"""simple docstring"""




import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor
class 		__A							(				unittest.TestCase	):






     def __init__(	self       ,  a__       ,  a__=7       ,  a__=3       ,  a__=18       ,  a__=30       ,  a__=400       ,  a__=True       ,  a__=None       ,  a__=True       ,  a__=None       ,  a__=True       ,  a__=[0.5, 0.5, 0.5]       ,  a__=[0.5, 0.5, 0.5]       ,  a__=False       ,  ):
       _lowerCAmelCase		: int						    =  size if size is not None else {"""height""": 20, """width""": 20}
       _lowerCAmelCase		: Any						    =  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
       _lowerCAmelCase		: Tuple						    =  parent
       _lowerCAmelCase		: str						    =  batch_size
       _lowerCAmelCase		: str						    =  num_channels
       _lowerCAmelCase		: Optional[int]						    =  image_size
       _lowerCAmelCase		: Dict						    =  min_resolution
       _lowerCAmelCase		: Any						    =  max_resolution
       _lowerCAmelCase		: Any						    =  do_resize
       _lowerCAmelCase		: Dict						    =  size
       _lowerCAmelCase		: Optional[Any]						    =  do_center_crop
       _lowerCAmelCase		: int						    =  crop_size
       _lowerCAmelCase		: Dict						    =  do_normalize
       _lowerCAmelCase		: int						    =  image_mean
       _lowerCAmelCase		: List[str]						    =  image_std
       _lowerCAmelCase		: Optional[int]						    =  do_reduce_labels






     def 					__A						(	self						):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_reduce_labels": self.do_reduce_labels,
       }







def 							SCREAMING_SNAKE_CASE  (    ) ->   List[str]:
  _lowerCAmelCase		: Optional[Any]						    =  load_dataset("""hf-internal-testing/fixtures_ade20k"""    ,split="""test"""			)

  _lowerCAmelCase		: List[str]						    =  Image.open(dataset[0]["""file"""]			)
  _lowerCAmelCase		: List[Any]						    =  Image.open(dataset[1]["""file"""]			)

  return image, map





def 							SCREAMING_SNAKE_CASE  (    ) ->   int:
  _lowerCAmelCase		: Dict						    =  load_dataset("""hf-internal-testing/fixtures_ade20k"""    ,split="""test"""			)

  _lowerCAmelCase		: Union[str, Any]						    =  Image.open(ds[0]["""file"""]			)
  _lowerCAmelCase		: int						    =  Image.open(ds[1]["""file"""]			)
  _lowerCAmelCase		: int						    =  Image.open(ds[2]["""file"""]			)
  _lowerCAmelCase		: str						    =  Image.open(ds[3]["""file"""]			)

  return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    List[str]	      =	BeitImageProcessor if is_vision_available() else None






     def 					__A						(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  BeitImageProcessingTester(self						)






     @property
     def 					__A						(	self						):
       return self.image_processor_tester.prepare_image_processor_dict()






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  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_normalize"""						)						)
       self.assertTrue(hasattr(a__       ,  """image_mean"""						)						)
       self.assertTrue(hasattr(a__       ,  """image_std"""						)						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[int]						    =  self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size       ,  {"""height""": 20, """width""": 20}						)
       self.assertEqual(image_processor.crop_size       ,  {"""height""": 18, """width""": 18}						)
       self.assertEqual(image_processor.do_reduce_labels       ,  a__						)

       _lowerCAmelCase		: Optional[Any]						    =  self.image_processing_class.from_dict(
           self.image_processor_dict       ,  size=42       ,  crop_size=84       ,  reduce_labels=a__						)
       self.assertEqual(image_processor.size       ,  {"""height""": 42, """width""": 42}						)
       self.assertEqual(image_processor.crop_size       ,  {"""height""": 84, """width""": 84}						)
       self.assertEqual(image_processor.do_reduce_labels       ,  a__						)






     def 					__A						(	self						):
       pass






     def 					__A						(	self						):
       # Initialize image_processing
       _lowerCAmelCase		: Dict						    =  self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       _lowerCAmelCase		: Tuple						    =  prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=a__						)
       for image in image_inputs:
         self.assertIsInstance(a__       ,  Image.Image						)

       # Test not batched input
       _lowerCAmelCase		: List[str]						    =  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
       _lowerCAmelCase		: Union[str, Any]						    =  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						):
       # Initialize image_processing
       _lowerCAmelCase		: List[Any]						    =  self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       _lowerCAmelCase		: int						    =  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
       _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.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )

       # Test batched
       _lowerCAmelCase		: Optional[Any]						    =  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						):
       # Initialize image_processing
       _lowerCAmelCase		: Optional[Any]						    =  self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       _lowerCAmelCase		: str						    =  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
       _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.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )

