




'''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 						snake_case_							(_a	:							Dict    ,   _a	:							Optional[int]    ,   _a	:							Dict    ,   _a	:							Optional[Any]    ,   _a	:							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
    UpperCAmelCase			       =		TapasConfig.from_json_file(_a     )
    # set absolute/relative position embeddings parameter
    UpperCAmelCase			       =		reset_position_index_per_cell

    # set remaining parameters of TapasConfig as well as the model based on the task
    if task == "SQA":
        UpperCAmelCase			       =		TapasForQuestionAnswering(config=_a     )
    elif task == "WTQ":
        # run_task_main.py hparams
        UpperCAmelCase			       =		4
        UpperCAmelCase			       =		True
        # hparam_utils.py hparams
        UpperCAmelCase			       =		0.66_4694
        UpperCAmelCase			       =		0.20_7951
        UpperCAmelCase			       =		0.12_1194
        UpperCAmelCase			       =		True
        UpperCAmelCase			       =		True
        UpperCAmelCase			       =		False
        UpperCAmelCase			       =		0.035_2513

        UpperCAmelCase			       =		TapasForQuestionAnswering(config=_a     )
    elif task == "WIKISQL_SUPERVISED":
        # run_task_main.py hparams
        UpperCAmelCase			       =		4
        UpperCAmelCase			       =		False
        # hparam_utils.py hparams
        UpperCAmelCase			       =		36.4519
        UpperCAmelCase			       =		0.90_3421
        UpperCAmelCase			       =		222.088
        UpperCAmelCase			       =		True
        UpperCAmelCase			       =		True
        UpperCAmelCase			       =		True
        UpperCAmelCase			       =		0.76_3141

        UpperCAmelCase			       =		TapasForQuestionAnswering(config=_a     )
    elif task == "TABFACT":
        UpperCAmelCase			       =		TapasForSequenceClassification(config=_a     )
    elif task == "MLM":
        UpperCAmelCase			       =		TapasForMaskedLM(config=_a     )
    elif task == "INTERMEDIATE_PRETRAINING":
        UpperCAmelCase			       =		TapasModel(config=_a     )
    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(_a    ,   _a    ,   _a     )

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

    # Save tokenizer files
    print(F"Save tokenizer files to {pytorch_dump_path}"     )
    UpperCAmelCase			       =		TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + '''vocab.txt'''    ,   model_max_length=5_1_2     )
    tokenizer.save_pretrained(_a     )

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


if __name__ == "__main__":
   A     =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.'
   )
   A     =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,
   )








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,
    get_resize_output_image_size,
    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, logging


UpperCAmelCase_      : Optional[int]  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Union[str, Any]        =						['''pixel_values''']
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Dict[str, int]] = None     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = PILImageResampling.BILINEAR     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int] = None     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Union[int, float] = 1 / 2_5_5     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			None:
             super().__init__(**SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  size if size is not None else {'shortest_edge': 2_5_6}
             a_					:		Any							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
             a_					:		Optional[int]							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             a_					:		List[str]							=  do_resize
             a_					:		Dict							=  size
             a_					:		Optional[Any]							=  resample
             a_					:		Optional[int]							=  do_center_crop
             a_					:		Dict							=  crop_size
             a_					:		int							=  do_rescale
             a_					:		int							=  rescale_factor
             a_					:		Tuple							=  do_normalize
             a_					:		int							=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             a_					:		Tuple							=  image_std if image_std is not None else IMAGENET_STANDARD_STD
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int]     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = PILImageResampling.BICUBIC     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			np.ndarray:
             a_					:		List[Any]							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             if "shortest_edge" not in size:
                    raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
             a_					:		Tuple							=  get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,							size=size['shortest_edge']     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             return resize(SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ,							resample=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			np.ndarray:
             a_					:		str							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             return center_crop(SCREAMING_SNAKE_CASE__     ,							size=(size['height'], size['width'])     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       float     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			np.ndarray:
             return rescale(SCREAMING_SNAKE_CASE__     ,							scale=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Union[float, List[float]]     ,							SCREAMING_SNAKE_CASE__				:       Union[float, List[float]]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       List[str]     ,							)		->			np.ndarray:
             return normalize(SCREAMING_SNAKE_CASE__     ,							mean=SCREAMING_SNAKE_CASE__     ,							std=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       ImageInput     ,							SCREAMING_SNAKE_CASE__				:       Optional[bool] = None     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int] = None     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = None     ,							SCREAMING_SNAKE_CASE__				:       bool = None     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[bool] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[float] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[bool] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, TensorType]] = None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, ChannelDimension] = ChannelDimension.FIRST     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			Union[str, Any]:
             a_					:		List[str]							=  do_resize if do_resize is not None else self.do_resize
             a_					:		Dict							=  size if size is not None else self.size
             a_					:		Dict							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  resample if resample is not None else self.resample
             a_					:		List[Any]							=  do_center_crop if do_center_crop is not None else self.do_center_crop
             a_					:		int							=  crop_size if crop_size is not None else self.crop_size
             a_					:		Optional[int]							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  do_rescale if do_rescale is not None else self.do_rescale
             a_					:		Dict							=  rescale_factor if rescale_factor is not None else self.rescale_factor
             a_					:		Any							=  do_normalize if do_normalize is not None else self.do_normalize
             a_					:		str							=  image_mean if image_mean is not None else self.image_mean
             a_					:		Dict							=  image_std if image_std is not None else self.image_std

             a_					:		Optional[int]							=  make_list_of_images(SCREAMING_SNAKE_CASE__     )

             if not valid_images(SCREAMING_SNAKE_CASE__     ):
                    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:
                    raise ValueError('Size 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.
             a_					:		Any							=  [to_numpy_array(SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_resize:
                    a_					:		str							=  [self.resize(image=SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ,							resample=SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_center_crop:
                    a_					:		int							=  [self.center_crop(image=SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_rescale:
                    a_					:		Optional[Any]							=  [self.rescale(image=SCREAMING_SNAKE_CASE__     ,							scale=SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_normalize:
                    a_					:		List[Any]							=  [self.normalize(image=SCREAMING_SNAKE_CASE__     ,							mean=SCREAMING_SNAKE_CASE__     ,							std=SCREAMING_SNAKE_CASE__     ) for image in images]

             a_					:		Dict							=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ) for image in images]

             a_					:		Tuple							=  {'pixel_values': images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,							tensor_type=SCREAMING_SNAKE_CASE__     )


