




import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Optional[Any] ,			UpperCAmelCase__				:     str=2 ,			UpperCAmelCase__				:     Union[str, Any]=3 ,			UpperCAmelCase__				:     str=64 ,			UpperCAmelCase__				:     List[str]=None)						->List[Any]:





           '''simple docstring'''

           A__							=   np.random.default_rng(UpperCAmelCase__)
           A__							=   length
           A__							=   rng.normal(size=(length,)).astype(np.floataa)
           A__							=   a * self.x + b + rng.normal(scale=0.1 ,			size=(length,)).astype(np.floataa)


    def __len__(      self				:     List[Any])						->int:





           '''simple docstring'''

           return self.length





    def __getitem__(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Any)						->Union[str, Any]:





           '''simple docstring'''

           return {"x": self.x[i], "y": self.y[i]}


class     UpperCamelCase_   (			torch.nn.Module					):

    '''simple docstring'''






    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     List[str]=0 ,			UpperCAmelCase__				:     List[str]=0 ,			UpperCAmelCase__				:     Tuple=False)						->Tuple:





           '''simple docstring'''

           super().__init__()
           A__							=   torch.nn.Parameter(torch.tensor([2, 3]).float())
           A__							=   torch.nn.Parameter(torch.tensor([2, 3]).float())
           A__							=   True





    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     str=None)						->Any:





           '''simple docstring'''

           if self.first_batch:
                  print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""")
                  A__							=   False
           return x * self.a[0] + self.b[0]


class     UpperCamelCase_   (			torch.nn.Module					):

    '''simple docstring'''






    def __init__(      self				:     Tuple ,			UpperCAmelCase__				:     int=0 ,			UpperCAmelCase__				:     Optional[Any]=0 ,			UpperCAmelCase__				:     Optional[int]=False)						->str:





           '''simple docstring'''

           super().__init__()
           A__							=   torch.nn.Parameter(torch.tensor(UpperCAmelCase__).float())
           A__							=   torch.nn.Parameter(torch.tensor(UpperCAmelCase__).float())
           A__							=   True





    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     List[Any]=None)						->Optional[int]:





           '''simple docstring'''

           if self.first_batch:
                  print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""")
                  A__							=   False
           return x * self.a + self.b







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_ = 16						)       ->	Dict:



       """simple docstring"""

       from datasets import load_dataset
       from transformers import AutoTokenizer

       A__							=   AutoTokenizer.from_pretrained('''bert-base-cased'''						)
       A__							=   {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
       A__							=   load_dataset('''csv'''			,    data_files=lowercase_						)
       A__							=   datasets['''train'''].unique('''label'''						)

       A__							=   {v: i for i, v in enumerate(lowercase_						)}

       def tokenize_function(lowercase_						):
              # max_length=None => use the model max length (it's actually the default)
              A__							=   tokenizer(
                  examples['''sentence1''']			,    examples['''sentence2''']			,    truncation=lowercase_			,    max_length=lowercase_			,    padding='''max_length'''						)
              if "label" in examples:
                     A__							=   [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
       A__							=   datasets.map(
           lowercase_			,    batched=lowercase_			,    remove_columns=['''sentence1''', '''sentence2''', '''label''']			,    )

       def collate_fn(lowercase_						):
              # 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(lowercase_			,    padding='''max_length'''			,    max_length=128			,    return_tensors='''pt'''						)
              return tokenizer.pad(lowercase_			,    padding='''longest'''			,    return_tensors='''pt'''						)

       # Instantiate dataloaders.
       A__							=   DataLoader(tokenized_datasets['''train''']			,    shuffle=lowercase_			,    collate_fn=lowercase_			,    batch_size=2						)
       A__							=   DataLoader(tokenized_datasets['''validation''']			,    shuffle=lowercase_			,    collate_fn=lowercase_			,    batch_size=1						)

       return train_dataloader, eval_dataloader








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


_lowerCamelCase     :		Tuple								=       {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		int								=       ["""Speech2TextTokenizer"""]

try:
  if not is_speech_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Optional[int]								=       ["""Speech2TextFeatureExtractor"""]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Optional[Any]								=       [
      """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFSpeech2TextForConditionalGeneration""",
      """TFSpeech2TextModel""",
      """TFSpeech2TextPreTrainedModel""",
  ]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		List[Any]								=       [
      """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2TextForConditionalGeneration""",
      """Speech2TextModel""",
      """Speech2TextPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
  from .processing_speech_to_text import SpeechaTextProcessor

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_speech_to_text import SpeechaTextTokenizer

  try:
    if not is_speech_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_speech_to_text import (
        TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSpeechaTextForConditionalGeneration,
        TFSpeechaTextModel,
        TFSpeechaTextPreTrainedModel,
    )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text import (
        SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextForConditionalGeneration,
        SpeechaTextModel,
        SpeechaTextPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase     :		Union[str, Any]								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


