring"""
if isinstance(__a , __a) and -len(self.__components) <= i < len(self.__components):
return self.__components[i]
else:
raise Exception('index out of range')
def SCREAMING_SNAKE_CASE__ (self , __a , __a) -> None:
"""simple docstring"""
assert -len(self.__components) <= pos < len(self.__components)
__snake_case : int = value
def SCREAMING_SNAKE_CASE__ (self) -> float:
"""simple docstring"""
if len(self.__components) == 0:
raise Exception('Vector is empty')
__snake_case : Tuple = [c**2 for c in self.__components]
return math.sqrt(sum(__a))
def SCREAMING_SNAKE_CASE__ (self , __a , __a = False) -> float:
"""simple docstring"""
__snake_case : Tuple = self * other
__snake_case : Optional[int] = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den))
else:
return math.acos(num / den)
def _SCREAMING_SNAKE_CASE ( A : int ) -> Vector:
"""simple docstring"""
assert isinstance(A , A )
return Vector([0] * dimension )
def _SCREAMING_SNAKE_CASE ( A : int , A : int ) -> Vector:
"""simple docstring"""
assert isinstance(A , A ) and (isinstance(A , A ))
__snake_case : Any = [0] * dimension
__snake_case : int = 1
return Vector(A )
def _SCREAMING_SNAKE_CASE ( A : float , A : Vector , A : Vector ) -> Vector:
"""simple docstring"""
assert (
isinstance(A , A )
and isinstance(A , A )
and (isinstance(A , (int, float) ))
)
return x * scalar + y
def _SCREAMING_SNAKE_CASE ( A : int , A : int , A : int ) -> Vector:
"""simple docstring"""
random.seed(A )
__snake_case : List[Any] = [random.randint(A , A ) for _ in range(A )]
return Vector(A )
class a_ :
def __init__(self , __a , __a , __a) -> None:
"""simple docstring"""
__snake_case : Union[str, Any] = matrix
__snake_case : int = w
__snake_case : str = h
def __str__(self) -> str:
"""simple docstring"""
__snake_case : Dict = ''
for i in range(self.__height):
ans += "|"
for j in range(self.__width):
if j < self.__width - 1:
ans += str(self.__matrix[i][j]) + ","
else:
ans += str(self.__matrix[i][j]) + "|\n"
return ans
def __add__(self , __a) -> Matrix:
"""simple docstring"""
if self.__width == other.width() and self.__height == other.height():
__snake_case : Tuple = []
for i in range(self.__height):
__snake_case : List[Any] = [
self.__matrix[i][j] + other.component(__a , __a)
for j in range(self.__width)
]
matrix.append(__a)
return Matrix(__a , self.__width , self.__height)
else:
raise Exception('matrix must have the same dimension!')
def __sub__(self , __a) -> Matrix:
"""simple docstring"""
if self.__width == other.width() and self.__height == other.height():
__snake_case : str = []
for i in range(self.__height):
__snake_case : List[str] = [
self.__matrix[i][j] - other.component(__a , __a)
for j in range(self.__width)
]
matrix.append(__a)
return Matrix(__a , self.__width , self.__height)
else:
raise Exception('matrices must have the same dimension!')
@overload
def __mul__(self , __a) -> Matrix:
"""simple docstring"""
...
@overload
def __mul__(self , __a) -> Vector:
"""simple docstring"""
...
def __mul__(self , __a) -> Vector | Matrix:
"""simple docstring"""
if isinstance(__a , __a): # matrix-vector
if len(__a) == self.__width:
__snake_case : Tuple = zero_vector(self.__height)
for i in range(self.__height):
__snake_case : Union[str, Any] = [
self.__matrix[i][j] * other.component(__a)
for j in range(self.__width)
]
ans.change_component(__a , sum(__a))
return ans
else:
raise Exception(
'vector must have the same size as the '
'number of columns of the matrix!')
elif isinstance(__a , (int, float)): # matrix-scalar
__snake_case : str = [
[self.__matrix[i][j] * other for j in range(self.__width)]
for i in range(self.__height)
]
return Matrix(__a , self.__width , self.__height)
return None
def SCREAMING_SNAKE_CASE__ (self) -> int:
"""simple docstring"""
return self.__height
def SCREAMING_SNAKE_CASE__ (self) -> int:
"""simple docstring"""
return self.__width
def SCREAMING_SNAKE_CASE__ (self , __a , __a) -> float:
"""simple docstring"""
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception('change_component: indices out of bounds')
def SCREAMING_SNAKE_CASE__ (self , __a , __a , __a) -> None:
"""simple docstring"""
if 0 <= x < self.__height and 0 <= y < self.__width:
__snake_case : List[Any] = value
else:
raise Exception('change_component: indices out of bounds')
def SCREAMING_SNAKE_CASE__ (self , __a , __a) -> float:
"""simple docstring"""
if self.__height != self.__width:
raise Exception('Matrix is not square')
__snake_case : List[Any] = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(__a)):
__snake_case : Tuple = minor[i][:y] + minor[i][y + 1 :]
return Matrix(__a , self.__width - 1 , self.__height - 1).determinant()
def SCREAMING_SNAKE_CASE__ (self , __a , __a) -> float:
"""simple docstring"""
if self.__height != self.__width:
raise Exception('Matrix is not square')
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(__a , __a)
else:
raise Exception('Indices out of bounds')
def SCREAMING_SNAKE_CASE__ (self) -> float:
"""simple docstring"""
if self.__height != self.__width:
raise Exception('Matrix is not square')
if self.__height < 1:
raise Exception('Matrix has no element')
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
__snake_case : Any = [
self.__matrix[0][y] * self.cofactor(0 , __a) for y in range(self.__width)
]
return sum(__a)
def _SCREAMING_SNAKE_CASE ( A : int ) -> Matrix:
"""simple docstring"""
__snake_case : list[list[float]] = [[0] * n for _ in range(A )]
return Matrix(A , A , A )
def _SCREAMING_SNAKE_CASE ( A : int , A : int , A : int , A : int ) -> Matrix:
"""simple docstring"""
random.seed(A )
__snake_case : list[list[float]] = [
[random.randint(A , A ) for _ in range(A )] for _ in range(A )
]
return Matrix(A , A , A ) | 61 | 1 |
def _lowerCamelCase ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
"""simple docstring"""
a_ : int = [False] * len(SCREAMING_SNAKE_CASE_ )
a_ : Tuple = []
queue.append(SCREAMING_SNAKE_CASE_ )
a_ : int = True
while queue:
a_ : List[str] = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(SCREAMING_SNAKE_CASE_ )
a_ : Dict = True
a_ : Optional[Any] = u
return visited[t]
def _lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Dict ):
"""simple docstring"""
a_ : List[str] = [-1] * (len(SCREAMING_SNAKE_CASE_ ))
a_ : Optional[Any] = 0
while bfs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
a_ : List[Any] = float("""Inf""" )
a_ : List[str] = sink
while s != source:
# Find the minimum value in select path
a_ : Any = min(SCREAMING_SNAKE_CASE_ , graph[parent[s]][s] )
a_ : Optional[Any] = parent[s]
max_flow += path_flow
a_ : str = sink
while v != source:
a_ : Optional[Any] = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
a_ : List[str] = parent[v]
return max_flow
SCREAMING_SNAKE_CASE : Optional[int] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = 0, 5
print(ford_fulkerson(graph, source, sink))
| 419 |
from ..utils import DummyObject, requires_backends
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Optional[int] = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> List[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Dict = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Union[str, Any] = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : List[str] = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> str:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Dict = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : int = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Dict = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Optional[int] = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> List[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Optional[Any] = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Any:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Optional[int] = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> List[str]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Optional[int] = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Dict = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Tuple = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> List[str]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : List[str] = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Any:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Optional[Any] = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Tuple = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> str:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : int = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Tuple:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : int = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Dict = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Tuple = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : int = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : str = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> List[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Tuple = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> int:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : List[str] = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : List[str] = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : int = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Dict = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> int:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Optional[Any] = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> str:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Any = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> str:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : int = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Any:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Any = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
| 419 | 1 |
"""simple docstring"""
from ... import PretrainedConfig
__A = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class a ( A_ ):
A_ : int = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
A_ : Optional[int] = '''nezha'''
def __init__( self : Tuple , lowerCamelCase_ : List[str]=2_11_28 , lowerCamelCase_ : str=7_68 , lowerCamelCase_ : Any=12 , lowerCamelCase_ : Optional[int]=12 , lowerCamelCase_ : Dict=30_72 , lowerCamelCase_ : int="gelu" , lowerCamelCase_ : Any=0.1 , lowerCamelCase_ : Tuple=0.1 , lowerCamelCase_ : str=5_12 , lowerCamelCase_ : List[str]=64 , lowerCamelCase_ : List[Any]=2 , lowerCamelCase_ : Optional[Any]=0.02 , lowerCamelCase_ : Dict=1E-12 , lowerCamelCase_ : List[str]=0.1 , lowerCamelCase_ : List[Any]=0 , lowerCamelCase_ : List[Any]=2 , lowerCamelCase_ : Tuple=3 , lowerCamelCase_ : Any=True , **lowerCamelCase_ : Tuple , ) -> str:
super().__init__(pad_token_id=lowerCamelCase_ , bos_token_id=lowerCamelCase_ , eos_token_id=lowerCamelCase_ , **lowerCamelCase_ )
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = hidden_act
__a = intermediate_size
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = max_relative_position
__a = type_vocab_size
__a = initializer_range
__a = layer_norm_eps
__a = classifier_dropout
__a = use_cache
| 173 | """simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : int = 1000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 173 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase (SCREAMING_SNAKE_CASE_ ):
__A = (DDPMScheduler,)
def _a ( self , **_lowerCAmelCase ) -> Any:
'''simple docstring'''
lowercase = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**snake_case__ )
return config
def _a ( self ) -> Tuple:
'''simple docstring'''
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=snake_case__ )
def _a ( self ) -> int:
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=snake_case__ , beta_end=snake_case__ )
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=snake_case__ )
def _a ( self ) -> Optional[int]:
'''simple docstring'''
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=snake_case__ )
def _a ( self ) -> int:
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=snake_case__ )
def _a ( self ) -> List[Any]:
'''simple docstring'''
self.check_over_configs(thresholding=snake_case__ )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=snake_case__ , prediction_type=snake_case__ , sample_max_value=snake_case__ , )
def _a ( self ) -> Any:
'''simple docstring'''
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=snake_case__ )
def _a ( self ) -> List[str]:
'''simple docstring'''
for t in [0, 500, 999]:
self.check_over_forward(time_step=snake_case__ )
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config()
lowercase = scheduler_class(**snake_case__ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config()
lowercase = scheduler_class(**snake_case__ )
lowercase = len(snake_case__ )
lowercase = self.dummy_model()
lowercase = self.dummy_sample_deter
lowercase = torch.manual_seed(0 )
for t in reversed(range(snake_case__ ) ):
# 1. predict noise residual
lowercase = model(snake_case__ , snake_case__ )
# 2. predict previous mean of sample x_t-1
lowercase = scheduler.step(snake_case__ , snake_case__ , snake_case__ , generator=snake_case__ ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
lowercase = pred_prev_sample
lowercase = torch.sum(torch.abs(snake_case__ ) )
lowercase = torch.mean(torch.abs(snake_case__ ) )
assert abs(result_sum.item() - 258.9606 ) < 1E-2
assert abs(result_mean.item() - 0.3372 ) < 1E-3
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config(prediction_type="""v_prediction""" )
lowercase = scheduler_class(**snake_case__ )
lowercase = len(snake_case__ )
lowercase = self.dummy_model()
lowercase = self.dummy_sample_deter
lowercase = torch.manual_seed(0 )
for t in reversed(range(snake_case__ ) ):
# 1. predict noise residual
lowercase = model(snake_case__ , snake_case__ )
# 2. predict previous mean of sample x_t-1
lowercase = scheduler.step(snake_case__ , snake_case__ , snake_case__ , generator=snake_case__ ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
lowercase = pred_prev_sample
lowercase = torch.sum(torch.abs(snake_case__ ) )
lowercase = torch.mean(torch.abs(snake_case__ ) )
assert abs(result_sum.item() - 202.0296 ) < 1E-2
assert abs(result_mean.item() - 0.2631 ) < 1E-3
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config()
lowercase = scheduler_class(**snake_case__ )
lowercase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=snake_case__ )
lowercase = scheduler.timesteps
for i, timestep in enumerate(snake_case__ ):
if i == len(snake_case__ ) - 1:
lowercase = -1
else:
lowercase = timesteps[i + 1]
lowercase = scheduler.previous_timestep(snake_case__ )
lowercase = prev_t.item()
self.assertEqual(snake_case__ , snake_case__ )
def _a ( self ) -> int:
'''simple docstring'''
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config()
lowercase = scheduler_class(**snake_case__ )
lowercase = [100, 87, 50, 51, 0]
with self.assertRaises(snake_case__ , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=snake_case__ )
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config()
lowercase = scheduler_class(**snake_case__ )
lowercase = [100, 87, 50, 1, 0]
lowercase = len(snake_case__ )
with self.assertRaises(snake_case__ , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=snake_case__ , timesteps=snake_case__ )
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config()
lowercase = scheduler_class(**snake_case__ )
lowercase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
snake_case__ , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=snake_case__ )
| 588 | from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
lowerCAmelCase_ = 42
lowerCAmelCase_ = 42
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : int , snake_case__ : int ) -> Any:
_lowerCamelCase = [[] for _ in range(snake_case__ )]
_lowerCamelCase = size
def __getitem__( self : Dict , snake_case__ : int ) -> Iterator[Edge]:
return iter(self._graph[vertex] )
@property
def _snake_case ( self : str ) -> int:
return self._size
def _snake_case ( self : List[Any] , snake_case__ : int , snake_case__ : int , snake_case__ : int ) -> Tuple:
if weight not in (0, 1):
raise ValueError('Edge weight must be either 0 or 1.' )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError('Vertex indexes must be in [0; size).' )
self._graph[from_vertex].append(Edge(snake_case__ , snake_case__ ) )
def _snake_case ( self : Dict , snake_case__ : int , snake_case__ : int ) -> int | None:
_lowerCamelCase = deque([start_vertex] )
_lowerCamelCase = [None] * self.size
_lowerCamelCase = 0
while queue:
_lowerCamelCase = queue.popleft()
_lowerCamelCase = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
_lowerCamelCase = current_distance + edge.weight
_lowerCamelCase = distances[edge.destination_vertex]
if (
isinstance(snake_case__ , snake_case__ )
and new_distance >= dest_vertex_distance
):
continue
_lowerCamelCase = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError('No path from start_vertex to finish_vertex.' )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod() | 544 | 0 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_lowercase : Any = _symbol_database.Default()
_lowercase : Tuple = _descriptor_pool.Default().AddSerializedFile(
b"\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"
)
_lowercase : Tuple = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, "sentencepiece_model_pb2", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
_lowercase : Tuple = None
_lowercase : Tuple = b"H\003"
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
_lowercase : Union[str, Any] = 45
_lowercase : Optional[Any] = 1581
_lowercase : Optional[int] = 1517
_lowercase : List[str] = 1570
_lowercase : int = 1584
_lowercase : Optional[Any] = 1793
_lowercase : Union[str, Any] = 1795
_lowercase : str = 1916
_lowercase : Optional[Any] = 1864
_lowercase : str = 1905
_lowercase : Any = 1919
_lowercase : str = 2429
_lowercase : Dict = 2208
_lowercase : Optional[Any] = 2418
_lowercase : List[str] = 2323
_lowercase : Any = 2407
# @@protoc_insertion_point(module_scope)
| 546 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowercase : Optional[Any] = 16
_lowercase : List[Any] = 32
def _lowerCAmelCase ( UpperCamelCase__: Accelerator , UpperCamelCase__: int = 16 ) -> Tuple:
"""simple docstring"""
A = AutoTokenizer.from_pretrained("""bert-base-cased""" )
A = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCamelCase__: List[Any] ):
# max_length=None => use the model max length (it's actually the default)
A = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
A = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCamelCase__: Union[str, Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
A = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
A = 16
elif accelerator.mixed_precision != "no":
A = 8
else:
A = None
return tokenizer.pad(
UpperCamelCase__ , padding="""longest""" , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
A = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
A = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowercase : List[Any] = mocked_dataloaders # noqa: F811
def _lowerCAmelCase ( UpperCamelCase__: Tuple , UpperCamelCase__: Any ) -> int:
"""simple docstring"""
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , UpperCamelCase__ ) == "1":
A = 2
# Initialize accelerator
A = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A = config["""lr"""]
A = int(config["""num_epochs"""] )
A = int(config["""seed"""] )
A = int(config["""batch_size"""] )
A = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
A = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
A = batch_size // MAX_GPU_BATCH_SIZE
A = MAX_GPU_BATCH_SIZE
set_seed(UpperCamelCase__ )
A , A = get_dataloaders(UpperCamelCase__ , UpperCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
A = model.to(accelerator.device )
# Instantiate optimizer
A = AdamW(params=model.parameters() , lr=UpperCamelCase__ )
# Instantiate scheduler
A = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__ , num_warmup_steps=1_00 , num_training_steps=(len(UpperCamelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A , A , A , A , A = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Now we train the model
for epoch in range(UpperCamelCase__ ):
model.train()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
A = model(**UpperCamelCase__ )
A = outputs.loss
A = loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
A = 0
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A = model(**UpperCamelCase__ )
A = outputs.logits.argmax(dim=-1 )
A , A = accelerator.gather((predictions, batch["""labels"""]) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(UpperCamelCase__ ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
A = predictions[: len(eval_dataloader.dataset ) - samples_seen]
A = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=UpperCamelCase__ , references=UpperCamelCase__ , )
A = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , UpperCamelCase__ )
def _lowerCAmelCase ( ) -> Dict:
"""simple docstring"""
A = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCamelCase__ , default=UpperCamelCase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
A = parser.parse_args()
A = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 546 | 1 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
A__ = models.Sequential()
# Step 1 - Convolution
# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
# (3,3) is the kernel size (filter matrix)
classifier.add(
layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="""relu""")
)
# Step 2 - Pooling
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(layers.ConvaD(32, (3, 3), activation="""relu"""))
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Step 3 - Flattening
classifier.add(layers.Flatten())
# Step 4 - Full connection
classifier.add(layers.Dense(units=128, activation="""relu"""))
classifier.add(layers.Dense(units=1, activation="""sigmoid"""))
# Compiling the CNN
classifier.compile(
optimizer="""adam""", loss="""binary_crossentropy""", metrics=["""accuracy"""]
)
# Part 2 - Fitting the CNN to the images
# Load Trained model weights
# from keras.models import load_model
# regressor=load_model('cnn.h5')
A__ = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
A__ = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)
A__ = train_datagen.flow_from_directory(
"""dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
)
A__ = test_datagen.flow_from_directory(
"""dataset/test_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
)
classifier.fit_generator(
training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
)
classifier.save("""cnn.h5""")
# Part 3 - Making new predictions
A__ = tf.keras.preprocessing.image.load_img(
"""dataset/single_prediction/image.png""", target_size=(64, 64)
)
A__ = tf.keras.preprocessing.image.img_to_array(test_image)
A__ = np.expand_dims(test_image, axis=0)
A__ = classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
A__ = """Normal"""
if result[0][0] == 1:
A__ = """Abnormality detected"""
| 166 | 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 ( a_ : Optional[int] , a_ : Optional[Any] , a_ : List[str]):
# Initialise PyTorch model
lowerCamelCase :int = BertConfig.from_json_file(a_)
print(F"Building PyTorch model from configuration: {config}")
lowerCamelCase :Union[str, Any] = BertForPreTraining(a_)
# Load weights from tf checkpoint
load_tf_weights_in_bert(a_ , a_ , a_)
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}")
torch.save(model.state_dict() , a_)
if __name__ == "__main__":
A__ = 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__ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 166 | 1 |
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 UpperCamelCase_ ( snake_case_ ):
'''simple docstring'''
lowerCAmelCase = ['''input_features''']
def __init__( self , a=80 , a=1_60_00 , a=1_60 , a=30 , a=4_00 , a=0.0 , a=False , **a , ) -> Dict:
super().__init__(
feature_size=a , sampling_rate=a , padding_value=a , return_attention_mask=a , **a , )
snake_case_ = n_fft
snake_case_ = hop_length
snake_case_ = chunk_length
snake_case_ = chunk_length * sampling_rate
snake_case_ = self.n_samples // hop_length
snake_case_ = sampling_rate
snake_case_ = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=a , min_frequency=0.0 , max_frequency=8_000.0 , sampling_rate=a , norm='slaney' , mel_scale='slaney' , )
def _UpperCamelCase ( self , a ) -> np.ndarray:
snake_case_ = spectrogram(
a , 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' , )
snake_case_ = log_spec[:, :-1]
snake_case_ = np.maximum(a , log_spec.max() - 8.0 )
snake_case_ = (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 _UpperCamelCase ( a , a , a = 0.0 ) -> List[np.ndarray]:
if attention_mask is not None:
snake_case_ = np.array(a , np.intaa )
snake_case_ = []
for vector, length in zip(a , attention_mask.sum(-1 ) ):
snake_case_ = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
snake_case_ = padding_value
normed_input_values.append(a )
else:
snake_case_ = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def __call__( self , a , a = True , a = None , a = None , a = None , a = "max_length" , a = None , a = None , a = None , **a , ) -> BatchFeature:
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.' )
snake_case_ = isinstance(a , 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}''' )
snake_case_ = is_batched_numpy or (
isinstance(a , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
snake_case_ = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(a , np.ndarray ):
snake_case_ = np.asarray(a , dtype=np.floataa )
elif isinstance(a , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
snake_case_ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
snake_case_ = [np.asarray([raw_speech] ).T]
snake_case_ = BatchFeature({'input_features': raw_speech} )
# convert into correct format for padding
snake_case_ = self.pad(
a , padding=a , max_length=max_length if max_length else self.n_samples , truncation=a , pad_to_multiple_of=a , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
snake_case_ = self.zero_mean_unit_var_norm(
padded_inputs['input_features'] , attention_mask=padded_inputs['attention_mask'] , padding_value=self.padding_value , )
snake_case_ = np.stack(padded_inputs['input_features'] , axis=0 )
# make sure list is in array format
snake_case_ = padded_inputs.get('input_features' ).transpose(2 , 0 , 1 )
