




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  lowercase    :Dict      	=						AutoConfig.from_pretrained(lowerCamelCase   )
  lowercase    :Union[str, Any]      	=						FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCamelCase   )
  lowercase    :str      	=						checkpoints.load_tax_checkpoint(lowerCamelCase   )

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

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

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

    # Layer Normalization
    lowercase    :Optional[int]      	=						tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

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

    lowercase    :str      	=						tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    lowercase    :Optional[int]      	=						tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    lowercase    :Union[str, Any]      	=						flax_model.params["encoder"]["block"][str(lowerCamelCase   )]["layer"]
    lowercase    :int      	=						tax_attention_key
    lowercase    :Dict      	=						tax_attention_out
    lowercase    :Union[str, Any]      	=						tax_attention_query
    lowercase    :Optional[int]      	=						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    :List[str]      	=						tax_global_layer_norm

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

    lowercase    :Tuple      	=						tax_mlp_wo
    lowercase    :Union[str, Any]      	=						tax_mlp_layer_norm

    lowercase    :Optional[int]      	=						flax_model_encoder_layer_block

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

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

  # Assigning
  lowercase    :Optional[Any]      	=						tax_model["target"]["encoder"]["encoder_norm"]["scale"]
  lowercase    :List[Any]      	=						tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers   ):
    lowercase    :Any      	=						F"layers_{str(lowerCamelCase   )}"

    # Self-Attention
    lowercase    :Tuple      	=						tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
    lowercase    :Any      	=						tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
    lowercase    :Optional[int]      	=						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    :Tuple      	=						tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
        "scale"
    ]

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

    # Layer Normalization
    lowercase    :int      	=						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    :str      	=						tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      lowercase    :List[Any]      	=						tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

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

    # Layer Normalization
    lowercase    :Union[str, Any]      	=						tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    lowercase    :int      	=						flax_model.params["decoder"]["block"][str(lowerCamelCase   )]["layer"]
    lowercase    :Union[str, Any]      	=						tax_attention_key
    lowercase    :List[str]      	=						tax_attention_out
    lowercase    :List[Any]      	=						tax_attention_query
    lowercase    :Tuple      	=						tax_attention_value

    lowercase    :Tuple      	=						tax_pre_attention_layer_norm

    lowercase    :Optional[Any]      	=						tax_enc_dec_attention_key
    lowercase    :List[Any]      	=						tax_enc_dec_attention_out
    lowercase    :Dict      	=						tax_enc_dec_attention_query
    lowercase    :str      	=						tax_enc_dec_attention_value

    lowercase    :Dict      	=						tax_cross_layer_norm

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

    lowercase    :Dict      	=						tax_mlp_wo

    lowercase    :Dict      	=						txa_mlp_layer_norm

    lowercase    :Any      	=						flax_model_decoder_layer_block

  # Decoder Normalization
  lowercase    :Tuple      	=						tax_model["target"]["decoder"]["decoder_norm"]["scale"]
  lowercase    :Union[str, Any]      	=						txa_decoder_norm

  # Only for layer 0:
  lowercase    :Optional[Any]      	=						tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
  lowercase    :str      	=						tax_decoder_rel_embedding

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

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

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


if __name__ == "__main__":
     _UpperCAmelCase	:						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."
     )
     _UpperCAmelCase	:						Any			=     parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
     import onnxruntime as ort


_UpperCAmelCase	:						Dict			=     logging.get_logger(__name__)

_UpperCAmelCase	:						List[Any]			=     {
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}




class    __lowerCAmelCase       :






       def __init__(   self:					Dict       ,					_lowerCAmelCase:					Union[str, Any]=None       ,					**_lowerCAmelCase:					Any				):
         logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."				)
         lowercase    :str      	=						model
         lowercase    :Any      	=						kwargs.get("model_save_dir"       ,					_lowerCAmelCase				)
         lowercase    :Dict      	=						kwargs.get("latest_model_name"       ,					_lowerCAmelCase				)






       def __call__(   self:					Optional[Any]       ,					**_lowerCAmelCase:					List[Any]				):
         lowercase    :List[str]      	=						{k: np.array(_lowerCAmelCase				) for k, v in kwargs.items()}
         return self.model.run(_lowerCAmelCase       ,					_lowerCAmelCase				)






