


import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__ ,   __magic_name__      ):
 _lowercase:       str						=				AutoConfig.from_pretrained(UpperCamelCase__      )
 _lowercase:       Optional[int]						=				FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__      )
 _lowercase:       List[str]						=				checkpoints.load_tax_checkpoint(UpperCamelCase__      )

 _lowercase:       Any						=				"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

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

  # Self-Attention
  _lowercase:       int						=				tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
  _lowercase:       List[str]						=				tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
  _lowercase:       Union[str, Any]						=				tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
  _lowercase:       Dict						=				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":
   _lowercase:       int						=				tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

  # Layer Normalization
  _lowercase:       str						=				tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

  if split_mlp_wi:
   _lowercase:       Union[str, Any]						=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
   _lowercase:       str						=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
  else:
   _lowercase:       int						=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

  _lowercase:       Optional[Any]						=				tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

  # Layer Normalization
  _lowercase:       List[Any]						=				tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

  # Assigning
  _lowercase:       List[Any]						=				flax_model.params["encoder"]["block"][str(UpperCamelCase__      )]["layer"]
  _lowercase:       Optional[int]						=				tax_attention_key
  _lowercase:       str						=				tax_attention_out
  _lowercase:       int						=				tax_attention_query
  _lowercase:       Optional[Any]						=				tax_attention_value

  _lowercase:       List[str]						=				tax_attention_layer_norm

  # Global input layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
   _lowercase:       Optional[Any]						=				tax_global_layer_norm

  if split_mlp_wi:
   _lowercase:       int						=				tax_mlp_wi_a
   _lowercase:       Union[str, Any]						=				tax_mlp_wi_a
  else:
   _lowercase:       int						=				tax_mlp_wi

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

  _lowercase:       str						=				flax_model_encoder_layer_block

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

 # Side/global relative position_bias + layer norm
 if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
  _lowercase:       Any						=				tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
  _lowercase:       int						=				tax_encoder_global_rel_embedding

 # Assigning
 _lowercase:       Union[str, Any]						=				tax_model["target"]["encoder"]["encoder_norm"]["scale"]
 _lowercase:       Optional[int]						=				tax_encoder_norm

 # Decoder
 for layer_index in range(config.num_layers      ):
  _lowercase:       List[Any]						=				f"layers_{str(UpperCamelCase__      )}"

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

  # Layer Normalization
  _lowercase:       Any						=				tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
      "scale"
  ]

  # Encoder-Decoder-Attention
  _lowercase:       str						=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
  _lowercase:       Union[str, Any]						=				tax_enc_dec_attention_module["key"]["kernel"]
  _lowercase:       Optional[int]						=				tax_enc_dec_attention_module["out"]["kernel"]
  _lowercase:       str						=				tax_enc_dec_attention_module["query"]["kernel"]
  _lowercase:       Union[str, Any]						=				tax_enc_dec_attention_module["value"]["kernel"]

  # Layer Normalization
  _lowercase:       List[str]						=				tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

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

  _lowercase:       str						=				tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

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

  # Assigning
  _lowercase:       Tuple						=				flax_model.params["decoder"]["block"][str(UpperCamelCase__      )]["layer"]
  _lowercase:       List[str]						=				tax_attention_key
  _lowercase:       Tuple						=				tax_attention_out
  _lowercase:       Dict						=				tax_attention_query
  _lowercase:       int						=				tax_attention_value

  _lowercase:       Union[str, Any]						=				tax_pre_attention_layer_norm

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

  _lowercase:       Optional[Any]						=				tax_cross_layer_norm

  if split_mlp_wi:
   _lowercase:       Optional[int]						=				tax_mlp_wi_a
   _lowercase:       str						=				tax_mlp_wi_a
  else:
   _lowercase:       Any						=				tax_mlp_wi

  _lowercase:       Dict						=				tax_mlp_wo

  _lowercase:       Tuple						=				txa_mlp_layer_norm

  _lowercase:       Any						=				flax_model_decoder_layer_block

 # Decoder Normalization
 _lowercase:       List[Any]						=				tax_model["target"]["decoder"]["decoder_norm"]["scale"]
 _lowercase:       int						=				txa_decoder_norm

