






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_		:						List[Any]             =    {
    '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__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=False							,       _UpperCAmelCase=True):
			if model_type not in MODEL_CLASSES:
						raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys())}.''')

			SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= MODEL_CLASSES[model_type]

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

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

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

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

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

						SCREAMING_SNAKE_CASE						= pto[0].numpy()
						SCREAMING_SNAKE_CASE						= tfo[0].numpy()
						SCREAMING_SNAKE_CASE						= 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(_UpperCAmelCase							,       save_format='h5')






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=None							,       _UpperCAmelCase=None							,       _UpperCAmelCase=False							,       _UpperCAmelCase=False							,       _UpperCAmelCase=False							,       _UpperCAmelCase=False							,       ):
			if args_model_type is None:
						SCREAMING_SNAKE_CASE						= list(MODEL_CLASSES.keys())
			else:
						SCREAMING_SNAKE_CASE						= [args_model_type]

			for j, model_type in enumerate(_UpperCAmelCase							,       start=1):
						print('=' * 100)
						print(F''' Converting model type {j}/{len(_UpperCAmelCase)}: {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())}.''')

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= MODEL_CLASSES[model_type]

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

						for i, (model_shortcut_name, config_shortcut_name) in enumerate(
						    zip(_UpperCAmelCase							,       _UpperCAmelCase)							,       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
												SCREAMING_SNAKE_CASE						= model_shortcut_name
									elif only_convert_finetuned_models:
												print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}''')
												continue
									print(
									    F'''    Converting checkpoint {i}/{len(_UpperCAmelCase)}: {model_shortcut_name} - model_type {model_type}''')
									print('-' * 100)

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

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

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

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


if __name__ == "__main__":
				a_		:						Any             =    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_		:						Tuple             =    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,
				)












from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from ..image_utils import load_image


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

a_		:						str             =    logging.get_logger(__name__)


a_		:						List[Any]             =    Dict[str, Any]
a_		:						Optional[int]             =    List[Prediction]

@add_end_docstrings(A__					)
class 		_snake_case  (  A__					):






			def __init__(       self		,					*a		,					**a)      ->       int:
						super().__init__(*a		,					**a)

						if self.framework == "tf":
									raise ValueError(f'''The {self.__class__} is only available in PyTorch.''')

						requires_backends(self		,					'vision')
						self.check_model_type(
						    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))






			def    SCREAMING_SNAKE_CASE__    (       self		,					**a)      ->       int:
						SCREAMING_SNAKE_CASE						= {}
						if "threshold" in kwargs:
									SCREAMING_SNAKE_CASE						= kwargs['threshold']
						return {}, {}, postprocess_kwargs






			def __call__(       self		,					*a		,					**a)      ->       Union[Predictions, List[Prediction]]:
						return super().__call__(*a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= load_image(a)
						SCREAMING_SNAKE_CASE						= torch.IntTensor([[image.height, image.width]])
						SCREAMING_SNAKE_CASE						= self.image_processor(images=[image]		,					return_tensors='pt')
						if self.tokenizer is not None:
									SCREAMING_SNAKE_CASE						= self.tokenizer(text=inputs['words']		,					boxes=inputs['boxes']		,					return_tensors='pt')
						SCREAMING_SNAKE_CASE						= target_size
						return inputs






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= model_inputs.pop('target_size')
						SCREAMING_SNAKE_CASE						= self.model(**a)
						SCREAMING_SNAKE_CASE						= outputs.__class__({'target_size': target_size, **outputs})
						if self.tokenizer is not None:
									SCREAMING_SNAKE_CASE						= model_inputs['bbox']
						return model_outputs






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=0.9)      ->       Dict:
						SCREAMING_SNAKE_CASE						= model_outputs['target_size']
						if self.tokenizer is not None:
									# This is a LayoutLMForTokenClassification variant.
									# The OCR got the boxes and the model classified the words.
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= target_size[0].tolist()

									def unnormalize(a):
												return self._get_bounding_box(
												    torch.Tensor(
												        [
												            (width * bbox[0] / 1000),
												            (height * bbox[1] / 1000),
												            (width * bbox[2] / 1000),
												            (height * bbox[3] / 1000),
												        ]))

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= model_outputs['logits'].squeeze(0).softmax(dim=-1).max(dim=-1)
									SCREAMING_SNAKE_CASE						= [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
									SCREAMING_SNAKE_CASE						= [unnormalize(a) for bbox in model_outputs['bbox'].squeeze(0)]
									SCREAMING_SNAKE_CASE						= ['score', 'label', 'box']
									SCREAMING_SNAKE_CASE						= [dict(zip(a		,					a)) for vals in zip(scores.tolist()		,					a		,					a) if vals[0] > threshold]
						else:
									# This is a regular ForObjectDetectionModel
									SCREAMING_SNAKE_CASE						= self.image_processor.post_process_object_detection(a		,					a		,					a)
									SCREAMING_SNAKE_CASE						= raw_annotations[0]
									SCREAMING_SNAKE_CASE						= raw_annotation['scores']
									SCREAMING_SNAKE_CASE						= raw_annotation['labels']
									SCREAMING_SNAKE_CASE						= raw_annotation['boxes']

									SCREAMING_SNAKE_CASE						= scores.tolist()
									SCREAMING_SNAKE_CASE						= [self.model.config.idalabel[label.item()] for label in labels]
									SCREAMING_SNAKE_CASE						= [self._get_bounding_box(a) for box in boxes]

									# {"scores": [...], ...} --> [{"score":x, ...}, ...]
									SCREAMING_SNAKE_CASE						= ['score', 'label', 'box']
									SCREAMING_SNAKE_CASE						= [
									    dict(zip(a		,					a))
									    for vals in zip(raw_annotation['scores']		,					raw_annotation['labels']		,					raw_annotation['boxes'])
									]

						return annotation







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict[str, int]:
						if self.framework != "pt":
									raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= box.int().tolist()
						SCREAMING_SNAKE_CASE						= {
						    'xmin': xmin,
						    'ymin': ymin,
						    'xmax': xmax,
						    'ymax': ymax,
						}
						return bbox




