"""simple docstring"""
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




@skip_mps
class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		TextToVideoSDPipeline
      a__        =		TEXT_TO_IMAGE_PARAMS
      a__        =		TEXT_TO_IMAGE_BATCH_PARAMS
      # No `output_type`.
      a__        =		frozenset(
          [
              """num_inference_steps""",
              """generator""",
              """latents""",
              """return_dict""",
              """callback""",
              """callback_steps""",
          ]				)



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Optional[int]										=	UNetaDConditionModel(
            block_out_channels=(32, 64, 64, 64)    ,			layers_per_block=2    ,			sample_size=32    ,			in_channels=4    ,			out_channels=4    ,			down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D')    ,			up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D')    ,			cross_attention_dim=32    ,			attention_head_dim=4    ,			)
        a__:     List[str]										=	DDIMScheduler(
            beta_start=0.00085    ,			beta_end=0.012    ,			beta_schedule='scaled_linear'    ,			clip_sample=lowercase    ,			set_alpha_to_one=lowercase    ,			)
        torch.manual_seed(0)
        a__:     Tuple										=	AutoencoderKL(
            block_out_channels=[32, 64]    ,			in_channels=3    ,			out_channels=3    ,			down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,			up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,			latent_channels=4    ,			sample_size=1_28    ,			)
        torch.manual_seed(0)
        a__:     Union[str, Any]										=	CLIPTextConfig(
            bos_token_id=0    ,			eos_token_id=2    ,			hidden_size=32    ,			intermediate_size=37    ,			layer_norm_eps=1e-05    ,			num_attention_heads=4    ,			num_hidden_layers=5    ,			pad_token_id=1    ,			vocab_size=10_00    ,			hidden_act='gelu'    ,			projection_dim=5_12    ,			)
        a__:     Dict										=	CLIPTextModel(lowercase)
        a__:     List[str]										=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

        a__:     int										=	{
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
        }
        return components



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=0)	->			Dict:





        '''simple docstring'''





        if str(lowercase).startswith('mps'):
          a__:     List[str]										=	torch.manual_seed(lowercase)
        else:
          a__:     Dict										=	torch.Generator(device=lowercase).manual_seed(lowercase)
        a__:     Tuple										=	{
            'prompt': 'A painting of a squirrel eating a burger',
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'pt',
        }
        return inputs



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:     int										=	self.get_dummy_components()
        a__:     Union[str, Any]										=	TextToVideoSDPipeline(**lowercase)
        a__:     Dict										=	sd_pipe.to(lowercase)
        sd_pipe.set_progress_bar_config(disable=lowercase)

        a__:     Optional[int]										=	self.get_dummy_inputs(lowercase)
        a__:     Dict										=	'np'
        a__:     Union[str, Any]										=	sd_pipe(**lowercase).frames
        a__:     Optional[int]										=	frames[0][-3:, -3:, -1]

        assert frames[0].shape == (64, 64, 3)
        a__:     Optional[Any]										=	np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowercase    ,			expected_max_diff=3e-3)



      @unittest.skipIf(
          torch_device != 'cuda' or not is_xformers_available()    ,			reason='XFormers attention is only available with CUDA and `xformers` installed'    ,			)
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowercase    ,			expected_max_diff=1e-2)



      @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.')
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        pass



      @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.')
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        pass



      @unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.')
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        pass



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        return super().test_progress_bar()







@slow
@skip_mps
class    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     List[Any]										=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy')

        a__:     Optional[Any]										=	TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b')
        a__:     int										=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
        a__:     Optional[Any]										=	pipe.to('cuda')

        a__:     str										=	'Spiderman is surfing'
        a__:     str										=	torch.Generator(device='cpu').manual_seed(0)

        a__:     str										=	pipe(lowercase    ,			generator=lowercase    ,			num_inference_steps=25    ,			output_type='pt').frames
        a__:     List[Any]										=	video_frames.cpu().numpy()

        assert np.abs(expected_video - video).mean() < 5e-2



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     Dict										=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy')

        a__:     int										=	TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b')
        a__:     Union[str, Any]										=	pipe.to('cuda')

        a__:     List[Any]										=	'Spiderman is surfing'
        a__:     Tuple										=	torch.Generator(device='cpu').manual_seed(0)

        a__:     Optional[int]										=	pipe(lowercase    ,			generator=lowercase    ,			num_inference_steps=2    ,			output_type='pt').frames
        a__:     str										=	video_frames.cpu().numpy()

        assert np.abs(expected_video - video).mean() < 5e-2



"""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, 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    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		KandinskyInpaintPipeline
      a__        =		["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
      a__        =		[
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
          """mask_image""",
      ]
      a__        =		[
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      a__        =		False



