






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_xlm_roberta import XLMRobertaTokenizer
else:
				__A       :					Optional[Any]     					=     None


__A       :					Optional[Any]     					=     logging.get_logger(__name__)

__A       :					List[str]     					=     {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__A       :					Union[str, Any]     					=     {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
        ),
    },
}

__A       :					Union[str, Any]     					=     {
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}

class 			A_ (lowerCAmelCase__    ):
	UpperCAmelCase__      =    VOCAB_FILES_NAMES
	UpperCAmelCase__      =    PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__      =    ['''input_ids''', '''attention_mask''']
	UpperCAmelCase__      =    XLMRobertaTokenizer
	def __init__(					self							,      _A=None							,      _A=None							,      _A="<s>"							,      _A="</s>"							,      _A="</s>"							,      _A="<s>"							,      _A="<unk>"							,      _A="<pad>"							,      _A="<mask>"							,      **_A							,      ):






				'''simple docstring'''







				UpperCAmelCase     	=       AddedToken(_lowerCamelCase							,      lstrip=_lowerCamelCase							,      rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,      _lowerCamelCase	) else mask_token

				super().__init__(
				    _lowerCamelCase							,      tokenizer_file=_lowerCamelCase							,      bos_token=_lowerCamelCase							,      eos_token=_lowerCamelCase							,      sep_token=_lowerCamelCase							,      cls_token=_lowerCamelCase							,      unk_token=_lowerCamelCase							,      pad_token=_lowerCamelCase							,      mask_token=_lowerCamelCase							,      **_lowerCamelCase							,      )

				UpperCAmelCase     	=       vocab_file
				UpperCAmelCase     	=       False if not self.vocab_file else True
	def   _lowercase       (					self							,      _A							,      _A = None	):






				'''simple docstring'''







				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase     	=       [self.cls_token_id]
				UpperCAmelCase     	=       [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep
	def   _lowercase       (					self							,      _A							,      _A = None	):






				'''simple docstring'''







				UpperCAmelCase     	=       [self.sep_token_id]
				UpperCAmelCase     	=       [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 + sep + token_ids_a + sep	) * [0]







	def   _lowercase       (					self							,      _A							,      _A = None	):






				'''simple docstring'''







				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
							    '''tokenizer.'''	)

				if not os.path.isdir(_lowerCamelCase	):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""	)
							return
				UpperCAmelCase     	=       os.path.join(
				    _lowerCamelCase							,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

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

				return (out_vocab_file,)










import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)						->       Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase     	=       TaConfig.from_pretrained(UpperCamelCase__				)
			UpperCAmelCase     	=       FlaxTaForConditionalGeneration(config=UpperCamelCase__				)
			UpperCAmelCase     	=       checkpoints.load_tax_checkpoint(UpperCamelCase__				)

			UpperCAmelCase     	=       '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

			# Encoder
			for layer_index in range(config.num_layers				):
						UpperCAmelCase     	=       F"""layers_{str(UpperCamelCase__				)}"""

						# Self-Attention
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

						if split_mlp_wi:
									UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						UpperCAmelCase     	=       tax_attention_key
						UpperCAmelCase     	=       tax_attention_out
						UpperCAmelCase     	=       tax_attention_query
						UpperCAmelCase     	=       tax_attention_value

						UpperCAmelCase     	=       tax_attention_layer_norm

						if split_mlp_wi:
									UpperCAmelCase     	=       tax_mlp_wi_a
									UpperCAmelCase     	=       tax_mlp_wi_a
						else:
									UpperCAmelCase     	=       tax_mlp_wi

						UpperCAmelCase     	=       tax_mlp_wo
						UpperCAmelCase     	=       tax_mlp_layer_norm

			# Only for layer 0:
			UpperCAmelCase     	=       tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
			UpperCAmelCase     	=       tax_encoder_rel_embedding

			# Assigning
			UpperCAmelCase     	=       tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
			UpperCAmelCase     	=       tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_decoder_layers				):
						UpperCAmelCase     	=       F"""layers_{str(UpperCamelCase__				)}"""

						# Self-Attention
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
						    '''scale'''
						]

						# Encoder-Decoder-Attention
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
						    '''kernel'''
						]
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
						    '''kernel'''
						]
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
						    '''kernel'''
						]
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
						    '''kernel'''
						]

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

						# MLP
						if split_mlp_wi:
									UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						UpperCAmelCase     	=       tax_attention_key
						UpperCAmelCase     	=       tax_attention_out
						UpperCAmelCase     	=       tax_attention_query
						UpperCAmelCase     	=       tax_attention_value

						UpperCAmelCase     	=       tax_pre_attention_layer_norm

						UpperCAmelCase     	=       tax_enc_dec_attention_key
						UpperCAmelCase     	=       tax_enc_dec_attention_out
						UpperCAmelCase     	=       tax_enc_dec_attention_query
						UpperCAmelCase     	=       tax_enc_dec_attention_value

						UpperCAmelCase     	=       tax_cross_layer_norm

						if split_mlp_wi:
									UpperCAmelCase     	=       tax_mlp_wi_a
									UpperCAmelCase     	=       tax_mlp_wi_a
						else:
									UpperCAmelCase     	=       tax_mlp_wi

						UpperCAmelCase     	=       tax_mlp_wo

						UpperCAmelCase     	=       txa_mlp_layer_norm

			# Decoder Normalization
			UpperCAmelCase     	=       tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
			UpperCAmelCase     	=       txa_decoder_norm

			# Only for layer 0:
			UpperCAmelCase     	=       tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
			UpperCAmelCase     	=       tax_decoder_rel_embedding

			# Token Embeddings
			UpperCAmelCase     	=       tax_model['''target''']['''token_embedder''']['''embedding''']
			UpperCAmelCase     	=       txa_token_embeddings

			# LM Head (only in v1.1 checkpoints)
			if "logits_dense" in tax_model["target"]["decoder"]:
						UpperCAmelCase     	=       tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

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


if __name__ == "__main__":
				__A       :					Union[str, Any]     					=     argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
				)
				parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
				parser.add_argument(
				    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
				)
				__A       :					Any     					=     parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)



