



'''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__ (		_A				,    _A				,    _A				,    _A				,    _A       ):
  # 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
  a		:      str       =  TapasConfig.from_json_file(_A       )
  # set absolute/relative position embeddings parameter
  a		:      Union[str, Any]       =  reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    a		:      Tuple       =  TapasForQuestionAnswering(config=_A       )
  elif task == "WTQ":
    # run_task_main.py hparams
    a		:      int       =  4
    a		:      int       =  True
    # hparam_utils.py hparams
    a		:      int       =  0.664694
    a		:      List[Any]       =  0.207951
    a		:      Union[str, Any]       =  0.121194
    a		:      Optional[Any]       =  True
    a		:      List[str]       =  True
    a		:      Any       =  False
    a		:      Dict       =  0.0352513

    a		:      List[str]       =  TapasForQuestionAnswering(config=_A       )
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    a		:      Optional[int]       =  4
    a		:      Optional[Any]       =  False
    # hparam_utils.py hparams
    a		:      List[Any]       =  36.4519
    a		:      Tuple       =  0.903421
    a		:      int       =  222.088
    a		:      List[str]       =  True
    a		:      Any       =  True
    a		:      Any       =  True
    a		:      List[Any]       =  0.763141

    a		:      str       =  TapasForQuestionAnswering(config=_A       )
  elif task == "TABFACT":
    a		:      Union[str, Any]       =  TapasForSequenceClassification(config=_A       )
  elif task == "MLM":
    a		:      List[str]       =  TapasForMaskedLM(config=_A       )
  elif task == "INTERMEDIATE_PRETRAINING":
    a		:      Tuple       =  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}"""       )
  a		:      Optional[Any]       =  TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'				,    model_max_length=512       )
  tokenizer.save_pretrained(_A       )

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


if __name__ == "__main__":
     lowerCAmelCase:      List[str]		 =		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:      Tuple		 =		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, Tuple, 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, is_torch_available, is_torch_tensor, logging


if is_torch_available():
     import torch


lowerCAmelCase:      Union[str, Any]		 =		logging.get_logger(__name__)

class 							a__(      lowerCamelCase__			):
    lowercase__				=					["""pixel_values"""]







    def __init__(   self				:						int		,    __snake_case				:						bool = True		,    __snake_case				:						Optional[Dict[str, int]] = None		,    __snake_case				:						PILImageResampling = PILImageResampling.BILINEAR		,    __snake_case				:						bool = True		,    __snake_case				:						Dict[str, int] = None		,    __snake_case				:						bool = True		,    __snake_case				:						Union[int, float] = 1 / 2_55		,    __snake_case				:						bool = True		,    __snake_case				:						Optional[Union[float, List[float]]] = None		,    __snake_case				:						Optional[Union[float, List[float]]] = None		,    **__snake_case				:						List[str]		,    ):
      super().__init__(**__snake_case			)
      a		:      Dict       =  size if size is not None else {'shortest_edge': 2_56}
      a		:      Optional[int]       =  get_size_dict(__snake_case		,    default_to_square=__snake_case			)
      a		:      List[str]       =  crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
      a		:      Dict       =  get_size_dict(__snake_case		,    param_name='crop_size'			)
      a		:      Optional[int]       =  do_resize
      a		:      Union[str, Any]       =  size
      a		:      Optional[Any]       =  resample
      a		:      int       =  do_center_crop
      a		:      List[str]       =  crop_size
      a		:      Tuple       =  do_rescale
      a		:      Any       =  rescale_factor
      a		:      str       =  do_normalize
      a		:      int       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      a		:      List[str]       =  image_std if image_std is not None else IMAGENET_STANDARD_STD







