
import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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 (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()
class 	A__   (						_a    ,						_a    ,						_a    ,						unittest.TestCase				):
							lowerCAmelCase__		:						Union[str, Any]    			=							StableDiffusionControlNetImgaImgPipeline
							lowerCAmelCase__		:						List[str]    			=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
							lowerCAmelCase__		:						str    			=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							lowerCAmelCase__		:						Union[str, Any]    			=							IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}				)
							lowerCAmelCase__		:						Dict    			=							IMAGE_TO_IMAGE_IMAGE_PARAMS





							def 			a__ ( self  :   Tuple				)						->		Union[str, Any]:



											"""simple docstring"""


											torch.manual_seed(0				)
											__lowercase								=					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     , )
											torch.manual_seed(0				)
											__lowercase								=					ControlNetModel(
											    block_out_channels=(32, 64)     , layers_per_block=2     , in_channels=4     , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     , cross_attention_dim=32     , conditioning_embedding_out_channels=(16, 32)     , )
											torch.manual_seed(0				)
											__lowercase								=					DDIMScheduler(
											    beta_start=0.00_085     , beta_end=0.012     , beta_schedule='scaled_linear'     , clip_sample=__UpperCAmelCase     , set_alpha_to_one=__UpperCAmelCase     , )
											torch.manual_seed(0				)
											__lowercase								=					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				)
											__lowercase								=					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=10_00     , )
											__lowercase								=					CLIPTextModel(__UpperCAmelCase				)
											__lowercase								=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

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





							def 			a__ ( self  :   Union[str, Any]     , _UpperCAmelCase  :   Optional[int]     , _UpperCAmelCase  :   Dict=0				)						->		Tuple:



											"""simple docstring"""


											if str(__UpperCAmelCase				).startswith('mps'				):
															__lowercase								=					torch.manual_seed(__UpperCAmelCase				)
											else:
															__lowercase								=					torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)

											__lowercase								=					2
											__lowercase								=					randn_tensor(
											    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)     , generator=__UpperCAmelCase     , device=torch.device(__UpperCAmelCase				)     , )
											__lowercase								=					floats_tensor(control_image.shape     , rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
											__lowercase								=					image.cpu().permute(0     , 2     , 3     , 1				)[0]
											__lowercase								=					Image.fromarray(np.uinta(__UpperCAmelCase				)				).convert('RGB'				).resize((64, 64)				)
											__lowercase								=					{
											    "prompt": "A painting of a squirrel eating a burger",
											    "generator": generator,
											    "num_inference_steps": 2,
											    "guidance_scale": 6.0,
											    "output_type": "numpy",
											    "image": image,
											    "control_image": control_image,
											}

											return inputs





							def 			a__ ( self  :   str				)						->		Dict:



											"""simple docstring"""


											return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3				)





							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()     , reason='XFormers attention is only available with CUDA and `xformers` installed'     , )
							def 			a__ ( self  :   Optional[int]				)						->		Dict:



											"""simple docstring"""


											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3				)





							def 			a__ ( self  :   str				)						->		Optional[Any]:



											"""simple docstring"""


											self._test_inference_batch_single_identical(expected_max_diff=2e-3				)






class 	A__   (						_a    ,						_a    ,						unittest.TestCase				):
							lowerCAmelCase__		:						Optional[Any]    			=							StableDiffusionControlNetImgaImgPipeline
							lowerCAmelCase__		:						List[str]    			=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
							lowerCAmelCase__		:						str    			=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							lowerCAmelCase__		:						List[Any]    			=							frozenset([]				)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess





							def 			a__ ( self  :   Dict				)						->		Union[str, Any]:



											"""simple docstring"""


											torch.manual_seed(0				)
											__lowercase								=					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     , )
											torch.manual_seed(0				)

											def init_weights(_UpperCAmelCase  :   Dict				):
															if isinstance(__UpperCAmelCase     , torch.nn.Convad				):
																			torch.nn.init.normal(m.weight				)
																			m.bias.data.fill_(1.0				)

