





'''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	)  ->	Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:   Tuple    						=TapasConfig.from_json_file(lowercase__	)
  # set absolute/relative position embeddings parameter
  lowerCamelCase__:   Optional[Any]    						=reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    lowerCamelCase__:   Dict    						=TapasForQuestionAnswering(config=lowercase__	)
  elif task == "WTQ":
    # run_task_main.py hparams
    lowerCamelCase__:   Optional[int]    						=4
    lowerCamelCase__:   Dict    						=True
    # hparam_utils.py hparams
    lowerCamelCase__:   int    						=0.6_6_4_6_9_4
    lowerCamelCase__:   List[str]    						=0.2_0_7_9_5_1
    lowerCamelCase__:   Optional[int]    						=0.1_2_1_1_9_4
    lowerCamelCase__:   Union[str, Any]    						=True
    lowerCamelCase__:   Optional[int]    						=True
    lowerCamelCase__:   int    						=False
    lowerCamelCase__:   List[Any]    						=0.0_3_5_2_5_1_3

    lowerCamelCase__:   Dict    						=TapasForQuestionAnswering(config=lowercase__	)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    lowerCamelCase__:   Dict    						=4
    lowerCamelCase__:   Tuple    						=False
    # hparam_utils.py hparams
    lowerCamelCase__:   Optional[int]    						=3_6.4_5_1_9
    lowerCamelCase__:   Optional[int]    						=0.9_0_3_4_2_1
    lowerCamelCase__:   List[Any]    						=2_2_2.0_8_8
    lowerCamelCase__:   List[str]    						=True
    lowerCamelCase__:   Any    						=True
    lowerCamelCase__:   Tuple    						=True
    lowerCamelCase__:   List[Any]    						=0.7_6_3_1_4_1

    lowerCamelCase__:   Optional[int]    						=TapasForQuestionAnswering(config=lowercase__	)
  elif task == "TABFACT":
    lowerCamelCase__:   Dict    						=TapasForSequenceClassification(config=lowercase__	)
  elif task == "MLM":
    lowerCamelCase__:   str    						=TapasForMaskedLM(config=lowercase__	)
  elif task == "INTERMEDIATE_PRETRAINING":
    lowerCamelCase__:   Optional[Any]    						=TapasModel(config=lowercase__	)
  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(lowercase__			,					lowercase__			,					lowercase__	)

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

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

  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, is_vision_available, logging


if is_vision_available():
     import PIL


__A             =       logging.get_logger(__name__)



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''
 lowercase_	=    ["pixel_values"]

 def __init__(self       :     List[str] ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     float = None ,				UpperCAmelCase_       :     PILImageResampling = PILImageResampling.BILINEAR ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Union[int, float] = 1 / 255 ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				**UpperCAmelCase_       :     List[str] ,				) ->None:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__:   int    						=size if size is not None else {"shortest_edge": 384}
   lowerCamelCase__:   Tuple    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)

   lowerCamelCase__:   Union[str, Any]    						=do_resize
   lowerCamelCase__:   Union[str, Any]    						=size
   # Default value set here for backwards compatibility where the value in config is None
   lowerCamelCase__:   str    						=crop_pct if crop_pct is not None else 224 / 256
   lowerCamelCase__:   Optional[int]    						=resample
   lowerCamelCase__:   str    						=do_rescale
   lowerCamelCase__:   List[Any]    						=rescale_factor
   lowerCamelCase__:   Dict    						=do_normalize
   lowerCamelCase__:   List[str]    						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   lowerCamelCase__:   Dict    						=image_std if image_std is not None else IMAGENET_STANDARD_STD

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int] ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Dict[str, int] ,				UpperCAmelCase_       :     float ,				UpperCAmelCase_       :     PILImageResampling = PILImageResampling.BICUBIC ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     str ,				) ->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)
   if "shortest_edge" not in size:
     raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}""")
   lowerCamelCase__:   Tuple    						=size["shortest_edge"]

   if shortest_edge < 384:
     # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
     lowerCamelCase__:   List[str]    						=int(shortest_edge / crop_pct)
     lowerCamelCase__:   List[str]    						=get_resize_output_image_size(UpperCAmelCase_ ,				size=UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)
     lowerCamelCase__:   List[str]    						=resize(image=UpperCAmelCase_ ,				size=UpperCAmelCase_ ,				resample=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)
     # then crop to (shortest_edge, shortest_edge)
     return center_crop(image=UpperCAmelCase_ ,				size=(shortest_edge, shortest_edge) ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)
   else:
     # warping (no cropping) when evaluated at 384 or larger
     return resize(
         UpperCAmelCase_ ,				size=(shortest_edge, shortest_edge) ,				resample=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int] ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Union[int, float] ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     str ,				) ->Tuple:



   '''simple docstring'''



   return rescale(UpperCAmelCase_ ,				scale=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Union[float, List[float]] ,				UpperCAmelCase_       :     Union[float, List[float]] ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     Optional[Any] ,				) ->np.ndarray:



   '''simple docstring'''



   return normalize(UpperCAmelCase_ ,				mean=UpperCAmelCase_ ,				std=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict ,				UpperCAmelCase_       :     ImageInput ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     float = None ,				UpperCAmelCase_       :     PILImageResampling = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     float = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[Union[str, TensorType]] = None ,				UpperCAmelCase_       :     ChannelDimension = ChannelDimension.FIRST ,				**UpperCAmelCase_       :     Dict ,				) ->PIL.Image.Image:



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						=do_resize if do_resize is not None else self.do_resize
   lowerCamelCase__:   List[Any]    						=crop_pct if crop_pct is not None else self.crop_pct
   lowerCamelCase__:   str    						=resample if resample is not None else self.resample
   lowerCamelCase__:   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__:   int    						=do_normalize if do_normalize is not None else self.do_normalize
   lowerCamelCase__:   Any    						=image_mean if image_mean is not None else self.image_mean
   lowerCamelCase__:   Any    						=image_std if image_std is not None else self.image_std

   lowerCamelCase__:   List[str]    						=size if size is not None else self.size
   lowerCamelCase__:   List[str]    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)

   lowerCamelCase__:   Any    						=make_list_of_images(UpperCAmelCase_)

   if not valid_images(UpperCAmelCase_):
     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_resize and size["shortest_edge"] < 384 and crop_pct is None:
     raise ValueError("crop_pct must be specified if size < 384.")

   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__:   List[Any]    						=[to_numpy_array(UpperCAmelCase_) for image in images]

   if do_resize:
     lowerCamelCase__:   Dict    						=[self.resize(image=UpperCAmelCase_ ,				size=UpperCAmelCase_ ,				crop_pct=UpperCAmelCase_ ,				resample=UpperCAmelCase_) for image in images]

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

   if do_normalize:
     lowerCamelCase__:   Tuple    						=[self.normalize(image=UpperCAmelCase_ ,				mean=UpperCAmelCase_ ,				std=UpperCAmelCase_) for image in images]

   lowerCamelCase__:   Optional[Any]    						=[to_channel_dimension_format(UpperCAmelCase_ ,				UpperCAmelCase_) for image in images]

   lowerCamelCase__:   Optional[Any]    						={"pixel_values": images}
   return BatchFeature(data=UpperCAmelCase_ ,				tensor_type=UpperCAmelCase_)

