






import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

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 transformers import OneFormerImageProcessor
              from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
              from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
       from PIL import Image
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase="shi-labs/oneformer_demo") ->			Union[str, Any]:
      """simple docstring"""

      with open(hf_hub_download(lowerCamelCase_       ,							lowerCamelCase_       ,							repo_type='dataset')       ,							'r') as f:
            UpperCamelCase					       =  json.load(lowerCamelCase_)
      UpperCamelCase					       =  {}
      UpperCamelCase					       =  []
      UpperCamelCase					       =  []
      for key, info in class_info.items():
            UpperCamelCase					       =  info['name']
            class_names.append(info['name'])
            if info["isthing"]:
                  thing_ids.append(int(lowerCamelCase_))
      UpperCamelCase					       =  thing_ids
      UpperCamelCase					       =  class_names
      return metadata





class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     def __init__( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Dict=7   ,						_SCREAMING_SNAKE_CASE      :    int=3   ,						_SCREAMING_SNAKE_CASE      :    List[str]=30   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=400   ,						_SCREAMING_SNAKE_CASE      :    Tuple=None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=True   ,						_SCREAMING_SNAKE_CASE      :    Dict=True   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=[0.5, 0.5, 0.5]   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=[0.5, 0.5, 0.5]   ,						_SCREAMING_SNAKE_CASE      :    Tuple=10   ,						_SCREAMING_SNAKE_CASE      :    Dict=False   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=255   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]="shi-labs/oneformer_demo"   ,						_SCREAMING_SNAKE_CASE      :    List[Any]="ade20k_panoptic.json"   ,						_SCREAMING_SNAKE_CASE      :    Dict=10   ,						):






           """simple docstring"""




           UpperCamelCase					       =  parent
           UpperCamelCase					       =  batch_size
           UpperCamelCase					       =  num_channels
           UpperCamelCase					       =  min_resolution
           UpperCamelCase					       =  max_resolution
           UpperCamelCase					       =  do_resize
           UpperCamelCase					       =  {'shortest_edge': 32, 'longest_edge': 1333} if size is None else size
           UpperCamelCase					       =  do_normalize
           UpperCamelCase					       =  image_mean
           UpperCamelCase					       =  image_std
           UpperCamelCase					       =  class_info_file
           UpperCamelCase					       =  prepare_metadata(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  num_text
           UpperCamelCase					       =  repo_path

           # for the post_process_functions
           UpperCamelCase					       =  2
           UpperCamelCase					       =  10
           UpperCamelCase					       =  10
           UpperCamelCase					       =  3
           UpperCamelCase					       =  4
           UpperCamelCase					       =  num_labels
           UpperCamelCase					       =  do_reduce_labels
           UpperCamelCase					       =  ignore_index







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "num_labels": self.num_labels,
               "do_reduce_labels": self.do_reduce_labels,
               "ignore_index": self.ignore_index,
               "class_info_file": self.class_info_file,
               "metadata": self.metadata,
               "num_text": self.num_text,
           }







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    Dict=False ):






           """simple docstring"""




           if not batched:
                 UpperCamelCase					       =  image_inputs[0]
                 if isinstance(_SCREAMING_SNAKE_CASE   ,						Image.Image ):
                       UpperCamelCase       ,  UpperCamelCase					       =  image.size
                 else:
                       UpperCamelCase       ,  UpperCamelCase					       =  image.shape[1], image.shape[2]
                 if w < h:
                       UpperCamelCase					       =  int(self.size['shortest_edge'] * h / w )
                       UpperCamelCase					       =  self.size['shortest_edge']
                 elif w > h:
                       UpperCamelCase					       =  self.size['shortest_edge']
                       UpperCamelCase					       =  int(self.size['shortest_edge'] * w / h )
                 else:
                       UpperCamelCase					       =  self.size['shortest_edge']
                       UpperCamelCase					       =  self.size['shortest_edge']

