

"""simple docstring"""






from datetime import datetime

import matplotlib.pyplot as plt
import torch





def 	lowercase      (   __UpperCamelCase						) ->      List[str]:
 for param in module.parameters():
  __magic_name__     =      False





def 	lowercase      (   ) ->      Optional[Any]:
 __magic_name__     =      '''cuda''' if torch.cuda.is_available() else '''cpu'''
 if torch.backends.mps.is_available() and torch.backends.mps.is_built():
  __magic_name__     =      '''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 	lowercase      (   __UpperCamelCase						) ->      Optional[int]:
 __magic_name__     =      plt.imshow(__UpperCamelCase						)
 fig.axes.get_xaxis().set_visible(__UpperCamelCase						)
 fig.axes.get_yaxis().set_visible(__UpperCamelCase						)
 plt.show()





def 	lowercase      (   ) ->      Union[str, Any]:
 __magic_name__     =      datetime.now()
 __magic_name__     =      current_time.strftime('''%H:%M:%S'''						)
 return timestamp




"""simple docstring"""






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class        _lowercase					(     unittest.TestCase     ):







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,						return_dict=UpperCamelCase_       ).to(UpperCamelCase_       )
      __magic_name__     =      AutoTokenizer.from_pretrained('''google/mt5-small'''       )

      __magic_name__     =      tokenizer('''Hello there''' ,						return_tensors='''pt'''       ).input_ids
      __magic_name__     =      tokenizer('''Hi I am''' ,						return_tensors='''pt'''       ).input_ids

      __magic_name__     =      model(input_ids.to(UpperCamelCase_       ) ,						labels=labels.to(UpperCamelCase_       )       ).loss
      __magic_name__     =      -(labels.shape[-1] * loss.item())

      __magic_name__     =      -8_4.9_1_2_7
      self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )

