



'''simple docstring'''

import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:

							class      __magic_name__						:







														@staticmethod
														def 							__lowercase      (							*_UpperCAmelCase :	List[str]     ,**_UpperCAmelCase :	Any		):
																pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class      __magic_name__						(   unittest.TestCase       ):
							lowerCAmelCase	:   List[Any]           = MODEL_FOR_OBJECT_DETECTION_MAPPING







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Tuple		):
									_a					:				str				=							ObjectDetectionPipeline(model=_A     ,image_processor=_A		)
									return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







							def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	Dict		):
									_a					:				Optional[int]				=							object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'     ,threshold=0.0		)

									self.assertGreater(len(_A		)     ,0		)
									for detected_object in outputs:
											self.assertEqual(
											    _A     ,{
											        'score': ANY(_A		),
											        'label': ANY(_A		),
											        'box': {'xmin': ANY(_A		), 'ymin': ANY(_A		), 'xmax': ANY(_A		), 'ymax': ANY(_A		)},
											    }     ,)

									import datasets

									_a					:				Dict				=							datasets.load_dataset('hf-internal-testing/fixtures_image_utils'     ,'image'     ,split='test'		)

									_a					:				Optional[Any]				=							[
									    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		),
									    'http://images.cocodataset.org/val2017/000000039769.jpg',
									    # RGBA
									    dataset[0]['file'],
									    # LA
									    dataset[1]['file'],
									    # L
									    dataset[2]['file'],
									]
									_a					:				Union[str, Any]				=							object_detector(_A     ,threshold=0.0		)

									self.assertEqual(len(_A		)     ,len(_A		)		)
									for outputs in batch_outputs:
											self.assertGreater(len(_A		)     ,0		)
											for detected_object in outputs:
													self.assertEqual(
													    _A     ,{
													        'score': ANY(_A		),
													        'label': ANY(_A		),
													        'box': {'xmin': ANY(_A		), 'ymin': ANY(_A		), 'xmax': ANY(_A		), 'ymax': ANY(_A		)},
													    }     ,)







							@require_tf
							@unittest.skip('Object detection not implemented in TF'		)
							def 							__lowercase      (							self :	List[Any]		):
									pass







							@require_torch
							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Union[str, Any]				=							'hf-internal-testing/tiny-detr-mobilenetsv3'

									_a					:				Optional[Any]				=							AutoModelForObjectDetection.from_pretrained(_A		)
									_a					:				str				=							AutoFeatureExtractor.from_pretrained(_A		)
									_a					:				int				=							ObjectDetectionPipeline(model=_A     ,feature_extractor=_A		)

									_a					:				Optional[int]				=							object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'     ,threshold=0.0		)

									self.assertEqual(
									    nested_simplify(_A     ,decimals=4		)     ,[
									        {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
									        {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
									    ]     ,)

									_a					:				Optional[Any]				=							object_detector(
									    [
									        'http://images.cocodataset.org/val2017/000000039769.jpg',
									        'http://images.cocodataset.org/val2017/000000039769.jpg',
									    ]     ,threshold=0.0     ,)

									self.assertEqual(
									    nested_simplify(_A     ,decimals=4		)     ,[
									        [
									            {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
									            {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
									        ],
									        [
									            {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
									            {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
									        ],
									    ]     ,)







							@require_torch
							@slow
							def 							__lowercase      (							self :	List[str]		):
									_a					:				List[Any]				=							'facebook/detr-resnet-50'

									_a					:				Union[str, Any]				=							AutoModelForObjectDetection.from_pretrained(_A		)
									_a					:				Optional[int]				=							AutoFeatureExtractor.from_pretrained(_A		)
									_a					:				Optional[Any]				=							ObjectDetectionPipeline(model=_A     ,feature_extractor=_A		)

									_a					:				List[str]				=							object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		)
									self.assertEqual(
									    nested_simplify(_A     ,decimals=4		)     ,[
									        {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
									        {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
									        {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
									        {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
									        {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
									    ]     ,)

									_a					:				List[str]				=							object_detector(
									    [
									        'http://images.cocodataset.org/val2017/000000039769.jpg',
									        'http://images.cocodataset.org/val2017/000000039769.jpg',
									    ]		)
									self.assertEqual(
									    nested_simplify(_A     ,decimals=4		)     ,[
									        [
									            {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
									            {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
									            {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
									            {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
									            {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
									        ],
									        [
									            {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
									            {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
									            {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
									            {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
									            {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
									        ],
									    ]     ,)







							@require_torch
							@slow
							def 							__lowercase      (							self :	str		):
									_a					:				Tuple				=							'facebook/detr-resnet-50'

