





"""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       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =TapasConfig.from_json_file(SCREAMING_SNAKE_CASE   )
							# set absolute/relative position embeddings parameter
							__lowerCamelCase      :      str         =reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__lowerCamelCase      :      Optional[Any]         =TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE   )
							elif task == "WTQ":
														# run_task_main.py hparams
														__lowerCamelCase      :      Optional[int]         =4
														__lowerCamelCase      :      int         =True
														# hparam_utils.py hparams
														__lowerCamelCase      :      Dict         =0.664_694
														__lowerCamelCase      :      Dict         =0.207_951
														__lowerCamelCase      :      Any         =0.121_194
														__lowerCamelCase      :      Any         =True
														__lowerCamelCase      :      Dict         =True
														__lowerCamelCase      :      int         =False
														__lowerCamelCase      :      Optional[int]         =0.0_352_513

														__lowerCamelCase      :      Dict         =TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE   )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__lowerCamelCase      :      str         =4
														__lowerCamelCase      :      List[str]         =False
														# hparam_utils.py hparams
														__lowerCamelCase      :      Optional[int]         =36.4_519
														__lowerCamelCase      :      Dict         =0.903_421
														__lowerCamelCase      :      List[Any]         =222.088
														__lowerCamelCase      :      Union[str, Any]         =True
														__lowerCamelCase      :      Optional[int]         =True
														__lowerCamelCase      :      Dict         =True
														__lowerCamelCase      :      List[Any]         =0.763_141

														__lowerCamelCase      :      Union[str, Any]         =TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE   )
							elif task == "TABFACT":
														__lowerCamelCase      :      Optional[int]         =TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE   )
							elif task == "MLM":
														__lowerCamelCase      :      Any         =TapasForMaskedLM(config=SCREAMING_SNAKE_CASE   )
							elif task == "INTERMEDIATE_PRETRAINING":
														__lowerCamelCase      :      Dict         =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}'   )
							__lowerCamelCase      :      List[Any]         =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__":
							_UpperCamelCase							=      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.'
							)
							_UpperCamelCase							=      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,
							)











"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


_UpperCamelCase							=      logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       List[str]	 =		["""pixel_values"""]




				def __init__(     self  :List[str]						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :Union[int, float] = 1 / 255						,    __lowercase  :bool = True						,    __lowercase  :bool = True						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    **__lowercase  :Union[str, Any]						,    ):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Dict         =size if size is not None else {'''height''': 256, '''width''': 256}
											__lowerCamelCase      :      Union[str, Any]         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      Tuple         =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											__lowerCamelCase      :      int         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      Tuple         =do_resize
											__lowerCamelCase      :      int         =size
											__lowerCamelCase      :      Union[str, Any]         =resample
											__lowerCamelCase      :      Dict         =do_center_crop
											__lowerCamelCase      :      Any         =crop_size
											__lowerCamelCase      :      Dict         =do_rescale
											__lowerCamelCase      :      List[Any]         =rescale_factor
											__lowerCamelCase      :      Any         =do_normalize
											__lowerCamelCase      :      int         =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase      :      List[str]         =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[int]						,    ):
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return resize(
											    __lowercase						,    size=(size['''height'''], size['''width'''])						,    resample=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[Any]						,    ):
											__lowerCamelCase      :      Dict         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return center_crop(__lowercase						,    size=(size['''height'''], size['''width'''])						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :int						,    __lowercase  :np.ndarray						,    __lowercase  :Union[int, float]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :List[Any]						,    ):
											return rescale(__lowercase						,    scale=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Dict						,    __lowercase  :np.ndarray						,    __lowercase  :Union[float, List[float]]						,    __lowercase  :Union[float, List[float]]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :int						,    ):
											return normalize(__lowercase						,    mean=__lowercase						,    std=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :ImageInput						,    __lowercase  :bool = None						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :Optional[Any]=None						,    __lowercase  :bool = None						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :bool = None						,    __lowercase  :float = None						,    __lowercase  :bool = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[str, TensorType]] = None						,    __lowercase  :ChannelDimension = ChannelDimension.FIRST						,    **__lowercase  :Any						,    ):
											__lowerCamelCase      :      Optional[Any]         =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase      :      List[Any]         =resample if resample is not None else self.resample
											__lowerCamelCase      :      Optional[int]         =do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowerCamelCase      :      List[str]         =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase      :      Optional[Any]         =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase      :      Optional[int]         =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase      :      Dict         =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase      :      Union[str, Any]         =image_std if image_std is not None else self.image_std

											__lowerCamelCase      :      Dict         =size if size is not None else self.size
											__lowerCamelCase      :      int         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      List[Any]         =crop_size if crop_size is not None else self.crop_size
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      int         =make_list_of_images(__lowercase			)

											if not valid_images(__lowercase			):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

											if do_resize and size is None or resample is None:
																		raise ValueError('''Size and resample must be specified if do_resize is True.'''			)

											if do_center_crop and crop_size is None:
																		raise ValueError('''Crop size must be specified if do_center_crop is True.'''			)

											if do_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

											if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('''Image mean and std must be specified if do_normalize is True.'''			)

											# All transformations expect numpy arrays.
											__lowerCamelCase      :      str         =[to_numpy_array(__lowercase			) for image in images]

											if do_resize:
																		__lowerCamelCase      :      Union[str, Any]         =[self.resize(image=__lowercase						,    size=__lowercase						,    resample=__lowercase			) for image in images]

											if do_center_crop:
																		__lowerCamelCase      :      Optional[Any]         =[self.center_crop(image=__lowercase						,    size=__lowercase			) for image in images]

											if do_rescale:
																		__lowerCamelCase      :      str         =[self.rescale(image=__lowercase						,    scale=__lowercase			) for image in images]

											if do_normalize:
																		__lowerCamelCase      :      int         =[self.normalize(image=__lowercase						,    mean=__lowercase						,    std=__lowercase			) for image in images]

											__lowerCamelCase      :      Tuple         =[to_channel_dimension_format(__lowercase						,    __lowercase			) for image in images]

											__lowerCamelCase      :      Any         ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase						,    tensor_type=__lowercase			)




