




"""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        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      List[str]:


			"""simple docstring"""
			# 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						:			Optional[Any]												=       TapasConfig.from_json_file(UpperCamelCase				)
			# set absolute/relative position embeddings parameter
			__UpperCAmelCase						:			Optional[Any]												=       reset_position_index_per_cell

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

						__UpperCAmelCase						:			Optional[int]												=       TapasForQuestionAnswering(config=UpperCamelCase				)
			elif task == "WIKISQL_SUPERVISED":
						# run_task_main.py hparams
						__UpperCAmelCase						:			int												=       4
						__UpperCAmelCase						:			Optional[int]												=       False
						# hparam_utils.py hparams
						__UpperCAmelCase						:			int												=       36.4519
						__UpperCAmelCase						:			str												=       0.903421
						__UpperCAmelCase						:			Dict												=       222.088
						__UpperCAmelCase						:			Dict												=       True
						__UpperCAmelCase						:			Union[str, Any]												=       True
						__UpperCAmelCase						:			Tuple												=       True
						__UpperCAmelCase						:			Any												=       0.763141

						__UpperCAmelCase						:			Optional[Any]												=       TapasForQuestionAnswering(config=UpperCamelCase				)
			elif task == "TABFACT":
						__UpperCAmelCase						:			Union[str, Any]												=       TapasForSequenceClassification(config=UpperCamelCase				)
			elif task == "MLM":
						__UpperCAmelCase						:			Tuple												=       TapasForMaskedLM(config=UpperCamelCase				)
			elif task == "INTERMEDIATE_PRETRAINING":
						__UpperCAmelCase						:			List[str]												=       TapasModel(config=UpperCamelCase				)
			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(UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)

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

			# Save tokenizer files
			print(f"Save tokenizer files to {pytorch_dump_path}"				)
			__UpperCAmelCase						:			str												=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"					,							model_max_length=512				)
			tokenizer.save_pretrained(UpperCamelCase				)

			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,
					)







'''simple docstring'''
from __future__ import annotations






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  position
						_UpperCamelCase   :						Any												=  [
						    (y + 1, x + 2),
						    (y - 1, x + 2),
						    (y + 1, x - 2),
						    (y - 1, x - 2),
						    (y + 2, x + 1),
						    (y + 2, x - 1),
						    (y - 2, x + 1),
						    (y - 2, x - 1),
						]
						_UpperCamelCase   :						Optional[Any]												=  []

						for position in positions:
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position
												if 0 <= y_test < n and 0 <= x_test < n:
																		permissible_positions.append(UpperCAmelCase_				)

						return permissible_positions






def A__       (	UpperCAmelCase_				):
						return not any(elem == 0 for row in board for elem in row				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):

						if is_complete(UpperCAmelCase_				):
												return True

						for position in get_valid_pos(UpperCAmelCase_    ,    len(UpperCAmelCase_				)				):
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position

												if board[y][x] == 0:
																		_UpperCamelCase   :						int												=  curr + 1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    UpperCAmelCase_    ,    curr + 1				):
																								return True
																		_UpperCamelCase   :						Union[str, Any]												=  0

						return False






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  [[0 for i in range(UpperCAmelCase_				)] for j in range(UpperCAmelCase_				)]

						for i in range(UpperCAmelCase_				):
												for j in range(UpperCAmelCase_				):
																		_UpperCamelCase   :						Tuple												=  1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    (i, j)    ,    1				):
																								return board
																		_UpperCamelCase   :						Union[str, Any]												=  0

						_UpperCamelCase   :						int												=  f'Open Kight Tour cannot be performed on a board of size {n}'
						raise ValueError(UpperCAmelCase_				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


