'''simple docstring'''




import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
      import torch






@require_torch
@require_optimum
@slow
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    """hf-internal-testing/tiny-random-t5"""
      __magic_name__						       =    AutoTokenizer.from_pretrained(snake_case_      )
      __magic_name__						       =    AutoModelForSeqaSeqLM.from_pretrained(snake_case_      )

      __magic_name__						       =    tokenizer('''This is me'''			,				return_tensors='''pt'''      )

      __magic_name__						       =    model.to_bettertransformer()

      self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

      __magic_name__						       =    model.generate(**snake_case_      )

      __magic_name__						       =    model.reverse_bettertransformer()

      self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

      with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(snake_case_      )

           __magic_name__						       =    AutoModelForSeqaSeqLM.from_pretrained(snake_case_      )

           self.assertFalse(
               any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()      )      )

           __magic_name__						       =    model_reloaded.generate(**snake_case_      )
           self.assertTrue(torch.allclose(snake_case_			,				snake_case_      )      )



 def 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__						       =    """hf-internal-testing/tiny-random-t5"""
      __magic_name__						       =    AutoModelForSeqaSeqLM.from_pretrained(snake_case_      )

      __magic_name__						       =    model.to_bettertransformer()

      with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(snake_case_      ):
                model.save_pretrained(snake_case_      )

           __magic_name__						       =    model.reverse_bettertransformer()
           model.save_pretrained(snake_case_      )









'''simple docstring'''




from random import randint, random




def 						UpperCamelCase	(			a		,						a		,						a		,						a = False		,						a = False		,						a = 5		,						)				->     list:






     '''simple docstring'''
     __magic_name__						       =    [[-1] * number_of_cells]  # Create a highway without any car
     __magic_name__						       =    0
     __magic_name__						       =    max(a		,						0			)
     while i < number_of_cells:
          __magic_name__						       =    (
              randint(0		,						a			) if random_speed else initial_speed
          )  # Place the cars
          i += (
              randint(1		,						max_speed * 2			) if random_frequency else frequency
          )  # Arbitrary number, may need tuning
     return highway




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    0
     __magic_name__						       =    highway_now[car_index + 1 :]
     for cell in range(len(a			)			):  # May need a better name for this
          if cells[cell] != -1:  # If the cell is not empty then
               return distance  # we have the distance we wanted
          distance += 1
     # Here if the car is near the end of the highway
     return distance + get_distance(a		,						-1			)




def 						UpperCamelCase	(			a		,						a		,						a			)				->     list:






     '''simple docstring'''
     __magic_name__						       =    len(a			)
     # Beforce calculations, the highway is empty
     __magic_name__						       =    [-1] * number_of_cells

     for car_index in range(a			):
          if highway_now[car_index] != -1:
               # Add 1 to the current speed of the car and cap the speed
               __magic_name__						       =    min(highway_now[car_index] + 1		,						a			)
               # Number of empty cell before the next car
               __magic_name__						       =    get_distance(a		,						a			) - 1
               # We can't have the car causing an accident
               __magic_name__						       =    min(next_highway[car_index]		,						a			)
               if random() < probability:
                    # Randomly, a driver will slow down
                    __magic_name__						       =    max(next_highway[car_index] - 1		,						0			)
     return next_highway




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     list:






     '''simple docstring'''
     __magic_name__						       =    len(highway[0]			)

     for i in range(a			):
          __magic_name__						       =    update(highway[i]		,						a		,						a			)
          __magic_name__						       =    [-1] * number_of_cells

          for car_index in range(a			):
               __magic_name__						       =    next_speeds_calculated[car_index]
               if speed != -1:
                    # Change the position based on the speed (with % to create the loop)
                    __magic_name__						       =    (car_index + speed) % number_of_cells
                    # Commit the change of position
                    __magic_name__						       =    speed
          highway.append(a			)

     return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()






