




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__			: Union[str, Any]    =  {
    """configuration_roc_bert""": ["""ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoCBertConfig"""],
    """tokenization_roc_bert""": ["""RoCBertTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       pass

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__			: Optional[int]    =  [
           """ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """RoCBertForCausalLM""",
           """RoCBertForMaskedLM""",
           """RoCBertForMultipleChoice""",
           """RoCBertForPreTraining""",
           """RoCBertForQuestionAnswering""",
           """RoCBertForSequenceClassification""",
           """RoCBertForTokenClassification""",
           """RoCBertLayer""",
           """RoCBertModel""",
           """RoCBertPreTrainedModel""",
           """load_tf_weights_in_roc_bert""",
       ]

if TYPE_CHECKING:
       from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
       from .tokenization_roc_bert import RoCBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              raise OptionalDependencyNotAvailable()

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roc_bert import (
                  ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoCBertForCausalLM,
                  RoCBertForMaskedLM,
                  RoCBertForMultipleChoice,
                  RoCBertForPreTraining,
                  RoCBertForQuestionAnswering,
                  RoCBertForSequenceClassification,
                  RoCBertForTokenClassification,
                  RoCBertLayer,
                  RoCBertModel,
                  RoCBertPreTrainedModel,
                  load_tf_weights_in_roc_bert,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__			: int    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











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    :
									'''simple docstring'''


									@staticmethod
									def       lowerCamelCase_				(  *snake_case    :				Dict       ,	**snake_case    :				List[Any]	):



														'''simple docstring'''


														pass


@is_pipeline_test
@require_torch
@require_vision
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		Any		=		MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				List[str]       ,	snake_case    :				int       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipeline('visual-question-answering'       ,	model='hf-internal-testing/tiny-vilt-random-vqa'	)
											SCREAMING_SNAKE_CASE							:     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       lowerCamelCase_				(  self    :				int       ,	snake_case    :				int       ,	snake_case    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				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       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


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

											SCREAMING_SNAKE_CASE							:     Optional[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	)}]	)

											SCREAMING_SNAKE_CASE							:     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       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


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

											SCREAMING_SNAKE_CASE							:     int						=				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'}]	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				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'}]	)

											SCREAMING_SNAKE_CASE							:     Any						=				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       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											pass