      @property
      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim * 4



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        return 1_00



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
        return tokenizer



      @property
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Dict										=	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=10_05    ,			)

        a__:     Optional[Any]										=	MultilingualCLIP(lowercase)
        a__:     int										=	text_encoder.eval()

        return text_encoder



      @property
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     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,
        }

        a__:     str										=	UNetaDConditionModel(**lowercase)
        return model



      @property
      def   lowerCamelCase_   (						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   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Any										=	VQModel(**self.dummy_movq_kwargs)
        return model



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Dict										=	self.dummy_text_encoder
        a__:     int										=	self.dummy_tokenizer
        a__:     str										=	self.dummy_unet
        a__:     Any										=	self.dummy_movq

        a__:     Tuple										=	DDIMScheduler(
            num_train_timesteps=10_00    ,			beta_schedule='linear'    ,			beta_start=0.00085    ,			beta_end=0.012    ,			clip_sample=lowercase    ,			set_alpha_to_one=lowercase    ,			steps_offset=1    ,			prediction_type='epsilon'    ,			thresholding=lowercase    ,			)

        a__:     Tuple										=	{
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=0)	->			Any:





        '''simple docstring'''





        a__:     List[Any]										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(seed + 1)).to(lowercase)
        # create init_image
        a__:     Optional[int]										=	floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	image.cpu().permute(0    ,			2    ,			3    ,			1)[0]
        a__:     Optional[int]										=	Image.fromarray(np.uinta(lowercase)).convert('RGB').resize((2_56, 2_56))
        # create mask
        a__:     Tuple										=	np.ones((64, 64)    ,			dtype=np.floataa)
        a__:     Optional[Any]										=	0

        if str(lowercase).startswith('mps'):
          a__:     str										=	torch.manual_seed(lowercase)
        else:
          a__:     Dict										=	torch.Generator(device=lowercase).manual_seed(lowercase)
        a__:     Optional[int]										=	{
            '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   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	'cpu'

        a__:     List[Any]										=	self.get_dummy_components()

        a__:     Optional[Any]										=	self.pipeline_class(**lowercase)
        a__:     str										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Optional[int]										=	pipe(**self.get_dummy_inputs(lowercase))
        a__:     List[str]										=	output.images

        a__:     int										=	pipe(
            **self.get_dummy_inputs(lowercase)    ,			return_dict=lowercase    ,			)[0]

        a__:     Optional[Any]										=	image[0, -3:, -3:, -1]
        a__:     List[Any]										=	image_from_tuple[0, -3:, -3:, -1]

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

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

        a__:     str										=	np.array(
            [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786])

        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   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





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







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



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





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

        a__:     int										=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
        a__:     Union[str, Any]										=	np.ones((7_68, 7_68)    ,			dtype=np.floataa)
        a__:     int										=	0

        a__:     Optional[int]										=	'a hat'

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

        a__:     Any										=	KandinskyInpaintPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-inpaint'    ,			torch_dtype=torch.floataa)
        a__:     Optional[Any]										=	pipeline.to(lowercase)
        pipeline.set_progress_bar_config(disable=lowercase)

        a__:     Dict										=	torch.Generator(device='cpu').manual_seed(0)
        a__							,    a__:     Optional[Any]										=	pipe_prior(
            lowercase    ,			generator=lowercase    ,			num_inference_steps=5    ,			negative_prompt=''    ,			).to_tuple()

        a__:     List[str]										=	pipeline(
            lowercase    ,			image=lowercase    ,			mask_image=lowercase    ,			image_embeds=lowercase    ,			negative_image_embeds=lowercase    ,			generator=lowercase    ,			num_inference_steps=1_00    ,			height=7_68    ,			width=7_68    ,			output_type='np'    ,			)

        a__:     str										=	output.images[0]

        assert image.shape == (7_68, 7_68, 3)

        assert_mean_pixel_difference(lowercase    ,			lowercase)


