



import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		AutoConfig.from_pretrained(_a			)
      lowerCamelCase             =		FlaxAutoModelForSeqaSeqLM.from_config(config=_a			)
      lowerCamelCase             =		checkpoints.load_tax_checkpoint(_a			)

      lowerCamelCase             =		"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

      if config.model_type == "t5":
            lowerCamelCase             =		"SelfAttention"
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            lowerCamelCase             =		"LocalSelfAttention"
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            lowerCamelCase             =		"TransientGlobalSelfAttention"
      else:
            raise ValueError(
                "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
                " attribute with a value from ['local', 'transient-global]."			)

      # Encoder
      for layer_index in range(config.num_layers			):
            lowerCamelCase             =		F"""layers_{str(_a			)}"""

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

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  lowerCamelCase             =		tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

            # Layer Normalization
            lowerCamelCase             =		tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

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

            lowerCamelCase             =		tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

            # Layer Normalization
            lowerCamelCase             =		tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

            # Assigning
            lowerCamelCase             =		flax_model.params["encoder"]["block"][str(_a			)]["layer"]
            lowerCamelCase             =		tax_attention_key
            lowerCamelCase             =		tax_attention_out
            lowerCamelCase             =		tax_attention_query
            lowerCamelCase             =		tax_attention_value

            lowerCamelCase             =		tax_attention_layer_norm

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  lowerCamelCase             =		tax_global_layer_norm

            if split_mlp_wi:
                  lowerCamelCase             =		tax_mlp_wi_a
                  lowerCamelCase             =		tax_mlp_wi_a
            else:
                  lowerCamelCase             =		tax_mlp_wi

            lowerCamelCase             =		tax_mlp_wo
            lowerCamelCase             =		tax_mlp_layer_norm

            lowerCamelCase             =		flax_model_encoder_layer_block

      # Only for layer 0:
      lowerCamelCase             =		tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
      lowerCamelCase             =		tax_encoder_rel_embedding

      # Side/global relative position_bias + layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            lowerCamelCase             =		tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
            lowerCamelCase             =		tax_encoder_global_rel_embedding

      # Assigning
      lowerCamelCase             =		tax_model["target"]["encoder"]["encoder_norm"]["scale"]
      lowerCamelCase             =		tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_layers			):
            lowerCamelCase             =		F"""layers_{str(_a			)}"""

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

            # Layer Normalization
            lowerCamelCase             =		tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
                "scale"
            ]

            # Encoder-Decoder-Attention
            lowerCamelCase             =		tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
            lowerCamelCase             =		tax_enc_dec_attention_module["key"]["kernel"]
            lowerCamelCase             =		tax_enc_dec_attention_module["out"]["kernel"]
            lowerCamelCase             =		tax_enc_dec_attention_module["query"]["kernel"]
            lowerCamelCase             =		tax_enc_dec_attention_module["value"]["kernel"]

            # Layer Normalization
            lowerCamelCase             =		tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

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

            lowerCamelCase             =		tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

            # Layer Normalization
            lowerCamelCase             =		tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

            # Assigning
            lowerCamelCase             =		flax_model.params["decoder"]["block"][str(_a			)]["layer"]
            lowerCamelCase             =		tax_attention_key
            lowerCamelCase             =		tax_attention_out
            lowerCamelCase             =		tax_attention_query
            lowerCamelCase             =		tax_attention_value

            lowerCamelCase             =		tax_pre_attention_layer_norm

            lowerCamelCase             =		tax_enc_dec_attention_key
            lowerCamelCase             =		tax_enc_dec_attention_out
            lowerCamelCase             =		tax_enc_dec_attention_query
            lowerCamelCase             =		tax_enc_dec_attention_value

            lowerCamelCase             =		tax_cross_layer_norm

            if split_mlp_wi:
                  lowerCamelCase             =		tax_mlp_wi_a
                  lowerCamelCase             =		tax_mlp_wi_a
            else:
                  lowerCamelCase             =		tax_mlp_wi

            lowerCamelCase             =		tax_mlp_wo

            lowerCamelCase             =		txa_mlp_layer_norm

            lowerCamelCase             =		flax_model_decoder_layer_block

      # Decoder Normalization
      lowerCamelCase             =		tax_model["target"]["decoder"]["decoder_norm"]["scale"]
      lowerCamelCase             =		txa_decoder_norm

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

      # Token Embeddings
      lowerCamelCase             =		tax_model["target"]["token_embedder"]["embedding"]
      lowerCamelCase             =		txa_token_embeddings

      # LM Head (only in v1.1 and LongT5 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
            lowerCamelCase             =		tax_model["target"]["decoder"]["logits_dense"]["kernel"]

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


if __name__ == "__main__":
     a_    :  int						  =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
     )
     parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
     parser.add_argument(
         '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
     )
     a_    :  List[Any]						  =		parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase													={
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=[
      "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MegaForCausalLM",
      "MegaForMaskedLM",
      "MegaForMultipleChoice",
      "MegaForQuestionAnswering",
      "MegaForSequenceClassification",
      "MegaForTokenClassification",
      "MegaModel",
      "MegaPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mega import (
        MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegaForCausalLM,
        MegaForMaskedLM,
        MegaForMultipleChoice,
        MegaForQuestionAnswering,
        MegaForSequenceClassification,
        MegaForTokenClassification,
        MegaModel,
        MegaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase													=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




