


'''simple docstring'''





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

from accelerate.utils.dataclasses import DistributedType




class 				UpperCAmelCase			:


 def __init__(      self							:	Optional[Any]			,       __lowerCamelCase							:	Tuple=2			,       __lowerCamelCase							:	str=3			,       __lowerCamelCase							:	Optional[Any]=6_4			,       __lowerCamelCase							:	Union[str, Any]=None ):
    UpperCAmelCase__	:List[str]      =      np.random.default_rng(__lowerCamelCase )
    UpperCAmelCase__	:Dict      =      length
    UpperCAmelCase__	:int      =      rng.normal(size=(length,) ).astype(np.floataa )
    UpperCAmelCase__	:Union[str, Any]      =      a * self.x + b + rng.normal(scale=0.1			,       size=(length,) ).astype(np.floataa )


 def __len__(      self							:	str ):
    return self.length





 def __getitem__(      self							:	List[Any]			,       __lowerCamelCase							:	List[str] ):
    return {"x": self.x[i], "y": self.y[i]}




class 				UpperCAmelCase			(			torch.nn.Module						):


 def __init__(      self							:	Dict			,       __lowerCamelCase							:	List[Any]=0			,       __lowerCamelCase							:	Optional[int]=0			,       __lowerCamelCase							:	Optional[Any]=False ):
    super().__init__()
    UpperCAmelCase__	:Any      =      torch.nn.Parameter(torch.tensor([2, 3] ).float() )
    UpperCAmelCase__	:str      =      torch.nn.Parameter(torch.tensor([2, 3] ).float() )
    UpperCAmelCase__	:List[Any]      =      True





 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	Union[str, Any]=None ):
    if self.first_batch:
       print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}''' )
       UpperCAmelCase__	:List[str]      =      False
    return x * self.a[0] + self.b[0]




class 				UpperCAmelCase			(			torch.nn.Module						):


 def __init__(      self							:	Union[str, Any]			,       __lowerCamelCase							:	Optional[int]=0			,       __lowerCamelCase							:	Tuple=0			,       __lowerCamelCase							:	Dict=False ):
    super().__init__()
    UpperCAmelCase__	:Any      =      torch.nn.Parameter(torch.tensor(__lowerCamelCase ).float() )
    UpperCAmelCase__	:Optional[Any]      =      torch.nn.Parameter(torch.tensor(__lowerCamelCase ).float() )
    UpperCAmelCase__	:Any      =      True





 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       __lowerCamelCase							:	Optional[int]=None ):
    if self.first_batch:
       print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}''' )
       UpperCAmelCase__	:int      =      False
    return x * self.a + self.b


def        a__     (					UpperCamelCase_						:      List[str],   UpperCamelCase_						:      int = 16						):
   from datasets import load_dataset
   from transformers import AutoTokenizer

   UpperCAmelCase__	:List[str]      =      AutoTokenizer.from_pretrained('''bert-base-cased'''						)
   UpperCAmelCase__	:List[Any]      =      {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
   UpperCAmelCase__	:Any      =      load_dataset('''csv''',   data_files=UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      datasets['''train'''].unique('''label'''						)

   UpperCAmelCase__	:Tuple      =      {v: i for i, v in enumerate(UpperCamelCase_						)}

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

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

   # Instantiate dataloaders.
   UpperCAmelCase__	:Optional[int]      =      DataLoader(tokenized_datasets['''train'''],   shuffle=UpperCamelCase_,   collate_fn=UpperCamelCase_,   batch_size=2						)
   UpperCAmelCase__	:int      =      DataLoader(tokenized_datasets['''validation'''],   shuffle=UpperCamelCase_,   collate_fn=UpperCamelCase_,   batch_size=1						)

   return train_dataloader, eval_dataloader







'''simple docstring'''





from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments



