





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		Dict:







  """simple docstring"""





  lowerCamelCase__:				List[Any]        =AutoConfig.from_pretrained(__a		)
  lowerCamelCase__:				Any        =FlaxAutoModelForSeqaSeqLM.from_config(config=__a		)
  lowerCamelCase__:				Any        =checkpoints.load_tax_checkpoint(__a		)

  lowerCamelCase__:				Tuple        ="wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

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

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

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

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

    lowerCamelCase__:				Tuple        =tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

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

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

    lowerCamelCase__:				Optional[int]        =tax_attention_layer_norm

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

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

    lowerCamelCase__:				Any        =tax_mlp_wo
    lowerCamelCase__:				Dict        =tax_mlp_layer_norm

    lowerCamelCase__:				Optional[Any]        =flax_model_encoder_layer_block

  # Only for layer 0:
  lowerCamelCase__:				List[str]        =tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
  lowerCamelCase__:				Optional[Any]        =tax_encoder_rel_embedding

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

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

  # Decoder
  for layer_index in range(config.num_layers		):
    lowerCamelCase__:				Optional[int]        =F"""layers_{str(__a		)}"""

    # 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__:				Union[str, Any]        =tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
    lowerCamelCase__:				Dict        =tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

    # Layer Normalization
    lowerCamelCase__:				int        =tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
        "scale"
    ]

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

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

    # MLP
    if split_mlp_wi:
      lowerCamelCase__:				Dict        =tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
      lowerCamelCase__:				Dict        =tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      lowerCamelCase__:				List[Any]        =tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

    lowerCamelCase__:				Union[str, Any]        =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__:				List[str]        =flax_model.params["decoder"]["block"][str(__a		)]["layer"]
    lowerCamelCase__:				List[Any]        =tax_attention_key
    lowerCamelCase__:				Any        =tax_attention_out
    lowerCamelCase__:				Tuple        =tax_attention_query
    lowerCamelCase__:				str        =tax_attention_value

    lowerCamelCase__:				Optional[int]        =tax_pre_attention_layer_norm

    lowerCamelCase__:				Dict        =tax_enc_dec_attention_key
    lowerCamelCase__:				Optional[int]        =tax_enc_dec_attention_out
    lowerCamelCase__:				List[str]        =tax_enc_dec_attention_query
    lowerCamelCase__:				Dict        =tax_enc_dec_attention_value

    lowerCamelCase__:				Optional[Any]        =tax_cross_layer_norm

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

    lowerCamelCase__:				Optional[Any]        =tax_mlp_wo

    lowerCamelCase__:				Union[str, Any]        =txa_mlp_layer_norm

    lowerCamelCase__:				List[Any]        =flax_model_decoder_layer_block

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

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

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

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

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


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








import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


__A     =    get_logger(__name__)



class        _SCREAMING_SNAKE_CASE						(     enum.Enum   ):







 '''simple docstring'''
 lowercase_		=      "all_checks"
 lowercase_		=      "basic_checks"
 lowercase_		=      "no_checks"



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a=None		)					->		Optional[int]:







  """simple docstring"""





  if expected_checksums is None:
    logger.info("Unable to verify checksums."		)
    return
  if len(set(__a		) - set(__a		)		) > 0:
    raise ExpectedMoreDownloadedFiles(str(set(__a		) - set(__a		)		)		)
  if len(set(__a		) - set(__a		)		) > 0:
    raise UnexpectedDownloadedFile(str(set(__a		) - set(__a		)		)		)
  lowerCamelCase__:				List[Any]        =[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
  lowerCamelCase__:				Union[str, Any]        =" for " + verification_name if verification_name is not None else ""
  if len(__a		) > 0:
    raise NonMatchingChecksumError(
        F"""Checksums didn't match{for_verification_name}:\n"""
        F"""{bad_urls}\n"""
        "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"		)
  logger.info("All the checksums matched successfully" + for_verification_name		)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Any:







  """simple docstring"""





  if expected_splits is None:
    logger.info("Unable to verify splits sizes."		)
    return
  if len(set(__a		) - set(__a		)		) > 0:
    raise ExpectedMoreSplits(str(set(__a		) - set(__a		)		)		)
  if len(set(__a		) - set(__a		)		) > 0:
    raise UnexpectedSplits(str(set(__a		) - set(__a		)		)		)
  lowerCamelCase__:				Optional[int]        =[
      {"expected": expected_splits[name], "recorded": recorded_splits[name]}
      for name in expected_splits
      if expected_splits[name].num_examples != recorded_splits[name].num_examples
  ]
  if len(__a		) > 0:
    raise NonMatchingSplitsSizesError(str(__a		)		)
  logger.info("All the splits matched successfully."		)





def 					lowerCAmelCase_			(	__a     ,   __a = True		)					->		dict:







  """simple docstring"""





  if record_checksum:
    lowerCamelCase__:				str        =shaaaa()
    with open(__a     ,   "rb"		) as f:
      for chunk in iter(lambda: f.read(1 << 20		)     ,   b""		):
        m.update(__a		)
      lowerCamelCase__:				Dict        =m.hexdigest()
  else:
    lowerCamelCase__:				List[str]        =None
  return {"num_bytes": os.path.getsize(__a		), "checksum": checksum}






def 					lowerCAmelCase_			(	__a		)					->		int:







  """simple docstring"""





  if dataset_size and config.IN_MEMORY_MAX_SIZE:
    return dataset_size < config.IN_MEMORY_MAX_SIZE
  else:
    return False

