



'''simple docstring'''



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


lowerCAmelCase_     :       List[Any]								=						logging.get_logger(__name__)

lowerCAmelCase_     :       Optional[Any]								=						{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_     :       Optional[int]								=						{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

lowerCAmelCase_     :       Union[str, Any]								=						{
    'camembert-base': 512,
}

lowerCAmelCase_     :       List[Any]								=						'▁'
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   List[Any]	     =				VOCAB_FILES_NAMES
						_lowerCAmelCase     :   Dict	     =				PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase     :   str	     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase     :   Any	     =				['input_ids', 'attention_mask']







						def __init__(  self   :      Optional[Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      int="<s>"				,    lowerCAmelCase__   :      Optional[int]="</s>"				,    lowerCAmelCase__   :      Any="</s>"				,    lowerCAmelCase__   :      str="<s>"				,    lowerCAmelCase__   :      List[Any]="<unk>"				,    lowerCAmelCase__   :      Optional[Any]="<pad>"				,    lowerCAmelCase__   :      Optional[Any]="<mask>"				,    lowerCAmelCase__   :      Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]				,    lowerCAmelCase__   :      Optional[Dict[str, Any]] = None				,    **lowerCAmelCase__   :      str				,    ):

													"""simple docstring"""
													# Mask token behave like a normal word, i.e. include the space before it
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			AddedToken(lowerCAmelCase__				,    lstrip=lowerCAmelCase__				,    rstrip=lowerCAmelCase__				) if isinstance(lowerCAmelCase__				,    lowerCAmelCase__				) else mask_token

													SCREAMING_SNAKE_CASE					:      str     					=			{} 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__				,    additional_special_tokens=lowerCAmelCase__				,    sp_model_kwargs=self.sp_model_kwargs				,    **lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      int     					=			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
													self.sp_model.Load(str(lowerCAmelCase__				)				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			vocab_file
													# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
													# sentencepiece vocabulary (this is the case for <s> and </s>
													SCREAMING_SNAKE_CASE					:      int     					=			{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
													SCREAMING_SNAKE_CASE					:      int     					=			len(self.fairseq_tokens_to_ids				)
													SCREAMING_SNAKE_CASE					:      str     					=			len(self.sp_model				) + len(self.fairseq_tokens_to_ids				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      List[int]				,    lowerCAmelCase__   :      Optional[List[int]] = None				):

													"""simple docstring"""

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







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      List[int]				,    lowerCAmelCase__   :      Optional[List[int]] = None				,    lowerCAmelCase__   :      bool = False				):

													"""simple docstring"""
													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 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      List[int]				,    lowerCAmelCase__   :      Optional[List[int]] = None				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			[self.sep_token_id]
													SCREAMING_SNAKE_CASE					:      str     					=			[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 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													return len(self.fairseq_tokens_to_ids				) + len(self.sp_model				)







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			{self.convert_ids_to_tokens(lowerCAmelCase__				): i for i in range(self.vocab_size				)}
													vocab.update(self.added_tokens_encoder				)
													return vocab







						def 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      str				):

													"""simple docstring"""
													return self.sp_model.encode(lowerCAmelCase__				,    out_type=lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Tuple				,    lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													if token in self.fairseq_tokens_to_ids:
																				return self.fairseq_tokens_to_ids[token]
													elif self.sp_model.PieceToId(lowerCAmelCase__				) == 0:
																				# Convert sentence piece unk token to fairseq unk token index
																				return self.unk_token_id
													return self.fairseq_offset + self.sp_model.PieceToId(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													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 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			[]
													SCREAMING_SNAKE_CASE					:      str     					=			''''''
													SCREAMING_SNAKE_CASE					:      List[str]     					=			False
													for token in tokens:
																				# make sure that special tokens are not decoded using sentencepiece model
																				if token in self.all_special_tokens:
																											if not prev_is_special:
																																		out_string += " "
																											out_string += self.sp_model.decode(lowerCAmelCase__				) + token
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			True
																											SCREAMING_SNAKE_CASE					:      int     					=			[]
																				else:
																											current_sub_tokens.append(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      int     					=			False
													out_string += self.sp_model.decode(lowerCAmelCase__				)
													return out_string.strip()







						def __getstate__(  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.__dict__.copy()
													SCREAMING_SNAKE_CASE					:      Any     					=			None
													return state







						def __setstate__(  self   :      str				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			d

													# for backward compatibility
													if not hasattr(self				,    '''sp_model_kwargs'''				):
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			{}

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
													self.sp_model.Load(self.vocab_file				)







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Optional[str] = None				):

													"""simple docstring"""
													if not os.path.isdir(lowerCAmelCase__				):
																				logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
																				return
													SCREAMING_SNAKE_CASE					:      List[str]     					=			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:
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			self.sp_model.serialized_model_proto()
																											fi.write(lowerCAmelCase__				)

													return (out_vocab_file,)









'''simple docstring'''



import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


lowerCAmelCase_     :       str								=						logging.get_logger(__name__)

lowerCAmelCase_     :       List[Any]								=						OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

lowerCAmelCase_     :       List[str]								=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def 					UpperCAmelCase   (						A  :				str				):
							for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			model_type_to_module_name(A				)