snake_case_ = [self._np_extract_fbank_features(a ) for waveform in input_features[0]]
if isinstance(input_features[0] , a ):
snake_case_ = [np.asarray(a , dtype=np.floataa ) for feature in input_features]
else:
snake_case_ = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
snake_case_ = padded_inputs['attention_mask'][:, :: self.hop_length]
if return_tensors is not None:
snake_case_ = padded_inputs.convert_to_tensors(a )
return padded_inputs
def _UpperCamelCase ( self ) -> Dict[str, Any]:
snake_case_ = copy.deepcopy(self.__dict__ )
snake_case_ = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 607 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , a , a=13 , a=7 , a=True , a=True , a=True , a=True , a=99 , a=32 , a=5 , a=4 , a=37 , a="gelu" , a=0.1 , a=0.1 , a=5_12 , a=16 , a=2 , a=0.02 , a=4 , ) -> Optional[int]:
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_attention_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_choices
def _UpperCamelCase ( self ) -> List[Any]:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_attention_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ = 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 , is_decoder=a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _UpperCamelCase ( self ) -> int:
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class UpperCamelCase_ ( snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase = True
lowerCAmelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _UpperCamelCase ( self ) -> Union[str, Any]:
snake_case_ = FlaxRoFormerModelTester(self )
@slow
def _UpperCamelCase ( self ) -> Any:
for model_class_name in self.all_model_classes:
snake_case_ = model_class_name.from_pretrained('junnyu/roformer_chinese_small' , from_pt=a )
snake_case_ = model(np.ones((1, 1) ) )
self.assertIsNotNone(a )
@require_flax
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _UpperCamelCase ( self ) -> str:
snake_case_ = FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
snake_case_ = jnp.array([[0, 1, 2, 3, 4, 5]] )
snake_case_ = model(a )[0]
snake_case_ = 5_00_00
snake_case_ = (1, 6, vocab_size)
self.assertEqual(output.shape , a )
snake_case_ = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , a , atol=1E-4 ) )
| 607 | 1 |
from ...configuration_utils import PretrainedConfig
A : Optional[int] = {
'google/tapas-base-finetuned-sqa': (
'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
),
'google/tapas-base-finetuned-wtq': (
'https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'
),
'google/tapas-base-finetuned-wikisql-supervised': (
'https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'
),
'google/tapas-base-finetuned-tabfact': (
'https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'
),
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''tapas'''
def __init__(self : Optional[int] , _UpperCAmelCase : int=3_0522 , _UpperCAmelCase : str=768 , _UpperCAmelCase : Optional[int]=12 , _UpperCAmelCase : int=12 , _UpperCAmelCase : Tuple=3072 , _UpperCAmelCase : Dict="gelu" , _UpperCAmelCase : List[Any]=0.1 , _UpperCAmelCase : Optional[Any]=0.1 , _UpperCAmelCase : List[str]=1024 , _UpperCAmelCase : Any=[3, 256, 256, 2, 256, 256, 10] , _UpperCAmelCase : List[Any]=0.02 , _UpperCAmelCase : Union[str, Any]=1E-1_2 , _UpperCAmelCase : Optional[int]=0 , _UpperCAmelCase : List[str]=10.0 , _UpperCAmelCase : Optional[int]=0 , _UpperCAmelCase : List[Any]=1.0 , _UpperCAmelCase : Dict=None , _UpperCAmelCase : Dict=1.0 , _UpperCAmelCase : str=False , _UpperCAmelCase : int=None , _UpperCAmelCase : List[str]=1.0 , _UpperCAmelCase : Union[str, Any]=1.0 , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Any="ratio" , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : Dict=None , _UpperCAmelCase : List[Any]=64 , _UpperCAmelCase : Any=32 , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : Tuple=False , _UpperCAmelCase : Any=True , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : Any=None , **_UpperCAmelCase : Dict , ) -> Dict:
"""simple docstring"""
super().__init__(pad_token_id=_UpperCAmelCase , **_UpperCAmelCase )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = hidden_act
lowercase__ = intermediate_size
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = type_vocab_sizes
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
# Fine-tuning task hyperparameters
lowercase__ = positive_label_weight
lowercase__ = num_aggregation_labels
lowercase__ = aggregation_loss_weight
lowercase__ = use_answer_as_supervision
lowercase__ = answer_loss_importance
lowercase__ = use_normalized_answer_loss
lowercase__ = huber_loss_delta
lowercase__ = temperature
lowercase__ = aggregation_temperature
lowercase__ = use_gumbel_for_cells
lowercase__ = use_gumbel_for_aggregation
lowercase__ = average_approximation_function
lowercase__ = cell_selection_preference
lowercase__ = answer_loss_cutoff
lowercase__ = max_num_rows
lowercase__ = max_num_columns
lowercase__ = average_logits_per_cell
lowercase__ = select_one_column
lowercase__ = allow_empty_column_selection
lowercase__ = init_cell_selection_weights_to_zero
lowercase__ = reset_position_index_per_cell
lowercase__ = disable_per_token_loss
# Aggregation hyperparameters
lowercase__ = aggregation_labels
lowercase__ = no_aggregation_label_index
if isinstance(self.aggregation_labels , _UpperCAmelCase ):
lowercase__ = {int(_UpperCAmelCase ): v for k, v in aggregation_labels.items()}
| 15 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def UpperCAmelCase_ ( ):
'''simple docstring'''
_a : str = 'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
_a : Any = Image.open(requests.get(A , stream=A ).raw ).convert('RGB' )
return image
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Optional[int] = []
# fmt: off
# vision encoder
rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding') )
rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding') )
rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight') )
rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias') )
rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight') )
rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.weight''', f'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.bias''', f'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.weight''', f'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.bias''', f'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.qkv.weight''', f'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.weight''', f'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.bias''', f'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight') )
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias') )
# fmt: on
return rename_keys
def UpperCAmelCase_ ( A , A , A ):
'''simple docstring'''
_a : Dict = dct.pop(A )
_a : Tuple = val
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
_a : Optional[Any] = state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias''' )
_a : str = state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
_a : Any = torch.cat((q_bias, torch.zeros_like(A , requires_grad=A ), v_bias) )
_a : Tuple = qkv_bias
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : int = 3_6_4 if 'coco' in model_name else 2_2_4
_a : List[Any] = InstructBlipVisionConfig(image_size=A ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
_a : Optional[Any] = TaConfig.from_pretrained('google/flan-t5-xl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
_a : Tuple = TaConfig.from_pretrained('google/flan-t5-xxl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
_a : int = LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf' , vocab_size=3_2_0_0_1 ).to_dict()
elif "vicuna-13b" in model_name:
_a : int = LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf' , vocab_size=3_2_0_0_1 ).to_dict()
else:
raise ValueError('Model name not supported' )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
_a : Tuple = InstructBlipQFormerConfig(vocab_size=3_0_5_2_3 ).to_dict()
_a : Optional[Any] = InstructBlipConfig(vision_config=A , text_config=A , qformer_config=A )
return config, image_size
@torch.no_grad()
def UpperCAmelCase_ ( A , A=None , A=False ):
'''simple docstring'''
_a : Tuple = AutoTokenizer.from_pretrained('bert-base-uncased' , truncation_side='left' )
qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'} )
if "t5" in model_name:
_a : int = TaTokenizerFast.from_pretrained('google/flan-t5-xl' , truncation_side='left' )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
_a : Optional[Any] = LlamaTokenizerFast.from_pretrained(
'huggyllama/llama-7b' , truncation_side='left' , bos_token='</s>' , unk_token='</s>' )
tokenizer.add_special_tokens({'pad_token': '[PAD]'} )
_a , _a : Tuple = get_blipa_config(A )
_a : Optional[Any] = InstructBlipForConditionalGeneration(A ).eval()
_a : Union[str, Any] = {
'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
}
_a , _a : Dict = model_name_to_original[model_name]
# load original model
print('Loading original model...' )
_a : int = 'cuda:1' if torch.cuda.is_available() else 'cpu'
_a : Any = 'cuda:2' if torch.cuda.is_available() else 'cpu'
_a , _a , _a : Optional[int] = load_model_and_preprocess(
name=A , model_type=A , is_eval=A , device=A )
original_model.eval()
print('Done!' )
# update state dict keys
_a : Tuple = original_model.state_dict()
_a : List[Any] = create_rename_keys(A )
for src, dest in rename_keys:
rename_key(A , A , A )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
_a : Union[str, Any] = state_dict.pop(A )
if key.startswith('Qformer.bert' ):
_a : Union[str, Any] = key.replace('Qformer.bert' , 'qformer' )
if "attention.self" in key:
_a : List[Any] = key.replace('self' , 'attention' )
if "llm_proj" in key:
_a : int = key.replace('llm_proj' , 'language_projection' )
if "t5_proj" in key:
_a : str = key.replace('t5_proj' , 'language_projection' )
if key.startswith('llm_model' ):
_a : int = key.replace('llm_model' , 'language_model' )
if key.startswith('t5' ):
_a : Tuple = key.replace('t5' , 'language' )
_a : Optional[Any] = val
# read in qv biases
read_in_q_v_bias(A , A )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(A , strict=A )
_a : Dict = load_demo_image()
_a : Any = 'What is unusual about this image?'
# create processor
_a : Any = BlipImageProcessor(
size={'height': image_size, 'width': image_size} , image_mean=A , image_std=A )
_a : Dict = InstructBlipProcessor(
image_processor=A , tokenizer=A , qformer_tokenizer=A , )
_a : str = processor(images=A , text=A , return_tensors='pt' ).to(A )
# make sure processor creates exact same pixel values
_a : str = vis_processors['eval'](A ).unsqueeze(0 ).to(A )
_a : Optional[int] = inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , A )
original_model.to(A )
hf_model.to(A )
with torch.no_grad():
if "vicuna" in model_name:
_a : Any = original_model({'image': original_pixel_values, 'text_input': [prompt]} ).logits
_a : str = hf_model(**A ).logits
else:
_a : Optional[Any] = original_model(
{'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']} ).logits
_a : List[Any] = tokenizer('\n' , return_tensors='pt' ).input_ids.to(A )
_a : str = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -1_0_0 )
_a : List[str] = hf_model(**A , labels=A ).logits
print('First values of original logits:' , original_logits[0, :3, :3] )
print('First values of HF logits:' , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
_a : str = 1E-4 if 'vicuna' in model_name else 1E-5
assert torch.allclose(original_logits.to(logits.device ) , A , atol=A )
print('Looks ok!' )
print('Generating with original model...' )
_a : Optional[int] = original_model.generate({'image': original_pixel_values, 'prompt': prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print('Generating with HF model...' )
_a : Dict = hf_model.generate(
**A , do_sample=A , num_beams=5 , max_length=2_5_6 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
_a : Tuple = 2
print('Original generation:' , A )
_a : Dict = processor.batch_decode(A , skip_special_tokens=A )
_a : Tuple = [text.strip() for text in output_text]
print('HF generation:' , A )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(A )
hf_model.save_pretrained(A )
if push_to_hub:
processor.push_to_hub(f'''Salesforce/{model_name}''' )
hf_model.push_to_hub(f'''Salesforce/{model_name}''' )
if __name__ == "__main__":
UpperCAmelCase_ : Optional[int] = argparse.ArgumentParser()
UpperCAmelCase_ : List[str] = [
"instructblip-vicuna-7b",
"instructblip-vicuna-13b",
"instructblip-flan-t5-xl",
"instructblip-flan-t5-xxl",
]
parser.add_argument(
"--model_name",
default="instructblip-flan-t5-xl",
choices=choices,
type=str,
help="Path to hf config.json of model to convert",
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub after converting",
)
UpperCAmelCase_ : Optional[int] = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 120 | 0 |
'''simple docstring'''
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='align_text_model'
def __init__( self : List[str] , a : List[str]=3_0522 , a : Tuple=768 , a : Dict=12 , a : Any=12 , a : Union[str, Any]=3072 , a : Tuple="gelu" , a : int=0.1 , a : Optional[Any]=0.1 , a : str=512 , a : Dict=2 , a : Union[str, Any]=0.02 , a : int=1e-12 , a : Any=0 , a : Union[str, Any]="absolute" , a : Any=True , **a : int , ) -> Tuple:
"""simple docstring"""
super().__init__(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE : List[str] = vocab_size
SCREAMING_SNAKE_CASE : str = hidden_size
SCREAMING_SNAKE_CASE : int = num_hidden_layers
SCREAMING_SNAKE_CASE : List[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_act
SCREAMING_SNAKE_CASE : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : List[str] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
SCREAMING_SNAKE_CASE : List[Any] = type_vocab_size
SCREAMING_SNAKE_CASE : List[str] = initializer_range
SCREAMING_SNAKE_CASE : Any = layer_norm_eps
SCREAMING_SNAKE_CASE : Union[str, Any] = position_embedding_type
SCREAMING_SNAKE_CASE : str = use_cache
SCREAMING_SNAKE_CASE : List[str] = pad_token_id
@classmethod
def __UpperCamelCase ( cls : Any , a : Tuple , **a : Union[str, Any] ) -> Dict:
"""simple docstring"""
cls._set_token_in_kwargs(_lowerCAmelCase )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : int = cls.get_config_dict(_lowerCAmelCase , **_lowerCAmelCase )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE : str = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
F"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(_lowerCAmelCase , **_lowerCAmelCase )
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='align_vision_model'
def __init__( self : Any , a : int = 3 , a : int = 600 , a : List[Any] = 2.0 , a : str = 3.1 , a : Dict = 8 , a : Tuple = [3, 3, 5, 3, 5, 5, 3] , a : str = [32, 16, 24, 40, 80, 112, 192] , a : Union[str, Any] = [16, 24, 40, 80, 112, 192, 320] , a : Optional[Any] = [] , a : str = [1, 2, 2, 2, 1, 2, 1] , a : Dict = [1, 2, 2, 3, 3, 4, 1] , a : str = [1, 6, 6, 6, 6, 6, 6] , a : int = 0.25 , a : Union[str, Any] = "swish" , a : Dict = 2560 , a : Optional[int] = "mean" , a : int = 0.02 , a : str = 0.001 , a : Union[str, Any] = 0.99 , a : Union[str, Any] = 0.2 , **a : str , ) -> List[str]:
"""simple docstring"""
super().__init__(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE : Any = num_channels
SCREAMING_SNAKE_CASE : Optional[Any] = image_size
SCREAMING_SNAKE_CASE : Dict = width_coefficient
SCREAMING_SNAKE_CASE : str = depth_coefficient
SCREAMING_SNAKE_CASE : List[str] = depth_divisor
SCREAMING_SNAKE_CASE : str = kernel_sizes
SCREAMING_SNAKE_CASE : int = in_channels
SCREAMING_SNAKE_CASE : int = out_channels
SCREAMING_SNAKE_CASE : Tuple = depthwise_padding
SCREAMING_SNAKE_CASE : List[Any] = strides
SCREAMING_SNAKE_CASE : Optional[int] = num_block_repeats
SCREAMING_SNAKE_CASE : Dict = expand_ratios
SCREAMING_SNAKE_CASE : str = squeeze_expansion_ratio
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE : str = hidden_dim
SCREAMING_SNAKE_CASE : Any = pooling_type
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : Optional[int] = batch_norm_eps
SCREAMING_SNAKE_CASE : int = batch_norm_momentum
SCREAMING_SNAKE_CASE : List[str] = drop_connect_rate
SCREAMING_SNAKE_CASE : List[Any] = sum(_lowerCAmelCase ) * 4
@classmethod
def __UpperCamelCase ( cls : List[str] , a : Dict , **a : Dict ) -> List[Any]:
"""simple docstring"""
cls._set_token_in_kwargs(_lowerCAmelCase )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = cls.get_config_dict(_lowerCAmelCase , **_lowerCAmelCase )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE : Tuple = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
F"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(_lowerCAmelCase , **_lowerCAmelCase )
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='align'
lowerCamelCase__ =True
def __init__( self : List[Any] , a : Any=None , a : Optional[Any]=None , a : List[Any]=640 , a : str=1.0 , a : str=0.02 , **a : Optional[Any] , ) -> Any:
"""simple docstring"""
super().__init__(**_lowerCAmelCase )
if text_config is None:
SCREAMING_SNAKE_CASE : List[str] = {}
logger.info("text_config is None. Initializing the AlignTextConfig with default values." )
if vision_config is None:
SCREAMING_SNAKE_CASE : List[str] = {}
logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )
SCREAMING_SNAKE_CASE : str = AlignTextConfig(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE : Optional[int] = AlignVisionConfig(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE : Optional[int] = projection_dim
SCREAMING_SNAKE_CASE : List[str] = temperature_init_value
SCREAMING_SNAKE_CASE : List[Any] = initializer_range
@classmethod
def __UpperCamelCase ( cls : List[Any] , a : Optional[Any] , a : Union[str, Any] , **a : Optional[int] ) -> Any:
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_lowerCAmelCase )
def __UpperCamelCase ( self : Tuple ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE : Dict = self.text_config.to_dict()
SCREAMING_SNAKE_CASE : Tuple = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE : List[Any] = self.__class__.model_type
return output | 709 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='gptj'
lowerCamelCase__ ={
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Dict , a : Optional[Any]=5_0400 , a : List[str]=2048 , a : List[Any]=4096 , a : int=28 , a : Union[str, Any]=16 , a : List[Any]=64 , a : int=None , a : Optional[int]="gelu_new" , a : Optional[Any]=0.0 , a : Any=0.0 , a : Union[str, Any]=0.0 , a : Union[str, Any]=1e-5 , a : Any=0.02 , a : Optional[int]=True , a : Tuple=5_0256 , a : Union[str, Any]=5_0256 , a : List[Any]=False , **a : str , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = n_positions
SCREAMING_SNAKE_CASE : Tuple = n_embd
SCREAMING_SNAKE_CASE : Tuple = n_layer
SCREAMING_SNAKE_CASE : List[Any] = n_head
SCREAMING_SNAKE_CASE : Tuple = n_inner
SCREAMING_SNAKE_CASE : Any = rotary_dim
SCREAMING_SNAKE_CASE : str = activation_function
SCREAMING_SNAKE_CASE : int = resid_pdrop
SCREAMING_SNAKE_CASE : Optional[int] = embd_pdrop
SCREAMING_SNAKE_CASE : Tuple = attn_pdrop
SCREAMING_SNAKE_CASE : List[str] = layer_norm_epsilon
SCREAMING_SNAKE_CASE : int = initializer_range
SCREAMING_SNAKE_CASE : Tuple = use_cache
SCREAMING_SNAKE_CASE : Union[str, Any] = bos_token_id
SCREAMING_SNAKE_CASE : List[Any] = eos_token_id
super().__init__(
bos_token_id=a , eos_token_id=a , tie_word_embeddings=a , **a )
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Optional[int] , a : PretrainedConfig , a : str = "default" , a : List[PatchingSpec] = None , a : bool = False , ) -> Any:
"""simple docstring"""
super().__init__(a , task=a , patching_specs=a , use_past=a )
if not getattr(self._config , "pad_token_id" , a ):
# TODO: how to do that better?
SCREAMING_SNAKE_CASE : Dict = 0
@property
def __UpperCamelCase ( self : Any ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(a , direction="inputs" )
SCREAMING_SNAKE_CASE : int = {0: "batch", 1: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE : Any = {0: "batch", 1: "sequence"}
return common_inputs
@property
def __UpperCamelCase ( self : Any ) -> int:
"""simple docstring"""
return self._config.n_layer
@property
def __UpperCamelCase ( self : str ) -> int:
"""simple docstring"""
return self._config.n_head
def __UpperCamelCase ( self : str , a : PreTrainedTokenizer , a : int = -1 , a : int = -1 , a : bool = False , a : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = super(a , self ).generate_dummy_inputs(
a , batch_size=a , seq_length=a , is_pair=a , framework=a )
# We need to order the input in the way they appears in the forward()
SCREAMING_SNAKE_CASE : Tuple = OrderedDict({"input_ids": common_inputs["input_ids"]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE : Any = seqlen + 2
SCREAMING_SNAKE_CASE : Dict = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
SCREAMING_SNAKE_CASE : str = [
(torch.zeros(a ), torch.zeros(a )) for _ in range(self.num_layers )
]
SCREAMING_SNAKE_CASE : Optional[int] = common_inputs["attention_mask"]
if self.use_past:
SCREAMING_SNAKE_CASE : List[str] = ordered_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE : Any = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(a , a , dtype=a )] , dim=1 )
return ordered_inputs
@property
def __UpperCamelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
return 13 | 193 | 0 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : Dict , lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
def get_matched_characters(lowerCAmelCase : Optional[int] , lowerCAmelCase : int ) -> str:
__magic_name__ : Optional[Any] = []
__magic_name__ : Optional[Any] = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
__magic_name__ : Tuple = int(max(0 , i - limit ) )
__magic_name__ : List[Any] = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(_A )
__magic_name__ : Optional[int] = f'{_stra[0:_stra.index(_A )]} {_stra[_stra.index(_A ) + 1:]}'
return "".join(_A )
# matching characters
__magic_name__ : List[Any] = get_matched_characters(_A , _A )
__magic_name__ : int = get_matched_characters(_A , _A )
__magic_name__ : str = len(_A )
# transposition
__magic_name__ : List[str] = (
len([(ca, ca) for ca, ca in zip(_A , _A ) if ca != ca] ) // 2
)
if not match_count:
__magic_name__ : Union[str, Any] = 0.0
else:
__magic_name__ : Tuple = (
1
/ 3
* (
match_count / len(_A )
+ match_count / len(_A )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
__magic_name__ : Union[str, Any] = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('''hello''', '''world''')) | 561 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase__ ( A__ , unittest.TestCase ):
"""simple docstring"""
a = TransfoXLTokenizer
a = False
a = False
def lowercase_ ( self : List[str] ) -> List[Any]:
super().setUp()
SCREAMING_SNAKE_CASE__ = [
'''<unk>''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''unwanted''',
'''wa''',
'''un''',
'''running''',
''',''',
'''low''',
'''l''',
]
SCREAMING_SNAKE_CASE__ = 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 lowercase_ ( self : int , **__lowerCamelCase : Any ) -> List[str]:
SCREAMING_SNAKE_CASE__ = True
return TransfoXLTokenizer.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def lowercase_ ( self : str , __lowerCamelCase : Dict ) -> str:
SCREAMING_SNAKE_CASE__ = '''<unk> UNwanted , running'''
SCREAMING_SNAKE_CASE__ = '''<unk> unwanted, running'''
return input_text, output_text
def lowercase_ ( self : int ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ = TransfoXLTokenizer(vocab_file=self.vocab_file , lower_case=__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = tokenizer.tokenize('''<unk> UNwanted , running''' )
self.assertListEqual(__lowerCamelCase , ['''<unk>''', '''unwanted''', ''',''', '''running'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , [0, 4, 8, 7] )
def lowercase_ ( self : Dict ) -> Dict:
SCREAMING_SNAKE_CASE__ = TransfoXLTokenizer(lower_case=__lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo ! how \n Are yoU ? ''' ) , ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
def lowercase_ ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE__ = TransfoXLTokenizer(lower_case=__lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo ! how \n Are yoU ? ''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def lowercase_ ( self : Tuple ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ = TransfoXLTokenizer(lower_case=__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = '''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
SCREAMING_SNAKE_CASE__ = [
'''Hello''',
'''(''',
'''bracket''',
''')''',
'''and''',
'''side''',
'''@-@''',
'''scrolled''',
'''[''',
'''and''',
''']''',
'''Henry''',
'''\'s''',
'''$''',
'''5''',
'''@,@''',
'''000''',
'''with''',
'''3''',
'''@.@''',
'''34''',
'''m''',
'''.''',
'''What''',
'''\'s''',
'''up''',
'''!''',
'''?''',
]
self.assertListEqual(tokenizer.tokenize(__lowerCamelCase ) , __lowerCamelCase )
self.assertEqual(tokenizer.convert_tokens_to_string(__lowerCamelCase ) , __lowerCamelCase )
def lowercase_ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = len(__lowerCamelCase )
tokenizer.add_tokens(['''new1''', '''new2'''] )
tokenizer.move_added_token('''new1''' , 1 )
# Check that moved token is not copied (duplicate)
self.assertEqual(len(__lowerCamelCase ) , original_len + 2 )
# Check that token is moved to specified id
self.assertEqual(tokenizer.encode('''new1''' ) , [1] )
self.assertEqual(tokenizer.decode([1] ) , '''new1''' )
| 493 | 0 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class lowercase__ :
@staticmethod
def A_ ( *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : Optional[Any] ):
pass
def _lowercase ( UpperCamelCase_ ) -> List[Any]:
'''simple docstring'''
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
__snake_case = (
"""https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class lowercase__ ( unittest.TestCase ):
A__ : List[str] =MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def A_ ( self : List[Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE__ = pipeline(
'document-question-answering' , model=UpperCAmelCase_ , tokenizer=UpperCAmelCase_ , image_processor=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = INVOICE_URL
SCREAMING_SNAKE_CASE__ = list(zip(*apply_tesseract(load_image(UpperCAmelCase_ ) , UpperCAmelCase_ , '' ) ) )
SCREAMING_SNAKE_CASE__ = 'What is the placebo?'
