"""simple docstring"""







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

import cva
import numpy as np

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



def     lowercase__( ):
     lowercase_							,						lowercase_			:       Optional[int]									=			get_dataset(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     for index in range(__SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Dict									=			random.sample(range(len(__SCREAMING_SNAKE_CASE   )   )  ,    4   )
          lowercase_							,						lowercase_							,						lowercase_			:       Optional[int]									=			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'
          lowercase_			:       str									=			random_chars(32   )
          lowercase_			:       Optional[Any]									=			path.split(os.sep   )[-1].rsplit('.'  ,    1   )[0]
          lowercase_			:       List[str]									=			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}'''   )
          lowercase_			:       Dict									=			[]
          for anno in new_annos:
               lowercase_			:       str									=			anno[3] - anno[1]
               lowercase_			:       List[Any]									=			anno[4] - anno[2]
               lowercase_			:       int									=			anno[1] + width / 2
               lowercase_			:       Dict									=			anno[2] + height / 2
               lowercase_			:       Tuple									=			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     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  str   ):
     lowercase_			:       Any									=			[]
     lowercase_			:       Optional[int]									=			[]
     for label_file in glob.glob(os.path.join(__SCREAMING_SNAKE_CASE  ,    '*.txt'   )   ):
          lowercase_			:       List[str]									=			label_file.split(os.sep   )[-1].rsplit('.'  ,    1   )[0]
          with open(__SCREAMING_SNAKE_CASE   ) as in_file:
               lowercase_			:       Optional[int]									=			in_file.readlines()
          lowercase_			:       Tuple									=			os.path.join(__SCREAMING_SNAKE_CASE  ,    F'''{label_name}.jpg'''   )

          lowercase_			:       Dict									=			[]
          for obj_list in obj_lists:
               lowercase_			:       Union[str, Any]									=			obj_list.rstrip('\n'   ).split(' '   )
               lowercase_			:       Optional[Any]									=			float(obj[1]   ) - float(obj[3]   ) / 2
               lowercase_			:       List[str]									=			float(obj[2]   ) - float(obj[4]   ) / 2
               lowercase_			:       Optional[int]									=			float(obj[1]   ) + float(obj[3]   ) / 2
               lowercase_			:       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(__SCREAMING_SNAKE_CASE   )
          labels.append(__SCREAMING_SNAKE_CASE   )
     return img_paths, labels



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  list[int]  ,    __SCREAMING_SNAKE_CASE							:  tuple[int, int]  ,    __SCREAMING_SNAKE_CASE							:  tuple[float, float]  ,    __SCREAMING_SNAKE_CASE							:  float = 0.0  ,    ):
     lowercase_			:       int									=			np.zeros([output_size[0], output_size[1], 3]  ,    dtype=np.uinta   )
     lowercase_			:       List[str]									=			scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
     lowercase_			:       Union[str, Any]									=			scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
     lowercase_			:       Any									=			int(scale_x * output_size[1]   )
     lowercase_			:       Union[str, Any]									=			int(scale_y * output_size[0]   )