    def     lowercase_ (   self				:						str		,    __snake_case				:						np.ndarray		,    __snake_case				:						Dict[str, int]		,    __snake_case				:						PILImageResampling = PILImageResampling.BICUBIC		,    __snake_case				:						Optional[Union[str, ChannelDimension]] = None		,    **__snake_case				:						Any		,    ):
      a		:      str       =  get_size_dict(__snake_case		,    default_to_square=__snake_case			)
      if "shortest_edge" not in size:
        raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""			)
      a		:      str       =  get_resize_output_image_size(__snake_case		,    size=size['shortest_edge']		,    default_to_square=__snake_case			)
      return resize(__snake_case		,    size=__snake_case		,    resample=__snake_case		,    data_format=__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						np.ndarray		,    __snake_case				:						Dict[str, int]		,    __snake_case				:						Optional[Union[str, ChannelDimension]] = None		,    **__snake_case				:						Any		,    ):
      a		:      Any       =  get_size_dict(__snake_case			)
      if "height" not in size or "width" not in size:
        raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""			)
      return center_crop(__snake_case		,    size=(size['height'], size['width'])		,    data_format=__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						str		,    __snake_case				:						np.ndarray		,    __snake_case				:						float		,    __snake_case				:						Optional[Union[str, ChannelDimension]] = None		,    **__snake_case				:						str			):
      return rescale(__snake_case		,    scale=__snake_case		,    data_format=__snake_case		,    **__snake_case			)







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







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						ImageInput		,    __snake_case				:						Optional[bool] = None		,    __snake_case				:						Dict[str, int] = None		,    __snake_case				:						PILImageResampling = None		,    __snake_case				:						bool = None		,    __snake_case				:						Dict[str, int] = None		,    __snake_case				:						Optional[bool] = None		,    __snake_case				:						Optional[float] = None		,    __snake_case				:						Optional[bool] = None		,    __snake_case				:						Optional[Union[float, List[float]]] = None		,    __snake_case				:						Optional[Union[float, List[float]]] = None		,    __snake_case				:						Optional[Union[str, TensorType]] = None		,    __snake_case				:						Union[str, ChannelDimension] = ChannelDimension.FIRST		,    **__snake_case				:						Union[str, Any]		,    ):
      a		:      Dict       =  do_resize if do_resize is not None else self.do_resize
      a		:      str       =  size if size is not None else self.size
      a		:      Dict       =  get_size_dict(__snake_case		,    default_to_square=__snake_case			)
      a		:      Optional[int]       =  resample if resample is not None else self.resample
      a		:      List[str]       =  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		:      List[str]       =  get_size_dict(__snake_case		,    param_name='crop_size'			)
      a		:      Optional[Any]       =  do_rescale if do_rescale is not None else self.do_rescale
      a		:      List[str]       =  rescale_factor if rescale_factor is not None else self.rescale_factor
      a		:      List[Any]       =  do_normalize if do_normalize is not None else self.do_normalize
      a		:      Dict       =  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		:      int       =  make_list_of_images(__snake_case			)

      if not valid_images(__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		:      int       =  [to_numpy_array(__snake_case			) for image in images]

      if do_resize:
        a		:      List[Any]       =  [self.resize(image=__snake_case		,    size=__snake_case		,    resample=__snake_case			) for image in images]

      if do_center_crop:
        a		:      Any       =  [self.center_crop(image=__snake_case		,    size=__snake_case			) for image in images]

      if do_rescale:
        a		:      Union[str, Any]       =  [self.rescale(image=__snake_case		,    scale=__snake_case			) for image in images]

      if do_normalize:
        a		:      List[str]       =  [self.normalize(image=__snake_case		,    mean=__snake_case		,    std=__snake_case			) for image in images]

      a		:      str       =  [to_channel_dimension_format(__snake_case		,    __snake_case			) for image in images]

      a		:      Tuple       =  {'pixel_values': images}
      return BatchFeature(data=__snake_case		,    tensor_type=__snake_case			)







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Tuple		,    __snake_case				:						List[Tuple] = None			):
      a		:      Union[str, Any]       =  outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
        if len(__snake_case			) != len(__snake_case			):
          raise ValueError(
              'Make sure that you pass in as many target sizes as the batch dimension of the logits'			)

        if is_torch_tensor(__snake_case			):
          a		:      Optional[int]       =  target_sizes.numpy()

        a		:      Union[str, Any]       =  []

        for idx in range(len(__snake_case			)			):
          a		:      Dict       =  torch.nn.functional.interpolate(
              logits[idx].unsqueeze(dim=0			)		,    size=target_sizes[idx]		,    mode='bilinear'		,    align_corners=__snake_case			)
          a		:      Optional[Any]       =  resized_logits[0].argmax(dim=0			)
          semantic_segmentation.append(__snake_case			)
      else:
        a		:      List[Any]       =  logits.argmax(dim=1			)
        a		:      List[str]       =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]			)]

      return semantic_segmentation