           else:
                 UpperCamelCase					       =  []
                 for image in image_inputs:
                       UpperCamelCase       ,  UpperCamelCase					       =  self.get_expected_values([image] )
                       expected_values.append((expected_height, expected_width) )
                 UpperCamelCase					       =  max(_SCREAMING_SNAKE_CASE   ,						key=lambda _SCREAMING_SNAKE_CASE : item[0] )[0]
                 UpperCamelCase					       =  max(_SCREAMING_SNAKE_CASE   ,						key=lambda _SCREAMING_SNAKE_CASE : item[1] )[1]

           return expected_height, expected_width







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           return OneFormerForUniversalSegmentationOutput(
               # +1 for null class
               class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) )   ,						masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) )   ,						)





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

     '''simple docstring'''






     snake_case__					=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
     # only for test_image_processing_common.test_image_proc_to_json_string
     snake_case__					=					image_processing_class







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  OneFormerImageProcessorTester(self )







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           return self.image_processing_tester.prepare_image_processor_dict()







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,						'image_mean' ) )
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,						'image_std' ) )
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,						'do_normalize' ) )
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,						'do_resize' ) )
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,						'size' ) )
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,						'ignore_index' ) )
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,						'class_info_file' ) )
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,						'num_text' ) )
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,						'repo_path' ) )
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,						'metadata' ) )
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,						'do_reduce_labels' ) )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




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

           # Test not batched input
           UpperCamelCase					       =  image_processor(image_inputs[0]   ,						['semantic']   ,						return_tensors='pt' ).pixel_values

           UpperCamelCase       ,  UpperCamelCase					       =  self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE )

           self.assertEqual(
               encoded_images.shape   ,						(1, self.image_processing_tester.num_channels, expected_height, expected_width)   ,						)

           # Test batched
           UpperCamelCase       ,  UpperCamelCase					       =  self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE   ,						batched=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  image_processor(
               _SCREAMING_SNAKE_CASE   ,						['semantic'] * len(_SCREAMING_SNAKE_CASE )   ,						return_tensors='pt' ).pixel_values
           self.assertEqual(
               encoded_images.shape   ,						(
                   self.image_processing_tester.batch_size,
                   self.image_processing_tester.num_channels,
                   expected_height,
                   expected_width,
               )   ,						)







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




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

           # Test not batched input
           UpperCamelCase					       =  image_processor(image_inputs[0]   ,						['semantic']   ,						return_tensors='pt' ).pixel_values

           UpperCamelCase       ,  UpperCamelCase					       =  self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE )

           self.assertEqual(
               encoded_images.shape   ,						(1, self.image_processing_tester.num_channels, expected_height, expected_width)   ,						)

           # Test batched
           UpperCamelCase       ,  UpperCamelCase					       =  self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE   ,						batched=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  image_processor(
               _SCREAMING_SNAKE_CASE   ,						['semantic'] * len(_SCREAMING_SNAKE_CASE )   ,						return_tensors='pt' ).pixel_values
           self.assertEqual(
               encoded_images.shape   ,						(
                   self.image_processing_tester.batch_size,
                   self.image_processing_tester.num_channels,
                   expected_height,
                   expected_width,
               )   ,						)







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




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

           # Test not batched input
           UpperCamelCase					       =  image_processor(image_inputs[0]   ,						['semantic']   ,						return_tensors='pt' ).pixel_values

           UpperCamelCase       ,  UpperCamelCase					       =  self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE )

           self.assertEqual(
               encoded_images.shape   ,						(1, self.image_processing_tester.num_channels, expected_height, expected_width)   ,						)

           # Test batched
           UpperCamelCase       ,  UpperCamelCase					       =  self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE   ,						batched=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  image_processor(
               _SCREAMING_SNAKE_CASE   ,						['semantic'] * len(_SCREAMING_SNAKE_CASE )   ,						return_tensors='pt' ).pixel_values
           self.assertEqual(
               encoded_images.shape   ,						(
                   self.image_processing_tester.batch_size,
                   self.image_processing_tester.num_channels,
                   expected_height,
                   expected_width,
               )   ,						)







