






'''simple docstring'''




import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible







class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Any					=					[
			    'safety_checker/pytorch_model.bin',
			    'safety_checker/model.safetensors',
			    'vae/diffusion_pytorch_model.bin',
			    'vae/diffusion_pytorch_model.safetensors',
			    'text_encoder/pytorch_model.bin',
			    'text_encoder/model.safetensors',
			    'unet/diffusion_pytorch_model.bin',
			    'unet/diffusion_pytorch_model.safetensors',
			]
			self.assertTrue(is_safetensors_compatible(UpperCAmelCase_))

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			UpperCamelCase__				:					Optional[Any]					=					[
			    'unet/diffusion_pytorch_model.bin',
			    'unet/diffusion_pytorch_model.safetensors',
			]
			self.assertTrue(is_safetensors_compatible(UpperCAmelCase_))

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					int					=					[
			    'safety_checker/pytorch_model.bin',
			    'safety_checker/model.safetensors',
			    'vae/diffusion_pytorch_model.bin',
			    'vae/diffusion_pytorch_model.safetensors',
			    'text_encoder/pytorch_model.bin',
			    'text_encoder/model.safetensors',
			    'unet/diffusion_pytorch_model.bin',
			    # Removed: 'unet/diffusion_pytorch_model.safetensors',
			]
			self.assertFalse(is_safetensors_compatible(UpperCAmelCase_))

	def      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					Dict					=					[
			    'text_encoder/pytorch_model.bin',
			    'text_encoder/model.safetensors',
			]
			self.assertTrue(is_safetensors_compatible(UpperCAmelCase_))

	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					int					=					[
			    'safety_checker/pytorch_model.bin',
			    'safety_checker/model.safetensors',
			    'vae/diffusion_pytorch_model.bin',
			    'vae/diffusion_pytorch_model.safetensors',
			    'text_encoder/pytorch_model.bin',
			    # Removed: 'text_encoder/model.safetensors',
			    'unet/diffusion_pytorch_model.bin',
			    'unet/diffusion_pytorch_model.safetensors',
			]
			self.assertFalse(is_safetensors_compatible(UpperCAmelCase_))

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					str					=					[
			    'safety_checker/pytorch_model.fp16.bin',
			    'safety_checker/model.fp16.safetensors',
			    'vae/diffusion_pytorch_model.fp16.bin',
			    'vae/diffusion_pytorch_model.fp16.safetensors',
			    'text_encoder/pytorch_model.fp16.bin',
			    'text_encoder/model.fp16.safetensors',
			    'unet/diffusion_pytorch_model.fp16.bin',
			    'unet/diffusion_pytorch_model.fp16.safetensors',
			]
			UpperCamelCase__				:					Union[str, Any]					=					'fp16'
			self.assertTrue(is_safetensors_compatible(UpperCAmelCase_  ,					variant=UpperCAmelCase_))

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Tuple					=					[
			    'unet/diffusion_pytorch_model.fp16.bin',
			    'unet/diffusion_pytorch_model.fp16.safetensors',
			]
			UpperCamelCase__				:					Any					=					'fp16'
			self.assertTrue(is_safetensors_compatible(UpperCAmelCase_  ,					variant=UpperCAmelCase_))

	def      __UpperCamelCase							(      self   :						int):
			# pass variant but use the non-variant filenames
			UpperCamelCase__				:					Any					=					[
			    'unet/diffusion_pytorch_model.bin',
			    'unet/diffusion_pytorch_model.safetensors',
			]
			UpperCamelCase__				:					Tuple					=					'fp16'
			self.assertTrue(is_safetensors_compatible(UpperCAmelCase_  ,					variant=UpperCAmelCase_))

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			UpperCamelCase__				:					Tuple					=					[
			    'safety_checker/pytorch_model.fp16.bin',
			    'safety_checker/model.fp16.safetensors',
			    'vae/diffusion_pytorch_model.fp16.bin',
			    'vae/diffusion_pytorch_model.fp16.safetensors',
			    'text_encoder/pytorch_model.fp16.bin',
			    'text_encoder/model.fp16.safetensors',
			    'unet/diffusion_pytorch_model.fp16.bin',
			    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
			]
			UpperCamelCase__				:					Any					=					'fp16'
			self.assertFalse(is_safetensors_compatible(UpperCAmelCase_  ,					variant=UpperCAmelCase_))

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					Union[str, Any]					=					[
			    'text_encoder/pytorch_model.fp16.bin',
			    'text_encoder/model.fp16.safetensors',
			]
			UpperCamelCase__				:					Optional[int]					=					'fp16'
			self.assertTrue(is_safetensors_compatible(UpperCAmelCase_  ,					variant=UpperCAmelCase_))

	def      __UpperCamelCase							(      self   :						List[Any]):
			# pass variant but use the non-variant filenames
			UpperCamelCase__				:					Union[str, Any]					=					[
			    'text_encoder/pytorch_model.bin',
			    'text_encoder/model.safetensors',
			]
			UpperCamelCase__				:					Any					=					'fp16'
			self.assertTrue(is_safetensors_compatible(UpperCAmelCase_  ,					variant=UpperCAmelCase_))