											__lowercase								=					ControlNetModel(
											    block_out_channels=(32, 64)     , layers_per_block=2     , in_channels=4     , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     , cross_attention_dim=32     , conditioning_embedding_out_channels=(16, 32)     , )
											controlneta.controlnet_down_blocks.apply(__UpperCAmelCase				)

											torch.manual_seed(0				)
											__lowercase								=					ControlNetModel(
											    block_out_channels=(32, 64)     , layers_per_block=2     , in_channels=4     , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     , cross_attention_dim=32     , conditioning_embedding_out_channels=(16, 32)     , )
											controlneta.controlnet_down_blocks.apply(__UpperCAmelCase				)

											torch.manual_seed(0				)
											__lowercase								=					DDIMScheduler(
											    beta_start=0.00_085     , beta_end=0.012     , beta_schedule='scaled_linear'     , clip_sample=__UpperCAmelCase     , set_alpha_to_one=__UpperCAmelCase     , )
											torch.manual_seed(0				)
											__lowercase								=					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				)
											__lowercase								=					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=10_00     , )
											__lowercase								=					CLIPTextModel(__UpperCAmelCase				)
											__lowercase								=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

											__lowercase								=					MultiControlNetModel([controlneta, controlneta]				)

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





							def 			a__ ( self  :   Tuple     , _UpperCAmelCase  :   Optional[Any]     , _UpperCAmelCase  :   Union[str, Any]=0				)						->		int:



											"""simple docstring"""


											if str(__UpperCAmelCase				).startswith('mps'				):
															__lowercase								=					torch.manual_seed(__UpperCAmelCase				)
											else:
															__lowercase								=					torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)

											__lowercase								=					2

											__lowercase								=					[
											    randn_tensor(
											        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)     , generator=__UpperCAmelCase     , device=torch.device(__UpperCAmelCase				)     , ),
											    randn_tensor(
											        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)     , generator=__UpperCAmelCase     , device=torch.device(__UpperCAmelCase				)     , ),
											]

											__lowercase								=					floats_tensor(control_image[0].shape     , rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
											__lowercase								=					image.cpu().permute(0     , 2     , 3     , 1				)[0]
											__lowercase								=					Image.fromarray(np.uinta(__UpperCAmelCase				)				).convert('RGB'				).resize((64, 64)				)
											__lowercase								=					{
											    "prompt": "A painting of a squirrel eating a burger",
											    "generator": generator,
											    "num_inference_steps": 2,
											    "guidance_scale": 6.0,
											    "output_type": "numpy",
											    "image": image,
											    "control_image": control_image,
											}

											return inputs





							def 			a__ ( self  :   Optional[int]				)						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								=					self.get_dummy_components()
											__lowercase								=					self.pipeline_class(**__UpperCAmelCase				)
											pipe.to(__UpperCAmelCase				)

											__lowercase								=					10.0
											__lowercase								=					4

											__lowercase								=					self.get_dummy_inputs(__UpperCAmelCase				)
											__lowercase								=					steps
											__lowercase								=					scale
											__lowercase								=					pipe(**__UpperCAmelCase				)[0]

											__lowercase								=					self.get_dummy_inputs(__UpperCAmelCase				)
											__lowercase								=					steps
											__lowercase								=					scale
											__lowercase								=					pipe(**__UpperCAmelCase     , control_guidance_start=0.1     , control_guidance_end=0.2				)[0]

											__lowercase								=					self.get_dummy_inputs(__UpperCAmelCase				)
											__lowercase								=					steps
											__lowercase								=					scale
											__lowercase								=					pipe(**__UpperCAmelCase     , control_guidance_start=[0.1, 0.3]     , control_guidance_end=[0.2, 0.7]				)[0]

											__lowercase								=					self.get_dummy_inputs(__UpperCAmelCase				)
											__lowercase								=					steps
											__lowercase								=					scale
											__lowercase								=					pipe(**__UpperCAmelCase     , control_guidance_start=0.4     , control_guidance_end=[0.5, 0.8]				)[0]

