



'''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 MobileViTImageProcessor

class 					SCREAMING_SNAKE_CASE					(    unittest.TestCase						):






    """simple docstring"""





    def __init__(    self :   List[str]    ,		UpperCamelCase__ :   List[Any]    ,		UpperCamelCase__ :   int=7    ,		UpperCamelCase__ :   Any=3    ,		UpperCamelCase__ :   List[Any]=1_8    ,		UpperCamelCase__ :   Dict=3_0    ,		UpperCamelCase__ :   Optional[int]=4_0_0    ,		UpperCamelCase__ :   List[str]=True    ,		UpperCamelCase__ :   Dict=None    ,		UpperCamelCase__ :   List[str]=True    ,		UpperCamelCase__ :   List[str]=None    ,		UpperCamelCase__ :   Union[str, Any]=True    ,		):


       """simple docstring"""







       UpperCamelCase      							=    size if size is not None else {'shortest_edge': 2_0}
       UpperCamelCase      							=    crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
       UpperCamelCase      							=    parent
       UpperCamelCase      							=    batch_size
       UpperCamelCase      							=    num_channels
       UpperCamelCase      							=    image_size
       UpperCamelCase      							=    min_resolution
       UpperCamelCase      							=    max_resolution
       UpperCamelCase      							=    do_resize
       UpperCamelCase      							=    size
       UpperCamelCase      							=    do_center_crop
       UpperCamelCase      							=    crop_size
       UpperCamelCase      							=    do_flip_channel_order






    def 					A			(    self :   Optional[int]					):


       """simple docstring"""







       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_flip_channel_order": self.do_flip_channel_order,
       }

@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE					(    _a  ,		unittest.TestCase						):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE          =    MobileViTImageProcessor if is_vision_available() else None






    def 					A			(    self :   List[Any]					):


       """simple docstring"""







       UpperCamelCase      							=    MobileViTImageProcessingTester(self					)






    @property
    def 					A			(    self :   Optional[int]					):


       """simple docstring"""







       return self.image_processor_tester.prepare_image_processor_dict()






    def 					A			(    self :   Dict					):


       """simple docstring"""







       UpperCamelCase      							=    self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(a_    ,		'do_resize'					)					)
       self.assertTrue(hasattr(a_    ,		'size'					)					)
       self.assertTrue(hasattr(a_    ,		'do_center_crop'					)					)
       self.assertTrue(hasattr(a_    ,		'center_crop'					)					)
       self.assertTrue(hasattr(a_    ,		'do_flip_channel_order'					)					)






    def 					A			(    self :   Optional[int]					):


       """simple docstring"""







       UpperCamelCase      							=    self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size    ,		{'shortest_edge': 2_0}					)
       self.assertEqual(image_processor.crop_size    ,		{'height': 1_8, 'width': 1_8}					)

       UpperCamelCase      							=    self.image_processing_class.from_dict(self.image_processor_dict    ,		size=4_2    ,		crop_size=8_4					)
       self.assertEqual(image_processor.size    ,		{'shortest_edge': 4_2}					)
       self.assertEqual(image_processor.crop_size    ,		{'height': 8_4, 'width': 8_4}					)






    def 					A			(    self :   Tuple					):


       """simple docstring"""







       pass






    def 					A			(    self :   List[Any]					):


       """simple docstring"""







       UpperCamelCase      							=    self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       UpperCamelCase      							=    prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=a_					)
       for image in image_inputs:
          self.assertIsInstance(a_    ,		Image.Image					)

       # Test not batched input
       UpperCamelCase      							=    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      							=    image_processing(a_    ,		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 					A			(    self :   Tuple					):


       """simple docstring"""







       UpperCamelCase      							=    self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       UpperCamelCase      							=    prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=a_    ,		numpify=a_					)
       for image in image_inputs:
          self.assertIsInstance(a_    ,		np.ndarray					)