       @staticmethod
       def 					SCREAMING_SNAKE_CASE    (   _lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					List[Any]=None       ,					_lowerCAmelCase:					List[Any]=None				):
         if provider is None:
           logger.info("No onnxruntime provider specified, using CPUExecutionProvider"				)
           lowercase    :int      	=						"CPUExecutionProvider"

         return ort.InferenceSession(_lowerCAmelCase       ,					providers=[provider]       ,					sess_options=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					Optional[str] = None       ,					**_lowerCAmelCase:					Any				):
         lowercase    :Optional[int]      	=						file_name if file_name is not None else ONNX_WEIGHTS_NAME

         lowercase    :Tuple      	=						self.model_save_dir.joinpath(self.latest_model_name				)
         lowercase    :Optional[int]      	=						Path(_lowerCAmelCase				).joinpath(_lowerCAmelCase				)
         try:
           shutil.copyfile(_lowerCAmelCase       ,					_lowerCAmelCase				)
         except shutil.SameFileError:
           pass

         # copy external weights (for models >2GB)
         lowercase    :Tuple      	=						self.model_save_dir.joinpath(_lowerCAmelCase				)
         if src_path.exists():
           lowercase    :Dict      	=						Path(_lowerCAmelCase				).joinpath(_lowerCAmelCase				)
           try:
             shutil.copyfile(_lowerCAmelCase       ,					_lowerCAmelCase				)
           except shutil.SameFileError:
             pass






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					_lowerCAmelCase:					Union[str, os.PathLike]       ,					**_lowerCAmelCase:					int       ,					):
         if os.path.isfile(_lowerCAmelCase				):
           logger.error(F"Provided path ({save_directory}) should be a directory, not a file"				)
           return

         os.makedirs(_lowerCAmelCase       ,					exist_ok=_lowerCAmelCase				)

         # saving model weights/files
         self._save_pretrained(_lowerCAmelCase       ,					**_lowerCAmelCase				)






       @classmethod
       def 					SCREAMING_SNAKE_CASE    (   cls:					Dict       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					Optional[Union[bool, str, None]] = None       ,					_lowerCAmelCase:					Optional[Union[str, None]] = None       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional["ort.SessionOptions"] = None       ,					**_lowerCAmelCase:					Optional[int]       ,					):
         lowercase    :List[str]      	=						file_name if file_name is not None else ONNX_WEIGHTS_NAME
         # load model from local directory
         if os.path.isdir(_lowerCAmelCase				):
           lowercase    :Union[str, Any]      	=						OnnxRuntimeModel.load_model(
               os.path.join(_lowerCAmelCase       ,					_lowerCAmelCase				)       ,					provider=_lowerCAmelCase       ,					sess_options=_lowerCAmelCase				)
           lowercase    :Optional[Any]      	=						Path(_lowerCAmelCase				)
         # load model from hub
         else:
           # download model
           lowercase    :str      	=						hf_hub_download(
               repo_id=_lowerCAmelCase       ,					filename=_lowerCAmelCase       ,					use_auth_token=_lowerCAmelCase       ,					revision=_lowerCAmelCase       ,					cache_dir=_lowerCAmelCase       ,					force_download=_lowerCAmelCase       ,					)
           lowercase    :Optional[int]      	=						Path(_lowerCAmelCase				).parent
           lowercase    :Tuple      	=						Path(_lowerCAmelCase				).name
           lowercase    :Tuple      	=						OnnxRuntimeModel.load_model(_lowerCAmelCase       ,					provider=_lowerCAmelCase       ,					sess_options=_lowerCAmelCase				)
         return cls(model=_lowerCAmelCase       ,					**_lowerCAmelCase				)






       @classmethod
       def 					SCREAMING_SNAKE_CASE    (   cls:					str       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					bool = True       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					**_lowerCAmelCase:					Any       ,					):
         lowercase    :List[str]      	=						None
         if len(str(_lowerCAmelCase				).split("@"				)				) == 2:
           lowercase	,    lowercase    :Tuple      	=						model_id.split("@"				)

         return cls._from_pretrained(
             model_id=_lowerCAmelCase       ,					revision=_lowerCAmelCase       ,					cache_dir=_lowerCAmelCase       ,					force_download=_lowerCAmelCase       ,					use_auth_token=_lowerCAmelCase       ,					**_lowerCAmelCase       ,					)



