



"""simple docstring"""





from random import randint, random




def 	UpperCAmelCase     (		A	:						int     ,				A	:						int     ,				A	:						int     ,				A	:						bool = False     ,				A	:						bool = False     ,				A	:						int = 5     ,				):

  '''simple docstring'''


  _UpperCAmelCase							 =		[[-1] * number_of_cells]  # Create a highway without any car
  _UpperCAmelCase							 =		0
  _UpperCAmelCase							 =		max(A     ,				0       )
  while i < number_of_cells:
    _UpperCAmelCase							 =		(
        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	:						list     ,				A	:						int       ):

  '''simple docstring'''


  _UpperCAmelCase							 =		0
  _UpperCAmelCase							 =		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	:						list     ,				A	:						float     ,				A	:						int       ):

  '''simple docstring'''


  _UpperCAmelCase							 =		len(A       )
  # Beforce calculations, the highway is empty
  _UpperCAmelCase							 =		[-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
      _UpperCAmelCase							 =		min(highway_now[car_index] + 1     ,				A       )
      # Number of empty cell before the next car
      _UpperCAmelCase							 =		get_distance(A     ,				A       ) - 1
      # We can't have the car causing an accident
      _UpperCAmelCase							 =		min(next_highway[car_index]     ,				A       )
      if random() < probability:
        # Randomly, a driver will slow down
        _UpperCAmelCase							 =		max(next_highway[car_index] - 1     ,				0       )
  return next_highway




def 	UpperCAmelCase     (		A	:						list     ,				A	:						int     ,				A	:						float     ,				A	:						int       ):

  '''simple docstring'''


  _UpperCAmelCase							 =		len(highway[0]       )

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

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

  return highway


if __name__ == "__main__":
     import doctest

     doctest.testmod()







"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
					import jax.numpy as jnp

if is_tf_available():
					import tensorflow as tf

if is_torch_available():
					import torch




class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		{
									    'task_specific_params': {
									        'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
									        'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
									        'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
									    }
									}
									_UpperCAmelCase							 =		{
									    'task_specific_params.summarization.length_penalty': 1.0,
									    'task_specific_params.summarization.max_length': 128,
									    'task_specific_params.summarization.min_length': 12,
									    'task_specific_params.summarization.num_beams': 4,
									    'task_specific_params.summarization_cnn.length_penalty': 2.0,
									    'task_specific_params.summarization_cnn.max_length': 142,
									    'task_specific_params.summarization_cnn.min_length': 56,
									    'task_specific_params.summarization_cnn.num_beams': 4,
									    'task_specific_params.summarization_xsum.length_penalty': 1.0,
									    'task_specific_params.summarization_xsum.max_length': 62,
									    'task_specific_params.summarization_xsum.min_length': 11,
									    'task_specific_params.summarization_xsum.num_beams': 6,
									}

									self.assertEqual(flatten_dict(snake_case						)	,       snake_case						)






							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									self.assertTrue(np.allclose(transpose(snake_case						)	,       x.transpose()						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									self.assertTrue(np.allclose(transpose(snake_case	,       axes=(1, 2, 0)						)	,       x.transpose((1, 2, 0)						)						)						)






							@require_torch
							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case						)	,       transpose(snake_case						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case	,       axes=(1, 2, 0)						)	,       transpose(snake_case	,       axes=(1, 2, 0)						).numpy()						)						)






							@require_tf
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case						)	,       transpose(snake_case						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case	,       axes=(1, 2, 0)						)	,       transpose(snake_case	,       axes=(1, 2, 0)						).numpy()						)						)






							@require_flax
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case						)	,       np.asarray(transpose(snake_case						)						)						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case	,       axes=(1, 2, 0)						)	,       np.asarray(transpose(snake_case	,       axes=(1, 2, 0)						)						)						)						)






							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (4, 3)						)	,       np.reshape(snake_case	,       (4, 3)						)						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (12, 5)						)	,       np.reshape(snake_case	,       (12, 5)						)						)						)






							@require_torch
							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (4, 3)						)	,       reshape(snake_case	,       (4, 3)						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (12, 5)						)	,       reshape(snake_case	,       (12, 5)						).numpy()						)						)






							@require_tf
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (4, 3)						)	,       reshape(snake_case	,       (4, 3)						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (12, 5)						)	,       reshape(snake_case	,       (12, 5)						).numpy()						)						)






							@require_flax
							def        lowerCamelCase_   (  self						)			->							Tuple:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (4, 3)						)	,       np.asarray(reshape(snake_case	,       (4, 3)						)						)						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (12, 5)						)	,       np.asarray(reshape(snake_case	,       (12, 5)						)						)						)						)






							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		np.random.randn(1	,       3	,       4						)
									self.assertTrue(np.allclose(squeeze(snake_case						)	,       np.squeeze(snake_case						)						)						)

									_UpperCAmelCase							 =		np.random.randn(1	,       4	,       1	,       5						)
									self.assertTrue(np.allclose(squeeze(snake_case	,       axis=2						)	,       np.squeeze(snake_case	,       axis=2						)						)						)






							@require_torch
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(1	,       3	,       4						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case						)	,       squeeze(snake_case						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(1	,       4	,       1	,       5						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case	,       axis=2						)	,       squeeze(snake_case	,       axis=2						).numpy()						)						)






							@require_tf
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									_UpperCAmelCase							 =		np.random.randn(1	,       3	,       4						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case						)	,       squeeze(snake_case						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(1	,       4	,       1	,       5						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case	,       axis=2						)	,       squeeze(snake_case	,       axis=2						).numpy()						)						)






							@require_flax
							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		np.random.randn(1	,       3	,       4						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case						)	,       np.asarray(squeeze(snake_case						)						)						)						)

									_UpperCAmelCase							 =		np.random.randn(1	,       4	,       1	,       5						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case	,       axis=2						)	,       np.asarray(squeeze(snake_case	,       axis=2						)						)						)						)






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									self.assertTrue(np.allclose(expand_dims(snake_case	,       axis=1						)	,       np.expand_dims(snake_case	,       axis=1						)						)						)






							@require_torch
							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(expand_dims(snake_case	,       axis=1						)	,       expand_dims(snake_case	,       axis=1						).numpy()						)						)






							@require_tf
							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(expand_dims(snake_case	,       axis=1						)	,       expand_dims(snake_case	,       axis=1						).numpy()						)						)






							@require_flax
							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(expand_dims(snake_case	,       axis=1						)	,       np.asarray(expand_dims(snake_case	,       axis=1						)						)						)						)


