





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   A      :
								'''simple docstring'''




								@staticmethod
								def        a_	(      *__lowerCAmelCase       :   Dict							,     **__lowerCAmelCase       :   Optional[Any]       )     ->     Tuple:







										"""simple docstring"""






										pass


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




			__lowerCamelCase     :	Optional[Any]          =   MODEL_FOR_OBJECT_DETECTION_MAPPING






			def        a_	(      self       :   str							,     __lowerCAmelCase       :   List[str]							,     __lowerCAmelCase       :   Tuple							,     __lowerCAmelCase       :   int       )     ->     List[str]:







					"""simple docstring"""






					A__  				=       ObjectDetectionPipeline(model=__lowerCAmelCase							,     image_processor=__lowerCAmelCase       )
					return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]






			def        a_	(      self       :   List[str]							,     __lowerCAmelCase       :   Any							,     __lowerCAmelCase       :   str       )     ->     Optional[Any]:







					"""simple docstring"""






					A__  				=       object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							,     threshold=0.0       )

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

					import datasets

					A__  				=       datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""							,     """image"""							,     split="""test"""       )

					A__  				=       [
					    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__  				=       object_detector(__lowerCAmelCase							,     threshold=0.0       )

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






			@require_tf
			@unittest.skip("""Object detection not implemented in TF"""       )
			def        a_	(      self       :   Dict       )     ->     int:







					"""simple docstring"""






					pass






			@require_torch
			def        a_	(      self       :   Optional[Any]       )     ->     str:







					"""simple docstring"""






					A__  				=       """hf-internal-testing/tiny-detr-mobilenetsv3"""

					A__  				=       AutoModelForObjectDetection.from_pretrained(__lowerCAmelCase       )
					A__  				=       AutoFeatureExtractor.from_pretrained(__lowerCAmelCase       )
					A__  				=       ObjectDetectionPipeline(model=__lowerCAmelCase							,     feature_extractor=__lowerCAmelCase       )

					A__  				=       object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""							,     threshold=0.0       )

					self.assertEqual(
					    nested_simplify(__lowerCAmelCase							,     decimals=4       )							,     [
					        {"""score""": 0.3_3_7_6, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
					        {"""score""": 0.3_3_7_6, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
					    ]							,     )

					A__  				=       object_detector(
					    [
					        """http://images.cocodataset.org/val2017/000000039769.jpg""",
					        """http://images.cocodataset.org/val2017/000000039769.jpg""",
					    ]							,     threshold=0.0							,     )

					self.assertEqual(
					    nested_simplify(__lowerCAmelCase							,     decimals=4       )							,     [
					        [
					            {"""score""": 0.3_3_7_6, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
					            {"""score""": 0.3_3_7_6, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
					        ],
					        [
					            {"""score""": 0.3_3_7_6, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
					            {"""score""": 0.3_3_7_6, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
					        ],
					    ]							,     )






			@require_torch
			@slow
			def        a_	(      self       :   Any       )     ->     int:







					"""simple docstring"""






					A__  				=       """facebook/detr-resnet-50"""

					A__  				=       AutoModelForObjectDetection.from_pretrained(__lowerCAmelCase       )
					A__  				=       AutoFeatureExtractor.from_pretrained(__lowerCAmelCase       )
					A__  				=       ObjectDetectionPipeline(model=__lowerCAmelCase							,     feature_extractor=__lowerCAmelCase       )

					A__  				=       object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""       )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase							,     decimals=4       )							,     [
					        {"""score""": 0.9_9_8_2, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
					        {"""score""": 0.9_9_6_0, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
					        {"""score""": 0.9_9_5_5, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
					        {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
					        {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
					    ]							,     )

					A__  				=       object_detector(
					    [
					        """http://images.cocodataset.org/val2017/000000039769.jpg""",
					        """http://images.cocodataset.org/val2017/000000039769.jpg""",
					    ]       )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase							,     decimals=4       )							,     [
					        [
					            {"""score""": 0.9_9_8_2, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
					            {"""score""": 0.9_9_6_0, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
					            {"""score""": 0.9_9_5_5, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
					            {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
					            {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
					        ],
					        [
					            {"""score""": 0.9_9_8_2, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
					            {"""score""": 0.9_9_6_0, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
					            {"""score""": 0.9_9_5_5, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
					            {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
					            {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
					        ],
					    ]							,     )






