

"""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				(UpperCamelCase_ ,						unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE    :Any							=			KandinskyImgaImgPipeline
     _SCREAMING_SNAKE_CASE    :int							=			["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
     _SCREAMING_SNAKE_CASE    :Optional[Any]							=			[
         """prompt""",
         """negative_prompt""",
         """image_embeds""",
         """negative_image_embeds""",
         """image""",
     ]
     _SCREAMING_SNAKE_CASE    :Union[str, Any]							=			[
         """generator""",
         """height""",
         """width""",
         """strength""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     _SCREAMING_SNAKE_CASE    :List[str]							=			False

     @property
     def        _a      (    self	)	->   Any:


           """simple docstring"""


           return 32

     @property
     def        _a      (    self	)	->   Optional[Any]:


           """simple docstring"""


           return 32

     @property
     def        _a      (    self	)	->   Any:


           """simple docstring"""


           return self.time_input_dim

     @property
     def        _a      (    self	)	->   str:


           """simple docstring"""


           return self.time_input_dim * 4

     @property
     def        _a      (    self	)	->   Union[str, Any]:


           """simple docstring"""


           return 100

     @property
     def        _a      (    self	)	->   Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   str   		=						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
           return tokenizer

     @property
     def        _a      (    self	)	->   Dict:


           """simple docstring"""


           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE__ :   Optional[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						,     )

           SCREAMING_SNAKE_CASE__ :   Tuple   		=						MultilingualCLIP(UpperCamelCase_	)
           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						text_encoder.eval()

           return text_encoder

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


           """simple docstring"""


           torch.manual_seed(0	)

           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						{
               '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,
           }

           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						UNetaDConditionModel(**UpperCamelCase_	)
           return model

     @property
     def        _a      (    self	)	->   Tuple:


           """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        _a      (    self	)	->   Dict:


           """simple docstring"""


           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						VQModel(**self.dummy_movq_kwargs	)
           return model

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						self.dummy_text_encoder
           SCREAMING_SNAKE_CASE__ :   int   		=						self.dummy_tokenizer
           SCREAMING_SNAKE_CASE__ :   int   		=						self.dummy_unet
           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						self.dummy_movq

           SCREAMING_SNAKE_CASE__ :   Any   		=						{
               'num_train_timesteps': 1_000,
               'beta_schedule': 'linear',
               'beta_start': 0.00_085,
               'beta_end': 0.012,
               'clip_sample': False,
               'set_alpha_to_one': False,
               'steps_offset': 0,
               'prediction_type': 'epsilon',
               'thresholding': False,
           }

           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						DDIMScheduler(**UpperCamelCase_	)

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

           return components

     def        _a      (    self						,     _a						,     _a=0	)	->   Tuple:


           """simple docstring"""


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

           if str(UpperCamelCase_	).startswith("""mps"""	):
                 SCREAMING_SNAKE_CASE__ :   Dict   		=						torch.manual_seed(UpperCamelCase_	)
           else:
                 SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						torch.Generator(device=UpperCamelCase_	).manual_seed(UpperCamelCase_	)
           SCREAMING_SNAKE_CASE__ :   Tuple   		=						{
               '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	)	->   str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						'cpu'

           SCREAMING_SNAKE_CASE__ :   Any   		=						self.get_dummy_components()

           SCREAMING_SNAKE_CASE__ :   Any   		=						self.pipeline_class(**UpperCamelCase_	)
           SCREAMING_SNAKE_CASE__ :   Tuple   		=						pipe.to(UpperCamelCase_	)

           pipe.set_progress_bar_config(disable=UpperCamelCase_	)

           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						pipe(**self.get_dummy_inputs(UpperCamelCase_	)	)
           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						output.images

           SCREAMING_SNAKE_CASE__ :   Dict   		=						pipe(
               **self.get_dummy_inputs(UpperCamelCase_	)						,     return_dict=UpperCamelCase_						,     )[0]

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

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

           SCREAMING_SNAKE_CASE__ :   Any   		=						np.array(
               [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]	)
           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):






     '''simple docstring'''

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


           """simple docstring"""


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





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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   str   		=						load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/kandinsky/kandinsky_img2img_frog.npy"""	)

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

           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						KandinskyPriorPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-1-prior"""						,     torch_dtype=torch.floataa	)
           pipe_prior.to(UpperCamelCase_	)

           SCREAMING_SNAKE_CASE__ :   Any   		=						KandinskyImgaImgPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-1"""						,     torch_dtype=torch.floataa	)
           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						pipeline.to(UpperCamelCase_	)

           pipeline.set_progress_bar_config(disable=UpperCamelCase_	)

           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						torch.Generator(device="""cpu"""	).manual_seed(0	)
           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						pipe_prior(
               UpperCamelCase_						,     generator=UpperCamelCase_						,     num_inference_steps=5						,     negative_prompt=""""""						,     ).to_tuple()

           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						pipeline(
               UpperCamelCase_						,     image=UpperCamelCase_						,     image_embeds=UpperCamelCase_						,     negative_image_embeds=UpperCamelCase_						,     generator=UpperCamelCase_						,     num_inference_steps=100						,     height=768						,     width=768						,     strength=0.2						,     output_type="""np"""						,     )

           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						output.images[0]

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

           assert_mean_pixel_difference(UpperCamelCase_						,     UpperCamelCase_	)






"""simple docstring"""



import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase__          =	'path-to-your-trained-model'
UpperCamelCase__          =	StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')

UpperCamelCase__          =	'A photo of sks dog in a bucket'
UpperCamelCase__          =	pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('dog-bucket.png')