	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			UpperCamelCase__				:					Optional[int]					=					[
			    'safety_checker/pytorch_model.fp16.bin',
			    'safety_checker/model.fp16.safetensors',
			    'vae/diffusion_pytorch_model.fp16.bin',
			    'vae/diffusion_pytorch_model.fp16.safetensors',
			    'text_encoder/pytorch_model.fp16.bin',
			    # 'text_encoder/model.fp16.safetensors',
			    'unet/diffusion_pytorch_model.fp16.bin',
			    'unet/diffusion_pytorch_model.fp16.safetensors',
			]
			UpperCamelCase__				:					Any					=					'fp16'
			self.assertFalse(is_safetensors_compatible(UpperCAmelCase_  ,					variant=UpperCAmelCase_))












'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__          =						logging.get_logger(__name__)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Any:
		UpperCamelCase__				:					Dict					=					DPTConfig()

		if "large" in checkpoint_url:
				UpperCamelCase__				:					List[str]					=					1_024
				UpperCamelCase__				:					List[str]					=					4_096
				UpperCamelCase__				:					Optional[int]					=					24
				UpperCamelCase__				:					List[str]					=					16
				UpperCamelCase__				:					List[str]					=					[5, 11, 17, 23]
				UpperCamelCase__				:					str					=					[256, 512, 1_024, 1_024]
				UpperCamelCase__				:					Union[str, Any]					=					(1, 384, 384)

		if "ade" in checkpoint_url:
				UpperCamelCase__				:					int					=					True

				UpperCamelCase__				:					Optional[Any]					=					150
				UpperCamelCase__				:					int					=					'huggingface/label-files'
				UpperCamelCase__				:					List[Any]					=					'ade20k-id2label.json'
				UpperCamelCase__				:					List[Any]					=					json.load(open(cached_download(hf_hub_url(lowerCamelCase_     ,      lowerCamelCase_     ,      repo_type='dataset'))     ,      'r'))
				UpperCamelCase__				:					int					=					{int(lowerCamelCase_): v for k, v in idalabel.items()}
				UpperCamelCase__				:					Union[str, Any]					=					idalabel
				UpperCamelCase__				:					List[str]					=					{v: k for k, v in idalabel.items()}
				UpperCamelCase__				:					Any					=					[1, 150, 480, 480]

