

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


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {"vocab_file": "vocab.txt"}

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

lowercase_       		=  {
    "openbmb/cpm-ant-10b": 1024,
}
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   collections.OrderedDict()
    with open(SCREAMING_SNAKE_CASE__    ,			'r'    ,			encoding='utf-8'      ) as reader:
        A__          =   reader.readlines()
    for index, token in enumerate(SCREAMING_SNAKE_CASE__      ):
        A__          =   token.rstrip('\n'      )
        A__          =   index
    return vocab


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def __init__(      self :       Union[str, Any],lowercase_ :       Any,lowercase_ :       Any="<unk>",lowercase_ :       List[Any]=2_0_0		)->    int:





        '''simple docstring'''







        A__          =   vocab
        A__          =   unk_token
        A__          =   max_input_chars_per_word


    def 						snake_case__     (      self :       int,lowercase_ :       Any		)->    int:





        '''simple docstring'''







        A__          =   list(lowercase_		)
        if len(lowercase_		) > self.max_input_chars_per_word:
            return [self.unk_token]

        A__          =   0
        A__          =   []
        while start < len(lowercase_		):
            A__          =   len(lowercase_		)
            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(lowercase_		)
                A__          =   end

        return sub_tokens




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							VOCAB_FILES_NAMES
    lowerCamelCase			=							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase			=							['input_ids', 'attention_mask']
    lowerCamelCase			=							False


    def __init__(      self :       str,lowercase_ :       List[str],lowercase_ :       Dict="<d>",lowercase_ :       int="</d>",lowercase_ :       int="<s>",lowercase_ :       Optional[Any]="</s>",lowercase_ :       Optional[Any]="<pad>",lowercase_ :       Optional[int]="<unk>",lowercase_ :       Any="</n>",lowercase_ :       Dict="</_>",lowercase_ :       Optional[int]="left",**lowercase_ :       int,)->    int:





        '''simple docstring'''







        requires_backends(self,['jieba']		)
        super().__init__(
            bod_token=lowercase_,eod_token=lowercase_,bos_token=lowercase_,eos_token=lowercase_,pad_token=lowercase_,unk_token=lowercase_,line_token=lowercase_,space_token=lowercase_,padding_side=lowercase_,**lowercase_,)
        A__          =   bod_token
        A__          =   eod_token
        A__          =   load_vocab(lowercase_		)
        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 lowercase_		: x[1]		)		)
        A__          =   {v: k for k, v in self.encoder.items()}

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


    @property
    def 						snake_case__     (      self :       Optional[Any]		)->    Any:





        '''simple docstring'''







        return self.encoder[self.bod_token]


    @property
    def 						snake_case__     (      self :       List[str]		)->    Dict:





        '''simple docstring'''







        return self.encoder[self.eod_token]


    @property
    def 						snake_case__     (      self :       Optional[Any]		)->    Union[str, Any]:





        '''simple docstring'''







        return self.encoder["\n"]


    @property
    def 						snake_case__     (      self :       Union[str, Any]		)->    int:





        '''simple docstring'''







        return len(self.encoder		)


    def 						snake_case__     (      self :       int		)->    str:





        '''simple docstring'''







        return dict(self.encoder,**self.added_tokens_encoder		)


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       Union[str, Any]		)->    str:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       List[str],lowercase_ :       Optional[Any],**lowercase_ :       int		)->    int:





        '''simple docstring'''







        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(lowercase_,**lowercase_		)


    def 						snake_case__     (      self :       Optional[int],lowercase_ :       Union[str, Any]		)->    Union[str, Any]:





        '''simple docstring'''







        return token in self.encoder


    def 						snake_case__     (      self :       List[str],lowercase_ :       List[str]		)->    str:





        '''simple docstring'''







        return "".join(lowercase_		)


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       List[Any]		)->    Any:





        '''simple docstring'''







        return self.encoder.get(lowercase_,self.encoder.get(self.unk_token		)		)


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       List[str]		)->    List[str]:





        '''simple docstring'''







        return self.decoder.get(lowercase_,self.unk_token		)


    def 						snake_case__     (      self :       Any,lowercase_ :       str,lowercase_ :       Optional[str] = None		)->    Tuple[str]:





        '''simple docstring'''







        if os.path.isdir(lowercase_		):
            A__          =   os.path.join(
                lowercase_,(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 lowercase_		: x[1]		)		)
        with open(lowercase_,'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 						snake_case__     (      self :       Optional[int],lowercase_ :       List[int],lowercase_ :       List[int] = None		)->    List[int]:





        '''simple docstring'''







        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 						snake_case__     (      self :       Optional[Any],lowercase_ :       List[int],lowercase_ :       Optional[List[int]] = None,lowercase_ :       bool = False		)->    List[int]:





        '''simple docstring'''







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

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




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}


class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'resnet'
    lowerCamelCase			=							['basic', 'bottleneck']


    def __init__(      self :       Optional[Any],lowercase_ :       int=3,lowercase_ :       List[str]=6_4,lowercase_ :       int=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8],lowercase_ :       Tuple=[3, 4, 6, 3],lowercase_ :       Union[str, Any]="bottleneck",lowercase_ :       List[str]="relu",lowercase_ :       Tuple=False,lowercase_ :       List[str]=None,lowercase_ :       List[Any]=None,**lowercase_ :       str,)->    Optional[Any]:





        '''simple docstring'''







        super().__init__(**lowercase_		)
        if layer_type not in self.layer_types:
            raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
        A__          =   num_channels
        A__          =   embedding_size
        A__          =   hidden_sizes
        A__          =   depths
        A__          =   layer_type
        A__          =   hidden_act
        A__          =   downsample_in_first_stage
        A__          =   ['stem'] + [F'stage{idx}' for idx in range(1,len(lowercase_		) + 1		)]
        A__      ,						A__          =   get_aligned_output_features_output_indices(
            out_features=lowercase_,out_indices=lowercase_,stage_names=self.stage_names		)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							version.parse('1.11' )


    @property
    def 						snake_case__     (      self :       List[Any]		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ]		)


    @property
    def 						snake_case__     (      self :       Any		)->    float:





        '''simple docstring'''







        return 1E-3

