

'''simple docstring'''

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				List[Any]     	=			argparse.ArgumentParser()

							parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

							parser.add_argument(
							    "--txt2img_unclip",
							    default="kakaobrain/karlo-v1-alpha",
							    type=str,
							    required=False,
							    help="The pretrained txt2img unclip.",
							)

							SCREAMING_SNAKE_CASE							:				Tuple     	=			parser.parse_args()

							SCREAMING_SNAKE_CASE							:				List[Any]     	=			UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							SCREAMING_SNAKE_CASE							:				Tuple     	=			CLIPImageProcessor()
							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)












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()
a__      :     Any         =		logging.get_logger(__name__)

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]				)	->				Optional[int]:
     '''simple docstring'''


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

     return new_state_dict

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Union[str, Any]				)	->				Tuple:
     '''simple docstring'''


     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_          =       state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'				)
               A_          =       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_          =       kv_weight[
                   : config.hidden_sizes[i], :
               ]
               A_          =       kv_bias[: config.hidden_sizes[i]]
               A_          =       kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               A_          =       kv_bias[config.hidden_sizes[i] :]

def      UpperCAmelCase_    (    )	->				str:
     '''simple docstring'''


     A_          =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     A_          =       Image.open(requests.get(_UpperCAmelCase  ,						stream=_UpperCAmelCase				).raw				)

     return image

@torch.no_grad()
def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Union[str, Any]=False  ,						_UpperCAmelCase							:Any=None				)	->				Optional[int]:
     '''simple docstring'''


     A_          =       GLPNConfig(hidden_sizes=[64, 128, 320, 512]  ,						decoder_hidden_size=64  ,						depths=[3, 8, 27, 3]				)

     # load image processor (only resize + rescale)
     A_          =       GLPNImageProcessor()

     # prepare image
     A_          =       prepare_img()
     A_          =       image_processor(images=_UpperCAmelCase  ,						return_tensors='''pt'''				).pixel_values

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

     # load original state dict
     A_          =       torch.load(_UpperCAmelCase  ,						map_location=torch.device('''cpu'''				)				)

     # rename keys
     A_          =       rename_keys(_UpperCAmelCase				)

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

     # create HuggingFace model and load state dict
     A_          =       GLPNForDepthEstimation(_UpperCAmelCase				)
     model.load_state_dict(_UpperCAmelCase				)
     model.eval()

     # forward pass
     A_          =       model(_UpperCAmelCase				)
     A_          =       outputs.predicted_depth

     # verify output
     if model_name is not None:
          if "nyu" in model_name:
               A_          =       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_          =       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_          =       torch.Size([1, 480, 640]				)

          assert predicted_depth.shape == expected_shape
          assert torch.allclose(predicted_depth[0, :3, :3]  ,						_UpperCAmelCase  ,						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(_UpperCAmelCase  ,						_UpperCAmelCase				)  ,						organization='''nielsr'''  ,						commit_message='''Add model'''  ,						use_temp_dir=_UpperCAmelCase  ,						)
          image_processor.push_to_hub(
              repo_path_or_name=Path(_UpperCAmelCase  ,						_UpperCAmelCase				)  ,						organization='''nielsr'''  ,						commit_message='''Add image processor'''  ,						use_temp_dir=_UpperCAmelCase  ,						)


if __name__ == "__main__":
      a__      :     Dict         =		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.',
      )
      a__      :     int         =		parser.parse_args()
      convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