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    List[str]=False   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]=False   ,						_SCREAMING_SNAKE_CASE      :    str="np" ):






           """simple docstring"""




           UpperCamelCase					       =  self.image_processing_class(**self.image_processor_dict )
           # prepare image and target
           UpperCamelCase					       =  self.image_processing_tester.num_labels
           UpperCamelCase					       =  None
           UpperCamelCase					       =  None
           UpperCamelCase					       =  prepare_image_inputs(self.image_processing_tester   ,						equal_resolution=_SCREAMING_SNAKE_CASE )
           if with_segmentation_maps:
                 UpperCamelCase					       =  num_labels
                 if is_instance_map:
                       UpperCamelCase					       =  list(range(_SCREAMING_SNAKE_CASE ) ) * 2
                       UpperCamelCase					       =  dict(enumerate(_SCREAMING_SNAKE_CASE ) )
                 UpperCamelCase					       =  [
                     np.random.randint(0   ,						high * 2   ,						(img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
                 ]
                 if segmentation_type == "pil":
                       UpperCamelCase					       =  [Image.fromarray(_SCREAMING_SNAKE_CASE ) for annotation in annotations]

           UpperCamelCase					       =  image_processor(
               _SCREAMING_SNAKE_CASE   ,						['semantic'] * len(_SCREAMING_SNAKE_CASE )   ,						_SCREAMING_SNAKE_CASE   ,						return_tensors='pt'   ,						instance_id_to_semantic_id=_SCREAMING_SNAKE_CASE   ,						pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE   ,						)

           return inputs







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           def common(_SCREAMING_SNAKE_CASE      :    int=False   ,						_SCREAMING_SNAKE_CASE      :    List[str]=None ):
                 UpperCamelCase					       =  self.comm_get_image_processor_inputs(
                     with_segmentation_maps=_SCREAMING_SNAKE_CASE   ,						is_instance_map=_SCREAMING_SNAKE_CASE   ,						segmentation_type=_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  inputs['mask_labels']
                 UpperCamelCase					       =  inputs['class_labels']
                 UpperCamelCase					       =  inputs['pixel_values']
                 UpperCamelCase					       =  inputs['text_inputs']

                 # check the batch_size
                 for mask_label, class_label, text_input in zip(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                       self.assertEqual(mask_label.shape[0]   ,						class_label.shape[0] )
                       # this ensure padding has happened
                       self.assertEqual(mask_label.shape[1:]   ,						pixel_values.shape[2:] )
                       self.assertEqual(len(_SCREAMING_SNAKE_CASE )   ,						self.image_processing_tester.num_text )

           common()
           common(is_instance_map=_SCREAMING_SNAKE_CASE )
           common(is_instance_map=_SCREAMING_SNAKE_CASE   ,						segmentation_type='pil' )
           common(is_instance_map=_SCREAMING_SNAKE_CASE   ,						segmentation_type='pil' )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  np.zeros((20, 50) )
           UpperCamelCase					       =  1
           UpperCamelCase					       =  1
           UpperCamelCase					       =  1

           UpperCamelCase					       =  binary_mask_to_rle(_SCREAMING_SNAKE_CASE )
           self.assertEqual(len(_SCREAMING_SNAKE_CASE )   ,						4 )
           self.assertEqual(rle[0]   ,						21 )
           self.assertEqual(rle[1]   ,						45 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  self.image_processing_class(
               num_labels=self.image_processing_tester.num_classes   ,						max_seq_length=77   ,						task_seq_length=77   ,						class_info_file='ade20k_panoptic.json'   ,						num_text=self.image_processing_tester.num_text   ,						repo_path='shi-labs/oneformer_demo'   ,						)
           UpperCamelCase					       =  self.image_processing_tester.get_fake_oneformer_outputs()

