




'''simple docstring'''



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


UpperCAmelCase_    :					Any					      =						'''none'''
UpperCAmelCase_    :					Any					      =						'''none'''







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[int]				,		_lowerCamelCase							:       List[Any]		)->				List[str]:




     '''simple docstring'''





     __snake_case         =					value.split(_lowerCamelCase		)
     return len(_lowerCamelCase		) > 1 and any((len(_lowerCamelCase		) == 3 for x in split)		)







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[Any]		)->				Any:




     '''simple docstring'''





     if isinstance(_lowerCamelCase				,		_lowerCamelCase		):
          return value
     if isinstance(_lowerCamelCase				,		_lowerCamelCase		):
          return float(_lowerCamelCase		)
     if not isinstance(_lowerCamelCase				,		_lowerCamelCase		):
          raise ValueError('''Argument value is not a string. Can\'t parse it as float'''		)
     __snake_case         =					value

     try:
          # Example: 1,000.7
          if "." in sanitized and "," in sanitized:
               return float(sanitized.replace(''','''				,		''''''		)		)
          # 1,000
          if "," in sanitized and _split_thousands(''','''				,		_lowerCamelCase		):
               return float(sanitized.replace(''','''				,		''''''		)		)
          # 5,5556
          if "," in sanitized and sanitized.count(''','''		) == 1 and not _split_thousands(''','''				,		_lowerCamelCase		):
               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(_lowerCamelCase		)
     except ValueError:
          # Avoid adding the sanitized value in the error message.
          raise ValueError('''Unable to convert value to float'''		)







def      _UpperCamelCase	(_lowerCamelCase							:       List[str]		)->				Tuple:




     '''simple docstring'''





     if answer is None:
          return None
     try:
          __snake_case         =					convert_to_float(_lowerCamelCase		)
          if isinstance(_lowerCamelCase				,		_lowerCamelCase		) and math.isnan(_lowerCamelCase		):
               return None
          return value
     except ValueError:
          return answer.lower()


UpperCAmelCase_    :					Dict					      =						{
    '''text''': lambda x: x,
    '''real''': convert_to_float,
}





class       lowerCAmelCase				(	enum.Enum):
       __lowercase					: Dict	    =     0
       __lowercase					: Tuple	    =     1
       __lowercase					: int	    =     2
       __lowercase					: Optional[int]	    =     3
       __lowercase					: Tuple	    =     4
       __lowercase					: int	    =     5





class       lowerCAmelCase				(	enum.Enum):
       __lowercase					: List[str]	    =     0
       __lowercase					: Union[str, Any]	    =     1
       __lowercase					: Any	    =     2





@dataclasses.dataclass
class       lowerCAmelCase				:
       __lowercase					: Text
       __lowercase					: _Operator
       __lowercase					: Any





UpperCAmelCase_    :					List[str]					      =						re.compile(R'''\w+|[^\w\s]+''', re.UNICODE | re.MULTILINE | re.DOTALL)







def      _UpperCamelCase	(_lowerCamelCase							:       str		)->				List[Any]:




     '''simple docstring'''





     return list(_TOKENIZER.findall(x.lower()		)		)







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[Any]				,		_lowerCamelCase							:       List[str]				,		_lowerCamelCase							:       Optional[int]		)->				Optional[int]:




     '''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	(_lowerCamelCase							:       Optional[Any]				,		_lowerCamelCase							:       Dict				,		_lowerCamelCase							:       List[Any]		)->				Dict:




     '''simple docstring'''





     __snake_case         =					table['''types''']
     return _TYPE_CONVERTER[types[column]](_lowerCamelCase		)







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[int]		)->				Dict:




     '''simple docstring'''





     return isinstance(_lowerCamelCase				,		_lowerCamelCase		)







def      _UpperCamelCase	(_lowerCamelCase							:       int				,		_lowerCamelCase							:       str				,		_lowerCamelCase							:       Any		)->				Optional[Any]:




     '''simple docstring'''





     for cond in conditions:
          __snake_case         =					row[cond.column]

          __snake_case         =					_parse_value(_lowerCamelCase				,		cond.column				,		cond.cmp_value		)

          if _is_string(_lowerCamelCase		) and _is_string(_lowerCamelCase		):
               __snake_case         =					_normalize_for_match(_lowerCamelCase		)
               __snake_case         =					_normalize_for_match(_lowerCamelCase		)

          if not isinstance(_lowerCamelCase				,		type(_lowerCamelCase		)		):
               raise ValueError('''Type difference {} != {}'''.format(type(_lowerCamelCase		)				,		type(_lowerCamelCase		)		)		)

          if not _compare(cond.operator				,		_lowerCamelCase				,		_lowerCamelCase		):
               return False
     return True







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[Any]				,		_lowerCamelCase							:       str				,		_lowerCamelCase							:       str		)->				Any:




     '''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(_lowerCamelCase		)		)

