



'''simple docstring'''







import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__     :  Optional[int]    					=			logging.get_logger(__name__)

lowercase__     :  List[Any]    					=			{
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowercase__     :  Optional[int]    					=			{
    'source_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'
    },
    'target_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'
    },
    'vocab': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'
    },
    'tokenizer_config_file': {
        'Helsinki-NLP/opus-mt-en-de': (
            'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'
        )
    },
}

lowercase__     :  Optional[Any]    					=			{'Helsinki-NLP/opus-mt-en-de': 5_12}
lowercase__     :  Tuple    					=			{}

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     Dict				=	VOCAB_FILES_NAMES
   A :     Optional[int]				=	PRETRAINED_VOCAB_FILES_MAP
   A :     Optional[int]				=	PRETRAINED_INIT_CONFIGURATION
   A :     List[Any]				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A :     Any				=	['''input_ids''', '''attention_mask''']
   A :     Any				=	re.compile(">>.+<<"  )  # type: re.Pattern


   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase="<unk>"     ,						__UpperCamelCase="</s>"     ,						__UpperCamelCase="<pad>"     ,						__UpperCamelCase=5_12     ,						__UpperCamelCase = None     ,						__UpperCamelCase=False     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
           source_lang=a_     ,						target_lang=a_     ,						unk_token=a_     ,						eos_token=a_     ,						pad_token=a_     ,						model_max_length=a_     ,						sp_model_kwargs=self.sp_model_kwargs     ,						target_vocab_file=a_     ,						separate_vocabs=a_     ,						**a_     ,						)
       assert Path(a_ ).exists(), f'cannot find spm source {source_spm}'

       UpperCamelCase__      =			separate_vocabs
       UpperCamelCase__      =			load_json(a_ )
       if self.unk_token not in self.encoder:
           raise KeyError("<unk> token must be in vocab" )
       assert self.pad_token in self.encoder

       if separate_vocabs:
           UpperCamelCase__      =			load_json(a_ )
           UpperCamelCase__      =			{v: k for k, v in self.target_encoder.items()}
           UpperCamelCase__      =			[]
       else:
           UpperCamelCase__      =			{v: k for k, v in self.encoder.items()}
           UpperCamelCase__      =			[k for k in self.encoder if k.startswith(">>" ) and k.endswith("<<" )]

       UpperCamelCase__      =			source_lang
       UpperCamelCase__      =			target_lang
       UpperCamelCase__      =			[source_spm, target_spm]

       # load SentencePiece model for pre-processing
       UpperCamelCase__      =			load_spm(a_     ,						self.sp_model_kwargs )
       UpperCamelCase__      =			load_spm(a_     ,						self.sp_model_kwargs )
       UpperCamelCase__      =			self.spm_source
       UpperCamelCase__      =			self.encoder

       # Multilingual target side: default to using first supported language code.

       self._setup_normalizer()


   def 	_UpperCamelCase       (  self ):
       try:
           from sacremoses import MosesPunctNormalizer

           UpperCamelCase__      =			MosesPunctNormalizer(self.source_lang ).normalize
       except (ImportError, FileNotFoundError):
           warnings.warn("Recommended: pip install sacremoses." )
           UpperCamelCase__      =			lambda __UpperCamelCase : x


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       return self.punc_normalizer(a_ ) if x else ""


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       return self.current_encoder.get(a_     ,						self.current_encoder[self.unk_token] )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.language_code_re.match(a_ )
       UpperCamelCase__      =			[match.group(0 )] if match else []
       return code, self.language_code_re.sub(""     ,						a_ )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__    ,  UpperCamelCase__      =			self.remove_language_code(a_ )
       UpperCamelCase__      =			self.current_spm.encode(a_     ,						out_type=a_ )
       return code + pieces


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       return self.decoder.get(a_     ,						self.unk_token )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						**__UpperCamelCase ):
       return super().batch_decode(a_     ,						**a_ )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						**__UpperCamelCase ):
       return super().decode(a_     ,						**a_ )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.spm_source if self._decode_use_source_tokenizer else self.spm_target
       UpperCamelCase__      =			[]
       UpperCamelCase__      =			""
       for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               out_string += sp_model.decode_pieces(a_ ) + token + " "
               UpperCamelCase__      =			[]
           else:
               current_sub_tokens.append(a_ )
       out_string += sp_model.decode_pieces(a_ )
       return out_string.strip()


