



'''simple docstring'''




from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCamelCase				:Optional[int]							       =   logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase				:Tuple							       =   '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''



def       a     (					lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__=8       ):
			'''simple docstring'''





			A_      :		Optional[Any]      =	height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			A_      :		Optional[Any]      =	width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor







class       _lowerCAmelCase				(     a__					):


	def __init__(self			, lowercase			, lowercase			, lowercase			, ):
				super().__init__()

				self.register_modules(
				    unet=_snake_case			, scheduler=_snake_case			, movq=_snake_case			, )
				A_      :		List[str]      =	2 ** (len(self.movq.config.block_out_channels				) - 1)


	def        _a      (self			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase			, lowercase				):






				if latents is None:
							A_      :		int      =	randn_tensor(_snake_case			, generator=_snake_case			, device=_snake_case			, dtype=_snake_case				)
				else:
							if latents.shape != shape:
										raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'				)
							A_      :		List[str]      =	latents.to(_snake_case				)

				A_      :		Tuple      =	latents * scheduler.init_noise_sigma
				return latents


	def        _a      (self			, lowercase=0				):






				if is_accelerate_available():
							from accelerate import cpu_offload
				else:
							raise ImportError("""Please install accelerate via `pip install accelerate`"""				)

				A_      :		Union[str, Any]      =	torch.device(F'cuda:{gpu_id}'				)

				A_      :		Dict      =	[
				    self.unet,
				    self.movq,
				]
				for cpu_offloaded_model in models:
							if cpu_offloaded_model is not None:
										cpu_offload(_snake_case			, _snake_case				)


	def        _a      (self			, lowercase=0				):






				if is_accelerate_available() and is_accelerate_version(""">="""			, """0.17.0.dev0"""				):
							from accelerate import cpu_offload_with_hook
				else:
							raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""				)

				A_      :		Dict      =	torch.device(F'cuda:{gpu_id}'				)

				if self.device.type != "cpu":
							self.to("""cpu"""			, silence_dtype_warnings=_snake_case				)
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

				A_      :		Optional[Any]      =	None
				for cpu_offloaded_model in [self.unet, self.movq]:
							A_,       A_      :		str      =	cpu_offload_with_hook(_snake_case			, _snake_case			, prev_module_hook=_snake_case				)

				# We'll offload the last model manually.
				A_      :		int      =	hook


	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def        _a      (self				):






				if not hasattr(self.unet			, """_hf_hook"""				):
							return self.device
				for module in self.unet.modules():
							if (
							    hasattr(_snake_case			, """_hf_hook"""				)
							    and hasattr(module._hf_hook			, """execution_device"""				)
							    and module._hf_hook.execution_device is not None
							):
										return torch.device(module._hf_hook.execution_device				)
				return self.device


	@torch.no_grad()
	@replace_example_docstring(_snake_case				)
	def __call__(self			, lowercase			, lowercase			, lowercase			, lowercase = 512			, lowercase = 512			, lowercase = 100			, lowercase = 4.0			, lowercase = 1			, lowercase = None			, lowercase = None			, lowercase = "pil"			, lowercase = True			, ):
				A_      :		Tuple      =	self._execution_device

				A_      :		List[str]      =	guidance_scale > 1.0

				if isinstance(_snake_case			, _snake_case				):
							A_      :		Optional[int]      =	torch.cat(_snake_case			, dim=0				)
				if isinstance(_snake_case			, _snake_case				):
							A_      :		int      =	torch.cat(_snake_case			, dim=0				)
				if isinstance(_snake_case			, _snake_case				):
							A_      :		List[str]      =	torch.cat(_snake_case			, dim=0				)

				A_      :		Optional[Any]      =	image_embeds.shape[0] * num_images_per_prompt

				if do_classifier_free_guidance:
							A_      :		Optional[int]      =	image_embeds.repeat_interleave(_snake_case			, dim=0				)
							A_      :		str      =	negative_image_embeds.repeat_interleave(_snake_case			, dim=0				)
							A_      :		Tuple      =	hint.repeat_interleave(_snake_case			, dim=0				)

