"""simple docstring"""




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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict			,_lowerCamelCase						:       List[str]="shi-labs/oneformer_demo"	)       ->      List[str]:
  with open(hf_hub_download(_lowerCamelCase			,_lowerCamelCase			,repo_type="""dataset"""	)			,"""r"""	) as f:
    _lowerCAmelCase						:       Tuple  			=     json.load(_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     {}
  _lowerCAmelCase						:       Dict  			=     []
  _lowerCAmelCase						:       Any  			=     []
  for key, info in class_info.items():
    _lowerCAmelCase						:       Tuple  			=     info["""name"""]
    class_names.append(info["""name"""]	)
    if info["isthing"]:
      thing_ids.append(int(_lowerCamelCase	)	)
  _lowerCAmelCase						:       int  			=     thing_ids
  _lowerCAmelCase						:       List[str]  			=     class_names
  return metadata
class 						__A							( unittest.TestCase		):






     def __init__(		self				,     a__				,     a__=7				,     a__=3				,     a__=30				,     a__=400				,     a__=None				,     a__=True				,     a__=True				,     a__=[0.5, 0.5, 0.5]				,     a__=[0.5, 0.5, 0.5]				,     a__=10				,     a__=False				,     a__=255				,     a__="shi-labs/oneformer_demo"				,     a__="ade20k_panoptic.json"				,     a__=10				,     ):
       _lowerCAmelCase						:       Optional[Any]  			=     parent
       _lowerCAmelCase						:       Optional[Any]  			=     batch_size
       _lowerCAmelCase						:       str  			=     num_channels
       _lowerCAmelCase						:       str  			=     min_resolution
       _lowerCAmelCase						:       int  			=     max_resolution
       _lowerCAmelCase						:       Any  			=     do_resize
       _lowerCAmelCase						:       Optional[int]  			=     {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
       _lowerCAmelCase						:       List[str]  			=     do_normalize
       _lowerCAmelCase						:       Dict  			=     image_mean
       _lowerCAmelCase						:       Any  			=     image_std
       _lowerCAmelCase						:       Dict  			=     class_info_file
       _lowerCAmelCase						:       Tuple  			=     prepare_metadata(a__				,     a__  )
       _lowerCAmelCase						:       Optional[int]  			=     num_text
       _lowerCAmelCase						:       int  			=     repo_path

       # for the post_process_functions
       _lowerCAmelCase						:       Any  			=     2
       _lowerCAmelCase						:       int  			=     10
       _lowerCAmelCase						:       List[str]  			=     10
       _lowerCAmelCase						:       Optional[int]  			=     3
       _lowerCAmelCase						:       Any  			=     4
       _lowerCAmelCase						:       Optional[int]  			=     num_labels
       _lowerCAmelCase						:       List[Any]  			=     do_reduce_labels
       _lowerCAmelCase						:       Dict  			=     ignore_index






     def     __A  (		self  ):
       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     __A  (		self				,     a__				,     a__=False  ):
       if not batched:
         _lowerCAmelCase						:       Any  			=     image_inputs[0]
         if isinstance(a__				,     Image.Image  ):
           _lowerCAmelCase			,	_lowerCAmelCase						:       Tuple  			=     image.size
         else:
           _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     image.shape[1], image.shape[2]
         if w < h:
           _lowerCAmelCase						:       str  			=     int(self.size["""shortest_edge"""] * h / w  )
           _lowerCAmelCase						:       List[str]  			=     self.size["""shortest_edge"""]
         elif w > h:
           _lowerCAmelCase						:       Optional[int]  			=     self.size["""shortest_edge"""]
           _lowerCAmelCase						:       Union[str, Any]  			=     int(self.size["""shortest_edge"""] * w / h  )
         else:
           _lowerCAmelCase						:       Dict  			=     self.size["""shortest_edge"""]
           _lowerCAmelCase						:       List[Any]  			=     self.size["""shortest_edge"""]

       else:
         _lowerCAmelCase						:       List[Any]  			=     []
         for image in image_inputs:
           _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     self.get_expected_values([image]  )
           expected_values.append((expected_height, expected_width)  )
         _lowerCAmelCase						:       Tuple  			=     max(a__				,     key=lambda a__  : item[0]  )[0]
         _lowerCAmelCase						:       Union[str, Any]  			=     max(a__				,     key=lambda a__  : item[1]  )[1]

       return expected_height, expected_width






     def     __A  (		self  ):
       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							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Dict							=		OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
     # only for test_image_processing_common.test_image_proc_to_json_string
     _UpperCamelCase							:			List[str]							=		image_processing_class






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     OneFormerImageProcessorTester(self  )






