
'''simple docstring'''

from typing import Mapping

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


__snake_case:  str								=			logging.get_logger(__name__)

__snake_case:  str								=			{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

					"""simple docstring"""

					a_    	=					"umt5"
					a_    	=					["past_key_values"]


					def __init__(      self							,		lowerCAmelCase_=25_01_12							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=64							,		lowerCAmelCase_=10_24							,		lowerCAmelCase_=8							,		lowerCAmelCase_=None							,		lowerCAmelCase_=6							,		lowerCAmelCase_=32							,		lowerCAmelCase_=1_28							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=1E-6							,		lowerCAmelCase_=1.0							,		lowerCAmelCase_="gated-gelu"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_="T5Tokenizer"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0							,		lowerCAmelCase_=1							,		lowerCAmelCase_=0							,		**lowerCAmelCase_							,		):

							'''simple docstring'''

							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_							,		)
							a_      :	List[Any]      =							vocab_size
							a_      :	List[str]      =							d_model
							a_      :	Optional[int]      =							d_kv
							a_      :	Optional[Any]      =							d_ff
							a_      :	Union[str, Any]      =							num_layers
							a_      :	Optional[Any]      =							(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							a_      :	int      =							num_heads
							a_      :	Any      =							relative_attention_num_buckets
							a_      :	List[Any]      =							relative_attention_max_distance
							a_      :	int      =							dropout_rate
							a_      :	List[Any]      =							layer_norm_epsilon
							a_      :	int      =							initializer_factor
							a_      :	Tuple      =							feed_forward_proj
							a_      :	List[Any]      =							use_cache

							a_      :	List[str]      =							self.feed_forward_proj.split("""-""" )
							a_      :	List[str]      =							act_info[-1]
							a_      :	Any      =							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'""" )

							if feed_forward_proj == "gated-gelu":
									a_      :	Optional[Any]      =							"""gelu_new"""


					@property
					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							return self.d_model


					@property
					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							return self.num_heads






					@property
					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							return self.num_layers







class  _UpperCAmelCase ( lowerCAmelCase__    ):

					"""simple docstring"""

					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	Dict      =							{
							    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
							    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
							}
							if self.use_past:
									a_      :	str      =							"""past_encoder_sequence + sequence"""
									a_      :	Tuple      =							{0: """batch"""}
									a_      :	str      =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
							else:
									a_      :	Any      =							{0: """batch""", 1: """decoder_sequence"""}
									a_      :	Dict      =							{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 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							return 13






					@property
					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							return 5E-4




'''simple docstring'''

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

__snake_case:  Tuple								=			{"vocab_file": "sentencepiece.model"}

__snake_case:  int								=			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

__snake_case:  Union[str, Any]								=			{
    "google/rembert": 2_56,
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					VOCAB_FILES_NAMES
     a_    	=					PRETRAINED_VOCAB_FILES_MAP
     a_    	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_="[CLS]"							,		lowerCAmelCase_="[SEP]"							,		lowerCAmelCase_="[UNK]"							,		lowerCAmelCase_="[SEP]"							,		lowerCAmelCase_="[PAD]"							,		lowerCAmelCase_="[CLS]"							,		lowerCAmelCase_="[MASK]"							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__(
           do_lower_case=lowerCAmelCase_							,		remove_space=lowerCAmelCase_							,		keep_accents=lowerCAmelCase_							,		bos_token=lowerCAmelCase_							,		eos_token=lowerCAmelCase_							,		unk_token=lowerCAmelCase_							,		sep_token=lowerCAmelCase_							,		pad_token=lowerCAmelCase_							,		cls_token=lowerCAmelCase_							,		mask_token=lowerCAmelCase_							,		**lowerCAmelCase_							,		)

       a_      :	int      =							do_lower_case
       a_      :	int      =							remove_space
       a_      :	Dict      =							keep_accents
       a_      :	Union[str, Any]      =							vocab_file

       a_      :	List[str]      =							spm.SentencePieceProcessor()
       self.sp_model.Load(lowerCAmelCase_ )


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return len(self.sp_model )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     def __getstate__(      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.__dict__.copy()
       a_      :	Optional[Any]      =							None
       return state


     def __setstate__(      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	int      =							d
       a_      :	Optional[int]      =							spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_=False ):

       '''simple docstring'''

       a_      :	Tuple      =							self.sp_model.EncodeAsPieces(lowerCAmelCase_ )
       return pieces


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return self.sp_model.PieceToId(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return self.sp_model.IdToPiece(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Tuple      =							self.sp_model.decode_pieces(lowerCAmelCase_ )
       return out_string


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       a_      :	List[Any]      =							[self.sep_token_id]
       a_      :	Tuple      =							[self.cls_token_id]
       if token_ids_a is None:
         return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = False ):

       '''simple docstring'''

       if already_has_special_tokens:
         if token_ids_a is not None:
           raise ValueError(
               """You should not supply a second sequence if the provided sequence of """
               """ids is already formatted with special tokens for the model.""" )
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
         return [1] + ([0] * len(lowerCAmelCase_ )) + [1] + ([0] * len(lowerCAmelCase_ )) + [1]
       return [1] + ([0] * len(lowerCAmelCase_ )) + [1]


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       a_      :	Optional[int]      =							[self.sep_token_id]
       a_      :	Optional[Any]      =							[self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep ) * [0]
       return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       if not os.path.isdir(lowerCAmelCase_ ):
         logger.error("""Vocabulary path ({}) should be a directory""".format(lowerCAmelCase_ ) )
         return
       a_      :	List[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,)


