





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   a__	:						str      		=  AutoConfig.from_pretrained(lowerCamelCase   )
   a__	:						Optional[int]      		=  FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCamelCase   )
   a__	:						Dict      		=  checkpoints.load_tax_checkpoint(lowerCamelCase   )

   a__	:						List[Any]      		=  "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

   if config.model_type == "t5":
      a__	:						Optional[Any]      		=  "SelfAttention"
   if config.model_type == "longt5" and config.encoder_attention_type == "local":
      a__	:						Union[str, Any]      		=  "LocalSelfAttention"
   elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      a__	:						Optional[Any]      		=  "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   ):
      a__	:						Union[str, Any]      		=  F"""layers_{str(lowerCamelCase   )}"""

      # Self-Attention
      a__	:						Optional[Any]      		=  tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
      a__	:						Any      		=  tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
      a__	:						List[Any]      		=  tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
      a__	:						Union[str, Any]      		=  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":
         a__	:						int      		=  tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

      # Layer Normalization
      a__	:						Optional[Any]      		=  tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

      if split_mlp_wi:
         a__	:						str      		=  tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
         a__	:						Tuple      		=  tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
         a__	:						Optional[int]      		=  tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

      a__	:						Any      		=  tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

      # Layer Normalization
      a__	:						Tuple      		=  tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

      # Assigning
      a__	:						int      		=  flax_model.params["encoder"]["block"][str(lowerCamelCase   )]["layer"]
      a__	:						Union[str, Any]      		=  tax_attention_key
      a__	:						Tuple      		=  tax_attention_out
      a__	:						Tuple      		=  tax_attention_query
      a__	:						List[Any]      		=  tax_attention_value

      a__	:						List[Any]      		=  tax_attention_layer_norm

      # Global input layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
         a__	:						List[Any]      		=  tax_global_layer_norm

      if split_mlp_wi:
         a__	:						Optional[int]      		=  tax_mlp_wi_a
         a__	:						Tuple      		=  tax_mlp_wi_a
      else:
         a__	:						int      		=  tax_mlp_wi

      a__	:						str      		=  tax_mlp_wo
      a__	:						str      		=  tax_mlp_layer_norm

      a__	:						Union[str, Any]      		=  flax_model_encoder_layer_block

   # Only for layer 0:
   a__	:						Optional[int]      		=  tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
   a__	:						Union[str, Any]      		=  tax_encoder_rel_embedding

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

   # Assigning
   a__	:						int      		=  tax_model["target"]["encoder"]["encoder_norm"]["scale"]
   a__	:						List[Any]      		=  tax_encoder_norm

   # Decoder
   for layer_index in range(config.num_layers   ):
      a__	:						Union[str, Any]      		=  F"""layers_{str(lowerCamelCase   )}"""

      # Self-Attention
      a__	:						Union[str, Any]      		=  tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
      a__	:						str      		=  tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
      a__	:						str      		=  tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
      a__	:						Tuple      		=  tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

      # Layer Normalization
      a__	:						str      		=  tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
          "scale"
      ]

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

      # Layer Normalization
      a__	:						Dict      		=  tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

      # MLP
      if split_mlp_wi:
         a__	:						int      		=  tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
         a__	:						Optional[int]      		=  tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
         a__	:						Dict      		=  tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

      a__	:						List[str]      		=  tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

      # Layer Normalization
      a__	:						Dict      		=  tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

      # Assigning
      a__	:						List[Any]      		=  flax_model.params["decoder"]["block"][str(lowerCamelCase   )]["layer"]
      a__	:						Optional[Any]      		=  tax_attention_key
      a__	:						Optional[Any]      		=  tax_attention_out
      a__	:						Any      		=  tax_attention_query
      a__	:						List[str]      		=  tax_attention_value

      a__	:						Optional[int]      		=  tax_pre_attention_layer_norm

      a__	:						Union[str, Any]      		=  tax_enc_dec_attention_key
      a__	:						int      		=  tax_enc_dec_attention_out
      a__	:						List[str]      		=  tax_enc_dec_attention_query
      a__	:						int      		=  tax_enc_dec_attention_value

      a__	:						List[str]      		=  tax_cross_layer_norm

      if split_mlp_wi:
         a__	:						Union[str, Any]      		=  tax_mlp_wi_a
         a__	:						int      		=  tax_mlp_wi_a
      else:
         a__	:						List[Any]      		=  tax_mlp_wi

      a__	:						Union[str, Any]      		=  tax_mlp_wo

      a__	:						int      		=  txa_mlp_layer_norm

      a__	:						Dict      		=  flax_model_decoder_layer_block

   # Decoder Normalization
   a__	:						Any      		=  tax_model["target"]["decoder"]["decoder_norm"]["scale"]
   a__	:						List[str]      		=  txa_decoder_norm

   # Only for layer 0:
   a__	:						Union[str, Any]      		=  tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
   a__	:						str      		=  tax_decoder_rel_embedding

   # Token Embeddings
   a__	:						int      		=  tax_model["target"]["token_embedder"]["embedding"]
   a__	:						Dict      		=  txa_token_embeddings

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

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__       :			Tuple			      =						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."""
  )
  SCREAMING_SNAKE_CASE__       :			List[Any]			      =						parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__       :			str			      =						{
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						["""DistilBertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Tuple			      =						[
      """DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DistilBertForMaskedLM""",
      """DistilBertForMultipleChoice""",
      """DistilBertForQuestionAnswering""",
      """DistilBertForSequenceClassification""",
      """DistilBertForTokenClassification""",
      """DistilBertModel""",
      """DistilBertPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			List[Any]			      =						[
      """TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFDistilBertForMaskedLM""",
      """TFDistilBertForMultipleChoice""",
      """TFDistilBertForQuestionAnswering""",
      """TFDistilBertForSequenceClassification""",
      """TFDistilBertForTokenClassification""",
      """TFDistilBertMainLayer""",
      """TFDistilBertModel""",
      """TFDistilBertPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Any			      =						[
      """FlaxDistilBertForMaskedLM""",
      """FlaxDistilBertForMultipleChoice""",
      """FlaxDistilBertForQuestionAnswering""",
      """FlaxDistilBertForSequenceClassification""",
      """FlaxDistilBertForTokenClassification""",
      """FlaxDistilBertModel""",
      """FlaxDistilBertPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_distilbert import (
      DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DistilBertConfig,
      DistilBertOnnxConfig,
  )
  from .tokenization_distilbert import DistilBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_distilbert_fast import DistilBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_distilbert import (
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DistilBertForMaskedLM,
        DistilBertForMultipleChoice,
        DistilBertForQuestionAnswering,
        DistilBertForSequenceClassification,
        DistilBertForTokenClassification,
        DistilBertModel,
        DistilBertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_distilbert import (
        TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDistilBertForMaskedLM,
        TFDistilBertForMultipleChoice,
        TFDistilBertForQuestionAnswering,
        TFDistilBertForSequenceClassification,
        TFDistilBertForTokenClassification,
        TFDistilBertMainLayer,
        TFDistilBertModel,
        TFDistilBertPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
        FlaxDistilBertPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

