






from typing import Mapping

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


A_	:	Dict				=				logging.get_logger(__name__)




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	Any								='''mt5'''
       a       :	Union[str, Any]								=['''past_key_values''']





       def __init__(      self						,    _lowerCamelCase=2_5_0_1_1_2						,    _lowerCamelCase=5_1_2						,    _lowerCamelCase=6_4						,    _lowerCamelCase=1_0_2_4						,    _lowerCamelCase=8						,    _lowerCamelCase=None						,    _lowerCamelCase=6						,    _lowerCamelCase=3_2						,    _lowerCamelCase=1_2_8						,    _lowerCamelCase=0.1						,    _lowerCamelCase=1e-6						,    _lowerCamelCase=1.0						,    _lowerCamelCase="gated-gelu"						,    _lowerCamelCase=True						,    _lowerCamelCase=True						,    _lowerCamelCase="T5Tokenizer"						,    _lowerCamelCase=False						,    _lowerCamelCase=0						,    _lowerCamelCase=1						,    _lowerCamelCase=0						,    **_lowerCamelCase						,    ):
         super().__init__(
             is_encoder_decoder=_lowerCamelCase						,    tokenizer_class=_lowerCamelCase						,    tie_word_embeddings=_lowerCamelCase						,    pad_token_id=_lowerCamelCase						,    eos_token_id=_lowerCamelCase						,    decoder_start_token_id=_lowerCamelCase						,    **_lowerCamelCase						,    )
         UpperCamelCase_:      Optional[Any]					   =       vocab_size
         UpperCamelCase_:      Union[str, Any]					   =       d_model
         UpperCamelCase_:      Tuple					   =       d_kv
         UpperCamelCase_:      int					   =       d_ff
         UpperCamelCase_:      Dict					   =       num_layers
         UpperCamelCase_:      Any					   =       (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         UpperCamelCase_:      Tuple					   =       num_heads
         UpperCamelCase_:      List[Any]					   =       relative_attention_num_buckets
         UpperCamelCase_:      int					   =       relative_attention_max_distance
         UpperCamelCase_:      Any					   =       dropout_rate
         UpperCamelCase_:      Tuple					   =       layer_norm_epsilon
         UpperCamelCase_:      str					   =       initializer_factor
         UpperCamelCase_:      Dict					   =       feed_forward_proj
         UpperCamelCase_:      str					   =       use_cache

         UpperCamelCase_:      Optional[Any]					   =       self.feed_forward_proj.split('-'							)
         UpperCamelCase_:      Optional[Any]					   =       act_info[-1]
         UpperCamelCase_:      Dict					   =       act_info[0] == 'gated'

         if len(_lowerCamelCase							) > 1 and act_info[0] != "gated" or len(_lowerCamelCase							) > 2:
           raise ValueError(
               f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
               'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
               '\'gated-gelu\' or \'relu\''							)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
           UpperCamelCase_:      List[str]					   =       'gelu_new'





       @property
       def     _a					(      self							):
         return self.d_model





       @property
       def     _a					(      self							):
         return self.num_heads





       @property
       def     _a					(      self							):
         return self.num_layers







class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def     _a					(      self							):
         UpperCamelCase_:      Dict					   =       {
             'input_ids': {0: 'batch', 1: 'encoder_sequence'},
             'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
         }
         if self.use_past:
           UpperCamelCase_:      Tuple					   =       'past_encoder_sequence + sequence'
           UpperCamelCase_:      List[Any]					   =       {0: 'batch'}
           UpperCamelCase_:      List[Any]					   =       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
           UpperCamelCase_:      int					   =       {0: 'batch', 1: 'decoder_sequence'}
           UpperCamelCase_:      List[Any]					   =       {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
           self.fill_with_past_key_values_(_lowerCamelCase						,    direction='inputs'							)

         return common_inputs





       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def     _a					(      self							):
         return 1_3





       @property
       def     _a					(      self							):
         return 5e-4







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

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


if is_sentencepiece_available():
   from .tokenization_reformer import ReformerTokenizer
else:
   A_	:	Union[str, Any]				=				None


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


A_	:	List[str]				=				'▁'

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

A_	:	Optional[Any]				=				{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

A_	:	List[Any]				=				{
    'google/reformer-crime-and-punishment': 524288,
}




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	int								=VOCAB_FILES_NAMES
       a       :	Optional[Any]								=PRETRAINED_VOCAB_FILES_MAP
       a       :	List[Any]								=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a       :	Tuple								=['''input_ids''', '''attention_mask''']
       a       :	Optional[Any]								=ReformerTokenizer





       def __init__(      self						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    _lowerCamelCase="</s>"						,    _lowerCamelCase="<unk>"						,    _lowerCamelCase=[]						,    **_lowerCamelCase						,    ):
         super().__init__(
             _lowerCamelCase						,    tokenizer_file=_lowerCamelCase						,    eos_token=_lowerCamelCase						,    unk_token=_lowerCamelCase						,    additional_special_tokens=_lowerCamelCase						,    **_lowerCamelCase						,    )

         UpperCamelCase_:      Any					   =       vocab_file
         UpperCamelCase_:      str					   =       False if not self.vocab_file else True





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

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

         return (out_vocab_file,)