
import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__		:     Any     			=      logging.get_logger(__name__)






def   lowerCamelCase__							(					a      )      ->   Optional[Any]:
   _A:  int					=       OrderedDict()
   for key, value in state_dict.items():
      if key.startswith('''module.encoder'''      ):
         _A:  Union[str, Any]					=       key.replace('''module.encoder'''		,		'''glpn.encoder'''      )
      if key.startswith('''module.decoder'''      ):
         _A:  Optional[int]					=       key.replace('''module.decoder'''		,		'''decoder.stages'''      )
      if "patch_embed" in key:
         # replace for example patch_embed1 by patch_embeddings.0
         _A:  List[str]					=       key[key.find('''patch_embed'''      ) + len('''patch_embed'''      )]
         _A:  List[str]					=       key.replace(f"""patch_embed{idx}"""		,		f"""patch_embeddings.{int(a      )-1}"""      )
      if "norm" in key:
         _A:  Tuple					=       key.replace('''norm'''		,		'''layer_norm'''      )
      if "glpn.encoder.layer_norm" in key:
         # replace for example layer_norm1 by layer_norm.0
         _A:  int					=       key[key.find('''glpn.encoder.layer_norm'''      ) + len('''glpn.encoder.layer_norm'''      )]
         _A:  Dict					=       key.replace(f"""layer_norm{idx}"""		,		f"""layer_norm.{int(a      )-1}"""      )
      if "layer_norm1" in key:
         _A:  Any					=       key.replace('''layer_norm1'''		,		'''layer_norm_1'''      )
      if "layer_norm2" in key:
         _A:  List[Any]					=       key.replace('''layer_norm2'''		,		'''layer_norm_2'''      )
      if "block" in key:
         # replace for example block1 by block.0
         _A:  Dict					=       key[key.find('''block'''      ) + len('''block'''      )]
         _A:  int					=       key.replace(f"""block{idx}"""		,		f"""block.{int(a      )-1}"""      )
      if "attn.q" in key:
         _A:  int					=       key.replace('''attn.q'''		,		'''attention.self.query'''      )
      if "attn.proj" in key:
         _A:  Tuple					=       key.replace('''attn.proj'''		,		'''attention.output.dense'''      )
      if "attn" in key:
         _A:  Optional[Any]					=       key.replace('''attn'''		,		'''attention.self'''      )
      if "fc1" in key:
         _A:  Optional[int]					=       key.replace('''fc1'''		,		'''dense1'''      )
      if "fc2" in key:
         _A:  List[Any]					=       key.replace('''fc2'''		,		'''dense2'''      )
      if "linear_pred" in key:
         _A:  str					=       key.replace('''linear_pred'''		,		'''classifier'''      )
      if "linear_fuse" in key:
         _A:  Optional[int]					=       key.replace('''linear_fuse.conv'''		,		'''linear_fuse'''      )
         _A:  Optional[Any]					=       key.replace('''linear_fuse.bn'''		,		'''batch_norm'''      )
      if "linear_c" in key:
         # replace for example linear_c4 by linear_c.3
         _A:  List[str]					=       key[key.find('''linear_c'''      ) + len('''linear_c'''      )]
         _A:  Union[str, Any]					=       key.replace(f"""linear_c{idx}"""		,		f"""linear_c.{int(a      )-1}"""      )
      if "bot_conv" in key:
         _A:  Optional[int]					=       key.replace('''bot_conv'''		,		'''0.convolution'''      )
      if "skip_conv1" in key:
         _A:  List[str]					=       key.replace('''skip_conv1'''		,		'''1.convolution'''      )
      if "skip_conv2" in key:
         _A:  str					=       key.replace('''skip_conv2'''		,		'''2.convolution'''      )
      if "fusion1" in key:
         _A:  Optional[Any]					=       key.replace('''fusion1'''		,		'''1.fusion'''      )
      if "fusion2" in key:
         _A:  Tuple					=       key.replace('''fusion2'''		,		'''2.fusion'''      )
      if "fusion3" in key:
         _A:  List[Any]					=       key.replace('''fusion3'''		,		'''3.fusion'''      )
      if "fusion" in key and "conv" in key:
         _A:  Union[str, Any]					=       key.replace('''conv'''		,		'''convolutional_layer'''      )
      if key.startswith('''module.last_layer_depth'''      ):
         _A:  str					=       key.replace('''module.last_layer_depth'''		,		'''head.head'''      )
      _A:  int					=       value

