'''simple docstring'''


import functools
import operator

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


__lowerCAmelCase	:       Dict						       =	logging.get_logger(__name__)

__lowerCAmelCase	:       Any						       =	{
    "microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}






class 						A   (   UpperCAmelCase   ):
							a_        =      '''wavlm'''





							def __init__(      self				:							int     ,		__a				:							int=3_2     ,		__a				:							Dict=7_6_8     ,		__a				:							Union[str, Any]=1_2     ,		__a				:							Tuple=1_2     ,		__a				:							Tuple=3_0_7_2     ,		__a				:							str="gelu"     ,		__a				:							Any=0.1     ,		__a				:							str=0.1     ,		__a				:							List[Any]=0.1     ,		__a				:							List[Any]=0.0     ,		__a				:							Dict=0.1     ,		__a				:							Tuple=0.1     ,		__a				:							Union[str, Any]=0.0_2     ,		__a				:							Union[str, Any]=1e-5     ,		__a				:							int="group"     ,		__a				:							Dict="gelu"     ,		__a				:							int=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,		__a				:							List[Any]=(5, 2, 2, 2, 2, 2, 2)     ,		__a				:							List[str]=(1_0, 3, 3, 3, 3, 2, 2)     ,		__a				:							Optional[Any]=False     ,		__a				:							List[Any]=1_2_8     ,		__a				:							Any=1_6     ,		__a				:							Optional[int]=3_2_0     ,		__a				:							str=8_0_0     ,		__a				:							Optional[int]=False     ,		__a				:							List[Any]=True     ,		__a				:							Optional[Any]=0.0_5     ,		__a				:							Tuple=1_0     ,		__a				:							List[str]=2     ,		__a				:							Optional[int]=0.0     ,		__a				:							Optional[int]=1_0     ,		__a				:							Optional[Any]=3_2_0     ,		__a				:							Optional[Any]=2     ,		__a				:							Tuple=0.1     ,		__a				:							Optional[Any]=1_0_0     ,		__a				:							Union[str, Any]=2_5_6     ,		__a				:							Union[str, Any]=2_5_6     ,		__a				:							Optional[Any]=0.1     ,		__a				:							List[Any]="mean"     ,		__a				:							Union[str, Any]=False     ,		__a				:							int=False     ,		__a				:							Any=2_5_6     ,		__a				:							Dict=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,		__a				:							List[Any]=(5, 3, 3, 1, 1)     ,		__a				:							List[str]=(1, 2, 3, 1, 1)     ,		__a				:							List[Any]=5_1_2     ,		__a				:							List[Any]=8_0     ,		__a				:							Dict=0     ,		__a				:							Union[str, Any]=1     ,		__a				:							Any=2     ,		__a				:							Optional[int]=False     ,		__a				:							Optional[Any]=3     ,		__a				:							Union[str, Any]=2     ,		__a				:							str=3     ,		__a				:							List[Any]=None     ,		**__a				:							Optional[Any]     ,		)							->	List[str]:
								super().__init__(**__a     ,		pad_token_id=__a     ,		bos_token_id=__a     ,		eos_token_id=__a	)
								__UpperCAmelCase							=			hidden_size
								__UpperCAmelCase							=			feat_extract_norm
								__UpperCAmelCase							=			feat_extract_activation
								__UpperCAmelCase							=			list(__a	)
								__UpperCAmelCase							=			list(__a	)
								__UpperCAmelCase							=			list(__a	)
								__UpperCAmelCase							=			conv_bias
								__UpperCAmelCase							=			num_buckets
								__UpperCAmelCase							=			max_bucket_distance
								__UpperCAmelCase							=			num_conv_pos_embeddings
								__UpperCAmelCase							=			num_conv_pos_embedding_groups
								__UpperCAmelCase							=			len(self.conv_dim	)
								__UpperCAmelCase							=			num_hidden_layers
								__UpperCAmelCase							=			intermediate_size
								__UpperCAmelCase							=			hidden_act
								__UpperCAmelCase							=			num_attention_heads
								__UpperCAmelCase							=			hidden_dropout
								__UpperCAmelCase							=			attention_dropout
								__UpperCAmelCase							=			activation_dropout
								__UpperCAmelCase							=			feat_proj_dropout
								__UpperCAmelCase							=			final_dropout
								__UpperCAmelCase							=			layerdrop
								__UpperCAmelCase							=			layer_norm_eps
								__UpperCAmelCase							=			initializer_range
								__UpperCAmelCase							=			num_ctc_classes
								__UpperCAmelCase							=			vocab_size
								__UpperCAmelCase							=			do_stable_layer_norm
								__UpperCAmelCase							=			use_weighted_layer_sum
								__UpperCAmelCase							=			classifier_proj_size

								if (
								    (len(self.conv_stride	) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel	) != self.num_feat_extract_layers)
								    or (len(self.conv_dim	) != self.num_feat_extract_layers)
								):
									raise ValueError(
									    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
									    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
									    f""" {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,"""
									    f""" `len(config.conv_kernel) = {len(self.conv_kernel	)}`."""	)

