


"""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      (      SCREAMING_SNAKE_CASE_		,		unittest.TestCase ):
    a_    :				List[Any]             =    UnCLIPImageVariationPipeline
    a_    :				List[str]             =    IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
    a_    :				Optional[int]             =    IMAGE_VARIATION_BATCH_PARAMS

    a_    :				List[str]             =    [
        'generator',
        'return_dict',
        'decoder_num_inference_steps',
        'super_res_num_inference_steps',
    ]
    a_    :				Tuple             =    False
    @property
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          return 32
    @property
    def      _UpperCamelCase       ( self							:					str				):
          return 32
    @property
    def      _UpperCamelCase       ( self							:					str				):
          return self.time_input_dim
    @property
    def      _UpperCamelCase       ( self							:					List[str]				):
          return self.time_input_dim * 4
    @property
    def      _UpperCamelCase       ( self							:					Any				):
          return 1_00
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)
          return tokenizer
    @property
    def      _UpperCamelCase       ( self							:					Tuple				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		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=10_00							,					)
          return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE__				)
    @property
    def      _UpperCamelCase       ( self							:					List[Any]				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		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(SCREAMING_SNAKE_CASE__				)
    @property
    def      _UpperCamelCase       ( self							:					int				):
          torch.manual_seed(0				)

          lowerCamelCase__           =		{
              'clip_embeddings_dim': self.text_embedder_hidden_size,
              'time_embed_dim': self.time_embed_dim,
              'cross_attention_dim': self.cross_attention_dim,
          }

          lowerCamelCase__           =		UnCLIPTextProjModel(**SCREAMING_SNAKE_CASE__				)
          return model
    @property
    def      _UpperCamelCase       ( self							:					str				):
          torch.manual_seed(0				)

          lowerCamelCase__           =		{
              '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',
          }

          lowerCamelCase__           =		UNetaDConditionModel(**SCREAMING_SNAKE_CASE__				)
          return model
    @property
    def      _UpperCamelCase       ( self							:					Any				):
          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      _UpperCamelCase       ( self							:					str				):
          torch.manual_seed(0				)

          lowerCamelCase__           =		UNetaDModel(**self.dummy_super_res_kwargs				)
          return model
    @property
    def      _UpperCamelCase       ( self							:					List[Any]				):
          # seeded differently to get different unet than `self.dummy_super_res_first`
          torch.manual_seed(1				)

          lowerCamelCase__           =		UNetaDModel(**self.dummy_super_res_kwargs				)
          return model
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		self.dummy_decoder
          lowerCamelCase__           =		self.dummy_text_proj
          lowerCamelCase__           =		self.dummy_text_encoder
          lowerCamelCase__           =		self.dummy_tokenizer
          lowerCamelCase__           =		self.dummy_super_res_first
          lowerCamelCase__           =		self.dummy_super_res_last

          lowerCamelCase__           =		UnCLIPScheduler(
              variance_type='learned_range'							,					prediction_type='epsilon'							,					num_train_timesteps=10_00							,					)

          lowerCamelCase__           =		UnCLIPScheduler(
              variance_type='fixed_small_log'							,					prediction_type='epsilon'							,					num_train_timesteps=10_00							,					)

          lowerCamelCase__           =		CLIPImageProcessor(crop_size=32							,					size=32				)

          lowerCamelCase__           =		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      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0							,					SCREAMING_SNAKE_CASE__							:					Tuple=True				):
          lowerCamelCase__           =		floats_tensor((1, 3, 32, 32)							,					rng=random.Random(SCREAMING_SNAKE_CASE__				)				).to(SCREAMING_SNAKE_CASE__				)
          if str(SCREAMING_SNAKE_CASE__				).startswith('mps'				):
                lowerCamelCase__           =		torch.manual_seed(SCREAMING_SNAKE_CASE__				)
          else:
                lowerCamelCase__           =		torch.Generator(device=SCREAMING_SNAKE_CASE__				).manual_seed(SCREAMING_SNAKE_CASE__				)

          if pil_image:
                lowerCamelCase__           =		input_image * 0.5 + 0.5
                lowerCamelCase__           =		input_image.clamp(0							,					1				)
                lowerCamelCase__           =		input_image.cpu().permute(0							,					2							,					3							,					1				).float().numpy()
                lowerCamelCase__           =		DiffusionPipeline.numpy_to_pil(SCREAMING_SNAKE_CASE__				)[0]

          return {
              "image": input_image,
              "generator": generator,
              "decoder_num_inference_steps": 2,
              "super_res_num_inference_steps": 2,
              "output_type": "np",
          }
    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__           =		'cpu'

          lowerCamelCase__           =		self.get_dummy_components()

          lowerCamelCase__           =		self.pipeline_class(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		pipe.to(SCREAMING_SNAKE_CASE__				)

          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							,					pil_image=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		output.images

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							,					pil_image=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		pipe(
              **SCREAMING_SNAKE_CASE__							,					return_dict=SCREAMING_SNAKE_CASE__							,					)[0]

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

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

          lowerCamelCase__           =		np.array(
              [
                  0.99_97,
                  0.00_02,
                  0.99_97,
                  0.99_97,
                  0.99_69,
                  0.00_23,
                  0.99_97,
                  0.99_69,
                  0.99_70,
              ]				)

          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      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		'cpu'

          lowerCamelCase__           =		self.get_dummy_components()

          lowerCamelCase__           =		self.pipeline_class(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		pipe.to(SCREAMING_SNAKE_CASE__				)

          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							,					pil_image=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		output.images

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							,					pil_image=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		pipe(
              **SCREAMING_SNAKE_CASE__							,					return_dict=SCREAMING_SNAKE_CASE__							,					)[0]