				A_      :		Any      =	torch.cat([negative_image_embeds, image_embeds]			, dim=0				).to(dtype=self.unet.dtype			, device=_snake_case				)
				A_      :		List[Any]      =	torch.cat([hint, hint]			, dim=0				).to(dtype=self.unet.dtype			, device=_snake_case				)
				self.scheduler.set_timesteps(_snake_case			, device=_snake_case				)
				A_      :		int      =	self.scheduler.timesteps

				A_      :		str      =	self.movq.config.latent_channels

				A_,       A_      :		Any      =	downscale_height_and_width(_snake_case			, _snake_case			, self.movq_scale_factor				)

				# create initial latent
				A_      :		Tuple      =	self.prepare_latents(
				    (batch_size, num_channels_latents, height, width)			, image_embeds.dtype			, _snake_case			, _snake_case			, _snake_case			, self.scheduler			, )

				for i, t in enumerate(self.progress_bar(_snake_case				)				):
							# expand the latents if we are doing classifier free guidance
							A_      :		Tuple      =	torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

							A_      :		Any      =	{"""image_embeds""": image_embeds, """hint""": hint}
							A_      :		int      =	self.unet(
							    sample=_snake_case			, timestep=_snake_case			, encoder_hidden_states=_snake_case			, added_cond_kwargs=_snake_case			, return_dict=_snake_case			, )[0]

							if do_classifier_free_guidance:
										A_,       A_      :		Optional[int]      =	noise_pred.split(latents.shape[1]			, dim=1				)
										A_,       A_      :		str      =	noise_pred.chunk(2				)
										A_,       A_      :		Optional[int]      =	variance_pred.chunk(2				)
										A_      :		Tuple      =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
										A_      :		Any      =	torch.cat([noise_pred, variance_pred_text]			, dim=1				)

							if not (
							    hasattr(self.scheduler.config			, """variance_type"""				)
							    and self.scheduler.config.variance_type in ["learned", "learned_range"]
							):
										A_,       A_      :		Any      =	noise_pred.split(latents.shape[1]			, dim=1				)

							# compute the previous noisy sample x_t -> x_t-1
							A_      :		Union[str, Any]      =	self.scheduler.step(
							    _snake_case			, _snake_case			, _snake_case			, generator=_snake_case			, )[0]
				# post-processing
				A_      :		Tuple      =	self.movq.decode(_snake_case			, force_not_quantize=_snake_case				)["""sample"""]

				if output_type not in ["pt", "np", "pil"]:
							raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'				)

				if output_type in ["np", "pil"]:
							A_      :		int      =	image * 0.5 + 0.5
							A_      :		Dict      =	image.clamp(0			, 1				)
							A_      :		int      =	image.cpu().permute(0			, 2			, 3			, 1				).float().numpy()

				if output_type == "pil":
							A_      :		Dict      =	self.numpy_to_pil(_snake_case				)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_snake_case				)






"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any			):
 lowerCAmelCase    				= SwinaSRConfig()

 if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
  lowerCAmelCase    				= 4
 elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
  lowerCAmelCase    				= 4
  lowerCAmelCase    				= 48
  lowerCAmelCase    				= 'pixelshuffle_aux'
 elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
  lowerCAmelCase    				= [6, 6, 6, 6]
  lowerCAmelCase    				= 60
  lowerCAmelCase    				= [6, 6, 6, 6]
  lowerCAmelCase    				= 'pixelshuffledirect'
 elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
  lowerCAmelCase    				= 4
  lowerCAmelCase    				= 'nearest+conv'
 elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
  lowerCAmelCase    				= 1
  lowerCAmelCase    				= 1
  lowerCAmelCase    				= 126
  lowerCAmelCase    				= 7
  lowerCAmelCase    				= 255.0
  lowerCAmelCase    				= ''

