
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					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)       ->      Dict:
     # 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(_lowercase			)
     # 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=_lowercase			)
     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=_lowercase			)
     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=_lowercase			)
     elif task == "TABFACT":
          UpperCamelCase						    =					TapasForSequenceClassification(config=_lowercase			)
     elif task == "MLM":
          UpperCamelCase						    =					TapasForMaskedLM(config=_lowercase			)
     elif task == "INTERMEDIATE_PRETRAINING":
          UpperCamelCase						    =					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}'			)
     UpperCamelCase						    =					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__":
 _snake_case      = 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.'''
 )
 _snake_case      = 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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


_snake_case      = logging.get_logger(__name__)

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Optional[Any]:
     UpperCamelCase						    =					b.T
     UpperCamelCase						    =					np.sum(np.square(_lowercase			)						,     axis=1			)
     UpperCamelCase						    =					np.sum(np.square(_lowercase			)						,     axis=0			)
     UpperCamelCase						    =					np.matmul(_lowercase						,     _lowercase			)
     UpperCamelCase						    =					aa[:, None] - 2 * ab + ba[None, :]
     return d

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      List[Any]:
     UpperCamelCase						    =					x.reshape(-1						,     3			)
     UpperCamelCase						    =					squared_euclidean_distance(_lowercase						,     _lowercase			)
     return np.argmin(_lowercase						,     axis=1			)







class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=["pixel_values"]

 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Optional[Union[List[List[int]], np.ndarray]] = None					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			Dict[str, int] = None					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = PILImageResampling.BILINEAR					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			bool = True					, **SCREAMING_SNAKE_CASE__			:			Optional[int]					, ):






      """simple docstring"""



      super().__init__(**SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					size if size is not None else {'height': 2_56, 'width': 2_56}
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__   ) if clusters is not None else None
      UpperCamelCase						    =					do_resize
      UpperCamelCase						    =					size
      UpperCamelCase						    =					resample
      UpperCamelCase						    =					do_normalize
      UpperCamelCase						    =					do_color_quantize

 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Dict[str, int]					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = PILImageResampling.BILINEAR					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, **SCREAMING_SNAKE_CASE__			:			List[str]					, ):






      """simple docstring"""



      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      if "height" not in size or "width" not in size:
           raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}'   )
      return resize(
          SCREAMING_SNAKE_CASE__					, size=(size['height'], size['width'])					, resample=SCREAMING_SNAKE_CASE__					, data_format=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, ):






      """simple docstring"""



      UpperCamelCase						    =					rescale(image=SCREAMING_SNAKE_CASE__					, scale=1 / 127.5					, data_format=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					image - 1
      return image






 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			ImageInput					, SCREAMING_SNAKE_CASE__			:			bool = None					, SCREAMING_SNAKE_CASE__			:			Dict[str, int] = None					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = None					, SCREAMING_SNAKE_CASE__			:			bool = None					, SCREAMING_SNAKE_CASE__			:			Optional[bool] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[List[List[int]], np.ndarray]] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, TensorType]] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					, **SCREAMING_SNAKE_CASE__			:			str					, ):






      """simple docstring"""



      UpperCamelCase						    =					do_resize if do_resize is not None else self.do_resize
      UpperCamelCase						    =					size if size is not None else self.size
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					resample if resample is not None else self.resample
      UpperCamelCase						    =					do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase						    =					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCamelCase						    =					clusters if clusters is not None else self.clusters
      UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__   )

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

      if do_color_quantize and clusters is None:
           raise ValueError('Clusters must be specified if do_color_quantize is True.'   )

      # All transformations expect numpy arrays.
      UpperCamelCase						    =					[to_numpy_array(SCREAMING_SNAKE_CASE__   ) for image in images]

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

      if do_normalize:
           UpperCamelCase						    =					[self.normalize(image=SCREAMING_SNAKE_CASE__   ) for image in images]

      if do_color_quantize:
           UpperCamelCase						    =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__					, ChannelDimension.LAST   ) for image in images]
           # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
           UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					color_quantize(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ).reshape(images.shape[:-1]   )

           # flatten to (batch_size, height*width)
           UpperCamelCase						    =					images.shape[0]
           UpperCamelCase						    =					images.reshape(SCREAMING_SNAKE_CASE__					, -1   )

           # We need to convert back to a list of images to keep consistent behaviour across processors.
           UpperCamelCase						    =					list(SCREAMING_SNAKE_CASE__   )
      else:
           UpperCamelCase						    =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) for image in images]

      UpperCamelCase						    =					{'input_ids': images}
      return BatchFeature(data=SCREAMING_SNAKE_CASE__					, tensor_type=SCREAMING_SNAKE_CASE__   )


