




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

from accelerate.utils.dataclasses import DistributedType






class  _lowerCAmelCase     :

						def __init__(   self      ,				__UpperCAmelCase=2      ,				__UpperCAmelCase=3      ,				__UpperCAmelCase=64      ,				__UpperCAmelCase=None		):
								lowerCAmelCase__				:  List[str]		       =     np.random.default_rng(__UpperCAmelCase		)
								lowerCAmelCase__				:  str		       =     length
								lowerCAmelCase__				:  int		       =     rng.normal(size=(length,)		).astype(np.floataa		)
								lowerCAmelCase__				:  List[str]		       =     a * self.x + b + rng.normal(scale=0.1      ,				size=(length,)		).astype(np.floataa		)

						def __len__(   self		):
								return self.length







						def __getitem__(   self      ,				__UpperCAmelCase		):
								return {"x": self.x[i], "y": self.y[i]}






class  _lowerCAmelCase     (  torch.nn.Module	):

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







						def 	__magic_name__(   self      ,				__UpperCAmelCase=None		):
								if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""		)
										lowerCAmelCase__				:  int		       =     False
								return x * self.a[0] + self.b[0]






class  _lowerCAmelCase     (  torch.nn.Module	):

						def __init__(   self      ,				__UpperCAmelCase=0      ,				__UpperCAmelCase=0      ,				__UpperCAmelCase=False		):
								super().__init__()
								lowerCAmelCase__				:  List[str]		       =     torch.nn.Parameter(torch.tensor(__UpperCAmelCase		).float()		)
								lowerCAmelCase__				:  Dict		       =     torch.nn.Parameter(torch.tensor(__UpperCAmelCase		).float()		)
								lowerCAmelCase__				:  Union[str, Any]		       =     True







						def 	__magic_name__(   self      ,				__UpperCAmelCase=None		):
								if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""		)
										lowerCAmelCase__				:  Dict		       =     False
								return x * self.a + self.b



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase = 16      )     ->	Tuple:
		from datasets import load_dataset
		from transformers import AutoTokenizer

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

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

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

		def collate_fn(UpperCamelCase      ):
				# 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.
		lowerCAmelCase__				:  Optional[Any]		       =     DataLoader(tokenized_datasets['''train''']	,      shuffle=UpperCamelCase	,      collate_fn=UpperCamelCase	,      batch_size=2      )
		lowerCAmelCase__				:  Any		       =     DataLoader(tokenized_datasets['''validation''']	,      shuffle=UpperCamelCase	,      collate_fn=UpperCamelCase	,      batch_size=1      )

		return train_dataloader, eval_dataloader







from datetime import datetime

import matplotlib.pyplot as plt
import torch



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	str:
  for param in module.parameters():
    lowerCAmelCase__				:  int		       =     False



def      __lowerCAmelCase			(					)     ->	Optional[Any]:
  lowerCAmelCase__				:  List[Any]		       =     '''cuda''' if torch.cuda.is_available() else '''cpu'''
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    lowerCAmelCase__				:  Optional[Any]		       =     '''mps'''
  if device == "mps":
    print(
        '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
        ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
        ''' with generations.'''      )
  return device



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     plt.imshow(UpperCamelCase      )
  fig.axes.get_xaxis().set_visible(UpperCamelCase      )
  fig.axes.get_yaxis().set_visible(UpperCamelCase      )
  plt.show()



def      __lowerCAmelCase			(					)     ->	str:
  lowerCAmelCase__				:  Dict		       =     datetime.now()
  lowerCAmelCase__				:  Optional[int]		       =     current_time.strftime('''%H:%M:%S'''      )
  return timestamp