 return config

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    Union[str, Any]			):
 if "patch_embed.proj" in name and "layers" not in name:
  lowerCAmelCase    				= name.replace('patch_embed.proj'  ,     'embeddings.patch_embeddings.projection'			)
 if "patch_embed.norm" in name:
  lowerCAmelCase    				= name.replace('patch_embed.norm'  ,     'embeddings.patch_embeddings.layernorm'			)
 if "layers" in name:
  lowerCAmelCase    				= name.replace('layers'  ,     'encoder.stages'			)
 if "residual_group.blocks" in name:
  lowerCAmelCase    				= name.replace('residual_group.blocks'  ,     'layers'			)
 if "attn.proj" in name:
  lowerCAmelCase    				= name.replace('attn.proj'  ,     'attention.output.dense'			)
 if "attn" in name:
  lowerCAmelCase    				= name.replace('attn'  ,     'attention.self'			)
 if "norm1" in name:
  lowerCAmelCase    				= name.replace('norm1'  ,     'layernorm_before'			)
 if "norm2" in name:
  lowerCAmelCase    				= name.replace('norm2'  ,     'layernorm_after'			)
 if "mlp.fc1" in name:
  lowerCAmelCase    				= name.replace('mlp.fc1'  ,     'intermediate.dense'			)
 if "mlp.fc2" in name:
  lowerCAmelCase    				= name.replace('mlp.fc2'  ,     'output.dense'			)
 if "q_bias" in name:
  lowerCAmelCase    				= name.replace('q_bias'  ,     'query.bias'			)
 if "k_bias" in name:
  lowerCAmelCase    				= name.replace('k_bias'  ,     'key.bias'			)
 if "v_bias" in name:
  lowerCAmelCase    				= name.replace('v_bias'  ,     'value.bias'			)
 if "cpb_mlp" in name:
  lowerCAmelCase    				= name.replace('cpb_mlp'  ,     'continuous_position_bias_mlp'			)
 if "patch_embed.proj" in name:
  lowerCAmelCase    				= name.replace('patch_embed.proj'  ,     'patch_embed.projection'			)

 if name == "norm.weight":
  lowerCAmelCase    				= 'layernorm.weight'
 if name == "norm.bias":
  lowerCAmelCase    				= 'layernorm.bias'

 if "conv_first" in name:
  lowerCAmelCase    				= name.replace('conv_first'  ,     'first_convolution'			)

 if (
     "upsample" in name
     or "conv_before_upsample" in name
     or "conv_bicubic" in name
     or "conv_up" in name
     or "conv_hr" in name
     or "conv_last" in name
     or "aux" in name
 ):
  # heads
  if "conv_last" in name:
   lowerCAmelCase    				= name.replace('conv_last'  ,     'final_convolution'			)
  if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
   if "conv_before_upsample.0" in name:
    lowerCAmelCase    				= name.replace('conv_before_upsample.0'  ,     'conv_before_upsample'			)
   if "upsample.0" in name:
    lowerCAmelCase    				= name.replace('upsample.0'  ,     'upsample.convolution_0'			)
   if "upsample.2" in name:
    lowerCAmelCase    				= name.replace('upsample.2'  ,     'upsample.convolution_1'			)
   lowerCAmelCase    				= 'upsample.' + name
  elif config.upsampler == "pixelshuffledirect":
   lowerCAmelCase    				= name.replace('upsample.0.weight'  ,     'upsample.conv.weight'			)
   lowerCAmelCase    				= name.replace('upsample.0.bias'  ,     'upsample.conv.bias'			)
  else:
   pass
 else:
  lowerCAmelCase    				= 'swin2sr.' + name

 return name

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    Dict			):
 for key in orig_state_dict.copy().keys():
  lowerCAmelCase    				= orig_state_dict.pop(_UpperCAmelCase			)

  if "qkv" in key:
   lowerCAmelCase    				= key.split('.'			)
   lowerCAmelCase    				= int(key_split[1]			)
   lowerCAmelCase    				= int(key_split[4]			)
   lowerCAmelCase    				= config.embed_dim

   if "weight" in key:
    lowerCAmelCase    				= val[:dim, :]
    lowerCAmelCase    				= val[dim : dim * 2, :]
    lowerCAmelCase    				= val[-dim:, :]
   else:
    lowerCAmelCase    				= val[:dim]
    lowerCAmelCase    				= val[dim : dim * 2]
    lowerCAmelCase    				= val[-dim:]
   pass
  else:
   lowerCAmelCase    				= val

