"""simple docstring"""





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  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						["image_processor"]
				a			=						"SamImageProcessor"





				def __init__(      self							:							Dict      ,    _A							:							Any):



											"""simple docstring"""







											super().__init__(_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.image_processor
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    -1_0
											_SCREAMING_SNAKE_CASE		:						str			  =    self.image_processor.size["""longest_edge"""]





				def __call__(      self							:							Union[str, Any]      ,    _A							:							Tuple=None      ,    _A							:							List[str]=None      ,    _A							:							Optional[int]=None      ,    _A							:							Tuple=None      ,    _A							:							Optional[Union[str, TensorType]] = None      ,    **_A							:							int      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    self.image_processor(
											    _A      ,    return_tensors=_A      ,    **_A      ,    )

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

											if hasattr(_A      ,    """numpy"""):  # Checks if Torch or TF tensor
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    original_sizes.numpy()

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self._check_and_preprocess_points(
											    input_points=_A      ,    input_labels=_A      ,    input_boxes=_A      ,    )

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self._normalize_and_convert(
											    _A      ,    _A      ,    input_points=_A      ,    input_labels=_A      ,    input_boxes=_A      ,    return_tensors=_A      ,    )

											return encoding_image_processor





				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							Optional[Any]      ,    _A							:							Tuple      ,    _A							:							Union[str, Any]=None      ,    _A							:							Tuple=None      ,    _A							:							int=None      ,    _A							:							List[str]="pt"      ,    ):



											"""simple docstring"""







											if input_points is not None:
																		if len(_A) != len(_A):
																									_SCREAMING_SNAKE_CASE		:						List[str]			  =    [
																									    self._normalize_coordinates(self.target_size      ,    _A      ,    original_sizes[0]) for point in input_points
																									]
																		else:
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    [
																									    self._normalize_coordinates(self.target_size      ,    _A      ,    _A)
																									    for point, original_size in zip(_A      ,    _A)
																									]
																		# 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:
																																_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self._pad_points_and_labels(_A      ,    _A)

																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    np.array(_A)

											if input_labels is not None:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    np.array(_A)

											if input_boxes is not None:
																		if len(_A) != len(_A):
																									_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    [
																									    self._normalize_coordinates(self.target_size      ,    _A      ,    original_sizes[0]      ,    is_bounding_box=_A)
																									    for box in input_boxes
																									]
																		else:
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    [
																									    self._normalize_coordinates(self.target_size      ,    _A      ,    _A      ,    is_bounding_box=_A)
																									    for box, original_size in zip(_A      ,    _A)
																									]
																		_SCREAMING_SNAKE_CASE		:						Any			  =    np.array(_A)

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



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    max([point.shape[0] for point in input_points])
											_SCREAMING_SNAKE_CASE		:						Any			  =    []
											for i, point in enumerate(_A):
																		if point.shape[0] != expected_nb_points:
																									_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    np.concatenate(
																									    [point, np.zeros((expected_nb_points - point.shape[0], 2)) + self.point_pad_value]      ,    axis=0)
																									_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    np.append(input_labels[i]      ,    [self.point_pad_value])
																		processed_input_points.append(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    processed_input_points
											return input_points, input_labels





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							int      ,    _A							:							np.ndarray      ,    _A							:							Union[str, Any]      ,    _A							:							Tuple=False):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    original_size
											_SCREAMING_SNAKE_CASE		:						Any			  =    self.image_processor._get_preprocess_shape(_A      ,    longest_edge=_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    deepcopy(_A).astype(_A)

											if is_bounding_box:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    coords.reshape(-1      ,    2      ,    2)

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

											if is_bounding_box:
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    coords.reshape(-1      ,    4)

											return coords





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							Dict=None      ,    _A							:							Dict=None      ,    _A							:							List[str]=None      ,    ):



											"""simple docstring"""







											if input_points is not None:
																		if hasattr(_A      ,    """numpy"""):  # Checks for TF or Torch tensor
																									_SCREAMING_SNAKE_CASE		:						Tuple			  =    input_points.numpy().tolist()

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

											if input_labels is not None:
																		if hasattr(_A      ,    """numpy"""):
																									_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    input_labels.numpy().tolist()

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

											if input_boxes is not None:
																		if hasattr(_A      ,    """numpy"""):
																									_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    input_boxes.numpy().tolist()

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

											return input_points, input_labels, input_boxes





				@property
				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.image_processor.model_input_names
											return list(dict.fromkeys(_A))





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    *_A							:							str      ,    **_A							:							Optional[int]):



											"""simple docstring"""







											return self.image_processor.post_process_masks(*_A      ,    **_A)



"""simple docstring"""





import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							# Initialise PyTorch model.
							# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
							# TapasConfig to False.

							# initialize configuration from json file
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TapasConfig.from_json_file(__SCREAMING_SNAKE_CASE  )
							# set absolute/relative position embeddings parameter
							_SCREAMING_SNAKE_CASE		:						Dict			  =    reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														_SCREAMING_SNAKE_CASE		:						str			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WTQ":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    4
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    0.66_46_94
														_SCREAMING_SNAKE_CASE		:						str			  =    0.20_79_51
														_SCREAMING_SNAKE_CASE		:						str			  =    0.12_11_94
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    False
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0.0_35_25_13

														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						int			  =    4
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    False
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    36.45_19
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0.90_34_21
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    2_22.0_88
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    True
														_SCREAMING_SNAKE_CASE		:						Dict			  =    0.76_31_41

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "TABFACT":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForSequenceClassification(config=__SCREAMING_SNAKE_CASE  )
							elif task == "MLM":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForMaskedLM(config=__SCREAMING_SNAKE_CASE  )
							elif task == "INTERMEDIATE_PRETRAINING":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasModel(config=__SCREAMING_SNAKE_CASE  )
							else:
														raise ValueError(F"""Task {task} not supported."""  )

							print(F"""Building PyTorch model from configuration: {config}"""  )
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							# Save pytorch-model (weights and configuration)
							print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
							model.save_pretrained(__SCREAMING_SNAKE_CASE  )

							# Save tokenizer files
							print(F"""Save tokenizer files to {pytorch_dump_path}"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""     ,	model_max_length=512  )
							tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE  )

							print("""Used relative position embeddings:"""     ,	model.config.reset_position_index_per_cell  )


if __name__ == "__main__":
			lowerCAmelCase_     =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
			)
			parser.add_argument(
			    '''--reset_position_index_per_cell''',
			    default=False,
			    action='''store_true''',
			    help='''Whether to use relative position embeddings or not. Defaults to True.''',
			)
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--tapas_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained TAPAS 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.'''
			)
			lowerCAmelCase_     =	parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.task,
			    args.reset_position_index_per_cell,
			    args.tf_checkpoint_path,
			    args.tapas_config_file,
			    args.pytorch_dump_path,
			)


