import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
   from PIL import Image
else:





   class 		_lowercase						:

    @staticmethod
    def 						SCREAMING_SNAKE_CASE__   ( *snake_case	:							Union[str, Any]   ,      **snake_case	:							Dict			)   ->						Optional[Any]:

     """simple docstring"""





     pass





@is_pipeline_test
@require_torch
@require_vision
class 		_lowercase						(					unittest.TestCase       ):
 lowercase				     =   MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							Optional[Any]   ,      snake_case	:							Optional[int]   ,      snake_case	:							str			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					pipeline('visual-question-answering'   ,      model='hf-internal-testing/tiny-vilt-random-vqa'			)
  UpperCamelCase_					:						Optional[int] =					[
      {
          'image': Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			),
          'question': 'How many cats are there?',
      },
      {
          'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
          'question': 'How many cats are there?',
      },
  ]
  return vqa_pipeline, examples

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							List[str]   ,      snake_case	:							List[Any]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					vqa_pipeline(snake_case   ,      top_k=1			)
  self.assertEqual(
      snake_case   ,      [
          [{'score': ANY(snake_case			), 'answer': ANY(snake_case			)}],
          [{'score': ANY(snake_case			), 'answer': ANY(snake_case			)}],
      ]   ,      )

 @require_torch
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					pipeline('visual-question-answering'   ,      model='hf-internal-testing/tiny-vilt-random-vqa'			)
  UpperCamelCase_					:						str =					'./tests/fixtures/tests_samples/COCO/000000039769.png'
  UpperCamelCase_					:						Optional[int] =					'How many cats are there?'

  UpperCamelCase_					:						Any =					vqa_pipeline(image=snake_case   ,      question='How many cats are there?'   ,      top_k=2			)
  self.assertEqual(
      snake_case   ,      [{'score': ANY(snake_case			), 'answer': ANY(snake_case			)}, {'score': ANY(snake_case			), 'answer': ANY(snake_case			)}]			)

  UpperCamelCase_					:						int =					vqa_pipeline({'image': image, 'question': question}   ,      top_k=2			)
  self.assertEqual(
      snake_case   ,      [{'score': ANY(snake_case			), 'answer': ANY(snake_case			)}, {'score': ANY(snake_case			), 'answer': ANY(snake_case			)}]			)

 @slow
 @require_torch
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Any =					pipeline('visual-question-answering'   ,      model='dandelin/vilt-b32-finetuned-vqa'			)
  UpperCamelCase_					:						str =					'./tests/fixtures/tests_samples/COCO/000000039769.png'
  UpperCamelCase_					:						List[str] =					'How many cats are there?'

  UpperCamelCase_					:						str =					vqa_pipeline(image=snake_case   ,      question=snake_case   ,      top_k=2			)
  self.assertEqual(
      nested_simplify(snake_case   ,      decimals=4			)   ,      [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]			)

  UpperCamelCase_					:						Any =					vqa_pipeline({'image': image, 'question': question}   ,      top_k=2			)
  self.assertEqual(
      nested_simplify(snake_case   ,      decimals=4			)   ,      [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]			)

  UpperCamelCase_					:						int =					vqa_pipeline(
      [{'image': image, 'question': question}, {'image': image, 'question': question}]   ,      top_k=2			)
  self.assertEqual(
      nested_simplify(snake_case   ,      decimals=4			)   ,      [[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2   ,      )


 @require_tf
 @unittest.skip('Visual question answering not implemented in TF'			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						List[str]:

  """simple docstring"""





  pass




from torch import nn





class 		_lowercase						(					nn.Module       ):

 def __init__( self	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_					:						List[Any] =					class_size
  UpperCamelCase_					:						List[Any] =					embed_size
  # self.mlp1 = nn.Linear(embed_size, embed_size)
  # self.mlp2 = (nn.Linear(embed_size, class_size))
  UpperCamelCase_					:						int =					nn.Linear(snake_case   ,      snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Any			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.mlp(snake_case			)
  return logits



