'''simple docstring'''

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast




@require_vision
class    lowerCamelCase__  (   unittest.TestCase     ):



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :Any       =tempfile.mkdtemp()

        _UpperCamelCase  :Union[str, Any]       =CLIPImageProcessor()
        _UpperCamelCase  :Dict       =BertTokenizer.from_pretrained(
            """hf-internal-testing/tiny-random-BertModel"""					,      model_input_names=["""input_ids""", """attention_mask"""]     )

        _UpperCamelCase  :Union[str, Any]       =GitProcessor(lowerCAmelCase__					,      lowerCAmelCase__     )

        processor.save_pretrained(self.tmpdirname     )



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     Dict:
        """simple docstring"""





        return AutoProcessor.from_pretrained(self.tmpdirname					,      **lowerCAmelCase__     ).tokenizer



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     Any:
        """simple docstring"""





        return AutoProcessor.from_pretrained(self.tmpdirname					,      **lowerCAmelCase__     ).image_processor



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        shutil.rmtree(self.tmpdirname     )



   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =[np.random.randint(255					,      size=(3, 30, 400)					,      dtype=np.uinta     )]

        _UpperCamelCase  :Any       =[Image.fromarray(np.moveaxis(lowerCAmelCase__					,      0					,      -1     )     ) for x in image_inputs]

        return image_inputs



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       =GitProcessor(tokenizer=self.get_tokenizer()					,      image_processor=self.get_image_processor()     )
        processor.save_pretrained(self.tmpdirname     )

        _UpperCamelCase  :Optional[Any]       =self.get_tokenizer(bos_token="""(BOS)"""					,      eos_token="""(EOS)"""     )
        _UpperCamelCase  :Dict       =self.get_image_processor(do_normalize=lowerCAmelCase__					,      padding_value=1.0     )

        _UpperCamelCase  :int       =GitProcessor.from_pretrained(
            self.tmpdirname					,      bos_token="""(BOS)"""					,      eos_token="""(EOS)"""					,      do_normalize=lowerCAmelCase__					,      padding_value=1.0     )

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

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



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =self.get_image_processor()
        _UpperCamelCase  :Optional[int]       =self.get_tokenizer()

        _UpperCamelCase  :Optional[int]       =GitProcessor(tokenizer=lowerCAmelCase__					,      image_processor=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[Any]       =self.prepare_image_inputs()

        _UpperCamelCase  :Tuple       =image_processor(lowerCAmelCase__					,      return_tensors="""np"""     )
        _UpperCamelCase  :Tuple       =processor(images=lowerCAmelCase__					,      return_tensors="""np"""     )

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



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =self.get_image_processor()
        _UpperCamelCase  :Any       =self.get_tokenizer()

        _UpperCamelCase  :Any       =GitProcessor(tokenizer=lowerCAmelCase__					,      image_processor=lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       ="""lower newer"""

        _UpperCamelCase  :List[str]       =processor(text=lowerCAmelCase__     )

        _UpperCamelCase  :List[Any]       =tokenizer(lowerCAmelCase__					,      return_token_type_ids=lowerCAmelCase__     )

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



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =self.get_image_processor()
        _UpperCamelCase  :Optional[int]       =self.get_tokenizer()

        _UpperCamelCase  :Optional[Any]       =GitProcessor(tokenizer=lowerCAmelCase__					,      image_processor=lowerCAmelCase__     )

        _UpperCamelCase  :Dict       ="""lower newer"""
        _UpperCamelCase  :Optional[Any]       =self.prepare_image_inputs()

        _UpperCamelCase  :Any       =processor(text=lowerCAmelCase__					,      images=lowerCAmelCase__     )

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

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



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.get_image_processor()
        _UpperCamelCase  :Tuple       =self.get_tokenizer()

        _UpperCamelCase  :Optional[int]       =GitProcessor(tokenizer=lowerCAmelCase__					,      image_processor=lowerCAmelCase__     )

        _UpperCamelCase  :Any       =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCamelCase  :int       =processor.batch_decode(lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =tokenizer.batch_decode(lowerCAmelCase__     )

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )




   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :Dict       =self.get_image_processor()
        _UpperCamelCase  :Optional[Any]       =self.get_tokenizer()

        _UpperCamelCase  :Any       =GitProcessor(tokenizer=lowerCAmelCase__					,      image_processor=lowerCAmelCase__     )

        _UpperCamelCase  :List[Any]       ="""lower newer"""
        _UpperCamelCase  :Dict       =self.prepare_image_inputs()

        _UpperCamelCase  :str       =processor(text=lowerCAmelCase__					,      images=lowerCAmelCase__     )

        # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
        self.assertListEqual(list(inputs.keys()     )					,      ["""input_ids""", """attention_mask""", """pixel_values"""]     )





'''simple docstring'''


from __future__ import annotations

import random






class 						lowerCAmelCase_	:







  '''simple docstring'''

  UpperCamelCase_			:      Optional[int]										=				6



  def __init__( self				:							Dict	)			->		Dict:







       '''simple docstring'''



       A:		Union[str, Any]          =		list(range(1					,  Dice.NUM_SIDES + 1	)	)




  def 					_snake_case     ( self				:							Optional[Any]	)			->		Any:







       '''simple docstring'''



       return random.choice(self.sides	)






def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase = 2	)			-> list[float]:
     A:		Union[str, Any]          =		[Dice() for i in range(__lowercase	)]
     A:		Union[str, Any]          =		[0] * (len(__lowercase	) * Dice.NUM_SIDES + 1)
     for _ in range(__lowercase	):
          count_of_sum[sum(dice.roll() for dice in dices	)] += 1
     A:		Dict          =		[round((count * 1_0_0) / num_throws		,				2	) for count in count_of_sum]
     return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
     import doctest

     doctest.testmod()






