

'''simple docstring'''




import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
  import numpy as np
  import torch

  from . import (
      AlbertForPreTraining,
      BartForConditionalGeneration,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      CamembertForMaskedLM,
      CTRLLMHeadModel,
      DistilBertForMaskedLM,
      DistilBertForQuestionAnswering,
      DPRContextEncoder,
      DPRQuestionEncoder,
      DPRReader,
      ElectraForPreTraining,
      FlaubertWithLMHeadModel,
      GPTaLMHeadModel,
      LayoutLMForMaskedLM,
      LxmertForPreTraining,
      LxmertVisualFeatureEncoder,
      OpenAIGPTLMHeadModel,
      RobertaForMaskedLM,
      RobertaForSequenceClassification,
      TaForConditionalGeneration,
      TransfoXLLMHeadModel,
      XLMRobertaForMaskedLM,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
  )


logging.set_verbosity_info()

_a					:			Optional[int]							=    {
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : Union[str, Any]							, SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : Optional[int]=False							, SCREAMING_SNAKE_CASE   : Optional[Any]=True    ):
  if model_type not in MODEL_CLASSES:
    raise ValueError(f'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()    )}.'''    )

  UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase    				=						MODEL_CLASSES[model_type]

  # Initialise TF model
  if config_file in aws_config_map:
    UpperCAmelCase    				=						cached_file(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, force_download=not use_cached_models    )
  UpperCAmelCase    				=						config_class.from_json_file(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						True
  UpperCAmelCase    				=						True
  print(f'''Building TensorFlow model from configuration: {config}'''    )
  UpperCAmelCase    				=						model_class(SCREAMING_SNAKE_CASE    )

  # Load weights from tf checkpoint
  if pytorch_checkpoint_path in aws_config_map.keys():
    UpperCAmelCase    				=						cached_file(
        SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, force_download=not use_cached_models    )
  # Load PyTorch checkpoint in tf2 model:
  UpperCAmelCase    				=						load_pytorch_checkpoint_in_tfa_model(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

  if compare_with_pt_model:
    UpperCAmelCase    				=						tf_model(tf_model.dummy_inputs							, training=SCREAMING_SNAKE_CASE    )  # build the network

    UpperCAmelCase    				=						torch.load(SCREAMING_SNAKE_CASE							, map_location='cpu'    )
    UpperCAmelCase    				=						pt_model_class.from_pretrained(
        pretrained_model_name_or_path=SCREAMING_SNAKE_CASE							, config=SCREAMING_SNAKE_CASE							, state_dict=SCREAMING_SNAKE_CASE    )

    with torch.no_grad():
      UpperCAmelCase    				=						pt_model(**pt_model.dummy_inputs    )

    UpperCAmelCase    				=						pto[0].numpy()
    UpperCAmelCase    				=						tfo[0].numpy()
    UpperCAmelCase    				=						np.amax(np.abs(np_pt - np_tf    )    )
    print(f'''Max absolute difference between models outputs {diff}'''    )
    assert diff <= 2E-2, f'''Error, model absolute difference is >2e-2: {diff}'''

  # Save pytorch-model
  print(f'''Save TensorFlow model to {tf_dump_path}'''    )
  tf_model.save_weights(SCREAMING_SNAKE_CASE							, save_format='h5'    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Any							, SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : List[Any]=None							, SCREAMING_SNAKE_CASE   : int=None							, SCREAMING_SNAKE_CASE   : List[Any]=False							, SCREAMING_SNAKE_CASE   : Optional[Any]=False							, SCREAMING_SNAKE_CASE   : str=False							, SCREAMING_SNAKE_CASE   : Optional[int]=False							, ):
  if args_model_type is None:
    UpperCAmelCase    				=						list(MODEL_CLASSES.keys()    )
  else:
    UpperCAmelCase    				=						[args_model_type]

  for j, model_type in enumerate(SCREAMING_SNAKE_CASE							, start=1    ):
    print('=' * 100    )
    print(f''' Converting model type {j}/{len(SCREAMING_SNAKE_CASE    )}: {model_type}'''    )
    print('=' * 100    )
    if model_type not in MODEL_CLASSES:
      raise ValueError(f'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()    )}.'''    )

    UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase    				=						MODEL_CLASSES[model_type]

    if model_shortcut_names_or_path is None:
      UpperCAmelCase    				=						list(aws_model_maps.keys()    )
    if config_shortcut_names_or_path is None:
      UpperCAmelCase    				=						model_shortcut_names_or_path

    for i, (model_shortcut_name, config_shortcut_name) in enumerate(
        zip(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )							, start=1    ):
      print('-' * 100    )
      if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
        if not only_convert_finetuned_models:
          print(f'''    Skipping finetuned checkpoint {model_shortcut_name}'''    )
          continue
        UpperCAmelCase    				=						model_shortcut_name
      elif only_convert_finetuned_models:
        print(f'''    Skipping not finetuned checkpoint {model_shortcut_name}'''    )
        continue
      print(
          f'''    Converting checkpoint {i}/{len(SCREAMING_SNAKE_CASE    )}: {model_shortcut_name} - model_type {model_type}'''    )
      print('-' * 100    )

