




'''simple docstring'''


import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class    __lowercase						(  __magic_name__       ,							__magic_name__       ,							unittest.TestCase ):
   _a							=      CycleDiffusionPipeline
   _a							=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
       """negative_prompt""",
       """height""",
       """width""",
       """negative_prompt_embeds""",
   }
   _a							=      PipelineTesterMixin.required_optional_params - {"""latents"""}
   _a							=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""} )
   _a							=      IMAGE_TO_IMAGE_IMAGE_PARAMS
   _a							=      IMAGE_TO_IMAGE_IMAGE_PARAMS







   def 		UpperCamelCase__			(	self      )				->    Dict:
     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=4      ,							out_channels=4      ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,							cross_attention_dim=32      ,							)
     __a			=					DDIMScheduler(
         beta_start=0.00_085      ,							beta_end=0.012      ,							beta_schedule='scaled_linear'      ,							num_train_timesteps=1000      ,							clip_sample=UpperCamelCase      ,							set_alpha_to_one=UpperCamelCase      ,							)
     torch.manual_seed(0      )
     __a			=					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      ,							)
     torch.manual_seed(0      )
     __a			=					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=1000      ,							)
     __a			=					CLIPTextModel(UpperCamelCase      )
     __a			=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

     __a			=					{
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'safety_checker': None,
         'feature_extractor': None,
     }
     return components







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=0      )				->    Any:
     __a			=					floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
     __a			=					image / 2 + 0.5
     if str(UpperCamelCase      ).startswith('mps'      ):
       __a			=					torch.manual_seed(UpperCamelCase      )
     else:
       __a			=					torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
     __a			=					{
         'prompt': 'An astronaut riding an elephant',
         'source_prompt': 'An astronaut riding a horse',
         'image': image,
         'generator': generator,
         'num_inference_steps': 2,
         'eta': 0.1,
         'strength': 0.8,
         'guidance_scale': 3,
         'source_guidance_scale': 1,
         'output_type': 'numpy',
     }
     return inputs







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					'cpu'  # ensure determinism for the device-dependent torch.Generator

     __a			=					self.get_dummy_components()
     __a			=					CycleDiffusionPipeline(**UpperCamelCase      )
     __a			=					pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe(**UpperCamelCase      )
     __a			=					output.images

     __a			=					images[0, -3:, -3:, -1]

     assert images.shape == (1, 32, 32, 3)
     __a			=					np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]      )

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







   @unittest.skipIf(torch_device != 'cuda'      ,							'This test requires a GPU'      )
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.get_dummy_components()
     for name, module in components.items():
       if hasattr(UpperCamelCase      ,							'half'      ):
         __a			=					module.half()
     __a			=					CycleDiffusionPipeline(**UpperCamelCase      )
     __a			=					pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe(**UpperCamelCase      )
     __a			=					output.images

     __a			=					images[0, -3:, -3:, -1]

     assert images.shape == (1, 32, 32, 3)
     __a			=					np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]      )

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







   @skip_mps
   def 		UpperCamelCase__			(	self      )				->    int:
     return super().test_save_load_local()







   @unittest.skip('non-deterministic pipeline'      )
   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     return super().test_inference_batch_single_identical()







   @skip_mps
   def 		UpperCamelCase__			(	self      )				->    Any:
     return super().test_dict_tuple_outputs_equivalent()







   @skip_mps
   def 		UpperCamelCase__			(	self      )				->    int:
     return super().test_save_load_optional_components()







   @skip_mps
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     return super().test_attention_slicing_forward_pass()





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







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







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/cycle-diffusion/black_colored_car.png'      )
     __a			=					load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'      )
     __a			=					init_image.resize((512, 512)      )

     __a			=					'CompVis/stable-diffusion-v1-4'
     __a			=					DDIMScheduler.from_pretrained(UpperCamelCase      ,							subfolder='scheduler'      )
     __a			=					CycleDiffusionPipeline.from_pretrained(
         UpperCamelCase      ,							scheduler=UpperCamelCase      ,							safety_checker=UpperCamelCase      ,							torch_dtype=torch.floataa      ,							revision='fp16'      )

     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )
     pipe.enable_attention_slicing()

     __a			=					'A black colored car'
     __a			=					'A blue colored car'

     __a			=					torch.manual_seed(0      )
     __a			=					pipe(
         prompt=UpperCamelCase      ,							source_prompt=UpperCamelCase      ,							image=UpperCamelCase      ,							num_inference_steps=100      ,							eta=0.1      ,							strength=0.85      ,							guidance_scale=3      ,							source_guidance_scale=1      ,							generator=UpperCamelCase      ,							output_type='np'      ,							)
     __a			=					output.images

     # the values aren't exactly equal, but the images look the same visually
     assert np.abs(image - expected_image      ).max() < 5e-1







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     __a			=					load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/cycle-diffusion/black_colored_car.png'      )
     __a			=					load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'      )
     __a			=					init_image.resize((512, 512)      )

