


'''simple docstring'''







import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
       from ...tokenization_utils_base import TextInput
from ...utils import logging


lowerCAmelCase    :     Tuple					=						logging.get_logger(__name__)

lowerCAmelCase    :     Union[str, Any]					=						{"""vocab_file""": """spiece.model"""}

lowerCAmelCase    :     Optional[Any]					=						{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCAmelCase    :     List[str]					=						{
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}

lowerCAmelCase    :     Tuple					=						"""▁"""
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    VOCAB_FILES_NAMES
     __magic_name__    			=    PRETRAINED_VOCAB_FILES_MAP
     __magic_name__    			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__    			=    ["input_ids", "attention_mask"]







     def __init__( self      ,	snake_case__      ,	snake_case__="</s>"      ,	snake_case__="<unk>"      ,	snake_case__="<pad>"      ,	snake_case__=100      ,	snake_case__=None      ,	snake_case__ = None      ,	snake_case__=True      ,	**snake_case__      ,	):

            '''simple docstring'''

            if extra_ids > 0 and additional_special_tokens is None:
                   _lowerCAmelCase    :							Optional[Any]   			=       [F'<extra_id_{i}>' for i in range(snake_case__   )]
            elif extra_ids > 0 and additional_special_tokens is not None:
                   # Check that we have the right number of extra_id special tokens
                   _lowerCAmelCase    :							Optional[int]   			=       len(set(filter(lambda snake_case__   : bool('extra_id' in str(snake_case__   )   )      ,	snake_case__   )   )   )
                   if extra_tokens != extra_ids:
                          raise ValueError(
                              F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                              ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                              ' tokens'   )
            if legacy:
                   logger.warning_once(
                       F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
                       ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'   )

            _lowerCAmelCase    :							List[Any]   			=       legacy
            _lowerCAmelCase    :							str   			=       {} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                eos_token=snake_case__      ,	unk_token=snake_case__      ,	pad_token=snake_case__      ,	extra_ids=snake_case__      ,	additional_special_tokens=snake_case__      ,	sp_model_kwargs=self.sp_model_kwargs      ,	legacy=snake_case__      ,	**snake_case__      ,	)

            _lowerCAmelCase    :							str   			=       vocab_file
            _lowerCAmelCase    :							List[str]   			=       extra_ids

            _lowerCAmelCase    :							List[str]   			=       spm.SentencePieceProcessor(**self.sp_model_kwargs   )
            self.sp_model.Load(snake_case__   )







     @staticmethod
     def a	( snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
                   _lowerCAmelCase    :							List[str]   			=       TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
                   if init_max_model_length is not None and init_max_model_length != max_model_length:
                          return init_max_model_length
                   elif init_max_model_length is None:
                          warnings.warn(
                              'This tokenizer was incorrectly instantiated with a model max length of'
                              F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                              ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                              ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                              F' {pretrained_model_name_or_path} automatically truncating your input to'
                              F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                              F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                              ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                              ' instantiate this tokenizer with `model_max_length` set to your preferred value.'      ,	snake_case__      ,	)

            return max_model_length







     @property
     def a	( self   ):

            '''simple docstring'''

            return self.sp_model.get_piece_size() + self._extra_ids







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       {self.convert_ids_to_tokens(snake_case__   ): i for i in range(self.vocab_size   )}
            vocab.update(self.added_tokens_encoder   )
            return vocab







     def a	( self      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = False   ):

            '''simple docstring'''

            if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=snake_case__      ,	token_ids_a=snake_case__      ,	already_has_special_tokens=snake_case__   )

            # normal case: some special tokens
            if token_ids_a is None:
                   return ([0] * len(snake_case__   )) + [1]
            return ([0] * len(snake_case__   )) + [1] + ([0] * len(snake_case__   )) + [1]







     def a	( self   ):

            '''simple docstring'''

            return list(
                set(filter(lambda snake_case__   : bool(re.search(R'<extra_id_\d+>'      ,	snake_case__   )   ) is not None      ,	self.additional_special_tokens   )   )   )







     def a	( self   ):

            '''simple docstring'''

            return [self._convert_token_to_id(snake_case__   ) for token in self.get_sentinel_tokens()]







