

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


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

A_       : List[str]         =			{"vocab_file": "vocab.txt"}

A_       : Any         =			{
    "vocab_file": {
        "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
    },
}

A_       : Optional[Any]         =			{
    "openbmb/cpm-ant-10b": 1024,
}

def 						UpperCamelCase					(lowercase_: int					)     ->      Optional[int]:
	A__							:    int        =		collections.OrderedDict()
	with open(SCREAMING_SNAKE_CASE__    ,      """r"""    ,      encoding="""utf-8"""					) as reader:
		A__							:    Any        =		reader.readlines()
	for index, token in enumerate(SCREAMING_SNAKE_CASE__					):
		A__							:    Union[str, Any]        =		token.rstrip("""\n"""					)
		A__							:    Union[str, Any]        =		index
	return vocab



class     _a						(__lowerCamelCase				):


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__="<unk>"    ,       A__=200			):
						A__							:    Union[str, Any]        =		vocab
						A__							:    Dict        =		unk_token
						A__							:    int        =		max_input_chars_per_word






					def   __A			( self    ,       A__			):
						A__							:    List[Any]        =		list(A__			)
						if len(A__			) > self.max_input_chars_per_word:
							return [self.unk_token]

						A__							:    Any        =		0
						A__							:    Tuple        =		[]
						while start < len(A__			):
							A__							:    str        =		len(A__			)
							A__							:    List[str]        =		None
							while start < end:
								A__							:    str        =		"""""".join(chars[start:end]			)
								if substr in self.vocab:
									A__							:    Union[str, Any]        =		substr
									break
								end -= 1
							if cur_substr is None:
								sub_tokens.append(self.unk_token			)
								start += 1
							else:
								sub_tokens.append(A__			)
								A__							:    List[str]        =		end

						return sub_tokens



class     _a						(__lowerCamelCase				):


					'''simple docstring'''






					UpperCAmelCase__:   str  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Dict  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   str  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   Any  							=       ['''input_ids''', '''attention_mask''']
					UpperCAmelCase__:   str  							=       False






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

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

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

						A__							:    Union[str, Any]        =		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    ,       A__			):
						A__							:    List[str]        =		[]
						for x in jieba.cut(A__    ,       cut_all=A__			):
							output_tokens.extend(self.wordpiece_tokenizer.tokenize(A__			)			)
						return output_tokens






					def   __A			( self    ,       A__    ,       **A__			):
						A__							:    List[Any]        =		[i for i in token_ids if i >= 0]
						A__							:    Dict        =		[
						    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(A__    ,       **A__			)






					def   __A			( self    ,       A__			):
						return token in self.encoder






					def   __A			( self    ,       A__			):
						return "".join(A__			)






					def   __A			( self    ,       A__			):
						return self.encoder.get(A__    ,       self.encoder.get(self.unk_token			)			)






					def   __A			( self    ,       A__			):
						return self.decoder.get(A__    ,       self.unk_token			)






					def   __A			( self    ,       A__    ,       A__ = None			):
						if os.path.isdir(A__			):
							A__							:    Dict        =		os.path.join(
							    A__    ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)
						else:
							A__							:    Union[str, Any]        =		(filename_prefix + """-""" if filename_prefix else """""") + save_directory
						A__							:    str        =		0
						if " " in self.encoder:
							A__							:    List[Any]        =		self.encoder[""" """]
							del self.encoder[" "]
						if "\n" in self.encoder:
							A__							:    int        =		self.encoder["""\n"""]
							del self.encoder["\n"]
						A__							:    Optional[Any]        =		collections.OrderedDict(sorted(self.encoder.items()    ,       key=lambda A__			: x[1]			)			)
						with open(A__    ,       """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__							:    Optional[Any]        =		token_index
								writer.write(token + """\n"""			)
								index += 1
						return (vocab_file,)






					def   __A			( self    ,       A__    ,       A__ = 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    ,       A__    ,       A__ = None    ,       A__ = False			):

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

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










from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Any							,				A :    List[str]=1							,				A :    str=0							,				A :    List[Any]=2							,				A :    Union[str, Any]=512							,				A :    Tuple="cls"							,				A :    Union[str, Any]=False							,				A :    Optional[Any]=True							,				**A :    Optional[int]							,				):
         super().__init__(pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				**A		)
         __snake_case:						str   =						project_dim
         __snake_case:						Optional[int]   =						pooler_fn
         __snake_case:						Dict   =						learn_encoder
         __snake_case:						str   =						use_attention_mask







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     [R"""pooler""", R"""logit_scale"""]
       lowerCAmelCase__      		=     [R"""position_ids""", R"""predictions.decoder.bias"""]
       lowerCAmelCase__      		=     """roberta"""
       lowerCAmelCase__      		=     RobertaSeriesConfig




       def __init__(  self :    Dict							,				A :    Dict		):
         super().__init__(A		)
         __snake_case:						Optional[Any]   =						XLMRobertaModel(A		)
         __snake_case:						List[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
         __snake_case:						Optional[int]   =						getattr(A							,				"""has_pre_transformation"""							,				A		)
         if self.has_pre_transformation:
           __snake_case:						Optional[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
           __snake_case:						Optional[Any]   =						nn.LayerNorm(config.hidden_size							,				eps=config.layer_norm_eps		)
         self.post_init()






       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[bool] = None							,				A :    Optional[bool] = None							,				A :    Optional[bool] = None							,				):
         __snake_case:						Any   =						return_dict if return_dict is not None else self.config.use_return_dict

         __snake_case:						Optional[int]   =						self.base_model(
             input_ids=A							,				attention_mask=A							,				token_type_ids=A							,				position_ids=A							,				head_mask=A							,				inputs_embeds=A							,				encoder_hidden_states=A							,				encoder_attention_mask=A							,				output_attentions=A							,				output_hidden_states=True if self.has_pre_transformation else output_hidden_states							,				return_dict=A							,				)

         if self.has_pre_transformation:
           __snake_case:						int   =						outputs["""hidden_states"""][-2]
           __snake_case:						List[str]   =						self.pre_LN(A		)
           __snake_case:						List[str]   =						self.transformation_pre(A		)

           return TransformationModelOutput(
               projection_state=A							,				last_hidden_state=outputs.last_hidden_state							,				hidden_states=outputs.hidden_states							,				attentions=outputs.attentions							,				)
         else:
           __snake_case:						Optional[int]   =						self.transformation(outputs.last_hidden_state		)
           return TransformationModelOutput(
               projection_state=A							,				last_hidden_state=outputs.last_hidden_state							,				hidden_states=outputs.hidden_states							,				attentions=outputs.attentions							,				)