           UpperCamelCase					       =  fature_extractor.post_process_semantic_segmentation(_SCREAMING_SNAKE_CASE )

           self.assertEqual(len(_SCREAMING_SNAKE_CASE )   ,						self.image_processing_tester.batch_size )
           self.assertEqual(
               segmentation[0].shape   ,						(
                   self.image_processing_tester.height,
                   self.image_processing_tester.width,
               )   ,						)

           UpperCamelCase					       =  [(1, 4) for i in range(self.image_processing_tester.batch_size )]
           UpperCamelCase					       =  fature_extractor.post_process_semantic_segmentation(_SCREAMING_SNAKE_CASE   ,						target_sizes=_SCREAMING_SNAKE_CASE )

           self.assertEqual(segmentation[0].shape   ,						target_sizes[0] )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  self.image_processing_class(
               num_labels=self.image_processing_tester.num_classes   ,						max_seq_length=77   ,						task_seq_length=77   ,						class_info_file='ade20k_panoptic.json'   ,						num_text=self.image_processing_tester.num_text   ,						repo_path='shi-labs/oneformer_demo'   ,						)
           UpperCamelCase					       =  self.image_processing_tester.get_fake_oneformer_outputs()
           UpperCamelCase					       =  image_processor.post_process_instance_segmentation(_SCREAMING_SNAKE_CASE   ,						threshold=0 )

           self.assertTrue(len(_SCREAMING_SNAKE_CASE ) == self.image_processing_tester.batch_size )
           for el in segmentation:
                 self.assertTrue('segmentation' in el )
                 self.assertTrue('segments_info' in el )
                 self.assertEqual(type(el['segments_info'] )   ,						_SCREAMING_SNAKE_CASE )
                 self.assertEqual(
                     el['segmentation'].shape   ,						(self.image_processing_tester.height, self.image_processing_tester.width) )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  self.image_processing_class(
               num_labels=self.image_processing_tester.num_classes   ,						max_seq_length=77   ,						task_seq_length=77   ,						class_info_file='ade20k_panoptic.json'   ,						num_text=self.image_processing_tester.num_text   ,						repo_path='shi-labs/oneformer_demo'   ,						)
           UpperCamelCase					       =  self.image_processing_tester.get_fake_oneformer_outputs()
           UpperCamelCase					       =  image_processor.post_process_panoptic_segmentation(_SCREAMING_SNAKE_CASE   ,						threshold=0 )

           self.assertTrue(len(_SCREAMING_SNAKE_CASE ) == self.image_processing_tester.batch_size )
           for el in segmentation:
                 self.assertTrue('segmentation' in el )
                 self.assertTrue('segments_info' in el )
                 self.assertEqual(type(el['segments_info'] )   ,						_SCREAMING_SNAKE_CASE )
                 self.assertEqual(
                     el['segmentation'].shape   ,						(self.image_processing_tester.height, self.image_processing_tester.width) )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__      :     Optional[int]       =					{
    'configuration_clap': [
        'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ClapAudioConfig',
        'ClapConfig',
        'ClapTextConfig',
    ],
    'processing_clap': ['ClapProcessor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__      :     str       =					[
							    'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ClapModel',
							    'ClapPreTrainedModel',
							    'ClapTextModel',
							    'ClapTextModelWithProjection',
							    'ClapAudioModel',
							    'ClapAudioModelWithProjection',
							]
							A__      :     List[Any]       =					['ClapFeatureExtractor']

if TYPE_CHECKING:
							from .configuration_clap import (
							    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
							    ClapAudioConfig,
							    ClapConfig,
							    ClapTextConfig,
							)
							from .processing_clap import ClapProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_clap import ClapFeatureExtractor
														from .modeling_clap import (
														    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ClapAudioModel,
														    ClapAudioModelWithProjection,
														    ClapModel,
														    ClapPreTrainedModel,
														    ClapTextModel,
														    ClapTextModelWithProjection,
														)


else:
							import sys

							A__      :     int       =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




