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

import cva
import numpy as np

# Parrameters
__a         =   (7_2_0, 1_2_8_0)  # Height, Width
__a         =   (0.4, 0.6)  # if height or width lower than this scale, drop it.
__a         =   1 / 1_0_0
__a         =   ''
__a         =   ''
__a         =   ''
__a         =   2_5_0




def 							a		(				):


				'''simple docstring'''
				lowercase_    ,      lowercase_	    =   get_dataset(snake_case__		,  snake_case__   )
				for index in range(snake_case__   ):
								lowercase_	    =   random.sample(range(len(snake_case__   )   )		,  4   )
								lowercase_    ,      lowercase_    ,      lowercase_	    =   update_image_and_anno(
								    snake_case__		,  snake_case__		,  snake_case__		,  snake_case__		,  snake_case__		,  filter_scale=snake_case__		,  )

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




def 							a		(				snake_case__:   str		,  snake_case__:   str   ):


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

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

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




def 							a		(				snake_case__:   list		,  snake_case__:   list		,  snake_case__:   list[int]		,  snake_case__:   tuple[int, int]		,  snake_case__:   tuple[float, float]		,  snake_case__:   float = 0.0		,  ):


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

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


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


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






import functools
import operator

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


__a         =   logging.get_logger(__name__)

__a         =   {
    'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Optional[int]       	=      'wav2vec2'
	def __init__(			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]=3_2			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=7_6_8			,				SCREAMING_SNAKE_CASE_    :     int=1_2			,				SCREAMING_SNAKE_CASE_    :     Any=1_2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=3_0_7_2			,				SCREAMING_SNAKE_CASE_    :     Dict="gelu"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=0.1			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     List[Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=0.0			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=0.0			,				SCREAMING_SNAKE_CASE_    :     Any=0.1			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     Any=0.02			,				SCREAMING_SNAKE_CASE_    :     Dict=1e-5			,				SCREAMING_SNAKE_CASE_    :     List[Any]="group"			,				SCREAMING_SNAKE_CASE_    :     List[str]="gelu"			,				SCREAMING_SNAKE_CASE_    :     List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)			,				SCREAMING_SNAKE_CASE_    :     Any=(5, 2, 2, 2, 2, 2, 2)			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=(1_0, 3, 3, 3, 3, 2, 2)			,				SCREAMING_SNAKE_CASE_    :     List[str]=False			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_2_8			,				SCREAMING_SNAKE_CASE_    :     str=1_6			,				SCREAMING_SNAKE_CASE_    :     Any=False			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     str=0.05			,				SCREAMING_SNAKE_CASE_    :     Dict=1_0			,				SCREAMING_SNAKE_CASE_    :     Tuple=2			,				SCREAMING_SNAKE_CASE_    :     List[str]=0.0			,				SCREAMING_SNAKE_CASE_    :     List[str]=1_0			,				SCREAMING_SNAKE_CASE_    :     List[Any]=0			,				SCREAMING_SNAKE_CASE_    :     Dict=3_2_0			,				SCREAMING_SNAKE_CASE_    :     Any=2			,				SCREAMING_SNAKE_CASE_    :     Tuple=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1_0_0			,				SCREAMING_SNAKE_CASE_    :     List[Any]=2_5_6			,				SCREAMING_SNAKE_CASE_    :     int=2_5_6			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     List[str]="sum"			,				SCREAMING_SNAKE_CASE_    :     Tuple=False			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=False			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=2_5_6			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)			,				SCREAMING_SNAKE_CASE_    :     List[Any]=(5, 3, 3, 1, 1)			,				SCREAMING_SNAKE_CASE_    :     List[Any]=(1, 2, 3, 1, 1)			,				SCREAMING_SNAKE_CASE_    :     Tuple=5_1_2			,				SCREAMING_SNAKE_CASE_    :     Dict=0			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=1			,				SCREAMING_SNAKE_CASE_    :     int=2			,				SCREAMING_SNAKE_CASE_    :     Dict=False			,				SCREAMING_SNAKE_CASE_    :     int=3			,				SCREAMING_SNAKE_CASE_    :     List[Any]=2			,				SCREAMING_SNAKE_CASE_    :     str=3			,				SCREAMING_SNAKE_CASE_    :     Any=None			,				SCREAMING_SNAKE_CASE_    :     int=None			,				**SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				)			->	Any:
					super().__init__(**SCREAMING_SNAKE_CASE_			,				pad_token_id=SCREAMING_SNAKE_CASE_			,				bos_token_id=SCREAMING_SNAKE_CASE_			,				eos_token_id=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   hidden_size
					lowercase_	    =   feat_extract_norm
					lowercase_	    =   feat_extract_activation
					lowercase_	    =   list(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   list(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   list(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   conv_bias
					lowercase_	    =   num_conv_pos_embeddings
					lowercase_	    =   num_conv_pos_embedding_groups
					lowercase_	    =   len(self.conv_dim			)
					lowercase_	    =   num_hidden_layers
					lowercase_	    =   intermediate_size
					lowercase_	    =   hidden_act
					lowercase_	    =   num_attention_heads
					lowercase_	    =   hidden_dropout
					lowercase_	    =   attention_dropout
					lowercase_	    =   activation_dropout
					lowercase_	    =   feat_proj_dropout
					lowercase_	    =   final_dropout
					lowercase_	    =   layerdrop
					lowercase_	    =   layer_norm_eps
					lowercase_	    =   initializer_range
					lowercase_	    =   vocab_size
					lowercase_	    =   do_stable_layer_norm
					lowercase_	    =   use_weighted_layer_sum

					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
					lowercase_	    =   apply_spec_augment
					lowercase_	    =   mask_time_prob
					lowercase_	    =   mask_time_length
					lowercase_	    =   mask_time_min_masks
					lowercase_	    =   mask_feature_prob
					lowercase_	    =   mask_feature_length
					lowercase_	    =   mask_feature_min_masks

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

					# ctc loss
					lowercase_	    =   ctc_loss_reduction
					lowercase_	    =   ctc_zero_infinity

					# adapter
					lowercase_	    =   add_adapter
					lowercase_	    =   adapter_kernel_size
					lowercase_	    =   adapter_stride
					lowercase_	    =   num_adapter_layers
					lowercase_	    =   output_hidden_size or hidden_size
					lowercase_	    =   adapter_attn_dim

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

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




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




