from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__magic_name__    		=logging.get_logger(__name__)


if is_vision_available():
     import PIL



class      _A (     _lowerCamelCase				):
  SCREAMING_SNAKE_CASE_						:						Union[str, Any]    =["pixel_values"]






  def __init__(self ,					SCREAMING_SNAKE_CASE_ = True ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC ,					SCREAMING_SNAKE_CASE_ = True ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = True ,					SCREAMING_SNAKE_CASE_ = 1 / 255 ,					SCREAMING_SNAKE_CASE_ = True ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = True ,					**SCREAMING_SNAKE_CASE_ ,					)		->   Optional[Any]:





       '''simple docstring'''





       super().__init__(**lowercase_						)
       UpperCamelCase__					    =   size if size is not None else {'''shortest_edge''': 224}
       UpperCamelCase__					    =   get_size_dict(lowercase_ ,					default_to_square=lowercase_						)
       UpperCamelCase__					    =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       UpperCamelCase__					    =   get_size_dict(lowercase_ ,					default_to_square=lowercase_ ,					param_name='''crop_size'''						)

       UpperCamelCase__					    =   do_resize
       UpperCamelCase__					    =   size
       UpperCamelCase__					    =   resample
       UpperCamelCase__					    =   do_center_crop
       UpperCamelCase__					    =   crop_size
       UpperCamelCase__					    =   do_rescale
       UpperCamelCase__					    =   rescale_factor
       UpperCamelCase__					    =   do_normalize
       UpperCamelCase__					    =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       UpperCamelCase__					    =   image_std if image_std is not None else OPENAI_CLIP_STD
       UpperCamelCase__					    =   do_convert_rgb






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC ,					SCREAMING_SNAKE_CASE_ = None ,					**SCREAMING_SNAKE_CASE_ ,					)		->   Optional[int]:





       '''simple docstring'''





       UpperCamelCase__					    =   get_size_dict(lowercase_ ,					default_to_square=lowercase_						)
       if "shortest_edge" not in size:
            raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"						)
       UpperCamelCase__					    =   get_resize_output_image_size(lowercase_ ,					size=size['''shortest_edge'''] ,					default_to_square=lowercase_						)
       return resize(lowercase_ ,					size=lowercase_ ,					resample=lowercase_ ,					data_format=lowercase_ ,					**lowercase_						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = None ,					**SCREAMING_SNAKE_CASE_ ,					)		->   List[Any]:





       '''simple docstring'''





       UpperCamelCase__					    =   get_size_dict(lowercase_						)
       if "height" not in size or "width" not in size:
            raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"						)
       return center_crop(lowercase_ ,					size=(size['''height'''], size['''width''']) ,					data_format=lowercase_ ,					**lowercase_						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = None ,					**SCREAMING_SNAKE_CASE_ ,					)		->   Tuple:





       '''simple docstring'''





       return rescale(lowercase_ ,					scale=lowercase_ ,					data_format=lowercase_ ,					**lowercase_						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = None ,					**SCREAMING_SNAKE_CASE_ ,					)		->   str:





       '''simple docstring'''





       return normalize(lowercase_ ,					mean=lowercase_ ,					std=lowercase_ ,					data_format=lowercase_ ,					**lowercase_						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST ,					**SCREAMING_SNAKE_CASE_ ,					)		->   Optional[int]:





       '''simple docstring'''





       UpperCamelCase__					    =   do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__					    =   size if size is not None else self.size
       UpperCamelCase__					    =   get_size_dict(lowercase_ ,					param_name='''size''' ,					default_to_square=lowercase_						)
       UpperCamelCase__					    =   resample if resample is not None else self.resample
       UpperCamelCase__					    =   do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase__					    =   crop_size if crop_size is not None else self.crop_size
       UpperCamelCase__					    =   get_size_dict(lowercase_ ,					param_name='''crop_size''' ,					default_to_square=lowercase_						)
       UpperCamelCase__					    =   do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__					    =   rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase__					    =   do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase__					    =   image_mean if image_mean is not None else self.image_mean
       UpperCamelCase__					    =   image_std if image_std is not None else self.image_std
       UpperCamelCase__					    =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       UpperCamelCase__					    =   make_list_of_images(lowercase_						)

       if not valid_images(lowercase_						):
            raise ValueError(
                '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

       if do_resize and size is None:
            raise ValueError('''Size must be specified if do_resize is True.'''						)

       if do_center_crop and crop_size is None:
            raise ValueError('''Crop size must be specified if do_center_crop is True.'''						)

       if do_rescale and rescale_factor is None:
            raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

       if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('''Image mean and std must be specified if do_normalize is True.'''						)

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
            UpperCamelCase__					    =   [convert_to_rgb(lowercase_						) for image in images]

       # All transformations expect numpy arrays.
       UpperCamelCase__					    =   [to_numpy_array(lowercase_						) for image in images]

       if do_resize:
            UpperCamelCase__					    =   [self.resize(image=lowercase_ ,					size=lowercase_ ,					resample=lowercase_						) for image in images]

       if do_center_crop:
            UpperCamelCase__					    =   [self.center_crop(image=lowercase_ ,					size=lowercase_						) for image in images]

       if do_rescale:
            UpperCamelCase__					    =   [self.rescale(image=lowercase_ ,					scale=lowercase_						) for image in images]

       if do_normalize:
            UpperCamelCase__					    =   [self.normalize(image=lowercase_ ,					mean=lowercase_ ,					std=lowercase_						) for image in images]

       UpperCamelCase__					    =   [to_channel_dimension_format(lowercase_ ,					lowercase_						) for image in images]

       UpperCamelCase__					    =   {'''pixel_values''': images}
       return BatchFeature(data=lowercase_ ,					tensor_type=lowercase_						)









'''simple docstring'''

import functools
import operator

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


a    :							List[Any]										=    logging.get_logger(__name__)

a    :							List[str]										=    {
    '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 		a							(  _lowerCamelCase	):
				snake_case_           =						"wavlm"


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

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

									# ctc loss
									snake_case_		     =							ctc_loss_reduction
									snake_case_		     =							ctc_zero_infinity

									# adapter
									snake_case_		     =							add_adapter
									snake_case_		     =							adapter_kernel_size
									snake_case_		     =							adapter_stride
									snake_case_		     =							num_adapter_layers
									snake_case_		     =							output_hidden_size or hidden_size

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

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


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

