






'''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        a_		:

       @staticmethod
       def 						A__		(   *_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Tuple:



             """simple docstring"""






             pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class        a_		(    unittest.TestCase       ):
  lowercase  	=   MODEL_FOR_OBJECT_DETECTION_MAPPING

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






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

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

        import datasets

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

        UpperCamelCase       						=      [
            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"""],
        ]
        UpperCamelCase       						=      object_detector(_SCREAMING_SNAKE_CASE    ,	threshold=0.0   )

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

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



        """simple docstring"""






        pass

  @require_torch
  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






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

        UpperCamelCase       						=      AutoModelForObjectDetection.from_pretrained(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      ObjectDetectionPipeline(model=_SCREAMING_SNAKE_CASE    ,	feature_extractor=_SCREAMING_SNAKE_CASE   )

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

        self.assertEqual(
            nested_simplify(_SCREAMING_SNAKE_CASE    ,	decimals=4   )    ,	[
                {"""score""": 0.3_3_7_6, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                {"""score""": 0.3_3_7_6, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
            ]    ,	)

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

        self.assertEqual(
            nested_simplify(_SCREAMING_SNAKE_CASE    ,	decimals=4   )    ,	[
                [
                    {"""score""": 0.3_3_7_6, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                    {"""score""": 0.3_3_7_6, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                ],
                [
                    {"""score""": 0.3_3_7_6, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                    {"""score""": 0.3_3_7_6, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                ],
            ]    ,	)

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



        """simple docstring"""






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

        UpperCamelCase       						=      AutoModelForObjectDetection.from_pretrained(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      ObjectDetectionPipeline(model=_SCREAMING_SNAKE_CASE    ,	feature_extractor=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
        self.assertEqual(
            nested_simplify(_SCREAMING_SNAKE_CASE    ,	decimals=4   )    ,	[
                {"""score""": 0.9_9_8_2, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                {"""score""": 0.9_9_6_0, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                {"""score""": 0.9_9_5_5, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
            ]    ,	)

        UpperCamelCase       						=      object_detector(
            [
                """http://images.cocodataset.org/val2017/000000039769.jpg""",
                """http://images.cocodataset.org/val2017/000000039769.jpg""",
            ]   )
        self.assertEqual(
            nested_simplify(_SCREAMING_SNAKE_CASE    ,	decimals=4   )    ,	[
                [
                    {"""score""": 0.9_9_8_2, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                    {"""score""": 0.9_9_6_0, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                    {"""score""": 0.9_9_5_5, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                    {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                    {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                ],
                [
                    {"""score""": 0.9_9_8_2, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                    {"""score""": 0.9_9_6_0, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                    {"""score""": 0.9_9_5_5, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                    {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                    {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                ],
            ]    ,	)

  @require_torch
  @slow
  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






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

        UpperCamelCase       						=      pipeline("""object-detection"""    ,	model=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
        self.assertEqual(
            nested_simplify(_SCREAMING_SNAKE_CASE    ,	decimals=4   )    ,	[
                {"""score""": 0.9_9_8_2, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                {"""score""": 0.9_9_6_0, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                {"""score""": 0.9_9_5_5, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
            ]    ,	)

        UpperCamelCase       						=      object_detector(
            [
                """http://images.cocodataset.org/val2017/000000039769.jpg""",
                """http://images.cocodataset.org/val2017/000000039769.jpg""",
            ]   )
        self.assertEqual(
            nested_simplify(_SCREAMING_SNAKE_CASE    ,	decimals=4   )    ,	[
                [
                    {"""score""": 0.9_9_8_2, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                    {"""score""": 0.9_9_6_0, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                    {"""score""": 0.9_9_5_5, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                    {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                    {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                ],
                [
                    {"""score""": 0.9_9_8_2, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                    {"""score""": 0.9_9_6_0, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                    {"""score""": 0.9_9_5_5, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                    {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                    {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                ],
            ]    ,	)

  @require_torch
  @slow
  def 						A__		(   self   )						->   int:



        """simple docstring"""






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

        UpperCamelCase       						=      pipeline("""object-detection"""    ,	model=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""    ,	threshold=_SCREAMING_SNAKE_CASE   )
        self.assertEqual(
            nested_simplify(_SCREAMING_SNAKE_CASE    ,	decimals=4   )    ,	[
                {"""score""": 0.9_9_8_8, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                {"""score""": 0.9_9_8_7, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
            ]    ,	)


  @require_torch
  @require_pytesseract
  @slow
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






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

        UpperCamelCase       						=      pipeline("""object-detection"""    ,	model=_SCREAMING_SNAKE_CASE    ,	threshold=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      object_detector(
            """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""   )
        self.assertEqual(
            nested_simplify(_SCREAMING_SNAKE_CASE    ,	decimals=4   )    ,	[
                {"""score""": 0.9_9_9_3, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
                {"""score""": 0.9_9_9_3, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
            ]    ,	)








'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 					lowercase__				(   __UpperCamelCase					)->				Any:
      UpperCamelCase       						=      [
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase	,		__UpperCamelCase					)







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase						,UpperCamelCase       						=      emb.weight.shape
      UpperCamelCase       						=      nn.Linear(__UpperCamelCase	,		__UpperCamelCase	,		bias=__UpperCamelCase					)
      UpperCamelCase       						=      emb.weight.data
      return lin_layer







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase       						=      torch.load(__UpperCamelCase	,		map_location="""cpu"""					)
      UpperCamelCase       						=      mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
      UpperCamelCase       						=      mam_aaa["""model"""]
      remove_ignore_keys_(__UpperCamelCase					)
      UpperCamelCase       						=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

      UpperCamelCase       						=      MaMaaaConfig(
          vocab_size=__UpperCamelCase	,		max_position_embeddings=1024	,		encoder_layers=args.encoder_layers	,		decoder_layers=args.decoder_layers	,		encoder_attention_heads=args.encoder_attention_heads	,		decoder_attention_heads=args.decoder_attention_heads	,		encoder_ffn_dim=args.encoder_ffn_embed_dim	,		decoder_ffn_dim=args.decoder_ffn_embed_dim	,		d_model=args.encoder_embed_dim	,		encoder_layerdrop=args.encoder_layerdrop	,		decoder_layerdrop=args.decoder_layerdrop	,		dropout=args.dropout	,		attention_dropout=args.attention_dropout	,		activation_dropout=args.activation_dropout	,		activation_function="""relu"""	,		)

      UpperCamelCase       						=      state_dict["""decoder.embed_tokens.weight"""]
      UpperCamelCase       						=      MaMaaaForConditionalGeneration(__UpperCamelCase					)
      model.model.load_state_dict(__UpperCamelCase	,		strict=__UpperCamelCase					)
      UpperCamelCase       						=      make_linear_from_emb(model.model.shared					)

      return model


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
     parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     SCREAMING_SNAKE_CASE__											=     convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)