SCREAMING_SNAKE_CASE__ = [
{
'image': load_image(UpperCAmelCase_ ),
'question': question,
},
{
'image': image,
'question': question,
},
{
'image': image,
'question': question,
'word_boxes': word_boxes,
},
]
return dqa_pipeline, examples
def A_ ( self : str , UpperCAmelCase_ : int , UpperCAmelCase_ : Dict ):
SCREAMING_SNAKE_CASE__ = dqa_pipeline(UpperCAmelCase_ , top_k=2 )
self.assertEqual(
UpperCAmelCase_ , [
[
{'score': ANY(UpperCAmelCase_ ), 'answer': ANY(UpperCAmelCase_ ), 'start': ANY(UpperCAmelCase_ ), 'end': ANY(UpperCAmelCase_ )},
{'score': ANY(UpperCAmelCase_ ), 'answer': ANY(UpperCAmelCase_ ), 'start': ANY(UpperCAmelCase_ ), 'end': ANY(UpperCAmelCase_ )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def A_ ( self : Tuple ):
SCREAMING_SNAKE_CASE__ = pipeline('document-question-answering' , model='hf-internal-testing/tiny-random-layoutlmv2' )
SCREAMING_SNAKE_CASE__ = INVOICE_URL
SCREAMING_SNAKE_CASE__ = 'How many cats are there?'
SCREAMING_SNAKE_CASE__ = [
{'score': 0.0_001, 'answer': 'oy 2312/2019', 'start': 38, 'end': 39},
{'score': 0.0_001, 'answer': 'oy 2312/2019 DUE', 'start': 38, 'end': 40},
]
SCREAMING_SNAKE_CASE__ = dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2 )
self.assertEqual(nested_simplify(UpperCAmelCase_ , decimals=4 ) , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(nested_simplify(UpperCAmelCase_ , decimals=4 ) , UpperCAmelCase_ )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
SCREAMING_SNAKE_CASE__ = './tests/fixtures/tests_samples/COCO/000000039769.png'
SCREAMING_SNAKE_CASE__ = dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2 )
self.assertEqual(UpperCAmelCase_ , [] )
# We can optionnally pass directly the words and bounding boxes
SCREAMING_SNAKE_CASE__ = './tests/fixtures/tests_samples/COCO/000000039769.png'
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , words=UpperCAmelCase_ , boxes=UpperCAmelCase_ , top_k=2 )
self.assertEqual(UpperCAmelCase_ , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def A_ ( self : Tuple ):
SCREAMING_SNAKE_CASE__ = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , )
SCREAMING_SNAKE_CASE__ = INVOICE_URL
SCREAMING_SNAKE_CASE__ = 'What is the invoice number?'
SCREAMING_SNAKE_CASE__ = dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
{'score': 0.9_944, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_009, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
SCREAMING_SNAKE_CASE__ = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
{'score': 0.9_944, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_009, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
SCREAMING_SNAKE_CASE__ = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
[
{'score': 0.9_944, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_009, 'answer': 'us-001', 'start': 16, 'end': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def A_ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE__ = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , max_seq_len=50 , )
SCREAMING_SNAKE_CASE__ = INVOICE_URL
SCREAMING_SNAKE_CASE__ = 'What is the invoice number?'
SCREAMING_SNAKE_CASE__ = dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
{'score': 0.9_974, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.9_948, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
SCREAMING_SNAKE_CASE__ = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
{'score': 0.9_974, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.9_948, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
SCREAMING_SNAKE_CASE__ = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
[
{'score': 0.9_974, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.9_948, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def A_ ( self : Dict ):
SCREAMING_SNAKE_CASE__ = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=UpperCAmelCase_ , revision='3dc6de3' , )
SCREAMING_SNAKE_CASE__ = INVOICE_URL
SCREAMING_SNAKE_CASE__ = 'What is the invoice number?'
SCREAMING_SNAKE_CASE__ = dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
{'score': 0.4_251, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_819, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
SCREAMING_SNAKE_CASE__ = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
{'score': 0.4_251, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_819, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
SCREAMING_SNAKE_CASE__ = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
[
{'score': 0.4_251, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_819, 'answer': '1110212019', 'start': 23, 'end': 23},
]
]
* 2 , )
SCREAMING_SNAKE_CASE__ = list(zip(*apply_tesseract(load_image(UpperCAmelCase_ ) , UpperCAmelCase_ , '' ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE__ = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
{'score': 0.4_251, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_819, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def A_ ( self : List[Any] ):
SCREAMING_SNAKE_CASE__ = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=UpperCAmelCase_ , revision='3dc6de3' , max_seq_len=50 , )
SCREAMING_SNAKE_CASE__ = INVOICE_URL
SCREAMING_SNAKE_CASE__ = 'What is the invoice number?'
SCREAMING_SNAKE_CASE__ = dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
{'score': 0.9_999, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.9_998, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
SCREAMING_SNAKE_CASE__ = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
[
{'score': 0.9_999, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.9_998, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 , )
SCREAMING_SNAKE_CASE__ = list(zip(*apply_tesseract(load_image(UpperCAmelCase_ ) , UpperCAmelCase_ , '' ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE__ = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
{'score': 0.9_999, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.9_998, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
@slow
@require_torch
def A_ ( self : int ):
SCREAMING_SNAKE_CASE__ = pipeline(
'document-question-answering' , model='naver-clova-ix/donut-base-finetuned-docvqa' , tokenizer=AutoTokenizer.from_pretrained('naver-clova-ix/donut-base-finetuned-docvqa' ) , feature_extractor='naver-clova-ix/donut-base-finetuned-docvqa' , )
SCREAMING_SNAKE_CASE__ = INVOICE_URL
SCREAMING_SNAKE_CASE__ = 'What is the invoice number?'
SCREAMING_SNAKE_CASE__ = dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2 )
self.assertEqual(nested_simplify(UpperCAmelCase_ , decimals=4 ) , [{'answer': 'us-001'}] )
@require_tf
@unittest.skip('Document question answering not implemented in TF' )
def A_ ( self : Any ):
pass
| 400 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__snake_case = logging.get_logger(__name__)
class lowercase__ ( _UpperCAmelCase ):
A__ : Dict =["""input_features""", """is_longer"""]
def __init__( self : Any , UpperCAmelCase_ : int=64 , UpperCAmelCase_ : List[str]=48000 , UpperCAmelCase_ : List[Any]=480 , UpperCAmelCase_ : Dict=10 , UpperCAmelCase_ : int=1024 , UpperCAmelCase_ : List[str]=0.0 , UpperCAmelCase_ : List[Any]=False , UpperCAmelCase_ : float = 0 , UpperCAmelCase_ : float = 14000 , UpperCAmelCase_ : int = None , UpperCAmelCase_ : str = "fusion" , UpperCAmelCase_ : str = "repeatpad" , **UpperCAmelCase_ : List[Any] , ):
super().__init__(
feature_size=UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , padding_value=UpperCAmelCase_ , return_attention_mask=UpperCAmelCase_ , **UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE__ = top_db
SCREAMING_SNAKE_CASE__ = truncation
SCREAMING_SNAKE_CASE__ = padding
SCREAMING_SNAKE_CASE__ = fft_window_size
SCREAMING_SNAKE_CASE__ = (fft_window_size >> 1) + 1
SCREAMING_SNAKE_CASE__ = hop_length
SCREAMING_SNAKE_CASE__ = max_length_s
SCREAMING_SNAKE_CASE__ = max_length_s * sampling_rate
SCREAMING_SNAKE_CASE__ = sampling_rate
SCREAMING_SNAKE_CASE__ = frequency_min
SCREAMING_SNAKE_CASE__ = frequency_max
SCREAMING_SNAKE_CASE__ = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCAmelCase_ , min_frequency=UpperCAmelCase_ , max_frequency=UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , norm=UpperCAmelCase_ , mel_scale='htk' , )
SCREAMING_SNAKE_CASE__ = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCAmelCase_ , min_frequency=UpperCAmelCase_ , max_frequency=UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , norm='slaney' , mel_scale='slaney' , )
def A_ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE__ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE__ = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def A_ ( self : Union[str, Any] , UpperCAmelCase_ : np.array , UpperCAmelCase_ : Optional[np.array] = None ):
SCREAMING_SNAKE_CASE__ = spectrogram(
UpperCAmelCase_ , window_function(self.fft_window_size , 'hann' ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=UpperCAmelCase_ , log_mel='dB' , )
return log_mel_spectrogram.T
def A_ ( self : str , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[str] ):
SCREAMING_SNAKE_CASE__ = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
SCREAMING_SNAKE_CASE__ = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
SCREAMING_SNAKE_CASE__ = [0]
# randomly choose index for each part
SCREAMING_SNAKE_CASE__ = np.random.choice(ranges[0] )
SCREAMING_SNAKE_CASE__ = np.random.choice(ranges[1] )
SCREAMING_SNAKE_CASE__ = np.random.choice(ranges[2] )
SCREAMING_SNAKE_CASE__ = mel[idx_front : idx_front + chunk_frames, :]
SCREAMING_SNAKE_CASE__ = mel[idx_middle : idx_middle + chunk_frames, :]
SCREAMING_SNAKE_CASE__ = mel[idx_back : idx_back + chunk_frames, :]
SCREAMING_SNAKE_CASE__ = torch.tensor(mel[None, None, :] )
SCREAMING_SNAKE_CASE__ = torch.nn.functional.interpolate(
UpperCAmelCase_ , size=[chunk_frames, 64] , mode='bilinear' , align_corners=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = mel_shrink[0][0].numpy()
SCREAMING_SNAKE_CASE__ = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def A_ ( self : Union[str, Any] , UpperCAmelCase_ : np.array , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : str ):
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
SCREAMING_SNAKE_CASE__ = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
SCREAMING_SNAKE_CASE__ = len(UpperCAmelCase_ ) - max_length
SCREAMING_SNAKE_CASE__ = np.random.randint(0 , overflow + 1 )
SCREAMING_SNAKE_CASE__ = waveform[idx : idx + max_length]
SCREAMING_SNAKE_CASE__ = self._np_extract_fbank_features(UpperCAmelCase_ , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
SCREAMING_SNAKE_CASE__ = self._np_extract_fbank_features(UpperCAmelCase_ , self.mel_filters )
SCREAMING_SNAKE_CASE__ = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
SCREAMING_SNAKE_CASE__ = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
SCREAMING_SNAKE_CASE__ = np.stack([mel, mel, mel, mel] , axis=0 )
SCREAMING_SNAKE_CASE__ = False
else:
SCREAMING_SNAKE_CASE__ = self._random_mel_fusion(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = True
else:
raise NotImplementedError(F'data_truncating {truncation} not implemented' )
else:
SCREAMING_SNAKE_CASE__ = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
SCREAMING_SNAKE_CASE__ = int(max_length / len(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE__ = np.stack(np.tile(UpperCAmelCase_ , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
SCREAMING_SNAKE_CASE__ = int(max_length / len(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE__ = np.stack(np.tile(UpperCAmelCase_ , UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE__ = np.pad(UpperCAmelCase_ , (0, max_length - waveform.shape[0]) , mode='constant' , constant_values=0 )
if truncation == "fusion":
SCREAMING_SNAKE_CASE__ = self._np_extract_fbank_features(UpperCAmelCase_ , self.mel_filters )
SCREAMING_SNAKE_CASE__ = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
SCREAMING_SNAKE_CASE__ = self._np_extract_fbank_features(UpperCAmelCase_ , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : List[str] , UpperCAmelCase_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , UpperCAmelCase_ : str = None , UpperCAmelCase_ : Optional[str] = None , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : Optional[Union[str, TensorType]] = None , **UpperCAmelCase_ : Optional[int] , ):
SCREAMING_SNAKE_CASE__ = truncation if truncation is not None else self.truncation
SCREAMING_SNAKE_CASE__ = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
F' was sampled with {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
SCREAMING_SNAKE_CASE__ = isinstance(UpperCAmelCase_ , 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}' )
SCREAMING_SNAKE_CASE__ = is_batched_numpy or (
isinstance(UpperCAmelCase_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
SCREAMING_SNAKE_CASE__ = [np.asarray(UpperCAmelCase_ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(UpperCAmelCase_ , np.ndarray ):
SCREAMING_SNAKE_CASE__ = np.asarray(UpperCAmelCase_ , dtype=np.floataa )
elif isinstance(UpperCAmelCase_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
SCREAMING_SNAKE_CASE__ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
SCREAMING_SNAKE_CASE__ = [np.asarray(UpperCAmelCase_ )]
# convert to mel spectrogram, truncate and pad if needed.
SCREAMING_SNAKE_CASE__ = [
self._get_input_mel(UpperCAmelCase_ , max_length if max_length else self.nb_max_samples , UpperCAmelCase_ , UpperCAmelCase_ )
for waveform in raw_speech
]
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
for mel, longer in padded_inputs:
input_mel.append(UpperCAmelCase_ )
is_longer.append(UpperCAmelCase_ )
if truncation == "fusion" and sum(UpperCAmelCase_ ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
SCREAMING_SNAKE_CASE__ = np.random.randint(0 , len(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE__ = True
if isinstance(input_mel[0] , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE__ = [np.asarray(UpperCAmelCase_ , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
SCREAMING_SNAKE_CASE__ = [[longer] for longer in is_longer]
SCREAMING_SNAKE_CASE__ = {'input_features': input_mel, 'is_longer': is_longer}
SCREAMING_SNAKE_CASE__ = BatchFeature(UpperCAmelCase_ )
if return_tensors is not None:
SCREAMING_SNAKE_CASE__ = input_features.convert_to_tensors(UpperCAmelCase_ )
return input_features
| 400 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , UpperCAmelCase__ : Union[str, Any]=2 , UpperCAmelCase__ : str=3 , UpperCAmelCase__ : Optional[int]=64 , UpperCAmelCase__ : Union[str, Any]=None ):
'''simple docstring'''
lowercase : Optional[Any] =np.random.default_rng(UpperCAmelCase__ )
lowercase : Union[str, Any] =length
lowercase : List[Any] =rng.normal(size=(length,) ).astype(np.floataa )
lowercase : Dict =a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : Tuple ):
'''simple docstring'''
return self.length
def __getitem__( self : Union[str, Any] , UpperCAmelCase__ : Any ):
'''simple docstring'''
return {"x": self.x[i], "y": self.y[i]}
class __SCREAMING_SNAKE_CASE ( torch.nn.Module ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : List[Any]=0 , UpperCAmelCase__ : List[Any]=0 , UpperCAmelCase__ : str=False ):
'''simple docstring'''
super().__init__()
lowercase : Any =torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowercase : List[str] =torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowercase : List[Any] =True
def lowerCamelCase_ ( self : str , UpperCAmelCase__ : Any=None ):
'''simple docstring'''
if self.first_batch:
print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}''' )
lowercase : Any =False
return x * self.a[0] + self.b[0]
class __SCREAMING_SNAKE_CASE ( torch.nn.Module ):
def __init__( self : List[Any] , UpperCAmelCase__ : Optional[Any]=0 , UpperCAmelCase__ : int=0 , UpperCAmelCase__ : Optional[Any]=False ):
'''simple docstring'''
super().__init__()
lowercase : Optional[int] =torch.nn.Parameter(torch.tensor(UpperCAmelCase__ ).float() )
lowercase : int =torch.nn.Parameter(torch.tensor(UpperCAmelCase__ ).float() )
lowercase : Tuple =True
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Optional[Any]=None ):
'''simple docstring'''
if self.first_batch:
print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}''' )
lowercase : List[Any] =False
return x * self.a + self.b
def _lowerCAmelCase ( __magic_name__ : Tuple , __magic_name__ : int = 16 ) -> Union[str, Any]:
from datasets import load_dataset
from transformers import AutoTokenizer
lowercase : Dict =AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowercase : Optional[int] ={'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
lowercase : Dict =load_dataset('''csv''' , data_files=__magic_name__ )
lowercase : int =datasets['''train'''].unique('''label''' )
lowercase : List[str] ={v: i for i, v in enumerate(__magic_name__ )}
def tokenize_function(__magic_name__ : Dict ):
# max_length=None => use the model max length (it's actually the default)
lowercase : Dict =tokenizer(
examples['''sentence1'''] , examples['''sentence2'''] , truncation=__magic_name__ , max_length=__magic_name__ , padding='''max_length''' )
if "label" in examples:
lowercase : List[Any] =[label_to_id[l] for l in examples['''label''']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowercase : Optional[int] =datasets.map(
__magic_name__ , batched=__magic_name__ , remove_columns=['''sentence1''', '''sentence2''', '''label'''] , )
def collate_fn(__magic_name__ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__magic_name__ , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return tokenizer.pad(__magic_name__ , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
lowercase : Union[str, Any] =DataLoader(tokenized_datasets['''train'''] , shuffle=__magic_name__ , collate_fn=__magic_name__ , batch_size=2 )
lowercase : Tuple =DataLoader(tokenized_datasets['''validation'''] , shuffle=__magic_name__ , collate_fn=__magic_name__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 92 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
_lowerCAmelCase = '''https://www.indeed.co.in/jobs?q=mobile+app+development&l='''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = "mumbai" ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = BeautifulSoup(requests.get(url + location ).content , """html.parser""" )
# This attribute finds out all the specifics listed in a job
for job in soup.find_all("""div""" , attrs={"""data-tn-component""": """organicJob"""} ):
lowerCAmelCase__ : Union[str, Any] = job.find("""a""" , attrs={"""data-tn-element""": """jobTitle"""} ).text.strip()
lowerCAmelCase__ : str = job.find("""span""" , {"""class""": """company"""} ).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs('''Bangalore'''), 1):
print(F"""Job {i:>2} is {job[0]} at {job[1]}""")
| 565 | 0 |
'''simple docstring'''
def A ( _UpperCAmelCase : int ,_UpperCAmelCase : Any ,_UpperCAmelCase : Any=False ) -> List[str]:
'''simple docstring'''
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
__lowerCAmelCase : int = len(set_a.intersection(_UpperCAmelCase ) )
if alternative_union:
__lowerCAmelCase : Union[str, Any] = len(_UpperCAmelCase ) + len(_UpperCAmelCase )
else:
__lowerCAmelCase : List[str] = len(set_a.union(_UpperCAmelCase ) )
return intersection / union
if isinstance(_UpperCAmelCase ,(list, tuple) ) and isinstance(_UpperCAmelCase ,(list, tuple) ):
__lowerCAmelCase : Union[str, Any] = [element for element in set_a if element in set_b]
if alternative_union:
__lowerCAmelCase : Dict = len(_UpperCAmelCase ) + len(_UpperCAmelCase )
return len(_UpperCAmelCase ) / union
else:
__lowerCAmelCase : Optional[Any] = set_a + [element for element in set_b if element not in set_a]
return len(_UpperCAmelCase ) / len(_UpperCAmelCase )
return len(_UpperCAmelCase ) / len(_UpperCAmelCase )
return None
if __name__ == "__main__":
A_ = {"a", "b", "c", "d", "e"}
A_ = {"c", "d", "e", "f", "h", "i"}
print(jaccard_similarity(set_a, set_b))
| 123 |
'''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def A ( ) -> Tuple:
'''simple docstring'''
__lowerCAmelCase : int = {
'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
'content': ['a ' * 2_0, 'a ' * 3_0, 'b ' * 7],
}
__lowerCAmelCase : Dict = Dataset.from_dict(_UpperCAmelCase )
return dataset
class UpperCamelCase__ ( a ):
'''simple docstring'''
def snake_case ( self ) -> Union[str, Any]:
__lowerCAmelCase : Dict = get_dataset()
__lowerCAmelCase : Union[str, Any] = make_duplicate_clusters(SCREAMING_SNAKE_CASE , 0.8_5 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def snake_case ( self ) -> Any:
__lowerCAmelCase : List[Any] = get_dataset()
__lowerCAmelCase , __lowerCAmelCase : List[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 )
| 123 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TrajectoryTransformerModel',
'TrajectoryTransformerPreTrainedModel',
'load_tf_weights_in_trajectory_transformer',
]
if TYPE_CHECKING:
from .configuration_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TrajectoryTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
load_tf_weights_in_trajectory_transformer,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 523 | '''simple docstring'''
import numpy as np
def UpperCamelCase__ ( _lowercase : np.array ) -> np.array:
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 523 | 1 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config_file""": """tokenizer_config.json""",
"""merges_file""": """merges.txt""",
}
_A = {
"""vocab_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
),
},
"""tokenizer_config_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
),
},
"""merges_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
),
},
}
_A = """</w>"""
_A = """@@ """
def lowercase_ ( __UpperCAmelCase ) -> List[str]:
lowerCAmelCase__ : Union[str, Any] = set()
lowerCAmelCase__ : Tuple = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCAmelCase__ : Dict = char
return pairs
# Speech2Text2 has no max input length
_A = {"""facebook/s2t-wav2vec2-large-en-de""": 1_0_2_4}
class _lowerCamelCase ( a_ ):
_lowerCamelCase :str = VOCAB_FILES_NAMES
_lowerCamelCase :Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase :str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase :str = ["input_ids", "attention_mask"]
def __init__( self : Optional[int] , UpperCamelCase : str , UpperCamelCase : str="<s>" , UpperCamelCase : Dict="<pad>" , UpperCamelCase : Dict="</s>" , UpperCamelCase : List[str]="<unk>" , UpperCamelCase : Tuple=False , UpperCamelCase : str=None , **UpperCamelCase : Dict , ) -> Tuple:
"""simple docstring"""
super().__init__(
unk_token=UpperCamelCase , bos_token=UpperCamelCase , eos_token=UpperCamelCase , pad_token=UpperCamelCase , do_lower_case=UpperCamelCase , **UpperCamelCase , )
lowerCAmelCase__ : Optional[Any] = do_lower_case
with open(UpperCamelCase , encoding="""utf-8""" ) as vocab_handle:
lowerCAmelCase__ : Tuple = json.load(UpperCamelCase )
lowerCAmelCase__ : Optional[int] = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding.""" )
lowerCAmelCase__ : str = None
lowerCAmelCase__ : Optional[Any] = None
else:
with open(UpperCamelCase , encoding="""utf-8""" ) as merges_handle:
lowerCAmelCase__ : int = merges_handle.read().split("""\n""" )[:-1]
lowerCAmelCase__ : List[str] = [tuple(merge.split()[:2] ) for merge in merges]
lowerCAmelCase__ : int = dict(zip(UpperCamelCase , range(len(UpperCamelCase ) ) ) )
lowerCAmelCase__ : str = {}
@property
def _lowerCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
return len(self.decoder )
def _lowerCAmelCase ( self : int ) -> Dict:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def _lowerCAmelCase ( self : Tuple , UpperCamelCase : Tuple ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : List[str] = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
lowerCAmelCase__ : List[Any] = get_pairs(UpperCamelCase )
if not pairs:
return token
while True:
lowerCAmelCase__ : Any = min(UpperCamelCase , key=lambda UpperCamelCase : self.bpe_ranks.get(UpperCamelCase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase__ : Dict = bigram
lowerCAmelCase__ : Optional[Any] = []
lowerCAmelCase__ : str = 0
while i < len(UpperCamelCase ):
try:
lowerCAmelCase__ : Union[str, Any] = word.index(UpperCamelCase , UpperCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase__ : Union[str, Any] = j
if word[i] == first and i < len(UpperCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase__ : Optional[int] = tuple(UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = new_word
if len(UpperCamelCase ) == 1:
break
else:
lowerCAmelCase__ : Any = get_pairs(UpperCamelCase )
lowerCAmelCase__ : Tuple = """ """.join(UpperCamelCase )
if word == "\n " + BPE_TOKEN_MERGES:
lowerCAmelCase__ : List[Any] = """\n""" + BPE_TOKEN_MERGES
if word.endswith(UpperCamelCase ):
lowerCAmelCase__ : Tuple = word.replace(UpperCamelCase , """""" )
lowerCAmelCase__ : Optional[Any] = word.replace(""" """ , UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = word
return word
def _lowerCAmelCase ( self : Optional[int] , UpperCamelCase : Any ) -> Tuple:
"""simple docstring"""
if self.bpe_ranks is None:
raise ValueError(
"""This tokenizer was instantiated without a `merges.txt` file, so"""
""" that it can only be used for decoding, not for encoding."""
