




'''simple docstring'''


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



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




     """simple docstring"""


     a_    :       Optional[int]    	=     TaConfig.from_pretrained(UpperCamelCase__							)
     a_    :       List[str]    	=     FlaxTaForConditionalGeneration(config=UpperCamelCase__							)
     a_    :       List[Any]    	=     checkpoints.load_tax_checkpoint(UpperCamelCase__							)

     a_    :       List[str]    	=     """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

     # Encoder
     for layer_index in range(config.num_layers							):
          a_    :       int    	=     F"layers_{str(UpperCamelCase__							)}"

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

          # Layer Normalization
          a_    :       Tuple    	=     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_    :       Optional[int]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_    :       Dict    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_    :       str    	=     tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_    :       Union[str, Any]    	=     tax_attention_key
          a_    :       Dict    	=     tax_attention_out
          a_    :       Any    	=     tax_attention_query
          a_    :       List[str]    	=     tax_attention_value

          a_    :       Union[str, Any]    	=     tax_attention_layer_norm

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

          a_    :       Optional[int]    	=     tax_mlp_wo
          a_    :       Tuple    	=     tax_mlp_layer_norm

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

     # Assigning
     a_    :       Dict    	=     tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
     a_    :       Optional[Any]    	=     tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers							):
          a_    :       int    	=     F"layers_{str(UpperCamelCase__							)}"

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

          # Layer Normalization
          a_    :       List[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"""]["""key"""][
              """kernel"""
          ]
          a_    :       str    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
              """kernel"""
          ]
          a_    :       Optional[int]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
              """kernel"""
          ]
          a_    :       List[Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
              """kernel"""
          ]

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

          # MLP
          if split_mlp_wi:
               a_    :       Tuple    	=     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_    :       Optional[Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_    :       List[str]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_    :       Any    	=     tax_attention_key
          a_    :       Any    	=     tax_attention_out
          a_    :       Any    	=     tax_attention_query
          a_    :       Union[str, Any]    	=     tax_attention_value

          a_    :       Any    	=     tax_pre_attention_layer_norm

          a_    :       Optional[int]    	=     tax_enc_dec_attention_key
          a_    :       List[Any]    	=     tax_enc_dec_attention_out
          a_    :       Any    	=     tax_enc_dec_attention_query
          a_    :       Tuple    	=     tax_enc_dec_attention_value

          a_    :       List[str]    	=     tax_cross_layer_norm

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

          a_    :       List[Any]    	=     tax_mlp_wo

          a_    :       Union[str, Any]    	=     txa_mlp_layer_norm

     # Decoder Normalization
     a_    :       str    	=     tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
     a_    :       Dict    	=     txa_decoder_norm

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

     # Token Embeddings
     a_    :       Union[str, Any]    	=     tax_model["""target"""]["""token_embedder"""]["""embedding"""]
     a_    :       Optional[Any]    	=     txa_token_embeddings

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

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


if __name__ == "__main__":
 lowerCAmelCase_			:     List[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_			:     str							    =     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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_			:     int							    =     {
    'configuration_dpr': ['DPR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPRConfig'],
    'tokenization_dpr': [
        'DPRContextEncoderTokenizer',
        'DPRQuestionEncoderTokenizer',
        'DPRReaderOutput',
        'DPRReaderTokenizer',
    ],
}


try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_			:     Union[str, Any]							    =     [
     'DPRContextEncoderTokenizerFast',
     'DPRQuestionEncoderTokenizerFast',
     'DPRReaderTokenizerFast',
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_			:     List[str]							    =     [
     'DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DPRContextEncoder',
     'DPRPretrainedContextEncoder',
     'DPRPreTrainedModel',
     'DPRPretrainedQuestionEncoder',
     'DPRPretrainedReader',
     'DPRQuestionEncoder',
     'DPRReader',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_			:     Dict							    =     [
     'TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDPRContextEncoder',
     'TFDPRPretrainedContextEncoder',
     'TFDPRPretrainedQuestionEncoder',
     'TFDPRPretrainedReader',
     'TFDPRQuestionEncoder',
     'TFDPRReader',
 ]


if TYPE_CHECKING:
 from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
 from .tokenization_dpr import (
     DPRContextEncoderTokenizer,
     DPRQuestionEncoderTokenizer,
     DPRReaderOutput,
     DPRReaderTokenizer,
 )

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_dpr_fast import (
      DPRContextEncoderTokenizerFast,
      DPRQuestionEncoderTokenizerFast,
      DPRReaderTokenizerFast,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_dpr import (
      DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
      DPRContextEncoder,
      DPRPretrainedContextEncoder,
      DPRPreTrainedModel,
      DPRPretrainedQuestionEncoder,
      DPRPretrainedReader,
      DPRQuestionEncoder,
      DPRReader,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_dpr import (
      TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDPRContextEncoder,
      TFDPRPretrainedContextEncoder,
      TFDPRPretrainedQuestionEncoder,
      TFDPRPretrainedReader,
      TFDPRQuestionEncoder,
      TFDPRReader,
  )

else:
 import sys

 lowerCAmelCase_			:     int							    =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