		return config, expected_shape







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[Any]:
		UpperCamelCase__				:					Tuple					=					['pretrained.model.head.weight', 'pretrained.model.head.bias']
		for k in ignore_keys:
				state_dict.pop(lowerCamelCase_     ,      lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[Any]:
		if (
		    "pretrained.model" in name
		    and "cls_token" not in name
		    and "pos_embed" not in name
		    and "patch_embed" not in name
		):
				UpperCamelCase__				:					Union[str, Any]					=					name.replace('pretrained.model'     ,      'dpt.encoder')
		if "pretrained.model" in name:
				UpperCamelCase__				:					Dict					=					name.replace('pretrained.model'     ,      'dpt.embeddings')
		if "patch_embed" in name:
				UpperCamelCase__				:					Tuple					=					name.replace('patch_embed'     ,      'patch_embeddings')
		if "pos_embed" in name:
				UpperCamelCase__				:					Optional[Any]					=					name.replace('pos_embed'     ,      'position_embeddings')
		if "attn.proj" in name:
				UpperCamelCase__				:					List[Any]					=					name.replace('attn.proj'     ,      'attention.output.dense')
		if "proj" in name and "project" not in name:
				UpperCamelCase__				:					Optional[Any]					=					name.replace('proj'     ,      'projection')
		if "blocks" in name:
				UpperCamelCase__				:					int					=					name.replace('blocks'     ,      'layer')
		if "mlp.fc1" in name:
				UpperCamelCase__				:					int					=					name.replace('mlp.fc1'     ,      'intermediate.dense')
		if "mlp.fc2" in name:
				UpperCamelCase__				:					Tuple					=					name.replace('mlp.fc2'     ,      'output.dense')
		if "norm1" in name:
				UpperCamelCase__				:					List[Any]					=					name.replace('norm1'     ,      'layernorm_before')
		if "norm2" in name:
				UpperCamelCase__				:					int					=					name.replace('norm2'     ,      'layernorm_after')
		if "scratch.output_conv" in name:
				UpperCamelCase__				:					Union[str, Any]					=					name.replace('scratch.output_conv'     ,      'head')
		if "scratch" in name:
				UpperCamelCase__				:					int					=					name.replace('scratch'     ,      'neck')
		if "layer1_rn" in name:
				UpperCamelCase__				:					Optional[Any]					=					name.replace('layer1_rn'     ,      'convs.0')
		if "layer2_rn" in name:
				UpperCamelCase__				:					List[Any]					=					name.replace('layer2_rn'     ,      'convs.1')
		if "layer3_rn" in name:
				UpperCamelCase__				:					List[Any]					=					name.replace('layer3_rn'     ,      'convs.2')
		if "layer4_rn" in name:
				UpperCamelCase__				:					List[str]					=					name.replace('layer4_rn'     ,      'convs.3')
		if "refinenet" in name:
				UpperCamelCase__				:					int					=					int(name[len('neck.refinenet') : len('neck.refinenet') + 1])
				# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
				UpperCamelCase__				:					Any					=					name.replace(f'refinenet{layer_idx}'     ,      f'fusion_stage.layers.{abs(layer_idx-4)}')
		if "out_conv" in name:
				UpperCamelCase__				:					Union[str, Any]					=					name.replace('out_conv'     ,      'projection')
		if "resConfUnit1" in name:
				UpperCamelCase__				:					int					=					name.replace('resConfUnit1'     ,      'residual_layer1')
		if "resConfUnit2" in name:
				UpperCamelCase__				:					Optional[Any]					=					name.replace('resConfUnit2'     ,      'residual_layer2')
		if "conv1" in name:
				UpperCamelCase__				:					Optional[Any]					=					name.replace('conv1'     ,      'convolution1')
		if "conv2" in name:
				UpperCamelCase__				:					int					=					name.replace('conv2'     ,      'convolution2')
		# readout blocks
		if "pretrained.act_postprocess1.0.project.0" in name:
				UpperCamelCase__				:					Any					=					name.replace('pretrained.act_postprocess1.0.project.0'     ,      'neck.reassemble_stage.readout_projects.0.0')
		if "pretrained.act_postprocess2.0.project.0" in name:
				UpperCamelCase__				:					Tuple					=					name.replace('pretrained.act_postprocess2.0.project.0'     ,      'neck.reassemble_stage.readout_projects.1.0')
		if "pretrained.act_postprocess3.0.project.0" in name:
				UpperCamelCase__				:					int					=					name.replace('pretrained.act_postprocess3.0.project.0'     ,      'neck.reassemble_stage.readout_projects.2.0')
		if "pretrained.act_postprocess4.0.project.0" in name:
				UpperCamelCase__				:					int					=					name.replace('pretrained.act_postprocess4.0.project.0'     ,      'neck.reassemble_stage.readout_projects.3.0')
		# resize blocks
		if "pretrained.act_postprocess1.3" in name:
				UpperCamelCase__				:					Tuple					=					name.replace('pretrained.act_postprocess1.3'     ,      'neck.reassemble_stage.layers.0.projection')
		if "pretrained.act_postprocess1.4" in name:
				UpperCamelCase__				:					Optional[Any]					=					name.replace('pretrained.act_postprocess1.4'     ,      'neck.reassemble_stage.layers.0.resize')
		if "pretrained.act_postprocess2.3" in name:
				UpperCamelCase__				:					Union[str, Any]					=					name.replace('pretrained.act_postprocess2.3'     ,      'neck.reassemble_stage.layers.1.projection')
		if "pretrained.act_postprocess2.4" in name:
				UpperCamelCase__				:					Dict					=					name.replace('pretrained.act_postprocess2.4'     ,      'neck.reassemble_stage.layers.1.resize')
		if "pretrained.act_postprocess3.3" in name:
				UpperCamelCase__				:					Any					=					name.replace('pretrained.act_postprocess3.3'     ,      'neck.reassemble_stage.layers.2.projection')
		if "pretrained.act_postprocess4.3" in name:
				UpperCamelCase__				:					List[Any]					=					name.replace('pretrained.act_postprocess4.3'     ,      'neck.reassemble_stage.layers.3.projection')
		if "pretrained.act_postprocess4.4" in name:
				UpperCamelCase__				:					Optional[Any]					=					name.replace('pretrained.act_postprocess4.4'     ,      'neck.reassemble_stage.layers.3.resize')
		if "pretrained" in name:
				UpperCamelCase__				:					List[str]					=					name.replace('pretrained'     ,      'dpt')
		if "bn" in name:
				UpperCamelCase__				:					Tuple					=					name.replace('bn'     ,      'batch_norm')
		if "head" in name:
				UpperCamelCase__				:					Union[str, Any]					=					name.replace('head'     ,      'head.head')
		if "encoder.norm" in name:
				UpperCamelCase__				:					int					=					name.replace('encoder.norm'     ,      'layernorm')
		if "auxlayer" in name:
				UpperCamelCase__				:					Union[str, Any]					=					name.replace('auxlayer'     ,      'auxiliary_head.head')

