



import functools
import operator

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


__snake_case    :		Optional[Any]  =logging.get_logger(__name__)

__snake_case    :		Optional[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__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""wavlm"""



		def __init__(self		,__lowerCamelCase=32		,__lowerCamelCase=7_68		,__lowerCamelCase=12		,__lowerCamelCase=12		,__lowerCamelCase=30_72		,__lowerCamelCase="gelu"		,__lowerCamelCase=0.1		,__lowerCamelCase=0.1		,__lowerCamelCase=0.1		,__lowerCamelCase=0.0		,__lowerCamelCase=0.1		,__lowerCamelCase=0.1		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-5		,__lowerCamelCase="group"		,__lowerCamelCase="gelu"		,__lowerCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)		,__lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)		,__lowerCamelCase=(10, 3, 3, 3, 3, 2, 2)		,__lowerCamelCase=False		,__lowerCamelCase=1_28		,__lowerCamelCase=16		,__lowerCamelCase=3_20		,__lowerCamelCase=8_00		,__lowerCamelCase=False		,__lowerCamelCase=True		,__lowerCamelCase=0.05		,__lowerCamelCase=10		,__lowerCamelCase=2		,__lowerCamelCase=0.0		,__lowerCamelCase=10		,__lowerCamelCase=3_20		,__lowerCamelCase=2		,__lowerCamelCase=0.1		,__lowerCamelCase=1_00		,__lowerCamelCase=2_56		,__lowerCamelCase=2_56		,__lowerCamelCase=0.1		,__lowerCamelCase="mean"		,__lowerCamelCase=False		,__lowerCamelCase=False		,__lowerCamelCase=2_56		,__lowerCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00)		,__lowerCamelCase=(5, 3, 3, 1, 1)		,__lowerCamelCase=(1, 2, 3, 1, 1)		,__lowerCamelCase=5_12		,__lowerCamelCase=80		,__lowerCamelCase=0		,__lowerCamelCase=1		,__lowerCamelCase=2		,__lowerCamelCase=False		,__lowerCamelCase=3		,__lowerCamelCase=2		,__lowerCamelCase=3		,__lowerCamelCase=None		,**__lowerCamelCase		,)					->      int:

						"""simple docstring"""






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

						# parameters for pretraining with codevector quantized representations
						lowerCAmelCase__					:    str												=					num_codevectors_per_group
						lowerCAmelCase__					:    Optional[Any]												=					num_codevector_groups
						lowerCAmelCase__					:    Tuple												=					contrastive_logits_temperature
						lowerCAmelCase__					:    List[Any]												=					num_negatives
						lowerCAmelCase__					:    List[Any]												=					codevector_dim
						lowerCAmelCase__					:    Dict												=					proj_codevector_dim
						lowerCAmelCase__					:    Any												=					diversity_loss_weight

						# ctc loss
						lowerCAmelCase__					:    Optional[int]												=					ctc_loss_reduction
						lowerCAmelCase__					:    int												=					ctc_zero_infinity

						# adapter
						lowerCAmelCase__					:    int												=					add_adapter
						lowerCAmelCase__					:    Optional[int]												=					adapter_kernel_size
						lowerCAmelCase__					:    Tuple												=					adapter_stride
						lowerCAmelCase__					:    Optional[Any]												=					num_adapter_layers
						lowerCAmelCase__					:    int												=					output_hidden_size or hidden_size

						# SequenceClassification-specific parameter. Feel free to ignore for other classes.
						lowerCAmelCase__					:    Optional[int]												=					classifier_proj_size

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



		@property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






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






import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor



class 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase=7		,__lowerCamelCase=3		,__lowerCamelCase=18		,__lowerCamelCase=30		,__lowerCamelCase=4_00		,__lowerCamelCase=True		,__lowerCamelCase=None		,__lowerCamelCase=True		,__lowerCamelCase=False		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase=[0.5, 0.5, 0.5]		,__lowerCamelCase=[0.5, 0.5, 0.5]		,)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					parent
						lowerCAmelCase__					:    Optional[Any]												=					batch_size
						lowerCAmelCase__					:    str												=					num_channels
						lowerCAmelCase__					:    List[str]												=					image_size
						lowerCAmelCase__					:    List[Any]												=					min_resolution
						lowerCAmelCase__					:    Union[str, Any]												=					max_resolution
						lowerCAmelCase__					:    Union[str, Any]												=					do_resize
						lowerCAmelCase__					:    str												=					size if size is not None else {'''height''': 18, '''width''': 20}
						lowerCAmelCase__					:    List[str]												=					do_thumbnail
						lowerCAmelCase__					:    str												=					do_align_axis
						lowerCAmelCase__					:    Optional[Any]												=					do_pad
						lowerCAmelCase__					:    Tuple												=					do_normalize
						lowerCAmelCase__					:    List[Any]												=					image_mean
						lowerCAmelCase__					:    List[str]												=					image_std



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_thumbnail": self.do_thumbnail,
						    "do_align_long_axis": self.do_align_axis,
						    "do_pad": self.do_pad,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						}



@require_torch
@require_vision
class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =DonutImageProcessor if is_vision_available() else None



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

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					DonutImageProcessingTester(self			)



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					self.image_processing_class(**self.image_processor_dict			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_resize'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''size'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_thumbnail'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_align_long_axis'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_pad'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_normalize'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''image_mean'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''image_std'''			)			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.image_processing_class.from_dict(self.image_processor_dict			)
						self.assertEqual(image_processor.size		,{'''height''': 18, '''width''': 20}			)

						lowerCAmelCase__					:    int												=					self.image_processing_class.from_dict(self.image_processor_dict		,size=42			)
						self.assertEqual(image_processor.size		,{'''height''': 42, '''width''': 42}			)

						# Previous config had dimensions in (width, height) order
						lowerCAmelCase__					:    Union[str, Any]												=					self.image_processing_class.from_dict(self.image_processor_dict		,size=(42, 84)			)
						self.assertEqual(image_processor.size		,{'''height''': 84, '''width''': 42}			)



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						pass



		@is_flaky()
		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






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

						# Test not batched input
						lowerCAmelCase__					:    Optional[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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

						# Test batched
						lowerCAmelCase__					:    Optional[int]												=					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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)



		@is_flaky()
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.image_processing_class(**self.image_processor_dict			)
						# create random numpy tensors
						lowerCAmelCase__					:    Any												=					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
						lowerCAmelCase__					:    Union[str, 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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

						# Test batched
						lowerCAmelCase__					:    int												=					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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)



		@is_flaky()
		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						lowerCAmelCase__					:    int												=					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
						lowerCAmelCase__					:    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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

						# Test batched
						lowerCAmelCase__					:    Dict												=					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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

