


"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class   __a      (   __UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Any       					=			UnCLIPImageVariationPipeline
 __lowercase			:							Optional[int]       					=			IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
 __lowercase			:							Tuple       					=			IMAGE_VARIATION_BATCH_PARAMS

 __lowercase			:							int       					=			[
     'generator',
     'return_dict',
     'decoder_num_inference_steps',
     'super_res_num_inference_steps',
 ]
 __lowercase			:							Dict       					=			False
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        return 32
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        return 32
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        return self.time_input_dim
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        return self.time_input_dim * 4
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        return 100
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Any        =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
        return tokenizer
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        torch.manual_seed(0			)
        lowercase__:       Optional[int]        =       CLIPTextConfig(
            bos_token_id=0    ,			eos_token_id=2    ,			hidden_size=self.text_embedder_hidden_size    ,			projection_dim=self.text_embedder_hidden_size    ,			intermediate_size=37    ,			layer_norm_eps=1E-05    ,			num_attention_heads=4    ,			num_hidden_layers=5    ,			pad_token_id=1    ,			vocab_size=1_000    ,			)
        return CLIPTextModelWithProjection(lowerCamelCase_			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        torch.manual_seed(0			)
        lowercase__:       Optional[int]        =       CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size    ,			projection_dim=self.text_embedder_hidden_size    ,			num_hidden_layers=5    ,			num_attention_heads=4    ,			image_size=32    ,			intermediate_size=37    ,			patch_size=1    ,			)
        return CLIPVisionModelWithProjection(lowerCamelCase_			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        torch.manual_seed(0			)

        lowercase__:       Optional[Any]        =       {
            'clip_embeddings_dim': self.text_embedder_hidden_size,
            'time_embed_dim': self.time_embed_dim,
            'cross_attention_dim': self.cross_attention_dim,
        }

        lowercase__:       List[Any]        =       UnCLIPTextProjModel(**lowerCamelCase_			)
        return model
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        torch.manual_seed(0			)

        lowercase__:       List[Any]        =       {
            'sample_size': 32,
            # RGB in channels
            'in_channels': 3,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 6,
            '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,
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': 'identity',
        }

        lowercase__:       Optional[Any]        =       UNetaDConditionModel(**lowerCamelCase_			)
        return model
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        return {
            "sample_size": 64,
            "layers_per_block": 1,
            "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
            "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "in_channels": 6,
            "out_channels": 3,
        }
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        torch.manual_seed(0			)

        lowercase__:       List[Any]        =       UNetaDModel(**self.dummy_super_res_kwargs			)
        return model
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        # seeded differently to get different unet than `self.dummy_super_res_first`
        torch.manual_seed(1			)

        lowercase__:       Optional[int]        =       UNetaDModel(**self.dummy_super_res_kwargs			)
        return model
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       str        =       self.dummy_decoder
        lowercase__:       List[Any]        =       self.dummy_text_proj
        lowercase__:       int        =       self.dummy_text_encoder
        lowercase__:       Any        =       self.dummy_tokenizer
        lowercase__:       Optional[Any]        =       self.dummy_super_res_first
        lowercase__:       List[Any]        =       self.dummy_super_res_last

        lowercase__:       List[str]        =       UnCLIPScheduler(
            variance_type='learned_range'    ,			prediction_type='epsilon'    ,			num_train_timesteps=1_000    ,			)

        lowercase__:       Optional[Any]        =       UnCLIPScheduler(
            variance_type='fixed_small_log'    ,			prediction_type='epsilon'    ,			num_train_timesteps=1_000    ,			)

        lowercase__:       List[str]        =       CLIPImageProcessor(crop_size=32    ,			size=32			)

        lowercase__:       Optional[Any]        =       self.dummy_image_encoder

        return {
            "decoder": decoder,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_proj": text_proj,
            "feature_extractor": feature_extractor,
            "image_encoder": image_encoder,
            "super_res_first": super_res_first,
            "super_res_last": super_res_last,
            "decoder_scheduler": decoder_scheduler,
            "super_res_scheduler": super_res_scheduler,
        }
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=True			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       floats_tensor((1, 3, 32, 32)    ,			rng=random.Random(lowerCamelCase_			)			).to(lowerCamelCase_			)
        if str(lowerCamelCase_			).startswith('mps'			):
               lowercase__:       Union[str, Any]        =       torch.manual_seed(lowerCamelCase_			)
        else:
               lowercase__:       Tuple        =       torch.Generator(device=lowerCamelCase_			).manual_seed(lowerCamelCase_			)

        if pil_image:
               lowercase__:       str        =       input_image * 0.5 + 0.5
               lowercase__:       Optional[Any]        =       input_image.clamp(0    ,			1			)
               lowercase__:       Optional[int]        =       input_image.cpu().permute(0    ,			2    ,			3    ,			1			).float().numpy()
               lowercase__:       int        =       DiffusionPipeline.numpy_to_pil(lowerCamelCase_			)[0]

