


'''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						,    __a=2						,    __a=3						,    __a=64						,    __a=None							):

      '''simple docstring'''





      __a      :	str    =  np.random.default_rng(__UpperCAmelCase							)
      __a      :	List[str]    =  length
      __a      :	List[Any]    =  rng.normal(size=(length,)							).astype(np.floataa							)
      __a      :	Union[str, Any]    =  a * self.x + b + rng.normal(scale=0.1						,    size=(length,)							).astype(np.floataa							)




 def __len__(       self							):

      '''simple docstring'''





      return self.length





 def __getitem__(       self						,    __a							):

      '''simple docstring'''





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





class 							__UpperCamelCase    (		torch.nn.Module ):




 def __init__(       self						,    __a=0						,    __a=0						,    __a=False							):

      '''simple docstring'''





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





 def 				__UpperCAmelCase							(       self						,    __a=None							):

      '''simple docstring'''





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





class 							__UpperCamelCase    (		torch.nn.Module ):




 def __init__(       self						,    __a=0						,    __a=0						,    __a=False							):

      '''simple docstring'''





      super().__init__()
      __a      :	Tuple    =  torch.nn.Parameter(torch.tensor(__UpperCAmelCase							).float()							)
      __a      :	List[str]    =  torch.nn.Parameter(torch.tensor(__UpperCAmelCase							).float()							)
      __a      :	str    =  True





 def 				__UpperCAmelCase							(       self						,    __a=None							):

      '''simple docstring'''





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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[int]						,				_SCREAMING_SNAKE_CASE				:       int = 16       ):
     from datasets import load_dataset
     from transformers import AutoTokenizer

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

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

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

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

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

     return train_dataloader, eval_dataloader







'''simple docstring'''



import re

from filelock import FileLock


try:
	import nltk

	__lowercase     :						Optional[Any]	=     True
except (ImportError, ModuleNotFoundError):
	__lowercase     :						Dict	=     False

if NLTK_AVAILABLE:
	with FileLock('.lock') as lock:
		nltk.download('punkt', quiet=True)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str       ):
					re.sub('<n>'						,				''						,				_SCREAMING_SNAKE_CASE       )  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE       )       )



