


'''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				(				a      ,				a      ,				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					int			      = StableDiffusionControlNetImgaImgPipeline
	_lowerCamelCase   :					int			      = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
	_lowerCamelCase   :					Any			      = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	_lowerCamelCase   :					Optional[Any]			      = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}							)
	_lowerCamelCase   :					Union[str, Any]			      = IMAGE_TO_IMAGE_IMAGE_PARAMS



	def 				lowercase     (					self    ):
		torch.manual_seed(0    )
		lowerCAmelCase					:    List[Any]      							= 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    )
		lowerCAmelCase					:    List[str]      							= 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    )
		lowerCAmelCase					:    Optional[Any]      							= DDIMScheduler(
		    beta_start=0.0_0_0_8_5 ,					beta_end=0.0_1_2 ,					beta_schedule='scaled_linear' ,					clip_sample=A__ ,					set_alpha_to_one=A__ ,					)
		torch.manual_seed(0    )
		lowerCAmelCase					:    Dict      							= 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					:    List[str]      							= 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=1000 ,					)
		lowerCAmelCase					:    Tuple      							= CLIPTextModel(A__    )
		lowerCAmelCase					:    Dict      							= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=0    ):
		if str(A__    ).startswith('mps'    ):
			lowerCAmelCase					:    Union[str, Any]      							= torch.manual_seed(A__    )
		else:
			lowerCAmelCase					:    Optional[Any]      							= torch.Generator(device=A__    ).manual_seed(A__    )

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



	def 				lowercase     (					self    ):
		self._test_inference_batch_single_identical(expected_max_diff=2e-3    )




class  lowerCAmelCase				(				a      ,				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Dict			      = StableDiffusionControlNetImgaImgPipeline
	_lowerCamelCase   :					str			      = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
	_lowerCamelCase   :					str			      = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	_lowerCamelCase   :					Tuple			      = frozenset([]							)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess



	def 				lowercase     (					self    ):
		torch.manual_seed(0    )
		lowerCAmelCase					:    str      							= 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__    ):
			if isinstance(A__ ,					torch.nn.Convad    ):
				torch.nn.init.normal(m.weight    )
				m.bias.data.fill_(1.0    )

		lowerCAmelCase					:    Optional[int]      							= 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(A__    )

		torch.manual_seed(0    )
		lowerCAmelCase					:    Tuple      							= 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(A__    )

		torch.manual_seed(0    )
		lowerCAmelCase					:    Optional[int]      							= DDIMScheduler(
		    beta_start=0.0_0_0_8_5 ,					beta_end=0.0_1_2 ,					beta_schedule='scaled_linear' ,					clip_sample=A__ ,					set_alpha_to_one=A__ ,					)
		torch.manual_seed(0    )
		lowerCAmelCase					:    Any      							= 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					:    List[Any]      							= 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=1000 ,					)
		lowerCAmelCase					:    Any      							= CLIPTextModel(A__    )
		lowerCAmelCase					:    Optional[Any]      							= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

		lowerCAmelCase					:    Optional[Any]      							= MultiControlNetModel([controlneta, controlneta]    )

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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=0    ):
		if str(A__    ).startswith('mps'    ):
			lowerCAmelCase					:    List[Any]      							= torch.manual_seed(A__    )
		else:
			lowerCAmelCase					:    List[Any]      							= torch.Generator(device=A__    ).manual_seed(A__    )

		lowerCAmelCase					:    List[str]      							= 2

		lowerCAmelCase					:    Optional[int]      							= [
		    randn_tensor(
		        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,					generator=A__ ,					device=torch.device(A__    ) ,					),
		    randn_tensor(
		        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,					generator=A__ ,					device=torch.device(A__    ) ,					),
		]

		lowerCAmelCase					:    List[Any]      							= floats_tensor(control_image[0].shape ,					rng=random.Random(A__    )    ).to(A__    )
		lowerCAmelCase					:    Optional[Any]      							= image.cpu().permute(0 ,					2 ,					3 ,					1    )[0]
		lowerCAmelCase					:    Union[str, Any]      							= Image.fromarray(np.uinta(A__    )    ).convert('RGB'    ).resize((64, 64)    )
		lowerCAmelCase					:    List[str]      							= {
		    """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 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= self.get_dummy_components()
		lowerCAmelCase					:    Tuple      							= self.pipeline_class(**A__    )
		pipe.to(A__    )

		lowerCAmelCase					:    List[str]      							= 10.0
		lowerCAmelCase					:    Union[str, Any]      							= 4

		lowerCAmelCase					:    Union[str, Any]      							= self.get_dummy_inputs(A__    )
		lowerCAmelCase					:    str      							= steps
		lowerCAmelCase					:    Dict      							= scale
		lowerCAmelCase					:    Optional[Any]      							= pipe(**A__    )[0]

		lowerCAmelCase					:    Union[str, Any]      							= self.get_dummy_inputs(A__    )
		lowerCAmelCase					:    Dict      							= steps
		lowerCAmelCase					:    Dict      							= scale
		lowerCAmelCase					:    List[str]      							= pipe(**A__ ,					control_guidance_start=0.1 ,					control_guidance_end=0.2    )[0]

		lowerCAmelCase					:    str      							= self.get_dummy_inputs(A__    )
		lowerCAmelCase					:    Dict      							= steps
		lowerCAmelCase					:    Optional[int]      							= scale
		lowerCAmelCase					:    Dict      							= pipe(**A__ ,					control_guidance_start=[0.1, 0.3] ,					control_guidance_end=[0.2, 0.7]    )[0]