        return {
            "image": input_image,
            "generator": generator,
            "decoder_num_inference_steps": 2,
            "super_res_num_inference_steps": 2,
            "output_type": "np",
        }
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       'cpu'

        lowercase__:       Dict        =       self.get_dummy_components()

        lowercase__:       Any        =       self.pipeline_class(**lowerCamelCase_			)
        lowercase__:       str        =       pipe.to(lowerCamelCase_			)

        pipe.set_progress_bar_config(disable=lowerCamelCase_			)

        lowercase__:       Any        =       self.get_dummy_inputs(lowerCamelCase_    ,			pil_image=lowerCamelCase_			)

        lowercase__:       Dict        =       pipe(**lowerCamelCase_			)
        lowercase__:       str        =       output.images

        lowercase__:       Dict        =       self.get_dummy_inputs(lowerCamelCase_    ,			pil_image=lowerCamelCase_			)

        lowercase__:       Tuple        =       pipe(
            **lowerCamelCase_    ,			return_dict=lowerCamelCase_    ,			)[0]

        lowercase__:       str        =       image[0, -3:, -3:, -1]
        lowercase__:       str        =       image_from_tuple[0, -3:, -3:, -1]

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

        lowercase__:       Any        =       np.array(
            [
                0.9_9_9_7,
                0.0_0_0_2,
                0.9_9_9_7,
                0.9_9_9_7,
                0.9_9_6_9,
                0.0_0_2_3,
                0.9_9_9_7,
                0.9_9_6_9,
                0.9_9_7_0,
            ]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       'cpu'

        lowercase__:       str        =       self.get_dummy_components()

        lowercase__:       Union[str, Any]        =       self.pipeline_class(**lowerCamelCase_			)
        lowercase__:       List[str]        =       pipe.to(lowerCamelCase_			)

        pipe.set_progress_bar_config(disable=lowerCamelCase_			)

        lowercase__:       List[Any]        =       self.get_dummy_inputs(lowerCamelCase_    ,			pil_image=lowerCamelCase_			)

        lowercase__:       Optional[int]        =       pipe(**lowerCamelCase_			)
        lowercase__:       Union[str, Any]        =       output.images

        lowercase__:       Dict        =       self.get_dummy_inputs(lowerCamelCase_    ,			pil_image=lowerCamelCase_			)

        lowercase__:       str        =       pipe(
            **lowerCamelCase_    ,			return_dict=lowerCamelCase_    ,			)[0]

        lowercase__:       List[str]        =       image[0, -3:, -3:, -1]
        lowercase__:       Any        =       image_from_tuple[0, -3:, -3:, -1]

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

        lowercase__:       Dict        =       np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       'cpu'

        lowercase__:       str        =       self.get_dummy_components()

        lowercase__:       Optional[Any]        =       self.pipeline_class(**lowerCamelCase_			)
        lowercase__:       Optional[int]        =       pipe.to(lowerCamelCase_			)

        pipe.set_progress_bar_config(disable=lowerCamelCase_			)

        lowercase__:       str        =       self.get_dummy_inputs(lowerCamelCase_    ,			pil_image=lowerCamelCase_			)
        lowercase__:       List[Any]        =       [
            pipeline_inputs['image'],
            pipeline_inputs['image'],
        ]

        lowercase__:       Dict        =       pipe(**lowerCamelCase_			)
        lowercase__:       int        =       output.images

        lowercase__:       Optional[Any]        =       self.get_dummy_inputs(lowerCamelCase_    ,			pil_image=lowerCamelCase_			)
        lowercase__:       Tuple        =       [
            tuple_pipeline_inputs['image'],
            tuple_pipeline_inputs['image'],
        ]

        lowercase__:       Optional[Any]        =       pipe(
            **lowerCamelCase_    ,			return_dict=lowerCamelCase_    ,			)[0]

        lowercase__:       List[Any]        =       image[0, -3:, -3:, -1]
        lowercase__:       Union[str, Any]        =       image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (2, 64, 64, 3)

        lowercase__:       str        =       np.array(
            [
                0.9_9_9_7,
                0.9_9_8_9,
                0.0_0_0_8,
                0.0_0_2_1,
                0.9_9_6_0,
                0.0_0_1_8,
                0.0_0_1_4,
                0.0_0_0_2,
                0.9_9_3_3,
            ]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       torch.device('cpu'			)





        class   __a      :
         __lowercase			:							Any       					=			1





        lowercase__:       Dict        =       self.get_dummy_components()

