



"""simple docstring"""
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 PoolFormerImageProcessor



class  SCREAMING_SNAKE_CASE__    (   unittest.TestCase					):

   def __init__(  self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=7 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=30 ,	_SCREAMING_SNAKE_CASE=400 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=0.9 ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,	_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,	)    -> Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase      :   Union[str, Any]      =      size if size is not None else {"shortest_edge": 30}
    UpperCAmelCase      :   Optional[int]      =      crop_size if crop_size is not None else {"height": 30, "width": 30}
    UpperCAmelCase      :   Optional[int]      =      parent
    UpperCAmelCase      :   Dict      =      batch_size
    UpperCAmelCase      :   Tuple      =      num_channels
    UpperCAmelCase      :   List[Any]      =      min_resolution
    UpperCAmelCase      :   int      =      max_resolution
    UpperCAmelCase      :   Dict      =      do_resize_and_center_crop
    UpperCAmelCase      :   Optional[Any]      =      size
    UpperCAmelCase      :   Dict      =      crop_pct
    UpperCAmelCase      :   Optional[int]      =      crop_size
    UpperCAmelCase      :   List[Any]      =      do_normalize
    UpperCAmelCase      :   List[Any]      =      image_mean
    UpperCAmelCase      :   Any      =      image_std




   def       SCREAMING_SNAKE_CASE	(  self		)    -> List[str]:





    '''simple docstring'''




    return {
        "size": self.size,
        "do_resize_and_center_crop": self.do_resize_and_center_crop,
        "crop_pct": self.crop_pct,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
    }



@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE__    (   UpperCAmelCase__ ,     unittest.TestCase					):
   __lowerCAmelCase    :  int		=		PoolFormerImageProcessor if is_vision_available() else None

   def       SCREAMING_SNAKE_CASE	(  self		)    -> Optional[int]:





    '''simple docstring'''




    UpperCAmelCase      :   Optional[int]      =      PoolFormerImageProcessingTester(self		)

   @property
   def       SCREAMING_SNAKE_CASE	(  self		)    -> List[Any]:





    '''simple docstring'''




    return self.image_processor_tester.prepare_image_processor_dict()

   def       SCREAMING_SNAKE_CASE	(  self		)    -> Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase      :   List[str]      =      self.image_processing_class(**self.image_processor_dict		)
    self.assertTrue(hasattr(snake_case__ ,	"""do_resize_and_center_crop"""		)		)
    self.assertTrue(hasattr(snake_case__ ,	"""size"""		)		)
    self.assertTrue(hasattr(snake_case__ ,	"""crop_pct"""		)		)
    self.assertTrue(hasattr(snake_case__ ,	"""do_normalize"""		)		)
    self.assertTrue(hasattr(snake_case__ ,	"""image_mean"""		)		)
    self.assertTrue(hasattr(snake_case__ ,	"""image_std"""		)		)

   def       SCREAMING_SNAKE_CASE	(  self		)    -> Dict:





    '''simple docstring'''




    UpperCAmelCase      :   List[str]      =      self.image_processing_class.from_dict(self.image_processor_dict		)
    self.assertEqual(image_processor.size ,	{"""shortest_edge""": 30}		)
    self.assertEqual(image_processor.crop_size ,	{"""height""": 30, """width""": 30}		)

    UpperCAmelCase      :   Optional[Any]      =      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       SCREAMING_SNAKE_CASE	(  self		)    -> Any:





    '''simple docstring'''




    pass

   def       SCREAMING_SNAKE_CASE	(  self		)    -> List[Any]:





    '''simple docstring'''




    UpperCAmelCase      :   List[Any]      =      self.image_processing_class(**self.image_processor_dict		)
    # create random PIL images
    UpperCAmelCase      :   Dict      =      prepare_image_inputs(self.image_processor_tester ,	equal_resolution=snake_case__		)
    for image in image_inputs:
     self.assertIsInstance(snake_case__ ,	Image.Image		)

