"""simple docstring"""






# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

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 lowerCamelCase		(_SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE     ,     unittest.TestCase   ):






					'''simple docstring'''


					a 						=		StableDiffusionControlNetImgaImgPipeline
					a 						=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
					a 						=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					a 						=		IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}   )
					a 						=		IMAGE_TO_IMAGE_IMAGE_PARAMS



					def       lowerCAmelCase_       (    self      :					str		)				->							List[str]:
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					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		)
									SCREAMING_SNAKE_CASE__			=					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		)
									SCREAMING_SNAKE_CASE__			=					DDIMScheduler(
									    beta_start=0.0_0085 ,			beta_end=0.012 ,			beta_schedule="scaled_linear" ,			clip_sample=_snake_case ,			set_alpha_to_one=_snake_case ,			)
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					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		)
									SCREAMING_SNAKE_CASE__			=					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 ,			)
									SCREAMING_SNAKE_CASE__			=					CLIPTextModel(_snake_case		)
									SCREAMING_SNAKE_CASE__			=					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

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



					def       lowerCAmelCase_       (    self      :					Optional[int] ,			_snake_case      :					Optional[int] ,			_snake_case      :					int=0		)				->							Any:
									if str(_snake_case		).startswith("mps"		):
													SCREAMING_SNAKE_CASE__			=					torch.manual_seed(_snake_case		)
									else:
													SCREAMING_SNAKE_CASE__			=					torch.Generator(device=_snake_case		).manual_seed(_snake_case		)

									SCREAMING_SNAKE_CASE__			=					2
									SCREAMING_SNAKE_CASE__			=					randn_tensor(
									    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,			generator=_snake_case ,			device=torch.device(_snake_case		) ,			)
									SCREAMING_SNAKE_CASE__			=					floats_tensor(control_image.shape ,			rng=random.Random(_snake_case		)		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__			=					image.cpu().permute(0 ,			2 ,			3 ,			1		)[0]
									SCREAMING_SNAKE_CASE__			=					Image.fromarray(np.uinta(_snake_case		)		).convert("RGB"		).resize((64, 64)		)
									SCREAMING_SNAKE_CASE__			=					{
									    "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       lowerCAmelCase_       (    self      :					Union[str, Any]		)				->							Union[str, Any]:
									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       lowerCAmelCase_       (    self      :					Optional[int]		)				->							List[str]:
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3		)



					def       lowerCAmelCase_       (    self      :					Any		)				->							Tuple:
									self._test_inference_batch_single_identical(expected_max_diff=2e-3		)



class lowerCamelCase		(_SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE     ,     unittest.TestCase   ):






					'''simple docstring'''


					a 						=		StableDiffusionControlNetImgaImgPipeline
					a 						=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
					a 						=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					a 						=		frozenset([]   )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess



					def       lowerCAmelCase_       (    self      :					Optional[int]		)				->							Optional[int]:
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					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(_snake_case      :					Dict		):
													if isinstance(_snake_case ,			torch.nn.Convad		):
																	torch.nn.init.normal(m.weight		)
																	m.bias.data.fill_(1.0		)

									SCREAMING_SNAKE_CASE__			=					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(_snake_case		)

									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					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(_snake_case		)

									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					DDIMScheduler(
									    beta_start=0.0_0085 ,			beta_end=0.012 ,			beta_schedule="scaled_linear" ,			clip_sample=_snake_case ,			set_alpha_to_one=_snake_case ,			)
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					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		)
									SCREAMING_SNAKE_CASE__			=					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 ,			)
									SCREAMING_SNAKE_CASE__			=					CLIPTextModel(_snake_case		)
									SCREAMING_SNAKE_CASE__			=					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

									SCREAMING_SNAKE_CASE__			=					MultiControlNetModel([controlneta, controlneta]		)

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



					def       lowerCAmelCase_       (    self      :					Any ,			_snake_case      :					Optional[Any] ,			_snake_case      :					Optional[int]=0		)				->							Dict:
									if str(_snake_case		).startswith("mps"		):
													SCREAMING_SNAKE_CASE__			=					torch.manual_seed(_snake_case		)
									else:
													SCREAMING_SNAKE_CASE__			=					torch.Generator(device=_snake_case		).manual_seed(_snake_case		)