 return orig_state_dict

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    Tuple			):
 lowerCAmelCase    				= get_config(_UpperCAmelCase			)
 lowerCAmelCase    				= SwinaSRForImageSuperResolution(_UpperCAmelCase			)
 model.eval()

 lowerCAmelCase    				= torch.hub.load_state_dict_from_url(_UpperCAmelCase  ,     map_location='cpu'			)
 lowerCAmelCase    				= convert_state_dict(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase				,lowerCAmelCase    				= model.load_state_dict(_UpperCAmelCase  ,     strict=_UpperCAmelCase			)

 if len(_UpperCAmelCase			) > 0:
  raise ValueError('Missing keys when converting: {}'.format(_UpperCAmelCase			)			)
 for key in unexpected_keys:
  if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
   raise ValueError(F'Unexpected key {key} in state_dict'			)

    # verify values
 lowerCAmelCase    				= 'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
 lowerCAmelCase    				= Image.open(requests.get(_UpperCAmelCase  ,     stream=_UpperCAmelCase			).raw			).convert('RGB'			)
 lowerCAmelCase    				= SwinaSRImageProcessor()
 # pixel_values = processor(image, return_tensors="pt").pixel_values

 lowerCAmelCase    				= 126 if 'Jpeg' in checkpoint_url else 256
 lowerCAmelCase    				= Compose(
     [
         Resize((image_size, image_size)			),
         ToTensor(),
         Normalize(mean=[0.485, 0.456, 0.406]  ,     std=[0.229, 0.224, 0.225]			),
     ]			)
 lowerCAmelCase    				= transforms(_UpperCAmelCase			).unsqueeze(0			)

 if config.num_channels == 1:
  lowerCAmelCase    				= pixel_values[:, 0, :, :].unsqueeze(1			)

 lowerCAmelCase    				= model(_UpperCAmelCase			)

 # assert values
 if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
  lowerCAmelCase    				= torch.Size([1, 3, 512, 512]			)
  lowerCAmelCase    				= torch.tensor(
      [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]			)
 elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
  lowerCAmelCase    				= torch.Size([1, 3, 1024, 1024]			)
  lowerCAmelCase    				= torch.tensor(
      [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]			)
 elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
  # TODO values didn't match exactly here
  lowerCAmelCase    				= torch.Size([1, 3, 1024, 1024]			)
  lowerCAmelCase    				= torch.tensor(
      [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]			)
 elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
  lowerCAmelCase    				= torch.Size([1, 3, 512, 512]			)
  lowerCAmelCase    				= torch.tensor(
      [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]			)
 elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
  lowerCAmelCase    				= torch.Size([1, 3, 1024, 1024]			)
  lowerCAmelCase    				= torch.tensor(
      [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]			)

 assert (
     outputs.reconstruction.shape == expected_shape
 ), F'Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'
 assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]  ,     _UpperCAmelCase  ,     atol=1e-3			)
 print('Looks ok!'			)

 lowerCAmelCase    				= {
     'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth': (
         'swin2SR-classical-sr-x2-64'
     ),
     'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth': (
         'swin2SR-classical-sr-x4-64'
     ),
     'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth': (
         'swin2SR-compressed-sr-x4-48'
     ),
     'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth': (
         'swin2SR-lightweight-x2-64'
     ),
     'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth': (
         'swin2SR-realworld-sr-x4-64-bsrgan-psnr'
     ),
 }
 lowerCAmelCase    				= url_to_name[checkpoint_url]

 if pytorch_dump_folder_path is not None:
  print(F'Saving model {model_name} to {pytorch_dump_folder_path}'			)
  model.save_pretrained(_UpperCAmelCase			)
  print(F'Saving image processor to {pytorch_dump_folder_path}'			)
  processor.save_pretrained(_UpperCAmelCase			)

 if push_to_hub:
  model.push_to_hub(F'caidas/{model_name}'			)
  processor.push_to_hub(F'caidas/{model_name}'			)


if __name__ == "__main__":
       __UpperCamelCase     :						Any						       =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint_url''',
           default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
           type=str,
           help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
       )
       parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')

       __UpperCamelCase     :						Optional[int]						       =  parser.parse_args()
       convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





