






import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Optional[Any]					=KandinskyInpaintPipeline
       __lowerCAmelCase    :		List[str]					=['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
       __lowerCAmelCase    :		str					=[
           '''prompt''',
           '''negative_prompt''',
           '''image_embeds''',
           '''negative_image_embeds''',
           '''image''',
           '''mask_image''',
       ]
       __lowerCAmelCase    :		Dict					=[
           '''generator''',
           '''height''',
           '''width''',
           '''latents''',
           '''guidance_scale''',
           '''negative_prompt''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       __lowerCAmelCase    :		Union[str, Any]					=False





       @property
       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          return 32





       @property
       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          return 32





       @property
       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return self.time_input_dim





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



          """simple docstring"""

          return self.time_input_dim * 4





       @property
       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return 100





       @property
       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
          return tokenizer





       @property
       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          torch.manual_seed(0)
          _lowercase              =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,      )

          _lowercase              =MultilingualCLIP(snake_case)
          _lowercase              =text_encoder.eval()

          return text_encoder





       @property
       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          torch.manual_seed(0)

          _lowercase              ={
              'in_channels': 9,
              # 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,
          }

          _lowercase              =UNetaDConditionModel(**snake_case)
          return model





       @property
       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          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 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          torch.manual_seed(0)
          _lowercase              =VQModel(**self.dummy_movq_kwargs)
          return model





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =self.dummy_text_encoder
          _lowercase              =self.dummy_tokenizer
          _lowercase              =self.dummy_unet
          _lowercase              =self.dummy_movq

          _lowercase              =DDIMScheduler(
              num_train_timesteps=1000,      beta_schedule='linear',      beta_start=0.0_0_0_8_5,      beta_end=0.0_1_2,      clip_sample=snake_case,      set_alpha_to_one=snake_case,      steps_offset=1,      prediction_type='epsilon',      thresholding=snake_case,      )

          _lowercase              ={
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }

          return components





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:Optional[Any],      snake_case	:List[Any]=0):



          """simple docstring"""

          _lowercase              =floats_tensor((1, self.cross_attention_dim),      rng=random.Random(snake_case)).to(snake_case)
          _lowercase              =floats_tensor((1, self.cross_attention_dim),      rng=random.Random(seed + 1)).to(snake_case)
          # create init_image
          _lowercase              =floats_tensor((1, 3, 64, 64),      rng=random.Random(snake_case)).to(snake_case)
          _lowercase              =image.cpu().permute(0,      2,      3,      1)[0]
          _lowercase              =Image.fromarray(np.uinta(snake_case)).convert('RGB').resize((256, 256))
          # create mask
          _lowercase              =np.ones((64, 64),      dtype=np.floataa)
          _lowercase              =0

          if str(snake_case).startswith('mps'):
             _lowercase              =torch.manual_seed(snake_case)
          else:
             _lowercase              =torch.Generator(device=snake_case).manual_seed(snake_case)
          _lowercase              ={
              'prompt': 'horse',
              'image': init_image,
              'mask_image': mask,
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'generator': generator,
              'height': 64,
              'width': 64,
              'num_inference_steps': 2,
              'guidance_scale': 4.0,
              'output_type': 'np',
          }
          return inputs





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              ='cpu'

          _lowercase              =self.get_dummy_components()

          _lowercase              =self.pipeline_class(**snake_case)
          _lowercase              =pipe.to(snake_case)

          pipe.set_progress_bar_config(disable=snake_case)

          _lowercase              =pipe(**self.get_dummy_inputs(snake_case))
          _lowercase              =output.images

          _lowercase              =pipe(
              **self.get_dummy_inputs(snake_case),      return_dict=snake_case,      )[0]

          _lowercase              =image[0, -3:, -3:, -1]
          _lowercase              =image_from_tuple[0, -3:, -3:, -1]

          print(f'''image.shape {image.shape}''')

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

          _lowercase              =np.array(
              [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6])

          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()}'''





       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          super().test_inference_batch_single_identical(expected_max_diff=3e-3)






@slow
@require_torch_gpu
class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()





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



          """simple docstring"""

          _lowercase              =load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')

          _lowercase              =load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
          _lowercase              =np.ones((768, 768),      dtype=np.floataa)
          _lowercase              =0

          _lowercase              ='a hat'

          _lowercase              =KandinskyPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-prior',      torch_dtype=torch.floataa)
          pipe_prior.to(snake_case)

          _lowercase              =KandinskyInpaintPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-inpaint',      torch_dtype=torch.floataa)
          _lowercase              =pipeline.to(snake_case)
          pipeline.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.Generator(device='cpu').manual_seed(0)
          _lowercase       ,       _lowercase              =pipe_prior(
              snake_case,      generator=snake_case,      num_inference_steps=5,      negative_prompt='',      ).to_tuple()

          _lowercase              =pipeline(
              snake_case,      image=snake_case,      mask_image=snake_case,      image_embeds=snake_case,      negative_image_embeds=snake_case,      generator=snake_case,      num_inference_steps=100,      height=768,      width=768,      output_type='np',      )

          _lowercase              =output.images[0]

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

          assert_mean_pixel_difference(snake_case,      snake_case)










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:
    _SCREAMING_SNAKE_CASE   =  False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def __init__(				self	:str,      snake_case	:int,      snake_case	:Dict=7,      snake_case	:List[Any]=3,      snake_case	:int=18,      snake_case	:List[str]=30,      snake_case	:str=400,      snake_case	:int=None,      snake_case	:List[Any]=True,      snake_case	:Optional[int]=True,      snake_case	:Union[str, Any]=None,      ):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              ='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
          _lowercase              =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 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Tuple					=PixaStructImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =PixaStructImageProcessingTester(self)





       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self.image_processor_tester.prepare_dummy_image()

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

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





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =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
          _lowercase              =(
              (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
             _lowercase              =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
             _lowercase              =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 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =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
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          _lowercase              =True

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

             _lowercase              ='Hello'

             _lowercase              =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
             _lowercase              =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 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _lowercase              =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)

          _lowercase              =(
              (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
             _lowercase              =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
             _lowercase              =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 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _lowercase              =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
          _lowercase              =(
              (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
             _lowercase              =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
             _lowercase              =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 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Optional[Any]					=PixaStructImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

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





       @property
       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =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
          _lowercase              =(
              (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
             _lowercase              =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
             _lowercase              =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),      )