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

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

          lowerCamelCase__           =		np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71]				)

          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      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		'cpu'

          lowerCamelCase__           =		self.get_dummy_components()

          lowerCamelCase__           =		self.pipeline_class(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		pipe.to(SCREAMING_SNAKE_CASE__				)

          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							,					pil_image=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              pipeline_inputs['image'],
              pipeline_inputs['image'],
          ]

          lowerCamelCase__           =		pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		output.images

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							,					pil_image=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		[
              tuple_pipeline_inputs['image'],
              tuple_pipeline_inputs['image'],
          ]

          lowerCamelCase__           =		pipe(
              **SCREAMING_SNAKE_CASE__							,					return_dict=SCREAMING_SNAKE_CASE__							,					)[0]

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

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

          lowerCamelCase__           =		np.array(
              [
                  0.99_97,
                  0.99_89,
                  0.00_08,
                  0.00_21,
                  0.99_60,
                  0.00_18,
                  0.00_14,
                  0.00_02,
                  0.99_33,
              ]				)

          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      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		torch.device('cpu'				)



          class   _a      :
              a_    :				Dict             =    1





          lowerCamelCase__           =		self.get_dummy_components()

          lowerCamelCase__           =		self.pipeline_class(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		pipe.to(SCREAMING_SNAKE_CASE__				)

          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		torch.Generator(device=SCREAMING_SNAKE_CASE__				).manual_seed(0				)
          lowerCamelCase__           =		pipe.decoder.dtype
          lowerCamelCase__           =		1

          lowerCamelCase__           =		(
              batch_size,
              pipe.decoder.config.in_channels,
              pipe.decoder.config.sample_size,
              pipe.decoder.config.sample_size,
          )
          lowerCamelCase__           =		pipe.prepare_latents(
              SCREAMING_SNAKE_CASE__							,					dtype=SCREAMING_SNAKE_CASE__							,					device=SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					latents=SCREAMING_SNAKE_CASE__							,					scheduler=DummyScheduler()				)

          lowerCamelCase__           =		(
              batch_size,
              pipe.super_res_first.config.in_channels // 2,
              pipe.super_res_first.config.sample_size,
              pipe.super_res_first.config.sample_size,
          )
          lowerCamelCase__           =		pipe.prepare_latents(
              SCREAMING_SNAKE_CASE__							,					dtype=SCREAMING_SNAKE_CASE__							,					device=SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					latents=SCREAMING_SNAKE_CASE__							,					scheduler=DummyScheduler()				)

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							,					pil_image=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		pipe(
              **SCREAMING_SNAKE_CASE__							,					decoder_latents=SCREAMING_SNAKE_CASE__							,					super_res_latents=SCREAMING_SNAKE_CASE__				).images

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							,					pil_image=SCREAMING_SNAKE_CASE__				)
          # Don't pass image, instead pass embedding
          lowerCamelCase__           =		pipeline_inputs.pop('image'				)
          lowerCamelCase__           =		pipe.image_encoder(SCREAMING_SNAKE_CASE__				).image_embeds

          lowerCamelCase__           =		pipe(
              **SCREAMING_SNAKE_CASE__							,					decoder_latents=SCREAMING_SNAKE_CASE__							,					super_res_latents=SCREAMING_SNAKE_CASE__							,					image_embeddings=SCREAMING_SNAKE_CASE__							,					).images

          # make sure passing text embeddings manually is identical
          assert np.abs(img_out_a - img_out_a				).max() < 1e-4
    @skip_mps
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		torch_device == 'cpu'

          # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
          lowerCamelCase__           =		1e-2

          self._test_attention_slicing_forward_pass(
              test_max_difference=SCREAMING_SNAKE_CASE__							,					expected_max_diff=SCREAMING_SNAKE_CASE__				)
    @skip_mps
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__           =		torch_device == 'cpu'
          lowerCamelCase__           =		True
          lowerCamelCase__           =		[
              'decoder_num_inference_steps',
              'super_res_num_inference_steps',
          ]

          self._test_inference_batch_single_identical(
              test_max_difference=SCREAMING_SNAKE_CASE__							,					relax_max_difference=SCREAMING_SNAKE_CASE__							,					additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		[
              'decoder_num_inference_steps',
              'super_res_num_inference_steps',
          ]

          if torch_device == "mps":
                # TODO: MPS errors with larger batch sizes
                lowerCamelCase__           =		[2, 3]
                self._test_inference_batch_consistent(
                    batch_sizes=SCREAMING_SNAKE_CASE__							,					additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE__							,					)
          else:
                self._test_inference_batch_consistent(
                    additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE__				)
    @skip_mps
    def      _UpperCamelCase       ( self							:					List[str]				):
          return super().test_dict_tuple_outputs_equivalent()
    @skip_mps
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          return super().test_save_load_local()


    @skip_mps
    def      _UpperCamelCase       ( self							:					List[Any]				):
          return super().test_save_load_optional_components()





@slow
@require_torch_gpu
class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					List[str]				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__           =		load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'				)
          lowerCamelCase__           =		load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'				)

          lowerCamelCase__           =		UnCLIPImageVariationPipeline.from_pretrained(
              'kakaobrain/karlo-v1-alpha-image-variations'							,					torch_dtype=torch.floataa				)
          lowerCamelCase__           =		pipeline.to(SCREAMING_SNAKE_CASE__				)
          pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		torch.Generator(device='cpu'				).manual_seed(0				)
          lowerCamelCase__           =		pipeline(
              SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					output_type='np'							,					)

          lowerCamelCase__           =		output.images[0]

          assert image.shape == (2_56, 2_56, 3)

          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					15				)







"""simple docstring"""


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)



