



"""simple docstring"""

import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


lowercase__					:						Optional[int]												=			threading.Lock()
lowercase__					:						Optional[logging.Handler]												=			None

lowercase__					:						Optional[int]												=			{
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

lowercase__					:						Tuple												=			logging.WARNING

lowercase__					:						Optional[int]												=			True




def __lowercase    (							):
    snake_case_				:  Dict       =			os.getenv('''TRANSFORMERS_VERBOSITY'''	,    _a					)
    if env_level_str:
        if env_level_str in log_levels:
            return log_levels[env_level_str]
        else:
            logging.getLogger().warning(
                f"Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, "
                f"has to be one of: { ', '.join(log_levels.keys()					) }"					)
    return _default_log_level




def __lowercase    (							):
    return __name__.split('''.'''					)[0]




def __lowercase    (							):
    return logging.getLogger(_get_library_name()					)




def __lowercase    (							):
    global _default_handler

    with _lock:
        if _default_handler:
            # This library has already configured the library root logger.
            return
        snake_case_				:  Tuple       =			logging.StreamHandler()  # Set sys.stderr as stream.
        snake_case_				:  str       =			sys.stderr.flush

        # Apply our default configuration to the library root logger.
        snake_case_				:  Optional[int]       =			_get_library_root_logger()
        library_root_logger.addHandler(_default_handler					)
        library_root_logger.setLevel(_get_default_logging_level()					)
        snake_case_				:  Optional[int]       =			False




def __lowercase    (							):
    global _default_handler

    with _lock:
        if not _default_handler:
            return

        snake_case_				:  List[Any]       =			_get_library_root_logger()
        library_root_logger.removeHandler(_default_handler					)
        library_root_logger.setLevel(logging.NOTSET					)
        snake_case_				:  Optional[Any]       =			None




def __lowercase    (							):
    return log_levels




def __lowercase    (							_a = None					):

    if name is None:
        snake_case_				:  int       =			_get_library_name()

    _configure_library_root_logger()
    return logging.getLogger(_a					)




def __lowercase    (							):
    _configure_library_root_logger()
    return _get_library_root_logger().getEffectiveLevel()




def __lowercase    (							_a					):
    _configure_library_root_logger()
    _get_library_root_logger().setLevel(_a					)




def __lowercase    (							):
    return set_verbosity(_a					)




def __lowercase    (							):
    return set_verbosity(_a					)




def __lowercase    (							):
    return set_verbosity(_a					)




def __lowercase    (							):
    return set_verbosity(_a					)




def __lowercase    (							):
    _configure_library_root_logger()

    assert _default_handler is not None
    _get_library_root_logger().removeHandler(_default_handler					)




def __lowercase    (							):
    _configure_library_root_logger()

    assert _default_handler is not None
    _get_library_root_logger().addHandler(_default_handler					)




def __lowercase    (							_a					):
    _configure_library_root_logger()

    assert handler is not None
    _get_library_root_logger().addHandler(_a					)




def __lowercase    (							_a					):
    _configure_library_root_logger()

    assert handler is not None and handler not in _get_library_root_logger().handlers
    _get_library_root_logger().removeHandler(_a					)




def __lowercase    (							):
    _configure_library_root_logger()
    snake_case_				:  Optional[int]       =			False




def __lowercase    (							):
    _configure_library_root_logger()
    snake_case_				:  str       =			True




def __lowercase    (							):
    snake_case_				:  Tuple       =			_get_library_root_logger().handlers

    for handler in handlers:
        snake_case_				:  str       =			logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s'''					)
        handler.setFormatter(_a					)




def __lowercase    (							):
    snake_case_				:  Optional[Any]       =			_get_library_root_logger().handlers

    for handler in handlers:
        handler.setFormatter(_a					)




def __lowercase    (							self	,    *_a	,    **_a					):
    snake_case_				:  str       =			os.getenv('''TRANSFORMERS_NO_ADVISORY_WARNINGS'''	,    _a					)
    if no_advisory_warnings:
        return
    self.warning(*_a	,    **_a					)


lowercase__					:						List[str]												=			warning_advice




@functools.lru_cache(_a					)
def __lowercase    (							self	,    *_a	,    **_a					):
    self.warning(*_a	,    **_a					)


lowercase__					:						int												=			warning_once




class  _UpperCAmelCase :

