



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase    :		List[Any]		      =    logging.get_logger(__name__)

_lowerCamelCase    :		Optional[int]		      =    {
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		str		=		'roformer'







						def __init__(  self    :				Optional[int]       ,	snake_case    :				List[str]=50000       ,	snake_case    :				List[Any]=None       ,	snake_case    :				int=768       ,	snake_case    :				Tuple=12       ,	snake_case    :				Dict=12       ,	snake_case    :				Optional[Any]=3072       ,	snake_case    :				List[str]="gelu"       ,	snake_case    :				Union[str, Any]=0.1       ,	snake_case    :				Dict=0.1       ,	snake_case    :				int=1536       ,	snake_case    :				Tuple=2       ,	snake_case    :				Tuple=0.02       ,	snake_case    :				Optional[Any]=1E-12       ,	snake_case    :				Union[str, Any]=0       ,	snake_case    :				str=False       ,	snake_case    :				Optional[int]=True       ,	**snake_case    :				Optional[int]       ,	):



											'''simple docstring'''


											super().__init__(pad_token_id=snake_case       ,	**snake_case	)

											SCREAMING_SNAKE_CASE							:     int						=				vocab_size
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				hidden_size if embedding_size is None else embedding_size
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				hidden_size
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				num_hidden_layers
											SCREAMING_SNAKE_CASE							:     str						=				num_attention_heads
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				hidden_act
											SCREAMING_SNAKE_CASE							:     List[Any]						=				intermediate_size
											SCREAMING_SNAKE_CASE							:     List[str]						=				hidden_dropout_prob
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				max_position_embeddings
											SCREAMING_SNAKE_CASE							:     int						=				type_vocab_size
											SCREAMING_SNAKE_CASE							:     Dict						=				initializer_range
											SCREAMING_SNAKE_CASE							:     int						=				layer_norm_eps
											SCREAMING_SNAKE_CASE							:     List[Any]						=				rotary_value
											SCREAMING_SNAKE_CASE							:     Dict						=				use_cache


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						@property
						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											if self.task == "multiple-choice":
																SCREAMING_SNAKE_CASE							:     List[str]						=				{0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																SCREAMING_SNAKE_CASE							:     Any						=				{0: 'batch', 1: 'sequence'}
											SCREAMING_SNAKE_CASE							:     Tuple						=				{0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]	)




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