from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore




class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							c_float



      @property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->int:


          """simple docstring"""


          return sizeof(self.value)







def     A__	(       __A ,			__A ,			__A = 0						):







    '''simple docstring'''
    # < 0 is possible if using a dynamic axis
    if dimension <= 0:
        _lowerCamelCase  :		str			    =    fixed_dimension

    dimension -= num_token_to_add
    return dimension







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    return num_parameters * dtype.size







def     A__	(       __A						):







    '''simple docstring'''
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

    try:
        return AutoProcessor.from_pretrained(__A						)
    except (ValueError, OSError, KeyError):
        _lowerCamelCase    ,				_lowerCamelCase  :		Union[str, Any]			    =    None, None
        try:
            _lowerCamelCase  :		Optional[int]			    =    AutoTokenizer.from_pretrained(__A						)
        except (OSError, KeyError):
            pass
        try:
            _lowerCamelCase  :		Optional[int]			    =    AutoFeatureExtractor.from_pretrained(__A						)
        except (OSError, KeyError):
            pass

        if tokenizer is not None and feature_extractor is not None:
            raise ValueError(
                F"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""						)
        elif tokenizer is None and feature_extractor is None:
            return None
        elif tokenizer is not None:
            return tokenizer
        else:
            return feature_extractor





import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Tuple			    =    TaConfig.from_pretrained(__A						)
    _lowerCamelCase  :		List[Any]			    =    FlaxTaForConditionalGeneration(config=__A						)
    _lowerCamelCase  :		Tuple			    =    checkpoints.load_tax_checkpoint(__A						)

    _lowerCamelCase  :		Union[str, Any]			    =    """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

    # Encoder
    for layer_index in range(config.num_layers						):
        _lowerCamelCase  :		Optional[int]			    =    F"""layers_{str(__A						)}"""

        # Self-Attention
        _lowerCamelCase  :		str			    =    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
        _lowerCamelCase  :		int			    =    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
        _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
        _lowerCamelCase  :		int			    =    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		str			    =    tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

        if split_mlp_wi:
            _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
            _lowerCamelCase  :		Union[str, Any]			    =    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
        else:
            _lowerCamelCase  :		str			    =    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

        _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

        # Assigning
        _lowerCamelCase  :		int			    =    tax_attention_key
        _lowerCamelCase  :		Tuple			    =    tax_attention_out
        _lowerCamelCase  :		List[str]			    =    tax_attention_query
        _lowerCamelCase  :		Tuple			    =    tax_attention_value

        _lowerCamelCase  :		Dict			    =    tax_attention_layer_norm

        if split_mlp_wi:
            _lowerCamelCase  :		Dict			    =    tax_mlp_wi_a
            _lowerCamelCase  :		Optional[int]			    =    tax_mlp_wi_a
        else:
            _lowerCamelCase  :		List[str]			    =    tax_mlp_wi

        _lowerCamelCase  :		Tuple			    =    tax_mlp_wo
        _lowerCamelCase  :		Optional[Any]			    =    tax_mlp_layer_norm

    # Only for layer 0:
    _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
    _lowerCamelCase  :		List[Any]			    =    tax_encoder_rel_embedding

    # Assigning
    _lowerCamelCase  :		Union[str, Any]			    =    tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
    _lowerCamelCase  :		int			    =    tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers						):
        _lowerCamelCase  :		Union[str, Any]			    =    F"""layers_{str(__A						)}"""

        # Self-Attention
        _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
        _lowerCamelCase  :		Dict			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
        _lowerCamelCase  :		Any			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
        _lowerCamelCase  :		Dict			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
            """scale"""
        ]

        # Encoder-Decoder-Attention
        _lowerCamelCase  :		Union[str, Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
            """kernel"""
        ]
        _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
            """kernel"""
        ]
        _lowerCamelCase  :		Dict			    =    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
            """kernel"""
        ]
        _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
            """kernel"""
        ]

        # Layer Normalization
        _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

        # MLP
        if split_mlp_wi:
            _lowerCamelCase  :		str			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
            _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
        else:
            _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

        _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

        # Assigning
        _lowerCamelCase  :		Optional[int]			    =    tax_attention_key
        _lowerCamelCase  :		Tuple			    =    tax_attention_out
        _lowerCamelCase  :		Optional[int]			    =    tax_attention_query
        _lowerCamelCase  :		Optional[int]			    =    tax_attention_value

        _lowerCamelCase  :		Optional[Any]			    =    tax_pre_attention_layer_norm

        _lowerCamelCase  :		str			    =    tax_enc_dec_attention_key
        _lowerCamelCase  :		str			    =    tax_enc_dec_attention_out
        _lowerCamelCase  :		Tuple			    =    tax_enc_dec_attention_query
        _lowerCamelCase  :		Dict			    =    tax_enc_dec_attention_value

        _lowerCamelCase  :		List[Any]			    =    tax_cross_layer_norm

        if split_mlp_wi:
            _lowerCamelCase  :		Optional[Any]			    =    tax_mlp_wi_a
            _lowerCamelCase  :		Tuple			    =    tax_mlp_wi_a
        else:
            _lowerCamelCase  :		Any			    =    tax_mlp_wi

        _lowerCamelCase  :		Optional[int]			    =    tax_mlp_wo

        _lowerCamelCase  :		List[str]			    =    txa_mlp_layer_norm

    # Decoder Normalization
    _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
    _lowerCamelCase  :		Union[str, Any]			    =    txa_decoder_norm

    # Only for layer 0:
    _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
    _lowerCamelCase  :		Tuple			    =    tax_decoder_rel_embedding

    # Token Embeddings
    _lowerCamelCase  :		List[str]			    =    tax_model["""target"""]["""token_embedder"""]["""embedding"""]
    _lowerCamelCase  :		List[str]			    =    txa_token_embeddings

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

    flax_model.save_pretrained(__A						)
    print("""T5X Model was sucessfully converted!"""						)


if __name__ == "__main__":
    lowerCAmelCase      :				Any     			=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      :				Any     			=parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