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






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__				,     """image_mean"""  )  )
       self.assertTrue(hasattr(a__				,     """image_std"""  )  )
       self.assertTrue(hasattr(a__				,     """do_normalize"""  )  )
       self.assertTrue(hasattr(a__				,     """do_resize"""  )  )
       self.assertTrue(hasattr(a__				,     """size"""  )  )
       self.assertTrue(hasattr(a__				,     """ignore_index"""  )  )
       self.assertTrue(hasattr(a__				,     """class_info_file"""  )  )
       self.assertTrue(hasattr(a__				,     """num_text"""  )  )
       self.assertTrue(hasattr(a__				,     """repo_path"""  )  )
       self.assertTrue(hasattr(a__				,     """metadata"""  )  )
       self.assertTrue(hasattr(a__				,     """do_reduce_labels"""  )  )






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       # Initialize image_processor
       _lowerCAmelCase						:       Union[str, Any]  			=     self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       _lowerCAmelCase						:       Optional[int]  			=     prepare_image_inputs(self.image_processing_tester				,     equal_resolution=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__				,     Image.Image  )

       # Test not batched input
       _lowerCAmelCase						:       int  			=     image_processor(image_inputs[0]				,     ["""semantic"""]				,     return_tensors="""pt"""  ).pixel_values

       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     self.image_processing_tester.get_expected_values(a__  )

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

       # Test batched
       _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     self.image_processing_tester.get_expected_values(a__				,     batched=a__  )

       _lowerCAmelCase						:       Any  			=     image_processor(
           a__				,     ["""semantic"""] * len(a__  )				,     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     __A  (		self  ):
       # Initialize image_processor
       _lowerCAmelCase						:       Union[str, Any]  			=     self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       _lowerCAmelCase						:       Optional[int]  			=     prepare_image_inputs(self.image_processing_tester				,     equal_resolution=a__				,     numpify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__				,     np.ndarray  )

       # Test not batched input
       _lowerCAmelCase						:       Tuple  			=     image_processor(image_inputs[0]				,     ["""semantic"""]				,     return_tensors="""pt"""  ).pixel_values

       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[Any]  			=     self.image_processing_tester.get_expected_values(a__  )

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

       # Test batched
       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[Any]  			=     self.image_processing_tester.get_expected_values(a__				,     batched=a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     image_processor(
           a__				,     ["""semantic"""] * len(a__  )				,     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     __A  (		self  ):
       # Initialize image_processor
       _lowerCAmelCase						:       int  			=     self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       _lowerCAmelCase						:       int  			=     prepare_image_inputs(self.image_processing_tester				,     equal_resolution=a__				,     torchify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__				,     torch.Tensor  )

       # Test not batched input
       _lowerCAmelCase						:       Union[str, Any]  			=     image_processor(image_inputs[0]				,     ["""semantic"""]				,     return_tensors="""pt"""  ).pixel_values

       _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     self.image_processing_tester.get_expected_values(a__  )

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

       # Test batched
       _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     self.image_processing_tester.get_expected_values(a__				,     batched=a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     image_processor(
           a__				,     ["""semantic"""] * len(a__  )				,     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     __A  (		self				,     a__=False				,     a__=False				,     a__="np"  ):
       _lowerCAmelCase						:       Optional[Any]  			=     self.image_processing_class(**self.image_processor_dict  )
       # prepare image and target
       _lowerCAmelCase						:       Any  			=     self.image_processing_tester.num_labels
       _lowerCAmelCase						:       List[str]  			=     None
       _lowerCAmelCase						:       List[str]  			=     None
       _lowerCAmelCase						:       List[str]  			=     prepare_image_inputs(self.image_processing_tester				,     equal_resolution=a__  )
       if with_segmentation_maps:
         _lowerCAmelCase						:       Tuple  			=     num_labels
         if is_instance_map:
           _lowerCAmelCase						:       Optional[int]  			=     list(range(a__  )  ) * 2
           _lowerCAmelCase						:       Optional[int]  			=     dict(enumerate(a__  )  )
         _lowerCAmelCase						:       int  			=     [
             np.random.randint(0				,     high * 2				,     (img.size[1], img.size[0])  ).astype(np.uinta  ) for img in image_inputs
         ]
         if segmentation_type == "pil":
           _lowerCAmelCase						:       Optional[int]  			=     [Image.fromarray(a__  ) for annotation in annotations]

       _lowerCAmelCase						:       Union[str, Any]  			=     image_processor(
           a__				,     ["""semantic"""] * len(a__  )				,     a__				,     return_tensors="""pt"""				,     instance_id_to_semantic_id=a__				,     pad_and_return_pixel_mask=a__				,     )

       return inputs






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       def common(a__=False				,     a__=None  ):
         _lowerCAmelCase						:       str  			=     self.comm_get_image_processor_inputs(
             with_segmentation_maps=a__				,     is_instance_map=a__				,     segmentation_type=a__  )