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								__UpperCAmelCase							=			apply_spec_augment
								__UpperCAmelCase							=			mask_time_prob
								__UpperCAmelCase							=			mask_time_length
								__UpperCAmelCase							=			mask_time_min_masks
								__UpperCAmelCase							=			mask_feature_prob
								__UpperCAmelCase							=			mask_feature_length

								# parameters for pretraining with codevector quantized representations
								__UpperCAmelCase							=			num_codevectors_per_group
								__UpperCAmelCase							=			num_codevector_groups
								__UpperCAmelCase							=			contrastive_logits_temperature
								__UpperCAmelCase							=			num_negatives
								__UpperCAmelCase							=			codevector_dim
								__UpperCAmelCase							=			proj_codevector_dim
								__UpperCAmelCase							=			diversity_loss_weight

								# ctc loss
								__UpperCAmelCase							=			ctc_loss_reduction
								__UpperCAmelCase							=			ctc_zero_infinity

								# adapter
								__UpperCAmelCase							=			add_adapter
								__UpperCAmelCase							=			adapter_kernel_size
								__UpperCAmelCase							=			adapter_stride
								__UpperCAmelCase							=			num_adapter_layers
								__UpperCAmelCase							=			output_hidden_size or hidden_size

								# SequenceClassification-specific parameter. Feel free to ignore for other classes.
								__UpperCAmelCase							=			classifier_proj_size

								# XVector-specific parameters. Feel free to ignore for other classes.
								__UpperCAmelCase							=			list(__a	)
								__UpperCAmelCase							=			list(__a	)
								__UpperCAmelCase							=			list(__a	)
								__UpperCAmelCase							=			xvector_output_dim






							@property
							def       snake_case__						(      self				:							Any	)							->	Any:
								return functools.reduce(operator.mul     ,		self.conv_stride     ,		1	)

'''simple docstring'''


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

import cva
__lowerCAmelCase	:       Any						       =	""
__lowerCAmelCase	:       int						       =	""
__lowerCAmelCase	:       Union[str, Any]						       =	""
__lowerCAmelCase	:       Any						       =	1  # (0 is vertical, 1 is horizontal)





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase				,			__UpperCAmelCase							=			get_dataset(UpperCamelCase__    ,					UpperCamelCase__       )
	print('''Processing...'''       )
	__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			update_image_and_anno(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )

	for index, image in enumerate(UpperCamelCase__       ):
		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		__UpperCAmelCase							=			random_chars(3_2       )
		__UpperCAmelCase							=			paths[index].split(os.sep       )[-1].rsplit('''.'''    ,					1       )[0]
		__UpperCAmelCase							=			f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
		cva.imwrite(f"""/{file_root}.jpg"""    ,					UpperCamelCase__    ,					[cva.IMWRITE_JPEG_QUALITY, 8_5]       )
		print(f"""Success {index+1}/{len(UpperCamelCase__       )} with {file_name}"""       )
		__UpperCAmelCase							=			[]
		for anno in new_annos[index]:
			__UpperCAmelCase							=			f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
			annos_list.append(UpperCamelCase__       )
		with open(f"""/{file_root}.txt"""    ,					'''w'''       ) as outfile:
			outfile.write('''\n'''.join(line for line in annos_list       )       )





def       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str       ):


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

		__UpperCAmelCase							=			[]
		for obj_list in obj_lists:
			__UpperCAmelCase							=			obj_list.rstrip('''\n'''       ).split(''' '''       )
			boxes.append(
			    [
			        int(obj[0]       ),
			        float(obj[1]       ),
			        float(obj[2]       ),
			        float(obj[3]       ),
			        float(obj[4]       ),
			    ]       )
		if not boxes:
			continue
		img_paths.append(UpperCamelCase__       )
		labels.append(UpperCamelCase__       )
	return img_paths, labels





def       lowerCAmelCase					(		UpperCamelCase__				:   list    ,					UpperCamelCase__				:   list    ,					UpperCamelCase__				:   int = 1       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			[]
	for idx in range(len(UpperCamelCase__       )       ):
		__UpperCAmelCase							=			[]
		__UpperCAmelCase							=			img_list[idx]
		path_list.append(UpperCamelCase__       )
		__UpperCAmelCase							=			anno_list[idx]
		__UpperCAmelCase							=			cva.imread(UpperCamelCase__       )
		if flip_type == 1:
			__UpperCAmelCase							=			cva.flip(UpperCamelCase__    ,					UpperCamelCase__       )
			for bbox in img_annos:
				__UpperCAmelCase							=			1 - bbox[1]
				new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]       )
		elif flip_type == 0:
			__UpperCAmelCase							=			cva.flip(UpperCamelCase__    ,					UpperCamelCase__       )
			for bbox in img_annos:
				__UpperCAmelCase							=			1 - bbox[2]
				new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]       )
		new_annos_lists.append(UpperCamelCase__       )
		new_imgs_list.append(UpperCamelCase__       )
	return new_imgs_list, new_annos_lists, path_list





def       lowerCAmelCase					(		UpperCamelCase__				:   int = 3_2       ):


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


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