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            if len(snake_case__   ) > 0 and token_ids[-1] == self.eos_token_id:
                   warnings.warn(
                       F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
                       ' eos tokens being added.'   )
                   return token_ids
            else:
                   return token_ids + [self.eos_token_id]







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       [self.eos_token_id]

            if token_ids_a is None:
                   return len(token_ids_a + eos   ) * [0]
            return len(token_ids_a + eos + token_ids_a + eos   ) * [0]







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self._add_eos_if_not_present(snake_case__   )
            if token_ids_a is None:
                   return token_ids_a
            else:
                   _lowerCAmelCase    :							int   			=       self._add_eos_if_not_present(snake_case__   )
                   return token_ids_a + token_ids_a







     def __getstate__( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.__dict__.copy()
            _lowerCAmelCase    :							str   			=       None
            return state







     def __setstate__( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       d

            # for backward compatibility
            if not hasattr(self      ,	'sp_model_kwargs'   ):
                   _lowerCAmelCase    :							Union[str, Any]   			=       {}

            _lowerCAmelCase    :							Any   			=       spm.SentencePieceProcessor(**self.sp_model_kwargs   )
            self.sp_model.Load(self.vocab_file   )







     def a	( self      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            if not self.legacy:
                   _lowerCAmelCase    :							Optional[int]   			=       SPIECE_UNDERLINE + text.replace(snake_case__      ,	' '   )
            return super().tokenize(snake_case__      ,	**snake_case__   )







     def a	( self      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            if not self.legacy:
                   _lowerCAmelCase    :							Dict   			=       text.startswith(snake_case__   )
                   if is_first:
                          _lowerCAmelCase    :							Optional[int]   			=       text[1:]

            _lowerCAmelCase    :							str   			=       self.sp_model.encode(snake_case__      ,	out_type=snake_case__   )

            if not self.legacy and not is_first and not text.startswith(' '   ) and tokens[0].startswith(snake_case__   ):
                   _lowerCAmelCase    :							List[str]   			=       ([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
            return tokens







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            if token.startswith('<extra_id_'   ):
                   _lowerCAmelCase    :							Union[str, Any]   			=       re.match(R'<extra_id_(\d+)>'      ,	snake_case__   )
                   _lowerCAmelCase    :							Any   			=       int(match.group(1   )   )
                   return self.vocab_size - num - 1
            return self.sp_model.piece_to_id(snake_case__   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            if index < self.sp_model.get_piece_size():
                   _lowerCAmelCase    :							Optional[Any]   			=       self.sp_model.IdToPiece(snake_case__   )
            else:
                   _lowerCAmelCase    :							List[Any]   			=       F'<extra_id_{self.vocab_size - 1 - index}>'
            return token







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       []
            _lowerCAmelCase    :							int   			=       ''
            _lowerCAmelCase    :							List[str]   			=       False
            for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                          if not prev_is_special:
                                 out_string += " "
                          out_string += self.sp_model.decode(snake_case__   ) + token
                          _lowerCAmelCase    :							Optional[int]   			=       True
                          _lowerCAmelCase    :							Optional[int]   			=       []
                   else:
                          current_sub_tokens.append(snake_case__   )
                          _lowerCAmelCase    :							str   			=       False
            out_string += self.sp_model.decode(snake_case__   )
            return out_string.strip()







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

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

            if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case__   ) and os.path.isfile(self.vocab_file   ):
                   copyfile(self.vocab_file      ,	snake_case__   )
            elif not os.path.isfile(self.vocab_file   ):
                   with open(snake_case__      ,	'wb'   ) as fi:
                          _lowerCAmelCase    :							List[str]   			=       self.sp_model.serialized_model_proto()
                          fi.write(snake_case__   )

            return (out_vocab_file,)