		lowerCAmelCase					:    List[str]      							= self.get_dummy_inputs(A__    )
		lowerCAmelCase					:    List[str]      							= steps
		lowerCAmelCase					:    Optional[int]      							= scale
		lowerCAmelCase					:    int      							= pipe(**A__ ,					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 				lowercase     (					self    ):
		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 				lowercase     (					self    ):
		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3    )



	def 				lowercase     (					self    ):
		self._test_inference_batch_single_identical(expected_max_diff=2e-3    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self.get_dummy_components()
		lowerCAmelCase					:    Optional[int]      							= self.pipeline_class(**A__    )
		pipe.to(A__    )
		pipe.set_progress_bar_config(disable=A__    )
		with tempfile.TemporaryDirectory() as tmpdir:
			try:
				# save_pretrained is not implemented for Multi-ControlNet
				pipe.save_pretrained(A__    )
			except NotImplementedError:
				pass




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



	def 				lowercase     (					self    ):
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'    )

		lowerCAmelCase					:    Union[str, Any]      							= StableDiffusionControlNetImgaImgPipeline.from_pretrained(
		    'runwayml/stable-diffusion-v1-5' ,					safety_checker=A__ ,					controlnet=A__    )
		pipe.enable_model_cpu_offload()
		pipe.set_progress_bar_config(disable=A__    )

		lowerCAmelCase					:    str      							= torch.Generator(device='cpu'    ).manual_seed(0    )
		lowerCAmelCase					:    List[Any]      							= """evil space-punk bird"""
		lowerCAmelCase					:    Union[str, Any]      							= load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'    ).resize((512, 512)    )
		lowerCAmelCase					:    Optional[int]      							= load_image(
		    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'    ).resize((512, 512)    )

		lowerCAmelCase					:    List[str]      							= pipe(
		    A__ ,					A__ ,					control_image=A__ ,					generator=A__ ,					output_type='np' ,					num_inference_steps=50 ,					strength=0.6 ,					)

		lowerCAmelCase					:    Optional[Any]      							= output.images[0]

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

		lowerCAmelCase					:    Union[str, Any]      							= 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 argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCAmelCase  :		List[str]		     =	logging.get_logger('transformers.models.speecht5')

def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Dict					,   _A    :				Any				)    ->      Union[str, Any]:




	"""simple docstring"""





	hf_model.apply_weight_norm()

	lowerCAmelCase					:    int      							= checkpoint['input_conv.weight_g']
	lowerCAmelCase					:    Optional[int]      							= checkpoint['input_conv.weight_v']
	lowerCAmelCase					:    Dict      							= checkpoint['input_conv.bias']

	for i in range(len(config.upsample_rates				)				):
		lowerCAmelCase					:    Optional[Any]      							= checkpoint[F"upsamples.{i}.1.weight_g"]
		lowerCAmelCase					:    str      							= checkpoint[F"upsamples.{i}.1.weight_v"]
		lowerCAmelCase					:    str      							= checkpoint[F"upsamples.{i}.1.bias"]

	for i in range(len(config.upsample_rates				) * len(config.resblock_kernel_sizes				)				):
		for j in range(len(config.resblock_dilation_sizes				)				):
			lowerCAmelCase					:    int      							= checkpoint[F"blocks.{i}.convs1.{j}.1.weight_g"]
			lowerCAmelCase					:    str      							= checkpoint[F"blocks.{i}.convs1.{j}.1.weight_v"]
			lowerCAmelCase					:    int      							= checkpoint[F"blocks.{i}.convs1.{j}.1.bias"]

			lowerCAmelCase					:    Optional[Any]      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]

	lowerCAmelCase					:    List[Any]      							= checkpoint['output_conv.1.weight_g']
	lowerCAmelCase					:    List[str]      							= checkpoint['output_conv.1.weight_v']
	lowerCAmelCase					:    Optional[Any]      							= checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()

@torch.no_grad()
def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Union[str, Any]					,   _A    :				List[Any]					,   _A    :				Any=None					,   _A    :				Any=None					,   )    ->      Dict:




	"""simple docstring"""





	if config_path is not None:
		lowerCAmelCase					:    Dict      							= SpeechTaHifiGanConfig.from_pretrained(_A				)
	else:
		lowerCAmelCase					:    Union[str, Any]      							= SpeechTaHifiGanConfig()

	lowerCAmelCase					:    List[Any]      							= SpeechTaHifiGan(_A				)

	lowerCAmelCase					:    List[str]      							= torch.load(_A				)
	load_weights(orig_checkpoint['model']['generator']					,   _A					,   _A				)

	lowerCAmelCase					:    Tuple      							= np.load(_A				)
	lowerCAmelCase					:    List[Any]      							= stats[0].reshape(-1				)
	lowerCAmelCase					:    int      							= stats[1].reshape(-1				)
	lowerCAmelCase					:    Union[str, Any]      							= torch.from_numpy(_A				).float()
	lowerCAmelCase					:    int      							= torch.from_numpy(_A				).float()

	model.save_pretrained(_A				)

	if repo_id:
		print('Pushing to the hub...'				)
		model.push_to_hub(_A				)


if __name__ == "__main__":
				_lowerCAmelCase  :		List[Any]		     =	argparse.ArgumentParser()
				parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
				parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				parser.add_argument(
				    '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
				)
				parser.add_argument(
				    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
				)

				_lowerCAmelCase  :		Union[str, Any]		     =	parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)
