




"""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,
)


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		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"""),
    ]
)

lowercase__  		=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)







def   _snake_case    (       lowercase__	):
				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												_lowerCamelCase  :    Optional[int]					=					model_type_to_module_name(lowercase__	)

												_lowerCamelCase  :    List[Any]					=					importlib.import_module(f'''.{module_name}'''							,					'transformers.models'	)
												try:
																return getattr(lowercase__							,					lowercase__	)
												except AttributeError:
																continue

				for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
								if getattr(lowercase__							,					'__name__'							,					lowercase__	) == 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.
				_lowerCamelCase  :    List[Any]					=					importlib.import_module('transformers'	)
				if hasattr(lowercase__							,					lowercase__	):
								return getattr(lowercase__							,					lowercase__	)

				return None







def   _snake_case    (       lowercase__							,					lowercase__ = None							,					lowercase__ = False							,					lowercase__ = False							,					lowercase__ = None							,					lowercase__ = None							,					lowercase__ = None							,					lowercase__ = False							,					**lowercase__							,					):
				_lowerCamelCase  :    Optional[int]					=					get_file_from_repo(
				    lowercase__							,					lowercase__							,					cache_dir=lowercase__							,					force_download=lowercase__							,					resume_download=lowercase__							,					proxies=lowercase__							,					use_auth_token=lowercase__							,					revision=lowercase__							,					local_files_only=lowercase__							,					)
				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(lowercase__							,					encoding='utf-8'	) as reader:
								return json.load(lowercase__	)






class lowerCAmelCase__			:






			'''simple docstring'''



			def __init__(						self						):
							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       A_					(						cls						,   lowercase						,   **lowercase						):
							_lowerCamelCase  :    Tuple					=					kwargs.pop('config'						,   lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					kwargs.pop('trust_remote_code'						,   lowercase						)
							_lowerCamelCase  :    Optional[int]					=					True

							_lowerCamelCase,				_lowerCamelCase  :    Dict					=					ImageProcessingMixin.get_image_processor_dict(lowercase						,   **lowercase						)
							_lowerCamelCase  :    Dict					=					config_dict.get('image_processor_type'						,   lowercase						)
							_lowerCamelCase  :    str					=					None
							if "AutoImageProcessor" in config_dict.get('auto_map'						,   {}						):
											_lowerCamelCase  :    List[str]					=					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:
											_lowerCamelCase  :    Optional[int]					=					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.'						)
															_lowerCamelCase  :    Tuple					=					feature_extractor_class.replace('FeatureExtractor'						,   'ImageProcessor'						)
											if "AutoFeatureExtractor" in config_dict.get('auto_map'						,   {}						):
															_lowerCamelCase  :    List[Any]					=					config_dict['auto_map']['AutoFeatureExtractor']
															_lowerCamelCase  :    Optional[int]					=					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						):
															_lowerCamelCase  :    str					=					AutoConfig.from_pretrained(lowercase						,   **lowercase						)
											# It could be in `config.image_processor_type``
											_lowerCamelCase  :    List[str]					=					getattr(lowercase						,   'image_processor_type'						,   lowercase						)
											if hasattr(lowercase						,   'auto_map'						) and "AutoImageProcessor" in config.auto_map:
															_lowerCamelCase  :    Union[str, Any]					=					config.auto_map['AutoImageProcessor']

							if image_processor_class is not None:
											_lowerCamelCase  :    Tuple					=					image_processor_class_from_name(lowercase						)

							_lowerCamelCase  :    List[Any]					=					image_processor_auto_map is not None
							_lowerCamelCase  :    List[Any]					=					image_processor_class is not None or type(lowercase						) in IMAGE_PROCESSOR_MAPPING
							_lowerCamelCase  :    Any					=					resolve_trust_remote_code(
							    lowercase						,   lowercase						,   lowercase						,   lowercase						)

							if has_remote_code and trust_remote_code:
											_lowerCamelCase  :    Optional[int]					=					get_class_from_dynamic_module(
											    lowercase						,   lowercase						,   **lowercase						)
											_lowerCamelCase  :    Any					=					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:
											_lowerCamelCase  :    Optional[Any]					=					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       A_					(						lowercase						,   lowercase						):
							IMAGE_PROCESSOR_MAPPING.register(lowercase						,   lowercase						)





"""simple docstring"""






from pathlib import Path

import fire







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    int					=					Path(lowercase__	)
				_lowerCamelCase  :    List[Any]					=					Path(lowercase__	)
				dest_dir.mkdir(exist_ok=lowercase__	)
				for path in src_dir.iterdir():
								_lowerCamelCase  :    str					=					[x.rstrip() for x in list(path.open().readlines()	)][:n]
								_lowerCamelCase  :    Any					=					dest_dir.joinpath(path.name	)
								print(lowercase__	)
								dest_path.open('w'	).write('\n'.join(lowercase__	)	)


if __name__ == "__main__":
			fire.Fire(minify)