											# make sure that all outputs are different
											assert np.sum(np.abs(output_a - output_a				)				) > 1e-3
											assert np.sum(np.abs(output_a - output_a				)				) > 1e-3
											assert np.sum(np.abs(output_a - output_a				)				) > 1e-3





							def 			a__ ( self  :   Tuple				)						->		Optional[int]:



											"""simple docstring"""


											return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3				)





							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()     , reason='XFormers attention is only available with CUDA and `xformers` installed'     , )
							def 			a__ ( self  :   Optional[Any]				)						->		str:



											"""simple docstring"""


											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3				)





							def 			a__ ( self  :   List[str]				)						->		int:



											"""simple docstring"""


											self._test_inference_batch_single_identical(expected_max_diff=2e-3				)





							def 			a__ ( self  :   Union[str, Any]				)						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								=					self.get_dummy_components()
											__lowercase								=					self.pipeline_class(**__UpperCAmelCase				)
											pipe.to(__UpperCAmelCase				)
											pipe.set_progress_bar_config(disable=__UpperCAmelCase				)
											with tempfile.TemporaryDirectory() as tmpdir:
															try:
																			# save_pretrained is not implemented for Multi-ControlNet
																			pipe.save_pretrained(__UpperCAmelCase				)
															except NotImplementedError:
																			pass






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





							def 			a__ ( self  :   List[str]				)						->		Union[str, Any]:



											"""simple docstring"""


											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





							def 			a__ ( self  :   Union[str, Any]				)						->		Dict:



											"""simple docstring"""


											__lowercase								=					ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'				)

											__lowercase								=					StableDiffusionControlNetImgaImgPipeline.from_pretrained(
											    'runwayml/stable-diffusion-v1-5'     , safety_checker=__UpperCAmelCase     , controlnet=__UpperCAmelCase				)
											pipe.enable_model_cpu_offload()
											pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

											__lowercase								=					torch.Generator(device='cpu'				).manual_seed(0				)
											__lowercase								=					"evil space-punk bird"
											__lowercase								=					load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'				).resize((5_12, 5_12)				)
											__lowercase								=					load_image(
											    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'				).resize((5_12, 5_12)				)

											__lowercase								=					pipe(
											    __UpperCAmelCase     , __UpperCAmelCase     , control_image=__UpperCAmelCase     , generator=__UpperCAmelCase     , output_type='np'     , num_inference_steps=50     , strength=0.6     , )

											__lowercase								=					output.images[0]

											assert image.shape == (5_12, 5_12, 3)

											__lowercase								=					load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'				)

											assert np.abs(expected_image - image				).max() < 9e-2









"""simple docstring"""







import pytest

from datasets import inspect_metric, list_metrics, load_metric


@pytest.fixture
def      lowercase			(				A_       )->      List[Any]:
  '''simple docstring'''







  monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"  ,   set()       )


@pytest.fixture
def      lowercase			(				A_       )->      Tuple:
  '''simple docstring'''







  class 						_A  :







      """simple docstring"""




      def __init__(					self				:    str				, __UpperCAmelCase				:    int):
        a						:		List[Any]      							=			metric_id






  class 						_A  :







      """simple docstring"""




      UpperCAmelCase       :							Union[str, Any]	=       [MetricMock(_a  ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]






      def 	__snake_case							(					self				:    List[str]):
        return self._metrics






  monkeypatch.setattr("datasets.inspect.huggingface_hub"  ,   HfhMock()       )







@pytest.mark.parametrize(
    "func, args"  ,   [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]       )
def      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_       )->      Any:
  '''simple docstring'''







  if "tmp_path" in args:
    a						:		Union[str, Any]      							=			tuple(arg if arg != "tmp_path" else tmp_path for arg in args       )
  with pytest.warns(A_  ,   match="https://huggingface.co/docs/evaluate"       ):
    func(*A_       )






