






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

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_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,
)


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

__UpperCamelCase      :      Any     						=						OrderedDict(
    [
        ("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
        ("""beit""", """BeitFeatureExtractor"""),
        ("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
        ("""clap""", """ClapFeatureExtractor"""),
        ("""clip""", """CLIPFeatureExtractor"""),
        ("""clipseg""", """ViTFeatureExtractor"""),
        ("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
        ("""convnext""", """ConvNextFeatureExtractor"""),
        ("""cvt""", """ConvNextFeatureExtractor"""),
        ("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
        ("""data2vec-vision""", """BeitFeatureExtractor"""),
        ("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
        ("""deit""", """DeiTFeatureExtractor"""),
        ("""detr""", """DetrFeatureExtractor"""),
        ("""dinat""", """ViTFeatureExtractor"""),
        ("""donut-swin""", """DonutFeatureExtractor"""),
        ("""dpt""", """DPTFeatureExtractor"""),
        ("""encodec""", """EncodecFeatureExtractor"""),
        ("""flava""", """FlavaFeatureExtractor"""),
        ("""glpn""", """GLPNFeatureExtractor"""),
        ("""groupvit""", """CLIPFeatureExtractor"""),
        ("""hubert""", """Wav2Vec2FeatureExtractor"""),
        ("""imagegpt""", """ImageGPTFeatureExtractor"""),
        ("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
        ("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
        ("""levit""", """LevitFeatureExtractor"""),
        ("""maskformer""", """MaskFormerFeatureExtractor"""),
        ("""mctct""", """MCTCTFeatureExtractor"""),
        ("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
        ("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
        ("""mobilevit""", """MobileViTFeatureExtractor"""),
        ("""nat""", """ViTFeatureExtractor"""),
        ("""owlvit""", """OwlViTFeatureExtractor"""),
        ("""perceiver""", """PerceiverFeatureExtractor"""),
        ("""poolformer""", """PoolFormerFeatureExtractor"""),
        ("""regnet""", """ConvNextFeatureExtractor"""),
        ("""resnet""", """ConvNextFeatureExtractor"""),
        ("""segformer""", """SegformerFeatureExtractor"""),
        ("""sew""", """Wav2Vec2FeatureExtractor"""),
        ("""sew-d""", """Wav2Vec2FeatureExtractor"""),
        ("""speech_to_text""", """Speech2TextFeatureExtractor"""),
        ("""speecht5""", """SpeechT5FeatureExtractor"""),
        ("""swiftformer""", """ViTFeatureExtractor"""),
        ("""swin""", """ViTFeatureExtractor"""),
        ("""swinv2""", """ViTFeatureExtractor"""),
        ("""table-transformer""", """DetrFeatureExtractor"""),
        ("""timesformer""", """VideoMAEFeatureExtractor"""),
        ("""tvlt""", """TvltFeatureExtractor"""),
        ("""unispeech""", """Wav2Vec2FeatureExtractor"""),
        ("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
        ("""van""", """ConvNextFeatureExtractor"""),
        ("""videomae""", """VideoMAEFeatureExtractor"""),
        ("""vilt""", """ViltFeatureExtractor"""),
        ("""vit""", """ViTFeatureExtractor"""),
        ("""vit_mae""", """ViTFeatureExtractor"""),
        ("""vit_msn""", """ViTFeatureExtractor"""),
        ("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
        ("""wavlm""", """Wav2Vec2FeatureExtractor"""),
        ("""whisper""", """WhisperFeatureExtractor"""),
        ("""xclip""", """CLIPFeatureExtractor"""),
        ("""yolos""", """YolosFeatureExtractor"""),
    ]
)

__UpperCamelCase      :      List[Any]     						=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
		if class_name in extractors:
			__lowercase					       =  model_type_to_module_name(lowerCamelCase					)

			__lowercase					       =  importlib.import_module(F'.{module_name}'				,  """transformers.models"""					)
			try:
				return getattr(lowerCamelCase				,  lowerCamelCase					)
			except AttributeError:
				continue

	for _, extractor in FEATURE_EXTRACTOR_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.
	__lowercase					       =  importlib.import_module("""transformers"""					)
	if hasattr(lowerCamelCase				,  lowerCamelCase					):
		return getattr(lowerCamelCase				,  lowerCamelCase					)

	return None





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase = None				,  lowerCamelCase = False				,  lowerCamelCase = False				,  lowerCamelCase = None				,  lowerCamelCase = None				,  lowerCamelCase = None				,  lowerCamelCase = False				,  **lowerCamelCase				,  ):




