"""simple docstring"""




import functools
import operator

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


__lowerCAmelCase     :				Dict    				=    logging.get_logger(__name__)

__lowerCAmelCase     :				Dict    				=    {
    "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    a_   (  __UpperCamelCase      ):
					UpperCamelCase_			:						Optional[Any]      =     "wavlm"




					def __init__(						self    :  int	,    snake_case__    :  int=32	,    snake_case__    :  Optional[int]=768	,    snake_case__    :  Union[str, Any]=12	,    snake_case__    :  int=12	,    snake_case__    :  Tuple=3072	,    snake_case__    :  List[str]="gelu"	,    snake_case__    :  Union[str, Any]=0.1	,    snake_case__    :  Dict=0.1	,    snake_case__    :  Dict=0.1	,    snake_case__    :  List[Any]=0.0	,    snake_case__    :  List[Any]=0.1	,    snake_case__    :  Any=0.1	,    snake_case__    :  str=0.02	,    snake_case__    :  str=1E-5	,    snake_case__    :  Tuple="group"	,    snake_case__    :  int="gelu"	,    snake_case__    :  Dict=(512, 512, 512, 512, 512, 512, 512)	,    snake_case__    :  Optional[int]=(5, 2, 2, 2, 2, 2, 2)	,    snake_case__    :  Optional[Any]=(10, 3, 3, 3, 3, 2, 2)	,    snake_case__    :  Any=False	,    snake_case__    :  int=128	,    snake_case__    :  int=16	,    snake_case__    :  Any=320	,    snake_case__    :  str=800	,    snake_case__    :  int=False	,    snake_case__    :  Any=True	,    snake_case__    :  Optional[int]=0.05	,    snake_case__    :  Any=10	,    snake_case__    :  Tuple=2	,    snake_case__    :  Optional[int]=0.0	,    snake_case__    :  List[str]=10	,    snake_case__    :  int=320	,    snake_case__    :  List[Any]=2	,    snake_case__    :  Dict=0.1	,    snake_case__    :  List[Any]=100	,    snake_case__    :  Union[str, Any]=256	,    snake_case__    :  List[Any]=256	,    snake_case__    :  int=0.1	,    snake_case__    :  List[str]="mean"	,    snake_case__    :  List[Any]=False	,    snake_case__    :  Any=False	,    snake_case__    :  Dict=256	,    snake_case__    :  Optional[Any]=(512, 512, 512, 512, 1500)	,    snake_case__    :  Tuple=(5, 3, 3, 1, 1)	,    snake_case__    :  Any=(1, 2, 3, 1, 1)	,    snake_case__    :  Optional[int]=512	,    snake_case__    :  Tuple=80	,    snake_case__    :  List[Any]=0	,    snake_case__    :  Union[str, Any]=1	,    snake_case__    :  int=2	,    snake_case__    :  Any=False	,    snake_case__    :  List[str]=3	,    snake_case__    :  Optional[int]=2	,    snake_case__    :  Optional[int]=3	,    snake_case__    :  Any=None	,    **snake_case__    :  Any	,    ):
							super().__init__(**snake_case__	,    pad_token_id=snake_case__	,    bos_token_id=snake_case__	,    eos_token_id=snake_case__  )
							lowerCAmelCase__   = hidden_size
							lowerCAmelCase__   = feat_extract_norm
							lowerCAmelCase__   = feat_extract_activation
							lowerCAmelCase__   = list(snake_case__  )
							lowerCAmelCase__   = list(snake_case__  )
							lowerCAmelCase__   = list(snake_case__  )
							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(snake_case__  )
							lowerCAmelCase__   = list(snake_case__  )
							lowerCAmelCase__   = list(snake_case__  )
							lowerCAmelCase__   = xvector_output_dim




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





"""simple docstring"""




import unittest

import numpy as np
import requests

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

			from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
			__lowerCAmelCase     :				int    				=    False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor

class    a_   (  unittest.TestCase      ):




					def __init__(						self    :  Tuple	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]=7	,    snake_case__    :  Optional[int]=3	,    snake_case__    :  int=18	,    snake_case__    :  Dict=30	,    snake_case__    :  Optional[Any]=400	,    snake_case__    :  Tuple=None	,    snake_case__    :  List[Any]=True	,    snake_case__    :  Any=True	,    snake_case__    :  Dict=None	,    ):
							lowerCAmelCase__   = size if size is not None else {"""height""": 20, """width""": 20}
							lowerCAmelCase__   = parent
							lowerCAmelCase__   = batch_size
							lowerCAmelCase__   = num_channels
							lowerCAmelCase__   = image_size
							lowerCAmelCase__   = min_resolution
							lowerCAmelCase__   = max_resolution
							lowerCAmelCase__   = size
							lowerCAmelCase__   = do_normalize
							lowerCAmelCase__   = do_convert_rgb
							lowerCAmelCase__   = [512, 1024, 2048, 4096]
							lowerCAmelCase__   = patch_size if patch_size is not None else {"""height""": 16, """width""": 16}




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
							lowerCAmelCase__   = Image.open(requests.get(snake_case__	,    stream=snake_case__  ).raw  ).convert("""RGB"""  )
							return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,		)
@require_torch
@require_vision
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						Optional[Any]      =     PixaStructImageProcessor if is_vision_available() else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							lowerCAmelCase__   = PixaStructImageProcessingTester(self  )




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(snake_case__	,    """do_normalize"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_convert_rgb"""  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.image_processor_tester.prepare_dummy_image()

							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							lowerCAmelCase__   = 2048

							lowerCAmelCase__   = image_processor(snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  )
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()	,    torch.tensor(0.0606  )	,    atol=1E-3	,    rtol=1E-3  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = 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
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = 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
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							lowerCAmelCase__   = True

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(snake_case__  ):
											lowerCAmelCase__   = image_processor(
											    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches

									lowerCAmelCase__   = """Hello"""

									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__	,    header_text=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__	,    header_text=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							lowerCAmelCase__   = 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  )

							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PyTorch tensors
							lowerCAmelCase__   = 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
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,		)
@require_torch
@require_vision
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						List[Any]      =     PixaStructImageProcessor if is_vision_available() else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = PixaStructImageProcessingTester(self	,    num_channels=4  )
							lowerCAmelCase__   = 3




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(snake_case__	,    """do_normalize"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_convert_rgb"""  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = 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
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * (self.image_processor_tester.num_channels - 1)
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




