code: 
  import dataclasses
  import enum
  import functools
  import math
  import re
  
  # The following script is adapted from the script of TaPas.
  # Original: https://github.com/google-research/tapas/master/wikisql_utils.py
  from typing import Any, List, Text
  
  
  lowercase__						=			'none'
  lowercase__						=			'none'
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Optional[Any]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Union[str, Any]     =	value.split(SCREAMING_SNAKE_CASE__ )
  		return len(SCREAMING_SNAKE_CASE__ ) > 1 and any((len(SCREAMING_SNAKE_CASE__ ) == 3 for x in split) )
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
  		'''simple docstring'''
  
  
  		if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
  				return value
  		if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
  				return float(SCREAMING_SNAKE_CASE__ )
  		if not isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
  				raise ValueError('''Argument value is not a string. Can\'t parse it as float''' )
  		snake_case     :					Any     =	value
  
  		try:
  				# Example: 1,000.7
  				if "." in sanitized and "," in sanitized:
  						return float(sanitized.replace(''','''   ,					'''''' ) )
  				# 1,000
  				if "," in sanitized and _split_thousands(''','''   ,					SCREAMING_SNAKE_CASE__ ):
  						return float(sanitized.replace(''','''   ,					'''''' ) )
  				# 5,5556
  				if "," in sanitized and sanitized.count(''',''' ) == 1 and not _split_thousands(''','''   ,					SCREAMING_SNAKE_CASE__ ):
  						return float(sanitized.replace(''','''   ,					'''.''' ) )
  				# 0.0.0.1
  				if sanitized.count('''.''' ) > 1:
  						return float(sanitized.replace('''.'''   ,					'''''' ) )
  				# 0,0,0,1
  				if sanitized.count(''',''' ) > 1:
  						return float(sanitized.replace(''','''   ,					'''''' ) )
  				return float(SCREAMING_SNAKE_CASE__ )
  		except ValueError:
  				# Avoid adding the sanitized value in the error message.
  				raise ValueError('''Unable to convert value to float''' )
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Union[str, Any]:
  
  
  		'''simple docstring'''
  
  
  		if answer is None:
  				return None
  		try:
  				snake_case     :					List[str]     =	convert_to_float(SCREAMING_SNAKE_CASE__ )
  				if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ) and math.isnan(SCREAMING_SNAKE_CASE__ ):
  						return None
  				return value
  		except ValueError:
  				return answer.lower()
  
  
  lowercase__						=			{
      'text': lambda x: x,
      'real': convert_to_float,
  }
  
  
  
  
  
  
  class   snake_case__					(			enum.Enum				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	0
  		lowerCamelCase           =	1
  		lowerCamelCase           =	2
  		lowerCamelCase           =	3
  		lowerCamelCase           =	4
  		lowerCamelCase           =	5
  
  
  
  
  
  
  
  class   snake_case__					(			enum.Enum				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	0
  		lowerCamelCase           =	1
  		lowerCamelCase           =	2
  
  
  
  
  
  
  
  @dataclasses.dataclass
  class   snake_case__					:
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	42
  		lowerCamelCase           =	42
  		lowerCamelCase           =	42
  
  
  
  
  
  
  
  lowercase__						=			re.compile(R"\w+|[^\w\s]+", re.UNICODE | re.MULTILINE | re.DOTALL)
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       List[str]:
  
  
  		'''simple docstring'''
  
  
  		return list(_TOKENIZER.findall(x.lower() ) )
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Tuple:
  
  
  		'''simple docstring'''
  
  
  		if operator == _Operator.EQUALS:
  				return src == tgt
  		elif operator == _Operator.GREATER:
  				return src > tgt
  		elif operator == _Operator.LESSER:
  				return src < tgt
  		raise ValueError(F'Unknown operator: {operator}' )
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Any     =	table['''types''']
  		return _TYPE_CONVERTER[types[column]](SCREAMING_SNAKE_CASE__ )
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       List[str]:
  
  
  		'''simple docstring'''
  
  
  		return isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Any:
  
  
  		'''simple docstring'''
  
  
  		for cond in conditions:
  				snake_case     :					Tuple     =	row[cond.column]
  
  				snake_case     :					Union[str, Any]     =	_parse_value(SCREAMING_SNAKE_CASE__   ,					cond.column   ,					cond.cmp_value )
  
  				if _is_string(SCREAMING_SNAKE_CASE__ ) and _is_string(SCREAMING_SNAKE_CASE__ ):
  						snake_case     :					Any     =	_normalize_for_match(SCREAMING_SNAKE_CASE__ )
  						snake_case     :					Dict     =	_normalize_for_match(SCREAMING_SNAKE_CASE__ )
  
  				if not isinstance(SCREAMING_SNAKE_CASE__   ,					type(SCREAMING_SNAKE_CASE__ ) ):
  						raise ValueError('''Type difference {} != {}'''.format(type(SCREAMING_SNAKE_CASE__ )   ,					type(SCREAMING_SNAKE_CASE__ ) ) )
  
  				if not _compare(cond.operator   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
  						return False
  		return True
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Tuple:
  
  
  		'''simple docstring'''
  
  
  		if not answer_coordinates:
  				if aggregation_op == _Aggregation.COUNT:
  						return 0.0
  				else:
  						return EMPTY_ANSWER_AGG
  
