'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import AlignProcessor, EfficientNetImageProcessor






@require_vision
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def 			snake_case__  (   self		:   str      ):
      __magic_name__						       =    tempfile.mkdtemp()

      __magic_name__						       =    [
          '''[UNK]''',
          '''[CLS]''',
          '''[SEP]''',
          '''[PAD]''',
          '''[MASK]''',
          '''want''',
          '''##want''',
          '''##ed''',
          '''wa''',
          '''un''',
          '''runn''',
          '''##ing''',
          ''',''',
          '''low''',
          '''lowest''',
      ]
      __magic_name__						       =    os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']      )
      with open(self.vocab_file			,				'''w'''			,				encoding='''utf-8'''      ) as vocab_writer:
           vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]      )      )

      __magic_name__						       =    {
          '''do_resize''': True,
          '''size''': 20,
          '''do_center_crop''': True,
          '''crop_size''': 18,
          '''do_normalize''': True,
          '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
          '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
      }
      __magic_name__						       =    os.path.join(self.tmpdirname			,				_SCREAMING_SNAKE_CASE      )
      with open(self.image_processor_file			,				'''w'''			,				encoding='''utf-8'''      ) as fp:
           json.dump(_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE      )



 def 			snake_case__  (   self		:   Dict			,				**a__		:   Dict      ):
      return BertTokenizer.from_pretrained(self.tmpdirname			,				**_SCREAMING_SNAKE_CASE      )



 def 			snake_case__  (   self		:   Any			,				**a__		:   int      ):
      return BertTokenizerFast.from_pretrained(self.tmpdirname			,				**_SCREAMING_SNAKE_CASE      )



 def 			snake_case__  (   self		:   Dict			,				**a__		:   List[str]      ):
      return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,				**_SCREAMING_SNAKE_CASE      )



 def 			snake_case__  (   self		:   int      ):
      shutil.rmtree(self.tmpdirname      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    [np.random.randint(255			,				size=(3, 30, 400)			,				dtype=np.uinta      )]
      __magic_name__						       =    [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE			,				0			,				-1      )      ) for x in image_inputs]
      return image_inputs



 def 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__						       =    self.get_tokenizer()
      __magic_name__						       =    self.get_rust_tokenizer()
      __magic_name__						       =    self.get_image_processor()

      __magic_name__						       =    AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,				image_processor=_SCREAMING_SNAKE_CASE      )
      processor_slow.save_pretrained(self.tmpdirname      )
      __magic_name__						       =    AlignProcessor.from_pretrained(self.tmpdirname			,				use_fast=_SCREAMING_SNAKE_CASE      )

      __magic_name__						       =    AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,				image_processor=_SCREAMING_SNAKE_CASE      )
      processor_fast.save_pretrained(self.tmpdirname      )
      __magic_name__						       =    AlignProcessor.from_pretrained(self.tmpdirname      )

      self.assertEqual(processor_slow.tokenizer.get_vocab()			,				tokenizer_slow.get_vocab()      )
      self.assertEqual(processor_fast.tokenizer.get_vocab()			,				tokenizer_fast.get_vocab()      )
      self.assertEqual(tokenizer_slow.get_vocab()			,				tokenizer_fast.get_vocab()      )
      self.assertIsInstance(processor_slow.tokenizer			,				_SCREAMING_SNAKE_CASE      )
      self.assertIsInstance(processor_fast.tokenizer			,				_SCREAMING_SNAKE_CASE      )

      self.assertEqual(processor_slow.image_processor.to_json_string()			,				image_processor.to_json_string()      )
      self.assertEqual(processor_fast.image_processor.to_json_string()			,				image_processor.to_json_string()      )
      self.assertIsInstance(processor_slow.image_processor			,				_SCREAMING_SNAKE_CASE      )
      self.assertIsInstance(processor_fast.image_processor			,				_SCREAMING_SNAKE_CASE      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    AlignProcessor(tokenizer=self.get_tokenizer()			,				image_processor=self.get_image_processor()      )
      processor.save_pretrained(self.tmpdirname      )

