


"""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


lowerCamelCase_     :     int    		=						"""none"""
lowerCamelCase_     :     Any    		=						"""none"""
def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    a					=value.split(lowercase	)
    return len(lowercase	) > 1 and any((len(lowercase	) == 3 for x in split)	)
def _A		(		lowercase	):







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

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







    """simple docstring"""
    if answer is None:
        return None
    try:
        a					=convert_to_float(lowercase	)
        if isinstance(lowercase ,					lowercase	) and math.isnan(lowercase	):
            return None
        return value
    except ValueError:
        return answer.lower()


lowerCamelCase_     :     Dict    		=						{
    """text""": lambda x: x,
    """real""": convert_to_float,
}






class    __A       ( enum.Enum    ):






 """simple docstring"""




 __lowerCAmelCase				      =						0
 __lowerCAmelCase				      =						1
 __lowerCAmelCase				      =						2
 __lowerCAmelCase				      =						3
 __lowerCAmelCase				      =						4
 __lowerCAmelCase				      =						5






class    __A       ( enum.Enum    ):






 """simple docstring"""




 __lowerCAmelCase				      =						0
 __lowerCAmelCase				      =						1
 __lowerCAmelCase				      =						2






@dataclasses.dataclass
class    __A       :






 """simple docstring"""




 __lowerCAmelCase				      =						42
 __lowerCAmelCase				      =						42
 __lowerCAmelCase				      =						42

lowerCamelCase_     :     Dict    		=						re.compile(R"""\w+|[^\w\s]+""", re.UNICODE | re.MULTILINE | re.DOTALL)
def _A		(		lowercase	):







    """simple docstring"""
    return list(_TOKENIZER.findall(x.lower()	)	)
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """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 _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    a					=table['''types''']
    return _TYPE_CONVERTER[types[column]](lowercase	)
def _A		(		lowercase	):







    """simple docstring"""
    return isinstance(lowercase ,					lowercase	)
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    for cond in conditions:
        a					=row[cond.column]

        a					=_parse_value(lowercase ,					cond.column ,					cond.cmp_value	)

        if _is_string(lowercase	) and _is_string(lowercase	):
            a					=_normalize_for_match(lowercase	)
            a					=_normalize_for_match(lowercase	)

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

        if not _compare(cond.operator ,					lowercase ,					lowercase	):
            return False
    return True
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """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(lowercase	)	)

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

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







    """simple docstring"""
    a					=sql_query['''agg''']
    if aggregation_op_index >= 3:
        a					=_Aggregation(lowercase	)
    else:
        a					=_Aggregation.NONE

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

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

    if not indices:
        return [], aggregation_op

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

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

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

    return indices, aggregation_op
def _A		(		lowercase ,					lowercase ,					lowercase	):







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





def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    a	,   a					=_get_answer_coordinates(lowercase ,					lowercase	)
    a					=_get_float_answer(lowercase ,					lowercase ,					lowercase	)
    a					=_get_answer_text(lowercase ,					lowercase ,					lowercase	)
    # keep the original data the same with TaPas
    if len(lowercase	) == 0:
        a					=[EMPTY_ANSWER]
    return answer_text



"""simple docstring"""







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    a					=TaConfig.from_pretrained(lowercase	)
    a					=FlaxTaForConditionalGeneration(config=lowercase	)
    a					=checkpoints.load_tax_checkpoint(lowercase	)

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

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

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

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

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

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

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

        # Assigning
        a					=tax_attention_key
        a					=tax_attention_out
        a					=tax_attention_query
        a					=tax_attention_value

        a					=tax_attention_layer_norm

        if split_mlp_wi:
            a					=tax_mlp_wi_a
            a					=tax_mlp_wi_a
        else:
            a					=tax_mlp_wi

        a					=tax_mlp_wo
        a					=tax_mlp_layer_norm

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

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

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

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

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

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

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

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

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

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

        # Assigning
        a					=tax_attention_key
        a					=tax_attention_out
        a					=tax_attention_query
        a					=tax_attention_value

        a					=tax_pre_attention_layer_norm

        a					=tax_enc_dec_attention_key
        a					=tax_enc_dec_attention_out
        a					=tax_enc_dec_attention_query
        a					=tax_enc_dec_attention_value

        a					=tax_cross_layer_norm

        if split_mlp_wi:
            a					=tax_mlp_wi_a
            a					=tax_mlp_wi_a
        else:
            a					=tax_mlp_wi

        a					=tax_mlp_wo

        a					=txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
       lowerCamelCase_     :     Dict    		=						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."""
       )
       lowerCamelCase_     :     Optional[int]    		=						parser.parse_args()
       convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)