    # Test not batched input
    UpperCAmelCase      :   Dict      =      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
    UpperCAmelCase      :   List[str]      =      image_processing(snake_case__ ,	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       SCREAMING_SNAKE_CASE	(  self		)    -> Dict:





    '''simple docstring'''




    UpperCAmelCase      :   List[str]      =      self.image_processing_class(**self.image_processor_dict		)
    # create random numpy tensors
    UpperCAmelCase      :   List[str]      =      prepare_image_inputs(self.image_processor_tester ,	equal_resolution=snake_case__ ,	numpify=snake_case__		)
    for image in image_inputs:
     self.assertIsInstance(snake_case__ ,	np.ndarray		)

    # Test not batched input
    UpperCAmelCase      :   int      =      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
    UpperCAmelCase      :   Optional[Any]      =      image_processing(snake_case__ ,	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       SCREAMING_SNAKE_CASE	(  self		)    -> List[str]:





    '''simple docstring'''




    UpperCAmelCase      :   int      =      self.image_processing_class(**self.image_processor_dict		)
    # create random PyTorch tensors
    UpperCAmelCase      :   int      =      prepare_image_inputs(self.image_processor_tester ,	equal_resolution=snake_case__ ,	torchify=snake_case__		)
    for image in image_inputs:
     self.assertIsInstance(snake_case__ ,	torch.Tensor		)

    # Test not batched input
    UpperCAmelCase      :   Any      =      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
    UpperCAmelCase      :   str      =      image_processing(snake_case__ ,	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"""],
        ) ,	)


'''simple docstring'''





import functools
import operator

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


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     =       {
    """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 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    List[str]				=							"wavlm"







 def __init__(					self		:  Optional[int]			,				snake_case__		:  Dict=32			,				snake_case__		:  Optional[Any]=7_68			,				snake_case__		:  Tuple=12			,				snake_case__		:  List[Any]=12			,				snake_case__		:  List[str]=30_72			,				snake_case__		:  Tuple="gelu"			,				snake_case__		:  Optional[Any]=0.1			,				snake_case__		:  List[str]=0.1			,				snake_case__		:  Any=0.1			,				snake_case__		:  List[Any]=0.0			,				snake_case__		:  Dict=0.1			,				snake_case__		:  List[Any]=0.1			,				snake_case__		:  Union[str, Any]=0.02			,				snake_case__		:  int=1e-5			,				snake_case__		:  Dict="group"			,				snake_case__		:  Any="gelu"			,				snake_case__		:  List[Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)			,				snake_case__		:  int=(5, 2, 2, 2, 2, 2, 2)			,				snake_case__		:  Dict=(10, 3, 3, 3, 3, 2, 2)			,				snake_case__		:  Tuple=False			,				snake_case__		:  int=1_28			,				snake_case__		:  Dict=16			,				snake_case__		:  int=3_20			,				snake_case__		:  Dict=8_00			,				snake_case__		:  List[str]=False			,				snake_case__		:  Union[str, Any]=True			,				snake_case__		:  str=0.05			,				snake_case__		:  List[Any]=10			,				snake_case__		:  Tuple=2			,				snake_case__		:  Optional[int]=0.0			,				snake_case__		:  int=10			,				snake_case__		:  Any=3_20			,				snake_case__		:  Dict=2			,				snake_case__		:  Optional[int]=0.1			,				snake_case__		:  Union[str, Any]=1_00			,				snake_case__		:  Dict=2_56			,				snake_case__		:  List[Any]=2_56			,				snake_case__		:  List[str]=0.1			,				snake_case__		:  List[Any]="mean"			,				snake_case__		:  int=False			,				snake_case__		:  Any=False			,				snake_case__		:  Any=2_56			,				snake_case__		:  Union[str, Any]=(5_12, 5_12, 5_12, 5_12, 15_00)			,				snake_case__		:  Union[str, Any]=(5, 3, 3, 1, 1)			,				snake_case__		:  Optional[Any]=(1, 2, 3, 1, 1)			,				snake_case__		:  List[Any]=5_12			,				snake_case__		:  str=80			,				snake_case__		:  Any=0			,				snake_case__		:  str=1			,				snake_case__		:  Union[str, Any]=2			,				snake_case__		:  int=False			,				snake_case__		:  Union[str, Any]=3			,				snake_case__		:  Union[str, Any]=2			,				snake_case__		:  List[str]=3			,				snake_case__		:  List[Any]=None			,				**snake_case__		:  int			,				):



