
'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						["image_processor", "tokenizer"]
							lowerCAmelCase_ 						=						"ViTImageProcessor"
							lowerCAmelCase_ 						=						("CLIPTokenizer", "CLIPTokenizerFast")

							def __init__(self  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  )					->       Dict:
														_snake_case       =  None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																					    """ instead."""  ,  UpperCAmelCase  ,  )
																					_snake_case       =  kwargs.pop("""feature_extractor"""  )

														_snake_case       =  image_processor if image_processor is not None else feature_extractor
														if image_processor is None:
																					raise ValueError("""You need to specify an `image_processor`."""  )
														if tokenizer is None:
																					raise ValueError("""You need to specify a `tokenizer`."""  )

														super().__init__(UpperCAmelCase  ,  UpperCAmelCase  )

							def __call__(self  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  )					->       Any:
														if text is None and visual_prompt is None and images is None:
																					raise ValueError("""You have to specify either text, visual prompt or images."""  )

														if text is not None and visual_prompt is not None:
																					raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""  )

														if text is not None:
																					_snake_case       =  self.tokenizer(UpperCAmelCase  ,  return_tensors=UpperCAmelCase  ,  **UpperCAmelCase  )

														if visual_prompt is not None:
																					_snake_case       =  self.image_processor(UpperCAmelCase  ,  return_tensors=UpperCAmelCase  ,  **UpperCAmelCase  )

														if images is not None:
																					_snake_case       =  self.image_processor(UpperCAmelCase  ,  return_tensors=UpperCAmelCase  ,  **UpperCAmelCase  )

														if visual_prompt is not None and images is not None:
																					_snake_case       =  {
																					    """pixel_values""": image_features.pixel_values,
																					    """conditional_pixel_values""": prompt_features.pixel_values,
																					}
																					return encoding
														elif text is not None and images is not None:
																					_snake_case       =  image_features.pixel_values
																					return encoding
														elif text is not None:
																					return encoding
														elif visual_prompt is not None:
																					_snake_case       =  {
																					    """conditional_pixel_values""": prompt_features.pixel_values,
																					}
																					return encoding
														else:
																					return BatchEncoding(data=dict(**UpperCAmelCase  )  ,  tensor_type=UpperCAmelCase  )

							def 	lowercase							(self  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       Any:
														return self.tokenizer.batch_decode(*UpperCAmelCase  ,  **UpperCAmelCase  )

							def 	lowercase							(self  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       int:
														return self.tokenizer.decode(*UpperCAmelCase  ,  **UpperCAmelCase  )

							@property
							def 	lowercase							(self  )					->       List[Any]:
														warnings.warn(
														    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,  UpperCAmelCase  ,  )
														return self.image_processor_class





							@property
							def 	lowercase							(self  )					->       List[Any]:
														warnings.warn(
														    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,  UpperCAmelCase  ,  )
														return self.image_processor

'''simple docstring'''
from random import randint, random






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE = False							, _SCREAMING_SNAKE_CASE = False							, _SCREAMING_SNAKE_CASE = 5							, ):
							_snake_case       =  [[-1] * number_of_cells]  # Create a highway without any car
							_snake_case       =  0
							_snake_case       =  max(_SCREAMING_SNAKE_CASE							, 0				)
							while i < number_of_cells:
														_snake_case       =  (
														    randint(0							, _SCREAMING_SNAKE_CASE				) 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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  0
							_snake_case       =  highway_now[car_index + 1 :]
							for cell in range(len(_SCREAMING_SNAKE_CASE				)				):  # 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(_SCREAMING_SNAKE_CASE							, -1				)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							# Beforce calculations, the highway is empty
							_snake_case       =  [-1] * number_of_cells

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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(highway[0]				)

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

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

							return highway


if __name__ == "__main__":
				import doctest

				doctest.testmod()