





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,
)


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		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"""),
    ]
)

_SCREAMING_SNAKE_CASE      	=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
		if class_name in extractors:
			snake_case_		:			List[Any]											=	model_type_to_module_name(__a   )

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

	for _, extractor in FEATURE_EXTRACTOR_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.
	snake_case_		:			int											=	importlib.import_module('transformers'   )
	if hasattr(__a   , __a   ):
		return getattr(__a   , __a   )

	return None


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a = None   , __a = False   , __a = False   , __a = None   , __a = None   , __a = None   , __a = False   , **__a   , ):
	snake_case_		:			int											=	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 feature extractor configuration file, will try to use the model config instead.'   )
		return {}

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


class SCREAMING_SNAKE_CASE_						:






						def __init__(      self      :      int	)							->    str:







							"""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 UpperCAmelCase_				(      cls      :      Dict		,   _A      :      Optional[int]		,   **_A      :      Dict	)							->    str:







							"""simple docstring"""


							snake_case_		:			List[str]											=	kwargs.pop('config'		,   _A	)
							snake_case_		:			Any											=	kwargs.pop('trust_remote_code'		,   _A	)
							snake_case_		:			Optional[int]											=	True

							snake_case_		:			Dict											=	FeatureExtractionMixin.get_feature_extractor_dict(_A		,   **_A	)
							snake_case_		:			List[Any]											=	config_dict.get('feature_extractor_type'		,   _A	)
							snake_case_		:			int											=	None
							if "AutoFeatureExtractor" in config_dict.get('auto_map'		,   {}	):
								snake_case_		:			str											=	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	):
									snake_case_		:			str											=	AutoConfig.from_pretrained(_A		,   **_A	)
								# It could be in `config.feature_extractor_type``
								snake_case_		:			Optional[int]											=	getattr(_A		,   'feature_extractor_type'		,   _A	)
								if hasattr(_A		,   'auto_map'	) and "AutoFeatureExtractor" in config.auto_map:
									snake_case_		:			Optional[Any]											=	config.auto_map['AutoFeatureExtractor']

							if feature_extractor_class is not None:
								snake_case_		:			str											=	feature_extractor_class_from_name(_A	)

							snake_case_		:			Dict											=	feature_extractor_auto_map is not None
							snake_case_		:			Union[str, Any]											=	feature_extractor_class is not None or type(_A	) in FEATURE_EXTRACTOR_MAPPING
							snake_case_		:			Dict											=	resolve_trust_remote_code(
							    _A		,   _A		,   _A		,   _A	)

							if has_remote_code and trust_remote_code:
								snake_case_		:			int											=	get_class_from_dynamic_module(
								    _A		,   _A		,   **_A	)
								snake_case_		:			Optional[int]											=	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:
								snake_case_		:			int											=	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 UpperCAmelCase_				(      _A      :      Optional[int]		,   _A      :      Any	)							->    Tuple:







							"""simple docstring"""


							FEATURE_EXTRACTOR_MAPPING.register(_A		,   _A	)











import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_SCREAMING_SNAKE_CASE      	=		(7_20, 12_80)  # Height, Width
_SCREAMING_SNAKE_CASE      	=		(0.4, 0.6)  # if height or width lower than this scale, drop it.
_SCREAMING_SNAKE_CASE      	=		1 / 1_00
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		2_50


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_						,snake_case_		:			Any											=	get_dataset(__a   , __a   )
	for index in range(__a   ):
		snake_case_		:			Dict											=	random.sample(range(len(__a   )   )   , 4   )
		snake_case_						,snake_case_						,snake_case_		:			Optional[int]											=	update_image_and_anno(
		    __a   , __a   , __a   , __a   , __a   , filter_scale=__a   , )

		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		snake_case_		:			List[str]											=	random_chars(32   )
		snake_case_		:			Dict											=	path.split(os.sep   )[-1].rsplit('.'   , 1   )[0]
		snake_case_		:			Any											=	f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
		cva.imwrite(f"""{file_root}.jpg"""   , __a   , [cva.IMWRITE_JPEG_QUALITY, 85]   )
		print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""   )
		snake_case_		:			Dict											=	[]
		for anno in new_annos:
			snake_case_		:			int											=	anno[3] - anno[1]
			snake_case_		:			Dict											=	anno[4] - anno[2]
			snake_case_		:			int											=	anno[1] + width / 2
			snake_case_		:			str											=	anno[2] + height / 2
			snake_case_		:			Union[str, Any]											=	f"""{anno[0]} {x_center} {y_center} {width} {height}"""
			annos_list.append(__a   )
		with open(f"""{file_root}.txt"""   , 'w'   ) as outfile:
			outfile.write('\n'.join(line for line in annos_list   )   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			List[Any]											=	[]
	snake_case_		:			Optional[int]											=	[]
	for label_file in glob.glob(os.path.join(__a   , '*.txt'   )   ):
		snake_case_		:			List[str]											=	label_file.split(os.sep   )[-1].rsplit('.'   , 1   )[0]
		with open(__a   ) as in_file:
			snake_case_		:			Dict											=	in_file.readlines()
		snake_case_		:			int											=	os.path.join(__a   , f"""{label_name}.jpg"""   )