   return new_state_dict






def   lowerCamelCase__							(					a		,		a      )      ->   Dict:
   # for each of the encoder blocks:
   for i in range(config.num_encoder_blocks      ):
      for j in range(config.depths[i]      ):
         # read in weights + bias of keys and values (which is a single matrix in the original implementation)
         _A:  Optional[int]					=       state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""      )
         _A:  Union[str, Any]					=       state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias"""      )
         # next, add keys and values (in that order) to the state dict
         _A:  Optional[Any]					=       kv_weight[
             : config.hidden_sizes[i], :
         ]
         _A:  Dict					=       kv_bias[: config.hidden_sizes[i]]
         _A:  int					=       kv_weight[
             config.hidden_sizes[i] :, :
         ]
         _A:  Union[str, Any]					=       kv_bias[config.hidden_sizes[i] :]






def   lowerCamelCase__							(					)      ->   Any:
   _A:  Dict					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   _A:  int					=       Image.open(requests.get(a		,		stream=a      ).raw      )

   return image






@torch.no_grad()
def   lowerCamelCase__							(					a		,		a		,		a=False		,		a=None      )      ->   int:
   _A:  str					=       GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]		,		decoder_hidden_size=64		,		depths=[3, 8, 27, 3]      )

   # load image processor (only resize + rescale)
   _A:  List[str]					=       GLPNImageProcessor()

   # prepare image
   _A:  Tuple					=       prepare_img()
   _A:  Dict					=       image_processor(images=a		,		return_tensors='''pt'''      ).pixel_values

   logger.info('''Converting model...'''      )

   # load original state dict
   _A:  List[Any]					=       torch.load(a		,		map_location=torch.device('''cpu'''      )      )

   # rename keys
   _A:  Dict					=       rename_keys(a      )

   # key and value matrices need special treatment
   read_in_k_v(a		,		a      )

   # create HuggingFace model and load state dict
   _A:  List[Any]					=       GLPNForDepthEstimation(a      )
   model.load_state_dict(a      )
   model.eval()

   # forward pass
   _A:  str					=       model(a      )
   _A:  Union[str, Any]					=       outputs.predicted_depth

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         _A:  Optional[int]					=       torch.tensor(
             [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]      )
      elif "kitti" in model_name:
         _A:  Tuple					=       torch.tensor(
             [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]      )
      else:
         raise ValueError(f"""Unknown model name: {model_name}"""      )

      _A:  Optional[Any]					=       torch.Size([1, 4_80, 6_40]      )

      assert predicted_depth.shape == expected_shape
      assert torch.allclose(predicted_depth[0, :3, :3]		,		a		,		atol=1E-4      )
      print('''Looks ok!'''      )

   # finally, push to hub if required
   if push_to_hub:
      logger.info('''Pushing model and image processor to the hub...'''      )
      model.push_to_hub(
          repo_path_or_name=Path(a		,		a      )		,		organization='''nielsr'''		,		commit_message='''Add model'''		,		use_temp_dir=a		,		)
      image_processor.push_to_hub(
          repo_path_or_name=Path(a		,		a      )		,		organization='''nielsr'''		,		commit_message='''Add image processor'''		,		use_temp_dir=a		,		)


if __name__ == "__main__":
   UpperCAmelCase__		:     List[str]     			=      argparse.ArgumentParser()

   parser.add_argument(
       '--checkpoint_path',
       default=None,
       type=str,
       help='Path to the original PyTorch checkpoint (.pth file).',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
   )
   parser.add_argument(
       '--model_name',
       default='glpn-kitti',
       type=str,
       help='Name of the model in case you\'re pushing to the hub.',
   )
   UpperCAmelCase__		:     List[str]     			=      parser.parse_args()
   convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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

UpperCAmelCase__		:     Dict     			=      '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'






def   lowerCamelCase__							(					a		,		a		,		a=8      )      ->   List[Any]:
   _A:  int					=       height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   _A:  str					=       width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor






def   lowerCamelCase__							(					a		,		a=5_12		,		a=5_12      )      ->   Dict:
   _A:  Union[str, Any]					=       pil_image.resize((w, h)		,		resample=Image.BICUBIC		,		reducing_gap=1      )
   _A:  Tuple					=       np.array(pil_image.convert('''RGB'''      )      )
   _A:  List[str]					=       arr.astype(np.floataa      ) / 127.5 - 1
   _A:  Tuple					=       np.transpose(a		,		[2, 0, 1]      )
   _A:  Any					=       torch.from_numpy(a      ).unsqueeze(0      )
   return image






