
"""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:
      UpperCamelCase     :    Union[str, Any]       =			False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor

class __lowerCAmelCase		(				unittest.TestCase      ):





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=7     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=18     ,					__UpperCAmelCase=30     ,					__UpperCAmelCase=400     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=None     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	size if size is not None else {'height': 20, 'width': 20}
      __UpperCamelCase      =	parent
      __UpperCamelCase      =	batch_size
      __UpperCamelCase      =	num_channels
      __UpperCamelCase      =	image_size
      __UpperCamelCase      =	min_resolution
      __UpperCamelCase      =	max_resolution
      __UpperCamelCase      =	size
      __UpperCamelCase      =	do_normalize
      __UpperCamelCase      =	do_convert_rgb
      __UpperCamelCase      =	[512, 1024, 2048, 4096]
      __UpperCamelCase      =	patch_size if patch_size is not None else {'height': 16, 'width': 16}





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
      __UpperCamelCase      =	Image.open(requests.get(__UpperCAmelCase     ,					stream=__UpperCAmelCase				).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 __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						PixaStructImageProcessor if is_vision_available() else None





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	PixaStructImageProcessingTester(self				)





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.image_processing_class(**self.image_processor_dict				)
      self.assertTrue(hasattr(__UpperCAmelCase     ,					'do_normalize'				)				)
      self.assertTrue(hasattr(__UpperCAmelCase     ,					'do_convert_rgb'				)				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.image_processor_tester.prepare_dummy_image()

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

      __UpperCamelCase      =	image_processor(__UpperCAmelCase     ,					return_tensors='pt'     ,					max_patches=__UpperCAmelCase				)
      self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     ,					torch.tensor(0.0_6_0_6				)     ,					atol=1E-3     ,					rtol=1E-3				)				)





     def 						UpperCAmelCase (     self				):

      '''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=__UpperCAmelCase				)
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase     ,					Image.Image				)

      # Test not batched input
      __UpperCamelCase      =	(
          (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
       __UpperCamelCase      =	image_processor(
           image_inputs[0]     ,					return_tensors='pt'     ,					max_patches=__UpperCAmelCase				).flattened_patches
       self.assertEqual(
           encoded_images.shape     ,					(1, max_patch, expected_hidden_dim)     ,					)

       # Test batched
       __UpperCamelCase      =	image_processor(
           __UpperCAmelCase     ,					return_tensors='pt'     ,					max_patches=__UpperCAmelCase				).flattened_patches
       self.assertEqual(
           encoded_images.shape     ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,					)





     def 						UpperCAmelCase (     self				):

      '''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=__UpperCAmelCase				)
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase     ,					Image.Image				)

      # Test not batched input
      __UpperCamelCase      =	(
          (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
          * self.image_processor_tester.num_channels
      ) + 2

      __UpperCamelCase      =	True

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

       __UpperCamelCase      =	'Hello'

       __UpperCamelCase      =	image_processor(
           image_inputs[0]     ,					return_tensors='pt'     ,					max_patches=__UpperCAmelCase     ,					header_text=__UpperCAmelCase				).flattened_patches
       self.assertEqual(
           encoded_images.shape     ,					(1, max_patch, expected_hidden_dim)     ,					)

       # Test batched
       __UpperCamelCase      =	image_processor(
           __UpperCAmelCase     ,					return_tensors='pt'     ,					max_patches=__UpperCAmelCase     ,					header_text=__UpperCAmelCase				).flattened_patches
       self.assertEqual(
           encoded_images.shape     ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,					)





     def 						UpperCAmelCase (     self				):

      '''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=__UpperCAmelCase     ,					numpify=__UpperCAmelCase				)
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase     ,					np.ndarray				)

      __UpperCamelCase      =	(
          (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
       __UpperCamelCase      =	image_processor(
           image_inputs[0]     ,					return_tensors='pt'     ,					max_patches=__UpperCAmelCase				).flattened_patches
       self.assertEqual(
           encoded_images.shape     ,					(1, max_patch, expected_hidden_dim)     ,					)

       # Test batched
       __UpperCamelCase      =	image_processor(
           __UpperCAmelCase     ,					return_tensors='pt'     ,					max_patches=__UpperCAmelCase				).flattened_patches
       self.assertEqual(
           encoded_images.shape     ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,					)







     def 						UpperCAmelCase (     self				):

      '''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=__UpperCAmelCase     ,					torchify=__UpperCAmelCase				)
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase     ,					torch.Tensor				)

      # Test not batched input
      __UpperCamelCase      =	(
          (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
       __UpperCamelCase      =	image_processor(
           image_inputs[0]     ,					return_tensors='pt'     ,					max_patches=__UpperCAmelCase				).flattened_patches
       self.assertEqual(
           encoded_images.shape     ,					(1, max_patch, expected_hidden_dim)     ,					)

       # Test batched
       __UpperCamelCase      =	image_processor(
           __UpperCAmelCase     ,					return_tensors='pt'     ,					max_patches=__UpperCAmelCase				).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 __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						PixaStructImageProcessor if is_vision_available() else None





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	PixaStructImageProcessingTester(self     ,					num_channels=4				)
      __UpperCamelCase      =	3





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.image_processing_class(**self.image_processor_dict				)
      self.assertTrue(hasattr(__UpperCAmelCase     ,					'do_normalize'				)				)
      self.assertTrue(hasattr(__UpperCAmelCase     ,					'do_convert_rgb'				)				)







     def 						UpperCAmelCase (     self				):

      '''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=__UpperCAmelCase				)
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase     ,					Image.Image				)

      # Test not batched input
      __UpperCamelCase      =	(
          (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
       __UpperCamelCase      =	image_processor(
           image_inputs[0]     ,					return_tensors='pt'     ,					max_patches=__UpperCAmelCase				).flattened_patches
       self.assertEqual(
           encoded_images.shape     ,					(1, max_patch, expected_hidden_dim)     ,					)

       # Test batched
       __UpperCamelCase      =	image_processor(
           __UpperCAmelCase     ,					return_tensors='pt'     ,					max_patches=__UpperCAmelCase				).flattened_patches
       self.assertEqual(
           encoded_images.shape     ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,					)





"""simple docstring"""

import functools
import operator

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


UpperCamelCase     :    str       =			logging.get_logger(__name__)

UpperCamelCase     :    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 __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						"wavlm"





     def __init__(     self     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=1E-5     ,					__UpperCAmelCase="group"     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)     ,					__UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)     ,					__UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=128     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=800     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=0.0_5     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=100     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase="mean"     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=(512, 512, 512, 512, 1500)     ,					__UpperCAmelCase=(5, 3, 3, 1, 1)     ,					__UpperCAmelCase=(1, 2, 3, 1, 1)     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=80     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




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

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

      # ctc loss
      __UpperCamelCase      =	ctc_loss_reduction
      __UpperCamelCase      =	ctc_zero_infinity

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

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

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







     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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



