
from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
		import torch

if is_tf_available():
		import tensorflow as tf




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  ['image_processor']
	_A			=  'SamImageProcessor'







	def __init__(      self	:Union[str, Any]  ,     a	:Tuple		)	->				Any:
								super().__init__(lowercase_		)
								__UpperCamelCase			:     Optional[int]         =      self.image_processor
								__UpperCamelCase			:     Optional[int]         =      -1_0
								__UpperCamelCase			:     Dict         =      self.image_processor.size["longest_edge"]







	def __call__(      self	:List[Any]  ,     a	:List[Any]=None  ,     a	:Dict=None  ,     a	:Optional[Any]=None  ,     a	:Any=None  ,     a	:Optional[Union[str, TensorType]] = None  ,     **a	:Optional[int]  ,     )	->				BatchEncoding:
								__UpperCamelCase			:     Union[str, Any]         =      self.image_processor(
								    lowercase_  ,     return_tensors=lowercase_  ,     **lowercase_  ,     )

								# pop arguments that are not used in the foward but used nevertheless
								__UpperCamelCase			:     Optional[int]         =      encoding_image_processor["original_sizes"]

								if hasattr(lowercase_  ,     "numpy"		):  # Checks if Torch or TF tensor
															__UpperCamelCase			:     Tuple         =      original_sizes.numpy()

								__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     int         =      self._check_and_preprocess_points(
								    input_points=lowercase_  ,     input_labels=lowercase_  ,     input_boxes=lowercase_  ,     )

								__UpperCamelCase			:     Dict         =      self._normalize_and_convert(
								    lowercase_  ,     lowercase_  ,     input_points=lowercase_  ,     input_labels=lowercase_  ,     input_boxes=lowercase_  ,     return_tensors=lowercase_  ,     )

								return encoding_image_processor







	def      _lowerCamelCase       (      self	:str  ,     a	:Optional[int]  ,     a	:Tuple  ,     a	:Union[str, Any]=None  ,     a	:Tuple=None  ,     a	:int=None  ,     a	:List[Any]="pt"  ,     )	->				List[str]:







								if input_points is not None:
															if len(lowercase_		) != len(lowercase_		):
																						__UpperCamelCase			:     int         =      [
																						    self._normalize_coordinates(self.target_size  ,     lowercase_  ,     original_sizes[0]		) for point in input_points
																						]
															else:
																						__UpperCamelCase			:     List[Any]         =      [
																						    self._normalize_coordinates(self.target_size  ,     lowercase_  ,     lowercase_		)
																						    for point, original_size in zip(lowercase_  ,     lowercase_		)
																						]
															# check that all arrays have the same shape
															if not all(point.shape == input_points[0].shape for point in input_points		):
																						if input_labels is not None:
																													__UpperCamelCase     ,		__UpperCamelCase			:     int         =      self._pad_points_and_labels(lowercase_  ,     lowercase_		)

															__UpperCamelCase			:     List[str]         =      np.array(lowercase_		)

								if input_labels is not None:
															__UpperCamelCase			:     Dict         =      np.array(lowercase_		)

								if input_boxes is not None:
															if len(lowercase_		) != len(lowercase_		):
																						__UpperCamelCase			:     Union[str, Any]         =      [
																						    self._normalize_coordinates(self.target_size  ,     lowercase_  ,     original_sizes[0]  ,     is_bounding_box=lowercase_		)
																						    for box in input_boxes
																						]
															else:
																						__UpperCamelCase			:     Any         =      [
																						    self._normalize_coordinates(self.target_size  ,     lowercase_  ,     lowercase_  ,     is_bounding_box=lowercase_		)
																						    for box, original_size in zip(lowercase_  ,     lowercase_		)
																						]
															__UpperCamelCase			:     List[Any]         =      np.array(lowercase_		)

								if input_boxes is not None:
															if return_tensors == "pt":
																						__UpperCamelCase			:     Union[str, Any]         =      torch.from_numpy(lowercase_		)
																						# boxes batch size of 1 by default
																						__UpperCamelCase			:     Optional[Any]         =      input_boxes.unsqueeze(1		) if len(input_boxes.shape		) != 3 else input_boxes
															elif return_tensors == "tf":
																						__UpperCamelCase			:     Union[str, Any]         =      tf.convert_to_tensor(lowercase_		)
																						# boxes batch size of 1 by default
																						__UpperCamelCase			:     Optional[Any]         =      tf.expand_dims(lowercase_  ,     1		) if len(input_boxes.shape		) != 3 else input_boxes
															encoding_image_processor.update({"input_boxes": input_boxes}		)
								if input_points is not None:
															if return_tensors == "pt":
																						__UpperCamelCase			:     str         =      torch.from_numpy(lowercase_		)
																						# point batch size of 1 by default
																						__UpperCamelCase			:     Union[str, Any]         =      input_points.unsqueeze(1		) if len(input_points.shape		) != 4 else input_points
															elif return_tensors == "tf":
																						__UpperCamelCase			:     Tuple         =      tf.convert_to_tensor(lowercase_		)
																						# point batch size of 1 by default
																						__UpperCamelCase			:     List[Any]         =      tf.expand_dims(lowercase_  ,     1		) if len(input_points.shape		) != 4 else input_points
															encoding_image_processor.update({"input_points": input_points}		)
								if input_labels is not None:
															if return_tensors == "pt":
																						__UpperCamelCase			:     int         =      torch.from_numpy(lowercase_		)
																						# point batch size of 1 by default
																						__UpperCamelCase			:     int         =      input_labels.unsqueeze(1		) if len(input_labels.shape		) != 3 else input_labels
															elif return_tensors == "tf":
																						__UpperCamelCase			:     Tuple         =      tf.convert_to_tensor(lowercase_		)
																						# point batch size of 1 by default
																						__UpperCamelCase			:     Dict         =      tf.expand_dims(lowercase_  ,     1		) if len(input_labels.shape		) != 3 else input_labels
															encoding_image_processor.update({"input_labels": input_labels}		)

