"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Any			=	{
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Dict           =		"""xlm"""
 _snake_case   :			Dict           =		{
     """hidden_size""": """emb_dim""",
     """num_attention_heads""": """n_heads""",
     """num_hidden_layers""": """n_layers""",
     """n_words""": """vocab_size""",  # For backward compatibility
 }


 def __init__(				self  ,						lowerCamelCase=30145  ,						lowerCamelCase=2048  ,						lowerCamelCase=12  ,						lowerCamelCase=16  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=True  ,						lowerCamelCase=False  ,						lowerCamelCase=False  ,						lowerCamelCase=False  ,						lowerCamelCase=1  ,						lowerCamelCase=True  ,						lowerCamelCase=512  ,						lowerCamelCase=2048**-0.5  ,						lowerCamelCase=1E-12  ,						lowerCamelCase=0.02  ,						lowerCamelCase=0  ,						lowerCamelCase=1  ,						lowerCamelCase=2  ,						lowerCamelCase=3  ,						lowerCamelCase=5  ,						lowerCamelCase=True  ,						lowerCamelCase="first"  ,						lowerCamelCase=True  ,						lowerCamelCase=None  ,						lowerCamelCase=True  ,						lowerCamelCase=0.1  ,						lowerCamelCase=5  ,						lowerCamelCase=5  ,						lowerCamelCase=0  ,						lowerCamelCase=0  ,						lowerCamelCase=2  ,						lowerCamelCase=0  ,						**lowerCamelCase  ,						):
        __a     				=  vocab_size
        __a     				=  emb_dim
        __a     				=  n_layers
        __a     				=  n_heads
        __a     				=  dropout
        __a     				=  attention_dropout
        __a     				=  gelu_activation
        __a     				=  sinusoidal_embeddings
        __a     				=  causal
        __a     				=  asm
        __a     				=  n_langs
        __a     				=  use_lang_emb
        __a     				=  layer_norm_eps
        __a     				=  bos_index
        __a     				=  eos_index
        __a     				=  pad_index
        __a     				=  unk_index
        __a     				=  mask_index
        __a     				=  is_encoder
        __a     				=  max_position_embeddings
        __a     				=  embed_init_std
        __a     				=  init_std
        __a     				=  summary_type
        __a     				=  summary_use_proj
        __a     				=  summary_activation
        __a     				=  summary_proj_to_labels
        __a     				=  summary_first_dropout
        __a     				=  start_n_top
        __a     				=  end_n_top
        __a     				=  mask_token_id
        __a     				=  lang_id

        if "n_words" in kwargs:
               __a     				=  kwargs["n_words"]

        super().__init__(pad_token_id=lowerCamelCase  ,						bos_token_id=lowerCamelCase  ,						**lowerCamelCase		)






class 				snake_case__     (   snake_case_   ):


 @property
 def 		a__ (				self		):
        if self.task == "multiple-choice":
               __a     				=  {0: "batch", 1: "choice", 2: "sequence"}
        else:
               __a     				=  {0: "batch", 1: "sequence"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
                ("token_type_ids", dynamic_axis),
            ]		)

"""simple docstring"""



import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
   import jieba

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


SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[Any]			=	{"""vocab_file""": """vocab.txt"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

SCREAMING_SNAKE_CASE__:Tuple			=	{
    """openbmb/cpm-ant-10b""": 1024,
}



def 					_lowerCamelCase(					a							):
       __a     				=  collections.OrderedDict()
       with open(a ,  "r" ,  encoding="utf-8"							) as reader:
              __a     				=  reader.readlines()
       for index, token in enumerate(a							):
              __a     				=  token.rstrip("\n"							)
              __a     				=  index
       return vocab






class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase=200		):
        __a     				=  vocab
        __a     				=  unk_token
        __a     				=  max_input_chars_per_word






 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  list(lowerCamelCase		)
        if len(lowerCamelCase		) > self.max_input_chars_per_word:
               return [self.unk_token]