																					SCREAMING_SNAKE_CASE					:      List[Any]     					=			importlib.import_module(F""".{module_name}"""					,		'''transformers.models'''				)
																					try:
																												return getattr(A					,		A				)
																					except AttributeError:
																												continue

							for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
														if getattr(A					,		'''__name__'''					,		A				) == class_name:
																					return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
							SCREAMING_SNAKE_CASE					:      List[str]     					=			importlib.import_module('''transformers'''				)
							if hasattr(A					,		A				):
														return getattr(A					,		A				)

							return None
def 					UpperCAmelCase   (						A  :				Union[str, os.PathLike]					,		A  :				Optional[Union[str, os.PathLike]] = None					,		A  :				bool = False					,		A  :				bool = False					,		A  :				Optional[Dict[str, str]] = None					,		A  :				Optional[Union[bool, str]] = None					,		A  :				Optional[str] = None					,		A  :				bool = False					,		**A  :				List[str]					,		):
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			get_file_from_repo(
							    A					,		A					,		cache_dir=A					,		force_download=A					,		resume_download=A					,		proxies=A					,		use_auth_token=A					,		revision=A					,		local_files_only=A					,		)
							if resolved_config_file is None:
														logger.info(
														    '''Could not locate the image processor configuration file, will try to use the model config instead.'''				)
														return {}

							with open(A					,		encoding='''utf-8'''				) as reader:
														return json.load(A				)






class 	lowerCamelCase_							:







						def __init__(  self   :      Tuple				):

													"""simple docstring"""
													raise EnvironmentError(
													    '''AutoImageProcessor is designed to be instantiated '''
													    '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''				)







						@classmethod
						@replace_list_option_in_docstrings(lowerCAmelCase__				)
						def 					__lowercase    (  cls   :      int				,    lowerCAmelCase__   :      Optional[int]				,    **lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			kwargs.pop('''config'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			kwargs.pop('''trust_remote_code'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			True

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      str     					=			ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase__				,    **lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			config_dict.get('''image_processor_type'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			None
													if "AutoImageProcessor" in config_dict.get('''auto_map'''				,    {}				):
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			config_dict['''auto_map''']['''AutoImageProcessor''']

													# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
													# and if so, infer the image processor class from there.
													if image_processor_class is None and image_processor_auto_map is None:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			config_dict.pop('''feature_extractor_type'''				,    lowerCAmelCase__				)
																				if feature_extractor_class is not None:
																											logger.warning(
																											    '''Could not find image processor class in the image processor config or the model config. Loading'''
																											    ''' based on pattern matching with the model\'s feature extractor configuration.'''				)
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			feature_extractor_class.replace('''FeatureExtractor'''				,    '''ImageProcessor'''				)
																				if "AutoFeatureExtractor" in config_dict.get('''auto_map'''				,    {}				):
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			config_dict['''auto_map''']['''AutoFeatureExtractor''']
																											SCREAMING_SNAKE_CASE					:      str     					=			feature_extractor_auto_map.replace('''FeatureExtractor'''				,    '''ImageProcessor'''				)
																											logger.warning(
																											    '''Could not find image processor auto map in the image processor config or the model config.'''
																											    ''' Loading based on pattern matching with the model\'s feature extractor configuration.'''				)

        # If we don't find the image processor class in the image processor config, let's try the model config.
													if image_processor_class is None and image_processor_auto_map is None:
																				if not isinstance(lowerCAmelCase__				,    lowerCAmelCase__				):
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			AutoConfig.from_pretrained(lowerCAmelCase__				,    **lowerCAmelCase__				)
																				# It could be in `config.image_processor_type``
																				SCREAMING_SNAKE_CASE					:      Any     					=			getattr(lowerCAmelCase__				,    '''image_processor_type'''				,    lowerCAmelCase__				)
																				if hasattr(lowerCAmelCase__				,    '''auto_map'''				) and "AutoImageProcessor" in config.auto_map:
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			config.auto_map['''AutoImageProcessor''']

													if image_processor_class is not None:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			image_processor_class_from_name(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			image_processor_auto_map is not None
													SCREAMING_SNAKE_CASE					:      str     					=			image_processor_class is not None or type(lowerCAmelCase__				) in IMAGE_PROCESSOR_MAPPING
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			resolve_trust_remote_code(
													    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)

													if has_remote_code and trust_remote_code:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			get_class_from_dynamic_module(
																				    lowerCAmelCase__				,    lowerCAmelCase__				,    **lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			kwargs.pop('''code_revision'''				,    lowerCAmelCase__				)
																				if os.path.isdir(lowerCAmelCase__				):
																											image_processor_class.register_for_auto_class()
																				return image_processor_class.from_dict(lowerCAmelCase__				,    **lowerCAmelCase__				)
													elif image_processor_class is not None:
																				return image_processor_class.from_dict(lowerCAmelCase__				,    **lowerCAmelCase__				)
													# Last try: we use the IMAGE_PROCESSOR_MAPPING.
													elif type(lowerCAmelCase__				) in IMAGE_PROCESSOR_MAPPING:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			IMAGE_PROCESSOR_MAPPING[type(lowerCAmelCase__				)]
																				return image_processor_class.from_dict(lowerCAmelCase__				,    **lowerCAmelCase__				)

													raise ValueError(
													    F"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
													    F"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
													    F"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()				)}"""				)







						@staticmethod
						def 					__lowercase    (  lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase__				,    lowerCAmelCase__				)




