"""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_    :		Optional[int]							=	logging.get_logger(__name__)

lowerCamelCase_    :		Dict							=	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_    :		Dict							=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def 							lowerCAmelCase(     __lowerCamelCase						):
					for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
										if class_name in extractors:
															__a		 =       model_type_to_module_name(__lowerCamelCase						)

															__a		 =       importlib.import_module(f'''.{module_name}'''				,    'transformers.models'						)
															try:
																				return getattr(__lowerCamelCase				,    __lowerCamelCase						)
															except AttributeError:
																				continue

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

					return None
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase = None				,    __lowerCamelCase = False				,    __lowerCamelCase = False				,    __lowerCamelCase = None				,    __lowerCamelCase = None				,    __lowerCamelCase = None				,    __lowerCamelCase = False				,    **__lowerCamelCase				,    ):
					__a		 =       get_file_from_repo(
					    __lowerCamelCase				,    __lowerCamelCase				,    cache_dir=__lowerCamelCase				,    force_download=__lowerCamelCase				,    resume_download=__lowerCamelCase				,    proxies=__lowerCamelCase				,    use_auth_token=__lowerCamelCase				,    revision=__lowerCamelCase				,    local_files_only=__lowerCamelCase				,    )
					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(__lowerCamelCase				,    encoding='utf-8'						) as reader:
										return json.load(__lowerCamelCase						)



class 						a__							:



			def __init__(						self   )			->   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(UpperCAmelCase   )
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   Dict:
								__a		 =       kwargs.pop('config'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('trust_remote_code'				,						UpperCAmelCase   )
								__a		 =       True

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

								__a		 =       image_processor_auto_map is not None
								__a		 =       image_processor_class is not None or type(UpperCAmelCase   ) in IMAGE_PROCESSOR_MAPPING
								__a		 =       resolve_trust_remote_code(
								    UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )

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

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :		Tuple							=	logging.get_logger(__name__)

lowerCamelCase_    :		List[Any]							=	{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}


class 						a__							(       __snake_case		):
			A__    :       Dict        =      'fnet'



			def __init__(						self				,						UpperCAmelCase=3_2_0_0_0				,						UpperCAmelCase=7_6_8				,						UpperCAmelCase=1_2				,						UpperCAmelCase=3_0_7_2				,						UpperCAmelCase="gelu_new"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=4				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1e-12				,						UpperCAmelCase=False				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=3				,						UpperCAmelCase=1				,						UpperCAmelCase=2				,						**UpperCAmelCase				,						)			->   Dict:
								super().__init__(pad_token_id=UpperCAmelCase				,						bos_token_id=UpperCAmelCase				,						eos_token_id=UpperCAmelCase				,						**UpperCAmelCase   )

								__a		 =       vocab_size
								__a		 =       max_position_embeddings
								__a		 =       hidden_size
								__a		 =       num_hidden_layers
								__a		 =       intermediate_size
								__a		 =       hidden_act
								__a		 =       hidden_dropout_prob
								__a		 =       initializer_range
								__a		 =       type_vocab_size
								__a		 =       layer_norm_eps
								__a		 =       use_tpu_fourier_optimizations
								__a		 =       tpu_short_seq_length