     __a			=					'CompVis/stable-diffusion-v1-4'
     __a			=					DDIMScheduler.from_pretrained(UpperCamelCase      ,							subfolder='scheduler'      )
     __a			=					CycleDiffusionPipeline.from_pretrained(UpperCamelCase      ,							scheduler=UpperCamelCase      ,							safety_checker=UpperCamelCase      )

     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )
     pipe.enable_attention_slicing()

     __a			=					'A black colored car'
     __a			=					'A blue colored car'

     __a			=					torch.manual_seed(0      )
     __a			=					pipe(
         prompt=UpperCamelCase      ,							source_prompt=UpperCamelCase      ,							image=UpperCamelCase      ,							num_inference_steps=100      ,							eta=0.1      ,							strength=0.85      ,							guidance_scale=3      ,							source_guidance_scale=1      ,							generator=UpperCamelCase      ,							output_type='np'      ,							)
     __a			=					output.images

     assert np.abs(image - expected_image      ).max() < 2e-2









'''simple docstring'''


import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class    __lowercase						:







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     torch.manual_seed(0      )
     __a			=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         sample_size=32      ,							layers_per_block=1      ,							block_out_channels=[32, 64]      ,							down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]      ,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'      ,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']      ,							in_channels=3      ,							out_channels=6      ,							cross_attention_dim=32      ,							encoder_hid_dim=32      ,							attention_head_dim=8      ,							addition_embed_type='text'      ,							addition_embed_type_num_heads=2      ,							cross_attention_norm='group_norm'      ,							resnet_time_scale_shift='scale_shift'      ,							act_fn='gelu'      ,							)
     unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							thresholding=UpperCamelCase      ,							dynamic_thresholding_ratio=0.95      ,							sample_max_value=1.0      ,							prediction_type='epsilon'      ,							variance_type='learned_range'      ,							)

     torch.manual_seed(0      )
     __a			=					IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     torch.manual_seed(0      )
     __a			=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         sample_size=32      ,							layers_per_block=[1, 2]      ,							block_out_channels=[32, 64]      ,							down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]      ,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'      ,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']      ,							in_channels=6      ,							out_channels=6      ,							cross_attention_dim=32      ,							encoder_hid_dim=32      ,							attention_head_dim=8      ,							addition_embed_type='text'      ,							addition_embed_type_num_heads=2      ,							cross_attention_norm='group_norm'      ,							resnet_time_scale_shift='scale_shift'      ,							act_fn='gelu'      ,							class_embed_type='timestep'      ,							mid_block_scale_factor=1.414      ,							time_embedding_act_fn='gelu'      ,							time_embedding_dim=32      ,							)
     unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							thresholding=UpperCamelCase      ,							dynamic_thresholding_ratio=0.95      ,							sample_max_value=1.0      ,							prediction_type='epsilon'      ,							variance_type='learned_range'      ,							)

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							)

     torch.manual_seed(0      )
     __a			=					IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "image_noising_scheduler": image_noising_scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )

     __a			=					inputs['prompt']
     __a			=					inputs['generator']
     __a			=					inputs['num_inference_steps']
     __a			=					inputs['output_type']

     if "image" in inputs:
       __a			=					inputs['image']
     else:
       __a			=					None

     if "mask_image" in inputs:
       __a			=					inputs['mask_image']
     else:
       __a			=					None

     if "original_image" in inputs:
       __a			=					inputs['original_image']
     else:
       __a			=					None

     __a	,       __a			=					pipe.encode_prompt(UpperCamelCase      )

     # inputs with prompt converted to embeddings
     __a			=					{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
       __a			=					image

     if mask_image is not None:
       __a			=					mask_image

     if original_image is not None:
       __a			=					original_image

     # set all optional components to None
     for optional_component in pipe._optional_components:
       setattr(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )

     __a			=					pipe(**UpperCamelCase      )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCamelCase      )
       __a			=					self.pipeline_class.from_pretrained(UpperCamelCase      )
       pipe_loaded.to(UpperCamelCase      )
       pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     for optional_component in pipe._optional_components:
       self.assertTrue(
           getattr(UpperCamelCase      ,							UpperCamelCase      ) is None      ,							f"`{optional_component}` did not stay set to None after loading."      ,							)

     __a			=					self.get_dummy_inputs(UpperCamelCase      )

     __a			=					inputs['generator']
     __a			=					inputs['num_inference_steps']
     __a			=					inputs['output_type']

     # inputs with prompt converted to embeddings
     __a			=					{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
       __a			=					image

     if mask_image is not None:
       __a			=					mask_image

     if original_image is not None:
       __a			=					original_image

     __a			=					pipe_loaded(**UpperCamelCase      )[0]

     __a			=					np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
     self.assertLess(UpperCamelCase      ,							1e-4      )







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe(**UpperCamelCase      )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCamelCase      )
       __a			=					self.pipeline_class.from_pretrained(UpperCamelCase      )
       pipe_loaded.to(UpperCamelCase      )
       pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe_loaded(**UpperCamelCase      )[0]

     __a			=					np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
     self.assertLess(UpperCamelCase      ,							1e-4      )



