





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 :		Dict					,  lowerCAmelCase_ :		List[str]=2					,  lowerCAmelCase_ :		Tuple=3					,  lowerCAmelCase_ :		int=6_4					,  lowerCAmelCase_ :		str=None     )							->						Tuple:
     __lowerCAmelCase								=	np.random.default_rng(lowerCAmelCase_     )
     __lowerCAmelCase								=	length
     __lowerCAmelCase								=	rng.normal(size=(length,)     ).astype(np.floataa     )
     __lowerCAmelCase								=	a * self.x + b + rng.normal(scale=0.1					,  size=(length,)     ).astype(np.floataa     )






  def __len__(				self :		Optional[Any]     )							->						Optional[int]:
     return self.length






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





class 				_UpperCAmelCase    (    torch.nn.Module    ):

  """simple docstring"""


  def __init__(				self :		int					,  lowerCAmelCase_ :		Any=0					,  lowerCAmelCase_ :		int=0					,  lowerCAmelCase_ :		Dict=False     )							->						Dict:
     super().__init__()
     __lowerCAmelCase								=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
     __lowerCAmelCase								=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
     __lowerCAmelCase								=	True






  def 				lowercase		(				self :		int					,  lowerCAmelCase_ :		Union[str, Any]=None     )							->						Optional[Any]:
     if self.first_batch:
        print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
        __lowerCAmelCase								=	False
     return x * self.a[0] + self.b[0]





class 				_UpperCAmelCase    (    torch.nn.Module    ):

  """simple docstring"""


  def __init__(				self :		Union[str, Any]					,  lowerCAmelCase_ :		List[str]=0					,  lowerCAmelCase_ :		Union[str, Any]=0					,  lowerCAmelCase_ :		List[str]=False     )							->						Optional[Any]:
     super().__init__()
     __lowerCAmelCase								=	torch.nn.Parameter(torch.tensor(lowerCAmelCase_     ).float()     )
     __lowerCAmelCase								=	torch.nn.Parameter(torch.tensor(lowerCAmelCase_     ).float()     )
     __lowerCAmelCase								=	True






  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		int=None     )							->						int:
     if self.first_batch:
        print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
        __lowerCAmelCase								=	False
     return x * self.a + self.b





def   a_    ( lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			int = 16	):
   from datasets import load_dataset
   from transformers import AutoTokenizer

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

   __lowerCAmelCase								=	{v: i for i, v in enumerate(lowerCAmelCase_	)}

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

   def collate_fn(lowerCAmelCase_      :			Tuple	):
      # 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.
   __lowerCAmelCase								=	DataLoader(tokenized_datasets['train'],	shuffle=lowerCAmelCase_,	collate_fn=lowerCAmelCase_,	batch_size=2	)
   __lowerCAmelCase								=	DataLoader(tokenized_datasets['validation'],	shuffle=lowerCAmelCase_,	collate_fn=lowerCAmelCase_,	batch_size=1	)

   return train_dataloader, eval_dataloader








import re

from filelock import FileLock


try:
     import nltk

     _snake_case  :    Any						    =  True
except (ImportError, ModuleNotFoundError):
     _snake_case  :    Union[str, Any]						    =  False

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

def   a_    ( lowerCAmelCase_      :			str	):
   re.sub('<n>',	'',	lowerCAmelCase_	)  # 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(lowerCAmelCase_	)	)

