




'''simple docstring'''


import functools
import operator

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


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {
    "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    __lowercase						(  __magic_name__ ):
   _a							=      """wavlm"""







   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=0.1      ,							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=320      ,							UpperCamelCase=800      ,							UpperCamelCase=False      ,							UpperCamelCase=True      ,							UpperCamelCase=0.05      ,							UpperCamelCase=10      ,							UpperCamelCase=2      ,							UpperCamelCase=0.0      ,							UpperCamelCase=10      ,							UpperCamelCase=320      ,							UpperCamelCase=2      ,							UpperCamelCase=0.1      ,							UpperCamelCase=100      ,							UpperCamelCase=256      ,							UpperCamelCase=256      ,							UpperCamelCase=0.1      ,							UpperCamelCase="mean"      ,							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=80      ,							UpperCamelCase=0      ,							UpperCamelCase=1      ,							UpperCamelCase=2      ,							UpperCamelCase=False      ,							UpperCamelCase=3      ,							UpperCamelCase=2      ,							UpperCamelCase=3      ,							UpperCamelCase=None      ,							**UpperCamelCase      ,							)				->    Optional[int]:
     super().__init__(**UpperCamelCase      ,							pad_token_id=UpperCamelCase      ,							bos_token_id=UpperCamelCase      ,							eos_token_id=UpperCamelCase      )
     __a			=					hidden_size
     __a			=					feat_extract_norm
     __a			=					feat_extract_activation
     __a			=					list(UpperCamelCase      )
     __a			=					list(UpperCamelCase      )
     __a			=					list(UpperCamelCase      )
     __a			=					conv_bias
     __a			=					num_buckets
     __a			=					max_bucket_distance
     __a			=					num_conv_pos_embeddings
     __a			=					num_conv_pos_embedding_groups
     __a			=					len(self.conv_dim      )
     __a			=					num_hidden_layers
     __a			=					intermediate_size
     __a			=					hidden_act
     __a			=					num_attention_heads
     __a			=					hidden_dropout
     __a			=					attention_dropout
     __a			=					activation_dropout
     __a			=					feat_proj_dropout
     __a			=					final_dropout
     __a			=					layerdrop
     __a			=					layer_norm_eps
     __a			=					initializer_range
     __a			=					num_ctc_classes
     __a			=					vocab_size
     __a			=					do_stable_layer_norm
     __a			=					use_weighted_layer_sum
     __a			=					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
     __a			=					apply_spec_augment
     __a			=					mask_time_prob
     __a			=					mask_time_length
     __a			=					mask_time_min_masks
     __a			=					mask_feature_prob
     __a			=					mask_feature_length

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

     # ctc loss
     __a			=					ctc_loss_reduction
     __a			=					ctc_zero_infinity

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

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

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







   @property
   def 		UpperCamelCase__			(	self      )				->    List[str]:
     return functools.reduce(operator.mul      ,							self.conv_stride      ,							1      )









'''simple docstring'''


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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


UpperCAmelCase_ 		=      logging.get_logger(__name__)





class    __lowercase						(  __magic_name__ ):
   _a							=      ["""pixel_values"""]







   def __init__(	self      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = PILImageResampling.BILINEAR      ,							UpperCamelCase = True      ,							UpperCamelCase = 1 / 255      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    None:
     super().__init__(**UpperCamelCase      )
     __a			=					size if size is not None else {'shortest_edge': 384}
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )

     __a			=					do_resize
     __a			=					size
     # Default value set here for backwards compatibility where the value in config is None
     __a			=					crop_pct if crop_pct is not None else 224 / 256
     __a			=					resample
     __a			=					do_rescale
     __a			=					rescale_factor
     __a			=					do_normalize
     __a			=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __a			=					image_std if image_std is not None else IMAGENET_STANDARD_STD







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = PILImageResampling.BICUBIC      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    np.ndarray:
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )
     if "shortest_edge" not in size:
       raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"      )
     __a			=					size['shortest_edge']

     if shortest_edge < 384:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       __a			=					int(shortest_edge / crop_pct      )
       __a			=					get_resize_output_image_size(UpperCamelCase      ,							size=UpperCamelCase      ,							default_to_square=UpperCamelCase      )
       __a			=					resize(image=UpperCamelCase      ,							size=UpperCamelCase      ,							resample=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=UpperCamelCase      ,							size=(shortest_edge, shortest_edge)      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           UpperCamelCase      ,							size=(shortest_edge, shortest_edge)      ,							resample=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    Any:
     return rescale(UpperCamelCase      ,							scale=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    np.ndarray:
     return normalize(UpperCamelCase      ,							mean=UpperCamelCase      ,							std=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = ChannelDimension.FIRST      ,							**UpperCamelCase      ,							)				->    PIL.Image.Image:
     __a			=					do_resize if do_resize is not None else self.do_resize
     __a			=					crop_pct if crop_pct is not None else self.crop_pct
     __a			=					resample if resample is not None else self.resample
     __a			=					do_rescale if do_rescale is not None else self.do_rescale
     __a			=					rescale_factor if rescale_factor is not None else self.rescale_factor
     __a			=					do_normalize if do_normalize is not None else self.do_normalize
     __a			=					image_mean if image_mean is not None else self.image_mean
     __a			=					image_std if image_std is not None else self.image_std

     __a			=					size if size is not None else self.size
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )

     __a			=					make_list_of_images(UpperCamelCase      )

     if not valid_images(UpperCamelCase      ):
       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 or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'      )

     if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
       raise ValueError('crop_pct must be specified if size < 384.'      )

     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.'      )

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

     if do_resize:
       __a			=					[self.resize(image=UpperCamelCase      ,							size=UpperCamelCase      ,							crop_pct=UpperCamelCase      ,							resample=UpperCamelCase      ) for image in images]

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

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

     __a			=					[to_channel_dimension_format(UpperCamelCase      ,							UpperCamelCase      ) for image in images]

     __a			=					{'pixel_values': images}
     return BatchFeature(data=UpperCamelCase      ,							tensor_type=UpperCamelCase      )