									_a					:				str				=							pipeline('object-detection'     ,model=_A		)

									_a					:				Optional[Any]				=							object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		)
									self.assertEqual(
									    nested_simplify(_A     ,decimals=4		)     ,[
									        {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
									        {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
									        {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
									        {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
									        {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
									    ]     ,)

									_a					:				Dict				=							object_detector(
									    [
									        'http://images.cocodataset.org/val2017/000000039769.jpg',
									        'http://images.cocodataset.org/val2017/000000039769.jpg',
									    ]		)
									self.assertEqual(
									    nested_simplify(_A     ,decimals=4		)     ,[
									        [
									            {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
									            {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
									            {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
									            {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
									            {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
									        ],
									        [
									            {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
									            {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
									            {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
									            {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
									            {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
									        ],
									    ]     ,)







							@require_torch
							@slow
							def 							__lowercase      (							self :	str		):
									_a					:				Dict				=							0.99_85
									_a					:				int				=							'facebook/detr-resnet-50'

									_a					:				List[str]				=							pipeline('object-detection'     ,model=_A		)

									_a					:				Union[str, Any]				=							object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'     ,threshold=_A		)
									self.assertEqual(
									    nested_simplify(_A     ,decimals=4		)     ,[
									        {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
									        {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
									    ]     ,)







							@require_torch
							@require_pytesseract
							@slow
							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Tuple				=							'Narsil/layoutlmv3-finetuned-funsd'
									_a					:				str				=							0.99_93

									_a					:				Any				=							pipeline('object-detection'     ,model=_A     ,threshold=_A		)

									_a					:				List[str]				=							object_detector(
									    'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'		)
									self.assertEqual(
									    nested_simplify(_A     ,decimals=4		)     ,[
									        {'score': 0.99_93, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
									        {'score': 0.99_93, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
									    ]     ,)









'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class      __magic_name__						:
							lowerCAmelCase	:   str           = field(
							    metadata={'help': 'The output directory where the model will be written.'}      ,						)
							lowerCAmelCase	:   str           = field(
							    metadata={
							        'help': (
							            'The encoder model checkpoint for weights initialization.'
							            'Don\'t set if you want to train an encoder model from scratch.'
							        )
							    }      ,						)
							lowerCAmelCase	:   str           = field(
							    metadata={
							        'help': (
							            'The decoder model checkpoint for weights initialization.'
							            'Don\'t set if you want to train a decoder model from scratch.'
							        )
							    }      ,						)
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}       )
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}       )



def 					__lowerCamelCase					(				)      ->				Union[str, Any]:
		_a					:				Any				=							HfArgumentParser((ModelArguments,)  )
		((_a)	, )					:				Dict				=							parser.parse_args_into_dataclasses()

		# Load pretrained model and tokenizer

		# Use explicit specified encoder config
		if model_args.encoder_config_name:
				_a					:				Optional[Any]				=							AutoConfig.from_pretrained(model_args.encoder_config_name  )
		# Use pretrained encoder model's config
		else:
				_a					:				Optional[Any]				=							AutoConfig.from_pretrained(model_args.encoder_model_name_or_path  )

		# Use explicit specified decoder config
		if model_args.decoder_config_name:
				_a					:				List[str]				=							AutoConfig.from_pretrained(model_args.decoder_config_name  )
		# Use pretrained decoder model's config
		else:
				_a					:				Optional[int]				=							AutoConfig.from_pretrained(model_args.decoder_model_name_or_path  )

		# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
		_a					:				List[Any]				=							True
		_a					:				int				=							True

		_a					:				Any				=							FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
		    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,      decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,      encoder_config=lowerCAmelCase_						,      decoder_config=lowerCAmelCase_						,      )

		# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
		_a					:				List[str]				=							decoder_config.decoder_start_token_id
		_a					:				Optional[int]				=							decoder_config.pad_token_id
		if decoder_start_token_id is None:
				_a					:				Tuple				=							decoder_config.bos_token_id
		if pad_token_id is None:
				_a					:				List[Any]				=							decoder_config.eos_token_id

		# This is necessary to make Flax's generate() work
		_a					:				Any				=							decoder_config.eos_token_id
		_a					:				Tuple				=							decoder_start_token_id
		_a					:				Any				=							pad_token_id

		_a					:				Dict				=							AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path  )

		_a					:				Dict				=							AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path  )
		_a					:				int				=							tokenizer.convert_ids_to_tokens(model.config.pad_token_id  )

		model.save_pretrained(model_args.output_dir  )
		image_processor.save_pretrained(model_args.output_dir  )
		tokenizer.save_pretrained(model_args.output_dir  )


if __name__ == "__main__":
							main()




