
'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  ):




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			TaConfig.from_pretrained(lowercase__  )
   _SCREAMING_SNAKE_CASE       	=			FlaxTaForConditionalGeneration(config=lowercase__  )
   _SCREAMING_SNAKE_CASE       	=			checkpoints.load_tax_checkpoint(lowercase__  )

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

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

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

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

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

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

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

      # Assigning
      _SCREAMING_SNAKE_CASE       	=			tax_attention_key
      _SCREAMING_SNAKE_CASE       	=			tax_attention_out
      _SCREAMING_SNAKE_CASE       	=			tax_attention_query
      _SCREAMING_SNAKE_CASE       	=			tax_attention_value

      _SCREAMING_SNAKE_CASE       	=			tax_attention_layer_norm

      if split_mlp_wi:
         _SCREAMING_SNAKE_CASE       	=			tax_mlp_wi_a
         _SCREAMING_SNAKE_CASE       	=			tax_mlp_wi_a
      else:
         _SCREAMING_SNAKE_CASE       	=			tax_mlp_wi

      _SCREAMING_SNAKE_CASE       	=			tax_mlp_wo
      _SCREAMING_SNAKE_CASE       	=			tax_mlp_layer_norm

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

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

   # Decoder
   for layer_index in range(config.num_decoder_layers  ):
      _SCREAMING_SNAKE_CASE       	=			F"layers_{str(lowercase__  )}"

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

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

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

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

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

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

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

      # Assigning
      _SCREAMING_SNAKE_CASE       	=			tax_attention_key
      _SCREAMING_SNAKE_CASE       	=			tax_attention_out
      _SCREAMING_SNAKE_CASE       	=			tax_attention_query
      _SCREAMING_SNAKE_CASE       	=			tax_attention_value

      _SCREAMING_SNAKE_CASE       	=			tax_pre_attention_layer_norm

      _SCREAMING_SNAKE_CASE       	=			tax_enc_dec_attention_key
      _SCREAMING_SNAKE_CASE       	=			tax_enc_dec_attention_out
      _SCREAMING_SNAKE_CASE       	=			tax_enc_dec_attention_query
      _SCREAMING_SNAKE_CASE       	=			tax_enc_dec_attention_value

      _SCREAMING_SNAKE_CASE       	=			tax_cross_layer_norm

      if split_mlp_wi:
         _SCREAMING_SNAKE_CASE       	=			tax_mlp_wi_a
         _SCREAMING_SNAKE_CASE       	=			tax_mlp_wi_a
      else:
         _SCREAMING_SNAKE_CASE       	=			tax_mlp_wi

      _SCREAMING_SNAKE_CASE       	=			tax_mlp_wo

      _SCREAMING_SNAKE_CASE       	=			txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
     UpperCamelCase__ :						List[str]    							=					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."
     )
     UpperCamelCase__ :						Tuple    							=					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_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase__ :						List[str]    							=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						Optional[Any]    							=					[
         "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTJForCausalLM",
         "GPTJForQuestionAnswering",
         "GPTJForSequenceClassification",
         "GPTJModel",
         "GPTJPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						List[str]    							=					[
         "TFGPTJForCausalLM",
         "TFGPTJForQuestionAnswering",
         "TFGPTJForSequenceClassification",
         "TFGPTJModel",
         "TFGPTJPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						List[Any]    							=					[
         "FlaxGPTJForCausalLM",
         "FlaxGPTJModel",
         "FlaxGPTJPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     UpperCamelCase__ :						Any    							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