     # If we have just one answer, if float returns it or try a conversion.
     __snake_case         =					[table['''rows'''][i][j] for (i, j) in answer_coordinates]
     if len(_lowerCamelCase		) == 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(_lowerCamelCase				,		(int, float)		) for v in values)		):
          return None

     if aggregation_op == _Aggregation.SUM:
          return float(sum(_lowerCamelCase		)		)
     elif aggregation_op == _Aggregation.AVERAGE:
          return sum(_lowerCamelCase		) / len(_lowerCamelCase		)
     else:
          raise ValueError(f'''Unknown aggregation: {aggregation_op}'''		)







def      _UpperCamelCase	(_lowerCamelCase							:       Tuple				,		_lowerCamelCase							:       Any		)->				Tuple:




     '''simple docstring'''





     __snake_case         =					sql_query['''agg''']
     if aggregation_op_index >= 3:
          __snake_case         =					_Aggregation(_lowerCamelCase		)
     else:
          __snake_case         =					_Aggregation.NONE

     __snake_case         =					sql_query['''sel''']
     __snake_case         =					[
         _Condition(_lowerCamelCase				,		_Operator(_lowerCamelCase		)				,		_lowerCamelCase		)
         for column, operator, cmp_value in zip(
             sql_query['''conds''']['''column_index''']				,		sql_query['''conds''']['''operator_index''']				,		sql_query['''conds''']['''condition''']		)
     ]

     __snake_case         =					[]
     for row in range(len(table['''rows''']		)		):
          if _respect_conditions(_lowerCamelCase				,		table['''rows'''][row]				,		_lowerCamelCase		):
               indices.append((row, target_column)		)

     if not indices:
          return [], aggregation_op

     if len(_lowerCamelCase		) == 1:
          return indices, aggregation_op

     # Parsing of MIN/MAX.
     if aggregation_op_index in (1, 2):
          __snake_case         =					{2: min, 1: max}
          __snake_case         =					[(table['''rows'''][i][j], index) for index, (i, j) in enumerate(_lowerCamelCase		)]
          __snake_case         =					functools.reduce(operators[sql_query['''agg''']]				,		_lowerCamelCase		)

          __snake_case         =					[indices[reduced[1]]]
          return ret, _Aggregation.NONE

     return indices, aggregation_op







def      _UpperCamelCase	(_lowerCamelCase							:       Tuple				,		_lowerCamelCase							:       List[str]				,		_lowerCamelCase							:       Dict		)->				Optional[Any]:




     '''simple docstring'''





     if float_answer is not None:
          return [str(_lowerCamelCase		)]
     return [str(table['''real_rows'''][r][c]		) for r, c in answer_coordinates]







def      _UpperCamelCase	(_lowerCamelCase							:       Union[str, Any]				,		_lowerCamelCase							:       Any		)->				List:




     '''simple docstring'''





     __snake_case   ,      __snake_case         =					_get_answer_coordinates(_lowerCamelCase				,		_lowerCamelCase		)
     __snake_case         =					_get_float_answer(_lowerCamelCase				,		_lowerCamelCase				,		_lowerCamelCase		)
     __snake_case         =					_get_answer_text(_lowerCamelCase				,		_lowerCamelCase				,		_lowerCamelCase		)
     # keep the original data the same with TaPas
     if len(_lowerCamelCase		) == 0:
          __snake_case         =					[EMPTY_ANSWER]
     return answer_text












'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[Any]				,		_lowerCamelCase							:       List[Any]				,		_lowerCamelCase							:       Dict		)->				Dict:




     '''simple docstring'''





     __snake_case         =					TaConfig.from_pretrained(_lowerCamelCase		)
     __snake_case         =					FlaxTaForConditionalGeneration(config=_lowerCamelCase		)
     __snake_case         =					checkpoints.load_tax_checkpoint(_lowerCamelCase		)

     __snake_case         =					'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

     # Encoder
     for layer_index in range(config.num_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

          # Self-Attention
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

          if split_mlp_wi:
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_attention_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo
          __snake_case         =					tax_mlp_layer_norm

     # Only for layer 0:
     __snake_case         =					tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
     __snake_case         =					tax_encoder_rel_embedding

     # Assigning
     __snake_case         =					tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
     __snake_case         =					tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

          # Self-Attention
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]

          # Encoder-Decoder-Attention
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

          # MLP
          if split_mlp_wi:
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_pre_attention_layer_norm

          __snake_case         =					tax_enc_dec_attention_key
          __snake_case         =					tax_enc_dec_attention_out
          __snake_case         =					tax_enc_dec_attention_query
          __snake_case         =					tax_enc_dec_attention_value

          __snake_case         =					tax_cross_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo

          __snake_case         =					txa_mlp_layer_norm

     # Decoder Normalization
     __snake_case         =					tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
     __snake_case         =					txa_decoder_norm

     # Only for layer 0:
     __snake_case         =					tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
     __snake_case         =					tax_decoder_rel_embedding

     # Token Embeddings
     __snake_case         =					tax_model['''target''']['''token_embedder''']['''embedding''']
     __snake_case         =					txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          __snake_case         =					tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

     flax_model.save_pretrained(_lowerCamelCase		)
     print('''T5X Model was sucessfully converted!'''		)


if __name__ == "__main__":
     UpperCAmelCase_    :					List[str]					      =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the TX5 checkpoint.'''
     )
     parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of T5 model.''')
     parser.add_argument(
         '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
     )
     UpperCAmelCase_    :					List[Any]					      =						parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






