


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:						Tuple   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Union[str, 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   _lowercase(					__a				:						str     ):
							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   _lowercase(					__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				:						Any    ,     ):
							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       lowercase_					:





				def __init__(				self)  -> str:
											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(lowercase_)
				def 						__UpperCamelCase    (				cls       ,  lowercase_       ,  **lowercase_)  -> str:
											a__      =kwargs.pop('config'       ,  lowercase_)
											a__      =kwargs.pop('trust_remote_code'       ,  lowercase_)
											a__      =True

											a__    ,     a__      =ImageProcessingMixin.get_image_processor_dict(lowercase_       ,  **lowercase_)
											a__      =config_dict.get('image_processor_type'       ,  lowercase_)
											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'       ,  lowercase_)
																		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(lowercase_       ,  lowercase_):
																									a__      =AutoConfig.from_pretrained(lowercase_       ,  **lowercase_)
																		# It could be in `config.image_processor_type``
																		a__      =getattr(lowercase_       ,  'image_processor_type'       ,  lowercase_)
																		if hasattr(lowercase_       ,  '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(lowercase_)

											a__      =image_processor_auto_map is not None
											a__      =image_processor_class is not None or type(lowercase_) in IMAGE_PROCESSOR_MAPPING
											a__      =resolve_trust_remote_code(
											    lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)

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

											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    (				lowercase_       ,  lowercase_)  -> Union[str, Any]:
											IMAGE_PROCESSOR_MAPPING.register(lowercase_       ,  lowercase_)





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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_mbart import MBartTokenizer
else:
				lowerCAmelCase       		=    None


lowerCAmelCase       		=    logging.get_logger(__name__)


lowerCAmelCase       		=    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase       		=    {
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

lowerCAmelCase       		=    {
    """facebook/mbart-large-en-ro""": 1_024,
    """facebook/mbart-large-cc25""": 1_024,
}

# fmt: off
lowerCAmelCase       		=    ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		List[str]										=						VOCAB_FILES_NAMES
	_lowerCAmelCase     :		Union[str, Any]										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase     :		str										=						PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase     :		Optional[int]										=						['''input_ids''', '''attention_mask''']
	_lowerCAmelCase     :		Optional[int]										=						MBartTokenizer

	_lowerCAmelCase     :		List[int]										=						[]
	_lowerCAmelCase     :		List[int]										=						[]


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

							super().__init__(
							    vocab_file=lowercase__				,     tokenizer_file=lowercase__				,     bos_token=lowercase__				,     eos_token=lowercase__				,     sep_token=lowercase__				,     cls_token=lowercase__				,     unk_token=lowercase__				,     pad_token=lowercase__				,     mask_token=lowercase__				,     src_lang=lowercase__				,     tgt_lang=lowercase__				,     additional_special_tokens=lowercase__				,     **lowercase__				,     )

							__UpperCAmelCase			:							Optional[Any]	    =	vocab_file
							__UpperCAmelCase			:							int	    =	False if not self.vocab_file else True

							__UpperCAmelCase			:							str	    =	FAIRSEQ_LANGUAGE_CODES.copy()

							if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													_additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in _additional_special_tokens])

							self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens})
							__UpperCAmelCase			:							Union[str, Any]	    =	{
							    lang_code: self.convert_tokens_to_ids(lowercase__) for lang_code in FAIRSEQ_LANGUAGE_CODES
							}

							__UpperCAmelCase			:							Any	    =	src_lang if src_lang is not None else '''en_XX'''
							__UpperCAmelCase			:							Optional[int]	    =	self.convert_tokens_to_ids(self._src_lang)
							__UpperCAmelCase			:							List[Any]	    =	tgt_lang
							self.set_src_lang_special_tokens(self._src_lang)


	@property
	def  A(    self):
							return self._src_lang


	@src_lang.setter
	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	new_src_lang
							self.set_src_lang_special_tokens(self._src_lang)


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							Optional[int]	    =	[self.sep_token_id]
							__UpperCAmelCase			:							int	    =	[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]


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__):
							if src_lang is None or tgt_lang is None:
													raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''')
							__UpperCAmelCase			:							Optional[Any]	    =	src_lang
							__UpperCAmelCase			:							Union[str, Any]	    =	self(lowercase__				,     add_special_tokens=lowercase__				,     return_tensors=lowercase__				,     **lowercase__)
							__UpperCAmelCase			:							int	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							Tuple	    =	tgt_lang_id
							return inputs


	def  A(    self				,     lowercase__				,     lowercase__ = "en_XX"				,     lowercase__ = None				,     lowercase__ = "ro_RO"				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Any	    =	src_lang
							__UpperCAmelCase			:							Tuple	    =	tgt_lang
							return super().prepare_seqaseq_batch(lowercase__				,     lowercase__				,     **lowercase__)


	def  A(    self):
							return self.set_src_lang_special_tokens(self.src_lang)


	def  A(    self):
							return self.set_tgt_lang_special_tokens(self.tgt_lang)


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							Optional[Any]	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[]
							__UpperCAmelCase			:							Dict	    =	[self.eos_token_id, self.cur_lang_code]

							__UpperCAmelCase			:							int	    =	self.convert_ids_to_tokens(self.prefix_tokens)
							__UpperCAmelCase			:							Dict	    =	self.convert_ids_to_tokens(self.suffix_tokens)

							__UpperCAmelCase			:							str	    =	processors.TemplateProcessing(
							    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str				,     pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str				,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,     self.prefix_tokens + self.suffix_tokens))				,     )


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							List[Any]	    =	[]
							__UpperCAmelCase			:							List[Any]	    =	[self.eos_token_id, self.cur_lang_code]

							__UpperCAmelCase			:							Tuple	    =	self.convert_ids_to_tokens(self.prefix_tokens)
							__UpperCAmelCase			:							Tuple	    =	self.convert_ids_to_tokens(self.suffix_tokens)

							__UpperCAmelCase			:							Optional[int]	    =	processors.TemplateProcessing(
							    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str				,     pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str				,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,     self.prefix_tokens + self.suffix_tokens))				,     )


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							if not self.can_save_slow_tokenizer:
													raise ValueError(
													    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
													    '''tokenizer.''')

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

							if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase__):
													copyfile(self.vocab_file				,     lowercase__)

							return (out_vocab_file,)

