from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase      :				List[str]     			=logging.get_logger(__name__)

lowerCAmelCase      :				str     			={
    "mnaylor/mega-base-wikitext": "https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json",
}




class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							'mega'



      def __init__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					str=3_0522     ,	_UpperCamelCase  :					Any=128     ,	_UpperCamelCase  :					Optional[Any]=4     ,	_UpperCamelCase  :					Tuple=256     ,	_UpperCamelCase  :					List[Any]=16     ,	_UpperCamelCase  :					Any=True     ,	_UpperCamelCase  :					Dict=64     ,	_UpperCamelCase  :					Dict=False     ,	_UpperCamelCase  :					Any=-1     ,	_UpperCamelCase  :					Any=None     ,	_UpperCamelCase  :					Optional[int]=True     ,	_UpperCamelCase  :					List[Any]="scalenorm"     ,	_UpperCamelCase  :					str=True     ,	_UpperCamelCase  :					List[str]="silu"     ,	_UpperCamelCase  :					int="softmax"     ,	_UpperCamelCase  :					int=0.1     ,	_UpperCamelCase  :					Optional[Any]=0.1     ,	_UpperCamelCase  :					Union[str, Any]=0.1     ,	_UpperCamelCase  :					List[str]=False     ,	_UpperCamelCase  :					Dict=True     ,	_UpperCamelCase  :					int=256     ,	_UpperCamelCase  :					str=True     ,	_UpperCamelCase  :					Optional[Any]=0.1     ,	_UpperCamelCase  :					Optional[int]=2048     ,	_UpperCamelCase  :					Union[str, Any]=False     ,	_UpperCamelCase  :					Union[str, Any]=2     ,	_UpperCamelCase  :					Optional[int]=0.0_2     ,	_UpperCamelCase  :					List[str]=0.2     ,	_UpperCamelCase  :					str=0.0_2     ,	_UpperCamelCase  :					List[Any]=1.0     ,	_UpperCamelCase  :					List[str]=1     ,	_UpperCamelCase  :					List[Any]=0     ,	_UpperCamelCase  :					List[str]=2     ,	_UpperCamelCase  :					Dict="rotary"     ,	_UpperCamelCase  :					Tuple=None     ,	_UpperCamelCase  :					Tuple=True     ,	_UpperCamelCase  :					str=True     ,	**_UpperCamelCase  :					Tuple     ,	)					->List[str]:


          """simple docstring"""


          super().__init__(pad_token_id=_UpperCamelCase     ,	bos_token_id=_UpperCamelCase     ,	eos_token_id=_UpperCamelCase     ,	**_UpperCamelCase)

          _lowerCamelCase  :		List[Any]			    =    vocab_size
          _lowerCamelCase  :		Union[str, Any]			    =    hidden_size
          _lowerCamelCase  :		Tuple			    =    num_hidden_layers
          _lowerCamelCase  :		List[Any]			    =    activation
          _lowerCamelCase  :		str			    =    attention_activation
          _lowerCamelCase  :		Dict			    =    intermediate_size
          _lowerCamelCase  :		Dict			    =    ema_projection_size
          _lowerCamelCase  :		List[Any]			    =    bidirectional
          _lowerCamelCase  :		Dict			    =    shared_representation_size
          _lowerCamelCase  :		str			    =    use_chunking
          _lowerCamelCase  :		List[str]			    =    chunk_size
          _lowerCamelCase  :		int			    =    truncation
          _lowerCamelCase  :		Any			    =    normalize_before_mega
          _lowerCamelCase  :		Optional[int]			    =    normalization_type
          _lowerCamelCase  :		Tuple			    =    norm_affine
          _lowerCamelCase  :		Tuple			    =    dropout_prob
          _lowerCamelCase  :		int			    =    hidden_dropout_prob
          _lowerCamelCase  :		List[str]			    =    attention_probs_dropout_prob
          _lowerCamelCase  :		List[Any]			    =    use_feature_dropout
          _lowerCamelCase  :		Tuple			    =    use_normalized_ffn
          _lowerCamelCase  :		str			    =    nffn_hidden_size
          _lowerCamelCase  :		int			    =    normalize_before_ffn
          _lowerCamelCase  :		Any			    =    nffn_activation_dropout_prob
          _lowerCamelCase  :		str			    =    max_positions
          _lowerCamelCase  :		Dict			    =    add_token_type_embeddings
          _lowerCamelCase  :		str			    =    type_vocab_size
          _lowerCamelCase  :		Tuple			    =    initializer_range
          _lowerCamelCase  :		Optional[Any]			    =    ema_delta_alpha_range
          _lowerCamelCase  :		Optional[int]			    =    ema_beta_range
          _lowerCamelCase  :		List[Any]			    =    ema_gamma_omega_range
          _lowerCamelCase  :		Any			    =    relative_positional_bias
          _lowerCamelCase  :		str			    =    use_cache
          _lowerCamelCase  :		Dict			    =    classifier_dropout
          _lowerCamelCase  :		Optional[Any]			    =    add_lm_hidden_dense_layer
          _lowerCamelCase  :		List[Any]			    =    1  # not used but required by Hugging Face





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      @property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->Mapping[str, Mapping[int, str]]:


          """simple docstring"""


          if self.task == "multiple-choice":
              _lowerCamelCase  :		Union[str, Any]			    =    {0: """batch""", 1: """choice""", 2: """sequence"""}
          else:
              _lowerCamelCase  :		Dict			    =    {0: """batch""", 1: """sequence"""}
          return OrderedDict(
              [
                  ("""input_ids""", dynamic_axis),
                  ("""attention_mask""", dynamic_axis),
              ])





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)




