
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,
)


UpperCAmelCase       =					logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase       =					'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=8				):
	lowercase	    =       height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	lowercase	    =       width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


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

		self.register_modules(
		    unet=snake_case						,       scheduler=snake_case						,       movq=snake_case						,       )
		lowercase	    =       2 ** (len(self.movq.config.block_out_channels			) - 1)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case			):
		if latents is None:
			lowercase	    =       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}'''			)
			lowercase	    =       latents.to(snake_case			)

		lowercase	    =       latents * scheduler.init_noise_sigma
		return latents





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case=0			):
		if is_accelerate_available():
			from accelerate import cpu_offload
		else:
			raise ImportError('Please install accelerate via `pip install accelerate`'			)

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

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





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case=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.'			)

		lowercase	    =       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)

		lowercase	    =       None
		for cpu_offloaded_model in [self.unet, self.movq]:
			lowercase    ,   lowercase	    =       cpu_offload_with_hook(snake_case						,       snake_case						,       prev_module_hook=snake_case			)

		# We'll offload the last model manually.
		lowercase	    =       hook





	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def       SCREAMING_SNAKE_CASE__		(			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						,       snake_case						,       snake_case						,       snake_case = 512						,       snake_case = 512						,       snake_case = 100						,       snake_case = 4.0						,       snake_case = 1						,       snake_case = None						,       snake_case = None						,       snake_case = "pil"						,       snake_case = True						,       ):
		lowercase	    =       self._execution_device

		lowercase	    =       guidance_scale > 1.0

		if isinstance(snake_case						,       snake_case			):
			lowercase	    =       torch.cat(snake_case						,       dim=0			)
		lowercase	    =       image_embeds.shape[0] * num_images_per_prompt
		if isinstance(snake_case						,       snake_case			):
			lowercase	    =       torch.cat(snake_case						,       dim=0			)

		if do_classifier_free_guidance:
			lowercase	    =       image_embeds.repeat_interleave(snake_case						,       dim=0			)
			lowercase	    =       negative_image_embeds.repeat_interleave(snake_case						,       dim=0			)

		lowercase	    =       torch.cat([negative_image_embeds, image_embeds]						,       dim=0			).to(dtype=self.unet.dtype						,       device=snake_case			)

		self.scheduler.set_timesteps(snake_case						,       device=snake_case			)
		lowercase	    =       self.scheduler.timesteps

		lowercase	    =       self.unet.config.in_channels

		lowercase    ,   lowercase	    =       downscale_height_and_width(snake_case						,       snake_case						,       self.movq_scale_factor			)

		# create initial latent
		lowercase	    =       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
			lowercase	    =       torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

			lowercase	    =       {'image_embeds': image_embeds}
			lowercase	    =       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:
				lowercase    ,   lowercase	    =       noise_pred.split(latents.shape[1]						,       dim=1			)
				lowercase    ,   lowercase	    =       noise_pred.chunk(2			)
				lowercase    ,   lowercase	    =       variance_pred.chunk(2			)
				lowercase	    =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
				lowercase	    =       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"]
			):
				lowercase    ,   lowercase	    =       noise_pred.split(latents.shape[1]						,       dim=1			)

			# compute the previous noisy sample x_t -> x_t-1
			lowercase	    =       self.scheduler.step(
			    snake_case						,       snake_case						,       snake_case						,       generator=snake_case						,       )[0]
		# post-processing
		lowercase	    =       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"]:
			lowercase	    =       image * 0.5 + 0.5
			lowercase	    =       image.clamp(0						,       1			)
			lowercase	    =       image.cpu().permute(0						,       2						,       3						,       1			).float().numpy()

		if output_type == "pil":
			lowercase	    =       self.numpy_to_pil(snake_case			)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=snake_case			)





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation


def    __lowerCamelCase   (      A__						: int					)     ->    List[str]:
   lowerCamelCase_						:   int		= 384
   if "tiny" in model_name:
      lowerCamelCase_						:   str		= [3, 3, 9, 3]
      lowerCamelCase_						:   List[str]		= [96, 192, 384, 768]
   if "small" in model_name:
      lowerCamelCase_						:   Any		= [3, 3, 27, 3]
      lowerCamelCase_						:   List[Any]		= [96, 192, 384, 768]
   if "base" in model_name:
      lowerCamelCase_						:   Optional[Any]		= [3, 3, 27, 3]
      lowerCamelCase_						:   int		= [128, 256, 512, 1024]
      lowerCamelCase_						:   int		= 512
   if "large" in model_name:
      lowerCamelCase_						:   Union[str, Any]		= [3, 3, 27, 3]
      lowerCamelCase_						:   Dict		= [192, 384, 768, 1536]
      lowerCamelCase_						:   Union[str, Any]		= 768
   if "xlarge" in model_name:
      lowerCamelCase_						:   Union[str, Any]		= [3, 3, 27, 3]
      lowerCamelCase_						:   Optional[int]		= [256, 512, 1024, 2048]
      lowerCamelCase_						:   List[str]		= 1024

