


'''simple docstring'''







import os
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_pegasus import PegasusTokenizer
else:
       lowerCAmelCase    :     Optional[int]					=						None


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


lowerCAmelCase    :     str					=						"""▁"""

lowerCAmelCase    :     str					=						{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase    :     List[str]					=						{
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

lowerCAmelCase    :     Tuple					=						{
    """google/pegasus-xsum""": 5_12,
}
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__    			=    PegasusTokenizer
     __magic_name__    			=    ["input_ids", "attention_mask"]







     def __init__( self      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__="<pad>"      ,	snake_case__="</s>"      ,	snake_case__="<unk>"      ,	snake_case__="<mask_2>"      ,	snake_case__="<mask_1>"      ,	snake_case__=None      ,	snake_case__=103      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       offset

            if additional_special_tokens is not None:
                   if not isinstance(snake_case__      ,	snake_case__   ):
                          raise TypeError(
                              F'additional_special_tokens should be of type {type(snake_case__   )}, but is'
                              F' {type(snake_case__   )}'   )

                   _lowerCAmelCase    :							Tuple   			=       (
                       ([mask_token_sent] + additional_special_tokens)
                       if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                       else additional_special_tokens
                   )
                   # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                   additional_special_tokens_extended += [
                       F'<unk_{i}>' for i in range(len(snake_case__   )      ,	self.offset - 1   )
                   ]

                   if len(set(snake_case__   )   ) != len(snake_case__   ):
                          raise ValueError(
                              'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
                              F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'   )
                   _lowerCAmelCase    :							Any   			=       additional_special_tokens_extended
            else:
                   _lowerCAmelCase    :							int   			=       [mask_token_sent] if mask_token_sent is not None else []
                   additional_special_tokens += [F'<unk_{i}>' for i in range(2      ,	self.offset   )]

            super().__init__(
                snake_case__      ,	tokenizer_file=snake_case__      ,	pad_token=snake_case__      ,	eos_token=snake_case__      ,	unk_token=snake_case__      ,	mask_token=snake_case__      ,	mask_token_sent=snake_case__      ,	offset=snake_case__      ,	additional_special_tokens=snake_case__      ,	**snake_case__      ,	)
            _lowerCAmelCase    :							Union[str, Any]   			=       vocab_file
            _lowerCAmelCase    :							str   			=       False if not self.vocab_file else True







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       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

            if all_special_ids != set(range(len(self.additional_special_tokens   ) + 3   )   ):
                   raise ValueError(
                       'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
                       F' {len(self.additional_special_tokens   )} additional_special_tokens, but got {all_special_ids}'   )

            return [1 if x in all_special_ids else 0 for x in seq]







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

            '''simple docstring'''

            if already_has_special_tokens:
                   return self._special_token_mask(snake_case__   )
            elif token_ids_a is None:
                   return self._special_token_mask(snake_case__   ) + [1]
            else:
                   return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]







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

            '''simple docstring'''

            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 a	( self      ,	snake_case__      ,	snake_case__ = 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(snake_case__   ):
                   logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
                   return
            _lowerCAmelCase    :							Union[str, Any]   			=       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__   ):
                   copyfile(self.vocab_file      ,	snake_case__   )

            return (out_vocab_file,)





'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase    :     str					=						logging.get_logger(__name__)

lowerCAmelCase    :     int					=						{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "data2vec-text"







     def __init__( self      ,	snake_case__=3_0522      ,	snake_case__=768      ,	snake_case__=12      ,	snake_case__=12      ,	snake_case__=3072      ,	snake_case__="gelu"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=512      ,	snake_case__=2      ,	snake_case__=0.02      ,	snake_case__=1E-12      ,	snake_case__=1      ,	snake_case__=0      ,	snake_case__=2      ,	snake_case__="absolute"      ,	snake_case__=True      ,	snake_case__=None      ,	**snake_case__      ,	):

            '''simple docstring'''

            super().__init__(pad_token_id=snake_case__      ,	bos_token_id=snake_case__      ,	eos_token_id=snake_case__      ,	**snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       vocab_size
            _lowerCAmelCase    :							Tuple   			=       hidden_size
            _lowerCAmelCase    :							Dict   			=       num_hidden_layers
            _lowerCAmelCase    :							int   			=       num_attention_heads
            _lowerCAmelCase    :							str   			=       hidden_act
            _lowerCAmelCase    :							Any   			=       intermediate_size
            _lowerCAmelCase    :							Any   			=       hidden_dropout_prob
            _lowerCAmelCase    :							Optional[int]   			=       attention_probs_dropout_prob
            _lowerCAmelCase    :							str   			=       max_position_embeddings
            _lowerCAmelCase    :							Any   			=       type_vocab_size
            _lowerCAmelCase    :							int   			=       initializer_range
            _lowerCAmelCase    :							List[str]   			=       layer_norm_eps
            _lowerCAmelCase    :							List[Any]   			=       position_embedding_type
            _lowerCAmelCase    :							str   			=       use_cache
            _lowerCAmelCase    :							Union[str, Any]   			=       classifier_dropout
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     @property
     def a	( self   ):

            '''simple docstring'''

            if self.task == "multiple-choice":
                   _lowerCAmelCase    :							Union[str, Any]   			=       {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   _lowerCAmelCase    :							List[Any]   			=       {0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ('input_ids', dynamic_axis),
                    ('attention_mask', dynamic_axis),
                ]   )

