

'''simple docstring'''

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

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


if is_sentencepiece_available():
       from .tokenization_ta import TaTokenizer
else:
       UpperCAmelCase					 =	None


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase					 =	{
    '''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''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase					 =	{
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}


class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			["input_ids", "attention_mask"]
       lowerCAmelCase_												=			TaTokenizer

       lowerCAmelCase_												=			[]







       def __init__(				self      :			Dict    ,   __lowercase      :			List[Any]=None    ,   __lowercase      :			Any=None    ,   __lowercase      :			Optional[Any]="</s>"    ,   __lowercase      :			Dict="<unk>"    ,   __lowercase      :			Dict="<pad>"    ,   __lowercase      :			List[Any]=100    ,   __lowercase      :			str=None    ,   **__lowercase      :			List[str]    ,   ):







            """simple docstring"""







            if extra_ids > 0 and additional_special_tokens is None:
                 __lowercase      		=[f'''<extra_id_{i}>''' for i in range(__lowercase   )]
            elif extra_ids > 0 and additional_special_tokens is not None:
                 # Check that we have the right number of extra special tokens
                 __lowercase      		=len(set(filter(lambda __lowercase   : bool('extra_id_' in str(__lowercase   )   )    ,   __lowercase   )   )   )
                 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'   )

            super().__init__(
                __lowercase    ,   tokenizer_file=__lowercase    ,   eos_token=__lowercase    ,   unk_token=__lowercase    ,   pad_token=__lowercase    ,   extra_ids=__lowercase    ,   additional_special_tokens=__lowercase    ,   **__lowercase    ,   )

            __lowercase      		=vocab_file
            __lowercase      		=False if not self.vocab_file else True
            __lowercase      		=extra_ids







       @staticmethod
       def       snake_case (				__lowercase      :			List[str]    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			Dict   ):







            """simple docstring"""







            if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                 __lowercase      		=TaTokenizerFast.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.'    ,   __lowercase    ,   )

            return max_model_length







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Optional[str] = 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(__lowercase   ):
                 logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
                 return
            __lowercase      		=os.path.join(
                __lowercase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

            if os.path.abspath(self.vocab_file   ) != os.path.abspath(__lowercase   ):
                 copyfile(self.vocab_file    ,   __lowercase   )
                 logger.info(f'''Copy vocab file to {out_vocab_file}'''   )

            return (out_vocab_file,)







       def       snake_case (				self      :			int    ,   __lowercase      :			List[int]    ,   __lowercase      :			Optional[List[int]] = None   ):







            """simple docstring"""







            __lowercase      		=token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a
            else:
                 __lowercase      		=token_ids_a + [self.eos_token_id]
                 return self.prefix_tokens + token_ids_a + token_ids_a







       def       snake_case (				self      :			Dict    ,   __lowercase      :			List[int]    ,   __lowercase      :			Optional[List[int]] = None   ):







            """simple docstring"""







            __lowercase      		=[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       snake_case (				self      :			str   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Any   ):







            """simple docstring"""







            return [self.convert_tokens_to_ids(__lowercase   ) for token in self.get_sentinel_tokens()]





'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase					 =	{
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase					 =	{
    '''gpt-neox-20b''': 2048,
}


class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			["input_ids", "attention_mask"]







       def __init__(				self      :			Tuple    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			str=None    ,   __lowercase      :			Optional[int]="<|endoftext|>"    ,   __lowercase      :			List[Any]="<|endoftext|>"    ,   __lowercase      :			Tuple="<|endoftext|>"    ,   __lowercase      :			Optional[int]=False    ,   **__lowercase      :			List[Any]    ,   ):







            """simple docstring"""







            super().__init__(
                __lowercase    ,   __lowercase    ,   tokenizer_file=__lowercase    ,   unk_token=__lowercase    ,   bos_token=__lowercase    ,   eos_token=__lowercase    ,   add_prefix_space=__lowercase    ,   **__lowercase    ,   )

            __lowercase      		=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
            if pre_tok_state.get('add_prefix_space'    ,   __lowercase   ) != add_prefix_space:
                 __lowercase      		=getattr(__lowercase    ,   pre_tok_state.pop('type'   )   )
                 __lowercase      		=add_prefix_space
                 __lowercase      		=pre_tok_class(**__lowercase   )

            __lowercase      		=add_prefix_space







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Optional[str] = None   ):







            """simple docstring"""







            __lowercase      		=self._tokenizer.model.save(__lowercase    ,   name=__lowercase   )
            return tuple(__lowercase   )







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			"Conversation"   ):







            """simple docstring"""







            __lowercase      		=[]
            for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(__lowercase    ,   add_special_tokens=__lowercase   ) + [self.eos_token_id]   )

            if len(__lowercase   ) > self.model_max_length:
                 __lowercase      		=input_ids[-self.model_max_length :]
            return input_ids


