



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

       return new_state_dict
def        lowercase_							(A : int		,					A : str      ):





       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)
                     snake_case__							:	str   	=	state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''      )
                     snake_case__							:	Dict   	=	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
                     snake_case__							:	Optional[int]   	=	kv_weight[
                         : config.hidden_sizes[i], :
                     ]
                     snake_case__							:	Dict   	=	kv_bias[: config.hidden_sizes[i]]
                     snake_case__							:	str   	=	kv_weight[
                         config.hidden_sizes[i] :, :
                     ]
                     snake_case__							:	List[str]   	=	kv_bias[config.hidden_sizes[i] :]
def        lowercase_							():
       snake_case__							:	List[str]   	=	"http://images.cocodataset.org/val2017/000000039769.jpg"
       snake_case__							:	Dict   	=	Image.open(requests.get(A		,					stream=A      ).raw      )

       return image
@torch.no_grad()
def        lowercase_							(A : List[str]		,					A : Tuple		,					A : List[str]=False		,					A : Union[str, Any]=None      ):
       snake_case__							:	List[str]   	=	GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]		,					decoder_hidden_size=6_4		,					depths=[3, 8, 2_7, 3]      )

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

       # prepare image
       snake_case__							:	Dict   	=	prepare_img()
       snake_case__							:	int   	=	image_processor(images=A		,					return_tensors='pt'      ).pixel_values

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

       # load original state dict
       snake_case__							:	Dict   	=	torch.load(A		,					map_location=torch.device('cpu'      )      )

       # rename keys
       snake_case__							:	Union[str, Any]   	=	rename_keys(A      )

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

       # create HuggingFace model and load state dict
       snake_case__							:	Dict   	=	GLPNForDepthEstimation(A      )
       model.load_state_dict(A      )
       model.eval()

       # forward pass
       snake_case__							:	int   	=	model(A      )
       snake_case__							:	Dict   	=	outputs.predicted_depth

       # verify output
       if model_name is not None:
              if "nyu" in model_name:
                     snake_case__							:	str   	=	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:
                     snake_case__							:	Dict   	=	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}'''      )

              snake_case__							:	Dict   	=	torch.Size([1, 4_8_0, 6_4_0]      )

              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__":
 a_   :Optional[int]        =      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_   :Optional[Any]        =      parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)










"""simple docstring"""



import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
  import onnxruntime as ort







class _lowerCAmelCase				(      snake_case_    ,							unittest.TestCase	):
   # TODO: is there an appropriate internal test set?
   __UpperCAmelCase :       Optional[Any]							  =  '''ssube/stable-diffusion-x4-upscaler-onnx'''







   def  lowerCamelCase		(	self						,	UpperCamelCase__=0						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    floats_tensor((1, 3, 128, 128)						,	rng=random.Random(UpperCamelCase__						)						)
       snake_case    :      Any      					=    torch.manual_seed(UpperCamelCase__						)
       snake_case    :      List[str]      					=    {
           "prompt": "A painting of a squirrel eating a burger",
           "image": image,
           "generator": generator,
           "num_inference_steps": 3,
           "guidance_scale": 7.5,
           "output_type": "numpy",
       }
       return inputs







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,	provider="CPUExecutionProvider"						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      Tuple      					=    self.get_dummy_inputs()
       snake_case    :      int      					=    pipe(**UpperCamelCase__						).images
       snake_case    :      Tuple      					=    image[0, -3:, -3:, -1].flatten()

       # started as 128, should now be 512
       assert image.shape == (1, 512, 512, 3)
       snake_case    :      Optional[int]      					=    np.array(
           [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]						)
       assert np.abs(image_slice - expected_slice						).max() < 1e-1







