




import functools
import operator

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


lowerCamelCase__			:       str         =							logging.get_logger(__name__)

lowerCamelCase__			:       List[Any]         =							{
    '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_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   "wavlm"



 def __init__(							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		Tuple=32    ,    _lowerCAmelCase  :		Dict=768    ,    _lowerCAmelCase  :		List[Any]=12    ,    _lowerCAmelCase  :		Tuple=12    ,    _lowerCAmelCase  :		Optional[int]=3_072    ,    _lowerCAmelCase  :		List[Any]="gelu"    ,    _lowerCAmelCase  :		int=0.1    ,    _lowerCAmelCase  :		Dict=0.1    ,    _lowerCAmelCase  :		Union[str, Any]=0.1    ,    _lowerCAmelCase  :		List[Any]=0.0    ,    _lowerCAmelCase  :		str=0.1    ,    _lowerCAmelCase  :		Optional[int]=0.1    ,    _lowerCAmelCase  :		List[str]=0.02    ,    _lowerCAmelCase  :		List[str]=1E-5    ,    _lowerCAmelCase  :		Union[str, Any]="group"    ,    _lowerCAmelCase  :		Union[str, Any]="gelu"    ,    _lowerCAmelCase  :		Any=(512, 512, 512, 512, 512, 512, 512)    ,    _lowerCAmelCase  :		List[Any]=(5, 2, 2, 2, 2, 2, 2)    ,    _lowerCAmelCase  :		List[Any]=(10, 3, 3, 3, 3, 2, 2)    ,    _lowerCAmelCase  :		List[str]=False    ,    _lowerCAmelCase  :		int=128    ,    _lowerCAmelCase  :		Tuple=16    ,    _lowerCAmelCase  :		str=320    ,    _lowerCAmelCase  :		int=800    ,    _lowerCAmelCase  :		int=False    ,    _lowerCAmelCase  :		List[Any]=True    ,    _lowerCAmelCase  :		Optional[int]=0.05    ,    _lowerCAmelCase  :		str=10    ,    _lowerCAmelCase  :		Any=2    ,    _lowerCAmelCase  :		List[str]=0.0    ,    _lowerCAmelCase  :		Any=10    ,    _lowerCAmelCase  :		int=320    ,    _lowerCAmelCase  :		List[str]=2    ,    _lowerCAmelCase  :		List[Any]=0.1    ,    _lowerCAmelCase  :		List[Any]=100    ,    _lowerCAmelCase  :		Dict=256    ,    _lowerCAmelCase  :		int=256    ,    _lowerCAmelCase  :		Optional[int]=0.1    ,    _lowerCAmelCase  :		List[Any]="mean"    ,    _lowerCAmelCase  :		List[Any]=False    ,    _lowerCAmelCase  :		Optional[Any]=False    ,    _lowerCAmelCase  :		int=256    ,    _lowerCAmelCase  :		Optional[int]=(512, 512, 512, 512, 1_500)    ,    _lowerCAmelCase  :		int=(5, 3, 3, 1, 1)    ,    _lowerCAmelCase  :		int=(1, 2, 3, 1, 1)    ,    _lowerCAmelCase  :		List[str]=512    ,    _lowerCAmelCase  :		int=80    ,    _lowerCAmelCase  :		int=0    ,    _lowerCAmelCase  :		Any=1    ,    _lowerCAmelCase  :		Optional[int]=2    ,    _lowerCAmelCase  :		Optional[Any]=False    ,    _lowerCAmelCase  :		Union[str, Any]=3    ,    _lowerCAmelCase  :		Union[str, Any]=2    ,    _lowerCAmelCase  :		Optional[Any]=3    ,    _lowerCAmelCase  :		Any=None    ,    **_lowerCAmelCase  :		Union[str, Any]    ,    ):
       super().__init__(**_lowerCAmelCase    ,    pad_token_id=_lowerCAmelCase    ,    bos_token_id=_lowerCAmelCase    ,    eos_token_id=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      hidden_size
       SCREAMING_SNAKE_CASE_     						=      feat_extract_norm
       SCREAMING_SNAKE_CASE_     						=      feat_extract_activation
       SCREAMING_SNAKE_CASE_     						=      list(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      list(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      list(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      conv_bias
       SCREAMING_SNAKE_CASE_     						=      num_buckets
       SCREAMING_SNAKE_CASE_     						=      max_bucket_distance
       SCREAMING_SNAKE_CASE_     						=      num_conv_pos_embeddings
       SCREAMING_SNAKE_CASE_     						=      num_conv_pos_embedding_groups
       SCREAMING_SNAKE_CASE_     						=      len(self.conv_dim						)
       SCREAMING_SNAKE_CASE_     						=      num_hidden_layers
       SCREAMING_SNAKE_CASE_     						=      intermediate_size
       SCREAMING_SNAKE_CASE_     						=      hidden_act
       SCREAMING_SNAKE_CASE_     						=      num_attention_heads
       SCREAMING_SNAKE_CASE_     						=      hidden_dropout
       SCREAMING_SNAKE_CASE_     						=      attention_dropout
       SCREAMING_SNAKE_CASE_     						=      activation_dropout
       SCREAMING_SNAKE_CASE_     						=      feat_proj_dropout
       SCREAMING_SNAKE_CASE_     						=      final_dropout
       SCREAMING_SNAKE_CASE_     						=      layerdrop
       SCREAMING_SNAKE_CASE_     						=      layer_norm_eps
       SCREAMING_SNAKE_CASE_     						=      initializer_range
       SCREAMING_SNAKE_CASE_     						=      num_ctc_classes
       SCREAMING_SNAKE_CASE_     						=      vocab_size
       SCREAMING_SNAKE_CASE_     						=      do_stable_layer_norm
       SCREAMING_SNAKE_CASE_     						=      use_weighted_layer_sum
       SCREAMING_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
       SCREAMING_SNAKE_CASE_     						=      apply_spec_augment
       SCREAMING_SNAKE_CASE_     						=      mask_time_prob
       SCREAMING_SNAKE_CASE_     						=      mask_time_length
       SCREAMING_SNAKE_CASE_     						=      mask_time_min_masks
       SCREAMING_SNAKE_CASE_     						=      mask_feature_prob
       SCREAMING_SNAKE_CASE_     						=      mask_feature_length