"""Make sure to provide `merges.txt` file at instantiation to enable """
"""encoding.""" )
if self.do_lower_case:
lowerCAmelCase__ : List[Any] = text.lower()
lowerCAmelCase__ : Tuple = text.split()
lowerCAmelCase__ : Dict = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(UpperCamelCase ).split(""" """ ) ) )
return split_tokens
def _lowerCAmelCase ( self : Dict , UpperCamelCase : str ) -> int:
"""simple docstring"""
return self.encoder.get(UpperCamelCase , self.encoder.get(self.unk_token ) )
def _lowerCAmelCase ( self : Optional[Any] , UpperCamelCase : int ) -> str:
"""simple docstring"""
lowerCAmelCase__ : List[Any] = self.decoder.get(UpperCamelCase , self.unk_token )
return result
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : List[str] ) -> str:
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = """ """.join(UpperCamelCase )
# make sure @@ tokens are concatenated
lowerCAmelCase__ : int = """""".join(string.split(UpperCamelCase ) )
return string
def _lowerCAmelCase ( self : Any , UpperCamelCase : str , UpperCamelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(UpperCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCAmelCase__ : Dict = os.path.join(
UpperCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase__ : List[str] = os.path.join(
UpperCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=UpperCamelCase , ensure_ascii=UpperCamelCase ) + """\n""" )
lowerCAmelCase__ : Union[str, Any] = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(UpperCamelCase , """w""" , encoding="""utf-8""" ) as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda UpperCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
""" Please check that the tokenizer is not corrupted!""" )
lowerCAmelCase__ : Optional[int] = token_index
writer.write(""" """.join(UpperCamelCase ) + """\n""" )
index += 1
return (vocab_file, merges_file)
| 716 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_A = logging.getLogger(__name__)
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> Optional[Any]:
# save results
if os.path.exists(__UpperCAmelCase ):
if os.path.exists(os.path.join(__UpperCAmelCase , """config.json""" ) ) and os.path.isfile(
os.path.join(__UpperCAmelCase , """config.json""" ) ):
os.remove(os.path.join(__UpperCAmelCase , """config.json""" ) )
if os.path.exists(os.path.join(__UpperCAmelCase , """pytorch_model.bin""" ) ) and os.path.isfile(
os.path.join(__UpperCAmelCase , """pytorch_model.bin""" ) ):
os.remove(os.path.join(__UpperCAmelCase , """pytorch_model.bin""" ) )
else:
os.makedirs(__UpperCAmelCase )
model.save_pretrained(__UpperCAmelCase )
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase=False ) -> str:
lowerCAmelCase__ : Dict = 2
if unlogit:
lowerCAmelCase__ : Tuple = torch.pow(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ : Any = p * torch.log(__UpperCAmelCase )
lowerCAmelCase__ : Optional[int] = 0
return -plogp.sum(dim=-1 )
def lowercase_ ( __UpperCAmelCase ) -> Any:
logger.info("""lv, h >\t""" + """\t""".join(f"""{x + 1}""" for x in range(len(__UpperCAmelCase ) ) ) )
for row in range(len(__UpperCAmelCase ) ):
if tensor.dtype != torch.long:
logger.info(f"""layer {row + 1}:\t""" + """\t""".join(f"""{x:.5f}""" for x in tensor[row].cpu().data ) )
else:
logger.info(f"""layer {row + 1}:\t""" + """\t""".join(f"""{x:d}""" for x in tensor[row].cpu().data ) )
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=None , __UpperCAmelCase=False ) -> List[Any]:
lowerCAmelCase__ , lowerCAmelCase__ : Tuple = model.config.num_hidden_layers, model.config.num_attention_heads
lowerCAmelCase__ : Dict = torch.zeros(__UpperCAmelCase , __UpperCAmelCase ).to(args.device )
lowerCAmelCase__ : str = torch.zeros(__UpperCAmelCase , __UpperCAmelCase ).to(args.device )
if head_mask is None:
lowerCAmelCase__ : Dict = torch.ones(__UpperCAmelCase , __UpperCAmelCase ).to(args.device )
head_mask.requires_grad_(requires_grad=__UpperCAmelCase )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
lowerCAmelCase__ : str = None
lowerCAmelCase__ : Tuple = 0.0
lowerCAmelCase__ : List[Any] = 0.0
for step, inputs in enumerate(tqdm(__UpperCAmelCase , desc="""Iteration""" , disable=args.local_rank not in [-1, 0] ) ):
lowerCAmelCase__ : Union[str, Any] = tuple(t.to(args.device ) for t in inputs )
((lowerCAmelCase__) , ) : Any = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
lowerCAmelCase__ : Any = model(__UpperCAmelCase , labels=__UpperCAmelCase , head_mask=__UpperCAmelCase )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : str = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(__UpperCAmelCase ):
lowerCAmelCase__ : Union[str, Any] = entropy(attn.detach() , __UpperCAmelCase )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(__UpperCAmelCase ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
lowerCAmelCase__ : Optional[int] = 2
lowerCAmelCase__ : Optional[Any] = torch.pow(torch.pow(__UpperCAmelCase , __UpperCAmelCase ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1E-20
if not args.dont_normalize_global_importance:
lowerCAmelCase__ : List[str] = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info("""Attention entropies""" )
print_ad_tensor(__UpperCAmelCase )
if compute_importance:
logger.info("""Head importance scores""" )
print_ad_tensor(__UpperCAmelCase )
logger.info("""Head ranked by importance scores""" )
lowerCAmelCase__ : Dict = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
lowerCAmelCase__ : Optional[Any] = torch.arange(
head_importance.numel() , device=args.device )
lowerCAmelCase__ : Tuple = head_ranks.view_as(__UpperCAmelCase )
print_ad_tensor(__UpperCAmelCase )
return attn_entropy, head_importance, total_loss
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Union[str, Any]:
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : List[str] = compute_heads_importance(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , compute_entropy=__UpperCAmelCase )
lowerCAmelCase__ : Any = 1 / loss # instead of downsteam score use the LM loss
logger.info("""Pruning: original score: %f, threshold: %f""" , __UpperCAmelCase , original_score * args.masking_threshold )
lowerCAmelCase__ : Any = torch.ones_like(__UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
lowerCAmelCase__ : Optional[int] = original_score
while current_score >= original_score * args.masking_threshold:
lowerCAmelCase__ : Tuple = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
lowerCAmelCase__ : Tuple = float("""Inf""" )
lowerCAmelCase__ : str = head_importance.view(-1 ).sort()[1]
if len(__UpperCAmelCase ) <= num_to_mask:
print("""BREAK BY num_to_mask""" )
break
# mask heads
lowerCAmelCase__ : List[Any] = current_heads_to_mask[:num_to_mask]
logger.info("""Heads to mask: %s""" , str(current_heads_to_mask.tolist() ) )
lowerCAmelCase__ : str = new_head_mask.view(-1 )
lowerCAmelCase__ : str = 0.0
lowerCAmelCase__ : Optional[int] = new_head_mask.view_as(__UpperCAmelCase )
lowerCAmelCase__ : Optional[int] = new_head_mask.clone().detach()
print_ad_tensor(__UpperCAmelCase )
# Compute metric and head importance again
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Optional[Any] = compute_heads_importance(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , compute_entropy=__UpperCAmelCase , head_mask=__UpperCAmelCase )
lowerCAmelCase__ : int = 1 / loss
logger.info(
"""Masking: current score: %f, remaining heads %d (%.1f percents)""" , __UpperCAmelCase , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info("""Final head mask""" )
print_ad_tensor(__UpperCAmelCase )
np.save(os.path.join(args.output_dir , """head_mask.npy""" ) , head_mask.detach().cpu().numpy() )
return head_mask
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
lowerCAmelCase__ : List[str] = datetime.now()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = compute_heads_importance(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , compute_entropy=__UpperCAmelCase , compute_importance=__UpperCAmelCase , head_mask=__UpperCAmelCase )
lowerCAmelCase__ : Tuple = 1 / loss
lowerCAmelCase__ : List[str] = datetime.now() - before_time
lowerCAmelCase__ : Optional[Any] = sum(p.numel() for p in model.parameters() )
lowerCAmelCase__ : List[str] = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__UpperCAmelCase ) )
}
for k, v in heads_to_prune.items():
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : int = [
v,
]
assert sum(len(__UpperCAmelCase ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(__UpperCAmelCase )
lowerCAmelCase__ : List[str] = sum(p.numel() for p in model.parameters() )
lowerCAmelCase__ : Dict = datetime.now()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Tuple = compute_heads_importance(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , compute_entropy=__UpperCAmelCase , compute_importance=__UpperCAmelCase , head_mask=__UpperCAmelCase , actually_pruned=__UpperCAmelCase , )
lowerCAmelCase__ : List[str] = 1 / loss
lowerCAmelCase__ : int = datetime.now() - before_time
logger.info(
"""Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)""" , __UpperCAmelCase , __UpperCAmelCase , pruned_num_params / original_num_params * 100 , )
logger.info("""Pruning: score with masking: %f score with pruning: %f""" , __UpperCAmelCase , __UpperCAmelCase )
logger.info("""Pruning: speed ratio (original timing / new timing): %f percents""" , original_time / new_time * 100 )
save_model(__UpperCAmelCase , args.output_dir )
def lowercase_ ( ) -> int:
lowerCAmelCase__ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--data_dir""" , default=__UpperCAmelCase , type=__UpperCAmelCase , required=__UpperCAmelCase , help="""The input data dir. Should contain the .tsv files (or other data files) for the task.""" , )
parser.add_argument(
"""--model_name_or_path""" , default=__UpperCAmelCase , type=__UpperCAmelCase , required=__UpperCAmelCase , help="""Path to pretrained model or model identifier from huggingface.co/models""" , )
parser.add_argument(
"""--output_dir""" , default=__UpperCAmelCase , type=__UpperCAmelCase , required=__UpperCAmelCase , help="""The output directory where the model predictions and checkpoints will be written.""" , )
# Other parameters
parser.add_argument(
"""--config_name""" , default="""""" , type=__UpperCAmelCase , help="""Pretrained config name or path if not the same as model_name_or_path""" , )
parser.add_argument(
"""--tokenizer_name""" , default="""""" , type=__UpperCAmelCase , help="""Pretrained tokenizer name or path if not the same as model_name_or_path""" , )
parser.add_argument(
"""--cache_dir""" , default=__UpperCAmelCase , type=__UpperCAmelCase , help="""Where do you want to store the pre-trained models downloaded from s3""" , )
parser.add_argument(
"""--data_subset""" , type=__UpperCAmelCase , default=-1 , help="""If > 0: limit the data to a subset of data_subset instances.""" )
parser.add_argument(
"""--overwrite_output_dir""" , action="""store_true""" , help="""Whether to overwrite data in output directory""" )
parser.add_argument(
"""--overwrite_cache""" , action="""store_true""" , help="""Overwrite the cached training and evaluation sets""" )
parser.add_argument(
"""--dont_normalize_importance_by_layer""" , action="""store_true""" , help="""Don't normalize importance score by layers""" )
parser.add_argument(
"""--dont_normalize_global_importance""" , action="""store_true""" , help="""Don't normalize all importance scores between 0 and 1""" , )
parser.add_argument(
"""--try_masking""" , action="""store_true""" , help="""Whether to try to mask head until a threshold of accuracy.""" )
parser.add_argument(
"""--masking_threshold""" , default=0.9 , type=__UpperCAmelCase , help="""masking threshold in term of metrics (stop masking when metric < threshold * original metric value).""" , )
parser.add_argument(
"""--masking_amount""" , default=0.1 , type=__UpperCAmelCase , help="""Amount to heads to masking at each masking step.""" )
parser.add_argument("""--metric_name""" , default="""acc""" , type=__UpperCAmelCase , help="""Metric to use for head masking.""" )
parser.add_argument(
"""--max_seq_length""" , default=128 , type=__UpperCAmelCase , help=(
"""The maximum total input sequence length after WordPiece tokenization. \n"""
"""Sequences longer than this will be truncated, sequences shorter padded."""
) , )
parser.add_argument("""--batch_size""" , default=1 , type=__UpperCAmelCase , help="""Batch size.""" )
parser.add_argument("""--seed""" , type=__UpperCAmelCase , default=42 )
parser.add_argument("""--local_rank""" , type=__UpperCAmelCase , default=-1 , help="""local_rank for distributed training on gpus""" )
parser.add_argument("""--no_cuda""" , action="""store_true""" , help="""Whether not to use CUDA when available""" )
parser.add_argument("""--server_ip""" , type=__UpperCAmelCase , default="""""" , help="""Can be used for distant debugging.""" )
parser.add_argument("""--server_port""" , type=__UpperCAmelCase , default="""""" , help="""Can be used for distant debugging.""" )
lowerCAmelCase__ : Dict = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("""Waiting for debugger attach""" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__UpperCAmelCase )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
lowerCAmelCase__ : Optional[int] = torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu""" )
lowerCAmelCase__ : List[Any] = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
lowerCAmelCase__ : Union[str, Any] = torch.device("""cuda""" , args.local_rank )
lowerCAmelCase__ : int = 1
torch.distributed.init_process_group(backend="""nccl""" ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info("""device: {} n_gpu: {}, distributed: {}""".format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
lowerCAmelCase__ : Optional[Any] = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
lowerCAmelCase__ : Union[str, Any] = nn.parallel.DistributedDataParallel(
__UpperCAmelCase , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=__UpperCAmelCase )
elif args.n_gpu > 1:
lowerCAmelCase__ : int = nn.DataParallel(__UpperCAmelCase )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=__UpperCAmelCase )
torch.save(__UpperCAmelCase , os.path.join(args.output_dir , """run_args.bin""" ) )
logger.info("""Training/evaluation parameters %s""" , __UpperCAmelCase )
# Prepare dataset
lowerCAmelCase__ : Union[str, Any] = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
lowerCAmelCase__ : Dict = (torch.from_numpy(__UpperCAmelCase ),)
lowerCAmelCase__ : Any = TensorDataset(*__UpperCAmelCase )
lowerCAmelCase__ : int = RandomSampler(__UpperCAmelCase )
lowerCAmelCase__ : Any = DataLoader(__UpperCAmelCase , sampler=__UpperCAmelCase , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
lowerCAmelCase__ : Any = mask_heads(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
prune_heads(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
main()
| 507 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TrajectoryTransformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TrajectoryTransformerModel""",
"""TrajectoryTransformerPreTrainedModel""",
"""load_tf_weights_in_trajectory_transformer""",
]
if TYPE_CHECKING:
from .configuration_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TrajectoryTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
load_tf_weights_in_trajectory_transformer,
)
else:
import sys
a = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 687 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self: int ):
_lowerCAmelCase :Optional[int] = 10
def SCREAMING_SNAKE_CASE__ ( self: Optional[Any] ):
_lowerCAmelCase :str = [1, 2, 3, 4]
_lowerCAmelCase :Union[str, Any] = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(_UpperCAmelCase , self.block_size , 0 ) , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self: int ):
_lowerCAmelCase :List[Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
_lowerCAmelCase :List[Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_UpperCAmelCase , self.block_size , 0 ) , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self: Optional[int] ):
_lowerCAmelCase :Dict = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
_lowerCAmelCase :Optional[int] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_UpperCAmelCase , self.block_size , 0 ) , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self: List[str] ):
_lowerCAmelCase :List[str] = 'It was the year of Our Lord one thousand seven hundred and\n seventy-five.\n\nSpiritual revelations were conceded to England at that\n favoured period, as at this.'
_lowerCAmelCase , _lowerCAmelCase :Optional[Any] = process_story(_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , [] )
def SCREAMING_SNAKE_CASE__ ( self: Any ):
_lowerCAmelCase :Optional[int] = ''
_lowerCAmelCase , _lowerCAmelCase :str = process_story(_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , [] )
self.assertEqual(_UpperCAmelCase , [] )
def SCREAMING_SNAKE_CASE__ ( self: str ):
_lowerCAmelCase :Optional[Any] = (
'It was the year of Our Lord one thousand seven hundred and '
'seventy-five\n\nSpiritual revelations were conceded to England '
'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
)
_lowerCAmelCase , _lowerCAmelCase :Optional[int] = process_story(_UpperCAmelCase )
_lowerCAmelCase :Optional[Any] = [
'It was the year of Our Lord one thousand seven hundred and seventy-five.',
'Spiritual revelations were conceded to England at that favoured period, as at this.',
]
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
_lowerCAmelCase :Optional[int] = ['It was the best of times.']