'''simple docstring'''







from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


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

lowerCAmelCase    :     str					=						{
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "gptj"
     __magic_name__    			=    {
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }







     def __init__( self      ,	snake_case__=5_0400      ,	snake_case__=2048      ,	snake_case__=4096      ,	snake_case__=28      ,	snake_case__=16      ,	snake_case__=64      ,	snake_case__=None      ,	snake_case__="gelu_new"      ,	snake_case__=0.0      ,	snake_case__=0.0      ,	snake_case__=0.0      ,	snake_case__=1E-5      ,	snake_case__=0.02      ,	snake_case__=True      ,	snake_case__=5_0256      ,	snake_case__=5_0256      ,	snake_case__=False      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       vocab_size
            _lowerCAmelCase    :							Optional[int]   			=       n_positions
            _lowerCAmelCase    :							Optional[int]   			=       n_embd
            _lowerCAmelCase    :							Optional[int]   			=       n_layer
            _lowerCAmelCase    :							str   			=       n_head
            _lowerCAmelCase    :							Tuple   			=       n_inner
            _lowerCAmelCase    :							Tuple   			=       rotary_dim
            _lowerCAmelCase    :							Optional[int]   			=       activation_function
            _lowerCAmelCase    :							Any   			=       resid_pdrop
            _lowerCAmelCase    :							List[str]   			=       embd_pdrop
            _lowerCAmelCase    :							int   			=       attn_pdrop
            _lowerCAmelCase    :							Any   			=       layer_norm_epsilon
            _lowerCAmelCase    :							Optional[int]   			=       initializer_range
            _lowerCAmelCase    :							List[str]   			=       use_cache

            _lowerCAmelCase    :							Dict   			=       bos_token_id
            _lowerCAmelCase    :							Any   			=       eos_token_id

            super().__init__(
                bos_token_id=snake_case__      ,	eos_token_id=snake_case__      ,	tie_word_embeddings=snake_case__      ,	**snake_case__   )
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__ = "default"      ,	snake_case__ = None      ,	snake_case__ = False      ,	):

            '''simple docstring'''

            super().__init__(snake_case__      ,	task=snake_case__      ,	patching_specs=snake_case__      ,	use_past=snake_case__   )
            if not getattr(self._config      ,	'pad_token_id'      ,	snake_case__   ):
                   # TODO: how to do that better?
                   _lowerCAmelCase    :							Any   			=       0







     @property
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
            if self.use_past:
                   self.fill_with_past_key_values_(snake_case__      ,	direction='inputs'   )
                   _lowerCAmelCase    :							int   			=       {0: 'batch', 1: 'past_sequence + sequence'}
            else:
                   _lowerCAmelCase    :							int   			=       {0: 'batch', 1: 'sequence'}

            return common_inputs







     @property
     def a	( self   ):

            '''simple docstring'''

            return self._config.n_layer







     @property
     def a	( self   ):

            '''simple docstring'''

            return self._config.n_head







     def a	( self      ,	snake_case__      ,	snake_case__ = -1      ,	snake_case__ = -1      ,	snake_case__ = False      ,	snake_case__ = None      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       super(snake_case__      ,	self   ).generate_dummy_inputs(
                snake_case__      ,	batch_size=snake_case__      ,	seq_length=snake_case__      ,	is_pair=snake_case__      ,	framework=snake_case__   )

            # We need to order the input in the way they appears in the forward()
            _lowerCAmelCase    :							Any   			=       OrderedDict({'input_ids': common_inputs['input_ids']}   )

            # Need to add the past_keys
            if self.use_past:
                   if not is_torch_available():
                          raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
                   else:
                          import torch

                          _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[Any]   			=       common_inputs['input_ids'].shape
                          # Not using the same length for past_key_values
                          _lowerCAmelCase    :							Any   			=       seqlen + 2
                          _lowerCAmelCase    :							Optional[int]   			=       (
                              batch,
                              self.num_attention_heads,
                              past_key_values_length,
                              self._config.hidden_size // self.num_attention_heads,
                          )
                          _lowerCAmelCase    :							Tuple   			=       [
                              (torch.zeros(snake_case__   ), torch.zeros(snake_case__   )) for _ in range(self.num_layers   )
                          ]

            _lowerCAmelCase    :							Tuple   			=       common_inputs['attention_mask']
            if self.use_past:
                   _lowerCAmelCase    :							Any   			=       ordered_inputs['attention_mask'].dtype
                   _lowerCAmelCase    :							Union[str, Any]   			=       torch.cat(
                       [ordered_inputs['attention_mask'], torch.ones(snake_case__      ,	snake_case__      ,	dtype=snake_case__   )]      ,	dim=1   )

            return ordered_inputs







     @property
     def a	( self   ):

            '''simple docstring'''

            return 13