     lowercase_			:       int									=			[]
     lowercase_			:       List[Any]									=			[]
     for i, index in enumerate(__SCREAMING_SNAKE_CASE   ):
          lowercase_			:       List[str]									=			all_img_list[index]
          path_list.append(__SCREAMING_SNAKE_CASE   )
          lowercase_			:       Any									=			all_annos[index]
          lowercase_			:       Optional[Any]									=			cva.imread(__SCREAMING_SNAKE_CASE   )
          if i == 0:  # top-left
               lowercase_			:       int									=			cva.resize(__SCREAMING_SNAKE_CASE  ,    (divid_point_x, divid_point_y)   )
               lowercase_			:       Optional[int]									=			img
               for bbox in img_annos:
                    lowercase_			:       Optional[Any]									=			bbox[1] * scale_x
                    lowercase_			:       str									=			bbox[2] * scale_y
                    lowercase_			:       Union[str, Any]									=			bbox[3] * scale_x
                    lowercase_			:       Tuple									=			bbox[4] * scale_y
                    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
          elif i == 1:  # top-right
               lowercase_			:       Dict									=			cva.resize(__SCREAMING_SNAKE_CASE  ,    (output_size[1] - divid_point_x, divid_point_y)   )
               lowercase_			:       List[str]									=			img
               for bbox in img_annos:
                    lowercase_			:       Union[str, Any]									=			scale_x + bbox[1] * (1 - scale_x)
                    lowercase_			:       List[str]									=			bbox[2] * scale_y
                    lowercase_			:       Union[str, Any]									=			scale_x + bbox[3] * (1 - scale_x)
                    lowercase_			:       Union[str, Any]									=			bbox[4] * scale_y
                    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
          elif i == 2:  # bottom-left
               lowercase_			:       List[Any]									=			cva.resize(__SCREAMING_SNAKE_CASE  ,    (divid_point_x, output_size[0] - divid_point_y)   )
               lowercase_			:       Any									=			img
               for bbox in img_annos:
                    lowercase_			:       Union[str, Any]									=			bbox[1] * scale_x
                    lowercase_			:       List[Any]									=			scale_y + bbox[2] * (1 - scale_y)
                    lowercase_			:       Tuple									=			bbox[3] * scale_x
                    lowercase_			:       Any									=			scale_y + bbox[4] * (1 - scale_y)
                    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
          else:  # bottom-right
               lowercase_			:       Tuple									=			cva.resize(
                   __SCREAMING_SNAKE_CASE  ,    (output_size[1] - divid_point_x, output_size[0] - divid_point_y)   )
               lowercase_			:       Dict									=			img
               for bbox in img_annos:
                    lowercase_			:       Dict									=			scale_x + bbox[1] * (1 - scale_x)
                    lowercase_			:       Optional[Any]									=			scale_y + bbox[2] * (1 - scale_y)
                    lowercase_			:       int									=			scale_x + bbox[3] * (1 - scale_x)
                    lowercase_			:       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:
          lowercase_			:       str									=			[
              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     lowercase__( __SCREAMING_SNAKE_CASE							:  int   ):
     assert number_char > 1, "The number of character should greater than 1"
     lowercase_			:       Dict									=			ascii_lowercase + digits
     return "".join(random.choice(__SCREAMING_SNAKE_CASE   ) for _ in range(__SCREAMING_SNAKE_CASE   )   )


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


"""simple docstring"""







import functools
import operator

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


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={
    "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  UpperCamelCase	(    lowercase_						):
   lowercase							=						'wav2vec2'
   def __init__(    self  ,__UpperCamelCase=32  ,__UpperCamelCase=768  ,__UpperCamelCase=12  ,__UpperCamelCase=12  ,__UpperCamelCase=3072  ,__UpperCamelCase="gelu"  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.02  ,__UpperCamelCase=1e-5  ,__UpperCamelCase="group"  ,__UpperCamelCase="gelu"  ,__UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)  ,__UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)  ,__UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)  ,__UpperCamelCase=False  ,__UpperCamelCase=128  ,__UpperCamelCase=16  ,__UpperCamelCase=False  ,__UpperCamelCase=True  ,__UpperCamelCase=0.05  ,__UpperCamelCase=10  ,__UpperCamelCase=2  ,__UpperCamelCase=0.0  ,__UpperCamelCase=10  ,__UpperCamelCase=0  ,__UpperCamelCase=320  ,__UpperCamelCase=2  ,__UpperCamelCase=0.1  ,__UpperCamelCase=100  ,__UpperCamelCase=256  ,__UpperCamelCase=256  ,__UpperCamelCase=0.1  ,__UpperCamelCase="sum"  ,__UpperCamelCase=False  ,__UpperCamelCase=False  ,__UpperCamelCase=256  ,__UpperCamelCase=(512, 512, 512, 512, 1500)  ,__UpperCamelCase=(5, 3, 3, 1, 1)  ,__UpperCamelCase=(1, 2, 3, 1, 1)  ,__UpperCamelCase=512  ,__UpperCamelCase=0  ,__UpperCamelCase=1  ,__UpperCamelCase=2  ,__UpperCamelCase=False  ,__UpperCamelCase=3  ,__UpperCamelCase=2  ,__UpperCamelCase=3  ,__UpperCamelCase=None  ,__UpperCamelCase=None  ,**__UpperCamelCase  ,)			->   Optional[Any]:


        '''simple docstring'''


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

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

        # ctc loss
        lowercase_			:       Tuple									=			ctc_loss_reduction
        lowercase_			:       int									=			ctc_zero_infinity

        # adapter
        lowercase_			:       int									=			add_adapter
        lowercase_			:       Dict									=			adapter_kernel_size
        lowercase_			:       List[str]									=			adapter_stride
        lowercase_			:       Dict									=			num_adapter_layers
        lowercase_			:       Dict									=			output_hidden_size or hidden_size
        lowercase_			:       Optional[Any]									=			adapter_attn_dim

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

        # XVector-specific parameters. Feel free to ignore for other classes.
        lowercase_			:       Any									=			list(__UpperCamelCase    )
        lowercase_			:       str									=			list(__UpperCamelCase    )
        lowercase_			:       Any									=			list(__UpperCamelCase    )
        lowercase_			:       Tuple									=			xvector_output_dim







   @property
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


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

