




"""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 					__magic_name__							( UpperCAmelCase__		,      UpperCAmelCase__		,      unittest.TestCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		CycleDiffusionPipeline
				__UpperCamelCase    						=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    "negative_prompt",
				    "height",
				    "width",
				    "negative_prompt_embeds",
				}
				__UpperCamelCase    						=		PipelineTesterMixin.required_optional_params - {"latents"}
				__UpperCamelCase    						=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}       )
				__UpperCamelCase    						=		IMAGE_TO_IMAGE_IMAGE_PARAMS
				__UpperCamelCase    						=		IMAGE_TO_IMAGE_IMAGE_PARAMS





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							torch.manual_seed(0				)
							lowerCamelCase       =      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	,				)
							lowerCamelCase       =      DDIMScheduler(
							    beta_start=0.00_085	,				beta_end=0.012	,				beta_schedule="""scaled_linear"""	,				num_train_timesteps=1_000	,				clip_sample=_a	,				set_alpha_to_one=_a	,				)
							torch.manual_seed(0				)
							lowerCamelCase       =      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				)
							lowerCamelCase       =      CLIPTextConfig(
							    bos_token_id=0	,				eos_token_id=2	,				hidden_size=32	,				intermediate_size=37	,				layer_norm_eps=1e-0_5	,				num_attention_heads=4	,				num_hidden_layers=5	,				pad_token_id=1	,				vocab_size=1_000	,				)
							lowerCamelCase       =      CLIPTextModel(_a				)
							lowerCamelCase       =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

							lowerCamelCase       =      {
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components





				def      _lowerCAmelCase       ( self	,				_a	,				_a=0				):





							"""simple docstring"""
							lowerCamelCase       =      floats_tensor((1, 3, 32, 32)	,				rng=random.Random(_a				)				).to(_a				)
							lowerCamelCase       =      image / 2 + 0.5
							if str(_a				).startswith("""mps"""				):
										lowerCamelCase       =      torch.manual_seed(_a				)
							else:
										lowerCamelCase       =      torch.Generator(device=_a				).manual_seed(_a				)
							lowerCamelCase       =      {
							    """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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      """cpu"""  # ensure determinism for the device-dependent torch.Generator

							lowerCamelCase       =      self.get_dummy_components()
							lowerCamelCase       =      CycleDiffusionPipeline(**_a				)
							lowerCamelCase       =      pipe.to(_a				)
							pipe.set_progress_bar_config(disable=_a				)

							lowerCamelCase       =      self.get_dummy_inputs(_a				)
							lowerCamelCase       =      pipe(**_a				)
							lowerCamelCase       =      output.images

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

							assert images.shape == (1, 32, 32, 3)
							lowerCamelCase       =      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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.get_dummy_components()
							for name, module in components.items():
										if hasattr(_a	,				"""half"""				):
													lowerCamelCase       =      module.half()
							lowerCamelCase       =      CycleDiffusionPipeline(**_a				)
							lowerCamelCase       =      pipe.to(_a				)
							pipe.set_progress_bar_config(disable=_a				)

							lowerCamelCase       =      self.get_dummy_inputs(_a				)
							lowerCamelCase       =      pipe(**_a				)
							lowerCamelCase       =      output.images

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

							assert images.shape == (1, 32, 32, 3)
							lowerCamelCase       =      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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return super().test_save_load_local()





				@unittest.skip("""non-deterministic pipeline"""				)
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return super().test_inference_batch_single_identical()





				@skip_mps
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return super().test_dict_tuple_outputs_equivalent()





				@skip_mps
				def      _lowerCAmelCase       ( self				):





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





				@skip_mps
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return super().test_attention_slicing_forward_pass()






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






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/cycle-diffusion/black_colored_car.png"""				)
							lowerCamelCase       =      load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""				)
							lowerCamelCase       =      init_image.resize((512, 512)				)

							lowerCamelCase       =      """CompVis/stable-diffusion-v1-4"""
							lowerCamelCase       =      DDIMScheduler.from_pretrained(_a	,				subfolder="""scheduler"""				)
							lowerCamelCase       =      CycleDiffusionPipeline.from_pretrained(
							    _a	,				scheduler=_a	,				safety_checker=_a	,				torch_dtype=torch.floataa	,				revision="""fp16"""				)

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

							lowerCamelCase       =      """A black colored car"""
							lowerCamelCase       =      """A blue colored car"""

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      pipe(
							    prompt=_a	,				source_prompt=_a	,				image=_a	,				num_inference_steps=100	,				eta=0.1	,				strength=0.85	,				guidance_scale=3	,				source_guidance_scale=1	,				generator=_a	,				output_type="""np"""	,				)
							lowerCamelCase       =      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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/cycle-diffusion/black_colored_car.png"""				)
							lowerCamelCase       =      load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""				)
							lowerCamelCase       =      init_image.resize((512, 512)				)

							lowerCamelCase       =      """CompVis/stable-diffusion-v1-4"""
							lowerCamelCase       =      DDIMScheduler.from_pretrained(_a	,				subfolder="""scheduler"""				)
							lowerCamelCase       =      CycleDiffusionPipeline.from_pretrained(_a	,				scheduler=_a	,				safety_checker=_a				)

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

							lowerCamelCase       =      """A black colored car"""
							lowerCamelCase       =      """A blue colored car"""

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

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









"""simple docstring"""




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:    List[str]  							=							{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:    Tuple  							=							[
					    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """AutoformerForPrediction""",
					    """AutoformerModel""",
					    """AutoformerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_autoformer import (
					    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    AutoformerConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_autoformer import (
										    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AutoformerForPrediction,
										    AutoformerModel,
										    AutoformerPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase							:    str  							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