									SCREAMING_SNAKE_CASE__			=					2

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

									SCREAMING_SNAKE_CASE__			=					floats_tensor(control_image[0].shape ,			rng=random.Random(_snake_case		)		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__			=					image.cpu().permute(0 ,			2 ,			3 ,			1		)[0]
									SCREAMING_SNAKE_CASE__			=					Image.fromarray(np.uinta(_snake_case		)		).convert("RGB"		).resize((64, 64)		)
									SCREAMING_SNAKE_CASE__			=					{
									    "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       lowerCAmelCase_       (    self      :					Union[str, Any]		)				->							str:
									SCREAMING_SNAKE_CASE__			=					self.get_dummy_components()
									SCREAMING_SNAKE_CASE__			=					self.pipeline_class(**_snake_case		)
									pipe.to(_snake_case		)

									SCREAMING_SNAKE_CASE__			=					10.0
									SCREAMING_SNAKE_CASE__			=					4

									SCREAMING_SNAKE_CASE__			=					self.get_dummy_inputs(_snake_case		)
									SCREAMING_SNAKE_CASE__			=					steps
									SCREAMING_SNAKE_CASE__			=					scale
									SCREAMING_SNAKE_CASE__			=					pipe(**_snake_case		)[0]

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

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

									SCREAMING_SNAKE_CASE__			=					self.get_dummy_inputs(_snake_case		)
									SCREAMING_SNAKE_CASE__			=					steps
									SCREAMING_SNAKE_CASE__			=					scale
									SCREAMING_SNAKE_CASE__			=					pipe(**_snake_case ,			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       lowerCAmelCase_       (    self      :					Tuple		)				->							Optional[Any]:
									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       lowerCAmelCase_       (    self      :					List[str]		)				->							List[str]:
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3		)



					def       lowerCAmelCase_       (    self      :					Tuple		)				->							Dict:
									self._test_inference_batch_single_identical(expected_max_diff=2e-3		)



					def       lowerCAmelCase_       (    self      :					Dict		)				->							Union[str, Any]:
									SCREAMING_SNAKE_CASE__			=					self.get_dummy_components()
									SCREAMING_SNAKE_CASE__			=					self.pipeline_class(**_snake_case		)
									pipe.to(_snake_case		)
									pipe.set_progress_bar_config(disable=_snake_case		)
									with tempfile.TemporaryDirectory() as tmpdir:
													try:
																	# save_pretrained is not implemented for Multi-ControlNet
																	pipe.save_pretrained(_snake_case		)
													except NotImplementedError:
																	pass



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






					'''simple docstring'''


					def       lowerCAmelCase_       (    self      :					Any		)				->							Dict:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



					def       lowerCAmelCase_       (    self      :					Tuple		)				->							int:
									SCREAMING_SNAKE_CASE__			=					ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"		)

									SCREAMING_SNAKE_CASE__			=					StableDiffusionControlNetImgaImgPipeline.from_pretrained(
									    "runwayml/stable-diffusion-v1-5" ,			safety_checker=_snake_case ,			controlnet=_snake_case		)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=_snake_case		)

									SCREAMING_SNAKE_CASE__			=					torch.Generator(device="cpu"		).manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					"evil space-punk bird"
									SCREAMING_SNAKE_CASE__			=					load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"		).resize((512, 512)		)
									SCREAMING_SNAKE_CASE__			=					load_image(
									    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"		).resize((512, 512)		)

									SCREAMING_SNAKE_CASE__			=					pipe(
									    _snake_case ,			_snake_case ,			control_image=_snake_case ,			generator=_snake_case ,			output_type="np" ,			num_inference_steps=50 ,			strength=0.6 ,			)

									SCREAMING_SNAKE_CASE__			=					output.images[0]

									assert image.shape == (512, 512, 3)

									SCREAMING_SNAKE_CASE__			=					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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a:     int   					=					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Dict   					=					['''MvpTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Tuple   					=					[
      '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MvpForCausalLM''',
      '''MvpForConditionalGeneration''',
      '''MvpForQuestionAnswering''',
      '''MvpForSequenceClassification''',
      '''MvpModel''',
      '''MvpPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
  from .tokenization_mvp import MvpTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mvp_fast import MvpTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mvp import (
        MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
        MvpForCausalLM,
        MvpForConditionalGeneration,
        MvpForQuestionAnswering,
        MvpForSequenceClassification,
        MvpModel,
        MvpPreTrainedModel,
    )

else:
  import sys

  __a:     Optional[Any]   					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)