




"""simple docstring"""


from random import randint, random



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase = False    ,		__UpperCAmelCase = False    ,		__UpperCAmelCase = 5    ,		)						->    list:
      lowerCAmelCase__      :			List[Any]   =	[[-1] * number_of_cells]  # Create a highway without any car
      lowerCAmelCase__      :			Any   =	0
      lowerCAmelCase__      :			Optional[Any]   =	max(__UpperCAmelCase    ,		0				)
      while i < number_of_cells:
            lowerCAmelCase__      :			Any   =	(
                randint(0    ,		__UpperCAmelCase				) 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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    int:
      lowerCAmelCase__      :			int   =	0
      lowerCAmelCase__      :			str   =	highway_now[car_index + 1 :]
      for cell in range(len(__UpperCAmelCase				)				):  # 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(__UpperCAmelCase    ,		-1				)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    list:
      lowerCAmelCase__      :			int   =	len(__UpperCAmelCase				)
      # Beforce calculations, the highway is empty
      lowerCAmelCase__      :			List[Any]   =	[-1] * number_of_cells

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



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    list:
      lowerCAmelCase__      :			Optional[Any]   =	len(highway[0]				)

      for i in range(__UpperCAmelCase				):
            lowerCAmelCase__      :			Optional[int]   =	update(highway[i]    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)
            lowerCAmelCase__      :			int   =	[-1] * number_of_cells

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

      return highway


if __name__ == "__main__":
  import doctest

  doctest.testmod()











"""simple docstring"""


from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Dict     				=     ["image_processor", "tokenizer"]
   _lowerCamelCase    :Dict     				=     "BlipImageProcessor"
   _lowerCamelCase    :Any     				=     ("BertTokenizer", "BertTokenizerFast")






   def __init__(		self       :		Dict   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		Union[str, Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	False
         super().__init__(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	self.image_processor






   def __call__(		self       :		int   ,     UpperCamelCase       :		ImageInput = None   ,     UpperCamelCase       :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Union[bool, str, PaddingStrategy] = False   ,     UpperCamelCase       :		Union[bool, str, TruncationStrategy] = None   ,     UpperCamelCase       :		Optional[int] = None   ,     UpperCamelCase       :		int = 0   ,     UpperCamelCase       :		Optional[int] = None   ,     UpperCamelCase       :		Optional[bool] = None   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Optional[Union[str, TensorType]] = None   ,     **UpperCamelCase       :		Optional[int]   ,     )			->						BatchEncoding:

         """simple docstring"""
         if images is None and text is None:
               raise ValueError("""You have to specify either images or text."""   )

         # Get only text
         if images is None:
               lowerCAmelCase__      :			Any   =	self.tokenizer
               lowerCAmelCase__      :			Optional[int]   =	self.tokenizer(
                   text=UpperCamelCase   ,     add_special_tokens=UpperCamelCase   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=UpperCamelCase   ,     stride=UpperCamelCase   ,     pad_to_multiple_of=UpperCamelCase   ,     return_attention_mask=UpperCamelCase   ,     return_overflowing_tokens=UpperCamelCase   ,     return_special_tokens_mask=UpperCamelCase   ,     return_offsets_mapping=UpperCamelCase   ,     return_token_type_ids=UpperCamelCase   ,     return_length=UpperCamelCase   ,     verbose=UpperCamelCase   ,     return_tensors=UpperCamelCase   ,     **UpperCamelCase   ,     )
               return text_encoding

         # add pixel_values
         lowerCAmelCase__      :			Tuple   =	self.image_processor(UpperCamelCase   ,     return_tensors=UpperCamelCase   )

         if text is not None:
               lowerCAmelCase__      :			Optional[int]   =	self.tokenizer(
                   text=UpperCamelCase   ,     add_special_tokens=UpperCamelCase   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=UpperCamelCase   ,     stride=UpperCamelCase   ,     pad_to_multiple_of=UpperCamelCase   ,     return_attention_mask=UpperCamelCase   ,     return_overflowing_tokens=UpperCamelCase   ,     return_special_tokens_mask=UpperCamelCase   ,     return_offsets_mapping=UpperCamelCase   ,     return_token_type_ids=UpperCamelCase   ,     return_length=UpperCamelCase   ,     verbose=UpperCamelCase   ,     return_tensors=UpperCamelCase   ,     **UpperCamelCase   ,     )
         else:
               lowerCAmelCase__      :			Tuple   =	None

         if text_encoding is not None:
               encoding_image_processor.update(UpperCamelCase   )

         return encoding_image_processor






   def 	_lowerCAmelCase   (		self       :		int   ,     *UpperCamelCase       :		Union[str, Any]   ,     **UpperCamelCase       :		List[str]   )			->						Dict:

         """simple docstring"""
         return self.tokenizer.batch_decode(*UpperCamelCase   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     *UpperCamelCase       :		Tuple   ,     **UpperCamelCase       :		List[str]   )			->						List[Any]:

         """simple docstring"""
         return self.tokenizer.decode(*UpperCamelCase   ,     **UpperCamelCase   )






   @property
   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.tokenizer.model_input_names
         lowerCAmelCase__      :			Union[str, Any]   =	self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





