




"""simple docstring"""


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'''





 lowercase__ 					=					"""wav2vec2"""


 def __init__(				self:		Optional[Any],      a_:		List[str]=32,      a_:		Optional[int]=768,      a_:		Optional[int]=12,      a_:		Any=12,      a_:		Union[str, Any]=3_072,      a_:		int="gelu",      a_:		str=0.1,      a_:		Union[str, Any]=0.1,      a_:		int=0.1,      a_:		Optional[Any]=0.0,      a_:		List[str]=0.0,      a_:		Optional[Any]=0.1,      a_:		Tuple=0.1,      a_:		Optional[int]=0.02,      a_:		Optional[Any]=1E-5,      a_:		Any="group",      a_:		int="gelu",      a_:		Optional[Any]=(512, 512, 512, 512, 512, 512, 512),      a_:		List[str]=(5, 2, 2, 2, 2, 2, 2),      a_:		Optional[Any]=(10, 3, 3, 3, 3, 2, 2),      a_:		Dict=False,      a_:		Tuple=128,      a_:		Optional[int]=16,      a_:		Any=False,      a_:		str=True,      a_:		str=0.05,      a_:		Optional[int]=10,      a_:		Dict=2,      a_:		List[str]=0.0,      a_:		str=10,      a_:		int=0,      a_:		str=320,      a_:		Dict=2,      a_:		Union[str, Any]=0.1,      a_:		List[str]=100,      a_:		List[str]=256,      a_:		List[Any]=256,      a_:		Optional[Any]=0.1,      a_:		Union[str, Any]="sum",      a_:		List[str]=False,      a_:		str=False,      a_:		Tuple=256,      a_:		List[str]=(512, 512, 512, 512, 1_500),      a_:		int=(5, 3, 3, 1, 1),      a_:		Union[str, Any]=(1, 2, 3, 1, 1),      a_:		Union[str, Any]=512,      a_:		Optional[int]=0,      a_:		Union[str, Any]=1,      a_:		Tuple=2,      a_:		Union[str, Any]=False,      a_:		Dict=3,      a_:		Any=2,      a_:		List[str]=3,      a_:		Union[str, Any]=None,      a_:		Any=None,      **a_:		Union[str, Any],      ):




   '''simple docstring'''



   super().__init__(**__lowercase,      pad_token_id=__lowercase,      bos_token_id=__lowercase,      eos_token_id=__lowercase		)
   _snake_case							:		Any								=  hidden_size
   _snake_case							:		int								=  feat_extract_norm
   _snake_case							:		Tuple								=  feat_extract_activation
   _snake_case							:		Union[str, Any]								=  list(__lowercase		)
   _snake_case							:		List[Any]								=  list(__lowercase		)
   _snake_case							:		int								=  list(__lowercase		)
   _snake_case							:		List[Any]								=  conv_bias
   _snake_case							:		Optional[int]								=  num_conv_pos_embeddings
   _snake_case							:		Dict								=  num_conv_pos_embedding_groups
   _snake_case							:		Any								=  len(self.conv_dim		)
   _snake_case							:		List[str]								=  num_hidden_layers
   _snake_case							:		int								=  intermediate_size
   _snake_case							:		str								=  hidden_act
   _snake_case							:		Any								=  num_attention_heads
   _snake_case							:		int								=  hidden_dropout
   _snake_case							:		Tuple								=  attention_dropout
   _snake_case							:		List[str]								=  activation_dropout
   _snake_case							:		Optional[Any]								=  feat_proj_dropout
   _snake_case							:		Any								=  final_dropout
   _snake_case							:		Any								=  layerdrop
   _snake_case							:		str								=  layer_norm_eps
   _snake_case							:		Optional[Any]								=  initializer_range
   _snake_case							:		List[str]								=  vocab_size
   _snake_case							:		str								=  do_stable_layer_norm
   _snake_case							:		Union[str, Any]								=  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
   _snake_case							:		List[Any]								=  apply_spec_augment
   _snake_case							:		Tuple								=  mask_time_prob
   _snake_case							:		int								=  mask_time_length
   _snake_case							:		Dict								=  mask_time_min_masks
   _snake_case							:		str								=  mask_feature_prob
   _snake_case							:		List[str]								=  mask_feature_length
   _snake_case							:		Union[str, Any]								=  mask_feature_min_masks

   # parameters for pretraining with codevector quantized representations
   _snake_case							:		Optional[Any]								=  num_codevectors_per_group
   _snake_case							:		List[Any]								=  num_codevector_groups
   _snake_case							:		Tuple								=  contrastive_logits_temperature
   _snake_case							:		Optional[int]								=  feat_quantizer_dropout
   _snake_case							:		Optional[int]								=  num_negatives
   _snake_case							:		List[Any]								=  codevector_dim
   _snake_case							:		str								=  proj_codevector_dim
   _snake_case							:		List[str]								=  diversity_loss_weight

   # ctc loss
   _snake_case							:		Tuple								=  ctc_loss_reduction
   _snake_case							:		Tuple								=  ctc_zero_infinity

   # adapter
   _snake_case							:		List[str]								=  add_adapter
   _snake_case							:		Tuple								=  adapter_kernel_size
   _snake_case							:		str								=  adapter_stride
   _snake_case							:		Tuple								=  num_adapter_layers
   _snake_case							:		Tuple								=  output_hidden_size or hidden_size
   _snake_case							:		Optional[Any]								=  adapter_attn_dim

   # SequenceClassification-specific parameter. Feel free to ignore for other classes.
   _snake_case							:		Optional[Any]								=  classifier_proj_size

   # XVector-specific parameters. Feel free to ignore for other classes.
   _snake_case							:		Optional[int]								=  list(__lowercase		)
   _snake_case							:		List[Any]								=  list(__lowercase		)
   _snake_case							:		List[Any]								=  list(__lowercase		)
   _snake_case							:		str								=  xvector_output_dim


 @property
 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   return functools.reduce(operator.mul,      self.conv_stride,      1		)



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

import cva
import numpy as np

# Parrameters
__lowercase							 =					(720, 1280)  # Height, Width
__lowercase							 =					(0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowercase							 =					1 / 100
__lowercase							 =					''''''
__lowercase							 =					''''''
__lowercase							 =					''''''
__lowercase							 =					250



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		get_dataset(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    for index in range(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[Any]			  =		random.sample(range(len(SCREAMING_SNAKE_CASE	)	)  ,			4	)
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :str			  =		update_image_and_anno(
            SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			filter_scale=SCREAMING_SNAKE_CASE  ,			)

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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

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

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 0.0  ,			):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		np.zeros([output_size[0], output_size[1], 3]  ,			dtype=np.uinta	)
    __UpperCamelCase     :List[Any]			  =		scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    __UpperCamelCase     :int			  =		scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    __UpperCamelCase     :Optional[int]			  =		int(scale_x * output_size[1]	)
    __UpperCamelCase     :Any			  =		int(scale_y * output_size[0]	)

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







    assert number_char > 1, "The number of character should greater than 1"
    __UpperCamelCase     :Optional[Any]			  =		ascii_lowercase + digits
    return "".join(random.choice(SCREAMING_SNAKE_CASE	) for _ in range(SCREAMING_SNAKE_CASE	)	)


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