      if config_shortcut_name in aws_config_map:
        UpperCAmelCase    				=						cached_file(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, force_download=not use_cached_models    )
      else:
        UpperCAmelCase    				=						config_shortcut_name

      if model_shortcut_name in aws_model_maps:
        UpperCAmelCase    				=						cached_file(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, force_download=not use_cached_models    )
      else:
        UpperCAmelCase    				=						model_shortcut_name

      if os.path.isfile(SCREAMING_SNAKE_CASE    ):
        UpperCAmelCase    				=						'converted_model'

      convert_pt_checkpoint_to_tf(
          model_type=SCREAMING_SNAKE_CASE							, pytorch_checkpoint_path=SCREAMING_SNAKE_CASE							, config_file=SCREAMING_SNAKE_CASE							, tf_dump_path=os.path.join(SCREAMING_SNAKE_CASE							, model_shortcut_name + '-tf_model.h5'    )							, compare_with_pt_model=SCREAMING_SNAKE_CASE							, )
      if remove_cached_files:
        os.remove(SCREAMING_SNAKE_CASE    )
        os.remove(SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
  _a					:			int							=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
  )
  parser.add_argument(
      '--model_type',
      default=None,
      type=str,
      help=(
          F'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
          'convert all the models from AWS.'
      ),
  )
  parser.add_argument(
      '--pytorch_checkpoint_path',
      default=None,
      type=str,
      help=(
          'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
          'If not given, will download and convert all the checkpoints from AWS.'
      ),
  )
  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      help=(
          'The config json file corresponding to the pre-trained model. \n'
          'This specifies the model architecture. If not given and '
          '--pytorch_checkpoint_path is not given or is a shortcut name '
          'use the configuration associated to the shortcut name on the AWS'
      ),
  )
  parser.add_argument(
      '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
  )
  parser.add_argument(
      '--use_cached_models',
      action='store_true',
      help='Use cached models if possible instead of updating to latest checkpoint versions.',
  )
  parser.add_argument(
      '--remove_cached_files',
      action='store_true',
      help='Remove pytorch models after conversion (save memory when converting in batches).',
  )
  parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
  _a					:			int							=    parser.parse_args()

  # if args.pytorch_checkpoint_path is not None:
  #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
  #                                 args.pytorch_checkpoint_path,
  #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
  #                                 args.tf_dump_path,
  #                                 compare_with_pt_model=args.compare_with_pt_model,
  #                                 use_cached_models=args.use_cached_models)
  # else:
  convert_all_pt_checkpoints_to_tf(
      args.model_type.lower() if args.model_type is not None else None,
      args.tf_dump_path,
      model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
      if args.pytorch_checkpoint_path is not None
      else None,
      config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
      compare_with_pt_model=args.compare_with_pt_model,
      use_cached_models=args.use_cached_models,
      remove_cached_files=args.remove_cached_files,
      only_convert_finetuned_models=args.only_convert_finetuned_models,
  )





'''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 							lowercase_						:

    '''simple docstring'''





    @staticmethod
    def     snake_case_      (  *a_ ,			**a_  )				->					Tuple:


      """simple docstring"""
      pass


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Image    ):
  UpperCAmelCase    				=						hashlib.mda(image.tobytes()    )
  return m.hexdigest()[:10]


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Image    ):
  UpperCAmelCase    				=						np.array(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						npimg.shape
  return {"hash": hashimage(SCREAMING_SNAKE_CASE    ), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			Tuple     	= dict(
      (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
  __lowerCAmelCase       :			Tuple     	= dict(
      (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						MaskGenerationPipeline(model=a_ ,			image_processor=a_  )
    return image_segmenter, [
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
    ]
  def     snake_case_      (  self ,			a_ ,			a_  )				->					Optional[Any]:


    """simple docstring"""
    pass
  @require_tf
  @unittest.skip('Image segmentation not implemented in TF'  )
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    pass
  @slow
  @require_torch
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						pipeline('mask-generation' ,			model='facebook/sam-vit-huge'  )

    UpperCAmelCase    				=						image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' ,			points_per_batch=2_5_6  )

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

    # fmt: off
    self.assertEqual(
        nested_simplify(a_ ,			decimals=4  ) ,			[
            {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0444},
            {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.021},
            {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0167},
            {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0132},
            {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0053},
            {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9967},
            {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.993},
            {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9909},
            {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9879},
            {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9834},
            {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9716},
            {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9612},
            {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9599},
            {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9552},
            {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9532},
            {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9516},
            {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9499},
            {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9483},
            {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9464},
            {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
            {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
            {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9408},
            {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9335},
            {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9326},
            {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9262},
            {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8999},
            {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8986},
            {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8984},
            {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8873},
            {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8871}
        ] ,			)
    # fmt: on





  @require_torch
  @slow
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						'facebook/sam-vit-huge'
    UpperCAmelCase    				=						pipeline('mask-generation' ,			model=a_  )

    UpperCAmelCase    				=						image_segmenter(
        'http://images.cocodataset.org/val2017/000000039769.jpg' ,			pred_iou_thresh=1 ,			points_per_batch=2_5_6  )

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

    self.assertEqual(
        nested_simplify(a_ ,			decimals=4  ) ,			[
            {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0444},
            {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0210},
            {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0167},
            {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0132},
            {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0053},
        ] ,			)