       # Test not batched input
       UpperCamelCase      							=    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      							=    image_processing(a_    ,		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 					A			(    self :   int					):


       """simple docstring"""







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

       # Test not batched input
       UpperCamelCase      							=    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      							=    image_processing(a_    ,		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'],
           )    ,		)






import functools
import operator

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


_lowerCAmelCase	:   List[str]    =   logging.get_logger(__name__)

_lowerCAmelCase	:   Tuple    =   {
    "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 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	'wavlm'
		def __init__(							self    ,a_=32    ,a_=768    ,a_=12    ,a_=12    ,a_=3072    ,a_="gelu"    ,a_=0.1    ,a_=0.1    ,a_=0.1    ,a_=0.0    ,a_=0.1    ,a_=0.1    ,a_=0.02    ,a_=1e-5    ,a_="group"    ,a_="gelu"    ,a_=(512, 512, 512, 512, 512, 512, 512)    ,a_=(5, 2, 2, 2, 2, 2, 2)    ,a_=(10, 3, 3, 3, 3, 2, 2)    ,a_=False    ,a_=128    ,a_=16    ,a_=320    ,a_=800    ,a_=False    ,a_=True    ,a_=0.05    ,a_=10    ,a_=2    ,a_=0.0    ,a_=10    ,a_=320    ,a_=2    ,a_=0.1    ,a_=100    ,a_=256    ,a_=256    ,a_=0.1    ,a_="mean"    ,a_=False    ,a_=False    ,a_=256    ,a_=(512, 512, 512, 512, 1500)    ,a_=(5, 3, 3, 1, 1)    ,a_=(1, 2, 3, 1, 1)    ,a_=512    ,a_=80    ,a_=0    ,a_=1    ,a_=2    ,a_=False    ,a_=3    ,a_=2    ,a_=3    ,a_=None    ,**a_    ,):







						"""simple docstring"""
						super().__init__(**a_    ,pad_token_id=a_    ,bos_token_id=a_    ,eos_token_id=a_						)
						lowerCAmelCase__		  =  hidden_size
						lowerCAmelCase__		  =  feat_extract_norm
						lowerCAmelCase__		  =  feat_extract_activation
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  conv_bias
						lowerCAmelCase__		  =  num_buckets
						lowerCAmelCase__		  =  max_bucket_distance
						lowerCAmelCase__		  =  num_conv_pos_embeddings
						lowerCAmelCase__		  =  num_conv_pos_embedding_groups
						lowerCAmelCase__		  =  len(self.conv_dim						)
						lowerCAmelCase__		  =  num_hidden_layers
						lowerCAmelCase__		  =  intermediate_size
						lowerCAmelCase__		  =  hidden_act
						lowerCAmelCase__		  =  num_attention_heads
						lowerCAmelCase__		  =  hidden_dropout
						lowerCAmelCase__		  =  attention_dropout
						lowerCAmelCase__		  =  activation_dropout
						lowerCAmelCase__		  =  feat_proj_dropout
						lowerCAmelCase__		  =  final_dropout
						lowerCAmelCase__		  =  layerdrop
						lowerCAmelCase__		  =  layer_norm_eps
						lowerCAmelCase__		  =  initializer_range
						lowerCAmelCase__		  =  num_ctc_classes
						lowerCAmelCase__		  =  vocab_size
						lowerCAmelCase__		  =  do_stable_layer_norm
						lowerCAmelCase__		  =  use_weighted_layer_sum
						lowerCAmelCase__		  =  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__		  =  apply_spec_augment
						lowerCAmelCase__		  =  mask_time_prob
						lowerCAmelCase__		  =  mask_time_length
						lowerCAmelCase__		  =  mask_time_min_masks
						lowerCAmelCase__		  =  mask_feature_prob
						lowerCAmelCase__		  =  mask_feature_length

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

						# ctc loss
						lowerCAmelCase__		  =  ctc_loss_reduction
						lowerCAmelCase__		  =  ctc_zero_infinity

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

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

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



		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







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