		return name







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Any:
		for i in range(config.num_hidden_layers):
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				UpperCamelCase__				:					Optional[int]					=					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight')
				UpperCamelCase__				:					Any					=					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias')
				# next, add query, keys and values (in that order) to the state dict
				UpperCamelCase__				:					List[str]					=					in_proj_weight[: config.hidden_size, :]
				UpperCamelCase__				:					List[Any]					=					in_proj_bias[: config.hidden_size]
				UpperCamelCase__				:					List[Any]					=					in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				UpperCamelCase__				:					List[Any]					=					in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				UpperCamelCase__				:					List[str]					=					in_proj_weight[
				    -config.hidden_size :, :
				]
				UpperCamelCase__				:					int					=					in_proj_bias[-config.hidden_size :]







def       __UpperCAmelCase						(  )							->     Optional[Any]:
		UpperCamelCase__				:					Tuple					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase__				:					List[Any]					=					Image.open(requests.get(lowerCamelCase_     ,      stream=lowerCamelCase_).raw)
		return im







@torch.no_grad()
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Dict:
		UpperCamelCase__,	UpperCamelCase__				:					Any					=					get_dpt_config(lowerCamelCase_)
		# load original state_dict from URL
		UpperCamelCase__				:					Tuple					=					torch.hub.load_state_dict_from_url(lowerCamelCase_     ,      map_location='cpu')
		# remove certain keys
		remove_ignore_keys_(lowerCamelCase_)
		# rename keys
		for key in state_dict.copy().keys():
				UpperCamelCase__				:					str					=					state_dict.pop(lowerCamelCase_)
				UpperCamelCase__				:					List[str]					=					val
		# read in qkv matrices
		read_in_q_k_v(lowerCamelCase_     ,      lowerCamelCase_)

		# load HuggingFace model
		UpperCamelCase__				:					str					=					DPTForSemanticSegmentation(lowerCamelCase_) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowerCamelCase_)
		model.load_state_dict(lowerCamelCase_)
		model.eval()

		# Check outputs on an image
		UpperCamelCase__				:					Any					=					480 if 'ade' in checkpoint_url else 384
		UpperCamelCase__				:					List[Any]					=					DPTImageProcessor(size=lowerCamelCase_)

		UpperCamelCase__				:					int					=					prepare_img()
		UpperCamelCase__				:					Optional[Any]					=					image_processor(lowerCamelCase_     ,      return_tensors='pt')

		# forward pass
		UpperCamelCase__				:					Any					=					model(**lowerCamelCase_).logits if 'ade' in checkpoint_url else model(**lowerCamelCase_).predicted_depth

		# Assert logits
		UpperCamelCase__				:					Tuple					=					torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]])
		if "ade" in checkpoint_url:
				UpperCamelCase__				:					List[str]					=					torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]])
		assert outputs.shape == torch.Size(lowerCamelCase_)
		assert (
		    torch.allclose(outputs[0, 0, :3, :3]     ,      lowerCamelCase_     ,      atol=1e-4)
		    if "ade" in checkpoint_url
		    else torch.allclose(outputs[0, :3, :3]     ,      lowerCamelCase_)
		)

		Path(lowerCamelCase_).mkdir(exist_ok=lowerCamelCase_)
		print(f'Saving model to {pytorch_dump_folder_path}')
		model.save_pretrained(lowerCamelCase_)
		print(f'Saving image processor to {pytorch_dump_folder_path}')
		image_processor.save_pretrained(lowerCamelCase_)

		if push_to_hub:
				print('Pushing model to hub...')
				model.push_to_hub(
				    repo_path_or_name=Path(lowerCamelCase_     ,      lowerCamelCase_)     ,      organization='nielsr'     ,      commit_message='Add model'     ,      use_temp_dir=lowerCamelCase_     ,      )
				image_processor.push_to_hub(
				    repo_path_or_name=Path(lowerCamelCase_     ,      lowerCamelCase_)     ,      organization='nielsr'     ,      commit_message='Add image processor'     ,      use_temp_dir=lowerCamelCase_     ,      )


if __name__ == "__main__":
		lowerCAmelCase__          =						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--checkpoint_url',
		    default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
		    type=str,
		    help='URL of the original DPT checkpoint you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=str,
		    required=True,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument(
		    '--push_to_hub',
		    action='store_true',
		)
		parser.add_argument(
		    '--model_name',
		    default='dpt-large',
		    type=str,
		    help='Name of the model, in case you\'re pushing to the hub.',
		)

		lowerCAmelCase__          =						parser.parse_args()
		convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