      # Count can support non numeric answers.
  		if aggregation_op == _Aggregation.COUNT:
  				return float(len(SCREAMING_SNAKE_CASE__ ) )
  
  		# If we have just one answer, if float returns it or try a conversion.
  		snake_case     :					List[Any]     =	[table['''rows'''][i][j] for (i, j) in answer_coordinates]
  		if len(SCREAMING_SNAKE_CASE__ ) == 1:
  				try:
  						return convert_to_float(values[0] )
  				except ValueError as e:
  						if aggregation_op != _Aggregation.NONE:
  								raise e
  
  		if aggregation_op == _Aggregation.NONE:
  				return None
  
  		# Other aggregation only support numeric values. Bail out if we have strings.
  		if not all((isinstance(SCREAMING_SNAKE_CASE__   ,					(int, float) ) for v in values) ):
  				return None
  
  		if aggregation_op == _Aggregation.SUM:
  				return float(sum(SCREAMING_SNAKE_CASE__ ) )
  		elif aggregation_op == _Aggregation.AVERAGE:
  				return sum(SCREAMING_SNAKE_CASE__ ) / len(SCREAMING_SNAKE_CASE__ )
  		else:
  				raise ValueError(F'Unknown aggregation: {aggregation_op}' )
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       List[str]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[int]     =	sql_query['''agg''']
  		if aggregation_op_index >= 3:
  				snake_case     :					Dict     =	_Aggregation(SCREAMING_SNAKE_CASE__ )
  		else:
  				snake_case     :					Dict     =	_Aggregation.NONE
  
  		snake_case     :					Optional[Any]     =	sql_query['''sel''']
  		snake_case     :					Union[str, Any]     =	[
  		    _Condition(SCREAMING_SNAKE_CASE__   ,					_Operator(SCREAMING_SNAKE_CASE__ )   ,					SCREAMING_SNAKE_CASE__ )
  		    for column, operator, cmp_value in zip(
  		        sql_query['''conds''']['''column_index''']   ,					sql_query['''conds''']['''operator_index''']   ,					sql_query['''conds''']['''condition'''] )
  		]
  
  		snake_case     :					Any     =	[]
  		for row in range(len(table['''rows'''] ) ):
  				if _respect_conditions(SCREAMING_SNAKE_CASE__   ,					table['''rows'''][row]   ,					SCREAMING_SNAKE_CASE__ ):
  						indices.append((row, target_column) )
  
  		if not indices:
  				return [], aggregation_op
  
  		if len(SCREAMING_SNAKE_CASE__ ) == 1:
  				return indices, aggregation_op
  
  		# Parsing of MIN/MAX.
  		if aggregation_op_index in (1, 2):
  				snake_case     :					List[str]     =	{2: min, 1: max}
  				snake_case     :					Optional[Any]     =	[(table['''rows'''][i][j], index) for index, (i, j) in enumerate(SCREAMING_SNAKE_CASE__ )]
  				snake_case     :					Tuple     =	functools.reduce(operators[sql_query['''agg''']]   ,					SCREAMING_SNAKE_CASE__ )
  
  				snake_case     :					Optional[Any]     =	[indices[reduced[1]]]
  				return ret, _Aggregation.NONE
  
  		return indices, aggregation_op
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       List[str]:
  
  
  		'''simple docstring'''
  
  
  		if float_answer is not None:
  				return [str(SCREAMING_SNAKE_CASE__ )]
  		return [str(table['''real_rows'''][r][c] ) for r, c in answer_coordinates]
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       List:
  
  
  		'''simple docstring'''
  
  
  		snake_case ,snake_case     :					Optional[int]     =	_get_answer_coordinates(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  		snake_case     :					Optional[Any]     =	_get_float_answer(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  		snake_case     :					Union[str, Any]     =	_get_answer_text(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  		# keep the original data the same with TaPas
  		if len(SCREAMING_SNAKE_CASE__ ) == 0:
  				snake_case     :					Tuple     =	[EMPTY_ANSWER]
  		return answer_text
  
  
  
style_context: 
  '''simple docstring'''
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Tuple     =	json.load(open(SCREAMING_SNAKE_CASE__   ,					'''r''' ) )
  		snake_case     :					Any     =	GPTNeoConfig(
  		    hidden_size=config_json['''n_embd''']   ,					num_layers=config_json['''n_layer''']   ,					num_heads=config_json['''n_head''']   ,					attention_types=config_json['''attention_types''']   ,					max_position_embeddings=config_json['''n_positions''']   ,					resid_dropout=config_json['''res_dropout''']   ,					embed_dropout=config_json['''embed_dropout''']   ,					attention_dropout=config_json['''attn_dropout''']   ,					)
  		print(F'Building PyTorch model from configuration: {config}' )
  		snake_case     :					Union[str, Any]     =	GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__ )
  
  		# Load weights from tf checkpoint
  		load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  
  		# Save pytorch-model
  		print(F'Save PyTorch model to {pytorch_dump_path}' )
  		model.save_pretrained(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
  						lowercase__						=			argparse.ArgumentParser()
  						# Required parameters
  						parser.add_argument(
  						    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  						)
  						parser.add_argument(
  						    "--config_file",
  						    default=None,
  						    type=str,
  						    required=True,
  						    help=(
  						        "The config json file corresponding to the pre-trained mesh-tf 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."
  						)
  						lowercase__						=			parser.parse_args()
  						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  