       # Test batched
       _lowerCAmelCase		: Optional[Any]						    =  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						):
       # Initialize image_processing
       _lowerCAmelCase		: Dict						    =  self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       _lowerCAmelCase		: List[Any]						    =  prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=a__       ,  torchify=a__						)
       _lowerCAmelCase		: Optional[int]						    =  []
       for image in image_inputs:
         self.assertIsInstance(a__       ,  torch.Tensor						)
         maps.append(torch.zeros(image.shape[-2:]						).long()						)

       # Test not batched input
       _lowerCAmelCase		: Optional[Any]						    =  image_processing(image_inputs[0]       ,  maps[0]       ,  return_tensors="""pt"""						)
       self.assertEqual(
           encoding["""pixel_values"""].shape       ,  (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )
       self.assertEqual(
           encoding["""labels"""].shape       ,  (
               1,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )
       self.assertEqual(encoding["""labels"""].dtype       ,  torch.long						)
       self.assertTrue(encoding["""labels"""].min().item() >= 0						)
       self.assertTrue(encoding["""labels"""].max().item() <= 255						)

       # Test batched
       _lowerCAmelCase		: Optional[Any]						    =  image_processing(a__       ,  a__       ,  return_tensors="""pt"""						)
       self.assertEqual(
           encoding["""pixel_values"""].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"""],
           )       ,  )
       self.assertEqual(
           encoding["""labels"""].shape       ,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )
       self.assertEqual(encoding["""labels"""].dtype       ,  torch.long						)
       self.assertTrue(encoding["""labels"""].min().item() >= 0						)
       self.assertTrue(encoding["""labels"""].max().item() <= 255						)

       # Test not batched input (PIL images)
       _lowerCAmelCase    ,			_lowerCAmelCase		: Dict						    =  prepare_semantic_single_inputs()

       _lowerCAmelCase		: Tuple						    =  image_processing(a__       ,  a__       ,  return_tensors="""pt"""						)
       self.assertEqual(
           encoding["""pixel_values"""].shape       ,  (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )
       self.assertEqual(
           encoding["""labels"""].shape       ,  (
               1,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )
       self.assertEqual(encoding["""labels"""].dtype       ,  torch.long						)
       self.assertTrue(encoding["""labels"""].min().item() >= 0						)
       self.assertTrue(encoding["""labels"""].max().item() <= 255						)

       # Test batched input (PIL images)
       _lowerCAmelCase    ,			_lowerCAmelCase		: Dict						    =  prepare_semantic_batch_inputs()

       _lowerCAmelCase		: List[Any]						    =  image_processing(a__       ,  a__       ,  return_tensors="""pt"""						)
       self.assertEqual(
           encoding["""pixel_values"""].shape       ,  (
               2,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )
       self.assertEqual(
           encoding["""labels"""].shape       ,  (
               2,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )
       self.assertEqual(encoding["""labels"""].dtype       ,  torch.long						)
       self.assertTrue(encoding["""labels"""].min().item() >= 0						)
       self.assertTrue(encoding["""labels"""].max().item() <= 255						)






     def 					__A						(	self						):
       # Initialize image_processing
       _lowerCAmelCase		: Optional[Any]						    =  self.image_processing_class(**self.image_processor_dict						)

       # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
       _lowerCAmelCase    ,			_lowerCAmelCase		: int						    =  prepare_semantic_single_inputs()
       _lowerCAmelCase		: Tuple						    =  image_processing(a__       ,  a__       ,  return_tensors="""pt"""						)
       self.assertTrue(encoding["""labels"""].min().item() >= 0						)
       self.assertTrue(encoding["""labels"""].max().item() <= 150						)

       _lowerCAmelCase		: Union[str, Any]						    =  True
       _lowerCAmelCase		: Any						    =  image_processing(a__       ,  a__       ,  return_tensors="""pt"""						)
       self.assertTrue(encoding["""labels"""].min().item() >= 0						)
       self.assertTrue(encoding["""labels"""].max().item() <= 255						)





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a   :	Tuple 					=     {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	str 					=     [
         'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'WavLMForAudioFrameClassification',
         'WavLMForCTC',
         'WavLMForSequenceClassification',
         'WavLMForXVector',
         'WavLMModel',
         'WavLMPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_wavlm import (
              WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              WavLMForAudioFrameClassification,
              WavLMForCTC,
              WavLMForSequenceClassification,
              WavLMForXVector,
              WavLMModel,
              WavLMPreTrainedModel,
          )

else:
     import sys

     _a   :	Any 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




