



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  snake_case__:

									"""simple docstring"""






									@staticmethod
									def 					snake_case	(				*SCREAMING_SNAKE_CASE   :      Dict ,  **SCREAMING_SNAKE_CASE   :      Dict	):
													pass

@is_pipeline_test
@require_torch
@require_vision
class  snake_case__(unittest.TestCase     ):

					"""simple docstring"""






					lowercase_								=						MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







					def 					snake_case	(				self   :      str ,  SCREAMING_SNAKE_CASE   :      Dict ,  SCREAMING_SNAKE_CASE   :      Dict ,  SCREAMING_SNAKE_CASE   :      Optional[Any]	):
									lowercase__	:       Tuple						=	pipeline("visual-question-answering" ,  model="hf-internal-testing/tiny-vilt-random-vqa"	)
									lowercase__	:       List[Any]						=	[
									    {
									        "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 					snake_case	(				self   :      List[Any] ,  SCREAMING_SNAKE_CASE   :      int ,  SCREAMING_SNAKE_CASE   :      Optional[Any]	):
									lowercase__	:       Optional[Any]						=	vqa_pipeline(_lowerCAmelCase ,  top_k=1	)
									self.assertEqual(
									    _lowerCAmelCase ,  [
									        [{"score": ANY(_lowerCAmelCase	), "answer": ANY(_lowerCAmelCase	)}],
									        [{"score": ANY(_lowerCAmelCase	), "answer": ANY(_lowerCAmelCase	)}],
									    ] ,  )







					@require_torch
					def 					snake_case	(				self   :      Optional[Any]	):
									lowercase__	:       Dict						=	pipeline("visual-question-answering" ,  model="hf-internal-testing/tiny-vilt-random-vqa"	)
									lowercase__	:       Any						=	"./tests/fixtures/tests_samples/COCO/000000039769.png"
									lowercase__	:       Any						=	"How many cats are there?"

									lowercase__	:       List[str]						=	vqa_pipeline(image=_lowerCAmelCase ,  question="How many cats are there?" ,  top_k=2	)
									self.assertEqual(
									    _lowerCAmelCase ,  [{"score": ANY(_lowerCAmelCase	), "answer": ANY(_lowerCAmelCase	)}, {"score": ANY(_lowerCAmelCase	), "answer": ANY(_lowerCAmelCase	)}]	)

									lowercase__	:       int						=	vqa_pipeline({"image": image, "question": question} ,  top_k=2	)
									self.assertEqual(
									    _lowerCAmelCase ,  [{"score": ANY(_lowerCAmelCase	), "answer": ANY(_lowerCAmelCase	)}, {"score": ANY(_lowerCAmelCase	), "answer": ANY(_lowerCAmelCase	)}]	)







					@slow
					@require_torch
					def 					snake_case	(				self   :      Dict	):
									lowercase__	:       Any						=	pipeline("visual-question-answering" ,  model="dandelin/vilt-b32-finetuned-vqa"	)
									lowercase__	:       str						=	"./tests/fixtures/tests_samples/COCO/000000039769.png"
									lowercase__	:       Union[str, Any]						=	"How many cats are there?"

									lowercase__	:       Any						=	vqa_pipeline(image=_lowerCAmelCase ,  question=_lowerCAmelCase ,  top_k=2	)
									self.assertEqual(
									    nested_simplify(_lowerCAmelCase ,  decimals=4	) ,  [{"score": 0.8_799, "answer": "2"}, {"score": 0.296, "answer": "1"}]	)

									lowercase__	:       Any						=	vqa_pipeline({"image": image, "question": question} ,  top_k=2	)
									self.assertEqual(
									    nested_simplify(_lowerCAmelCase ,  decimals=4	) ,  [{"score": 0.8_799, "answer": "2"}, {"score": 0.296, "answer": "1"}]	)

									lowercase__	:       str						=	vqa_pipeline(
									    [{"image": image, "question": question}, {"image": image, "question": question}] ,  top_k=2	)
									self.assertEqual(
									    nested_simplify(_lowerCAmelCase ,  decimals=4	) ,  [[{"score": 0.8_799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2 ,  )







					@require_tf
					@unittest.skip("Visual question answering not implemented in TF"	)
					def 					snake_case	(				self   :      Any	):
									pass








from collections import deque

from .hash_table import HashTable


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							int     ,					*_lowerCAmelCase							:							List[Any]     ,					**_lowerCAmelCase							:							Any	) ->			Union[str, Any]:




     """simple docstring"""
     super().__init__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							List[Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       deque([]	) if self.values[key] is None else self.values[key]
     self.values[key].appendleft(_lowerCAmelCase	)
     snake_case_            =       self.values[key]



  def        lowerCAmelCase__	(  self							:							List[str]	) ->			List[Any]:




     """simple docstring"""
     return (
         sum(self.charge_factor - len(_lowerCAmelCase	) for slot in self.values	)
         / self.size_table
         * self.charge_factor
     )



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							List[str]=None	) ->			int:




     """simple docstring"""
     if not (
         len(self.values[key]	) == self.charge_factor and self.values.count(_lowerCAmelCase	) == 0
     ):
        return key
     return super()._collision_resolution(_lowerCAmelCase     ,					_lowerCAmelCase	)




