

"""simple docstring"""




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

import sentencepiece as spm

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


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {"vocab_file": "sentencepiece.model"}

lowercase_			=  {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

lowercase_			=  {
    "google/rembert": 256,
}


class 			A						(							__lowercase			):







    """simple docstring"""





    lowerCamelCase		     =							VOCAB_FILES_NAMES
    lowerCamelCase		     =							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


    def __init__(				self			:	Dict,lowercase_			:	Dict,lowercase_			:	Tuple=False,lowercase_			:	Dict=True,lowercase_			:	Tuple=True,lowercase_			:	int="[CLS]",lowercase_			:	List[Any]="[SEP]",lowercase_			:	int="[UNK]",lowercase_			:	Union[str, Any]="[SEP]",lowercase_			:	Optional[int]="[PAD]",lowercase_			:	Dict="[CLS]",lowercase_			:	List[str]="[MASK]",**lowercase_			:	Optional[Any],)->   str:





        '''simple docstring'''







        super().__init__(
            do_lower_case=_a,remove_space=_a,keep_accents=_a,bos_token=_a,eos_token=_a,unk_token=_a,sep_token=_a,pad_token=_a,cls_token=_a,mask_token=_a,**_a,)

        A__   				=       do_lower_case
        A__   				=       remove_space
        A__   				=       keep_accents
        A__   				=       vocab_file

        A__   				=       spm.SentencePieceProcessor()
        self.sp_model.Load(_a		)


    @property
    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        return len(self.sp_model		)


    def 						snake_case__					(				self			:	Union[str, Any]		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       {self.convert_ids_to_tokens(_a		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab


    def __getstate__(				self			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       self.__dict__.copy()
        A__   				=       None
        return state


    def __setstate__(				self			:	Dict,lowercase_			:	List[str]		)->   Any:





        '''simple docstring'''







        A__   				=       d
        A__   				=       spm.SentencePieceProcessor()
        self.sp_model.Load(self.vocab_file		)


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Optional[Any],lowercase_			:	Optional[int]=False		)->   Any:





        '''simple docstring'''







        A__   				=       self.sp_model.EncodeAsPieces(_a		)
        return pieces


    def 						snake_case__					(				self			:	Any,lowercase_			:	List[str]		)->   Any:





        '''simple docstring'''







        return self.sp_model.PieceToId(_a		)


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	List[Any]		)->   List[Any]:





        '''simple docstring'''







        return self.sp_model.IdToPiece(_a		)


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	List[str]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       self.sp_model.decode_pieces(_a		)
        return out_string


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	Union[str, Any],lowercase_			:	Union[str, Any] = None		)->   List[int]:





        '''simple docstring'''







        A__   				=       [self.sep_token_id]
        A__   				=       [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			:	Optional[Any],lowercase_			:	List[str],lowercase_			:	List[Any] = None,lowercase_			:	List[Any] = False		)->   List[int]:





        '''simple docstring'''







        if already_has_special_tokens:
            if token_ids_a is not None:
                raise ValueError(
                    'You should not supply a second sequence if the provided sequence of '
                    'ids is already formatted with special tokens for the model.'		)
            return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

        if token_ids_a is not None:
            return [1] + ([0] * len(_a		)) + [1] + ([0] * len(_a		)) + [1]
        return [1] + ([0] * len(_a		)) + [1]


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	str,lowercase_			:	List[Any] = None		)->   List[int]:





        '''simple docstring'''







        A__   				=       [self.sep_token_id]
        A__   				=       [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			:	Dict,lowercase_			:	Optional[Any],lowercase_			:	List[Any] = None		)->   Tuple[str]:





        '''simple docstring'''







        if not os.path.isdir(_a		):
            logger.error('Vocabulary path ({}) should be a directory'.format(_a		)		)
            return
        A__   				=       os.path.join(
            _a,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

        return (out_vocab_file,)




import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]="attention"				)							->				Union[str, Any]:





    '''simple docstring'''






    A__   				=       params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
    A__   				=       params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
    A__   				=       params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
    A__   				=       params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
    return k, o, q, v
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Dict=False				)							->				str:





    '''simple docstring'''






    if split_mlp_wi:
        A__   				=       params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
        A__   				=       params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
        A__   				=       (wi_a, wi_a)
    else:
        A__   				=       params[f'{prefix}/layers_{i}/mlp/wi/kernel']

    A__   				=       params[f'{prefix}/layers_{i}/mlp/wo/kernel']
    return wi, wo
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				str:





    '''simple docstring'''






    return params[f'{prefix}/layers_{i}/{layer_name}/scale']
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     dict   ,      *, SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     bool				)							->				int:





    '''simple docstring'''






    A__   				=       traverse_util.flatten_dict(variables['target']				)
    A__   				=       {'/'.join(SCREAMING_SNAKE_CASE__				): v for k, v in old.items()}

