



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__lowerCamelCase         =			logging.get_logger(__name__)

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

__lowerCamelCase         =			{
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

__lowerCamelCase         =			{
    '''roberta-base''': 512,
    '''roberta-large''': 512,
    '''roberta-large-mnli''': 512,
    '''distilroberta-base''': 512,
    '''roberta-base-openai-detector''': 512,
    '''roberta-large-openai-detector''': 512,
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				VOCAB_FILES_NAMES
       _lowerCamelCase			    =				PRETRAINED_VOCAB_FILES_MAP
       _lowerCamelCase			    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowerCamelCase			    =				["""input_ids""", """attention_mask"""]
       _lowerCamelCase			    =				RobertaTokenizer



       def __init__(     self     ,lowercase=None     ,lowercase=None     ,lowercase=None     ,lowercase="replace"     ,lowercase="<s>"     ,lowercase="</s>"     ,lowercase="</s>"     ,lowercase="<s>"     ,lowercase="<unk>"     ,lowercase="<pad>"     ,lowercase="<mask>"     ,lowercase=False     ,lowercase=True     ,**lowercase     ,):

             """simple docstring"""





             super().__init__(
                 lowercase     ,lowercase     ,tokenizer_file=lowercase     ,errors=lowercase     ,bos_token=lowercase     ,eos_token=lowercase     ,sep_token=lowercase     ,cls_token=lowercase     ,unk_token=lowercase     ,pad_token=lowercase     ,mask_token=lowercase     ,add_prefix_space=lowercase     ,trim_offsets=lowercase     ,**lowercase     ,)

             UpperCAmelCase_  :	List[str]				      =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
             if pre_tok_state.get("add_prefix_space"     ,lowercase) != add_prefix_space:
                   UpperCAmelCase_  :	List[str]				      =		getattr(lowercase     ,pre_tok_state.pop("type"))
                   UpperCAmelCase_  :	Optional[Any]				      =		add_prefix_space
                   UpperCAmelCase_  :	List[Any]				      =		pre_tok_class(**lowercase)

             UpperCAmelCase_  :	Tuple				      =		add_prefix_space

             UpperCAmelCase_  :	Optional[Any]				      =		"post_processor"
             UpperCAmelCase_  :	int				      =		getattr(self.backend_tokenizer     ,lowercase     ,lowercase)
             if tokenizer_component_instance:
                   UpperCAmelCase_  :	Optional[int]				      =		json.loads(tokenizer_component_instance.__getstate__())

                   # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                   if "sep" in state:
                         UpperCAmelCase_  :	str				      =		tuple(state["sep"])
                   if "cls" in state:
                         UpperCAmelCase_  :	Any				      =		tuple(state["cls"])

                   UpperCAmelCase_  :	Dict				      =		False

                   if state.get("add_prefix_space"     ,lowercase) != add_prefix_space:
                         UpperCAmelCase_  :	Optional[Any]				      =		add_prefix_space
                         UpperCAmelCase_  :	Any				      =		True

                   if state.get("trim_offsets"     ,lowercase) != trim_offsets:
                         UpperCAmelCase_  :	Any				      =		trim_offsets
                         UpperCAmelCase_  :	List[str]				      =		True

                   if changes_to_apply:
                         UpperCAmelCase_  :	int				      =		getattr(lowercase     ,state.pop("type"))
                         UpperCAmelCase_  :	Tuple				      =		component_class(**lowercase)
                         setattr(self.backend_tokenizer     ,lowercase     ,lowercase)



       @property
       def  A_   (     self):

             """simple docstring"""





             if self._mask_token is None:
                   if self.verbose:
                         logger.error("Using mask_token, but it is not set yet.")
                   return None
             return str(self._mask_token)



       @mask_token.setter
       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		AddedToken(lowercase     ,lstrip=lowercase     ,rstrip=lowercase) if isinstance(lowercase     ,lowercase) else value
             UpperCAmelCase_  :	Any				      =		value



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		kwargs.get("is_split_into_words"     ,lowercase)
             assert self.add_prefix_space or not is_split_into_words, (
                 F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                 "to use it with pretokenized inputs."
             )

             return super()._batch_encode_plus(*lowercase     ,**lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		kwargs.get("is_split_into_words"     ,lowercase)

             assert self.add_prefix_space or not is_split_into_words, (
                 F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                 "to use it with pretokenized inputs."
             )

             return super()._encode_plus(*lowercase     ,**lowercase)



