


'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType




class      lowerCamelCase_  :






      def __init__(   self							:       Dict		,		_A							:       str=2		,		_A							:       Tuple=3		,		_A							:       Tuple=64		,		_A							:       str=None					):


          '''simple docstring'''


          UpperCAmelCase__     :						int						=							np.random.default_rng(_A					)
          UpperCAmelCase__     :						Any						=							length
          UpperCAmelCase__     :						int						=							rng.normal(size=(length,)					).astype(np.floataa					)
          UpperCAmelCase__     :						Optional[Any]						=							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]		,		_A							:       Optional[Any]					):


          '''simple docstring'''


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




class      lowerCamelCase_  (      torch.nn.Module ):






      def __init__(   self							:       str		,		_A							:       List[str]=0		,		_A							:       List[Any]=0		,		_A							:       int=False					):


          '''simple docstring'''


          super().__init__()
          UpperCAmelCase__     :						Dict						=							torch.nn.Parameter(torch.tensor([2, 3]					).float()					)
          UpperCAmelCase__     :						Dict						=							torch.nn.Parameter(torch.tensor([2, 3]					).float()					)
          UpperCAmelCase__     :						List[Any]						=							True






      def lowercase_				(   self							:       Any		,		_A							:       List[Any]=None					):


          '''simple docstring'''


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




class      lowerCamelCase_  (      torch.nn.Module ):






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


          '''simple docstring'''


          super().__init__()
          UpperCAmelCase__     :						Dict						=							torch.nn.Parameter(torch.tensor(_A					).float()					)
          UpperCAmelCase__     :						Optional[Any]						=							torch.nn.Parameter(torch.tensor(_A					).float()					)
          UpperCAmelCase__     :						str						=							True






      def lowercase_				(   self							:       str		,		_A							:       List[Any]=None					):


          '''simple docstring'''


          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__			(   lowerCAmelCase__    ,   lowerCAmelCase__ = 16		)       ->      Dict:
    from datasets import load_dataset
    from transformers import AutoTokenizer

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

    UpperCAmelCase__     :						Union[str, Any]						=							{v: i for i, v in enumerate(lowerCAmelCase__		)}

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

    def collate_fn(lowerCAmelCase__		):
        # 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(lowerCAmelCase__    ,   padding='''max_length'''    ,   max_length=1_28    ,   return_tensors='''pt'''		)
        return tokenizer.pad(lowerCAmelCase__    ,   padding='''longest'''    ,   return_tensors='''pt'''		)

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

    return train_dataloader, eval_dataloader




import os

try:
 from .build_directory_md import good_file_paths
except ImportError:
 from build_directory_md import good_file_paths  # type: ignore

SCREAMING_SNAKE_CASE			: int				   =					list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

SCREAMING_SNAKE_CASE			: Dict				   =					[file for file in filepaths if file != file.lower()]
if upper_files:
 print(F"{len(upper_files)} files contain uppercase characters:")
 print("\n".join(upper_files) + "\n")

SCREAMING_SNAKE_CASE			: List[Any]				   =					[file for file in filepaths if " " in file]
if space_files:
 print(F"{len(space_files)} files contain space characters:")
 print("\n".join(space_files) + "\n")

SCREAMING_SNAKE_CASE			: Any				   =					[file for file in filepaths if "-" in file]
if hyphen_files:
 print(F"{len(hyphen_files)} files contain hyphen characters:")
 print("\n".join(hyphen_files) + "\n")

SCREAMING_SNAKE_CASE			: str				   =					[file for file in filepaths if os.sep not in file]
if nodir_files:
 print(F"{len(nodir_files)} files are not in a directory:")
 print("\n".join(nodir_files) + "\n")

SCREAMING_SNAKE_CASE			: Tuple				   =					len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
 import sys

 sys.exit(bad_files)