      __magic_name__						       =    self.get_tokenizer(bos_token='''(BOS)'''			,				eos_token='''(EOS)'''      )
      __magic_name__						       =    self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE			,				padding_value=1.0      )

      __magic_name__						       =    AlignProcessor.from_pretrained(
          self.tmpdirname			,				bos_token='''(BOS)'''			,				eos_token='''(EOS)'''			,				do_normalize=_SCREAMING_SNAKE_CASE			,				padding_value=1.0      )

      self.assertEqual(processor.tokenizer.get_vocab()			,				tokenizer_add_kwargs.get_vocab()      )
      self.assertIsInstance(processor.tokenizer			,				_SCREAMING_SNAKE_CASE      )

      self.assertEqual(processor.image_processor.to_json_string()			,				image_processor_add_kwargs.to_json_string()      )
      self.assertIsInstance(processor.image_processor			,				_SCREAMING_SNAKE_CASE      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    self.get_image_processor()
      __magic_name__						       =    self.get_tokenizer()

      __magic_name__						       =    AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,				image_processor=_SCREAMING_SNAKE_CASE      )

      __magic_name__						       =    self.prepare_image_inputs()

      __magic_name__						       =    image_processor(_SCREAMING_SNAKE_CASE			,				return_tensors='''np'''      )
      __magic_name__						       =    processor(images=_SCREAMING_SNAKE_CASE			,				return_tensors='''np'''      )

      for key in input_image_proc.keys():
           self.assertAlmostEqual(input_image_proc[key].sum()			,				input_processor[key].sum()			,				delta=1E-2      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    self.get_image_processor()
      __magic_name__						       =    self.get_tokenizer()

      __magic_name__						       =    AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,				image_processor=_SCREAMING_SNAKE_CASE      )

      __magic_name__						       =    '''lower newer'''

      __magic_name__						       =    processor(text=_SCREAMING_SNAKE_CASE      )

      __magic_name__						       =    tokenizer(_SCREAMING_SNAKE_CASE			,				padding='''max_length'''			,				max_length=64      )

      for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]			,				encoded_processor[key]      )



 def 			snake_case__  (   self		:   str      ):
      __magic_name__						       =    self.get_image_processor()
      __magic_name__						       =    self.get_tokenizer()

      __magic_name__						       =    AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,				image_processor=_SCREAMING_SNAKE_CASE      )

      __magic_name__						       =    '''lower newer'''
      __magic_name__						       =    self.prepare_image_inputs()

      __magic_name__						       =    processor(text=_SCREAMING_SNAKE_CASE			,				images=_SCREAMING_SNAKE_CASE      )

      self.assertListEqual(list(inputs.keys()      )			,				['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']      )

      # test if it raises when no input is passed
      with pytest.raises(_SCREAMING_SNAKE_CASE      ):
           processor()



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    self.get_image_processor()
      __magic_name__						       =    self.get_tokenizer()

      __magic_name__						       =    AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,				image_processor=_SCREAMING_SNAKE_CASE      )

      __magic_name__						       =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      __magic_name__						       =    processor.batch_decode(_SCREAMING_SNAKE_CASE      )
      __magic_name__						       =    tokenizer.batch_decode(_SCREAMING_SNAKE_CASE      )

      self.assertListEqual(_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE      )



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    self.get_image_processor()
      __magic_name__						       =    self.get_tokenizer()

      __magic_name__						       =    AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,				image_processor=_SCREAMING_SNAKE_CASE      )

      __magic_name__						       =    '''lower newer'''
      __magic_name__						       =    self.prepare_image_inputs()

      __magic_name__						       =    processor(text=_SCREAMING_SNAKE_CASE			,				images=_SCREAMING_SNAKE_CASE      )

      self.assertListEqual(list(inputs.keys()      )			,				processor.model_input_names      )









'''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()






