




"""simple docstring"""







import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       {
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       '''detr'''
   A_				      =       ['''past_key_values''']
   A_				      =       {
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
   }







   def __init__(		self       ,					lowerCamelCase_=True       ,					lowerCamelCase_=None       ,					lowerCamelCase_=3       ,					lowerCamelCase_=1_0_0       ,					lowerCamelCase_=6       ,					lowerCamelCase_=2_0_4_8       ,					lowerCamelCase_=8       ,					lowerCamelCase_=6       ,					lowerCamelCase_=2_0_4_8       ,					lowerCamelCase_=8       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=True       ,					lowerCamelCase_="relu"       ,					lowerCamelCase_=2_5_6       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=1.0       ,					lowerCamelCase_=False       ,					lowerCamelCase_="sine"       ,					lowerCamelCase_="resnet50"       ,					lowerCamelCase_=True       ,					lowerCamelCase_=False       ,					lowerCamelCase_=1       ,					lowerCamelCase_=5       ,					lowerCamelCase_=2       ,					lowerCamelCase_=1       ,					lowerCamelCase_=1       ,					lowerCamelCase_=5       ,					lowerCamelCase_=2       ,					lowerCamelCase_=0.1       ,					**lowerCamelCase_       ,					)				->   List[Any]:
       if backbone_config is not None and use_timm_backbone:
           raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')

       if not use_timm_backbone:
           if backbone_config is None:
               logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
               UpperCamelCase              =	CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
           elif isinstance(lowerCamelCase_       ,					lowerCamelCase_):
               UpperCamelCase              =	backbone_config.get('''model_type''')
               UpperCamelCase              =	CONFIG_MAPPING[backbone_model_type]
               UpperCamelCase              =	config_class.from_dict(lowerCamelCase_)
           # set timm attributes to None
           UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase              =	None, None, None

       UpperCamelCase              =	use_timm_backbone
       UpperCamelCase              =	backbone_config
       UpperCamelCase              =	num_channels
       UpperCamelCase              =	num_queries
       UpperCamelCase              =	d_model
       UpperCamelCase              =	encoder_ffn_dim
       UpperCamelCase              =	encoder_layers
       UpperCamelCase              =	encoder_attention_heads
       UpperCamelCase              =	decoder_ffn_dim
       UpperCamelCase              =	decoder_layers
       UpperCamelCase              =	decoder_attention_heads
       UpperCamelCase              =	dropout
       UpperCamelCase              =	attention_dropout
       UpperCamelCase              =	activation_dropout
       UpperCamelCase              =	activation_function
       UpperCamelCase              =	init_std
       UpperCamelCase              =	init_xavier_std
       UpperCamelCase              =	encoder_layerdrop
       UpperCamelCase              =	decoder_layerdrop
       UpperCamelCase              =	encoder_layers
       UpperCamelCase              =	auxiliary_loss
       UpperCamelCase              =	position_embedding_type
       UpperCamelCase              =	backbone
       UpperCamelCase              =	use_pretrained_backbone
       UpperCamelCase              =	dilation
       # Hungarian matcher
       UpperCamelCase              =	class_cost
       UpperCamelCase              =	bbox_cost
       UpperCamelCase              =	giou_cost
       # Loss coefficients
       UpperCamelCase              =	mask_loss_coefficient
       UpperCamelCase              =	dice_loss_coefficient
       UpperCamelCase              =	bbox_loss_coefficient
       UpperCamelCase              =	giou_loss_coefficient
       UpperCamelCase              =	eos_coefficient
       super().__init__(is_encoder_decoder=lowerCamelCase_       ,					**lowerCamelCase_)







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return self.encoder_attention_heads







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return self.d_model







   @classmethod
   def   UpperCAmelCase__			(		cls       ,					lowerCamelCase_       ,					**lowerCamelCase_)				->   List[str]:
       return cls(backbone_config=lowerCamelCase_       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Dict[str, any]:
       UpperCamelCase              =	copy.deepcopy(self.__dict__)
       if output["backbone_config"] is not None:
           UpperCamelCase              =	self.backbone_config.to_dict()
       UpperCamelCase              =	self.__class__.model_type
       return output

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       version.parse('''1.11'''			)







   @property
   def   UpperCAmelCase__			(		self)				->   Mapping[str, Mapping[int, str]]:
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ('''pixel_mask''', {0: '''batch'''}),
           ])







   @property
   def   UpperCAmelCase__			(		self)				->   float:
       return 1e-5







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return 1_2




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