






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,
)


__snake_case    :    Tuple         =     logging.get_logger(__name__)

__snake_case    :    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"""),
    ]
)

__snake_case    :    Tuple         =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def 	_UpperCamelCase   (    UpperCamelCase_       :					str		)  ->   Union[str, Any]:

						"""simple docstring"""



						for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
												if class_name in extractors:
																		lowerCAmelCase__ 						=						model_type_to_module_name(UpperCamelCase_		)

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

						for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
												if getattr(UpperCamelCase_    ,       '__name__'    ,       UpperCamelCase_		) == 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__ 						=						importlib.import_module('transformers'		)
						if hasattr(UpperCamelCase_    ,       UpperCamelCase_		):
												return getattr(UpperCamelCase_    ,       UpperCamelCase_		)

						return None







def 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, os.PathLike]    ,       UpperCamelCase_       :					Optional[Union[str, os.PathLike]] = None    ,       UpperCamelCase_       :					bool = False    ,       UpperCamelCase_       :					bool = False    ,       UpperCamelCase_       :					Optional[Dict[str, str]] = None    ,       UpperCamelCase_       :					Optional[Union[bool, str]] = None    ,       UpperCamelCase_       :					Optional[str] = None    ,       UpperCamelCase_       :					bool = False    ,       **UpperCamelCase_       :					Optional[int]    ,       )  ->   int:

						"""simple docstring"""



						lowerCAmelCase__ 						=						get_file_from_repo(
						    UpperCamelCase_    ,       UpperCamelCase_    ,       cache_dir=UpperCamelCase_    ,       force_download=UpperCamelCase_    ,       resume_download=UpperCamelCase_    ,       proxies=UpperCamelCase_    ,       use_auth_token=UpperCamelCase_    ,       revision=UpperCamelCase_    ,       local_files_only=UpperCamelCase_    ,       )
						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(UpperCamelCase_    ,       encoding='utf-8'		) as reader:
												return json.load(UpperCamelCase_		)


class     __SCREAMING_SNAKE_CASE					:






						def __init__(	self   ):






												"""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(_UpperCamelCase   )
						def       UpperCamelCase__      (	cls     ,						_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						kwargs.pop('config'     ,						_UpperCamelCase   )
												lowerCAmelCase__ 						=						kwargs.pop('trust_remote_code'     ,						_UpperCamelCase   )
												lowerCAmelCase__ 						=						True

												lowerCAmelCase__						,       lowerCAmelCase__ 						=						FeatureExtractionMixin.get_feature_extractor_dict(_UpperCamelCase     ,						**_UpperCamelCase   )
												lowerCAmelCase__ 						=						config_dict.get('feature_extractor_type'     ,						_UpperCamelCase   )
												lowerCAmelCase__ 						=						None
												if "AutoFeatureExtractor" in config_dict.get('auto_map'     ,						{}   ):
																		lowerCAmelCase__ 						=						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(_UpperCamelCase     ,						_UpperCamelCase   ):
																								lowerCAmelCase__ 						=						AutoConfig.from_pretrained(_UpperCamelCase     ,						**_UpperCamelCase   )
																		# It could be in `config.feature_extractor_type``
																		lowerCAmelCase__ 						=						getattr(_UpperCamelCase     ,						'feature_extractor_type'     ,						_UpperCamelCase   )
																		if hasattr(_UpperCamelCase     ,						'auto_map'   ) and "AutoFeatureExtractor" in config.auto_map:
																								lowerCAmelCase__ 						=						config.auto_map['AutoFeatureExtractor']

												if feature_extractor_class is not None:
																		lowerCAmelCase__ 						=						feature_extractor_class_from_name(_UpperCamelCase   )

												lowerCAmelCase__ 						=						feature_extractor_auto_map is not None
												lowerCAmelCase__ 						=						feature_extractor_class is not None or type(_UpperCamelCase   ) in FEATURE_EXTRACTOR_MAPPING
												lowerCAmelCase__ 						=						resolve_trust_remote_code(
												    _UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )

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

												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__      (	_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												FEATURE_EXTRACTOR_MAPPING.register(_UpperCamelCase     ,						_UpperCamelCase   )













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

import cva
import numpy as np

# Parrameters
__snake_case    :    str         =     (7_20, 12_80)  # Height, Width
__snake_case    :    Dict         =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__snake_case    :    Dict         =     1 / 1_00
__snake_case    :    Optional[int]         =     """"""
__snake_case    :    Union[str, Any]         =     """"""
__snake_case    :    List[str]         =     """"""
__snake_case    :    List[str]         =     2_50







def 	_UpperCamelCase   (    )  ->   None:

						"""simple docstring"""



						lowerCAmelCase__						,       lowerCAmelCase__    						=						get_dataset(UpperCamelCase_    ,       UpperCamelCase_		)
						for index in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						random.sample(range(len(UpperCamelCase_		)		)    ,       4		)
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						update_image_and_anno(
												    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       filter_scale=UpperCamelCase_    ,       )

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







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					str		)  ->   tuple[list, list]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[]
						lowerCAmelCase__    						=						[]
						for label_file in glob.glob(os.path.join(UpperCamelCase_    ,       '*.txt'		)		):
												lowerCAmelCase__    						=						label_file.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												with open(UpperCamelCase_		) as in_file:
																		lowerCAmelCase__    						=						in_file.readlines()
												lowerCAmelCase__    						=						os.path.join(UpperCamelCase_    ,       F"{label_name}.jpg"		)

												lowerCAmelCase__    						=						[]
												for obj_list in obj_lists:
																		lowerCAmelCase__    						=						obj_list.rstrip('\n'		).split(' '		)
																		lowerCAmelCase__    						=						float(obj[1]		) - float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) - float(obj[4]		) / 2
																		lowerCAmelCase__    						=						float(obj[1]		) + float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) + float(obj[4]		) / 2

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







def 	_UpperCamelCase   (    UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					tuple[int, int]    ,       UpperCamelCase_       :					tuple[float, float]    ,       UpperCamelCase_       :					float = 0.0    ,       )  ->   tuple[list, list, str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.zeros([output_size[0], output_size[1], 3]    ,       dtype=np.uinta		)
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						int(scale_x * output_size[1]		)
						lowerCAmelCase__    						=						int(scale_y * output_size[0]		)

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

						"""simple docstring"""



						assert number_char > 1, "The number of character should greater than 1"
						lowerCAmelCase__    						=						ascii_lowercase + digits
						return "".join(random.choice(UpperCamelCase_		) for _ in range(UpperCamelCase_		)		)


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