         _lowerCAmelCase						:       List[Any]  			=     inputs["""mask_labels"""]
         _lowerCAmelCase						:       Any  			=     inputs["""class_labels"""]
         _lowerCAmelCase						:       Tuple  			=     inputs["""pixel_values"""]
         _lowerCAmelCase						:       Any  			=     inputs["""text_inputs"""]

         # check the batch_size
         for mask_label, class_label, text_input in zip(a__				,     a__				,     a__  ):
           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(a__  )				,     self.image_processing_tester.num_text  )

       common()
       common(is_instance_map=a__  )
       common(is_instance_map=a__				,     segmentation_type="""pil"""  )
       common(is_instance_map=a__				,     segmentation_type="""pil"""  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     np.zeros((20, 50)  )
       _lowerCAmelCase						:       Any  			=     1
       _lowerCAmelCase						:       Dict  			=     1
       _lowerCAmelCase						:       Union[str, Any]  			=     1

       _lowerCAmelCase						:       Optional[int]  			=     binary_mask_to_rle(a__  )
       self.assertEqual(len(a__  )				,     4  )
       self.assertEqual(rle[0]				,     21  )
       self.assertEqual(rle[1]				,     45  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     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"""				,     )
       _lowerCAmelCase						:       Any  			=     self.image_processing_tester.get_fake_oneformer_outputs()

       _lowerCAmelCase						:       Tuple  			=     fature_extractor.post_process_semantic_segmentation(a__  )

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

       _lowerCAmelCase						:       Tuple  			=     [(1, 4) for i in range(self.image_processing_tester.batch_size  )]
       _lowerCAmelCase						:       Union[str, Any]  			=     fature_extractor.post_process_semantic_segmentation(a__				,     target_sizes=a__  )

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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     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"""				,     )
       _lowerCAmelCase						:       List[Any]  			=     self.image_processing_tester.get_fake_oneformer_outputs()
       _lowerCAmelCase						:       Union[str, Any]  			=     image_processor.post_process_instance_segmentation(a__				,     threshold=0  )

       self.assertTrue(len(a__  ) == 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"""]  )				,     a__  )
         self.assertEqual(
             el["""segmentation"""].shape				,     (self.image_processing_tester.height, self.image_processing_tester.width)  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     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"""				,     )
       _lowerCAmelCase						:       Dict  			=     self.image_processing_tester.get_fake_oneformer_outputs()
       _lowerCAmelCase						:       Any  			=     image_processor.post_process_panoptic_segmentation(a__				,     threshold=0  )

       self.assertTrue(len(a__  ) == 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"""]  )				,     a__  )
         self.assertEqual(
             el["""segmentation"""].shape				,     (self.image_processing_tester.height, self.image_processing_tester.width)  )





"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[Any]							=		KandinskyImgaImgPipeline
     _UpperCamelCase							:			Optional[Any]							=		["prompt", "image_embeds", "negative_image_embeds", "image"]
     _UpperCamelCase							:			List[Any]							=		[
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
     ]
     _UpperCamelCase							:			Dict							=		[
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     _UpperCamelCase							:			Union[str, Any]							=		False






     @property
     def     __A  (		self  ):
       return 32






     @property
     def     __A  (		self  ):
       return 32






     @property
     def     __A  (		self  ):
       return self.time_input_dim






     @property
     def     __A  (		self  ):
       return self.time_input_dim * 4






     @property
     def     __A  (		self  ):
       return 100






     @property
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""  )
       return tokenizer






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       List[Any]  			=     MCLIPConfig(
           numDims=self.cross_attention_dim				,     transformerDimensions=self.text_embedder_hidden_size				,     hidden_size=self.text_embedder_hidden_size				,     intermediate_size=37				,     num_attention_heads=4				,     num_hidden_layers=5				,     vocab_size=1005				,     )

       _lowerCAmelCase						:       int  			=     MultilingualCLIP(a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     text_encoder.eval()

       return text_encoder






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )

       _lowerCAmelCase						:       str  			=     {
           """in_channels""": 4,
           # Out channels is double in channels because predicts mean and variance
           """out_channels""": 8,
           """addition_embed_type""": """text_image""",
           """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
           """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
           """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
           """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
           """layers_per_block""": 1,
           """encoder_hid_dim""": self.text_embedder_hidden_size,
           """encoder_hid_dim_type""": """text_image_proj""",
           """cross_attention_dim""": self.cross_attention_dim,
           """attention_head_dim""": 4,
           """resnet_time_scale_shift""": """scale_shift""",
           """class_embed_type""": None,
       }

