
"""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 lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		):
							# 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
							SCREAMING_SNAKE_CASE__		: Any     =     TapasConfig.from_json_file(_snake_case		)
							# set absolute/relative position embeddings parameter
							SCREAMING_SNAKE_CASE__		: List[Any]     =     reset_position_index_per_cell

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

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

														SCREAMING_SNAKE_CASE__		: Dict     =     TapasForQuestionAnswering(config=_snake_case		)
							elif task == "TABFACT":
														SCREAMING_SNAKE_CASE__		: int     =     TapasForSequenceClassification(config=_snake_case		)
							elif task == "MLM":
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     TapasForMaskedLM(config=_snake_case		)
							elif task == "INTERMEDIATE_PRETRAINING":
														SCREAMING_SNAKE_CASE__		: Dict     =     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}'''		)
							SCREAMING_SNAKE_CASE__		: Dict     =     TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""     ,model_max_length=512		)
							tokenizer.save_pretrained(_snake_case		)

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


if __name__ == "__main__":
					UpperCAmelCase__   :					Optional[Any]												=				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.'
					)
					UpperCAmelCase__   :					Any												=				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 math
import os
import sys

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     """"""
							try:
														with open(_snake_case     ,"""rb"""		) as binary_file:
																					SCREAMING_SNAKE_CASE__		: str     =     binary_file.read()
														for dat in data:
																					SCREAMING_SNAKE_CASE__		: int     =     f'''{dat:08b}'''
																					result += curr_byte
														return result
							except OSError:
														print("""File not accessible"""		)
														sys.exit()

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case		):
							lexicon.pop(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     last_match_id

							if math.loga(_snake_case		).is_integer():
														for curr_key in lexicon:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     """0""" + lexicon[curr_key]

							SCREAMING_SNAKE_CASE__		: Tuple     =     bin(_snake_case		)[2:]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     {"""0""": """0""", """1""": """1"""}
							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: str     =     """""", """"""
							SCREAMING_SNAKE_CASE__		: int     =     len(_snake_case		)

							for i in range(len(_snake_case		)		):
														curr_string += data_bits[i]
														if curr_string not in lexicon:
																					continue

														SCREAMING_SNAKE_CASE__		: Dict     =     lexicon[curr_string]
														result += last_match_id
														add_key_to_lexicon(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		)
														index += 1
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """"""

							while curr_string != "" and curr_string not in lexicon:
														curr_string += "0"

							if curr_string != "":
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     lexicon[curr_string]
														result += last_match_id

							return result

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: str     =     os.path.getsize(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Any     =     bin(_snake_case		)[2:]
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     len(_snake_case		)

							return "0" * (length_length - 1) + file_length_binary + compressed

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     8
							try:
														with open(_snake_case     ,"""wb"""		) as opened_file:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     [
																					    to_write[i : i + byte_length]
																					    for i in range(0     ,len(_snake_case		)     ,_snake_case		)
																					]

																					if len(result_byte_array[-1]		) % byte_length == 0:
																												result_byte_array.append("""10000000"""		)
																					else:
																												result_byte_array[-1] += "1" + "0" * (
																												    byte_length - len(result_byte_array[-1]		) - 1
																												)

																					for elem in result_byte_array:
																												opened_file.write(int(_snake_case     ,2		).to_bytes(1     ,byteorder="""big"""		)		)
							except OSError:
														print("""File not accessible"""		)
														sys.exit()

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     read_file_binary(_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     compress_data(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     add_file_length(_snake_case     ,_snake_case		)
							write_file_binary(_snake_case     ,_snake_case		)


if __name__ == "__main__":
					compress(sys.argv[1], sys.argv[2])




