

"""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 					_lowerCAmelCase  (	lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Union[str, Any]   		=	CycleDiffusionPipeline
 __UpperCAmelCase	:					Optional[Any]   		=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     "negative_prompt",
     "height",
     "width",
     "negative_prompt_embeds",
 }
 __UpperCAmelCase	:					Optional[Any]   		=	PipelineTesterMixin.required_optional_params - {"latents"}
 __UpperCAmelCase	:					List[Any]   		=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}   )
 __UpperCAmelCase	:					Tuple   		=	IMAGE_TO_IMAGE_IMAGE_PARAMS
 __UpperCAmelCase	:					int   		=	IMAGE_TO_IMAGE_IMAGE_PARAMS


 def 					_lowercase (			self							:			List[str]	):
    torch.manual_seed(0	)
    __lowercase								=						UNetaDConditionModel(
        block_out_channels=(3_2, 6_4),					layers_per_block=2,					sample_size=3_2,					in_channels=4,					out_channels=4,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),					cross_attention_dim=3_2,					)
    __lowercase								=						DDIMScheduler(
        beta_start=0.00_085,					beta_end=0.012,					beta_schedule="scaled_linear",					num_train_timesteps=1_0_0_0,					clip_sample=UpperCAmelCase__,					set_alpha_to_one=UpperCAmelCase__,					)
    torch.manual_seed(0	)
    __lowercase								=						AutoencoderKL(
        block_out_channels=[3_2, 6_4],					in_channels=3,					out_channels=3,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],					latent_channels=4,					)
    torch.manual_seed(0	)
    __lowercase								=						CLIPTextConfig(
        bos_token_id=0,					eos_token_id=2,					hidden_size=3_2,					intermediate_size=3_7,					layer_norm_eps=1E-05,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1_0_0_0,					)
    __lowercase								=						CLIPTextModel(UpperCAmelCase__	)
    __lowercase								=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]=0	):
    __lowercase								=						floats_tensor((1, 3, 3_2, 3_2),					rng=random.Random(UpperCAmelCase__	)	).to(UpperCAmelCase__	)
    __lowercase								=						image / 2 + 0.5
    if str(UpperCAmelCase__	).startswith("mps"	):
       __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    else:
       __lowercase								=						torch.Generator(device=UpperCAmelCase__	).manual_seed(UpperCAmelCase__	)
    __lowercase								=						{
        "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 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						"cpu"  # ensure determinism for the device-dependent torch.Generator

    __lowercase								=						self.get_dummy_components()
    __lowercase								=						CycleDiffusionPipeline(**UpperCAmelCase__	)
    __lowercase								=						pipe.to(UpperCAmelCase__	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs(UpperCAmelCase__	)
    __lowercase								=						pipe(**UpperCAmelCase__	)
    __lowercase								=						output.images

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

    assert images.shape == (1, 3_2, 3_2, 3)
    __lowercase								=						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 					_lowercase (			self							:			Any	):
    __lowercase								=						self.get_dummy_components()
    for name, module in components.items():
       if hasattr(UpperCAmelCase__,					"half"	):
          __lowercase								=						module.half()
    __lowercase								=						CycleDiffusionPipeline(**UpperCAmelCase__	)
    __lowercase								=						pipe.to(UpperCAmelCase__	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs(UpperCAmelCase__	)
    __lowercase								=						pipe(**UpperCAmelCase__	)
    __lowercase								=						output.images

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

    assert images.shape == (1, 3_2, 3_2, 3)
    __lowercase								=						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 					_lowercase (			self							:			List[Any]	):
    return super().test_save_load_local()


 @unittest.skip("non-deterministic pipeline"	)
 def 					_lowercase (			self							:			Dict	):
    return super().test_inference_batch_single_identical()


 @skip_mps
 def 					_lowercase (			self							:			Dict	):
    return super().test_dict_tuple_outputs_equivalent()


 @skip_mps
 def 					_lowercase (			self							:			Tuple	):
    return super().test_save_load_optional_components()



 @skip_mps
 def 					_lowercase (			self							:			Union[str, Any]	):
    return super().test_attention_slicing_forward_pass()







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







 """simple docstring"""







 def 					_lowercase (			self							:			str	):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/cycle-diffusion/black_colored_car.png"	)
    __lowercase								=						load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"	)
    __lowercase								=						init_image.resize((5_1_2, 5_1_2)	)

    __lowercase								=						"CompVis/stable-diffusion-v1-4"
    __lowercase								=						DDIMScheduler.from_pretrained(UpperCAmelCase__,					subfolder="scheduler"	)
    __lowercase								=						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()

    __lowercase								=						"A black colored car"
    __lowercase								=						"A blue colored car"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					source_prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					num_inference_steps=1_0_0,					eta=0.1,					strength=0.85,					guidance_scale=3,					source_guidance_scale=1,					generator=UpperCAmelCase__,					output_type="np",					)
    __lowercase								=						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 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/cycle-diffusion/black_colored_car.png"	)
    __lowercase								=						load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"	)
    __lowercase								=						init_image.resize((5_1_2, 5_1_2)	)

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

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

    __lowercase								=						"A black colored car"
    __lowercase								=						"A blue colored car"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					source_prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					num_inference_steps=1_0_0,					eta=0.1,					strength=0.85,					guidance_scale=3,					source_guidance_scale=1,					generator=UpperCAmelCase__,					output_type="np",					)
    __lowercase								=						output.images

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







"""simple docstring"""





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class 					_lowerCAmelCase  (	unittest.TestCase     ,lowercase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						load_tool("text-classification"	)
    self.tool.setup()
    __lowercase								=						load_tool("text-classification",					remote=UpperCAmelCase__	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.tool("That's quite cool",					["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.remote_tool("That's quite cool",					["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.tool(text="That's quite cool",					labels=["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)



 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						self.remote_tool(text="That's quite cool",					labels=["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)