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self: Tuple ):
_lowerCAmelCase :Union[str, Any] = torch.tensor([1, 2, 3, 4] )
_lowerCAmelCase :List[Any] = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(_UpperCAmelCase , 0 ).numpy() , expected.numpy() )
def SCREAMING_SNAKE_CASE__ ( self: Optional[int] ):
_lowerCAmelCase :List[Any] = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
_lowerCAmelCase :Optional[int] = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_UpperCAmelCase , 23 ).numpy() , expected.numpy() )
def SCREAMING_SNAKE_CASE__ ( self: Optional[Any] ):
_lowerCAmelCase :Tuple = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
_lowerCAmelCase :List[Any] = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_UpperCAmelCase , 1 ).numpy() , expected.numpy() )
def SCREAMING_SNAKE_CASE__ ( self: str ):
_lowerCAmelCase :List[str] = 101
_lowerCAmelCase :Dict = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
_lowerCAmelCase :int = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
_lowerCAmelCase :List[str] = compute_token_type_ids(_UpperCAmelCase , _UpperCAmelCase )
np.testing.assert_array_equal(_UpperCAmelCase , _UpperCAmelCase ) | 687 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def A__ (self):
'''simple docstring'''
__UpperCAmelCase =tf.convert_to_tensor(
[
[
8.222_0991, # 3rd highest value; idx. 0
-0.562_0044,
5.2322_9752,
4.038_6393,
-6.879_8378,
-0.5478_5802,
-3.201_2153,
2.9277_7176,
1.8817_1953,
7.3534_1276, # 5th highest value; idx. 9
8.4320_7833, # 2nd highest value; idx. 10
-9.8571_1836,
-5.9620_9236,
-1.1303_9161,
-7.111_5294,
-0.836_9633,
-5.318_6408,
7.0642_7407,
0.8136_9344,
-0.8202_3817,
-5.917_9796,
0.5881_3443,
-6.9977_8438,
4.7155_1189,
-0.1877_1637,
7.4402_0759, # 4th highest value; idx. 25
9.3845_0987, # 1st highest value; idx. 26
2.1266_2941,
-9.3256_2038,
2.3565_2522,
], # cummulative prob of 5 highest values <= 0.6
[
0.5842_5518,
4.5313_9238,
-5.5751_0464,
-6.2803_0699,
-7.1952_9503,
-4.0212_2551,
1.3933_7037,
-6.0670_7057,
1.5948_0517,
-9.64_3119,
0.0390_7799,
0.6723_1762,
-8.8820_6726,
6.2711_5922, # 4th highest value; idx. 13
2.2852_0723,
4.8276_7506,
4.3042_1368,
8.827_5313, # 2nd highest value; idx. 17
5.4402_9958, # 5th highest value; idx. 18
-4.473_5794,
7.3857_9536, # 3rd highest value; idx. 20
-2.9105_1663,
2.6194_6077,
-2.567_4762,
-9.4895_9302,
-4.0292_2645,
-1.3541_6918,
9.6770_2323, # 1st highest value; idx. 27
-5.8947_8553,
1.8537_0467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
__UpperCAmelCase =tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 1_0], [0, 2_5], [0, 2_6], [1, 1_3], [1, 1_7], [1, 1_8], [1, 2_0], [1, 2_7]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
__UpperCAmelCase =tf.convert_to_tensor(
[8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023] , dtype=tf.floataa , ) # expected non filtered values as noted above
__UpperCAmelCase =tf_top_k_top_p_filtering(UpperCAmelCase , top_k=1_0 , top_p=0.6 , min_tokens_to_keep=4)
__UpperCAmelCase =output[output != -float('''inf''')]
__UpperCAmelCase =tf.cast(
tf.where(tf.not_equal(UpperCAmelCase , tf.constant(-float('''inf''') , dtype=tf.floataa))) , dtype=tf.intaa , )
tf.debugging.assert_near(UpperCAmelCase , UpperCAmelCase , rtol=1e-12)
tf.debugging.assert_equal(UpperCAmelCase , UpperCAmelCase)
@require_tf
class _SCREAMING_SNAKE_CASE ( unittest.TestCase , _lowerCAmelCase ):
# setting framework_dependent_parameters needs to be gated, just like its contents' imports
if is_tf_available():
a_ : int = {
'''AutoModelForCausalLM''': TFAutoModelForCausalLM,
'''AutoModelForSpeechSeq2Seq''': TFAutoModelForSpeechSeqaSeq,
'''AutoModelForSeq2SeqLM''': TFAutoModelForSeqaSeqLM,
'''AutoModelForVision2Seq''': TFAutoModelForVisionaSeq,
'''LogitsProcessorList''': TFLogitsProcessorList,
'''MinLengthLogitsProcessor''': TFMinLengthLogitsProcessor,
'''create_tensor_fn''': tf.convert_to_tensor,
'''floats_tensor''': floats_tensor,
'''return_tensors''': '''tf''',
}
@slow
def A__ (self):
'''simple docstring'''
__UpperCAmelCase =TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
__UpperCAmelCase =2
__UpperCAmelCase =2
class _SCREAMING_SNAKE_CASE ( tf.Module ):
def __init__(self , UpperCAmelCase):
'''simple docstring'''
super(UpperCAmelCase , self).__init__()
__UpperCAmelCase =model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name='''input_ids'''),
tf.TensorSpec((None, input_length) , tf.intaa , name='''attention_mask'''),
) , jit_compile=UpperCAmelCase , )
def A__ (self , UpperCAmelCase , UpperCAmelCase):
'''simple docstring'''
__UpperCAmelCase =self.model.generate(
input_ids=UpperCAmelCase , attention_mask=UpperCAmelCase , max_new_tokens=UpperCAmelCase , return_dict_in_generate=UpperCAmelCase , )
return {"sequences": outputs["sequences"]}
__UpperCAmelCase =[[2, 0], [1_0_2, 1_0_3]]
__UpperCAmelCase =[[1, 0], [1, 1]]
__UpperCAmelCase =DummyModel(model=UpperCAmelCase)
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(UpperCAmelCase , UpperCAmelCase , signatures={'''serving_default''': dummy_model.serving})
__UpperCAmelCase =tf.saved_model.load(UpperCAmelCase).signatures['''serving_default''']
for batch_size in range(1 , len(UpperCAmelCase) + 1):
__UpperCAmelCase ={
'''input_ids''': tf.constant(dummy_input_ids[:batch_size]),
'''attention_mask''': tf.constant(dummy_attention_masks[:batch_size]),
}
__UpperCAmelCase =serving_func(**UpperCAmelCase)['''sequences''']
__UpperCAmelCase =test_model.generate(**UpperCAmelCase , max_new_tokens=UpperCAmelCase)
tf.debugging.assert_equal(UpperCAmelCase , UpperCAmelCase)
@slow
def A__ (self):
'''simple docstring'''
__UpperCAmelCase =TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
__UpperCAmelCase =1
__UpperCAmelCase =2
class _SCREAMING_SNAKE_CASE ( tf.Module ):
def __init__(self , UpperCAmelCase):
'''simple docstring'''
super(UpperCAmelCase , self).__init__()
__UpperCAmelCase =model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name='''input_ids'''),
tf.TensorSpec((batch_size, None) , tf.intaa , name='''attention_mask'''),
) , jit_compile=UpperCAmelCase , )
def A__ (self , UpperCAmelCase , UpperCAmelCase):
'''simple docstring'''
__UpperCAmelCase =self.model.generate(
input_ids=UpperCAmelCase , attention_mask=UpperCAmelCase , max_new_tokens=UpperCAmelCase , return_dict_in_generate=UpperCAmelCase , )
return {"sequences": outputs["sequences"]}
__UpperCAmelCase =[[2], [1_0_2, 1_0_3]]
__UpperCAmelCase =[[1], [1, 1]]
__UpperCAmelCase =DummyModel(model=UpperCAmelCase)
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(UpperCAmelCase , UpperCAmelCase , signatures={'''serving_default''': dummy_model.serving})
__UpperCAmelCase =tf.saved_model.load(UpperCAmelCase).signatures['''serving_default''']
for input_row in range(len(UpperCAmelCase)):
__UpperCAmelCase ={
'''input_ids''': tf.constant([dummy_input_ids[input_row]]),
'''attention_mask''': tf.constant([dummy_attention_masks[input_row]]),
}
__UpperCAmelCase =serving_func(**UpperCAmelCase)['''sequences''']
__UpperCAmelCase =test_model.generate(**UpperCAmelCase , max_new_tokens=UpperCAmelCase)
tf.debugging.assert_equal(UpperCAmelCase , UpperCAmelCase)
@slow
@require_tensorflow_text
def A__ (self):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id='''google/flan-t5-small''' , filename='''spiece.model''' , local_dir=UpperCAmelCase)
class _SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
def __init__(self):
'''simple docstring'''
super().__init__()
__UpperCAmelCase =text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(UpperCAmelCase , '''spiece.model''') , '''rb''').read())
__UpperCAmelCase =TFAutoModelForSeqaSeqLM.from_pretrained('''hf-internal-testing/tiny-random-t5''')
def A__ (self , UpperCAmelCase , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
__UpperCAmelCase =self.tokenizer.tokenize(UpperCAmelCase)
__UpperCAmelCase , __UpperCAmelCase =text.pad_model_inputs(
UpperCAmelCase , max_seq_length=6_4 , pad_value=self.model.config.pad_token_id)
__UpperCAmelCase =self.model.generate(input_ids=UpperCAmelCase , attention_mask=UpperCAmelCase)
return self.tokenizer.detokenize(UpperCAmelCase)
__UpperCAmelCase =CompleteSentenceTransformer()
__UpperCAmelCase =tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name='''inputs''')
__UpperCAmelCase =complete_model(UpperCAmelCase)
__UpperCAmelCase =tf.keras.Model(UpperCAmelCase , UpperCAmelCase)
keras_model.save(UpperCAmelCase)
def A__ (self):
'''simple docstring'''
__UpperCAmelCase ={
'''do_sample''': True,
'''num_beams''': 1,
'''top_p''': 0.7,
'''top_k''': 1_0,
'''temperature''': 0.7,
}
__UpperCAmelCase =1_4
__UpperCAmelCase =AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
__UpperCAmelCase ='''Hello, my dog is cute and'''
__UpperCAmelCase =tokenizer(UpperCAmelCase , return_tensors='''tf''')
__UpperCAmelCase =TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
__UpperCAmelCase =6_3_8
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(''':/CPU:0'''):
tf.random.set_seed(0)
__UpperCAmelCase =model.generate(**UpperCAmelCase , eos_token_id=UpperCAmelCase , **UpperCAmelCase)
self.assertTrue(expectation == len(generated_tokens[0]))
__UpperCAmelCase =[6_3_8, 1_9_8]
with tf.device(''':/CPU:0'''):
tf.random.set_seed(0)
__UpperCAmelCase =model.generate(**UpperCAmelCase , eos_token_id=UpperCAmelCase , **UpperCAmelCase)
self.assertTrue(expectation == len(generated_tokens[0]))
def A__ (self):
'''simple docstring'''
__UpperCAmelCase =AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bart''')
__UpperCAmelCase ='''Hugging Face is a technology company based in New York and Paris.'''
__UpperCAmelCase =bart_tokenizer(UpperCAmelCase , return_tensors='''tf''').input_ids
__UpperCAmelCase =TFBartForConditionalGeneration.from_pretrained('''hf-internal-testing/tiny-random-bart''')
__UpperCAmelCase =bart_model.generate(UpperCAmelCase).numpy()
class _SCREAMING_SNAKE_CASE ( _lowerCAmelCase ):
def A__ (self , UpperCAmelCase , UpperCAmelCase=None , **UpperCAmelCase):
'''simple docstring'''
return super().call(UpperCAmelCase , **UpperCAmelCase)
__UpperCAmelCase =FakeBart.from_pretrained('''hf-internal-testing/tiny-random-bart''')
__UpperCAmelCase =bart_model.generate(UpperCAmelCase , foo='''bar''').numpy()
self.assertTrue(np.array_equal(UpperCAmelCase , UpperCAmelCase))
class _SCREAMING_SNAKE_CASE ( bart_model.model.encoder.__class__ ):
def A__ (self , UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
return super().call(UpperCAmelCase , **UpperCAmelCase)
__UpperCAmelCase =FakeEncoder(bart_model.config , bart_model.model.shared)
__UpperCAmelCase =fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
__UpperCAmelCase =bart_model.generate(UpperCAmelCase).numpy()
with self.assertRaises(UpperCAmelCase):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(UpperCAmelCase , foo='''bar''')
| 705 |
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=_lowerCAmelCase ):
a_ : Dict = ['''torch''', '''transformers''', '''onnx''']
def __init__(self , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''])
@classmethod
def A__ (cls , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''])
@classmethod
def A__ (cls , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''])
class _SCREAMING_SNAKE_CASE ( metaclass=_lowerCAmelCase ):
a_ : Dict = ['''torch''', '''transformers''', '''onnx''']
def __init__(self , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''])
@classmethod
def A__ (cls , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''])
@classmethod
def A__ (cls , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''])
class _SCREAMING_SNAKE_CASE ( metaclass=_lowerCAmelCase ):
a_ : List[Any] = ['''torch''', '''transformers''', '''onnx''']
def __init__(self , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''])
@classmethod
def A__ (cls , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''])
@classmethod
def A__ (cls , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''])
class _SCREAMING_SNAKE_CASE ( metaclass=_lowerCAmelCase ):
a_ : str = ['''torch''', '''transformers''', '''onnx''']
def __init__(self , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''])
@classmethod
def A__ (cls , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''])
@classmethod
def A__ (cls , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''])
class _SCREAMING_SNAKE_CASE ( metaclass=_lowerCAmelCase ):
a_ : List[str] = ['''torch''', '''transformers''', '''onnx''']
def __init__(self , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''])
@classmethod
def A__ (cls , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''])
@classmethod
def A__ (cls , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''])
class _SCREAMING_SNAKE_CASE ( metaclass=_lowerCAmelCase ):
a_ : List[Any] = ['''torch''', '''transformers''', '''onnx''']
def __init__(self , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''])
@classmethod
def A__ (cls , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''])
@classmethod
def A__ (cls , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''])
| 142 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def A ( _A, _A = 2, _A = 1, _A = 3, ):
"""simple docstring"""
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError("The input value cannot be less than 2" )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(_A, _A, _A ) -> int:
return (pow(snake_case_, 2 ) + step) % modulus
for _ in range(snake_case_ ):
# These track the position within the cycle detection logic.
snake_case_ :Optional[Any] = seed
snake_case_ :str = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
snake_case_ :int = rand_fn(snake_case_, snake_case_, snake_case_ )
snake_case_ :Dict = rand_fn(snake_case_, snake_case_, snake_case_ )
snake_case_ :Union[str, Any] = rand_fn(snake_case_, snake_case_, snake_case_ )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
snake_case_ :str = gcd(hare - tortoise, snake_case_ )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
snake_case_ :Optional[int] = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
__UpperCAmelCase : Any = argparse.ArgumentParser()
parser.add_argument(
'num',
type=int,
help='The value to find a divisor of',
)
parser.add_argument(
'--attempts',
type=int,
default=3,
help='The number of attempts before giving up',
)
__UpperCAmelCase : Optional[int] = parser.parse_args()
__UpperCAmelCase : Optional[int] = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F'''{args.num} is probably prime''')
else:
__UpperCAmelCase : Optional[Any] = args.num // divisor
print(F'''{args.num} = {divisor} * {quotient}''')
| 584 |
"""simple docstring"""
def A_ ( snake_case_ : int = 1_0_0_0_0_0_0 ):
'''simple docstring'''
UpperCamelCase : List[Any] = [i - 1 for i in range(limit + 1 )]
for i in range(2 ,limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i ,limit + 1 ,snake_case_ ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 499 | 0 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a_ ( __UpperCamelCase ):
def __init__( self : Optional[Any] , snake_case__ : UNetaDModel , snake_case__ : UNetaDModel , snake_case__ : DDPMScheduler , snake_case__ : Optional[Any] , ):
super().__init__()
lowerCAmelCase__ = value_function
lowerCAmelCase__ = unet
lowerCAmelCase__ = scheduler
lowerCAmelCase__ = env
lowerCAmelCase__ = env.get_dataset()
lowerCAmelCase__ = {}
for key in self.data.keys():
try:
lowerCAmelCase__ = self.data[key].mean()
except: # noqa: E722
pass
lowerCAmelCase__ = {}
for key in self.data.keys():
try:
lowerCAmelCase__ = self.data[key].std()
except: # noqa: E722
pass
lowerCAmelCase__ = env.observation_space.shape[0]
lowerCAmelCase__ = env.action_space.shape[0]
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : str , snake_case__ : Tuple ):
return (x_in - self.means[key]) / self.stds[key]
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : Any , snake_case__ : Optional[int] ):
return x_in * self.stds[key] + self.means[key]
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Union[str, Any] ):
if type(snake_case__ ) is dict:
return {k: self.to_torch(snake_case__ ) for k, v in x_in.items()}
elif torch.is_tensor(snake_case__ ):
return x_in.to(self.unet.device )
return torch.tensor(snake_case__ , device=self.unet.device )
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Tuple , snake_case__ : Any , snake_case__ : Any ):
for key, val in cond.items():
lowerCAmelCase__ = val.clone()
return x_in
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : str , snake_case__ : str , snake_case__ : Union[str, Any] , snake_case__ : Dict ):
lowerCAmelCase__ = x.shape[0]
lowerCAmelCase__ = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
lowerCAmelCase__ = torch.full((batch_size,) , snake_case__ , device=self.unet.device , dtype=torch.long )
for _ in range(snake_case__ ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
lowerCAmelCase__ = self.value_function(x.permute(0 , 2 , 1 ) , snake_case__ ).sample
lowerCAmelCase__ = torch.autograd.grad([y.sum()] , [x] )[0]
lowerCAmelCase__ = self.scheduler._get_variance(snake_case__ )
lowerCAmelCase__ = torch.exp(0.5 * posterior_variance )
lowerCAmelCase__ = model_std * grad
lowerCAmelCase__ = 0
lowerCAmelCase__ = x.detach()
lowerCAmelCase__ = x + scale * grad
lowerCAmelCase__ = self.reset_xa(snake_case__ , snake_case__ , self.action_dim )
lowerCAmelCase__ = self.unet(x.permute(0 , 2 , 1 ) , snake_case__ ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
lowerCAmelCase__ = self.scheduler.step(snake_case__ , snake_case__ , snake_case__ , predict_epsilon=snake_case__ )["""prev_sample"""]
# apply conditions to the trajectory (set the initial state)
lowerCAmelCase__ = self.reset_xa(snake_case__ , snake_case__ , self.action_dim )
lowerCAmelCase__ = self.to_torch(snake_case__ )
return x, y
def __call__( self : int , snake_case__ : Optional[int] , snake_case__ : int=64 , snake_case__ : Dict=32 , snake_case__ : Any=2 , snake_case__ : Optional[Any]=0.1 ):
# normalize the observations and create batch dimension
lowerCAmelCase__ = self.normalize(snake_case__ , """observations""" )
lowerCAmelCase__ = obs[None].repeat(snake_case__ , axis=0 )
lowerCAmelCase__ = {0: self.to_torch(snake_case__ )}
lowerCAmelCase__ = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
lowerCAmelCase__ = randn_tensor(snake_case__ , device=self.unet.device )
lowerCAmelCase__ = self.reset_xa(snake_case__ , snake_case__ , self.action_dim )
lowerCAmelCase__ = self.to_torch(snake_case__ )
# run the diffusion process
lowerCAmelCase__ , lowerCAmelCase__ = self.run_diffusion(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# sort output trajectories by value
lowerCAmelCase__ = y.argsort(0 , descending=snake_case__ ).squeeze()
lowerCAmelCase__ = x[sorted_idx]
lowerCAmelCase__ = sorted_values[:, :, : self.action_dim]
lowerCAmelCase__ = actions.detach().cpu().numpy()
lowerCAmelCase__ = self.de_normalize(snake_case__ , key="""actions""" )
# select the action with the highest value
if y is not None:
lowerCAmelCase__ = 0
else:
# if we didn't run value guiding, select a random action
lowerCAmelCase__ = np.random.randint(0 , snake_case__ )
lowerCAmelCase__ = denorm_actions[selected_index, 0]
return denorm_actions
| 707 | """simple docstring"""
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(nlp.__version__) != version.parse("0.8.3"):
raise Exception("requires gluonnlp == 0.8.3")
if version.parse(mx.__version__) != version.parse("1.5.0"):
raise Exception("requires mxnet == 1.5.0")
logging.set_verbosity_info()
