




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     __a(	_a							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = KandinskyInpaintPipeline
    lowerCAmelCase         = ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
    lowerCAmelCase         = [
        '''prompt''',
        '''negative_prompt''',
        '''image_embeds''',
        '''negative_image_embeds''',
        '''image''',
        '''mask_image''',
    ]
    lowerCAmelCase         = [
        '''generator''',
        '''height''',
        '''width''',
        '''latents''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''num_inference_steps''',
        '''return_dict''',
        '''guidance_scale''',
        '''num_images_per_prompt''',
        '''output_type''',
        '''return_dict''',
    ]
    lowerCAmelCase         = False





    @property
    def 				a__ ( self    )					->						Tuple:
         return 32





    @property
    def 				a__ ( self    )					->						Union[str, Any]:
         return 32





    @property
    def 				a__ ( self    )					->						List[Any]:
         return self.time_input_dim





    @property
    def 				a__ ( self    )					->						List[Any]:
         return self.time_input_dim * 4





    @property
    def 				a__ ( self    )					->						Union[str, Any]:
         return 100





    @property
    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_     :				Dict  =     XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''    )
         return tokenizer





    @property
    def 				a__ ( self    )					->						Optional[Any]:
         torch.manual_seed(0    )
         UpperCAmelCase_     :				int  =     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=1_005  ,)

         UpperCAmelCase_     :				Tuple  =     MultilingualCLIP(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[str]  =     text_encoder.eval()

         return text_encoder





    @property
    def 				a__ ( self    )					->						List[str]:
         torch.manual_seed(0    )

         UpperCAmelCase_     :				Union[str, Any]  =     {
             '''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,
         }

         UpperCAmelCase_     :				Any  =     UNetaDConditionModel(**_SCREAMING_SNAKE_CASE    )
         return model





    @property
    def 				a__ ( self    )					->						str:
         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    )					->						str:
         torch.manual_seed(0    )
         UpperCAmelCase_     :				List[str]  =     VQModel(**self.dummy_movq_kwargs    )
         return model





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				str  =     self.dummy_text_encoder
         UpperCAmelCase_     :				Optional[int]  =     self.dummy_tokenizer
         UpperCAmelCase_     :				Any  =     self.dummy_unet
         UpperCAmelCase_     :				List[Any]  =     self.dummy_movq

         UpperCAmelCase_     :				Any  =     DDIMScheduler(
             num_train_timesteps=1_000  ,beta_schedule='''linear'''  ,beta_start=0.0_00_85  ,beta_end=0.0_12  ,clip_sample=_SCREAMING_SNAKE_CASE  ,set_alpha_to_one=_SCREAMING_SNAKE_CASE  ,steps_offset=1  ,prediction_type='''epsilon'''  ,thresholding=_SCREAMING_SNAKE_CASE  ,)

         UpperCAmelCase_     :				int  =     {
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=0    )					->						Optional[Any]:
         UpperCAmelCase_     :				List[Any]  =     floats_tensor((1, self.cross_attention_dim)  ,rng=random.Random(_SCREAMING_SNAKE_CASE    )    ).to(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[str]  =     floats_tensor((1, self.cross_attention_dim)  ,rng=random.Random(seed + 1    )    ).to(_SCREAMING_SNAKE_CASE    )
         # create init_image
         UpperCAmelCase_     :				Union[str, Any]  =     floats_tensor((1, 3, 64, 64)  ,rng=random.Random(_SCREAMING_SNAKE_CASE    )    ).to(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[str]  =     image.cpu().permute(0  ,2  ,3  ,1    )[0]
         UpperCAmelCase_     :				Dict  =     Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE    )    ).convert('''RGB'''    ).resize((256, 256)    )
         # create mask
         UpperCAmelCase_     :				List[str]  =     np.ones((64, 64)  ,dtype=np.floataa    )
         UpperCAmelCase_     :				Union[str, Any]  =     0

         if str(_SCREAMING_SNAKE_CASE    ).startswith('''mps'''    ):
              UpperCAmelCase_     :				Optional[int]  =     torch.manual_seed(_SCREAMING_SNAKE_CASE    )
         else:
              UpperCAmelCase_     :				Union[str, Any]  =     torch.Generator(device=_SCREAMING_SNAKE_CASE    ).manual_seed(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Tuple  =     {
             '''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 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				List[str]  =     '''cpu'''