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

 # Token Embeddings
 _lowercase:       str						=				tax_model["target"]["token_embedder"]["embedding"]
 _lowercase:       List[str]						=				txa_token_embeddings

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

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


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE     :			Dict        =							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.'
       )
       _SCREAMING_SNAKE_CASE     :			Tuple        =							parser.parse_args()
       convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





"""simple docstring"""

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCAmelCase__					=logging.get_logger(__name__)

UpperCAmelCase__					={
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class  lowerCamelCase__    (		_a     ):
      a			:  Tuple           =		"""gptj"""
      a			:  Union[str, Any]           =		{
          """max_position_embeddings""": """n_positions""",
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }
      def __init__(		self						:						int   ,							A_						:						Optional[int]=5_0_4_0_0   ,							A_						:						Optional[Any]=2_0_4_8   ,							A_						:						Optional[Any]=4_0_9_6   ,							A_						:						Any=2_8   ,							A_						:						Union[str, Any]=1_6   ,							A_						:						int=6_4   ,							A_						:						int=None   ,							A_						:						str="gelu_new"   ,							A_						:						str=0.0   ,							A_						:						Optional[Any]=0.0   ,							A_						:						Dict=0.0   ,							A_						:						Dict=1e-5   ,							A_						:						Optional[int]=0.02   ,							A_						:						List[str]=True   ,							A_						:						List[Any]=5_0_2_5_6   ,							A_						:						Optional[int]=5_0_2_5_6   ,							A_						:						List[Any]=False   ,							**A_						:						int   ,							):






       '''simple docstring'''


       __lowercase						=						vocab_size
       __lowercase						=						n_positions
       __lowercase						=						n_embd
       __lowercase						=						n_layer
       __lowercase						=						n_head
       __lowercase						=						n_inner
       __lowercase						=						rotary_dim
       __lowercase						=						activation_function
       __lowercase						=						resid_pdrop
       __lowercase						=						embd_pdrop
       __lowercase						=						attn_pdrop
       __lowercase						=						layer_norm_epsilon
       __lowercase						=						initializer_range
       __lowercase						=						use_cache

       __lowercase						=						bos_token_id
       __lowercase						=						eos_token_id

       super().__init__(
           bos_token_id=A_   ,							eos_token_id=A_   ,							tie_word_embeddings=A_   ,							**A_				)






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						int   ,							A_						:						PretrainedConfig   ,							A_						:						str = "default"   ,							A_						:						List[PatchingSpec] = None   ,							A_						:						bool = False   ,							):






       '''simple docstring'''


       super().__init__(A_   ,							task=A_   ,							patching_specs=A_   ,							use_past=A_				)
       if not getattr(self._config   ,							"""pad_token_id"""   ,							A_				):
        # TODO: how to do that better?
        __lowercase						=						0
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}				)
       if self.use_past:
        self.fill_with_past_key_values_(A_   ,							direction="""inputs"""				)
        __lowercase						=						{0: """batch""", 1: """past_sequence + sequence"""}
       else:
        __lowercase						=						{0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       return self._config.n_layer
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       return self._config.n_head
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						PreTrainedTokenizer   ,							A_						:						int = -1   ,							A_						:						int = -1   ,							A_						:						bool = False   ,							A_						:						Optional[TensorType] = None   ,							):






       '''simple docstring'''


       __lowercase						=						super(A_   ,							self				).generate_dummy_inputs(
           A_   ,							batch_size=A_   ,							seq_length=A_   ,							is_pair=A_   ,							framework=A_				)

       # We need to order the input in the way they appears in the forward()
       __lowercase						=						OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}				)

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
        else:
         import torch

         __lowercase				,		__lowercase						=						common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         __lowercase						=						seqlen + 2
         __lowercase						=						(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         __lowercase						=						[
             (torch.zeros(A_				), torch.zeros(A_				)) for _ in range(self.num_layers				)
         ]

       __lowercase						=						common_inputs["""attention_mask"""]
       if self.use_past:
        __lowercase						=						ordered_inputs["""attention_mask"""].dtype
        __lowercase						=						torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(A_   ,							A_   ,							dtype=A_				)]   ,							dim=1				)

       return ordered_inputs






      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return 1_3