	'''simple docstring'''







	__lowercase					       =  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 feature extractor configuration file, will try to use the model config instead."""					)
		return {}

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




class        __UpperCamelCase    :







	def __init__(  self      :							Dict			)		-> Dict:
		"""simple docstring"""







		raise EnvironmentError(
		    """AutoFeatureExtractor is designed to be instantiated """
		    """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""			)







	@classmethod
	@replace_list_option_in_docstrings(__a			)
	def  _a   (  cls      :							Optional[int]       ,				_lowerCAmelCase      :							int       ,				**_lowerCAmelCase      :							str			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase					       =  kwargs.pop("""config"""       ,				__a			)
		__lowercase					       =  kwargs.pop("""trust_remote_code"""       ,				__a			)
		__lowercase					       =  True

		__lowercase					       =  FeatureExtractionMixin.get_feature_extractor_dict(__a       ,				**__a			)
		__lowercase					       =  config_dict.get("""feature_extractor_type"""       ,				__a			)
		__lowercase					       =  None
		if "AutoFeatureExtractor" in config_dict.get("""auto_map"""       ,				{}			):
			__lowercase					       =  config_dict['auto_map']['AutoFeatureExtractor']

		# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
		if feature_extractor_class is None and feature_extractor_auto_map is None:
			if not isinstance(__a       ,				__a			):
				__lowercase					       =  AutoConfig.from_pretrained(__a       ,				**__a			)
			# It could be in `config.feature_extractor_type``
			__lowercase					       =  getattr(__a       ,				"""feature_extractor_type"""       ,				__a			)
			if hasattr(__a       ,				"""auto_map"""			) and "AutoFeatureExtractor" in config.auto_map:
				__lowercase					       =  config.auto_map['AutoFeatureExtractor']

		if feature_extractor_class is not None:
			__lowercase					       =  feature_extractor_class_from_name(__a			)

		__lowercase					       =  feature_extractor_auto_map is not None
		__lowercase					       =  feature_extractor_class is not None or type(__a			) in FEATURE_EXTRACTOR_MAPPING
		__lowercase					       =  resolve_trust_remote_code(
		    __a       ,				__a       ,				__a       ,				__a			)

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

		raise ValueError(
		    F'Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '
		    F'`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '
		    F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()			)}'			)







	@staticmethod
	def  _a   (  _lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							List[Any]			)		-> Dict:
		"""simple docstring"""







		FEATURE_EXTRACTOR_MAPPING.register(__a       ,				__a			)









import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if (ksize % 2) == 0:
		__lowercase             =  ksize + 1
	__lowercase             =  np.zeros((ksize, ksize)				,  dtype=np.floataa					)

	# each value
	for y in range(lowerCamelCase					):
		for x in range(lowerCamelCase					):
			# distance from center
			__lowercase             =  x - ksize // 2
			__lowercase             =  y - ksize // 2

			# degree to radiant
			__lowercase             =  theta / 180 * np.pi
			__lowercase             =  np.cos(_theta					)
			__lowercase             =  np.sin(_theta					)

			# get kernel x
			__lowercase             =  cos_theta * px + sin_theta * py

			# get kernel y
			__lowercase             =  -sin_theta * px + cos_theta * py

			# fill kernel
			__lowercase             =  np.exp(
			    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)					) * np.cos(2 * np.pi * _x / lambd + psi					)

	return gabor


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# read original image
						__UpperCamelCase      :      List[Any]     						=						imread("""../image_data/lena.jpg""")
						# turn image in gray scale value
						__UpperCamelCase      :      Union[str, Any]     						=						cvtColor(img, COLOR_BGR2GRAY)

						# Apply multiple Kernel to detect edges
						__UpperCamelCase      :      Union[str, Any]     						=						np.zeros(gray.shape[:2])
						for theta in [0, 30, 60, 90, 120, 150]:
												__UpperCamelCase      :      Tuple     						=						gabor_filter_kernel(10, 8, theta, 10, 0, 0)
												out += filteraD(gray, CV_8UC3, kernel_aa)
						__UpperCamelCase      :      List[str]     						=						out / out.max() * 255
						__UpperCamelCase      :      List[str]     						=						out.astype(np.uinta)

						imshow("""Original""", gray)
						imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

						waitKey(0)