         UpperCAmelCase_     :				Any  =     self.get_dummy_components()

         UpperCAmelCase_     :				Optional[Any]  =     self.pipeline_class(**_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Any  =     pipe.to(_SCREAMING_SNAKE_CASE    )

         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Tuple  =     pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				Union[str, Any]  =     output.images

         UpperCAmelCase_     :				Dict  =     pipe(
             **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )  ,return_dict=_SCREAMING_SNAKE_CASE  ,)[0]

         UpperCAmelCase_     :				Optional[int]  =     image[0, -3:, -3:, -1]
         UpperCAmelCase_     :				Tuple  =     image_from_tuple[0, -3:, -3:, -1]

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

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

         UpperCAmelCase_     :				Union[str, Any]  =     np.array(
             [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]    )

         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 				a__ ( self    )					->						Any:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3    )


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






    """simple docstring"""





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





    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				List[str]  =     load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''    )

         UpperCAmelCase_     :				Union[str, Any]  =     load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''    )
         UpperCAmelCase_     :				Optional[Any]  =     np.ones((768, 768)  ,dtype=np.floataa    )
         UpperCAmelCase_     :				List[Any]  =     0

         UpperCAmelCase_     :				List[Any]  =     '''a hat'''

         UpperCAmelCase_     :				Tuple  =     KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''  ,torch_dtype=torch.floataa    )
         pipe_prior.to(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[str]  =     KandinskyInpaintPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-inpaint'''  ,torch_dtype=torch.floataa    )
         UpperCAmelCase_     :				Tuple  =     pipeline.to(_SCREAMING_SNAKE_CASE    )
         pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Union[str, Any]  =     torch.Generator(device='''cpu'''    ).manual_seed(0    )
         UpperCAmelCase_,       UpperCAmelCase_     :				Dict  =     pipe_prior(
             _SCREAMING_SNAKE_CASE  ,generator=_SCREAMING_SNAKE_CASE  ,num_inference_steps=5  ,negative_prompt=''''''  ,).to_tuple()

         UpperCAmelCase_     :				Tuple  =     pipeline(
             _SCREAMING_SNAKE_CASE  ,image=_SCREAMING_SNAKE_CASE  ,mask_image=_SCREAMING_SNAKE_CASE  ,image_embeds=_SCREAMING_SNAKE_CASE  ,negative_image_embeds=_SCREAMING_SNAKE_CASE  ,generator=_SCREAMING_SNAKE_CASE  ,num_inference_steps=100  ,height=768  ,width=768  ,output_type='''np'''  ,)

         UpperCAmelCase_     :				Optional[Any]  =     output.images[0]

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

         assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
     import torch

     from transformers import AutoModelForImageClassification

if is_vision_available():
     from transformers import AutoImageProcessor

@require_torch
@require_vision
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    @slow
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         model.to(_SCREAMING_SNAKE_CASE    )

         from datasets import load_dataset

         UpperCAmelCase_     :				Optional[int]  =     load_dataset('''nielsr/rvlcdip-demo'''    )

         UpperCAmelCase_     :				Optional[Any]  =     dataset['''train'''][0]['''image'''].convert('''RGB'''    )

         UpperCAmelCase_     :				str  =     image_processor(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).to(_SCREAMING_SNAKE_CASE    )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_     :				Optional[int]  =     model(**_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				List[Any]  =     outputs.logits

         UpperCAmelCase_     :				Tuple  =     torch.Size((1, 16)    )
         self.assertEqual(logits.shape  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     torch.tensor(
             [-0.41_58, -0.40_92, -0.43_47]  ,device=_SCREAMING_SNAKE_CASE  ,dtype=torch.float  ,)
         self.assertTrue(torch.allclose(logits[0, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-4    )    )