__lowerCAmelCase : Any = logging.get_logger(__name__)
__lowerCAmelCase : Any = "The Nymphenburg Palace is a beautiful palace in Munich!"
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = {
"""attention_cell""": """multi_head""",
"""num_layers""": 4,
"""units""": 1024,
"""hidden_size""": 768,
"""max_length""": 512,
"""num_heads""": 8,
"""scaled""": True,
"""dropout""": 0.1,
"""use_residual""": True,
"""embed_size""": 1024,
"""embed_dropout""": 0.1,
"""word_embed""": None,
"""layer_norm_eps""": 1e-5,
"""token_type_vocab_size""": 2,
}
lowerCAmelCase__ = bort_4_8_768_1024_hparams
# Let's construct the original Bort model here
# Taken from official BERT implementation, see:
# https://github.com/alexa/bort/blob/master/bort/bort.py
lowerCAmelCase__ = BERTEncoder(
attention_cell=predefined_args["""attention_cell"""] , num_layers=predefined_args["""num_layers"""] , units=predefined_args["""units"""] , hidden_size=predefined_args["""hidden_size"""] , max_length=predefined_args["""max_length"""] , num_heads=predefined_args["""num_heads"""] , scaled=predefined_args["""scaled"""] , dropout=predefined_args["""dropout"""] , output_attention=lowerCamelCase__ , output_all_encodings=lowerCamelCase__ , use_residual=predefined_args["""use_residual"""] , activation=predefined_args.get("""activation""" , """gelu""" ) , layer_norm_eps=predefined_args.get("""layer_norm_eps""" , lowerCamelCase__ ) , )
# Vocab information needs to be fetched first
# It's the same as RoBERTa, so RobertaTokenizer can be used later
lowerCAmelCase__ = """openwebtext_ccnews_stories_books_cased"""
# Specify download folder to Gluonnlp's vocab
lowerCAmelCase__ = os.path.join(get_home_dir() , """models""" )
lowerCAmelCase__ = _load_vocab(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , cls=lowerCamelCase__ )
lowerCAmelCase__ = nlp.model.BERTModel(
lowerCamelCase__ , len(lowerCamelCase__ ) , units=predefined_args["""units"""] , embed_size=predefined_args["""embed_size"""] , embed_dropout=predefined_args["""embed_dropout"""] , word_embed=predefined_args["""word_embed"""] , use_pooler=lowerCamelCase__ , use_token_type_embed=lowerCamelCase__ , token_type_vocab_size=predefined_args["""token_type_vocab_size"""] , use_classifier=lowerCamelCase__ , use_decoder=lowerCamelCase__ , )
original_bort.load_parameters(lowerCamelCase__ , cast_dtype=lowerCamelCase__ , ignore_extra=lowerCamelCase__ )
lowerCAmelCase__ = original_bort._collect_params_with_prefix()
# Build our config 🤗
lowerCAmelCase__ = {
"""architectures""": ["""BertForMaskedLM"""],
"""attention_probs_dropout_prob""": predefined_args["""dropout"""],
"""hidden_act""": """gelu""",
"""hidden_dropout_prob""": predefined_args["""dropout"""],
"""hidden_size""": predefined_args["""embed_size"""],
"""initializer_range""": 0.02,
"""intermediate_size""": predefined_args["""hidden_size"""],
"""layer_norm_eps""": predefined_args["""layer_norm_eps"""],
"""max_position_embeddings""": predefined_args["""max_length"""],
"""model_type""": """bort""",
"""num_attention_heads""": predefined_args["""num_heads"""],
"""num_hidden_layers""": predefined_args["""num_layers"""],
"""pad_token_id""": 1, # 2 = BERT, 1 = RoBERTa
"""type_vocab_size""": 1, # 2 = BERT, 1 = RoBERTa
"""vocab_size""": len(lowerCamelCase__ ),
}
lowerCAmelCase__ = BertConfig.from_dict(lowerCamelCase__ )
lowerCAmelCase__ = BertForMaskedLM(lowerCamelCase__ )
hf_bort_model.eval()
# Parameter mapping table (Gluonnlp to Transformers)
# * denotes layer index
#
# | Gluon Parameter | Transformers Parameter
# | -------------------------------------------------------------- | ----------------------
# | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias`
# | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight`
# | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight`
# | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
# | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight`
# | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight`
# | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias`
# | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight`
# Helper function to convert MXNET Arrays to PyTorch
def to_torch(lowerCamelCase__ ) -> nn.Parameter:
return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )
# Check param shapes and map new HF param back
def check_and_map_params(lowerCamelCase__ , lowerCamelCase__ ):
lowerCAmelCase__ = hf_param.shape
lowerCAmelCase__ = to_torch(params[gluon_param] )
lowerCAmelCase__ = gluon_param.shape
assert (
shape_hf == shape_gluon
), f"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""
return gluon_param
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.word_embeddings.weight , """word_embed.0.weight""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.position_embeddings.weight , """encoder.position_weight""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.bias , """encoder.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.weight , """encoder.layer_norm.gamma""" )
# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
lowerCAmelCase__ = torch.zeros_like(
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )
for i in range(hf_bort_config.num_hidden_layers ):
lowerCAmelCase__ = hf_bort_model.bert.encoder.layer[i]
# self attention
lowerCAmelCase__ = layer.attention.self
lowerCAmelCase__ = check_and_map_params(
self_attn.key.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.key.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.query.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.query.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.value.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.value.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight""" )
# self attention output
lowerCAmelCase__ = layer.attention.output
lowerCAmelCase__ = check_and_map_params(
self_output.dense.bias , f"""encoder.transformer_cells.{i}.proj.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_output.dense.weight , f"""encoder.transformer_cells.{i}.proj.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_output.LayerNorm.bias , f"""encoder.transformer_cells.{i}.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
self_output.LayerNorm.weight , f"""encoder.transformer_cells.{i}.layer_norm.gamma""" )
# intermediate
lowerCAmelCase__ = layer.intermediate
lowerCAmelCase__ = check_and_map_params(
intermediate.dense.bias , f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias""" )
lowerCAmelCase__ = check_and_map_params(
intermediate.dense.weight , f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight""" )
# output
lowerCAmelCase__ = layer.output
lowerCAmelCase__ = check_and_map_params(
bert_output.dense.bias , f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.dense.weight , f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.LayerNorm.bias , f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.LayerNorm.weight , f"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma""" )
# Save space and energy 🎄
hf_bort_model.half()
# Compare output of both models
lowerCAmelCase__ = RobertaTokenizer.from_pretrained("""roberta-base""" )
lowerCAmelCase__ = tokenizer.encode_plus(lowerCamelCase__ )["""input_ids"""]
# Get gluon output
lowerCAmelCase__ = mx.nd.array([input_ids] )
lowerCAmelCase__ = original_bort(inputs=lowerCamelCase__ , token_types=[] )
# Get Transformer output (save and reload model again)
hf_bort_model.save_pretrained(lowerCamelCase__ )
lowerCAmelCase__ = BertModel.from_pretrained(lowerCamelCase__ )
hf_bort_model.eval()
lowerCAmelCase__ = tokenizer.encode_plus(lowerCamelCase__ , return_tensors="""pt""" )
lowerCAmelCase__ = hf_bort_model(**lowerCamelCase__ )[0]
lowerCAmelCase__ = output_gluon[0].asnumpy()
lowerCAmelCase__ = output_hf[0].detach().numpy()
lowerCAmelCase__ = np.max(np.abs(hf_layer - gluon_layer ) ).item()
lowerCAmelCase__ = np.allclose(lowerCamelCase__ , lowerCamelCase__ , atol=1e-3 )
if success:
print("""✔️ Both model do output the same tensors""" )
else:
print("""❌ Both model do **NOT** output the same tensors""" )
print("""Absolute difference is:""" , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--bort_checkpoint_path", default=None, type=str, required=True, help="Path the official Bort params file."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase : str = parser.parse_args()
convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
| 674 | 0 |
'''simple docstring'''
import argparse
import datetime
def _UpperCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
__magic_name__ : Union[str, Any] = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Saturday",
}
__magic_name__ : Dict = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(UpperCamelCase__ ) < 11:
raise ValueError("Must be 10 characters long" )
# Get month
__magic_name__ : int = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError("Month must be between 1 - 12" )
__magic_name__ : str = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'" )
# Get day
__magic_name__ : int = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError("Date must be between 1 - 31" )
# Get second separator
__magic_name__ : str = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'" )
# Get year
__magic_name__ : int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 8500:
raise ValueError(
"Year out of range. There has to be some sort of limit...right?" )
# Get datetime obj for validation
__magic_name__ : Dict = datetime.date(int(UpperCamelCase__ ) , int(UpperCamelCase__ ) , int(UpperCamelCase__ ) )
# Start math
if m <= 2:
__magic_name__ : Optional[int] = y - 1
__magic_name__ : Any = m + 12
# maths var
__magic_name__ : int = int(str(UpperCamelCase__ )[:2] )
__magic_name__ : int = int(str(UpperCamelCase__ )[2:] )
__magic_name__ : int = int(2.6 * m - 5.39 )
__magic_name__ : int = int(c / 4 )
__magic_name__ : int = int(k / 4 )
__magic_name__ : int = int(d + k )
__magic_name__ : int = int(t + u + v + x )
__magic_name__ : int = int(z - (2 * c) )
__magic_name__ : int = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError("The date was evaluated incorrectly. Contact developer." )
# Response
__magic_name__ : str = F"""Your date {date_input}, is a {days[str(UpperCamelCase__ )]}!"""
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
_SCREAMING_SNAKE_CASE : Union[str, Any] = argparse.ArgumentParser(
description=(
"Find out what day of the week nearly any date is or was. Enter "
"date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
)
)
parser.add_argument(
"date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
)
_SCREAMING_SNAKE_CASE : str = parser.parse_args()
zeller(args.date_input) | 436 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_SCREAMING_SNAKE_CASE : List[Any] = Lock()
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
__magic_name__ : Dict = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
__magic_name__ : int = min(UpperCamelCase__ , UpperCamelCase__ )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
__magic_name__ : Optional[Any] = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
__magic_name__ : List[str] = max(UpperCamelCase__ , UpperCamelCase__ )
# after all swaps are performed, send the values back to main
result_pipe[1].send(UpperCamelCase__ )
def _UpperCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
__magic_name__ : int = []
__magic_name__ : Union[str, Any] = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
__magic_name__ : Union[str, Any] = Pipe()
__magic_name__ : List[str] = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) )
__magic_name__ : int = temp_rs
__magic_name__ : List[str] = temp_rr
for i in range(1 , len(UpperCamelCase__ ) - 1 ):
__magic_name__ : Optional[int] = Pipe()
__magic_name__ : Optional[int] = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) )
__magic_name__ : int = temp_rs
__magic_name__ : Union[str, Any] = temp_rr
process_array_.append(
Process(
target=UpperCamelCase__ , args=(
len(UpperCamelCase__ ) - 1,
arr[len(UpperCamelCase__ ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(UpperCamelCase__ ) - 1],
) , ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(UpperCamelCase__ ) ):
__magic_name__ : str = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def _UpperCamelCase ( ):
"""simple docstring"""
__magic_name__ : int = list(range(10 , 0 , -1 ) )
print("Initial List" )
print(*UpperCamelCase__ )
__magic_name__ : Tuple = odd_even_transposition(UpperCamelCase__ )
print("Sorted List\n" )
print(*UpperCamelCase__ )
if __name__ == "__main__":
main() | 436 | 1 |
'''simple docstring'''
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__lowerCamelCase : Tuple = re.compile(r"""^(?P<major>\d+)""" r"""\.(?P<minor>\d+)""" r"""\.(?P<patch>\d+)$""")
@total_ordering
@dataclass
class lowerCAmelCase__ :
A = 42
A = None
A = None
A = None
A = None
def __UpperCamelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ : int = _str_to_version_tuple(self.version_str )
def __repr__( self : List[Any] ) -> int:
"""simple docstring"""
return F"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""
@property
def __UpperCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
return self.major, self.minor, self.patch
def __UpperCamelCase ( self : Optional[Any] , UpperCamelCase_ : Tuple ) -> Any:
"""simple docstring"""
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
return Version(UpperCamelCase_ )
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ):
return other
raise TypeError(F"""{other} (type {type(UpperCamelCase_ )}) cannot be compared to version.""" )
def __eq__( self : str , UpperCamelCase_ : List[Any] ) -> Dict:
"""simple docstring"""
try:
lowerCamelCase_ : List[Any] = self._validate_operand(UpperCamelCase_ )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self : Union[str, Any] , UpperCamelCase_ : List[Any] ) -> str:
"""simple docstring"""
lowerCamelCase_ : Any = self._validate_operand(UpperCamelCase_ )
return self.tuple < other.tuple
def __hash__( self : Optional[int] ) -> str:
"""simple docstring"""
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def __UpperCamelCase ( cls : Union[str, Any] , UpperCamelCase_ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ : Optional[int] = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def __UpperCamelCase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
return self.version_str
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase_ : str = _VERSION_REG.match(__UpperCAmelCase )
if not res:
raise ValueError(F"""Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits.""" )
return tuple(int(__UpperCAmelCase ) for v in [res.group('''major''' ), res.group('''minor''' ), res.group('''patch''' )] )
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
return ".".join(str(__UpperCAmelCase ) for v in version_tuple )
| 418 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Tuple = {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : int = ["""FNetTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = ["""FNetTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Optional[int] = [
"""FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FNetForMaskedLM""",
"""FNetForMultipleChoice""",
"""FNetForNextSentencePrediction""",
"""FNetForPreTraining""",
"""FNetForQuestionAnswering""",
"""FNetForSequenceClassification""",
"""FNetForTokenClassification""",
"""FNetLayer""",
"""FNetModel""",
"""FNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
__lowerCamelCase : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 418 | 1 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
@staticmethod
def lowercase__ ( *snake_case__ , **snake_case__ ):
"""simple docstring"""
pass
def a__ ( SCREAMING_SNAKE_CASE : Image ):
'''simple docstring'''
lowerCAmelCase : Dict = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
a : Optional[Any] =MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def lowercase__ ( self , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : int = DepthEstimationPipeline(model=snake_case__ , image_processor=snake_case__ )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def lowercase__ ( self , snake_case__ , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png" )
self.assertEqual({"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )} , snake_case__ )
import datasets
lowerCAmelCase : List[Any] = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
lowerCAmelCase : List[str] = depth_estimator(
[
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["file"],
# LA
dataset[1]["file"],
# L
dataset[2]["file"],
] )
self.assertEqual(
[
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
] , snake_case__ , )
@require_tf
@unittest.skip("Depth estimation is not implemented in TF" )
def lowercase__ ( self ):
"""simple docstring"""
pass
@slow
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = "Intel/dpt-large"
lowerCAmelCase : List[str] = pipeline("depth-estimation" , model=snake_case__ )
lowerCAmelCase : Any = depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg" )
lowerCAmelCase : Optional[Any] = hashimage(outputs["depth"] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item() ) , 2.662 )
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT" )
| 645 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''openai/imagegpt-small''': '''''',
'''openai/imagegpt-medium''': '''''',
'''openai/imagegpt-large''': '''''',
}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : int ="imagegpt"
a : Union[str, Any] =["past_key_values"]
a : Optional[Any] ={
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , snake_case__=512 + 1 , snake_case__=32 * 32 , snake_case__=512 , snake_case__=24 , snake_case__=8 , snake_case__=None , snake_case__="quick_gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=1e-5 , snake_case__=0.02 , snake_case__=True , snake_case__=True , snake_case__=False , snake_case__=False , snake_case__=False , **snake_case__ , ):
"""simple docstring"""
lowerCAmelCase : Tuple = vocab_size
lowerCAmelCase : List[Any] = n_positions
lowerCAmelCase : Union[str, Any] = n_embd
lowerCAmelCase : str = n_layer
lowerCAmelCase : Tuple = n_head
lowerCAmelCase : Optional[Any] = n_inner
lowerCAmelCase : Dict = activation_function
lowerCAmelCase : str = resid_pdrop
lowerCAmelCase : Optional[int] = embd_pdrop
lowerCAmelCase : Optional[int] = attn_pdrop
lowerCAmelCase : Union[str, Any] = layer_norm_epsilon
lowerCAmelCase : Any = initializer_range
lowerCAmelCase : Union[str, Any] = scale_attn_weights
lowerCAmelCase : int = use_cache
lowerCAmelCase : List[Any] = scale_attn_by_inverse_layer_idx
lowerCAmelCase : Optional[int] = reorder_and_upcast_attn
lowerCAmelCase : int = tie_word_embeddings
super().__init__(tie_word_embeddings=snake_case__ , **snake_case__ )
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
@property
def lowercase__ ( self ):
"""simple docstring"""
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
] )
def lowercase__ ( self , snake_case__ , snake_case__ = 1 , snake_case__ = -1 , snake_case__ = False , snake_case__ = None , snake_case__ = 3 , snake_case__ = 32 , snake_case__ = 32 , ):
"""simple docstring"""
lowerCAmelCase : Tuple = self._generate_dummy_images(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
lowerCAmelCase : Union[str, Any] = dict(preprocessor(images=snake_case__ , return_tensors=snake_case__ ) )
return inputs
| 645 | 1 |
'''simple docstring'''
__UpperCAmelCase = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
__UpperCAmelCase = [{"type": "code", "content": INSTALL_CONTENT}]
__UpperCAmelCase = {
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
} | 692 |
'''simple docstring'''
import os
import sys
import unittest
__UpperCAmelCase = 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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase = os.path.join(git_repo_path, "src", "transformers")
__UpperCAmelCase = "\n{0} = None\n"
__UpperCAmelCase = "\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n"
__UpperCAmelCase = "\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n"
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = find_backend(' _import_structure["models.albert"].append("AlbertTokenizerFast")' )
self.assertIsNone(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = find_backend(' if not is_tokenizers_available():' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'tokenizers' )
snake_case: List[Any] = find_backend(' if not is_tensorflow_text_available():' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'tensorflow_text' )
snake_case: int = find_backend(' if not (is_sentencepiece_available() and is_tokenizers_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tokenizers' )
snake_case: Optional[Any] = find_backend(
' if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tensorflow_text' )
snake_case: Dict = find_backend(
' if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tokenizers_and_vision' )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , SCREAMING_SNAKE_CASE__ )
self.assertIn('tensorflow_text' , SCREAMING_SNAKE_CASE__ )
self.assertIn('sentencepiece_and_tokenizers' , SCREAMING_SNAKE_CASE__ )
# Likewise, we can't assert on the exact content of a key
self.assertIn('BertModel' , objects['torch'] )
self.assertIn('TFBertModel' , objects['tf'] )
self.assertIn('FlaxBertModel' , objects['flax'] )
self.assertIn('BertModel' , objects['torch'] )
self.assertIn('TFBertTokenizer' , objects['tensorflow_text'] )
self.assertIn('convert_slow_tokenizer' , objects['sentencepiece_and_tokenizers'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = create_dummy_object('CONSTANT' , '\'torch\'' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , '\nCONSTANT = None\n' )
snake_case: Any = create_dummy_object('function' , '\'torch\'' )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n' )
snake_case: Optional[int] = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n'
snake_case: Tuple = create_dummy_object('FakeClass' , '\'torch\'' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n'
snake_case: Optional[int] = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
self.assertEqual(dummy_files['torch'] , SCREAMING_SNAKE_CASE__ ) | 692 | 1 |
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
lowercase__ = str(bin(SCREAMING_SNAKE_CASE ) )[2:] # remove the leading "0b"
lowercase__ = str(bin(SCREAMING_SNAKE_CASE ) )[2:] # remove the leading "0b"
lowercase__ = max(len(SCREAMING_SNAKE_CASE ) , len(SCREAMING_SNAKE_CASE ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE ) , b_binary.zfill(SCREAMING_SNAKE_CASE ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 43 |
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return "".join([hex(SCREAMING_SNAKE_CASE )[2:].zfill(2 ).upper() for byte in list(SCREAMING_SNAKE_CASE )] )
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if (len(SCREAMING_SNAKE_CASE ) % 2) != 0:
raise ValueError(
'''Base16 encoded data is invalid:
Data does not have an even number of hex digits.''' )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(SCREAMING_SNAKE_CASE ) <= set('''0123456789ABCDEF''' ):
raise ValueError(
'''Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.''' )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(SCREAMING_SNAKE_CASE ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 43 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, 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_(SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
a_ : Optional[int] = ShapEImgaImgPipeline
a_ : Union[str, Any] = ["""image"""]
a_ : Union[str, Any] = ["""image"""]
a_ : Optional[Any] = [
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
a_ : int = False
@property
def _lowerCAmelCase ( self ):
return 32
@property
def _lowerCAmelCase ( self ):
return 32
@property
def _lowerCAmelCase ( self ):
return self.time_input_dim * 4
@property
def _lowerCAmelCase ( self ):
return 8
@property
def _lowerCAmelCase ( self ):
torch.manual_seed(0 )
_lowerCamelCase : int = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
_lowerCamelCase : int = CLIPVisionModel(A )
return model
@property
def _lowerCAmelCase ( self ):
_lowerCamelCase : str = CLIPImageProcessor(
crop_size=224 , do_center_crop=A , do_normalize=A , do_resize=A , image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , resample=3 , size=224 , )
return image_processor
@property
def _lowerCAmelCase ( self ):
torch.manual_seed(0 )
_lowerCamelCase : Union[str, Any] = {
'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',
'embedding_proj_norm_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
_lowerCamelCase : Optional[Any] = PriorTransformer(**A )
return model
