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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						'▁'

_SCREAMING_SNAKE_CASE										=						{'vocab_file': 'sentencepiece.bpe.model'}

_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'
        ),
    }
}

_SCREAMING_SNAKE_CASE										=						{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Tuple    			=		VOCAB_FILES_NAMES
 lowerCamelCase	:Any    			=		PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase	:Optional[int]    			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase	:int    			=		['''input_ids''', '''attention_mask''']



 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_="<s>"		,				lowerCAmelCase_="</s>"		,				lowerCAmelCase_="</s>"		,				lowerCAmelCase_="<s>"		,				lowerCAmelCase_="<unk>"		,				lowerCAmelCase_="<pad>"		,				lowerCAmelCase_="<mask>"		,				lowerCAmelCase_ = None		,				**lowerCAmelCase_		,				)  ->					None:
        # Mask token behave like a normal word, i.e. include the space before it
        _A        =  AddedToken(lowerCAmelCase_		,				lstrip=lowerCAmelCase_		,				rstrip=lowerCAmelCase_							) if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							) else mask_token

        _A        =  {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=lowerCAmelCase_		,				eos_token=lowerCAmelCase_		,				unk_token=lowerCAmelCase_		,				sep_token=lowerCAmelCase_		,				cls_token=lowerCAmelCase_		,				pad_token=lowerCAmelCase_		,				mask_token=lowerCAmelCase_		,				sp_model_kwargs=self.sp_model_kwargs		,				**lowerCAmelCase_		,				)

        _A        =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
        self.sp_model.Load(str(lowerCAmelCase_							)							)
        _A        =  vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # Mimic fairseq token-to-id alignment for the first 4 token
        _A        =  {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        _A        =  1

        _A        =  len(self.sp_model							) + self.fairseq_offset
        _A        =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}



 def __getstate__(					self							)  ->					Union[str, Any]:
        _A        =  self.__dict__.copy()
        _A        =  None
        _A        =  self.sp_model.serialized_model_proto()
        return state



 def __setstate__(					self		,				lowerCAmelCase_							)  ->					List[str]:
        _A        =  d

        # for backward compatibility
        if not hasattr(self		,				"""sp_model_kwargs"""							):
               _A        =  {}

        _A        =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None							)  ->					List[int]:

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



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = False							)  ->					List[int]:

        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 None:
               return [1] + ([0] * len(lowerCAmelCase_							)) + [1]
        return [1] + ([0] * len(lowerCAmelCase_							)) + [1, 1] + ([0] * len(lowerCAmelCase_							)) + [1]



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None							)  ->					List[int]:
        _A        =  [self.sep_token_id]
        _A        =  [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]



 @property
 def      UpperCAmelCase							(					self							)  ->					int:
        return len(self.sp_model							) + self.fairseq_offset + 1  # Add the <mask> token



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  {self.convert_ids_to_tokens(lowerCAmelCase_							): i for i in range(self.vocab_size							)}
        vocab.update(self.added_tokens_encoder							)
        return vocab



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					List[str]:
        return self.sp_model.encode(lowerCAmelCase_		,				out_type=lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					Dict:
        if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
        _A        =  self.sp_model.PieceToId(lowerCAmelCase_							)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					Tuple:
        if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					List[Any]:
        _A        =  """""".join(lowerCAmelCase_							).replace(lowerCAmelCase_		,				""" """							).strip()
        return out_string



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None							)  ->					Tuple[str]:
        if not os.path.isdir(lowerCAmelCase_							):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
               return
        _A        =  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_							) and os.path.isfile(self.vocab_file							):
               copyfile(self.vocab_file		,				lowerCAmelCase_							)
        elif not os.path.isfile(self.vocab_file							):
               with open(lowerCAmelCase_		,				"""wb"""							) as fi:
                      _A        =  self.sp_model.serialized_model_proto()
                      fi.write(lowerCAmelCase_							)

        return (out_vocab_file,)





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


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

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



class 				a							(			__lowerCAmelCase       ,			__lowerCAmelCase       ):







 """simple docstring"""






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



 def __init__(					self		,				lowerCAmelCase_=3		,				lowerCAmelCase_=64		,				lowerCAmelCase_=[2_56, 5_12, 10_24, 20_48]		,				lowerCAmelCase_=[3, 4, 6, 3]		,				lowerCAmelCase_="bottleneck"		,				lowerCAmelCase_="relu"		,				lowerCAmelCase_=False		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				**lowerCAmelCase_		,				)  ->					Union[str, Any]:
        super().__init__(**lowerCAmelCase_							)
        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(lowerCAmelCase_							) + 1							)]
        _A   ,  _A        =  get_aligned_output_features_output_indices(
            out_features=lowerCAmelCase_		,				out_indices=lowerCAmelCase_		,				stage_names=self.stage_names							)



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Optional[Any]    			=		version.parse('''1.11'''       )



 @property
 def      UpperCAmelCase							(					self							)  ->					Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ]							)



 @property
 def      UpperCAmelCase							(					self							)  ->					float:
        return 1E-3




