


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:
    a_ :Union[str, Any]  							=			None


a_ :str  							=			logging.get_logger(__name__)

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

a_ :Union[str, 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',
    },
    '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
a_ :Optional[int]  							=			{
    't5-small': 5_12,
    't5-base': 5_12,
    't5-large': 5_12,
    't5-3b': 5_12,
    't5-11b': 5_12,
}

class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			Any      = VOCAB_FILES_NAMES
     lowerCamelCase			:			Dict      = PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase			:			Optional[Any]      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase			:			str      = ['''input_ids''', '''attention_mask''']
     lowerCamelCase			:			Dict      = TaTokenizer

     lowerCamelCase			:			List[int]      = []







     def __init__(						self :						Optional[int]   ,   _lowercase :						Any=None   ,   _lowercase :						List[Any]=None   ,   _lowercase :						Dict="</s>"   ,   _lowercase :						int="<unk>"   ,   _lowercase :						int="<pad>"   ,   _lowercase :						List[Any]=1_00   ,   _lowercase :						Dict=None   ,   **_lowercase :						Tuple   ,   ):
           # Add extra_ids to the special token list
           if extra_ids > 0 and additional_special_tokens is None:
                 SCREAMING_SNAKE_CASE__       :  List[str]						     =		[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
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		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   ,   )

           SCREAMING_SNAKE_CASE__       :  int						     =		vocab_file
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		False if not self.vocab_file else True
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		extra_ids







     @staticmethod
     def 							lowercase__       (						_lowercase :						Any   ,   _lowercase :						str   ,   _lowercase :						Union[str, Any]      ):
           if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		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 							lowercase__       (						self :						List[str]   ,   _lowercase :						str   ,   _lowercase :						Optional[str] = None      ):
           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
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		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 							lowercase__       (						self :						List[Any]   ,   _lowercase :						List[int]   ,   _lowercase :						Optional[List[int]] = None      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a
           else:
                 SCREAMING_SNAKE_CASE__       :  Any						     =		token_ids_a + [self.eos_token_id]
                 return self.prefix_tokens + token_ids_a + token_ids_a







     def 							lowercase__       (						self :						List[str]   ,   _lowercase :						List[int]   ,   _lowercase :						Optional[List[int]] = None      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[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 							lowercase__       (						self :						List[str]      ):
           return list(
               set(filter(lambda _lowercase      : bool(re.search(R'''<extra_id_\d+>'''   ,   _lowercase      )      ) is not None   ,   self.additional_special_tokens      )      )      )







     def 							lowercase__       (						self :						List[str]      ):
           return [self.convert_tokens_to_ids(_lowercase      ) for token in self.get_sentinel_tokens()]








'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase          =						logging.get_logger(__name__)

_lowercase          =						{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: str										=  '''dpr'''




						def __init__(   self       ,  _lowercase=30_522       ,  _lowercase=768       ,  _lowercase=12       ,  _lowercase=12       ,  _lowercase=3_072       ,  _lowercase="gelu"       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=512       ,  _lowercase=2       ,  _lowercase=0.02       ,  _lowercase=1e-12       ,  _lowercase=0       ,  _lowercase="absolute"       ,  _lowercase = 0       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(pad_token_id=_lowercase       ,  **_lowercase )

												_lowerCAmelCase    		=					vocab_size
												_lowerCAmelCase    		=					hidden_size
												_lowerCAmelCase    		=					num_hidden_layers
												_lowerCAmelCase    		=					num_attention_heads
												_lowerCAmelCase    		=					hidden_act
												_lowerCAmelCase    		=					intermediate_size
												_lowerCAmelCase    		=					hidden_dropout_prob
												_lowerCAmelCase    		=					attention_probs_dropout_prob
												_lowerCAmelCase    		=					max_position_embeddings
												_lowerCAmelCase    		=					type_vocab_size
												_lowerCAmelCase    		=					initializer_range
												_lowerCAmelCase    		=					layer_norm_eps
												_lowerCAmelCase    		=					projection_dim
												_lowerCAmelCase    		=					position_embedding_type