								return encoding_image_processor







	def      _lowerCamelCase       (      self	:Tuple  ,     a	:int  ,     a	:str		)	->				Union[str, Any]:
								__UpperCamelCase			:     Dict         =      max([point.shape[0] for point in input_points]		)
								__UpperCamelCase			:     Optional[Any]         =      []
								for i, point in enumerate(lowercase_		):
															if point.shape[0] != expected_nb_points:
																						__UpperCamelCase			:     Dict         =      np.concatenate(
																						    [point, np.zeros((expected_nb_points - point.shape[0], 2)		) + self.point_pad_value]  ,     axis=0		)
																						__UpperCamelCase			:     Dict         =      np.append(input_labels[i]  ,     [self.point_pad_value]		)
															processed_input_points.append(lowercase_		)
								__UpperCamelCase			:     List[str]         =      processed_input_points
								return input_points, input_labels







	def      _lowerCamelCase       (      self	:Tuple  ,     a	:int  ,     a	:np.ndarray  ,     a	:str  ,     a	:str=False		)	->				np.ndarray:
								__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      original_size
								__UpperCamelCase     ,		__UpperCamelCase			:     List[str]         =      self.image_processor._get_preprocess_shape(lowercase_  ,     longest_edge=lowercase_		)
								__UpperCamelCase			:     str         =      deepcopy(lowercase_		).astype(lowercase_		)

								if is_bounding_box:
															__UpperCamelCase			:     str         =      coords.reshape(-1  ,     2  ,     2		)

								__UpperCamelCase			:     Optional[Any]         =      coords[..., 0] * (new_w / old_w)
								__UpperCamelCase			:     str         =      coords[..., 1] * (new_h / old_h)

								if is_bounding_box:
															__UpperCamelCase			:     Union[str, Any]         =      coords.reshape(-1  ,     4		)

								return coords







	def      _lowerCamelCase       (      self	:Optional[int]  ,     a	:List[str]=None  ,     a	:Optional[int]=None  ,     a	:Union[str, Any]=None  ,     )	->				Optional[Any]:







								if input_points is not None:
															if hasattr(lowercase_  ,     "numpy"		):  # Checks for TF or Torch tensor
																						__UpperCamelCase			:     str         =      input_points.numpy().tolist()

															if not isinstance(lowercase_  ,     lowercase_		) or not isinstance(input_points[0]  ,     lowercase_		):
																						raise ValueError("Input points must be a list of list of floating points."		)
															__UpperCamelCase			:     Any         =      [np.array(lowercase_		) for input_point in input_points]
								else:
															__UpperCamelCase			:     Tuple         =      None

								if input_labels is not None:
															if hasattr(lowercase_  ,     "numpy"		):
																						__UpperCamelCase			:     Union[str, Any]         =      input_labels.numpy().tolist()

															if not isinstance(lowercase_  ,     lowercase_		) or not isinstance(input_labels[0]  ,     lowercase_		):
																						raise ValueError("Input labels must be a list of list integers."		)
															__UpperCamelCase			:     str         =      [np.array(lowercase_		) for label in input_labels]
								else:
															__UpperCamelCase			:     Union[str, Any]         =      None

								if input_boxes is not None:
															if hasattr(lowercase_  ,     "numpy"		):
																						__UpperCamelCase			:     int         =      input_boxes.numpy().tolist()

															if (
															    not isinstance(lowercase_  ,     lowercase_		)
															    or not isinstance(input_boxes[0]  ,     lowercase_		)
															    or not isinstance(input_boxes[0][0]  ,     lowercase_		)
															):
																						raise ValueError("Input boxes must be a list of list of list of floating points."		)
															__UpperCamelCase			:     Dict         =      [np.array(lowercase_		).astype(np.floataa		) for box in input_boxes]
								else:
															__UpperCamelCase			:     Any         =      None

								return input_points, input_labels, input_boxes







	@property
	def      _lowerCamelCase       (      self	:int		)	->				Union[str, Any]:
								__UpperCamelCase			:     Union[str, Any]         =      self.image_processor.model_input_names
								return list(dict.fromkeys(lowercase_		)		)







	def      _lowerCamelCase       (      self	:Dict  ,     *a	:Optional[int]  ,     **a	:Any		)	->				Union[str, Any]:
								return self.image_processor.post_process_masks(*lowercase_  ,     **lowercase_		)

import argparse

import torch

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


logging.set_verbosity_info()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any])      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase			:     List[Any]         =      BertForPreTraining(_lowerCamelCase)

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

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


if __name__ == "__main__":
		lowercase						:      Any									= 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.'
		)
		lowercase						:      Tuple									= parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)