@property
def _lowerCAmelCase ( self ):
torch.manual_seed(0 )
_lowerCamelCase : Tuple = {
'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,
),
}
_lowerCamelCase : Any = ShapERenderer(**A )
return model
def _lowerCAmelCase ( self ):
_lowerCamelCase : Tuple = self.dummy_prior
_lowerCamelCase : Union[str, Any] = self.dummy_image_encoder
_lowerCamelCase : Optional[int] = self.dummy_image_processor
_lowerCamelCase : str = self.dummy_renderer
_lowerCamelCase : List[Any] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=A , clip_sample=A , clip_sample_range=1.0 , )
_lowerCamelCase : List[str] = {
'prior': prior,
'image_encoder': image_encoder,
'image_processor': image_processor,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def _lowerCAmelCase ( self , A , A=0 ):
_lowerCamelCase : Any = floats_tensor((1, 3, 64, 64) , rng=random.Random(A ) ).to(A )
if str(A ).startswith('mps' ):
_lowerCamelCase : Any = torch.manual_seed(A )
else:
_lowerCamelCase : Union[str, Any] = torch.Generator(device=A ).manual_seed(A )
_lowerCamelCase : Optional[int] = {
'image': input_image,
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def _lowerCAmelCase ( self ):
_lowerCamelCase : int = 'cpu'
_lowerCamelCase : Union[str, Any] = self.get_dummy_components()
_lowerCamelCase : int = self.pipeline_class(**A )
_lowerCamelCase : Tuple = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
_lowerCamelCase : Optional[Any] = pipe(**self.get_dummy_inputs(A ) )
_lowerCamelCase : Tuple = output.images[0]
_lowerCamelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
_lowerCamelCase : List[Any] = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowerCAmelCase ( self ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def _lowerCAmelCase ( self ):
_lowerCamelCase : List[Any] = torch_device == 'cpu'
_lowerCamelCase : Optional[int] = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=A , relax_max_difference=A , )
def _lowerCAmelCase ( self ):
_lowerCamelCase : str = self.get_dummy_components()
_lowerCamelCase : int = self.pipeline_class(**A )
_lowerCamelCase : Union[str, Any] = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
_lowerCamelCase : List[Any] = 1
_lowerCamelCase : Tuple = 2
_lowerCamelCase : Union[str, Any] = self.get_dummy_inputs(A )
for key in inputs.keys():
if key in self.batch_params:
_lowerCamelCase : List[Any] = batch_size * [inputs[key]]
_lowerCamelCase : int = pipe(**A , num_images_per_prompt=A )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class A_(unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCAmelCase ( self ):
_lowerCamelCase : List[str] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png' )
_lowerCamelCase : str = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_img2img_out.npy' )
_lowerCamelCase : List[str] = ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img' )
_lowerCamelCase : Optional[Any] = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
_lowerCamelCase : Dict = torch.Generator(device=A ).manual_seed(0 )
_lowerCamelCase : List[Any] = pipe(
A , generator=A , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(A , A )
| 349 |
"""simple docstring"""
import numpy as np
def UpperCAmelCase_ ( __a : np.array ):
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__ ( self ) -> List[str]:
"""simple docstring"""
lowercase_ : str = tf.convert_to_tensor(
[
[
8.2220991, # 3rd highest value; idx. 0
-0.5620044,
5.23229752,
4.0386393,
-6.8798378,
-0.54785802,
-3.2012153,
2.92777176,
1.88171953,
7.35341276, # 5th highest value; idx. 9
8.43207833, # 2nd highest value; idx. 10
-9.85711836,
-5.96209236,
-1.13039161,
-7.1115294,
-0.8369633,
-5.3186408,
7.06427407,
0.81369344,
-0.82023817,
-5.9179796,
0.58813443,
-6.99778438,
4.71551189,
-0.18771637,
7.44020759, # 4th highest value; idx. 25
9.38450987, # 1st highest value; idx. 26
2.12662941,
-9.32562038,
2.35652522,
], # cummulative prob of 5 highest values <= 0.6
[
0.58425518,
4.53139238,
-5.57510464,
-6.28030699,
-7.19529503,
-4.02122551,
1.39337037,
-6.06707057,
1.59480517,
-9.643119,
0.03907799,
0.67231762,
-8.88206726,
6.27115922, # 4th highest value; idx. 13
2.28520723,
4.82767506,
4.30421368,
8.8275313, # 2nd highest value; idx. 17
5.44029958, # 5th highest value; idx. 18
-4.4735794,
7.38579536, # 3rd highest value; idx. 20
-2.91051663,
2.61946077,
-2.5674762,
-9.48959302,
-4.02922645,
-1.35416918,
9.67702323, # 1st highest value; idx. 27
-5.89478553,
1.85370467,
], # cummulative prob of 5 highest values <= 0.6
], dtype=tf.floataa, )
lowercase_ : int = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]], dtype=tf.intaa, ) # expected non filtered idx as noted above
lowercase_ : List[Any] = tf.convert_to_tensor(
[8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023], dtype=tf.floataa, ) # expected non filtered values as noted above
lowercase_ : int = tf_top_k_top_p_filtering(snake_case__, top_k=10, top_p=0.6, min_tokens_to_keep=4 )
lowercase_ : Dict = output[output != -float("""inf""" )]
lowercase_ : Tuple = tf.cast(
tf.where(tf.not_equal(snake_case__, tf.constant(-float("""inf""" ), dtype=tf.floataa ) ) ), dtype=tf.intaa, )
tf.debugging.assert_near(snake_case__, snake_case__, rtol=1E-12 )
tf.debugging.assert_equal(snake_case__, snake_case__ )
@require_tf
class UpperCamelCase__ ( unittest.TestCase , lowerCamelCase__ ):
'''simple docstring'''
if is_tf_available():
__a : Optional[int] = {
"""AutoModelForCausalLM""": TFAutoModelForCausalLM,
"""AutoModelForSpeechSeq2Seq""": TFAutoModelForSpeechSeqaSeq,
"""AutoModelForSeq2SeqLM""": TFAutoModelForSeqaSeqLM,
"""AutoModelForVision2Seq""": TFAutoModelForVisionaSeq,
"""LogitsProcessorList""": TFLogitsProcessorList,
"""MinLengthLogitsProcessor""": TFMinLengthLogitsProcessor,
"""create_tensor_fn""": tf.convert_to_tensor,
"""floats_tensor""": floats_tensor,
"""return_tensors""": """tf""",
}
@slow
def snake_case__ ( self ) -> str:
"""simple docstring"""
# TF-only test: tf.saved_model export
lowercase_ : Optional[Any] = TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
lowercase_ : int = 2
lowercase_ : Optional[Any] = 2
class UpperCamelCase__ ( tf.Module ):
'''simple docstring'''
def __init__( self, snake_case__ ) -> Any:
"""simple docstring"""
super(snake_case__, self ).__init__()
lowercase_ : int = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length), tf.intaa, name="""input_ids""" ),
tf.TensorSpec((None, input_length), tf.intaa, name="""attention_mask""" ),
), jit_compile=snake_case__, )
def snake_case__ ( self, snake_case__, snake_case__ ) -> int:
"""simple docstring"""
lowercase_ : Tuple = self.model.generate(
input_ids=snake_case__, attention_mask=snake_case__, max_new_tokens=snake_case__, return_dict_in_generate=snake_case__, )
return {"sequences": outputs["sequences"]}
lowercase_ : Union[str, Any] = [[2, 0], [1_02, 1_03]]
lowercase_ : Tuple = [[1, 0], [1, 1]]
lowercase_ : int = DummyModel(model=snake_case__ )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(snake_case__, snake_case__, signatures={"""serving_default""": dummy_model.serving} )
lowercase_ : List[Any] = tf.saved_model.load(snake_case__ ).signatures["""serving_default"""]
for batch_size in range(1, len(snake_case__ ) + 1 ):
lowercase_ : str = {
"""input_ids""": tf.constant(dummy_input_ids[:batch_size] ),
"""attention_mask""": tf.constant(dummy_attention_masks[:batch_size] ),
}
lowercase_ : Optional[int] = serving_func(**snake_case__ )["""sequences"""]
lowercase_ : Optional[int] = test_model.generate(**snake_case__, max_new_tokens=snake_case__ )
tf.debugging.assert_equal(snake_case__, snake_case__ )
@slow
def snake_case__ ( self ) -> Tuple:
"""simple docstring"""
# TF-only test: tf.saved_model export
lowercase_ : int = TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
lowercase_ : Tuple = 1
lowercase_ : Tuple = 2
class UpperCamelCase__ ( tf.Module ):
'''simple docstring'''
def __init__( self, snake_case__ ) -> List[str]:
"""simple docstring"""
super(snake_case__, self ).__init__()
lowercase_ : Union[str, Any] = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None), tf.intaa, name="""input_ids""" ),
tf.TensorSpec((batch_size, None), tf.intaa, name="""attention_mask""" ),
), jit_compile=snake_case__, )
def snake_case__ ( self, snake_case__, snake_case__ ) -> Optional[Any]:
"""simple docstring"""
lowercase_ : str = self.model.generate(
input_ids=snake_case__, attention_mask=snake_case__, max_new_tokens=snake_case__, return_dict_in_generate=snake_case__, )
return {"sequences": outputs["sequences"]}
lowercase_ : Union[str, Any] = [[2], [1_02, 1_03]]
lowercase_ : List[str] = [[1], [1, 1]]
lowercase_ : Union[str, Any] = DummyModel(model=snake_case__ )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(snake_case__, snake_case__, signatures={"""serving_default""": dummy_model.serving} )
lowercase_ : Optional[Any] = tf.saved_model.load(snake_case__ ).signatures["""serving_default"""]
for input_row in range(len(snake_case__ ) ):
lowercase_ : List[str] = {
"""input_ids""": tf.constant([dummy_input_ids[input_row]] ),
"""attention_mask""": tf.constant([dummy_attention_masks[input_row]] ),
}
lowercase_ : Union[str, Any] = serving_func(**snake_case__ )["""sequences"""]
lowercase_ : Optional[Any] = test_model.generate(**snake_case__, max_new_tokens=snake_case__ )
tf.debugging.assert_equal(snake_case__, snake_case__ )
@slow
@require_tensorflow_text
def snake_case__ ( self ) -> Any:
"""simple docstring"""
# TF-only test: tf.saved_model export
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id="""google/flan-t5-small""", filename="""spiece.model""", local_dir=snake_case__ )
class UpperCamelCase__ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
lowercase_ : Tuple = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(snake_case__, """spiece.model""" ), """rb""" ).read() )
lowercase_ : Dict = TFAutoModelForSeqaSeqLM.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
def snake_case__ ( self, snake_case__, *snake_case__, **snake_case__ ) -> Optional[Any]:
"""simple docstring"""
lowercase_ : Union[str, Any] = self.tokenizer.tokenize(snake_case__ )
lowercase_ , lowercase_ : Union[str, Any] = text.pad_model_inputs(
snake_case__, max_seq_length=64, pad_value=self.model.config.pad_token_id )
lowercase_ : str = self.model.generate(input_ids=snake_case__, attention_mask=snake_case__ )
return self.tokenizer.detokenize(snake_case__ )
lowercase_ : Optional[int] = CompleteSentenceTransformer()
lowercase_ : Union[str, Any] = tf.keras.layers.Input(shape=(1,), dtype=tf.string, name="""inputs""" )
lowercase_ : Any = complete_model(snake_case__ )
lowercase_ : Optional[Any] = tf.keras.Model(snake_case__, snake_case__ )
keras_model.save(snake_case__ )
def snake_case__ ( self ) -> Optional[int]:
"""simple docstring"""
# Has PT equivalent: this test relies on random sampling
lowercase_ : Any = {
"""do_sample""": True,
"""num_beams""": 1,
"""top_p""": 0.7,
"""top_k""": 10,
"""temperature""": 0.7,
}
lowercase_ : List[str] = 14
lowercase_ : Optional[Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
lowercase_ : Dict = """Hello, my dog is cute and"""
lowercase_ : List[str] = tokenizer(snake_case__, return_tensors="""tf""" )
lowercase_ : Tuple = TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
lowercase_ : int = 6_38
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(""":/CPU:0""" ):
tf.random.set_seed(0 )
lowercase_ : Optional[int] = model.generate(**snake_case__, eos_token_id=snake_case__, **snake_case__ )
self.assertTrue(expectation == len(generated_tokens[0] ) )
lowercase_ : int = [6_38, 1_98]
with tf.device(""":/CPU:0""" ):
tf.random.set_seed(0 )
lowercase_ : Optional[int] = model.generate(**snake_case__, eos_token_id=snake_case__, **snake_case__ )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def snake_case__ ( self ) -> str:
"""simple docstring"""
# Has PT equivalent: ample use of framework-specific code
lowercase_ : List[Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bart""" )
lowercase_ : int = """Hugging Face is a technology company based in New York and Paris."""
lowercase_ : int = bart_tokenizer(snake_case__, return_tensors="""tf""" ).input_ids
lowercase_ : Tuple = TFBartForConditionalGeneration.from_pretrained("""hf-internal-testing/tiny-random-bart""" )
lowercase_ : Union[str, Any] = bart_model.generate(snake_case__ ).numpy()
class UpperCamelCase__ ( lowerCamelCase__ ):
'''simple docstring'''
def snake_case__ ( self, snake_case__, snake_case__=None, **snake_case__ ) -> str:
"""simple docstring"""
return super().call(snake_case__, **snake_case__ )
lowercase_ : Tuple = FakeBart.from_pretrained("""hf-internal-testing/tiny-random-bart""" )
lowercase_ : str = bart_model.generate(snake_case__, foo="""bar""" ).numpy()
self.assertTrue(np.array_equal(snake_case__, snake_case__ ) )
class UpperCamelCase__ ( bart_model.model.encoder.__class__ ):
'''simple docstring'''
def snake_case__ ( self, snake_case__, **snake_case__ ) -> List[str]:
"""simple docstring"""
return super().call(snake_case__, **snake_case__ )
lowercase_ : Optional[int] = FakeEncoder(bart_model.config, bart_model.model.shared )
lowercase_ : Any = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
lowercase_ : Any = bart_model.generate(snake_case__ ).numpy()
with self.assertRaises(snake_case__ ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(snake_case__, foo="""bar""" ) | 458 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __magic_name__ ( ) -> Union[str, Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(lowercase ):
requests.request("""GET""" , """https://huggingface.co""" )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request("""GET""" , """https://huggingface.co""" , timeout=1.0 )
@pytest.mark.integration
def __magic_name__ ( ) -> Union[str, Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request("""GET""" , """https://huggingface.co""" )
def __magic_name__ ( ) -> Dict:
"""simple docstring"""
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(lowercase ):
http_head("""https://huggingface.co""" ) | 458 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def _A ( A__ ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(A__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _A ( A__ ):
"""simple docstring"""
__lowercase = str(A__ )
__lowercase = [n]
for i in range(1 , len(A__ ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def _A ( A__ ):
"""simple docstring"""
if len(str(A__ ) ) > 3:
if not is_prime(int(str(A__ )[-3:] ) ) or not is_prime(int(str(A__ )[:3] ) ):
return False
return True
def _A ( A__ = 11 ):
"""simple docstring"""
__lowercase = []
__lowercase = 13
while len(A__ ) != count:
if validate(A__ ):
__lowercase = list_truncated_nums(A__ )
if all(is_prime(A__ ) for i in list_nums ):
list_truncated_primes.append(A__ )
num += 2
return list_truncated_primes
def _A ( ):
"""simple docstring"""
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f'{sum(compute_truncated_primes(11)) = }')
| 702 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowercase__ ,'''embed_dim''' ) )
self.parent.assertTrue(hasattr(lowercase__ ,'''num_heads''' ) )
class lowercase_ :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowercase__ : Optional[int] ,lowercase__ : Optional[int]=1_3 ,lowercase__ : List[Any]=6_4 ,lowercase__ : Optional[int]=3 ,lowercase__ : Dict=[1_6, 4_8, 9_6] ,lowercase__ : Optional[Any]=[1, 3, 6] ,lowercase__ : Tuple=[1, 2, 1_0] ,lowercase__ : Optional[int]=[7, 3, 3] ,lowercase__ : str=[4, 2, 2] ,lowercase__ : Dict=[2, 1, 1] ,lowercase__ : Tuple=[2, 2, 2] ,lowercase__ : Tuple=[False, False, True] ,lowercase__ : int=[0.0, 0.0, 0.0] ,lowercase__ : str=0.0_2 ,lowercase__ : Union[str, Any]=1e-1_2 ,lowercase__ : Optional[int]=True ,lowercase__ : Union[str, Any]=True ,lowercase__ : Optional[Any]=2 ,):
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_sizes
__lowercase = patch_stride
__lowercase = patch_padding
__lowercase = is_training
__lowercase = use_labels
__lowercase = num_labels
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = num_heads
__lowercase = stride_kv
__lowercase = depth
__lowercase = cls_token
__lowercase = attention_drop_rate
__lowercase = initializer_range
__lowercase = layer_norm_eps
def SCREAMING_SNAKE_CASE ( self : List[str] ):
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] ,self.num_labels )
__lowercase = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE ( self : str ):
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 : Dict ,lowercase__ : List[str] ,lowercase__ : Any ,lowercase__ : List[str] ):
__lowercase = CvtModel(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ )
__lowercase = (self.image_size, self.image_size)
__lowercase , __lowercase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
__lowercase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
__lowercase = 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__ : Any ,lowercase__ : List[Any] ,lowercase__ : Dict ):
__lowercase = self.num_labels
__lowercase = CvtForImageClassification(lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ ,labels=lowercase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase_ (lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
SCREAMING_SNAKE_CASE : Optional[int] = (
{'feature-extraction': CvtModel, 'image-classification': CvtForImageClassification}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE : Optional[int] = False
SCREAMING_SNAKE_CASE : Union[str, Any] = False
SCREAMING_SNAKE_CASE : int = False
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : str = False
def SCREAMING_SNAKE_CASE ( self : List[str] ):
__lowercase = CvtModelTester(self )
__lowercase = ConfigTester(self ,config_class=lowercase__ ,has_text_modality=lowercase__ ,hidden_size=3_7 )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
self.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()
def SCREAMING_SNAKE_CASE ( self : str ):
return
@unittest.skip(reason='''Cvt does not output attentions''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
pass
@unittest.skip(reason='''Cvt does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
pass
@unittest.skip(reason='''Cvt does not support input and output embeddings''' )
def SCREAMING_SNAKE_CASE ( self : str ):
pass
def SCREAMING_SNAKE_CASE ( self : List[str] ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowercase__ )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
def check_hidden_states_output(lowercase__ : Union[str, Any] ,lowercase__ : Union[str, Any] ,lowercase__ : str ):
__lowercase = model_class(lowercase__ )
model.to(lowercase__ )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(lowercase__ ,lowercase__ ) )
__lowercase = outputs.hidden_states
__lowercase = len(self.model_tester.depth )
self.assertEqual(len(lowercase__ ) ,lowercase__ )
# 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 , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(lowercase__ ,lowercase__ ,lowercase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(lowercase__ ,lowercase__ ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase__ )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
pass
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = CvtModel.from_pretrained(lowercase__ )
self.assertIsNotNone(lowercase__ )
def _A ( ):
"""simple docstring"""
__lowercase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowercase_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def SCREAMING_SNAKE_CASE ( self : List[str] ):
__lowercase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(lowercase__ )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=lowercase__ ,return_tensors='''pt''' ).to(lowercase__ )
# forward pass
with torch.no_grad():
__lowercase = model(**lowercase__ )
# verify the logits
__lowercase = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape ,lowercase__ )
__lowercase = torch.tensor([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0] ).to(lowercase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,lowercase__ ,atol=1e-4 ) )
| 624 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''image_processor''', '''tokenizer''']
lowerCAmelCase_ = '''CLIPImageProcessor'''
lowerCAmelCase_ = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
def __init__( self : Dict , _A : List[str]=None , _A : List[Any]=None , **_A : Any ):
"""simple docstring"""
__SCREAMING_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.''' , _A , )
__SCREAMING_SNAKE_CASE : str = kwargs.pop('''feature_extractor''' )
__SCREAMING_SNAKE_CASE : Optional[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 : List[str] , _A : Union[str, Any]=None , _A : Union[str, Any]=None , _A : Tuple=None , **_A : Optional[int] ):
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
__SCREAMING_SNAKE_CASE : Optional[int] = self.tokenizer(_A , return_tensors=_A , **_A )
if images is not None:
__SCREAMING_SNAKE_CASE : str = self.image_processor(_A , return_tensors=_A , **_A )
if text is not None and images is not None:
__SCREAMING_SNAKE_CASE : Optional[Any] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_A ) , tensor_type=_A )
def UpperCAmelCase__ ( self : List[Any] , *_A : Tuple , **_A : Any ):
"""simple docstring"""
return self.tokenizer.batch_decode(*_A , **_A )
def UpperCAmelCase__ ( self : Optional[int] , *_A : Optional[Any] , **_A : Optional[Any] ):
"""simple docstring"""
return self.tokenizer.decode(*_A , **_A )
@property
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = self.tokenizer.model_input_names
__SCREAMING_SNAKE_CASE : List[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
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 UpperCAmelCase__ ( self : Any ):
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , _A , )
return self.image_processor
| 74 |
def lowerCAmelCase ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: int = [0 for i in range(r + 1 )]
# nc0 = 1
__SCREAMING_SNAKE_CASE: Dict = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
__SCREAMING_SNAKE_CASE: Optional[Any] = min(UpperCamelCase__ , UpperCamelCase__ )
while j > 0:
c[j] += c[j - 1]
j -= 1
return c[r]
print(binomial_coefficient(n=10, r=5))
| 202 | 0 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
__lowerCamelCase : Dict = pytest.mark.integration
__lowerCamelCase : int = {'comet'}
__lowerCamelCase : Dict = importlib.util.find_spec('fairseq') is not None
__lowerCamelCase : Tuple = {'code_eval'}
__lowerCamelCase : Optional[Any] = os.name == 'nt'
__lowerCamelCase : Union[str, Any] = {'bertscore', 'frugalscore', 'perplexity'}
__lowerCamelCase : Union[str, Any] = importlib.util.find_spec('transformers') is not None
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
@wraps(__A )
def wrapper(self , __SCREAMING_SNAKE_CASE ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest('''\"test requires Fairseq\"''' )
else:
test_case(self , __A )
return wrapper
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
@wraps(__A )
def wrapper(self , __SCREAMING_SNAKE_CASE ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest('''\"test requires transformers\"''' )
else:
test_case(self , __A )
return wrapper
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
@wraps(__A )
def wrapper(self , __SCREAMING_SNAKE_CASE ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest('''\"test not supported on Windows\"''' )
else:
test_case(self , __A )
return wrapper
def _a ():
"""simple docstring"""
_UpperCamelCase =[metric_dir.split(os.sep )[-2] for metric_dir in glob.glob('''./metrics/*/''' )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names())
@for_all_test_methods(
_snake_case , _snake_case , _snake_case)
@local
class UpperCAmelCase ( parameterized.TestCase):
"""simple docstring"""
lowerCAmelCase_ = {}
lowerCAmelCase_ = None
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning''' )
@pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning''' )
def UpperCamelCase__ ( self : List[str] , UpperCamelCase__ : Tuple ) -> Dict:
_UpperCamelCase ='''[...]'''
_UpperCamelCase =importlib.import_module(
datasets.load.metric_module_factory(os.path.join('''metrics''' , lowerCAmelCase__ ) ).module_path )
_UpperCamelCase =datasets.load.import_main_class(metric_module.__name__ , dataset=lowerCAmelCase__ )
# check parameters
_UpperCamelCase =inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(lowerCAmelCase__ , metric_module.__name__ ):
with self.use_local_metrics():
try:
_UpperCamelCase =doctest.testmod(lowerCAmelCase__ , verbose=lowerCAmelCase__ , raise_on_error=lowerCAmelCase__ )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def UpperCamelCase__ ( self : str , UpperCamelCase__ : Optional[int] ) -> str:
_UpperCamelCase ='''[...]'''
