






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





def 	_a(					UpperCamelCase__			:  int,		UpperCamelCase__			:  int,		UpperCamelCase__			:  int,		UpperCamelCase__			:  bool = False,		UpperCamelCase__			:  bool = False,		UpperCamelCase__			:  int = 5,		):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=[[-1] * number_of_cells]  # Create a highway without any car
      SCREAMING_SNAKE_CASE__       :			Tuple													=0
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=max(UpperCamelCase__,		0							)
      while i < number_of_cells:
            SCREAMING_SNAKE_CASE__       :			Dict													=(
                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 	_a(					UpperCamelCase__			:  list,		UpperCamelCase__			:  int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=0
      SCREAMING_SNAKE_CASE__       :			List[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 	_a(					UpperCamelCase__			:  list,		UpperCamelCase__			:  float,		UpperCamelCase__			:  int							):




      '''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  list,		UpperCamelCase__			:  int,		UpperCamelCase__			:  float,		UpperCamelCase__			:  int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=len(highway[0]							)

      for i in range(UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__       :			List[str]													=update(highway[i],		UpperCamelCase__,		UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=[-1] * number_of_cells

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

      return highway


if __name__ == "__main__":
  import doctest

  doctest.testmod()







'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							"""facebook/bart-large-mnli"""
 snake_case_												=							(
     """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
     """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
     """It returns the most likely label in the list of provided `labels` for the input text."""
 )
 snake_case_												=							"""text_classifier"""
 snake_case_												=							AutoTokenizer
 snake_case_												=							AutoModelForSequenceClassification

 snake_case_												=							["""text""", ["""text"""]]
 snake_case_												=							["""text"""]




 def        __magic_name__		( self		:							Any )		->  Any:
       super().setup()
       SCREAMING_SNAKE_CASE__       :			int													=self.model.config
       SCREAMING_SNAKE_CASE__       :			int													=-1
       for idx, label in config.idalabel.items():
             if label.lower().startswith('''entail''' ):
                   SCREAMING_SNAKE_CASE__       :			Any													=int(__lowercase )
       if self.entailment_id == -1:
             raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''' )




 def        __magic_name__		( self		:							str   , __lowercase		:							Any   , __lowercase		:							Optional[Any] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Dict													=labels
       return self.pre_processor(
           [text] * len(__lowercase )   , [F"This example is {label}" for label in labels]   , return_tensors='''pt'''   , padding='''max_length'''   , )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							str )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			int													=outputs.logits
       SCREAMING_SNAKE_CASE__       :			Any													=torch.argmax(logits[:, 2] ).item()
       return self._labels[label_id]