






'''simple docstring'''
from typing import TYPE_CHECKING

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


a_					=	{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
      'UniSpeechForCTC',
      'UniSpeechForPreTraining',
      'UniSpeechForSequenceClassification',
      'UniSpeechModel',
      'UniSpeechPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_unispeech import (
        UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
        UniSpeechForCTC,
        UniSpeechForPreTraining,
        UniSpeechForSequenceClassification,
        UniSpeechModel,
        UniSpeechPreTrainedModel,
    )

else:
  import sys

  a_					=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Any													=AutoConfig.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=checkpoints.load_tax_checkpoint(UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

      if config.model_type == "t5":
            SCREAMING_SNAKE_CASE__       :			str													='''SelfAttention'''
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''LocalSelfAttention'''
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            SCREAMING_SNAKE_CASE__       :			Dict													='''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							):
            SCREAMING_SNAKE_CASE__       :			Dict													=f"layers_{str(UpperCamelCase__							)}"

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

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''encoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			Dict													=tax_attention_layer_norm

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_global_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE__       :			Any													=tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=tax_mlp_wi

            SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wo
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=flax_model_encoder_layer_block

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

      # Side/global relative position_bias + layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_encoder_global_rel_embedding

      # Assigning
      SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			int													=tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_layers							):
            SCREAMING_SNAKE_CASE__       :			int													=f"layers_{str(UpperCamelCase__							)}"

            # Self-Attention
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                '''scale'''
            ]

            # Encoder-Decoder-Attention
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_enc_dec_attention_module['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			Any													=tax_enc_dec_attention_module['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_enc_dec_attention_module['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_enc_dec_attention_module['''value''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

            # MLP
            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''decoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_pre_attention_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_enc_dec_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_enc_dec_attention_out
            SCREAMING_SNAKE_CASE__       :			str													=tax_enc_dec_attention_query
            SCREAMING_SNAKE_CASE__       :			str													=tax_enc_dec_attention_value

            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_cross_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi

            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_mlp_wo

            SCREAMING_SNAKE_CASE__       :			str													=txa_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[Any]													=flax_model_decoder_layer_block

      # Decoder Normalization
      SCREAMING_SNAKE_CASE__       :			Any													=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=txa_decoder_norm

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

      # Token Embeddings
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''token_embedder''']['''embedding''']
      SCREAMING_SNAKE_CASE__       :			List[Any]													=txa_token_embeddings

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

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


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