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		self._tokenizer.model.save(lowercase     ,name=lowercase)
             return tuple(lowercase)



       def  A_   (     self     ,lowercase     ,lowercase=None):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                   return output

             return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		[self.sep_token_id]
             UpperCAmelCase_  :	int				      =		[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 + sep + token_ids_a + sep) * [0]





from typing import Mapping

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


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''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   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""umt5"""
       _lowerCamelCase			    =				["""past_key_values"""]



       def __init__(     self     ,lowercase=250112     ,lowercase=512     ,lowercase=64     ,lowercase=1024     ,lowercase=8     ,lowercase=None     ,lowercase=6     ,lowercase=32     ,lowercase=128     ,lowercase=0.1     ,lowercase=1E-6     ,lowercase=1.0     ,lowercase="gated-gelu"     ,lowercase=True     ,lowercase=True     ,lowercase="T5Tokenizer"     ,lowercase=True     ,lowercase=0     ,lowercase=1     ,lowercase=0     ,**lowercase     ,):

             """simple docstring"""





             super().__init__(
                 is_encoder_decoder=lowercase     ,tokenizer_class=lowercase     ,tie_word_embeddings=lowercase     ,pad_token_id=lowercase     ,eos_token_id=lowercase     ,decoder_start_token_id=lowercase     ,**lowercase     ,)
             UpperCAmelCase_  :	Optional[int]				      =		vocab_size
             UpperCAmelCase_  :	Any				      =		d_model
             UpperCAmelCase_  :	Any				      =		d_kv
             UpperCAmelCase_  :	int				      =		d_ff
             UpperCAmelCase_  :	Tuple				      =		num_layers
             UpperCAmelCase_  :	int				      =		(
                 num_decoder_layers if num_decoder_layers is not None else self.num_layers
             )  # default = symmetry
             UpperCAmelCase_  :	Optional[int]				      =		num_heads
             UpperCAmelCase_  :	str				      =		relative_attention_num_buckets
             UpperCAmelCase_  :	Any				      =		relative_attention_max_distance
             UpperCAmelCase_  :	Optional[Any]				      =		dropout_rate
             UpperCAmelCase_  :	Union[str, Any]				      =		layer_norm_epsilon
             UpperCAmelCase_  :	Optional[Any]				      =		initializer_factor
             UpperCAmelCase_  :	int				      =		feed_forward_proj
             UpperCAmelCase_  :	str				      =		use_cache

             UpperCAmelCase_  :	List[str]				      =		self.feed_forward_proj.split("-")
             UpperCAmelCase_  :	Any				      =		act_info[-1]
             UpperCAmelCase_  :	Optional[int]				      =		act_info[0] == "gated"

             if len(lowercase) > 1 and act_info[0] != "gated" or len(lowercase) > 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":
                   UpperCAmelCase_  :	Tuple				      =		"gelu_new"



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.d_model



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.num_heads



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.num_layers


class   snake_case_ (lowercase__  ):




       """simple docstring"""






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

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		{
                 "input_ids": {0: "batch", 1: "encoder_sequence"},
                 "attention_mask": {0: "batch", 1: "encoder_sequence"},
             }
             if self.use_past:
                   UpperCAmelCase_  :	Union[str, Any]				      =		"past_encoder_sequence + sequence"
                   UpperCAmelCase_  :	Optional[int]				      =		{0: "batch"}
                   UpperCAmelCase_  :	Union[str, Any]				      =		{0: "batch", 1: "past_decoder_sequence + sequence"}
             else:
                   UpperCAmelCase_  :	Optional[int]				      =		{0: "batch", 1: "decoder_sequence"}
                   UpperCAmelCase_  :	Dict				      =		{0: "batch", 1: "decoder_sequence"}

             if self.use_past:
                   self.fill_with_past_key_values_(lowercase     ,direction="inputs")

             return common_inputs



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

             """simple docstring"""





             return 13



       @property
       def  A_   (     self):

             """simple docstring"""





             return 5E-4