		snake_case_		:			int											=	[]
		for obj_list in obj_lists:
			snake_case_		:			str											=	obj_list.rstrip('\n'   ).split(' '   )
			snake_case_		:			Tuple											=	float(obj[1]   ) - float(obj[3]   ) / 2
			snake_case_		:			str											=	float(obj[2]   ) - float(obj[4]   ) / 2
			snake_case_		:			Optional[Any]											=	float(obj[1]   ) + float(obj[3]   ) / 2
			snake_case_		:			List[Any]											=	float(obj[2]   ) + float(obj[4]   ) / 2

			boxes.append([int(obj[0]   ), xmin, ymin, xmax, ymax]   )
		if not boxes:
			continue
		img_paths.append(__a   )
		labels.append(__a   )
	return img_paths, labels


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a   , __a = 0.0   , ):
	snake_case_		:			str											=	np.zeros([output_size[0], output_size[1], 3]   , dtype=np.uinta   )
	snake_case_		:			Tuple											=	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	snake_case_		:			int											=	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	snake_case_		:			Optional[Any]											=	int(scale_x * output_size[1]   )
	snake_case_		:			int											=	int(scale_y * output_size[0]   )

	snake_case_		:			str											=	[]
	snake_case_		:			Dict											=	[]
	for i, index in enumerate(__a   ):
		snake_case_		:			Any											=	all_img_list[index]
		path_list.append(__a   )
		snake_case_		:			List[str]											=	all_annos[index]
		snake_case_		:			Any											=	cva.imread(__a   )
		if i == 0:  # top-left
			snake_case_		:			Optional[Any]											=	cva.resize(__a   , (divid_point_x, divid_point_y)   )
			snake_case_		:			Dict											=	img
			for bbox in img_annos:
				snake_case_		:			Dict											=	bbox[1] * scale_x
				snake_case_		:			Tuple											=	bbox[2] * scale_y
				snake_case_		:			Optional[Any]											=	bbox[3] * scale_x
				snake_case_		:			str											=	bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
		elif i == 1:  # top-right
			snake_case_		:			Tuple											=	cva.resize(__a   , (output_size[1] - divid_point_x, divid_point_y)   )
			snake_case_		:			Optional[Any]											=	img
			for bbox in img_annos:
				snake_case_		:			int											=	scale_x + bbox[1] * (1 - scale_x)
				snake_case_		:			Dict											=	bbox[2] * scale_y
				snake_case_		:			Union[str, Any]											=	scale_x + bbox[3] * (1 - scale_x)
				snake_case_		:			Union[str, Any]											=	bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
		elif i == 2:  # bottom-left
			snake_case_		:			Optional[Any]											=	cva.resize(__a   , (divid_point_x, output_size[0] - divid_point_y)   )
			snake_case_		:			int											=	img
			for bbox in img_annos:
				snake_case_		:			int											=	bbox[1] * scale_x
				snake_case_		:			List[str]											=	scale_y + bbox[2] * (1 - scale_y)
				snake_case_		:			List[str]											=	bbox[3] * scale_x
				snake_case_		:			Union[str, Any]											=	scale_y + bbox[4] * (1 - scale_y)
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
		else:  # bottom-right
			snake_case_		:			str											=	cva.resize(
			    __a   , (output_size[1] - divid_point_x, output_size[0] - divid_point_y)   )
			snake_case_		:			Dict											=	img
			for bbox in img_annos:
				snake_case_		:			Dict											=	scale_x + bbox[1] * (1 - scale_x)
				snake_case_		:			Tuple											=	scale_y + bbox[2] * (1 - scale_y)
				snake_case_		:			Union[str, Any]											=	scale_x + bbox[3] * (1 - scale_x)
				snake_case_		:			List[Any]											=	scale_y + bbox[4] * (1 - scale_y)
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )

    # Remove bounding box small than scale of filter
	if filter_scale > 0:
		snake_case_		:			Dict											=	[
		    anno
		    for anno in new_anno
		    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
		]

	return output_img, new_anno, path_list[0]


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	assert number_char > 1, "The number of character should greater than 1"
	snake_case_		:			Tuple											=	ascii_lowercase + digits
	return "".join(random.choice(__a   ) for _ in range(__a   )   )


if __name__ == "__main__":
						main()
						print("""DONE ✅""")