        lowercase__:       Union[str, Any]        =       self.pipeline_class(**lowerCamelCase_			)
        lowercase__:       List[str]        =       pipe.to(lowerCamelCase_			)

        pipe.set_progress_bar_config(disable=lowerCamelCase_			)

        lowercase__:       List[str]        =       torch.Generator(device=lowerCamelCase_			).manual_seed(0			)
        lowercase__:       Dict        =       pipe.decoder.dtype
        lowercase__:       int        =       1

        lowercase__:       List[str]        =       (
            batch_size,
            pipe.decoder.config.in_channels,
            pipe.decoder.config.sample_size,
            pipe.decoder.config.sample_size,
        )
        lowercase__:       Dict        =       pipe.prepare_latents(
            lowerCamelCase_    ,			dtype=lowerCamelCase_    ,			device=lowerCamelCase_    ,			generator=lowerCamelCase_    ,			latents=lowerCamelCase_    ,			scheduler=DummyScheduler()			)

        lowercase__:       int        =       (
            batch_size,
            pipe.super_res_first.config.in_channels // 2,
            pipe.super_res_first.config.sample_size,
            pipe.super_res_first.config.sample_size,
        )
        lowercase__:       int        =       pipe.prepare_latents(
            lowerCamelCase_    ,			dtype=lowerCamelCase_    ,			device=lowerCamelCase_    ,			generator=lowerCamelCase_    ,			latents=lowerCamelCase_    ,			scheduler=DummyScheduler()			)

        lowercase__:       Tuple        =       self.get_dummy_inputs(lowerCamelCase_    ,			pil_image=lowerCamelCase_			)

        lowercase__:       str        =       pipe(
            **lowerCamelCase_    ,			decoder_latents=lowerCamelCase_    ,			super_res_latents=lowerCamelCase_			).images

        lowercase__:       List[str]        =       self.get_dummy_inputs(lowerCamelCase_    ,			pil_image=lowerCamelCase_			)
        # Don't pass image, instead pass embedding
        lowercase__:       Any        =       pipeline_inputs.pop('image'			)
        lowercase__:       str        =       pipe.image_encoder(lowerCamelCase_			).image_embeds

        lowercase__:       Any        =       pipe(
            **lowerCamelCase_    ,			decoder_latents=lowerCamelCase_    ,			super_res_latents=lowerCamelCase_    ,			image_embeddings=lowerCamelCase_    ,			).images

        # make sure passing text embeddings manually is identical
        assert np.abs(img_out_a - img_out_a			).max() < 1E-4
 @skip_mps
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Any        =       torch_device == 'cpu'

        # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
        lowercase__:       str        =       1E-2

        self._test_attention_slicing_forward_pass(
            test_max_difference=lowerCamelCase_    ,			expected_max_diff=lowerCamelCase_			)
 @skip_mps
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       torch_device == 'cpu'
        lowercase__:       Any        =       True
        lowercase__:       Dict        =       [
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        self._test_inference_batch_single_identical(
            test_max_difference=lowerCamelCase_    ,			relax_max_difference=lowerCamelCase_    ,			additional_params_copy_to_batched_inputs=lowerCamelCase_    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       Any        =       [
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        if torch_device == "mps":
               # TODO: MPS errors with larger batch sizes
               lowercase__:       List[Any]        =       [2, 3]
               self._test_inference_batch_consistent(
                   batch_sizes=lowerCamelCase_    ,			additional_params_copy_to_batched_inputs=lowerCamelCase_    ,			)
        else:
               self._test_inference_batch_consistent(
                   additional_params_copy_to_batched_inputs=lowerCamelCase_			)
 @skip_mps
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return super().test_dict_tuple_outputs_equivalent()
 @skip_mps
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return super().test_save_load_local()
 @skip_mps
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        return super().test_save_load_optional_components()





@slow
@require_torch_gpu
class   __a      (   unittest.TestCase				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'			)
        lowercase__:       int        =       load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'			)

        lowercase__:       List[Any]        =       UnCLIPImageVariationPipeline.from_pretrained(
            'kakaobrain/karlo-v1-alpha-image-variations'    ,			torch_dtype=torch.floataa			)
        lowercase__:       Optional[int]        =       pipeline.to(lowerCamelCase_			)
        pipeline.set_progress_bar_config(disable=lowerCamelCase_			)

        lowercase__:       str        =       torch.Generator(device='cpu'			).manual_seed(0			)
        lowercase__:       Any        =       pipeline(
            lowerCamelCase_    ,			generator=lowerCamelCase_    ,			output_type='np'    ,			)

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

        assert image.shape == (256, 256, 3)

        assert_mean_pixel_difference(lowerCamelCase_    ,			lowerCamelCase_    ,			15			)






import torch

from diffusers import StableDiffusionPipeline


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

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

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


