




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(							__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__       )					->				str:





     '''simple docstring'''


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

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

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

          lowercase   :  Any        =					TapasForQuestionAnswering(config=__snake_case       )
     elif task == "TABFACT":
          lowercase   :  int        =					TapasForSequenceClassification(config=__snake_case       )
     elif task == "MLM":
          lowercase   :  int        =					TapasForMaskedLM(config=__snake_case       )
     elif task == "INTERMEDIATE_PRETRAINING":
          lowercase   :  Tuple        =					TapasModel(config=__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(__snake_case					,						__snake_case					,						__snake_case       )

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

     # Save tokenizer files
     print(F"""Save tokenizer files to {pytorch_dump_path}"""       )
     lowercase   :  str        =					TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + '''vocab.txt'''					,						model_max_length=5_12       )
     tokenizer.save_pretrained(__snake_case       )

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


if __name__ == "__main__":
 lowerCAmelCase_						 =					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_						 =					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'''




import mpmath  # for roots of unity
import numpy as np


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None):

          '''simple docstring'''






          __A		:					List[Any]						=			list(poly_a or [0])[:]
          __A		:					Optional[int]						=			list(poly_b or [0])[:]

          # Remove leading zero coefficients
          while self.polyA[-1] == 0:
               self.polyA.pop()
          __A		:					Union[str, Any]						=			len(self.polyA)

          while self.polyB[-1] == 0:
               self.polyB.pop()
          __A		:					Optional[int]						=			len(self.polyB)

          # Add 0 to make lengths equal a power of 2
          __A		:					Optional[Any]						=			int(
              2 ** np.ceil(np.loga(len(self.polyA) + len(self.polyB) - 1)))

          while len(self.polyA) < self.c_max_length:
               self.polyA.append(0)
          while len(self.polyB) < self.c_max_length:
               self.polyB.append(0)
          # A complex root used for the fourier transform
          __A		:					str						=			complex(mpmath.root(x=1		,      n=self.c_max_length		,      k=1))

          # The product
          __A		:					Tuple						=			self.__multiply()


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Optional[int]						=			[[x] for x in self.polyA] if which == 'A' else [[x] for x in self.polyB]
          # Corner case
          if len(_UpperCAmelCase) <= 1:
               return dft[0]
          #
          __A		:					Dict						=			self.c_max_length // 2
          while next_ncol > 0:
               __A		:					Optional[Any]						=			[[] for i in range(_UpperCAmelCase)]
               __A		:					Tuple						=			self.root**next_ncol

               # First half of next step
               __A		:					Optional[Any]						=			1
               for j in range(self.c_max_length // (next_ncol * 2)):
                    for i in range(_UpperCAmelCase):
                         new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
                    current_root *= root
               # Second half of next step
               __A		:					List[str]						=			1
               for j in range(self.c_max_length // (next_ncol * 2)):
                    for i in range(_UpperCAmelCase):
                         new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
                    current_root *= root
               # Update
               __A		:					Optional[int]						=			new_dft
               __A		:					Tuple						=			next_ncol // 2
          return dft[0]


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			self.__dft('A')
          __A		:					Optional[Any]						=			self.__dft('B')
          __A		:					str						=			[[dft_a[i] * dft_b[i] for i in range(self.c_max_length)]]
          del dft_a
          del dft_b

          # Corner Case
          if len(inverce_c[0]) <= 1:
               return inverce_c[0]
          # Inverse DFT
          __A		:					Dict						=			2
          while next_ncol <= self.c_max_length:
               __A		:					Optional[int]						=			[[] for i in range(_UpperCAmelCase)]
               __A		:					Any						=			self.root ** (next_ncol // 2)
               __A		:					Tuple						=			1
               # First half of next step
               for j in range(self.c_max_length // next_ncol):
                    for i in range(next_ncol // 2):
                         # Even positions
                         new_inverse_c[i].append(
                             (
                                 inverce_c[i][j]
                                 + inverce_c[i][j + self.c_max_length // next_ncol]
                             )
                             / 2)
                         # Odd positions
                         new_inverse_c[i + next_ncol // 2].append(
                             (
                                 inverce_c[i][j]
                                 - inverce_c[i][j + self.c_max_length // next_ncol]
                             )
                             / (2 * current_root))
                    current_root *= root
               # Update
               __A		:					int						=			new_inverse_c
               next_ncol *= 2
          # Unpack
          __A		:					Optional[int]						=			[round(x[0].real		,      8) + round(x[0].imag		,      8) * 1j for x in inverce_c]

          # Remove leading 0's
          while inverce_c[-1] == 0:
               inverce_c.pop()
          return inverce_c


     def __str__(		self):

          '''simple docstring'''






          __A		:					int						=			'A = ' + ' + '.join(
              F'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]))
          __A		:					Optional[Any]						=			'B = ' + ' + '.join(
              F'{coef}*x^{i}' for coef, i in enumerate(self.polyB[: self.len_B]))
          __A		:					str						=			'A*B = ' + ' + '.join(
              F'{coef}*x^{i}' for coef, i in enumerate(self.product))

          return F'{a}\n{b}\n{c}'


# Unit tests
if __name__ == "__main__":
  import doctest

  doctest.testmod()