





'''simple docstring'''




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def   a_							(   __snake_case							:    Optional[Any]     , __snake_case							:    Tuple     , __snake_case							:    Optional[Any]     )							->		Optional[Any]:



       """simple docstring"""
       lowerCamelCase_							=TaConfig.from_pretrained(__snake_case     )
       lowerCamelCase_							=FlaxTaForConditionalGeneration(config=__snake_case     )
       lowerCamelCase_							=checkpoints.load_tax_checkpoint(__snake_case     )

       lowerCamelCase_							='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

       # Encoder
       for layer_index in range(config.num_layers     ):
              lowerCamelCase_							=F'''layers_{str(__snake_case     )}'''

              # 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''']

              # 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_							=tax_attention_key
              lowerCamelCase_							=tax_attention_out
              lowerCamelCase_							=tax_attention_query
              lowerCamelCase_							=tax_attention_value

              lowerCamelCase_							=tax_attention_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

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

       # Assigning
       lowerCamelCase_							=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
       lowerCamelCase_							=tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_decoder_layers     ):
              lowerCamelCase_							=F'''layers_{str(__snake_case     )}'''

              # 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''']['''key'''][
                  '''kernel'''
              ]
              lowerCamelCase_							=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
                  '''kernel'''
              ]
              lowerCamelCase_							=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
                  '''kernel'''
              ]
              lowerCamelCase_							=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''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_							=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

       # 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 checkpoints)
       if "logits_dense" in tax_model["target"]["decoder"]:
              lowerCamelCase_							=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

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


if __name__ == "__main__":
     a_   :	Optional[int]							   = 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."""
     )
     a_   :	Union[str, 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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_   :	Union[str, Any]							   = {
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_   :	int							   = [
         """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """OpenAIGPTDoubleHeadsModel""",
         """OpenAIGPTForSequenceClassification""",
         """OpenAIGPTLMHeadModel""",
         """OpenAIGPTModel""",
         """OpenAIGPTPreTrainedModel""",
         """load_tf_weights_in_openai_gpt""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_   :	Dict							   = [
         """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFOpenAIGPTDoubleHeadsModel""",
         """TFOpenAIGPTForSequenceClassification""",
         """TFOpenAIGPTLMHeadModel""",
         """TFOpenAIGPTMainLayer""",
         """TFOpenAIGPTModel""",
         """TFOpenAIGPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
     from .tokenization_openai import OpenAIGPTTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_openai_fast import OpenAIGPTTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_openai import (
              OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OpenAIGPTDoubleHeadsModel,
              OpenAIGPTForSequenceClassification,
              OpenAIGPTLMHeadModel,
              OpenAIGPTModel,
              OpenAIGPTPreTrainedModel,
              load_tf_weights_in_openai_gpt,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_openai import (
              TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFOpenAIGPTDoubleHeadsModel,
              TFOpenAIGPTForSequenceClassification,
              TFOpenAIGPTLMHeadModel,
              TFOpenAIGPTMainLayer,
              TFOpenAIGPTModel,
              TFOpenAIGPTPreTrainedModel,
          )

else:
     import sys

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




