'''simple docstring'''
import functools
import operator

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


a=  logging.get_logger(__name__)

a=  {
    '''microsoft/unispeech-sat-base-100h-libri-ft''': (
        '''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'''
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''unispeech-sat'''







     def __init__(		self     ,			_lowerCamelCase=3_2     ,			_lowerCamelCase=7_6_8     ,			_lowerCamelCase=1_2     ,			_lowerCamelCase=1_2     ,			_lowerCamelCase=3_0_7_2     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=1E-5     ,			_lowerCamelCase="group"     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,			_lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)     ,			_lowerCamelCase=(1_0, 3, 3, 3, 3, 2, 2)     ,			_lowerCamelCase=False     ,			_lowerCamelCase=1_2_8     ,			_lowerCamelCase=1_6     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			_lowerCamelCase=0.0_5     ,			_lowerCamelCase=1_0     ,			_lowerCamelCase=2     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=1_0     ,			_lowerCamelCase=0     ,			_lowerCamelCase=3_2_0     ,			_lowerCamelCase=2     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=1_0_0     ,			_lowerCamelCase=2_5_6     ,			_lowerCamelCase=2_5_6     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase="mean"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=2_5_6     ,			_lowerCamelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,			_lowerCamelCase=(5, 3, 3, 1, 1)     ,			_lowerCamelCase=(1, 2, 3, 1, 1)     ,			_lowerCamelCase=5_1_2     ,			_lowerCamelCase=0     ,			_lowerCamelCase=1     ,			_lowerCamelCase=2     ,			_lowerCamelCase=5_0_4     ,			**_lowerCamelCase     ,			):
         super().__init__(**_lowerCamelCase     ,			pad_token_id=_lowerCamelCase     ,			bos_token_id=_lowerCamelCase     ,			eos_token_id=_lowerCamelCase    )
         __UpperCamelCase	:	Dict		    =  hidden_size
         __UpperCamelCase	:	Tuple		    =  feat_extract_norm
         __UpperCamelCase	:	str		    =  feat_extract_activation
         __UpperCamelCase	:	Any		    =  list(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  list(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  list(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  conv_bias
         __UpperCamelCase	:	Optional[Any]		    =  num_conv_pos_embeddings
         __UpperCamelCase	:	int		    =  num_conv_pos_embedding_groups
         __UpperCamelCase	:	Tuple		    =  len(self.conv_dim    )
         __UpperCamelCase	:	List[Any]		    =  num_hidden_layers
         __UpperCamelCase	:	List[Any]		    =  intermediate_size
         __UpperCamelCase	:	Union[str, Any]		    =  hidden_act
         __UpperCamelCase	:	Any		    =  num_attention_heads
         __UpperCamelCase	:	Union[str, Any]		    =  hidden_dropout
         __UpperCamelCase	:	Any		    =  attention_dropout
         __UpperCamelCase	:	List[Any]		    =  activation_dropout
         __UpperCamelCase	:	List[Any]		    =  feat_proj_dropout
         __UpperCamelCase	:	Any		    =  final_dropout
         __UpperCamelCase	:	Optional[int]		    =  layerdrop
         __UpperCamelCase	:	List[Any]		    =  layer_norm_eps
         __UpperCamelCase	:	Dict		    =  initializer_range
         __UpperCamelCase	:	Tuple		    =  vocab_size
         __UpperCamelCase	:	Union[str, Any]		    =  num_clusters
         __UpperCamelCase	:	Dict		    =  do_stable_layer_norm
         __UpperCamelCase	:	Dict		    =  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
         __UpperCamelCase	:	List[Any]		    =  apply_spec_augment
         __UpperCamelCase	:	List[Any]		    =  mask_time_prob
         __UpperCamelCase	:	int		    =  mask_time_length
         __UpperCamelCase	:	Dict		    =  mask_time_min_masks
         __UpperCamelCase	:	Any		    =  mask_feature_prob
         __UpperCamelCase	:	Dict		    =  mask_feature_length
         __UpperCamelCase	:	List[str]		    =  mask_feature_min_masks

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

         # ctc loss
         __UpperCamelCase	:	List[Any]		    =  ctc_loss_reduction
         __UpperCamelCase	:	Dict		    =  ctc_zero_infinity

         # SequenceClassification-specific parameter. Feel free to ignore for other classes.
         __UpperCamelCase	:	List[str]		    =  classifier_proj_size

         # XVector-specific parameters. Feel free to ignore for other classes.
         __UpperCamelCase	:	Tuple		    =  list(_lowerCamelCase    )
         __UpperCamelCase	:	Dict		    =  list(_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  list(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  xvector_output_dim







     @property
     def lowerCAmelCase     (		self    ):
         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
a=  ''''''
a=  ''''''
a=  ''''''
a=  1  # (0 is vertical, 1 is horizontal)







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  get_dataset(_a				,     _a			)
    print('Processing...'			)
    __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	List[Any]		    =  update_image_and_anno(_a				,     _a				,     _a			)

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







def  _UpperCamelCase (     _a	:	str				,     _a	:	str			):




    """simple docstring"""



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

        __UpperCamelCase	:	Optional[Any]		    =  []
        for obj_list in obj_lists:
            __UpperCamelCase	:	Optional[Any]		    =  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(_a			)
        labels.append(_a			)
    return img_paths, labels







def  _UpperCamelCase (     _a	:	list				,     _a	:	list				,     _a	:	int = 1			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  []
    __UpperCamelCase	:	str		    =  []
    __UpperCamelCase	:	str		    =  []
    for idx in range(len(_a			)			):
        __UpperCamelCase	:	Union[str, Any]		    =  []
        __UpperCamelCase	:	List[str]		    =  img_list[idx]
        path_list.append(_a			)
        __UpperCamelCase	:	Dict		    =  anno_list[idx]
        __UpperCamelCase	:	List[str]		    =  cva.imread(_a			)
        if flip_type == 1:
            __UpperCamelCase	:	List[Any]		    =  cva.flip(_a				,     _a			)
            for bbox in img_annos:
                __UpperCamelCase	:	List[str]		    =  1 - bbox[1]
                new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
        elif flip_type == 0:
            __UpperCamelCase	:	Optional[Any]		    =  cva.flip(_a				,     _a			)
            for bbox in img_annos:
                __UpperCamelCase	:	List[str]		    =  1 - bbox[2]
                new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
        new_annos_lists.append(_a			)
        new_imgs_list.append(_a			)
    return new_imgs_list, new_annos_lists, path_list







def  _UpperCamelCase (     _a	:	int = 3_2			):




    """simple docstring"""



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


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