_UpperCamelCase =importlib.import_module(
datasets.load.metric_module_factory(os.path.join('''metrics''' , lowerCAmelCase__ ) ).module_path )
# run doctest
with self.use_local_metrics():
_UpperCamelCase =doctest.testmod(lowerCAmelCase__ , verbose=lowerCAmelCase__ , raise_on_error=lowerCAmelCase__ )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def UpperCamelCase__ ( self : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Optional[Any] ) -> Dict:
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCAmelCase__ ):
yield
else:
yield
@contextmanager
def UpperCamelCase__ ( self : List[str] ) -> int:
def load_local_metric(UpperCamelCase__ : Tuple , *UpperCamelCase__ : str , **UpperCamelCase__ : Dict ):
return load_metric(os.path.join('''metrics''' , lowerCAmelCase__ ) , *lowerCAmelCase__ , **lowerCAmelCase__ )
with patch('''datasets.load_metric''' ) as mock_load_metric:
_UpperCamelCase =load_local_metric
yield
@classmethod
def UpperCamelCase__ ( cls : List[Any] , UpperCamelCase__ : Any ) -> str:
def wrapper(UpperCamelCase__ : List[Any] ):
_UpperCamelCase =contextmanager(lowerCAmelCase__ )
_UpperCamelCase =patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher('''bleurt''' )
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string('''sv''' , '''''' , '''''' ) # handle pytest cli flags
class UpperCAmelCase ( _snake_case):
"""simple docstring"""
def UpperCamelCase__ ( self : Tuple , UpperCamelCase__ : Any ) -> int:
assert len(input_dict['''input_ids'''] ) == 2
return np.array([1.03, 1.04] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch('''bleurt.score._create_predictor''' ) as mock_create_predictor:
_UpperCamelCase =MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher('''bertscore''' )
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
import torch
def bert_cos_score_idf(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(__A ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch('''bert_score.scorer.get_model''' ), patch(
'''bert_score.scorer.bert_cos_score_idf''' ) as mock_bert_cos_score_idf:
_UpperCamelCase =bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher('''comet''' )
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def load_from_checkpoint(__SCREAMING_SNAKE_CASE ):
class UpperCAmelCase :
"""simple docstring"""
def UpperCamelCase__ ( self : Optional[Any] , UpperCamelCase__ : Optional[int] , *UpperCamelCase__ : Dict , **UpperCamelCase__ : str ) -> Dict:
assert len(lowerCAmelCase__ ) == 2
_UpperCamelCase =[0.19, 0.92]
return scores, sum(lowerCAmelCase__ ) / len(lowerCAmelCase__ )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch('''comet.download_model''' ) as mock_download_model:
_UpperCamelCase =None
with patch('''comet.load_from_checkpoint''' ) as mock_load_from_checkpoint:
_UpperCamelCase =load_from_checkpoint
yield
def _a ():
"""simple docstring"""
_UpperCamelCase =load_metric(os.path.join('''metrics''' , '''seqeval''' ) )
_UpperCamelCase ='''ERROR'''
_UpperCamelCase =f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
with pytest.raises(__A , match=re.escape(__A ) ):
metric.compute(predictions=[] , references=[] , scheme=__A )
| 701 |
'''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 PoolFormerImageProcessor
class UpperCAmelCase ( unittest.TestCase):
"""simple docstring"""
def __init__( self : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Any=7 , UpperCamelCase__ : List[Any]=3 , UpperCamelCase__ : Dict=30 , UpperCamelCase__ : Union[str, Any]=400 , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : List[str]=None , UpperCamelCase__ : List[str]=0.9 , UpperCamelCase__ : Any=None , UpperCamelCase__ : Any=True , UpperCamelCase__ : Optional[Any]=[0.5, 0.5, 0.5] , UpperCamelCase__ : List[str]=[0.5, 0.5, 0.5] , ) -> Dict:
_UpperCamelCase =size if size is not None else {'''shortest_edge''': 30}
_UpperCamelCase =crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
_UpperCamelCase =parent
_UpperCamelCase =batch_size
_UpperCamelCase =num_channels
_UpperCamelCase =min_resolution
_UpperCamelCase =max_resolution
_UpperCamelCase =do_resize_and_center_crop
_UpperCamelCase =size
_UpperCamelCase =crop_pct
_UpperCamelCase =crop_size
_UpperCamelCase =do_normalize
_UpperCamelCase =image_mean
_UpperCamelCase =image_std
def UpperCamelCase__ ( self : str ) -> Union[str, Any]:
return {
"size": self.size,
"do_resize_and_center_crop": self.do_resize_and_center_crop,
"crop_pct": self.crop_pct,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class UpperCAmelCase ( lowercase_ , unittest.TestCase):
"""simple docstring"""
lowerCAmelCase_ = PoolFormerImageProcessor if is_vision_available() else None
def UpperCamelCase__ ( self : Optional[int] ) -> int:
_UpperCamelCase =PoolFormerImageProcessingTester(self )
@property
def UpperCamelCase__ ( self : Tuple ) -> Optional[int]:
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase__ ( self : Optional[Any] ) -> int:
_UpperCamelCase =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase__ , '''do_resize_and_center_crop''' ) )
self.assertTrue(hasattr(UpperCamelCase__ , '''size''' ) )
self.assertTrue(hasattr(UpperCamelCase__ , '''crop_pct''' ) )
self.assertTrue(hasattr(UpperCamelCase__ , '''do_normalize''' ) )
self.assertTrue(hasattr(UpperCamelCase__ , '''image_mean''' ) )
self.assertTrue(hasattr(UpperCamelCase__ , '''image_std''' ) )
def UpperCamelCase__ ( self : Any ) -> Dict:
_UpperCamelCase =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 30} )
self.assertEqual(image_processor.crop_size , {'''height''': 30, '''width''': 30} )
_UpperCamelCase =self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def UpperCamelCase__ ( self : Dict ) -> Optional[Any]:
pass
def UpperCamelCase__ ( self : Union[str, Any] ) -> List[Any]:
# Initialize image_processing
_UpperCamelCase =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCamelCase =prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , Image.Image )
# Test not batched input
_UpperCamelCase =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
_UpperCamelCase =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,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def UpperCamelCase__ ( self : Union[str, Any] ) -> Optional[int]:
# Initialize image_processing
_UpperCamelCase =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCamelCase =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
_UpperCamelCase =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
_UpperCamelCase =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,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def UpperCamelCase__ ( self : str ) -> List[Any]:
# Initialize image_processing
_UpperCamelCase =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCamelCase =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
_UpperCamelCase =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
_UpperCamelCase =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,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 271 | 0 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _A ( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = IFImgaImgSuperResolutionPipeline
lowerCamelCase : Tuple = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
lowerCamelCase : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'} )
lowerCamelCase : Tuple = PipelineTesterMixin.required_optional_params - {'latents'}
def _a ( self : Any ) -> List[Any]:
return self._get_superresolution_dummy_components()
def _a ( self : str , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any]=0 ) -> int:
if str(__SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
__UpperCAmelCase =torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
__UpperCAmelCase =torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =floats_tensor((1, 3, 32, 32) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =floats_tensor((1, 3, 16, 16) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase ={
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""original_image""": original_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _a ( self : Optional[Any] ) -> Dict:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def _a ( self : str ) -> str:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def _a ( self : Optional[Any] ) -> str:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def _a ( self : Any ) -> Optional[int]:
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def _a ( self : List[Any] ) -> Tuple:
self._test_save_load_local()
def _a ( self : List[Any] ) -> Union[str, Any]:
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 68 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class lowercase__ ( unittest.TestCase , __SCREAMING_SNAKE_CASE ):
def _UpperCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = load_tool("text-to-speech" )
self.tool.setup()
def _UpperCAmelCase ( self : Dict ):
"""simple docstring"""
torch.manual_seed(0 )
UpperCAmelCase__ = self.tool("hey" )
UpperCAmelCase__ = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
def _UpperCAmelCase ( self : Optional[int] ):
"""simple docstring"""
torch.manual_seed(0 )
UpperCAmelCase__ = self.tool("hey" )
UpperCAmelCase__ = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
| 475 | 0 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def UpperCAmelCase__ ( lowerCamelCase_ : Union[str, Any] ):
__a : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(lowerCamelCase_ ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
__a : Dict = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError('This matrix has no inverse.' )
# Creates a copy of the matrix with swapped positions of the elements
__a : List[Any] = [[0.0, 0.0], [0.0, 0.0]]
__a , __a : List[str] = matrix[1][1], matrix[0][0]
__a , __a : Tuple = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(lowerCamelCase_ ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(lowerCamelCase_ ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
__a : List[Any] = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError('This matrix has no inverse.' )
# Creating cofactor matrix
__a : Tuple = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
__a : Optional[Any] = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
__a : List[Any] = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
__a : List[Any] = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
__a : Optional[int] = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
__a : Tuple = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
__a : List[str] = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
__a : Union[str, Any] = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
__a : List[Any] = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
__a : List[str] = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
__a : Optional[Any] = array(lowerCamelCase_ )
for i in range(3 ):
for j in range(3 ):
__a : Optional[int] = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
__a : Union[str, Any] = array(lowerCamelCase_ )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(lowerCamelCase_ )
# Calculate the inverse of the matrix
return [[float(d(lowerCamelCase_ ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError('Please provide a matrix of size 2x2 or 3x3.' )
| 720 |
def UpperCAmelCase__ ( lowerCamelCase_ : list[int] , lowerCamelCase_ : list[int] ):
# Check if the input is valid
if not len(lowerCamelCase_ ) == len(lowerCamelCase_ ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError('Both a & b of two equations can\'t be zero.' )
# Extract the coefficients
__a , __a , __a : int = equationa
__a , __a , __a : Dict = equationa
# Calculate the determinants of the matrices
__a : Dict = aa * ba - aa * ba
__a : List[Any] = ca * ba - ca * ba
__a : Tuple = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError('Infinite solutions. (Consistent system)' )
else:
raise ValueError('No solution. (Inconsistent system)' )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
__a : int = determinant_x / determinant
__a : List[str] = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 577 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :str) -> list[int]:
return [ord(a_) - 96 for elem in plain]
def __A ( a_ :list[int]) -> str:
return "".join(chr(elem + 96) for elem in encoded)
def __A ( ) -> None:
__a : Dict = encode(input('''-> ''').strip().lower())
print('''Encoded: ''' , a_)
print('''Decoded:''' , decode(a_))
if __name__ == "__main__":
main() | 52 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class lowerCAmelCase ( __lowerCAmelCase , unittest.TestCase):
__lowercase : int = BarthezTokenizer
__lowercase : Any = BarthezTokenizerFast
__lowercase : Dict = True
__lowercase : Optional[int] = True
def lowerCAmelCase ( self ) -> Dict:
'''simple docstring'''
super().setUp()
__snake_case = BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=__SCREAMING_SNAKE_CASE )
__snake_case = tokenizer
def lowerCAmelCase ( self ) -> Dict:
'''simple docstring'''
__snake_case = '''<pad>'''
__snake_case = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
__snake_case = 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(__SCREAMING_SNAKE_CASE ) , 10_1122 )
def lowerCAmelCase ( self ) -> Any:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 10_1122 )
@require_torch
def lowerCAmelCase ( self ) -> Tuple:
'''simple docstring'''
__snake_case = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
__snake_case = [0, 57, 3018, 7_0307, 91, 2]
__snake_case = self.tokenizer(
__SCREAMING_SNAKE_CASE , max_length=len(__SCREAMING_SNAKE_CASE ) , padding=__SCREAMING_SNAKE_CASE , truncation=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
__snake_case = batch.input_ids.tolist()[0]
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__snake_case = self.get_tokenizer()
__snake_case = self.get_rust_tokenizer()
__snake_case = '''I was born in 92000, and this is falsé.'''
__snake_case = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
__snake_case = rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__snake_case = tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
__snake_case = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__snake_case = self.get_rust_tokenizer()
__snake_case = tokenizer.encode(__SCREAMING_SNAKE_CASE )
__snake_case = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@slow
def lowerCAmelCase ( self ) -> int:
'''simple docstring'''
__snake_case = {'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 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], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
__snake_case = [
'''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
'''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
'''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
'''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
'''telles que la traduction et la synthèse de texte.''',
]
self.tokenizer_integration_test_util(
expected_encoding=__SCREAMING_SNAKE_CASE , model_name='''moussaKam/mbarthez''' , revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' , sequences=__SCREAMING_SNAKE_CASE , )
| 24 | 0 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
# Throw an error if user passes both BYO and on-demand cluster args
# Otherwise, use default values
lowercase = argparse.ArgumentParser()
parser.add_argument('''--user''', type=str, default='''ubuntu''')
parser.add_argument('''--host''', type=str, default='''localhost''')
parser.add_argument('''--key_path''', type=str, default=None)
parser.add_argument('''--instance''', type=str, default='''V100:1''')
parser.add_argument('''--provider''', type=str, default='''cheapest''')
parser.add_argument('''--use_spot''', type=bool, default=False)
parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
lowercase , lowercase = parser.parse_known_args()
if args.host != "localhost":
if args.instance != "V100:1" or args.provider != "cheapest":
raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
lowercase = rh.cluster(
name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
)
else:
lowercase = rh.cluster(
name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
)
lowercase = args.example.rsplit('''/''', 1)[0]
# Set up remote environment
cluster.install_packages(['''pip:./''']) # Installs transformers from local source
# Note transformers is copied into the home directory on the remote machine, so we can install from there
cluster.run([F"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])
# Run example. You can bypass the CLI wrapper and paste your own code here.
cluster.run([F"""python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"""])
# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
# from my_script... import train
# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
# launch_train_gpu = rh.function(fn=train,
# system=gpu,
# reqs=reqs,
# name='train_bert_glue')
#
# We can pass in arguments just like we would to a function:
# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
# stream_logs=True)
| 700 |
'''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
lowercase = logging.get_logger(__name__)
lowercase = '''T5Config'''
class __lowerCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case__ : Optional[int] = '''mt5'''
snake_case__ : Dict = MTaConfig
class __lowerCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case__ : List[str] = '''mt5'''
snake_case__ : List[str] = MTaConfig
class __lowerCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case__ : Optional[int] = '''mt5'''
snake_case__ : Union[str, Any] = MTaConfig
| 564 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 0 , _lowerCAmelCase = 0 )-> int:
__UpperCAmelCase = right or len(__lowercase ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(__lowercase , __lowercase , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 126 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise ValueError("Resistance cannot be negative" )
if voltage == 0:
return {"voltage": float(current * resistance )}
elif current == 0:
return {"current": voltage / resistance}
elif resistance == 0:
return {"resistance": voltage / current}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod() | 670 | 0 |
def lowercase ( _a ) -> int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def lowercase ( _a ) -> bool:
UpperCAmelCase_: Any = 0
UpperCAmelCase_: List[str] = number
while duplicate > 0:
UpperCAmelCase_ , UpperCAmelCase_: List[Any] = divmod(_a ,10 )
fact_sum += factorial(_a )
return fact_sum == number
if __name__ == "__main__":
print("""Program to check whether a number is a Krisnamurthy Number or not.""")
_lowerCAmelCase = int(input("""Enter number: """).strip())
print(
F"""{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."""
) | 306 |
from __future__ import annotations
_lowerCAmelCase = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowerCAmelCase = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowercase ( _a ) -> list[float]:
UpperCAmelCase_: Dict = []
UpperCAmelCase_: List[Any] = len(_a )
for i in range(_a ):
UpperCAmelCase_: float = -1
for j in range(i + 1 ,_a ):
if arr[i] < arr[j]:
UpperCAmelCase_: List[str] = arr[j]
break
result.append(_a )
return result
def lowercase ( _a ) -> list[float]:
UpperCAmelCase_: List[Any] = []
for i, outer in enumerate(_a ):
UpperCAmelCase_: float = -1
for inner in arr[i + 1 :]:
if outer < inner:
UpperCAmelCase_: Union[str, Any] = inner
break
result.append(_a )
return result
def lowercase ( _a ) -> list[float]:
UpperCAmelCase_: Union[str, Any] = len(_a )
UpperCAmelCase_: list[float] = []
UpperCAmelCase_: list[float] = [-1] * arr_size
for index in reversed(range(_a ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
UpperCAmelCase_: Union[str, Any] = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
_lowerCAmelCase = (
"""from __main__ import arr, next_greatest_element_slow, """
"""next_greatest_element_fast, next_greatest_element"""
)
print(
"""next_greatest_element_slow():""",
timeit("""next_greatest_element_slow(arr)""", setup=setup),
)
print(
"""next_greatest_element_fast():""",
timeit("""next_greatest_element_fast(arr)""", setup=setup),
)
print(
""" next_greatest_element():""",
timeit("""next_greatest_element(arr)""", setup=setup),
) | 306 | 1 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 521 |
from __future__ import annotations
def __UpperCamelCase ( lowerCAmelCase__ : list[float] , lowerCAmelCase__ : int ):
print(f"Vertex\tShortest Distance from vertex {src}" )
for i, d in enumerate(lowerCAmelCase__ ):
print(f"{i}\t\t{d}" )
def __UpperCamelCase ( lowerCAmelCase__ : list[dict[str, int]] , lowerCAmelCase__ : list[float] , lowerCAmelCase__ : int ):
for j in range(lowerCAmelCase__ ):
__a , __a , __a : Optional[Any] = (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
if distance[u] != float('''inf''' ) and distance[u] + w < distance[v]:
return True
return False
def __UpperCamelCase ( lowerCAmelCase__ : list[dict[str, int]] , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
__a : Optional[Any] = [float('''inf''' )] * vertex_count
__a : Tuple = 0.0
for _ in range(vertex_count - 1 ):
for j in range(lowerCAmelCase__ ):
__a , __a , __a : int = (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
if distance[u] != float('''inf''' ) and distance[u] + w < distance[v]:
__a : Any = distance[u] + w
__a : Union[str, Any] = check_negative_cycle(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
if negative_cycle_exists:
raise Exception('''Negative cycle found''' )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase__ =int(input('Enter number of vertices: ').strip())
lowercase__ =int(input('Enter number of edges: ').strip())
lowercase__ =[{} for _ in range(E)]
for i in range(E):
print('Edge ', i + 1)
lowercase__ , lowercase__ , lowercase__ =(
int(x)
for x in input('Enter source, destination, weight: ').strip().split(' ')
)
lowercase__ ={'src': src, 'dst': dest, 'weight': weight}
lowercase__ =int(input('\nEnter shortest path source:').strip())
lowercase__ =bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 521 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
__lowerCAmelCase = tuple[int, int]
class SCREAMING_SNAKE_CASE :
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : set[int] , __SCREAMING_SNAKE_CASE : Mapping[EdgeT, int] ) -> None:
a_ : set[int] = vertices
a_ : dict[EdgeT, int] = {
(min(__SCREAMING_SNAKE_CASE ), max(__SCREAMING_SNAKE_CASE )): weight for edge, weight in edges.items()
}
def SCREAMING_SNAKE_CASE ( self : Dict , __SCREAMING_SNAKE_CASE : EdgeT , __SCREAMING_SNAKE_CASE : int ) -> None:
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
a_ : Optional[Any] = weight
def SCREAMING_SNAKE_CASE ( self : Any ) -> Graph:
a_ : Graph = Graph({min(self.vertices )} , {} )
a_ : EdgeT
a_ : int
a_ : EdgeT
a_ : int
while len(subgraph.vertices ) < len(self.vertices ):
a_ : List[Any] = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
a_ : Any = edge
a_ : List[Any] = weight
subgraph.add_edge(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return subgraph
def _UpperCAmelCase ( __A : str = "p107_network.txt" ):
a_ : str = os.path.abspath(os.path.dirname(__A ) )
a_ : str = os.path.join(__A , __A )
a_ : dict[EdgeT, int] = {}
a_ : list[str]
a_ : int
a_ : int
with open(__A ) as f:
a_ : str = f.read().strip().split('''\n''' )
a_ : List[Any] = [line.split(''',''' ) for line in data]
for edgea in range(1 , len(__A ) ):
for edgea in range(__A ):
if adjaceny_matrix[edgea][edgea] != "-":
a_ : Tuple = int(adjaceny_matrix[edgea][edgea] )
a_ : Graph = Graph(set(range(len(__A ) ) ) , __A )
a_ : Graph = graph.prims_algorithm()
a_ : int = sum(graph.edges.values() )
a_ : int = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(F"""{solution() = }""")
| 714 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
__lowerCAmelCase = logging.get_logger(__name__)
logging.set_verbosity_info()
def _UpperCAmelCase ( __A : str , __A : str ):
if "xprophetnet" in prophetnet_checkpoint_path:
a_ : Tuple = XLMProphetNetForConditionalGenerationOld.from_pretrained(__A )
a_ , a_ : Optional[Any] = XLMProphetNetForConditionalGeneration.from_pretrained(
__A , output_loading_info=__A )
else:
a_ : List[Any] = ProphetNetForConditionalGenerationOld.from_pretrained(__A )
a_ , a_ : Any = ProphetNetForConditionalGeneration.from_pretrained(
__A , output_loading_info=__A )
a_ : str = ['''key_proj''', '''value_proj''', '''query_proj''']
a_ : Tuple = {
'''self_attn''': '''ngram_self_attn''',
'''cross_attn''': '''encoder_attn''',
'''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
'''feed_forward_layer_norm''': '''final_layer_norm''',
'''feed_forward''': '''''',
'''intermediate''': '''fc1''',
'''output''': '''fc2''',
'''key_proj''': '''k_proj''',
'''query_proj''': '''q_proj''',
'''value_proj''': '''v_proj''',
'''word_embeddings''': '''embed_tokens''',
'''embeddings_layer_norm''': '''emb_layer_norm''',
'''relative_pos_embeddings''': '''relative_linear''',
'''ngram_embeddings''': '''ngram_input_embed''',
'''position_embeddings''': '''embed_positions''',
}
for key in loading_info["missing_keys"]:
a_ : List[str] = key.split('''.''' )
if attributes[0] == "lm_head":
a_ : List[str] = prophet
a_ : Dict = prophet_old
else:
a_ : str = prophet.prophetnet
a_ : int = prophet_old.model
a_ : str = False
for attribute in attributes:
if attribute in mapping:
a_ : Dict = mapping[attribute]
if not hasattr(__A , __A ) and len(__A ) > 0:
a_ : List[str] = attribute
elif hasattr(__A , __A ):
a_ : Union[str, Any] = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
a_ : Tuple = old_model.weight
logger.info(f'{attribute} is initialized.' )
a_ : Union[str, Any] = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
a_ : Union[str, Any] = old_model.bias
logger.info(f'{attribute} is initialized' )
a_ : Dict = True
break
elif attribute in special_keys and hasattr(__A , '''in_proj_weight''' ):
a_ : Tuple = old_model.in_proj_weight.shape[0] // 3
a_ : Any = getattr(__A , __A )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
a_ : Union[str, Any] = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
a_ : Optional[Any] = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
a_ : List[Any] = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
a_ : Optional[int] = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
a_ : Tuple = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
a_ : Any = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
a_ : Dict = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
a_ : Union[str, Any] = nn.Parameter(old_model.embed_positions.weight[:5_12, :] )
a_ : Optional[Any] = True
break
if attribute.isdigit():
a_ : Union[str, Any] = model[int(__A )]
a_ : str = old_model[int(__A )]
else:
a_ : Tuple = getattr(__A , __A )
if old_attribute == "":
a_ : List[str] = old_model
else:
if not hasattr(__A , __A ):
raise ValueError(f'{old_model} does not have {old_attribute}' )
a_ : Optional[Any] = getattr(__A , __A )
if not is_key_init:
raise ValueError(f'{key} was not correctly initialized!' )
print(f'Saving model to {pytorch_dump_folder_path}' )
prophet.save_pretrained(__A )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
__lowerCAmelCase = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 666 | 0 |
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