


import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
snake_case				:    List[str]       				=     logging.getLogger(__name__)


snake_case				:    Dict       				=     '''Hello world! cécé herlolip'''


snake_case				:    int       				=     namedtuple(
    '''BertAbsConfig''',
    [
        '''temp_dir''',
        '''large''',
        '''use_bert_emb''',
        '''finetune_bert''',
        '''encoder''',
        '''share_emb''',
        '''max_pos''',
        '''enc_layers''',
        '''enc_hidden_size''',
        '''enc_heads''',
        '''enc_ff_size''',
        '''enc_dropout''',
        '''dec_layers''',
        '''dec_hidden_size''',
        '''dec_heads''',
        '''dec_ff_size''',
        '''dec_dropout''',
    ],
)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Dict ,  UpperCAmelCase_						:		Tuple						):




      """simple docstring"""





      a						:Tuple 							=       BertAbsConfig(
          temp_dir='''.''' ,  finetune_bert=__lowerCAmelCase ,  large=__lowerCAmelCase ,  share_emb=__lowerCAmelCase ,  use_bert_emb=__lowerCAmelCase ,  encoder='''bert''' ,  max_pos=512 ,  enc_layers=6 ,  enc_hidden_size=512 ,  enc_heads=8 ,  enc_ff_size=512 ,  enc_dropout=0.2 ,  dec_layers=6 ,  dec_hidden_size=768 ,  dec_heads=8 ,  dec_ff_size=2048 ,  dec_dropout=0.2 ,  )
      a						:str 							=       torch.load(__lowerCAmelCase ,  lambda UpperCAmelCase_ ,  UpperCAmelCase_						: storage						)
      a						:Any 							=       AbsSummarizer(__lowerCAmelCase ,  torch.device('''cpu'''						) ,  __lowerCAmelCase						)
      original.eval()

      a						:Optional[int] 							=       BertAbsSummarizer(__lowerCAmelCase ,  torch.device('''cpu'''						)						)
      new_model.eval()

      # -------------------
      # Convert the weights
      # -------------------

      logging.info('''convert the model'''						)
      new_model.bert.load_state_dict(original.bert.state_dict()						)
      new_model.decoder.load_state_dict(original.decoder.state_dict()						)
      new_model.generator.load_state_dict(original.generator.state_dict()						)

      # ----------------------------------
      # Make sure the outpus are identical
      # ----------------------------------

      logging.info('''Make sure that the models\' outputs are identical'''						)
      a						:Optional[Any] 							=       BertTokenizer.from_pretrained('''bert-base-uncased'''						)

      # prepare the model inputs
      a						:Dict 							=       tokenizer.encode('''This is sample éàalj\'-.'''						)
      encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCAmelCase						))						)
      a						:int 							=       torch.tensor(__lowerCAmelCase						).unsqueeze(0						)
      a						:Dict 							=       tokenizer.encode('''This is sample 3 éàalj\'-.'''						)
      decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCAmelCase						))						)
      a						:Union[str, Any] 							=       torch.tensor(__lowerCAmelCase						).unsqueeze(0						)

      # failsafe to make sure the weights reset does not affect the
      # loaded weights.
      assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight						)						) == 0

      # forward pass
      a						:Any 							=       encoder_input_ids
      a						:Optional[int] 							=       decoder_input_ids
      a						:Tuple 							=       None
      a						:Any 							=       None
      a						:Union[str, Any] 							=       None
      a						:Optional[Any] 							=       None
      a						:Union[str, Any] 							=       None

      # The original model does not apply the geneator layer immediatly but rather in
      # the beam search (where it combines softmax + linear layer). Since we already
      # apply the softmax in our generation process we only apply the linear layer here.
      # We make sure that the outputs of the full stack are identical
      a						:Optional[int] 							=       original(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase						)[0]
      a						:Optional[int] 							=       original.generator(__lowerCAmelCase						)

      a						:str 							=       new_model(
          __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase						)[0]
      a						:Tuple 							=       new_model.generator(__lowerCAmelCase						)

      a						:Any 							=       torch.max(torch.abs(output_converted_model - output_original_model						)						).item()
      print('''Maximum absolute difference beween weights: {:.2f}'''.format(__lowerCAmelCase						)						)
      a						:Optional[Any] 							=       torch.max(torch.abs(output_converted_generator - output_original_generator						)						).item()
      print('''Maximum absolute difference beween weights: {:.2f}'''.format(__lowerCAmelCase						)						)

      a						:List[str] 							=       torch.allclose(__lowerCAmelCase ,  __lowerCAmelCase ,  atol=1E-3						)
      if are_identical:
            logging.info('''all weights are equal up to 1e-3'''						)
      else:
            raise ValueError('''the weights are different. The new model is likely different from the original one.'''						)

      # The model has been saved with torch.save(model) and this is bound to the exact
      # directory structure. We save the state_dict instead.
      logging.info('''saving the model\'s state dictionary'''						)
      torch.save(
          new_model.state_dict() ,  '''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin'''						)


if __name__ == "__main__":
     snake_case				:    Optional[Any]       				=     argparse.ArgumentParser()
     parser.add_argument(
         '''--bertabs_checkpoint_path''',
         default=None,
         type=str,
         required=True,
         help='''Path the official PyTorch dump.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default=None,
         type=str,
         required=True,
         help='''Path to the output PyTorch model.''',
     )
     snake_case				:    List[str]       				=     parser.parse_args()

     convert_bertabs_checkpoints(
         args.bertabs_checkpoint_path,
         args.pytorch_dump_folder_path,
     )







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 	_lowerCAmelCase					:




							@staticmethod
							def      __a						(					*_UpperCamelCase				,				**_UpperCamelCase     )				->      str:
										pass





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 	_lowerCAmelCase					(	unittest.TestCase       ):
				_lowercase					=MODEL_FOR_OBJECT_DETECTION_MAPPING




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				image_processor=_UpperCamelCase     )
							return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Any:
							lowerCAmelCase_						=	object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"				,				threshold=0.0     )

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

							import datasets

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

							lowerCAmelCase_						=	[
							    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"],
							]
							lowerCAmelCase_						=	object_detector(_UpperCamelCase				,				threshold=0.0     )

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




				@require_tf
				@unittest.skip("Object detection not implemented in TF"     )
				def      __a						(					self     )				->      Optional[int]:
							pass




				@require_torch
				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	"hf-internal-testing/tiny-detr-mobilenetsv3"

							lowerCAmelCase_						=	AutoModelForObjectDetection.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	AutoFeatureExtractor.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				feature_extractor=_UpperCamelCase     )

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

							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							    ]				,				)

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

							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        [
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        ],
							        [
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        ],
							    ]				,				)




				@require_torch
				@slow
				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	AutoModelForObjectDetection.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	AutoFeatureExtractor.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				feature_extractor=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]				,				)

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							    ]				,				)




				@require_torch
				@slow
				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]				,				)

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							    ]				,				)




				@require_torch
				@slow
				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	0.9985
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"				,				threshold=_UpperCamelCase     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]				,				)




				@require_torch
				@require_pytesseract
				@slow
				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	"Narsil/layoutlmv3-finetuned-funsd"
							lowerCAmelCase_						=	0.9993

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase				,				threshold=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector(
							    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
							        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
							    ]				,				)