        __a     				=  0
        __a     				=  []
        while start < len(lowerCamelCase		):
               __a     				=  len(lowerCamelCase		)
               __a     				=  None
               while start < end:
                      __a     				=  "".join(chars[start:end]		)
                      if substr in self.vocab:
                             __a     				=  substr
                             break
                      end -= 1
               if cur_substr is None:
                      sub_tokens.append(self.unk_token		)
                      start += 1
               else:
                      sub_tokens.append(lowerCamelCase		)
                      __a     				=  end

        return sub_tokens






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[int]           =		VOCAB_FILES_NAMES
 _snake_case   :			Optional[int]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[Any]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			int           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			int           =		False


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="<d>"  ,						lowerCamelCase="</d>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="</n>"  ,						lowerCamelCase="</_>"  ,						lowerCamelCase="left"  ,						**lowerCamelCase  ,						):
        requires_backends(self  ,						["jieba"]		)
        super().__init__(
            bod_token=lowerCamelCase  ,						eod_token=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						line_token=lowerCamelCase  ,						space_token=lowerCamelCase  ,						padding_side=lowerCamelCase  ,						**lowerCamelCase  ,						)
        __a     				=  bod_token
        __a     				=  eod_token
        __a     				=  load_vocab(lowerCamelCase		)
        __a     				=  self.encoder[space_token]
        __a     				=  self.encoder[line_token]

        del self.encoder[space_token]
        del self.encoder[line_token]

        __a     				=  collections.OrderedDict(sorted(self.encoder.items()  ,						key=lambda lowerCamelCase		: x[1]		)		)
        __a     				=  {v: k for k, v in self.encoder.items()}

        __a     				=  WordpieceTokenizer(vocab=self.encoder  ,						unk_token=self.unk_token		)


 @property
 def 		a__ (				self		):
        return self.encoder[self.bod_token]


 @property
 def 		a__ (				self		):
        return self.encoder[self.eod_token]


 @property
 def 		a__ (				self		):
        return self.encoder["\n"]


 @property
 def 		a__ (				self		):
        return len(self.encoder		)


 def 		a__ (				self		):
        return dict(self.encoder  ,						**self.added_tokens_encoder		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        for x in jieba.cut(lowerCamelCase  ,						cut_all=lowerCamelCase		):
               output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCamelCase		)		)
        return output_tokens


 def 		a__ (				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        __a     				=  [i for i in token_ids if i >= 0]
        __a     				=  [
            x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
        ]
        return super()._decode(lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return token in self.encoder


 def 		a__ (				self  ,						lowerCamelCase		):
        return "".join(lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.encoder.get(lowerCamelCase  ,						self.encoder.get(self.unk_token		)		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.decoder.get(lowerCamelCase  ,						self.unk_token		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if os.path.isdir(lowerCamelCase		):
               __a     				=  os.path.join(
                   lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
        else:
               __a     				=  (filename_prefix + "-" if filename_prefix else "") + save_directory
        __a     				=  0
        if " " in self.encoder:
               __a     				=  self.encoder[" "]
               del self.encoder[" "]
        if "\n" in self.encoder:
               __a     				=  self.encoder["\n"]
               del self.encoder["\n"]
        __a     				=  collections.OrderedDict(sorted(self.encoder.items()  ,						key=lambda lowerCamelCase		: x[1]		)		)
        with open(lowerCamelCase  ,						"w"  ,						encoding="utf-8"		) as writer:
               for token, token_index in self.encoder.items():
                      if index != token_index:
                             logger.warning(
                                 F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                                 " Please check that the vocabulary is not corrupted!"		)
                             __a     				=  token_index
                      writer.write(token + "\n"		)
                      index += 1
        return (vocab_file,)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if token_ids_a is None:
               return [self.bos_token_id] + token_ids_a
        return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = False		):

        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase  ,						token_ids_a=lowerCamelCase  ,						already_has_special_tokens=lowerCamelCase		)

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