






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    a   (			unittest.TestCase       ):



      """simple docstring"""


      def      UpperCAmelCase				(				self :      Tuple )							->				List[str]:
        __UpperCAmelCase							:     Any									=  tempfile.mkdtemp()

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

        __UpperCAmelCase							:     int									=  GitProcessor(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE )

        processor.save_pretrained(self.tmpdirname )
      def      UpperCAmelCase				(				self :      str		,				**__lowercase :      Dict )							->				List[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**__SCREAMING_SNAKE_CASE ).tokenizer
      def      UpperCAmelCase				(				self :      Any		,				**__lowercase :      Optional[Any] )							->				int:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**__SCREAMING_SNAKE_CASE ).image_processor
      def      UpperCAmelCase				(				self :      str )							->				List[str]:
        shutil.rmtree(self.tmpdirname )
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Optional[Any]:
        __UpperCAmelCase							:     Union[str, Any]									=  [np.random.randint(255		,				size=(3, 30, 400)		,				dtype=np.uinta )]

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

        return image_inputs
      def      UpperCAmelCase				(				self :      Tuple )							->				List[str]:
        __UpperCAmelCase							:     Optional[int]									=  GitProcessor(tokenizer=self.get_tokenizer()		,				image_processor=self.get_image_processor() )
        processor.save_pretrained(self.tmpdirname )

        __UpperCAmelCase							:     int									=  self.get_tokenizer(bos_token="""(BOS)"""		,				eos_token="""(EOS)""" )
        __UpperCAmelCase							:     Optional[int]									=  self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE		,				padding_value=1.0 )

        __UpperCAmelCase							:     str									=  GitProcessor.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      UpperCAmelCase				(				self :      str )							->				Optional[Any]:
        __UpperCAmelCase							:     str									=  self.get_image_processor()
        __UpperCAmelCase							:     List[str]									=  self.get_tokenizer()

        __UpperCAmelCase							:     List[Any]									=  GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE		,				image_processor=__SCREAMING_SNAKE_CASE )

        __UpperCAmelCase							:     List[Any]									=  self.prepare_image_inputs()

        __UpperCAmelCase							:     Any									=  image_processor(__SCREAMING_SNAKE_CASE		,				return_tensors="""np""" )
        __UpperCAmelCase							:     str									=  processor(images=__SCREAMING_SNAKE_CASE		,				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 :      str )							->				str:
        __UpperCAmelCase							:     str									=  self.get_image_processor()
        __UpperCAmelCase							:     Tuple									=  self.get_tokenizer()

        __UpperCAmelCase							:     Union[str, Any]									=  GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE		,				image_processor=__SCREAMING_SNAKE_CASE )

        __UpperCAmelCase							:     Optional[Any]									=  """lower newer"""

        __UpperCAmelCase							:     Optional[Any]									=  processor(text=__SCREAMING_SNAKE_CASE )

        __UpperCAmelCase							:     Union[str, Any]									=  tokenizer(__SCREAMING_SNAKE_CASE		,				return_token_type_ids=__SCREAMING_SNAKE_CASE )

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]		,				encoded_processor[key] )
      def      UpperCAmelCase				(				self :      Any )							->				List[Any]:
        __UpperCAmelCase							:     int									=  self.get_image_processor()
        __UpperCAmelCase							:     Optional[int]									=  self.get_tokenizer()

        __UpperCAmelCase							:     int									=  GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE		,				image_processor=__SCREAMING_SNAKE_CASE )

        __UpperCAmelCase							:     Dict									=  """lower newer"""
        __UpperCAmelCase							:     Dict									=  self.prepare_image_inputs()

        __UpperCAmelCase							:     str									=  processor(text=__SCREAMING_SNAKE_CASE		,				images=__SCREAMING_SNAKE_CASE )

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

        # test if it raises when no input is passed
        with pytest.raises(__SCREAMING_SNAKE_CASE ):
          processor()
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Any:
        __UpperCAmelCase							:     Any									=  self.get_image_processor()
        __UpperCAmelCase							:     Union[str, Any]									=  self.get_tokenizer()

        __UpperCAmelCase							:     Tuple									=  GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE		,				image_processor=__SCREAMING_SNAKE_CASE )

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

        __UpperCAmelCase							:     Optional[int]									=  processor.batch_decode(__SCREAMING_SNAKE_CASE )
        __UpperCAmelCase							:     int									=  tokenizer.batch_decode(__SCREAMING_SNAKE_CASE )

        self.assertListEqual(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE )




      def      UpperCAmelCase				(				self :      List[Any] )							->				int:
        __UpperCAmelCase							:     List[Any]									=  self.get_image_processor()
        __UpperCAmelCase							:     List[str]									=  self.get_tokenizer()

        __UpperCAmelCase							:     int									=  GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE		,				image_processor=__SCREAMING_SNAKE_CASE )

        __UpperCAmelCase							:     Any									=  """lower newer"""
        __UpperCAmelCase							:     Union[str, Any]									=  self.prepare_image_inputs()

        __UpperCAmelCase							:     Tuple									=  processor(text=__SCREAMING_SNAKE_CASE		,				images=__SCREAMING_SNAKE_CASE )

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










def     lowerCamelCase__				(   ):

  for a in range(300		):
    for b in range(a + 1  ,		400		):
      for c in range(b + 1  ,		500		):
        if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
          return a * b * c

  return -1






def     lowerCamelCase__				(   ):

  for a in range(300		):
    for b in range(400		):
      __UpperCAmelCase							:     Optional[Any]									=  1000 - a - b
      if a < b < c and (a**2) + (b**2) == (c**2):
        return a * b * c

  return -1






def     lowerCamelCase__				(   ):
  import timeit

  print(
      timeit.timeit("""solution()"""  ,		setup="""from __main__ import solution"""  ,		number=1000		)		)
  print(
      timeit.timeit(
          """solution_fast()"""  ,		setup="""from __main__ import solution_fast"""  ,		number=1000		)		)


if __name__ == "__main__":
       print(f"""{solution() = }""")


