
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




'''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)