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase=None ):
       if token_ids_a is None:
           return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.spm_source
       UpperCamelCase__      =			self.encoder


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.spm_target
       if self.separate_vocabs:
           UpperCamelCase__      =			self.target_encoder


   @property
   def 	_UpperCamelCase       (  self ):
       return len(self.encoder )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase = None ):
       if not os.path.isdir(a_ ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
           return
       UpperCamelCase__      =			[]

       if self.separate_vocabs:
           UpperCamelCase__      =			os.path.join(
               a_     ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]     ,						)
           UpperCamelCase__      =			os.path.join(
               a_     ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]     ,						)
           save_json(self.encoder     ,						a_ )
           save_json(self.target_encoder     ,						a_ )
           saved_files.append(a_ )
           saved_files.append(a_ )
       else:
           UpperCamelCase__      =			os.path.join(
               a_     ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"] )
           save_json(self.encoder     ,						a_ )
           saved_files.append(a_ )

       for spm_save_filename, spm_orig_path, spm_model in zip(
           [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]     ,						self.spm_files     ,						[self.spm_source, self.spm_target]     ,						):
           UpperCamelCase__      =			os.path.join(
               a_     ,						(filename_prefix + "-" if filename_prefix else "") + spm_save_filename )
           if os.path.abspath(a_ ) != os.path.abspath(a_ ) and os.path.isfile(a_ ):
               copyfile(a_     ,						a_ )
               saved_files.append(a_ )
           elif not os.path.isfile(a_ ):
               with open(a_     ,						"wb" ) as fi:
                   UpperCamelCase__      =			spm_model.serialized_model_proto()
                   fi.write(a_ )
               saved_files.append(a_ )

       return tuple(a_ )


   def 	_UpperCamelCase       (  self ):
       return self.get_src_vocab()


   def 	_UpperCamelCase       (  self ):
       return dict(self.encoder     ,						**self.added_tokens_encoder )


   def 	_UpperCamelCase       (  self ):
       return dict(self.target_encoder     ,						**self.added_tokens_decoder )


   def __getstate__(  self ):
       UpperCamelCase__      =			self.__dict__.copy()
       state.update(
           {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]} )
       return state


   def __setstate__(  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			d

       # for backward compatibility
       if not hasattr(self     ,						"sp_model_kwargs" ):
           UpperCamelCase__      =			{}

       UpperCamelCase__    ,  UpperCamelCase__      =			(load_spm(a_     ,						self.sp_model_kwargs ) for f in self.spm_files)
       UpperCamelCase__      =			self.spm_source
       self._setup_normalizer()


   def 	_UpperCamelCase       (  self     ,						*__UpperCamelCase     ,						**__UpperCamelCase ):
       return 1


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			set(self.all_special_ids )  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special
       return [1 if x in all_special_ids else 0 for x in seq]







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase = None     ,						__UpperCamelCase = False ):
       if already_has_special_tokens:
           return self._special_token_mask(a_ )
       elif token_ids_a is None:
           return self._special_token_mask(a_ ) + [1]
       else:
           return self._special_token_mask(token_ids_a + token_ids_a ) + [1]





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			sentencepiece.SentencePieceProcessor(**UpperCamelCase__	)
    spm.Load(UpperCamelCase__	)
    return spm





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_	):






    with open(UpperCamelCase__					,  "w"	) as f:
        json.dump(UpperCamelCase__					,  UpperCamelCase__					,  indent=2	)





def 						snake_case__ (lowerCAmelCase_	):






    with open(UpperCamelCase__					,  "r"	) as f:
        return json.load(UpperCamelCase__	)









'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			TaConfig.from_pretrained(lowerCAmelCase_	)
    UpperCamelCase__      =			FlaxTaForConditionalGeneration(config=lowerCAmelCase_	)
    UpperCamelCase__      =			checkpoints.load_tax_checkpoint(lowerCAmelCase_	)

    UpperCamelCase__      =			"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

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

        # 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(lowerCAmelCase_	)}'

        # 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(lowerCAmelCase_	)
    print("T5X Model was sucessfully converted!"	)


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




