





"""simple docstring"""





import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
  from PIL import Image
else:






  class 					_lowerCAmelCase   :
         """simple docstring"""




         @staticmethod
         def      snake_case						(    *__UpperCAmelCase    , **__UpperCAmelCase      ):



           '''simple docstring'''

           pass
def  __A   (_SCREAMING_SNAKE_CASE			)					->str:





  """simple docstring"""


  lowerCAmelCase__			:str											=							hashlib.mda(image.tobytes()			)
  return m.hexdigest()[:10]
def  __A   (_SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:Optional[Any]											=							np.array(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Tuple											=							npimg.shape
  return {"hash": hashimage(_SCREAMING_SNAKE_CASE			), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       __magic_name__     :Dict		  =					dict(
           (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])       )
       __magic_name__     :Optional[Any]		  =					dict(
           (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])       )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							MaskGenerationPipeline(model=__UpperCAmelCase    , image_processor=__UpperCAmelCase      )
         return image_segmenter, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         pass






       @require_tf
       @unittest.skip('Image segmentation not implemented in TF'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






       @slow
       @require_torch
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							pipeline('mask-generation'    , model='facebook/sam-vit-huge'      )

         lowerCAmelCase__			:List[Any]											=							image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'    , points_per_batch=2_5_6      )

         # Shortening by hashing
         lowerCAmelCase__			:Any											=							[]
         for i, o in enumerate(outputs['masks']      ):
           new_outupt += [{"mask": mask_to_test_readable(__UpperCAmelCase      ), "scores": outputs["scores"][i]}]

         # fmt: off
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.04_44},
                 {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_21},
                 {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                 {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                 {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
                 {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_67},
                 {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_93},
                 {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_09},
                 {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_79},
                 {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_34},
                 {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.97_16},
                 {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.96_12},
                 {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_99},
                 {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_52},
                 {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_32},
                 {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_16},
                 {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_99},
                 {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_83},
                 {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_64},
                 {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                 {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                 {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_08},
                 {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_35},
                 {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_26},
                 {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.92_62},
                 {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_99},
                 {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_86},
                 {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_84},
                 {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_73},
                 {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_71}
             ]    , )
         # fmt: on






       @require_torch
       @slow
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							'facebook/sam-vit-huge'
         lowerCAmelCase__			:str											=							pipeline('mask-generation'    , model=__UpperCAmelCase      )

         lowerCAmelCase__			:List[str]											=							image_segmenter(
             'http://images.cocodataset.org/val2017/000000039769.jpg'    , pred_iou_thresh=1    , points_per_batch=2_5_6      )

         # Shortening by hashing
         lowerCAmelCase__			:Optional[Any]											=							[]
         for i, o in enumerate(outputs['masks']      ):
           new_outupt += [{"mask": mask_to_test_readable(__UpperCAmelCase      ), "scores": outputs["scores"][i]}]

         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.04_44},
                 {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.02_10},
                 {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                 {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                 {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
             ]    , )







"""simple docstring"""





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:str											=							BertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
  print(F"Building PyTorch model from configuration: {config}"			)
  lowerCAmelCase__			:int											=							BertForPreTraining(_SCREAMING_SNAKE_CASE			)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  # Save pytorch-model
  print(F"Save PyTorch model to {pytorch_dump_path}"			)
  torch.save(model.state_dict()     ,   _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  __A											=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--bert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __A											=		parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