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

       # ctc loss
       SCREAMING_SNAKE_CASE_     						=      ctc_loss_reduction
       SCREAMING_SNAKE_CASE_     						=      ctc_zero_infinity

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

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

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




 @property
 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       return functools.reduce(operator.mul    ,    self.conv_stride    ,    1						)





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor





class 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 def __init__(							self  :		str    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Any=7    ,    _lowerCAmelCase  :		Tuple=3    ,    _lowerCAmelCase  :		List[str]=18    ,    _lowerCAmelCase  :		Any=30    ,    _lowerCAmelCase  :		List[Any]=400    ,    _lowerCAmelCase  :		Dict=True    ,    _lowerCAmelCase  :		Tuple=None    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		Union[str, Any]=None    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		List[Any]=[0.5, 0.5, 0.5]    ,    _lowerCAmelCase  :		Union[str, Any]=[0.5, 0.5, 0.5]    ,    ):
       SCREAMING_SNAKE_CASE_     						=      size if size is not None else {'shortest_edge': 18}
       SCREAMING_SNAKE_CASE_     						=      crop_size if crop_size is not None else {'height': 18, 'width': 18}
       SCREAMING_SNAKE_CASE_     						=      parent
       SCREAMING_SNAKE_CASE_     						=      batch_size
       SCREAMING_SNAKE_CASE_     						=      num_channels
       SCREAMING_SNAKE_CASE_     						=      image_size
       SCREAMING_SNAKE_CASE_     						=      min_resolution
       SCREAMING_SNAKE_CASE_     						=      max_resolution
       SCREAMING_SNAKE_CASE_     						=      do_resize
       SCREAMING_SNAKE_CASE_     						=      size
       SCREAMING_SNAKE_CASE_     						=      do_center_crop
       SCREAMING_SNAKE_CASE_     						=      crop_size
       SCREAMING_SNAKE_CASE_     						=      do_normalize
       SCREAMING_SNAKE_CASE_     						=      image_mean
       SCREAMING_SNAKE_CASE_     						=      image_std




 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "do_center_crop": self.do_center_crop,
           "size": self.size,
           "crop_size": self.crop_size,
       }





@require_torch
@require_vision
class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   LevitImageProcessor if is_vision_available() else None



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      LevitImageProcessingTester(self						)



 @property
 def 				lowerCAmelCase_  (							self  :		Dict						):
       return self.image_processor_tester.prepare_image_processor_dict()



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'image_mean'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'image_std'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_normalize'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_resize'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_center_crop'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'size'						)						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size    ,    {'shortest_edge': 18}						)
       self.assertEqual(image_processor.crop_size    ,    {'height': 18, 'width': 18}						)

       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class.from_dict(self.image_processor_dict    ,    size=42    ,    crop_size=84						)
       self.assertEqual(image_processor.size    ,    {'shortest_edge': 42}						)
       self.assertEqual(image_processor.crop_size    ,    {'height': 84, 'width': 84}						)



 def 				lowerCAmelCase_  (							self  :		Dict						):
       pass



 def 				lowerCAmelCase_  (							self  :		int						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    Image.Image						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )

       # Test batched
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )



 def 				lowerCAmelCase_  (							self  :		str						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase    ,    numpify=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    np.ndarray						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )

       # Test batched
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )




 def 				lowerCAmelCase_  (							self  :		Tuple						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase    ,    torchify=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    torch.Tensor						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )

       # Test batched
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )