




"""simple docstring"""






import functools
import operator

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


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """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 lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """wavlm"""
			def __init__(						self						,   lowercase=32						,   lowercase=768						,   lowercase=12						,   lowercase=12						,   lowercase=3072						,   lowercase="gelu"						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=0.0						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=0.02						,   lowercase=1E-5						,   lowercase="group"						,   lowercase="gelu"						,   lowercase=(512, 512, 512, 512, 512, 512, 512)						,   lowercase=(5, 2, 2, 2, 2, 2, 2)						,   lowercase=(10, 3, 3, 3, 3, 2, 2)						,   lowercase=False						,   lowercase=128						,   lowercase=16						,   lowercase=320						,   lowercase=800						,   lowercase=False						,   lowercase=True						,   lowercase=0.05						,   lowercase=10						,   lowercase=2						,   lowercase=0.0						,   lowercase=10						,   lowercase=320						,   lowercase=2						,   lowercase=0.1						,   lowercase=100						,   lowercase=256						,   lowercase=256						,   lowercase=0.1						,   lowercase="mean"						,   lowercase=False						,   lowercase=False						,   lowercase=256						,   lowercase=(512, 512, 512, 512, 1500)						,   lowercase=(5, 3, 3, 1, 1)						,   lowercase=(1, 2, 3, 1, 1)						,   lowercase=512						,   lowercase=80						,   lowercase=0						,   lowercase=1						,   lowercase=2						,   lowercase=False						,   lowercase=3						,   lowercase=2						,   lowercase=3						,   lowercase=None						,   **lowercase						,   ):
							super().__init__(**lowercase						,   pad_token_id=lowercase						,   bos_token_id=lowercase						,   eos_token_id=lowercase						)
							_lowerCamelCase  :    int					=					hidden_size
							_lowerCamelCase  :    Dict					=					feat_extract_norm
							_lowerCamelCase  :    Optional[Any]					=					feat_extract_activation
							_lowerCamelCase  :    int					=					list(lowercase						)
							_lowerCamelCase  :    Optional[int]					=					list(lowercase						)
							_lowerCamelCase  :    Optional[int]					=					list(lowercase						)
							_lowerCamelCase  :    str					=					conv_bias
							_lowerCamelCase  :    Optional[Any]					=					num_buckets
							_lowerCamelCase  :    Optional[Any]					=					max_bucket_distance
							_lowerCamelCase  :    str					=					num_conv_pos_embeddings
							_lowerCamelCase  :    Any					=					num_conv_pos_embedding_groups
							_lowerCamelCase  :    Optional[Any]					=					len(self.conv_dim						)
							_lowerCamelCase  :    Optional[Any]					=					num_hidden_layers
							_lowerCamelCase  :    Tuple					=					intermediate_size
							_lowerCamelCase  :    Optional[int]					=					hidden_act
							_lowerCamelCase  :    str					=					num_attention_heads
							_lowerCamelCase  :    Tuple					=					hidden_dropout
							_lowerCamelCase  :    List[Any]					=					attention_dropout
							_lowerCamelCase  :    Tuple					=					activation_dropout
							_lowerCamelCase  :    int					=					feat_proj_dropout
							_lowerCamelCase  :    int					=					final_dropout
							_lowerCamelCase  :    Optional[Any]					=					layerdrop
							_lowerCamelCase  :    List[Any]					=					layer_norm_eps
							_lowerCamelCase  :    Any					=					initializer_range
							_lowerCamelCase  :    int					=					num_ctc_classes
							_lowerCamelCase  :    Any					=					vocab_size
							_lowerCamelCase  :    Dict					=					do_stable_layer_norm
							_lowerCamelCase  :    List[Any]					=					use_weighted_layer_sum
							_lowerCamelCase  :    Any					=					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
							_lowerCamelCase  :    List[Any]					=					apply_spec_augment
							_lowerCamelCase  :    List[Any]					=					mask_time_prob
							_lowerCamelCase  :    Dict					=					mask_time_length
							_lowerCamelCase  :    Any					=					mask_time_min_masks
							_lowerCamelCase  :    Any					=					mask_feature_prob
							_lowerCamelCase  :    List[Any]					=					mask_feature_length

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

							# ctc loss
							_lowerCamelCase  :    List[Any]					=					ctc_loss_reduction
							_lowerCamelCase  :    Any					=					ctc_zero_infinity

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

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

							# XVector-specific parameters. Feel free to ignore for other classes.
							_lowerCamelCase  :    Tuple					=					list(lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					list(lowercase						)
							_lowerCamelCase  :    Optional[Any]					=					list(lowercase						)
							_lowerCamelCase  :    str					=					xvector_output_dim






			@property
			def       A_					(						self						):
							return functools.reduce(operator.mul						,   self.conv_stride						,   1						)





"""simple docstring"""






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt






class lowerCAmelCase__			:






			'''simple docstring'''



			def __init__(						self						):
							_lowerCamelCase  :    Tuple					=					''
							_lowerCamelCase  :    Any					=					''
							_lowerCamelCase  :    List[Any]					=					[]
							_lowerCamelCase  :    Tuple					=					0
							_lowerCamelCase  :    Dict					=					256
							_lowerCamelCase  :    Optional[Any]					=					0
							_lowerCamelCase  :    List[Any]					=					0
							_lowerCamelCase  :    int					=					0
							_lowerCamelCase  :    Dict					=					0
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Any					=					cva.imread(lowercase						,   0						)
							_lowerCamelCase  :    Optional[int]					=					copy.deepcopy(self.img						)
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					plt.hist(self.img.ravel()						,   256						,   [0, 256]						,   label='x'						)
							_lowerCamelCase  :    Optional[int]					=					np.sum(lowercase						)
							for i in range(len(lowercase						)						):
											_lowerCamelCase  :    List[Any]					=					x[i] / self.k
											self.sk += prk
											_lowerCamelCase  :    int					=					(self.L - 1) * self.sk
											if self.rem != 0:
															_lowerCamelCase  :    Union[str, Any]					=					int(last % last						)
											_lowerCamelCase  :    int					=					int(last + 1 if self.rem >= 0.5 else last						)
											self.last_list.append(lowercase						)
											_lowerCamelCase  :    List[str]					=					int(np.ma.count(self.img						) / self.img[1].size						)
											_lowerCamelCase  :    int					=					self.img[1].size
							for i in range(self.number_of_cols						):
											for j in range(self.number_of_rows						):
															_lowerCamelCase  :    Optional[Any]					=					self.img[j][i]
															if num != self.last_list[num]:
																			_lowerCamelCase  :    Any					=					self.last_list[num]
							cva.imwrite('output_data/output.jpg'						,   self.img						)
			def       A_					(						self						):
							plt.hist(self.img.ravel()						,   256						,   [0, 256]						)






			def       A_					(						self						):
							cva.imshow('Output-Image'						,   self.img						)
							cva.imshow('Input-Image'						,   self.original_image						)
							cva.waitKey(5000						)
							cva.destroyAllWindows()


if __name__ == "__main__":
			lowercase__  		=		os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
			lowercase__  		=		ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()