





import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig






def 				_lowerCAmelCase			(	_a :       List[str]       , _a :       Tuple       , _a :       List[str]      )						->      Dict:
     lowerCAmelCase_       :							Tuple			 =		TaConfig.from_pretrained(_UpperCamelCase      )
     lowerCAmelCase_       :							Optional[Any]			 =		FlaxTaForConditionalGeneration(config=_UpperCamelCase      )
     lowerCAmelCase_       :							int			 =		checkpoints.load_tax_checkpoint(_UpperCamelCase      )

     lowerCAmelCase_       :							int			 =		"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

     # Encoder
     for layer_index in range(config.num_layers      ):
          lowerCAmelCase_       :							Optional[Any]			 =		F'layers_{str(_UpperCamelCase      )}'

          # Self-Attention
          lowerCAmelCase_       :							Tuple			 =		tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
          lowerCAmelCase_       :							Optional[int]			 =		tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
          lowerCAmelCase_       :							Tuple			 =		tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
          lowerCAmelCase_       :							Any			 =		tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

          # Layer Normalization
          lowerCAmelCase_       :							Dict			 =		tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

          if split_mlp_wi:
               lowerCAmelCase_       :							Optional[Any]			 =		tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               lowerCAmelCase_       :							Optional[Any]			 =		tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               lowerCAmelCase_       :							List[Any]			 =		tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          lowerCAmelCase_       :							Dict			 =		tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

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

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

          lowerCAmelCase_       :							Union[str, Any]			 =		tax_attention_layer_norm

          if split_mlp_wi:
               lowerCAmelCase_       :							Any			 =		tax_mlp_wi_a
               lowerCAmelCase_       :							Optional[Any]			 =		tax_mlp_wi_a
          else:
               lowerCAmelCase_       :							List[str]			 =		tax_mlp_wi

          lowerCAmelCase_       :							List[str]			 =		tax_mlp_wo
          lowerCAmelCase_       :							Optional[Any]			 =		tax_mlp_layer_norm

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

     # Assigning
     lowerCAmelCase_       :							int			 =		tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
     lowerCAmelCase_       :							Optional[int]			 =		tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers      ):
          lowerCAmelCase_       :							Union[str, Any]			 =		F'layers_{str(_UpperCamelCase      )}'

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

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

          # Encoder-Decoder-Attention
          lowerCAmelCase_       :							Optional[Any]			 =		tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
              """kernel"""
          ]
          lowerCAmelCase_       :							Any			 =		tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
              """kernel"""
          ]
          lowerCAmelCase_       :							str			 =		tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
              """kernel"""
          ]
          lowerCAmelCase_       :							Any			 =		tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
              """kernel"""
          ]

          # Layer Normalization
          lowerCAmelCase_       :							Optional[Any]			 =		tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

          # MLP
          if split_mlp_wi:
               lowerCAmelCase_       :							Union[str, Any]			 =		tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               lowerCAmelCase_       :							Optional[int]			 =		tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               lowerCAmelCase_       :							Tuple			 =		tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          lowerCAmelCase_       :							int			 =		tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          lowerCAmelCase_       :							Any			 =		tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          lowerCAmelCase_       :							int			 =		tax_attention_key
          lowerCAmelCase_       :							int			 =		tax_attention_out
          lowerCAmelCase_       :							List[Any]			 =		tax_attention_query
          lowerCAmelCase_       :							List[Any]			 =		tax_attention_value

          lowerCAmelCase_       :							List[Any]			 =		tax_pre_attention_layer_norm

          lowerCAmelCase_       :							Union[str, Any]			 =		tax_enc_dec_attention_key
          lowerCAmelCase_       :							Dict			 =		tax_enc_dec_attention_out
          lowerCAmelCase_       :							Any			 =		tax_enc_dec_attention_query
          lowerCAmelCase_       :							str			 =		tax_enc_dec_attention_value

          lowerCAmelCase_       :							List[str]			 =		tax_cross_layer_norm

          if split_mlp_wi:
               lowerCAmelCase_       :							List[Any]			 =		tax_mlp_wi_a
               lowerCAmelCase_       :							List[Any]			 =		tax_mlp_wi_a
          else:
               lowerCAmelCase_       :							Optional[Any]			 =		tax_mlp_wi

          lowerCAmelCase_       :							Tuple			 =		tax_mlp_wo

          lowerCAmelCase_       :							str			 =		txa_mlp_layer_norm

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

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

     # Token Embeddings
     lowerCAmelCase_       :							Optional[Any]			 =		tax_model["""target"""]["""token_embedder"""]["""embedding"""]
     lowerCAmelCase_       :							int			 =		txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          lowerCAmelCase_       :							Union[str, Any]			 =		tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

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


if __name__ == "__main__":
      UpperCAmelCase_       : 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."""
      )
      UpperCAmelCase_       : int							=    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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__       =	{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizer"]

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "OpenLlamaForCausalLM",
					    "OpenLlamaModel",
					    "OpenLlamaPreTrainedModel",
					    "OpenLlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_open_llama import (
										    OpenLlamaForCausalLM,
										    OpenLlamaForSequenceClassification,
										    OpenLlamaModel,
										    OpenLlamaPreTrainedModel,
										)


else:
					import sys

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


