
"""simple docstring"""


import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, 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 					lowerCAmelCase_					:



								'''simple docstring'''


								@staticmethod
								def     _SCREAMING_SNAKE_CASE   (			*A_  :   Any	,**A_  :   int   )  -> List[Any]:
									pass







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



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   Tuple	,A_  :   Union[str, Any]	,A_  :   str   )  -> Dict:
							A     = pipeline(
							    'zero-shot-object-detection'	,model='hf-internal-testing/tiny-random-owlvit-object-detection'   )

							A     = [
							    {
							        'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
							        'candidate_labels': ['cat', 'remote', 'couch'],
							    }
							]
							return object_detector, examples


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   Union[str, Any]	,A_  :   List[str]   )  -> List[Any]:
							A     = object_detector(examples[0]	,threshold=0.0   )

							A     = len(A_   )
							self.assertGreater(A_	,0   )
							self.assertEqual(
							    A_	,[
							        {
							            'score': ANY(A_   ),
							            'label': ANY(A_   ),
							            'box': {'xmin': ANY(A_   ), 'ymin': ANY(A_   ), 'xmax': ANY(A_   ), 'ymax': ANY(A_   )},
							        }
							        for i in range(A_   )
							    ]	,)


						@require_tf
						@unittest.skip('Zero Shot Object Detection not implemented in TF'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Dict:
							pass


						@require_torch
						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Union[str, Any]:
							A     = pipeline(
							    'zero-shot-object-detection'	,model='hf-internal-testing/tiny-random-owlvit-object-detection'   )

							A     = object_detector(
							    './tests/fixtures/tests_samples/COCO/000000039769.png'	,candidate_labels=['cat', 'remote', 'couch']	,threshold=0.64	,)

							self.assertEqual(
							    nested_simplify(A_	,decimals=4   )	,[
							        {'score': 0.72_35, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
							        {'score': 0.72_18, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
							        {'score': 0.71_84, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
							        {'score': 0.67_48, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
							        {'score': 0.66_56, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
							        {'score': 0.66_14, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
							        {'score': 0.64_56, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
							        {'score': 0.6_42, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
							        {'score': 0.64_19, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
							    ]	,)

							A     = object_detector(
							    [
							        {
							            'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
							            'candidate_labels': ['cat', 'remote', 'couch'],
							        }
							    ]	,threshold=0.64	,)

							self.assertEqual(
							    nested_simplify(A_	,decimals=4   )	,[
							        [
							            {'score': 0.72_35, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
							            {'score': 0.72_18, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
							            {'score': 0.71_84, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
							            {'score': 0.67_48, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
							            {'score': 0.66_56, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
							            {'score': 0.66_14, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
							            {'score': 0.64_56, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
							            {'score': 0.6_42, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
							            {'score': 0.64_19, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
							        ]
							    ]	,)


						@require_torch
						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[int]:
							A     = pipeline('zero-shot-object-detection'   )

							A     = object_detector(
							    'http://images.cocodataset.org/val2017/000000039769.jpg'	,candidate_labels=['cat', 'remote', 'couch']	,)
							self.assertEqual(
							    nested_simplify(A_	,decimals=4   )	,[
							        {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
							        {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
							        {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
							        {'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
							        {'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
							    ]	,)

							A     = object_detector(
							    [
							        {
							            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
							            'candidate_labels': ['cat', 'remote', 'couch'],
							        },
							        {
							            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
							            'candidate_labels': ['cat', 'remote', 'couch'],
							        },
							    ]	,)
							self.assertEqual(
							    nested_simplify(A_	,decimals=4   )	,[
							        [
							            {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
							            {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
							            {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
							            {'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
							            {'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
							        ],
							        [
							            {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
							            {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
							            {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
							            {'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
							            {'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
							        ],
							    ]	,)


						@require_tf
						@unittest.skip('Zero Shot Object Detection not implemented in TF'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> List[str]:
							pass


						@require_torch
						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> List[Any]:
							A     = 0.2
							A     = pipeline('zero-shot-object-detection'   )

							A     = object_detector(
							    'http://images.cocodataset.org/val2017/000000039769.jpg'	,candidate_labels=['cat', 'remote', 'couch']	,threshold=A_	,)
							self.assertEqual(
							    nested_simplify(A_	,decimals=4   )	,[
							        {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
							        {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
							        {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
							    ]	,)




						@require_torch
						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Dict:
							A     = 2
							A     = pipeline('zero-shot-object-detection'   )

							A     = object_detector(
							    'http://images.cocodataset.org/val2017/000000039769.jpg'	,candidate_labels=['cat', 'remote', 'couch']	,top_k=A_	,)
							self.assertEqual(
							    nested_simplify(A_	,decimals=4   )	,[
							        {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
							        {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
							    ]	,)




'''simple docstring'''





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

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


if TYPE_CHECKING:
       from ...processing_utils import ProcessorMixin
       from ...utils import TensorType