   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      Tuple      					=    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,	provider="CPUExecutionProvider"						)
       snake_case    :      Optional[Any]      					=    PNDMScheduler.from_config(pipe.scheduler.config						,	skip_prk_steps=UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      Tuple      					=    self.get_dummy_inputs()
       snake_case    :      Optional[Any]      					=    pipe(**UpperCamelCase__						).images
       snake_case    :      List[str]      					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       snake_case    :      Dict      					=    np.array(
           [0.6898892, 0.59240556, 0.52499527, 0.58866215, 0.52258235, 0.52572715, 0.62414473, 0.6174387, 0.6214964]						)
       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,	provider="CPUExecutionProvider"						)
       snake_case    :      Optional[Any]      					=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      Any      					=    self.get_dummy_inputs()
       snake_case    :      List[Any]      					=    pipe(**UpperCamelCase__						).images
       snake_case    :      List[str]      					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       snake_case    :      Optional[Any]      					=    np.array(
           [0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,	provider="CPUExecutionProvider"						)
       snake_case    :      Any      					=    EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      Dict      					=    self.get_dummy_inputs()
       snake_case    :      Union[str, Any]      					=    pipe(**UpperCamelCase__						).images
       snake_case    :      str      					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       snake_case    :      Union[str, Any]      					=    np.array(
           [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]						)
       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,	provider="CPUExecutionProvider"						)
       snake_case    :      Optional[Any]      					=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      Any      					=    self.get_dummy_inputs()
       snake_case    :      int      					=    pipe(**UpperCamelCase__						).images
       snake_case    :      List[Any]      					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       snake_case    :      Union[str, Any]      					=    np.array(
           [0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1







@nightly
@require_onnxruntime
@require_torch_gpu
class _lowerCAmelCase				(      unittest.TestCase	):







   @property
   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )







   @property
   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    ort.SessionOptions()
       snake_case    :      str      					=    False
       return options







   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      str      					=    load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/img2img/sketch-mountains-input.jpg"						)
       snake_case    :      Tuple      					=    init_image.resize((128, 128)						)
       # using the PNDM scheduler by default
       snake_case    :      Tuple      					=    OnnxStableDiffusionUpscalePipeline.from_pretrained(
           "ssube/stable-diffusion-x4-upscaler-onnx"						,	provider=self.gpu_provider						,	sess_options=self.gpu_options						,	)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      List[Any]      					=    "A fantasy landscape, trending on artstation"

       snake_case    :      Union[str, Any]      					=    torch.manual_seed(0						)
       snake_case    :      int      					=    pipe(
           prompt=UpperCamelCase__						,	image=UpperCamelCase__						,	guidance_scale=7.5						,	num_inference_steps=10						,	generator=UpperCamelCase__						,	output_type="np"						,	)
       snake_case    :      str      					=    output.images
       snake_case    :      Tuple      					=    images[0, 255:258, 383:386, -1]

       assert images.shape == (1, 512, 512, 3)
       snake_case    :      int      					=    np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]						)
       # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 2e-2







   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




       snake_case    :      Tuple      					=    load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/img2img/sketch-mountains-input.jpg"						)
       snake_case    :      Optional[Any]      					=    init_image.resize((128, 128)						)
       snake_case    :      Union[str, Any]      					=    LMSDiscreteScheduler.from_pretrained(
           "ssube/stable-diffusion-x4-upscaler-onnx"						,	subfolder="scheduler"						)
       snake_case    :      List[Any]      					=    OnnxStableDiffusionUpscalePipeline.from_pretrained(
           "ssube/stable-diffusion-x4-upscaler-onnx"						,	scheduler=UpperCamelCase__						,	provider=self.gpu_provider						,	sess_options=self.gpu_options						,	)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      Union[str, Any]      					=    "A fantasy landscape, trending on artstation"

       snake_case    :      Tuple      					=    torch.manual_seed(0						)
       snake_case    :      str      					=    pipe(
           prompt=UpperCamelCase__						,	image=UpperCamelCase__						,	guidance_scale=7.5						,	num_inference_steps=20						,	generator=UpperCamelCase__						,	output_type="np"						,	)
       snake_case    :      List[Any]      					=    output.images
       snake_case    :      List[Any]      					=    images[0, 255:258, 383:386, -1]

       assert images.shape == (1, 512, 512, 3)
       snake_case    :      int      					=    np.array(
           [0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566]						)
       # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 2e-2