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''


   def __init__(					self     :							int			,			lowerCAmelCase_     :							UNetaDConditionModel			,			lowerCAmelCase_     :							DDPMScheduler			,			lowerCAmelCase_     :							VQModel			,			):

      """simple docstring"""
      super().__init__()

      self.register_modules(
          unet=lowerCAmelCase_			,			scheduler=lowerCAmelCase_			,			movq=lowerCAmelCase_			,			)
      _A:  List[Any]					=       2 ** (len(self.movq.config.block_out_channels     ) - 1)


   def  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      # get the original timestep using init_timestep
      _A:  Union[str, Any]					=       min(int(num_inference_steps * strength     )			,			lowerCAmelCase_     )

      _A:  str					=       max(num_inference_steps - init_timestep			,			0     )
      _A:  str					=       self.scheduler.timesteps[t_start:]

      return timesteps, num_inference_steps - t_start


   def  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Optional[int]=None     ):

      """simple docstring"""
      if not isinstance(lowerCAmelCase_			,			(torch.Tensor, PIL.Image.Image, list)     ):
         raise ValueError(
             F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCAmelCase_     )}"""     )

      _A:  Optional[int]					=       image.to(device=lowerCAmelCase_			,			dtype=lowerCAmelCase_     )

      _A:  Union[str, Any]					=       batch_size * num_images_per_prompt

      if image.shape[1] == 4:
         _A:  Optional[int]					=       image

      else:
         if isinstance(lowerCAmelCase_			,			lowerCAmelCase_     ) and len(lowerCAmelCase_     ) != batch_size:
            raise ValueError(
                F"""You have passed a list of generators of length {len(lowerCAmelCase_     )}, but requested an effective batch"""
                F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""     )

         elif isinstance(lowerCAmelCase_			,			lowerCAmelCase_     ):
            _A:  List[Any]					=       [
                self.movq.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(lowerCAmelCase_     )
            ]
            _A:  Optional[Any]					=       torch.cat(lowerCAmelCase_			,			dim=0     )
         else:
            _A:  Optional[int]					=       self.movq.encode(lowerCAmelCase_     ).latent_dist.sample(lowerCAmelCase_     )

         _A:  int					=       self.movq.config.scaling_factor * init_latents

      _A:  Optional[Any]					=       torch.cat([init_latents]			,			dim=0     )

      _A:  Any					=       init_latents.shape
      _A:  Optional[Any]					=       randn_tensor(lowerCAmelCase_			,			generator=lowerCAmelCase_			,			device=lowerCAmelCase_			,			dtype=lowerCAmelCase_     )

      # get latents
      _A:  Union[str, Any]					=       self.scheduler.add_noise(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_     )

      _A:  List[str]					=       init_latents

      return latents


   def  __magic_name__				(					self     :							Optional[int]			,			lowerCAmelCase_     :							Optional[int]=0     ):

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

      _A:  Any					=       torch.device(F"""cuda:{gpu_id}"""     )

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


   def  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							Any=0     ):

      """simple docstring"""
      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:  Any					=       torch.device(F"""cuda:{gpu_id}"""     )

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

      _A:  int					=       None
      for cpu_offloaded_model in [self.unet, self.movq]:
         _A			,  _A:  List[Any]					=       cpu_offload_with_hook(lowerCAmelCase_			,			lowerCAmelCase_			,			prev_module_hook=lowerCAmelCase_     )

      # We'll offload the last model manually.
      _A:  Tuple					=       hook


   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      if not hasattr(self.unet			,			'''_hf_hook'''     ):
         return self.device
      for module in self.unet.modules():
         if (
             hasattr(lowerCAmelCase_			,			'''_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(lowerCAmelCase_     )
   def __call__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Union[torch.FloatTensor, List[torch.FloatTensor]]			,			lowerCAmelCase_     :							Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]			,			lowerCAmelCase_     :							Union[torch.FloatTensor, List[torch.FloatTensor]]			,			lowerCAmelCase_     :							int = 5_1_2			,			lowerCAmelCase_     :							int = 5_1_2			,			lowerCAmelCase_     :							int = 1_0_0			,			lowerCAmelCase_     :							float = 4.0			,			lowerCAmelCase_     :							float = 0.3			,			lowerCAmelCase_     :							int = 1			,			lowerCAmelCase_     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None			,			lowerCAmelCase_     :							Optional[str] = "pil"			,			lowerCAmelCase_     :							bool = True			,			):