   # set label information
   lowerCamelCase_						:   str		= 150
   lowerCamelCase_						:   List[Any]		= """huggingface/label-files"""
   lowerCamelCase_						:   int		= """ade20k-id2label.json"""
   lowerCamelCase_						:   List[Any]		= json.load(open(hf_hub_download(A__						,						A__						,						repo_type="""dataset"""					)						,						"""r"""					)					)
   lowerCamelCase_						:   Optional[Any]		= {int(A__					): v for k, v in idalabel.items()}
   lowerCamelCase_						:   int		= {v: k for k, v in idalabel.items()}

   lowerCamelCase_						:   Dict		= ConvNextConfig(
       depths=A__						,						hidden_sizes=A__						,						out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					)
   lowerCamelCase_						:   str		= UperNetConfig(
       backbone_config=A__						,						auxiliary_in_channels=A__						,						num_labels=A__						,						idalabel=A__						,						labelaid=A__						,						)

   return config


def    __lowerCamelCase   (      A__						: Optional[Any]					)     ->    Optional[Any]:
   lowerCamelCase_						:   Tuple		= []

   # fmt: off
   # stem
   rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")					)
   rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")					)
   rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")					)
   rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""")					)
   # stages
   for i in range(len(config.backbone_config.depths					)					):
      for j in range(config.backbone_config.depths[i]					):
         rename_keys.append((f'''backbone.stages.{i}.{j}.gamma''', f'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.norm.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.norm.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''')					)
      if i > 0:
         rename_keys.append((f'''backbone.downsample_layers.{i}.0.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''')					)
         rename_keys.append((f'''backbone.downsample_layers.{i}.0.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''')					)
         rename_keys.append((f'''backbone.downsample_layers.{i}.1.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''')					)
         rename_keys.append((f'''backbone.downsample_layers.{i}.1.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''')					)

      rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''')					)
      rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''')					)

   # decode head
   rename_keys.extend(
       [
           ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
           ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
           ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
           ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
       ]					)
   # fmt: on

   return rename_keys


def    __lowerCamelCase   (      A__						: Optional[Any]						,						A__						: List[Any]						,						A__						: int					)     ->    Union[str, Any]:
   lowerCamelCase_						:   Dict		= dct.pop(A__					)
   lowerCamelCase_						:   int		= val


def    __lowerCamelCase   (      A__						: Tuple						,						A__						: str						,						A__						: Any					)     ->    Optional[int]:
   lowerCamelCase_						:   int		= {
       """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
       """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
       """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
       """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
       """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
   }
   lowerCamelCase_						:   List[str]		= model_name_to_url[model_name]
   lowerCamelCase_						:   Dict		= torch.hub.load_state_dict_from_url(A__						,						map_location="""cpu"""					)["""state_dict"""]

   lowerCamelCase_						:   List[Any]		= get_upernet_config(A__					)
   lowerCamelCase_						:   Any		= UperNetForSemanticSegmentation(A__					)
   model.eval()

   # replace "bn" => "batch_norm"
   for key in state_dict.copy().keys():
      lowerCamelCase_						:   Tuple		= state_dict.pop(A__					)
      if "bn" in key:
         lowerCamelCase_						:   Dict		= key.replace("""bn"""						,						"""batch_norm"""					)
      lowerCamelCase_						:   Tuple		= val

   # rename keys
   lowerCamelCase_						:   List[Any]		= create_rename_keys(A__					)
   for src, dest in rename_keys:
      rename_key(A__						,						A__						,						A__					)

   model.load_state_dict(A__					)

   # verify on image
   lowerCamelCase_						:   Any		= """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
   lowerCamelCase_						:   Optional[int]		= Image.open(requests.get(A__						,						stream=A__					).raw					).convert("""RGB"""					)

   lowerCamelCase_						:   List[str]		= SegformerImageProcessor()
   lowerCamelCase_						:   Union[str, Any]		= processor(A__						,						return_tensors="""pt"""					).pixel_values

   with torch.no_grad():
      lowerCamelCase_						:   List[str]		= model(A__					)

   if model_name == "upernet-convnext-tiny":
      lowerCamelCase_						:   Dict		= torch.tensor(
          [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]					)
   elif model_name == "upernet-convnext-small":
      lowerCamelCase_						:   Optional[Any]		= torch.tensor(
          [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]					)
   elif model_name == "upernet-convnext-base":
      lowerCamelCase_						:   str		= torch.tensor(
          [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]					)
   elif model_name == "upernet-convnext-large":
      lowerCamelCase_						:   List[str]		= torch.tensor(
          [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]					)
   elif model_name == "upernet-convnext-xlarge":
      lowerCamelCase_						:   Any		= torch.tensor(
          [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]					)
   print("""Logits:"""						,						outputs.logits[0, 0, :3, :3]					)
   assert torch.allclose(outputs.logits[0, 0, :3, :3]						,						A__						,						atol=1e-4					)
   print("""Looks ok!"""					)

   if pytorch_dump_folder_path is not None:
      print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''					)
      model.save_pretrained(A__					)
      print(f'''Saving processor to {pytorch_dump_folder_path}'''					)
      processor.save_pretrained(A__					)

   if push_to_hub:
      print(f'''Pushing model and processor for {model_name} to hub'''					)
      model.push_to_hub(f'''openmmlab/{model_name}'''					)
      processor.push_to_hub(f'''openmmlab/{model_name}'''					)


if __name__ == "__main__":
      snake_case__				:	Dict					       =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model_name',
          default='upernet-convnext-tiny',
          type=str,
          choices=[F'upernet-convnext-{size}' for size in ['tiny', 'small', 'base', 'large', 'xlarge']],
          help='Name of the ConvNext UperNet model 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 or not to push the converted model to the 🤗 hub.'
      )

      snake_case__				:	List[Any]					       =					parser.parse_args()
      convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