			@require_torch
			@slow
			def        a_	(      self       :   Dict       )     ->     Any:







					"""simple docstring"""






					A__  				=       """facebook/detr-resnet-50"""

					A__  				=       pipeline("""object-detection"""							,     model=__lowerCAmelCase       )

					A__  				=       object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""       )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase							,     decimals=4       )							,     [
					        {"""score""": 0.9_9_8_2, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
					        {"""score""": 0.9_9_6_0, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
					        {"""score""": 0.9_9_5_5, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
					        {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
					        {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
					    ]							,     )

					A__  				=       object_detector(
					    [
					        """http://images.cocodataset.org/val2017/000000039769.jpg""",
					        """http://images.cocodataset.org/val2017/000000039769.jpg""",
					    ]       )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase							,     decimals=4       )							,     [
					        [
					            {"""score""": 0.9_9_8_2, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
					            {"""score""": 0.9_9_6_0, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
					            {"""score""": 0.9_9_5_5, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
					            {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
					            {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
					        ],
					        [
					            {"""score""": 0.9_9_8_2, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
					            {"""score""": 0.9_9_6_0, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
					            {"""score""": 0.9_9_5_5, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
					            {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
					            {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
					        ],
					    ]							,     )






			@require_torch
			@slow
			def        a_	(      self       :   List[str]       )     ->     Tuple:







					"""simple docstring"""






					A__  				=       0.9_9_8_5
					A__  				=       """facebook/detr-resnet-50"""

					A__  				=       pipeline("""object-detection"""							,     model=__lowerCAmelCase       )

					A__  				=       object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""							,     threshold=__lowerCAmelCase       )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase							,     decimals=4       )							,     [
					        {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
					        {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
					    ]							,     )







			@require_torch
			@require_pytesseract
			@slow
			def        a_	(      self       :   List[str]       )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       """Narsil/layoutlmv3-finetuned-funsd"""
					A__  				=       0.9_9_9_3

					A__  				=       pipeline("""object-detection"""							,     model=__lowerCAmelCase							,     threshold=__lowerCAmelCase       )

					A__  				=       object_detector(
					    """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""       )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase							,     decimals=4       )							,     [
					        {"""score""": 0.9_9_9_3, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_94, """ymin""": 2_54, """xmax""": 3_43, """ymax""": 2_64}},
					        {"""score""": 0.9_9_9_3, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_94, """ymin""": 2_54, """xmax""": 3_43, """ymax""": 2_64}},
					    ]							,     )










import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					A   : List[str]			=				argparse.ArgumentParser(
					    description=(
					        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
					        ''' Distillation'''
					    )
					)
					parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
					parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
					parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
					parser.add_argument('''--vocab_transform''', action='''store_true''')
					A   : Tuple			=				parser.parse_args()

					if args.model_type == "bert":
										A   : Dict			=				BertForMaskedLM.from_pretrained(args.model_name)
										A   : List[str]			=				'''bert'''
					else:
										raise ValueError('''args.model_type should be "bert".''')

					A   : Optional[Any]			=				model.state_dict()
					A   : int			=				{}

					for w in ["word_embeddings", "position_embeddings"]:
										A   : str			=				state_dict[F'''{prefix}.embeddings.{w}.weight''']
					for w in ["weight", "bias"]:
										A   : Any			=				state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

					A   : Tuple			=				0
					for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
										for w in ["weight", "bias"]:
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
															]

															A   : int			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
															]
															A   : List[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
															]

															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
															]
															A   : Union[str, Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
															]
															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
															]
										std_idx += 1

					A   : int			=				state_dict['''cls.predictions.decoder.weight''']
					A   : str			=				state_dict['''cls.predictions.bias''']
					if args.vocab_transform:
										for w in ["weight", "bias"]:
															A   : List[Any]			=				state_dict[F'''cls.predictions.transform.dense.{w}''']
															A   : List[str]			=				state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

					print(F'''N layers selected for distillation: {std_idx}''')
					print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

					print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
					torch.save(compressed_sd, args.dump_checkpoint)