       _lowerCAmelCase						:       Optional[Any]  			=     UNetaDConditionModel(**a__  )
       return model






     @property
     def     __A  (		self  ):
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       str  			=     VQModel(**self.dummy_movq_kwargs  )
       return model






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.dummy_text_encoder
       _lowerCAmelCase						:       List[Any]  			=     self.dummy_tokenizer
       _lowerCAmelCase						:       int  			=     self.dummy_unet
       _lowerCAmelCase						:       Dict  			=     self.dummy_movq

       _lowerCAmelCase						:       Tuple  			=     {
           """num_train_timesteps""": 1000,
           """beta_schedule""": """linear""",
           """beta_start""": 0.0_0_0_8_5,
           """beta_end""": 0.0_1_2,
           """clip_sample""": False,
           """set_alpha_to_one""": False,
           """steps_offset""": 0,
           """prediction_type""": """epsilon""",
           """thresholding""": False,
       }

       _lowerCAmelCase						:       Optional[Any]  			=     DDIMScheduler(**a__  )

       _lowerCAmelCase						:       List[Any]  			=     {
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """unet""": unet,
           """scheduler""": scheduler,
           """movq""": movq,
       }

       return components






     def     __A  (		self				,     a__				,     a__=0  ):
       _lowerCAmelCase						:       Optional[int]  			=     floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(a__  )  ).to(a__  )
       _lowerCAmelCase						:       int  			=     floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(seed + 1  )  ).to(a__  )
       # create init_image
       _lowerCAmelCase						:       List[Any]  			=     floats_tensor((1, 3, 64, 64)				,     rng=random.Random(a__  )  ).to(a__  )
       _lowerCAmelCase						:       int  			=     image.cpu().permute(0				,     2				,     3				,     1  )[0]
       _lowerCAmelCase						:       List[Any]  			=     Image.fromarray(np.uinta(a__  )  ).convert("""RGB"""  ).resize((256, 256)  )

       if str(a__  ).startswith("""mps"""  ):
         _lowerCAmelCase						:       List[Any]  			=     torch.manual_seed(a__  )
       else:
         _lowerCAmelCase						:       Tuple  			=     torch.Generator(device=a__  ).manual_seed(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     {
           """prompt""": """horse""",
           """image""": init_image,
           """image_embeds""": image_embeds,
           """negative_image_embeds""": negative_image_embeds,
           """generator""": generator,
           """height""": 64,
           """width""": 64,
           """num_inference_steps""": 10,
           """guidance_scale""": 7.0,
           """strength""": 0.2,
           """output_type""": """np""",
       }
       return inputs






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     """cpu"""

       _lowerCAmelCase						:       int  			=     self.get_dummy_components()

       _lowerCAmelCase						:       int  			=     self.pipeline_class(**a__  )
       _lowerCAmelCase						:       Optional[int]  			=     pipe.to(a__  )

       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Tuple  			=     pipe(**self.get_dummy_inputs(a__  )  )
       _lowerCAmelCase						:       List[Any]  			=     output.images

       _lowerCAmelCase						:       Tuple  			=     pipe(
           **self.get_dummy_inputs(a__  )				,     return_dict=a__				,     )[0]

       _lowerCAmelCase						:       Dict  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       List[Any]  			=     image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       _lowerCAmelCase						:       str  			=     np.array(
           [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]  )
       assert (
           np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







@slow
@require_torch_gpu
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinsky/kandinsky_img2img_frog.npy"""  )

       _lowerCAmelCase						:       List[str]  			=     load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""  )
       _lowerCAmelCase						:       Union[str, Any]  			=     """A red cartoon frog, 4k"""

       _lowerCAmelCase						:       int  			=     KandinskyPriorPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1-prior"""				,     torch_dtype=torch.floataa  )
       pipe_prior.to(a__  )

       _lowerCAmelCase						:       Tuple  			=     KandinskyImgaImgPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1"""				,     torch_dtype=torch.floataa  )
       _lowerCAmelCase						:       Any  			=     pipeline.to(a__  )

       pipeline.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Any  			=     torch.Generator(device="""cpu"""  ).manual_seed(0  )
       _lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     pipe_prior(
           a__				,     generator=a__				,     num_inference_steps=5				,     negative_prompt=""""""				,     ).to_tuple()

       _lowerCAmelCase						:       Union[str, Any]  			=     pipeline(
           a__				,     image=a__				,     image_embeds=a__				,     negative_image_embeds=a__				,     generator=a__				,     num_inference_steps=100				,     height=768				,     width=768				,     strength=0.2				,     output_type="""np"""				,     )

       _lowerCAmelCase						:       Dict  			=     output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(a__				,     a__  )