 def __init__(  self	: Any						,  *lowercase_	: List[str]						,  **lowercase_	: Union[str, Any]       ):  # pylint: disable=unused-argument
     snake_case_				:  Optional[Any]       =			args[0] if args else None

 def __iter__(  self	: int       ):
     return iter(self._iterator       )

 def __getattr__(  self	: List[str]						,  lowercase_	: str       ):

     def empty_fn(*lowercase_	: Optional[Any]						,  **lowercase_	: Tuple       ):  # pylint: disable=unused-argument
         return

     return empty_fn

 def __enter__(  self	: int       ):
     return self






 def __exit__(  self	: Tuple						,  lowercase_	: Union[str, Any]						,  lowercase_	: int						,  lowercase_	: Any       ):
     return





class  _UpperCAmelCase :

 def __call__(  self	: Tuple						,  *lowercase_	: Optional[int]						,  **lowercase_	: int       ):
     if _tqdm_active:
         return tqdm_lib.tqdm(*lowercase_						,  **lowercase_       )
     else:
         return EmptyTqdm(*lowercase_						,  **lowercase_       )

 def 		_snake_case      (  self	: List[Any]						,  *lowercase_	: str						,  **lowercase_	: str       ):
     snake_case_				:  Union[str, Any]       =			None
     if _tqdm_active:
         return tqdm_lib.tqdm.set_lock(*lowercase_						,  **lowercase_       )






 def 		_snake_case      (  self	: str       ):
     if _tqdm_active:
         return tqdm_lib.tqdm.get_lock()





lowercase__					:						Dict												=			_tqdm_cls()




def __lowercase    (							):
    global _tqdm_active
    return bool(_tqdm_active					)




def __lowercase    (							):
    global _tqdm_active
    snake_case_				:  str       =			True
    hf_hub_utils.enable_progress_bars()






def __lowercase    (							):
    global _tqdm_active
    snake_case_				:  int       =			False
    hf_hub_utils.disable_progress_bars()










"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			AutoConfig.from_pretrained(_a					)
    snake_case_				:  Tuple       =			FlaxAutoModelForSeqaSeqLM.from_config(config=_a					)
    snake_case_				:  Union[str, Any]       =			checkpoints.load_tax_checkpoint(_a					)

    snake_case_				:  Optional[int]       =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        snake_case_				:  str       =			'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        snake_case_				:  List[str]       =			'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  Dict       =			'''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					):
        snake_case_				:  Any       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  Dict       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[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":
            snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  str       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[Any]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Optional[Any]       =			flax_model.params['''encoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  List[str]       =			tax_attention_key
        snake_case_				:  Optional[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  str       =			tax_attention_value

        snake_case_				:  Dict       =			tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Union[str, Any]       =			tax_global_layer_norm

        if split_mlp_wi:
            snake_case_				:  Any       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  Union[str, Any]       =			tax_mlp_wi

        snake_case_				:  List[Any]       =			tax_mlp_wo
        snake_case_				:  int       =			tax_mlp_layer_norm

        snake_case_				:  Any       =			flax_model_encoder_layer_block

    # Only for layer 0:
    snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Any       =			tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        snake_case_				:  Tuple       =			tax_encoder_global_rel_embedding

    # Assigning
    snake_case_				:  Dict       =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    snake_case_				:  Any       =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Tuple       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  str       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

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

        # Layer Normalization
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Dict       =			flax_model.params['''decoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  int       =			tax_attention_key
        snake_case_				:  List[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  Dict       =			tax_attention_value

        snake_case_				:  str       =			tax_pre_attention_layer_norm

        snake_case_				:  Any       =			tax_enc_dec_attention_key
        snake_case_				:  str       =			tax_enc_dec_attention_out
        snake_case_				:  int       =			tax_enc_dec_attention_query
        snake_case_				:  Any       =			tax_enc_dec_attention_value

        snake_case_				:  Optional[Any]       =			tax_cross_layer_norm

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

        snake_case_				:  Dict       =			tax_mlp_wo

        snake_case_				:  List[Any]       =			txa_mlp_layer_norm

        snake_case_				:  Optional[int]       =			flax_model_decoder_layer_block

    # Decoder Normalization
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    snake_case_				:  Tuple       =			txa_decoder_norm

    # Only for layer 0:
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Optional[Any]       =			tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''token_embedder''']['''embedding''']
    snake_case_				:  Optional[int]       =			txa_token_embeddings

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

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


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