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    A__   				=       'encoder/layers_0/mlp/wi_0/kernel' in old
    print('Split MLP:'   ,      SCREAMING_SNAKE_CASE__				)

    A__   				=       collections.OrderedDict()

    # Shared embeddings.
    A__   				=       old['token_embedder/embedding']

    # Encoder.
    for i in range(SCREAMING_SNAKE_CASE__				):
        # Block i, layer 0 (Self Attention).
        A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'encoder'   ,      'pre_attention_layer_norm'				)
        A__				,						A__				,						A__				,						A__   				=       tax_attention_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'encoder'   ,      'attention'				)
        A__   				=       layer_norm
        A__   				=       k.T
        A__   				=       o.T
        A__   				=       q.T
        A__   				=       v.T

        # Block i, layer 1 (MLP).
        A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'encoder'   ,      'pre_mlp_layer_norm'				)
        A__				,						A__   				=       tax_mlp_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'encoder'   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       layer_norm
        if split_mlp_wi:
            A__   				=       wi[0].T
            A__   				=       wi[1].T
        else:
            A__   				=       wi.T
        A__   				=       wo.T

    A__   				=       old[
        'encoder/relpos_bias/rel_embedding'
    ].T
    A__   				=       old['encoder/encoder_norm/scale']

    if not is_encoder_only:
        # Decoder.
        for i in range(SCREAMING_SNAKE_CASE__				):
            # Block i, layer 0 (Self Attention).
            A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'pre_self_attention_layer_norm'				)
            A__				,						A__				,						A__				,						A__   				=       tax_attention_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'self_attention'				)
            A__   				=       layer_norm
            A__   				=       k.T
            A__   				=       o.T
            A__   				=       q.T
            A__   				=       v.T

            # Block i, layer 1 (Cross Attention).
            A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'pre_cross_attention_layer_norm'				)
            A__				,						A__				,						A__				,						A__   				=       tax_attention_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'encoder_decoder_attention'				)
            A__   				=       layer_norm
            A__   				=       k.T
            A__   				=       o.T
            A__   				=       q.T
            A__   				=       v.T

            # Block i, layer 2 (MLP).
            A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'pre_mlp_layer_norm'				)
            A__				,						A__   				=       tax_mlp_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      SCREAMING_SNAKE_CASE__				)
            A__   				=       layer_norm
            if split_mlp_wi:
                A__   				=       wi[0].T
                A__   				=       wi[1].T
            else:
                A__   				=       wi.T
            A__   				=       wo.T

        A__   				=       old['decoder/decoder_norm/scale']
        A__   				=       old[
            'decoder/relpos_bias/rel_embedding'
        ].T

        # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
        if "decoder/logits_dense/kernel" in old:
            A__   				=       old['decoder/logits_dense/kernel'].T

    return new
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     bool				)							->				Dict:





    '''simple docstring'''






    A__   				=       collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

    # Add what is missing.
    if "encoder.embed_tokens.weight" not in state_dict:
        A__   				=       state_dict['shared.weight']

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            A__   				=       state_dict['shared.weight']

        if "lm_head.weight" not in state_dict:  # For old 1.0 models.
            print('Using shared word embeddings as lm_head.'				)
            A__   				=       state_dict['shared.weight']

    return state_dict
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     Tuple				)							->				int:





    '''simple docstring'''






    A__   				=       checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__				)
    A__   				=       convert_tax_to_pytorch(SCREAMING_SNAKE_CASE__   ,      num_layers=config.num_layers   ,      is_encoder_only=SCREAMING_SNAKE_CASE__				)
    A__   				=       make_state_dict(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    model.load_state_dict(SCREAMING_SNAKE_CASE__   ,      strict=SCREAMING_SNAKE_CASE__				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     bool = False				)							->				Any:





    '''simple docstring'''






    A__   				=       TaConfig.from_json_file(SCREAMING_SNAKE_CASE__				)
    print(f'Building PyTorch model from configuration: {config}'				)
    # Non-v1.1 checkpoints could also use T5Model, but this works for all.
    # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
    if is_encoder_only:
        A__   				=       TaEncoderModel(SCREAMING_SNAKE_CASE__				)
    else:
        A__   				=       TaForConditionalGeneration(SCREAMING_SNAKE_CASE__				)

    # Load weights from tf checkpoint
    load_tax_weights_in_ta(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'				)
    model.save_pretrained(SCREAMING_SNAKE_CASE__				)

    # Verify that we can load the checkpoint.
    model.from_pretrained(SCREAMING_SNAKE_CASE__				)
    print('Done'				)


if __name__ == "__main__":
     lowercase_			=  argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
     # Required parameters
     parser.add_argument(
         "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
     )
     parser.add_argument(
         "--config_file",
         default=None,
         type=str,
         required=True,
         help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
     )
     lowercase_			=  parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )

