






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 				lowerCamelCase__				(_UpperCAmelCase):
			return ConvertCommand(
			    args.model_type							,       args.tf_checkpoint							,       args.pytorch_dump_output							,       args.config							,       args.finetuning_task_name)


a_		:						Dict             =    '\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'







class 		__A  (  A__					):






			@staticmethod
			def    SCREAMING_SNAKE_CASE__    (       a)      ->       Any:
						SCREAMING_SNAKE_CASE						= parser.add_parser(
						    'convert'		,					help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'		,					)
						train_parser.add_argument('--model_type'		,					type=a		,					required=a		,					help='Model\'s type.')
						train_parser.add_argument(
						    '--tf_checkpoint'		,					type=a		,					required=a		,					help='TensorFlow checkpoint path or folder.')
						train_parser.add_argument(
						    '--pytorch_dump_output'		,					type=a		,					required=a		,					help='Path to the PyTorch saved model output.')
						train_parser.add_argument('--config'		,					type=a		,					default=''		,					help='Configuration file path or folder.')
						train_parser.add_argument(
						    '--finetuning_task_name'		,					type=a		,					default=a		,					help='Optional fine-tuning task name if the TF model was a finetuned model.'		,					)
						train_parser.set_defaults(func=a)






			def __init__(       self		,					a		,					a		,					a		,					a		,					a		,					*a		,					)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= logging.get_logger('transformers-cli/converting')

						self._logger.info(f'''Loading model {model_type}''')
						SCREAMING_SNAKE_CASE						= model_type
						SCREAMING_SNAKE_CASE						= tf_checkpoint
						SCREAMING_SNAKE_CASE						= pytorch_dump_output
						SCREAMING_SNAKE_CASE						= config
						SCREAMING_SNAKE_CASE						= finetuning_task_name







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						if self._model_type == "albert":
									try:
												from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
									except ImportError:
												raise ImportError(a)

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						elif self._model_type == "bert":
									try:
												from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
									except ImportError:
												raise ImportError(a)

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						elif self._model_type == "funnel":
									try:
												from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
									except ImportError:
												raise ImportError(a)

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						elif self._model_type == "t5":
									try:
												from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
									except ImportError:
												raise ImportError(a)

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						elif self._model_type == "gpt":
									from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
									    convert_openai_checkpoint_to_pytorch,
									)

									convert_openai_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						elif self._model_type == "transfo_xl":
									try:
												from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
												    convert_transfo_xl_checkpoint_to_pytorch,
												)
									except ImportError:
												raise ImportError(a)

									if "ckpt" in self._tf_checkpoint.lower():
												SCREAMING_SNAKE_CASE						= self._tf_checkpoint
												SCREAMING_SNAKE_CASE						= ''
									else:
												SCREAMING_SNAKE_CASE						= self._tf_checkpoint
												SCREAMING_SNAKE_CASE						= ''
									convert_transfo_xl_checkpoint_to_pytorch(
									    a		,					self._config		,					self._pytorch_dump_output		,					a)
						elif self._model_type == "gpt2":
									try:
												from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
												    convert_gpta_checkpoint_to_pytorch,
												)
									except ImportError:
												raise ImportError(a)

									convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						elif self._model_type == "xlnet":
									try:
												from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
												    convert_xlnet_checkpoint_to_pytorch,
												)
									except ImportError:
												raise ImportError(a)

									convert_xlnet_checkpoint_to_pytorch(
									    self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output		,					self._finetuning_task_name)
						elif self._model_type == "xlm":
									from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
									    convert_xlm_checkpoint_to_pytorch,
									)

									convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint		,					self._pytorch_dump_output)
						elif self._model_type == "lxmert":
									from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
									    convert_lxmert_checkpoint_to_pytorch,
									)

									convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint		,					self._pytorch_dump_output)
						elif self._model_type == "rembert":
									from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
									    convert_rembert_tf_checkpoint_to_pytorch,
									)

									convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						else:
									raise ValueError(
									    '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]')












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




