





'''simple docstring'''


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

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


if is_sentencepiece_available():
 from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
 _SCREAMING_SNAKE_CASE			=							None


_SCREAMING_SNAKE_CASE			=							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=							{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE			=							{
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
        ),
    },
}

_SCREAMING_SNAKE_CASE			=							{
    "xlm-roberta-base": 5_12,
    "xlm-roberta-large": 5_12,
    "xlm-roberta-large-finetuned-conll02-dutch": 5_12,
    "xlm-roberta-large-finetuned-conll02-spanish": 5_12,
    "xlm-roberta-large-finetuned-conll03-english": 5_12,
    "xlm-roberta-large-finetuned-conll03-german": 5_12,
}

class 				lowerCAmelCase_						(						__magic_name__						):
 __lowerCamelCase  :							str    					=   VOCAB_FILES_NAMES
 __lowerCamelCase  :							List[str]    					=   PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase  :							Any    					=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase  :							Optional[Any]    					=   ['input_ids', 'attention_mask']
 __lowerCamelCase  :							Optional[int]    					=   XLMRobertaTokenizer




 def __init__(		self     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase="<s>"     ,       _lowerCAmelCase="</s>"     ,       _lowerCAmelCase="</s>"     ,       _lowerCAmelCase="<s>"     ,       _lowerCAmelCase="<unk>"     ,       _lowerCAmelCase="<pad>"     ,       _lowerCAmelCase="<mask>"     ,       **_lowerCAmelCase     ,       )      ->							Tuple:
     _lowerCAmelCase		     = AddedToken(_lowerCAmelCase     ,       lstrip=_lowerCAmelCase     ,       rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase     ,       _lowerCAmelCase						) else mask_token

     super().__init__(
         _lowerCAmelCase     ,       tokenizer_file=_lowerCAmelCase     ,       bos_token=_lowerCAmelCase     ,       eos_token=_lowerCAmelCase     ,       sep_token=_lowerCAmelCase     ,       cls_token=_lowerCAmelCase     ,       unk_token=_lowerCAmelCase     ,       pad_token=_lowerCAmelCase     ,       mask_token=_lowerCAmelCase     ,       **_lowerCAmelCase     ,       )

     _lowerCAmelCase		     = vocab_file
     _lowerCAmelCase		     = False if not self.vocab_file else True




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None						)      ->							Tuple:






     if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     _lowerCAmelCase		     = [self.cls_token_id]
     _lowerCAmelCase		     = [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None						)      ->							Dict:
     _lowerCAmelCase		     = [self.sep_token_id]
     _lowerCAmelCase		     = [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]





 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None						)      ->							Tuple:






     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
     _lowerCAmelCase		     = 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,)









'''simple docstring'''


from typing import Mapping

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


_SCREAMING_SNAKE_CASE			=							logging.get_logger(__name__)

class 				lowerCAmelCase_						(						__magic_name__						):
 __lowerCamelCase  :							str    					=   "mt5"
 __lowerCamelCase  :							Dict    					=   ["past_key_values"]




 def __init__(		self     ,       _lowerCAmelCase=250112     ,       _lowerCAmelCase=512     ,       _lowerCAmelCase=64     ,       _lowerCAmelCase=1024     ,       _lowerCAmelCase=8     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=6     ,       _lowerCAmelCase=32     ,       _lowerCAmelCase=128     ,       _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     ,       )      ->							Optional[Any]:
     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     ,       )
     _lowerCAmelCase		     = vocab_size
     _lowerCAmelCase		     = d_model
     _lowerCAmelCase		     = d_kv
     _lowerCAmelCase		     = d_ff
     _lowerCAmelCase		     = num_layers
     _lowerCAmelCase		     = (
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     _lowerCAmelCase		     = num_heads
     _lowerCAmelCase		     = relative_attention_num_buckets
     _lowerCAmelCase		     = relative_attention_max_distance
     _lowerCAmelCase		     = dropout_rate
     _lowerCAmelCase		     = layer_norm_epsilon
     _lowerCAmelCase		     = initializer_factor
     _lowerCAmelCase		     = feed_forward_proj
     _lowerCAmelCase		     = use_cache

     _lowerCAmelCase		     = self.feed_forward_proj.split("-"						)
     _lowerCAmelCase		     = act_info[-1]
     _lowerCAmelCase		     = 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":
         _lowerCAmelCase		     = "gelu_new"




 @property
 def 							_snake_case				(		self						)      ->							Any:
     return self.d_model




 @property
 def 							_snake_case				(		self						)      ->							Optional[Any]:
     return self.num_heads





 @property
 def 							_snake_case				(		self						)      ->							List[str]:
     return self.num_layers







class 				lowerCAmelCase_						(						__magic_name__						):




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 							_snake_case				(		self						)      ->							Mapping[str, Mapping[int, str]]:
     _lowerCAmelCase		     = {
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
         _lowerCAmelCase		     = "past_encoder_sequence + sequence"
         _lowerCAmelCase		     = {0: "batch"}
         _lowerCAmelCase		     = {0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
         _lowerCAmelCase		     = {0: "batch", 1: "decoder_sequence"}
         _lowerCAmelCase		     = {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 							_snake_case				(		self						)      ->							int:
     return 13





 @property
 def 							_snake_case				(		self						)      ->							float:
     return 5E-4


