
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,
)


SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							: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'),
    ]
)

SCREAMING_SNAKE_CASE							:Optional[int]							 =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)


def 						UpperCAmelCase			(	a_						)		->	Tuple:







				"""simple docstring"""



				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												__A   			= model_type_to_module_name(a_						)

												__A   			= 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.
				__A   			= importlib.import_module("transformers"						)
				if hasattr(a_		,   a_						):
								return getattr(a_		,   a_						)

				return None


def 						UpperCAmelCase			(	a_		,   a_ = None		,   a_ = False		,   a_ = False		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = False		,   **a_		,   )		->	List[str]:







				"""simple docstring"""



				__A   			= 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 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: Optional[int] ):
					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(A )
	def 				UpperCamelCase_  (       cls					: Union[str, Any]							,A					: Tuple							,**A					: Optional[int] ):
					__A   			= kwargs.pop("config"							,A )
					__A   			= kwargs.pop("trust_remote_code"							,A )
					__A   			= True

					__A			,							__A   			= ImageProcessingMixin.get_image_processor_dict(A							,**A )
					__A   			= config_dict.get("image_processor_type"							,A )
					__A   			= None
					if "AutoImageProcessor" in config_dict.get("auto_map"							,{} ):
									__A   			= 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:
									__A   			= config_dict.pop("feature_extractor_type"							,A )
									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." )
													__A   			= feature_extractor_class.replace("FeatureExtractor"							,"ImageProcessor" )
									if "AutoFeatureExtractor" in config_dict.get("auto_map"							,{} ):
													__A   			= config_dict["auto_map"]["AutoFeatureExtractor"]
													__A   			= 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(A							,A ):
													__A   			= AutoConfig.from_pretrained(A							,**A )
									# It could be in `config.image_processor_type``
									__A   			= getattr(A							,"image_processor_type"							,A )
									if hasattr(A							,"auto_map" ) and "AutoImageProcessor" in config.auto_map:
													__A   			= config.auto_map["AutoImageProcessor"]

					if image_processor_class is not None:
									__A   			= image_processor_class_from_name(A )

					__A   			= image_processor_auto_map is not None
					__A   			= image_processor_class is not None or type(A ) in IMAGE_PROCESSOR_MAPPING
					__A   			= resolve_trust_remote_code(
					    A							,A							,A							,A )

					if has_remote_code and trust_remote_code:
									__A   			= get_class_from_dynamic_module(
									    A							,A							,**A )
									__A   			= kwargs.pop("code_revision"							,A )
									if os.path.isdir(A ):
													image_processor_class.register_for_auto_class()
									return image_processor_class.from_dict(A							,**A )
					elif image_processor_class is not None:
									return image_processor_class.from_dict(A							,**A )
					# Last try: we use the IMAGE_PROCESSOR_MAPPING.
					elif type(A ) in IMAGE_PROCESSOR_MAPPING:
									__A   			= IMAGE_PROCESSOR_MAPPING[type(A )]
									return image_processor_class.from_dict(A							,**A )

					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 				UpperCamelCase_  (       A					: Optional[Any]							,A					: Any ):
					IMAGE_PROCESSOR_MAPPING.register(A							,A )


'''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         =    logging.get_logger(__name__)

__lowerCAmelCase         =    """▁"""

__lowerCAmelCase         =    {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

__lowerCAmelCase         =    {
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

__lowerCAmelCase         =    {"""vinai/bartpho-syllable""": 1_0_2_4}







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[Any]   =					VOCAB_FILES_NAMES
  __UpperCAmelCase				:				Dict   =					PRETRAINED_VOCAB_FILES_MAP
  __UpperCAmelCase				:				Optional[int]   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __UpperCAmelCase				:				Dict   =					['''input_ids''', '''attention_mask''']


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


     '''simple docstring'''






     _a				:	Any			=   AddedToken(_a							,lstrip=_a							,rstrip=_a       ) if isinstance(_a							,_a       ) else mask_token

     _a				:	Optional[Any]			=   {} if sp_model_kwargs is None else sp_model_kwargs

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

     _a				:	Optional[int]			=   vocab_file
     _a				:	Union[str, Any]			=   monolingual_vocab_file
     _a				:	List[Any]			=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.Load(str(_a       )       )

     # Load the reduced vocab

     # Keep order of special tokens for backward compatibility
     _a				:	Union[str, Any]			=   {}
     _a				:	int			=   0
     for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
        if str(_a       ) not in self.fairseq_tokens_to_ids:
           _a				:	int			=   cnt
           cnt += 1
     with open(_a							,'r'							,encoding='utf-8'       ) as f:
        for line in f.readlines():
           _a				:	str			=   line.strip().split()[0]
           _a				:	Tuple			=   len(self.fairseq_tokens_to_ids       )
     if str(_a       ) not in self.fairseq_tokens_to_ids:
        _a				:	List[str]			=   len(self.fairseq_tokens_to_ids       )

     _a				:	Tuple			=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}


  def __getstate__(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	int			=   self.__dict__.copy()
     _a				:	str			=   None
     _a				:	Optional[Any]			=   self.sp_model.serialized_model_proto()
     return state


  def __setstate__(		self      :     Tuple							,_a      :     Tuple       ):


     '''simple docstring'''






     _a				:	Tuple			=   d

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

     _a				:	List[Any]			=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )


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


     '''simple docstring'''






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


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


     '''simple docstring'''






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


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


     '''simple docstring'''






     _a				:	List[str]			=   [self.sep_token_id]
     _a				:	List[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      :     Dict       ):


     '''simple docstring'''






     return len(self.fairseq_ids_to_tokens       )


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	List[str]			=   {self.convert_ids_to_tokens(_a       ): i for i in range(self.vocab_size       )}
     vocab.update(self.added_tokens_encoder       )
     return vocab


  def   __lowercase		(		self      :     Tuple							,_a      :     str       ):


     '''simple docstring'''






     return self.sp_model.encode(_a							,out_type=_a       )


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


     '''simple docstring'''






     if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
     else:
        return self.unk_token_id


  def   __lowercase		(		self      :     Any							,_a      :     int       ):


     '''simple docstring'''






     return self.fairseq_ids_to_tokens[index]


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


     '''simple docstring'''






     _a				:	str			=   ''.join(_a       ).replace(_a							,' '       ).strip()
     return out_string


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     str							,_a      :     Optional[str] = None       ):


     '''simple docstring'''






     if not os.path.isdir(_a       ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
        return
     _a				:	int			=   os.path.join(
         _a							,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
     _a				:	int			=   os.path.join(
         _a							,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']							,)

     if os.path.abspath(self.vocab_file       ) != os.path.abspath(_a       ) and os.path.isfile(self.vocab_file       ):
        copyfile(self.vocab_file							,_a       )
     elif not os.path.isfile(self.vocab_file       ):
        with open(_a							,'wb'       ) as fi:
           _a				:	List[Any]			=   self.sp_model.serialized_model_proto()
           fi.write(_a       )

     if os.path.abspath(self.monolingual_vocab_file       ) != os.path.abspath(
         _a       ) and os.path.isfile(self.monolingual_vocab_file       ):
        copyfile(self.monolingual_vocab_file							,_a       )
     elif not os.path.isfile(self.monolingual_vocab_file       ):
        with open(_a							,'w'							,encoding='utf-8'       ) as fp:
           for token in self.fairseq_tokens_to_ids:
              if token not in self.all_special_tokens:
                 fp.write(F"""{str(_a       )} \n"""       )

     return out_vocab_file, out_monolingual_vocab_file