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"layoutlmv3"




    def __init__(       self   :	Any					,	snake_case_   :	Dict=50_265					,	snake_case_   :	Union[str, Any]=768					,	snake_case_   :	Union[str, Any]=12					,	snake_case_   :	Optional[int]=12					,	snake_case_   :	Union[str, Any]=3_072					,	snake_case_   :	List[str]="gelu"					,	snake_case_   :	List[Any]=0.1					,	snake_case_   :	Union[str, Any]=0.1					,	snake_case_   :	Optional[int]=512					,	snake_case_   :	List[str]=2					,	snake_case_   :	List[str]=0.02					,	snake_case_   :	List[Any]=1E-5					,	snake_case_   :	Any=1					,	snake_case_   :	Any=0					,	snake_case_   :	int=2					,	snake_case_   :	Optional[Any]=1_024					,	snake_case_   :	int=128					,	snake_case_   :	Union[str, Any]=128					,	snake_case_   :	int=True					,	snake_case_   :	Tuple=32					,	snake_case_   :	str=128					,	snake_case_   :	Optional[int]=64					,	snake_case_   :	Optional[Any]=256					,	snake_case_   :	List[str]=True					,	snake_case_   :	str=True					,	snake_case_   :	Dict=True					,	snake_case_   :	int=224					,	snake_case_   :	Any=3					,	snake_case_   :	int=16					,	snake_case_   :	Tuple=None					,	**snake_case_   :	Optional[Any]					,	):
          super().__init__(
              vocab_size=snake_case_					,	hidden_size=snake_case_					,	num_hidden_layers=snake_case_					,	num_attention_heads=snake_case_					,	intermediate_size=snake_case_					,	hidden_act=snake_case_					,	hidden_dropout_prob=snake_case_					,	attention_probs_dropout_prob=snake_case_					,	max_position_embeddings=snake_case_					,	type_vocab_size=snake_case_					,	initializer_range=snake_case_					,	layer_norm_eps=snake_case_					,	pad_token_id=snake_case_					,	bos_token_id=snake_case_					,	eos_token_id=snake_case_					,	**snake_case_					,	)
          snake_case__    :       List[Any]       		= max_ad_position_embeddings
          snake_case__    :       Optional[int]       		= coordinate_size
          snake_case__    :       int       		= shape_size
          snake_case__    :       Optional[int]       		= has_relative_attention_bias
          snake_case__    :       Dict       		= rel_pos_bins
          snake_case__    :       List[Any]       		= max_rel_pos
          snake_case__    :       str       		= has_spatial_attention_bias
          snake_case__    :       Optional[Any]       		= rel_ad_pos_bins
          snake_case__    :       Optional[int]       		= max_rel_ad_pos
          snake_case__    :       Union[str, Any]       		= text_embed
          snake_case__    :       List[Any]       		= visual_embed
          snake_case__    :       Union[str, Any]       		= input_size
          snake_case__    :       int       		= num_channels
          snake_case__    :       Optional[Any]       		= patch_size
          snake_case__    :       Dict       		= classifier_dropout







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						version.parse("1.12"      )




    @property
    def lowerCamelCase			(       self   :	Tuple		):
          # The order of inputs is different for question answering and sequence classification
          if self.task in ["question-answering", "sequence-classification"]:
                return OrderedDict(
                    [
                        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                        ("""bbox""", {0: """batch""", 1: """sequence"""}),
                        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                    ]		)
          else:
                return OrderedDict(
                    [
                        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                        ("""bbox""", {0: """batch""", 1: """sequence"""}),
                        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
                    ]		)




    @property
    def lowerCamelCase			(       self   :	str		):
          return 1E-5




    @property
    def lowerCamelCase			(       self   :	List[Any]		):
          return 12




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	"ProcessorMixin"					,	snake_case_   :	int = -1					,	snake_case_   :	int = -1					,	snake_case_   :	bool = False					,	snake_case_   :	Optional["TensorType"] = None					,	snake_case_   :	int = 3					,	snake_case_   :	int = 40					,	snake_case_   :	int = 40					,	):
          setattr(processor.image_processor					,	"""apply_ocr"""					,	snake_case_		)

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          snake_case__    :       Optional[Any]       		= compute_effective_axis_dimension(
              snake_case_					,	fixed_dimension=OnnxConfig.default_fixed_batch					,	num_token_to_add=0		)
          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          snake_case__    :       List[Any]       		= processor.tokenizer.num_special_tokens_to_add(snake_case_		)
          snake_case__    :       Tuple       		= compute_effective_axis_dimension(
              snake_case_					,	fixed_dimension=OnnxConfig.default_fixed_sequence					,	num_token_to_add=snake_case_		)
          # Generate dummy inputs according to compute batch and sequence
          snake_case__    :       int       		= [[""" """.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

          # Generate dummy bounding boxes
          snake_case__    :       int       		= [[[48, 84, 73, 128]]] * batch_size

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
          snake_case__    :       Optional[int]       		= self._generate_dummy_images(snake_case_					,	snake_case_					,	snake_case_					,	snake_case_		)

          snake_case__    :       Optional[int]       		= dict(
              processor(
                  snake_case_					,	text=snake_case_					,	boxes=snake_case_					,	return_tensors=snake_case_					,	)		)

          return inputs