   '''simple docstring'''




   super().__init__(**snake_case__			,				pad_token_id=snake_case__			,				bos_token_id=snake_case__			,				eos_token_id=snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							hidden_size
   UpperCAmelCase__      :					List[str]				       =							feat_extract_norm
   UpperCAmelCase__      :					int				       =							feat_extract_activation
   UpperCAmelCase__      :					Optional[int]				       =							list(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							list(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							list(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							conv_bias
   UpperCAmelCase__      :					List[Any]				       =							num_buckets
   UpperCAmelCase__      :					Union[str, Any]				       =							max_bucket_distance
   UpperCAmelCase__      :					Tuple				       =							num_conv_pos_embeddings
   UpperCAmelCase__      :					Union[str, Any]				       =							num_conv_pos_embedding_groups
   UpperCAmelCase__      :					Optional[Any]				       =							len(self.conv_dim )
   UpperCAmelCase__      :					Optional[int]				       =							num_hidden_layers
   UpperCAmelCase__      :					Tuple				       =							intermediate_size
   UpperCAmelCase__      :					List[str]				       =							hidden_act
   UpperCAmelCase__      :					List[str]				       =							num_attention_heads
   UpperCAmelCase__      :					Any				       =							hidden_dropout
   UpperCAmelCase__      :					List[Any]				       =							attention_dropout
   UpperCAmelCase__      :					Optional[int]				       =							activation_dropout
   UpperCAmelCase__      :					Optional[Any]				       =							feat_proj_dropout
   UpperCAmelCase__      :					List[str]				       =							final_dropout
   UpperCAmelCase__      :					int				       =							layerdrop
   UpperCAmelCase__      :					int				       =							layer_norm_eps
   UpperCAmelCase__      :					Tuple				       =							initializer_range
   UpperCAmelCase__      :					Union[str, Any]				       =							num_ctc_classes
   UpperCAmelCase__      :					Any				       =							vocab_size
   UpperCAmelCase__      :					Dict				       =							do_stable_layer_norm
   UpperCAmelCase__      :					List[Any]				       =							use_weighted_layer_sum
   UpperCAmelCase__      :					Optional[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
   UpperCAmelCase__      :					List[str]				       =							apply_spec_augment
   UpperCAmelCase__      :					Union[str, Any]				       =							mask_time_prob
   UpperCAmelCase__      :					List[Any]				       =							mask_time_length
   UpperCAmelCase__      :					List[str]				       =							mask_time_min_masks
   UpperCAmelCase__      :					List[str]				       =							mask_feature_prob
   UpperCAmelCase__      :					Dict				       =							mask_feature_length

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

   # ctc loss
   UpperCAmelCase__      :					Tuple				       =							ctc_loss_reduction
   UpperCAmelCase__      :					Any				       =							ctc_zero_infinity

   # adapter
   UpperCAmelCase__      :					int				       =							add_adapter
   UpperCAmelCase__      :					List[Any]				       =							adapter_kernel_size
   UpperCAmelCase__      :					Dict				       =							adapter_stride
   UpperCAmelCase__      :					Dict				       =							num_adapter_layers
   UpperCAmelCase__      :					Union[str, Any]				       =							output_hidden_size or hidden_size

   # SequenceClassification-specific parameter. Feel free to ignore for other classes.
   UpperCAmelCase__      :					str				       =							classifier_proj_size

   # XVector-specific parameters. Feel free to ignore for other classes.
   UpperCAmelCase__      :					Any				       =							list(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							list(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							list(snake_case__ )
   UpperCAmelCase__      :					Dict				       =							xvector_output_dim







 @property
 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   return functools.reduce(operator.mul			,				self.conv_stride			,				1 )



