


'''simple docstring'''







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

from accelerate.utils.dataclasses import DistributedType


class  lowerCAmelCase_						:





  """simple docstring"""



  def __init__(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							int=2				,							SCREAMING_SNAKE_CASE__     :							Tuple=3				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=6_4				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=None						):





       '''simple docstring'''

       __a       =   np.random.default_rng(SCREAMING_SNAKE_CASE__						)
       __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     :							Tuple						):





       '''simple docstring'''

       return self.length


  def __getitem__(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							List[str]						):





       '''simple docstring'''

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


class  lowerCAmelCase_						(       torch.nn.Module      ):





  """simple docstring"""



  def __init__(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=0				,							SCREAMING_SNAKE_CASE__     :							str=0				,							SCREAMING_SNAKE_CASE__     :							Tuple=False						):





       '''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 __a						(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=None						):





       '''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  lowerCAmelCase_						(       torch.nn.Module      ):





  """simple docstring"""



  def __init__(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							List[Any]=0				,							SCREAMING_SNAKE_CASE__     :							Dict=0				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=False						):





       '''simple docstring'''

       super().__init__()
       __a       =   torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__						).float()						)
       __a       =   torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__						).float()						)
       __a       =   True


  def __a						(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=None						):





       '''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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE = 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=__SCREAMING_SNAKE_CASE		)
     __a       =   datasets["""train"""].unique("""label"""		)

     __a       =   {v: i for i, v in enumerate(__SCREAMING_SNAKE_CASE		)}

     def tokenize_function(__SCREAMING_SNAKE_CASE		):
          # max_length=None => use the model max length (it's actually the default)
          __a       =   tokenizer(
              examples["""sentence1"""]				,  examples["""sentence2"""]				,  truncation=__SCREAMING_SNAKE_CASE				,  max_length=__SCREAMING_SNAKE_CASE				,  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(
         __SCREAMING_SNAKE_CASE				,  batched=__SCREAMING_SNAKE_CASE				,  remove_columns=["""sentence1""", """sentence2""", """label"""]				,  )

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

     # Instantiate dataloaders.
     __a       =   DataLoader(tokenized_datasets["""train"""]				,  shuffle=__SCREAMING_SNAKE_CASE				,  collate_fn=__SCREAMING_SNAKE_CASE				,  batch_size=2		)
     __a       =   DataLoader(tokenized_datasets["""validation"""]				,  shuffle=__SCREAMING_SNAKE_CASE				,  collate_fn=__SCREAMING_SNAKE_CASE				,  batch_size=1		)

     return train_dataloader, eval_dataloader










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_      =			{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     'RagModel',
     'RagPreTrainedModel',
     'RagSequenceForGeneration',
     'RagTokenForGeneration',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     'TFRagModel',
     'TFRagPreTrainedModel',
     'TFRagSequenceForGeneration',
     'TFRagTokenForGeneration',
 ]


if TYPE_CHECKING:
 from .configuration_rag import RagConfig
 from .retrieval_rag import RagRetriever
 from .tokenization_rag import RagTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rag import (
      TFRagModel,
      TFRagPreTrainedModel,
      TFRagSequenceForGeneration,
      TFRagTokenForGeneration,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE_      =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