      """simple docstring"""
      _A:  Any					=       self._execution_device

      _A:  Any					=       guidance_scale > 1.0

      if isinstance(lowerCAmelCase_			,			lowerCAmelCase_     ):
         _A:  Any					=       torch.cat(lowerCAmelCase_			,			dim=0     )
      _A:  int					=       image_embeds.shape[0]
      if isinstance(lowerCAmelCase_			,			lowerCAmelCase_     ):
         _A:  Dict					=       torch.cat(lowerCAmelCase_			,			dim=0     )

      if do_classifier_free_guidance:
         _A:  Any					=       image_embeds.repeat_interleave(lowerCAmelCase_			,			dim=0     )
         _A:  str					=       negative_image_embeds.repeat_interleave(lowerCAmelCase_			,			dim=0     )

      _A:  Dict					=       torch.cat([negative_image_embeds, image_embeds]			,			dim=0     ).to(dtype=self.unet.dtype			,			device=lowerCAmelCase_     )

      if not isinstance(lowerCAmelCase_			,			lowerCAmelCase_     ):
         _A:  List[str]					=       [image]
      if not all(isinstance(lowerCAmelCase_			,			(PIL.Image.Image, torch.Tensor)     ) for i in image     ):
         raise ValueError(
             F"""Input is in incorrect format: {[type(lowerCAmelCase_     ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""     )

      _A:  List[str]					=       torch.cat([prepare_image(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_     ) for i in image]			,			dim=0     )
      _A:  Tuple					=       image.to(dtype=image_embeds.dtype			,			device=lowerCAmelCase_     )

      _A:  Optional[Any]					=       self.movq.encode(lowerCAmelCase_     )['''latents''']
      _A:  Optional[int]					=       latents.repeat_interleave(lowerCAmelCase_			,			dim=0     )
      self.scheduler.set_timesteps(lowerCAmelCase_			,			device=lowerCAmelCase_     )
      _A			,  _A:  List[Any]					=       self.get_timesteps(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_     )
      _A:  Dict					=       timesteps[:1].repeat(batch_size * num_images_per_prompt     )
      _A			,  _A:  Optional[int]					=       downscale_height_and_width(lowerCAmelCase_			,			lowerCAmelCase_			,			self.movq_scale_factor     )
      _A:  Any					=       self.prepare_latents(
          lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			image_embeds.dtype			,			lowerCAmelCase_			,			lowerCAmelCase_     )
      for i, t in enumerate(self.progress_bar(lowerCAmelCase_     )     ):
         # expand the latents if we are doing classifier free guidance
         _A:  Dict					=       torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

         _A:  str					=       {'''image_embeds''': image_embeds}
         _A:  Optional[int]					=       self.unet(
             sample=lowerCAmelCase_			,			timestep=lowerCAmelCase_			,			encoder_hidden_states=lowerCAmelCase_			,			added_cond_kwargs=lowerCAmelCase_			,			return_dict=lowerCAmelCase_			,			)[0]

         if do_classifier_free_guidance:
            _A			,  _A:  str					=       noise_pred.split(latents.shape[1]			,			dim=1     )
            _A			,  _A:  int					=       noise_pred.chunk(2     )
            _A			,  _A:  int					=       variance_pred.chunk(2     )
            _A:  Dict					=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
            _A:  List[str]					=       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:  Optional[Any]					=       noise_pred.split(latents.shape[1]			,			dim=1     )

         # compute the previous noisy sample x_t -> x_t-1
         _A:  Any					=       self.scheduler.step(
             lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			generator=lowerCAmelCase_			,			)[0]

      # post-processing
      _A:  Tuple					=       self.movq.decode(lowerCAmelCase_			,			force_not_quantize=lowerCAmelCase_     )['''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:  Any					=       image.clamp(0			,			1     )
         _A:  Any					=       image.cpu().permute(0			,			2			,			3			,			1     ).float().numpy()

      if output_type == "pil":
         _A:  Union[str, Any]					=       self.numpy_to_pil(lowerCAmelCase_     )

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=lowerCAmelCase_     )



