






"""simple docstring"""







import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       int:
				lowercase    :      Tuple       = AutoConfig.from_pretrained(_A )
				lowercase    :      Any       = FlaxAutoModelForSeqaSeqLM.from_config(config=_A )
				lowercase    :      Union[str, Any]       = checkpoints.load_tax_checkpoint(_A )

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

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

								# Self-Attention
								lowercase    :      str       = tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
								lowercase    :      int       = tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
								lowercase    :      Tuple       = tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
								lowercase    :      List[str]       = 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    :      Tuple       = 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    :      Optional[int]       = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
												lowercase    :      Dict       = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
								else:
												lowercase    :      Union[str, Any]       = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

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

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

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

								lowercase    :      List[Any]       = tax_attention_layer_norm

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												lowercase    :      int       = tax_global_layer_norm

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

								lowercase    :      Optional[int]       = tax_mlp_wo
								lowercase    :      Tuple       = tax_mlp_layer_norm

								lowercase    :      Dict       = flax_model_encoder_layer_block

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

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

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

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

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

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

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

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

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

								lowercase    :      str       = 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    :      Tuple       = flax_model.params["""decoder"""]["""block"""][str(_A )]["""layer"""]
								lowercase    :      Dict       = tax_attention_key
								lowercase    :      Optional[int]       = tax_attention_out
								lowercase    :      Optional[int]       = tax_attention_query
								lowercase    :      List[Any]       = tax_attention_value

								lowercase    :      Any       = tax_pre_attention_layer_norm

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

								lowercase    :      Union[str, Any]       = tax_cross_layer_norm

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

								lowercase    :      Optional[Any]       = tax_mlp_wo

								lowercase    :      Dict       = txa_mlp_layer_norm

								lowercase    :      Union[str, 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    :      Optional[Any]       = tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
				lowercase    :      Union[str, Any]       = tax_decoder_rel_embedding

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

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

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


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








"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_fnet import FNetTokenizer
else:
				_lowerCAmelCase            =	None

_lowerCAmelCase            =	logging.get_logger(__name__)
_lowerCAmelCase            =	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase            =	{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

_lowerCAmelCase            =	{
    'google/fnet-base': 5_12,
    'google/fnet-large': 5_12,
}

_lowerCAmelCase            =	'▁'


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Dict												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		["""input_ids""", """token_type_ids"""]
						_SCREAMING_SNAKE_CASE  : Union[str, Any]												=		FNetTokenizer






						def __init__(		self  :Optional[Any]			, __magic_name__  :Union[str, Any]=None			, __magic_name__  :Any=None			, __magic_name__  :List[str]=False			, __magic_name__  :Any=True			, __magic_name__  :Any=True			, __magic_name__  :Union[str, Any]="<unk>"			, __magic_name__  :List[Any]="[SEP]"			, __magic_name__  :Union[str, Any]="<pad>"			, __magic_name__  :Tuple="[CLS]"			, __magic_name__  :Optional[int]="[MASK]"			, **__magic_name__  :Optional[int]			, )      ->Tuple:
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowercase    :      Union[str, Any]       = (
										    AddedToken(__magic_name__			, lstrip=__magic_name__			, rstrip=__magic_name__			, normalized=__magic_name__				)
										    if isinstance(__magic_name__			, __magic_name__				)
										    else mask_token
										)

										super().__init__(
										    __magic_name__			, tokenizer_file=__magic_name__			, do_lower_case=__magic_name__			, remove_space=__magic_name__			, keep_accents=__magic_name__			, unk_token=__magic_name__			, sep_token=__magic_name__			, pad_token=__magic_name__			, cls_token=__magic_name__			, mask_token=__magic_name__			, **__magic_name__			, )

										lowercase    :      Tuple       = do_lower_case
										lowercase    :      Tuple       = remove_space
										lowercase    :      List[str]       = keep_accents
										lowercase    :      Union[str, Any]       = vocab_file
										lowercase    :      int       = False if not self.vocab_file else True






						def      __snake_case      (		self  :Any			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      List[str]       = [self.sep_token_id]
										lowercase    :      Any       = [self.cls_token_id]
										if token_ids_a is None:
														return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep






						def      __snake_case      (		self  :int			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      Tuple       = [self.sep_token_id]
										lowercase    :      Union[str, Any]       = [self.cls_token_id]

										if token_ids_a is None:
														return len(cls + token_ids_a + sep				) * [0]
										return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]






						def      __snake_case      (		self  :int			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										if not os.path.isdir(__magic_name__				):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
														return
										lowercase    :      str       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

										if os.path.abspath(self.vocab_file				) != os.path.abspath(__magic_name__				):
														copyfile(self.vocab_file			, __magic_name__				)

										return (out_vocab_file,)
