

"""simple docstring"""




import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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      _UpperCAmelCase   (							_lowerCAmelCase		,    unittest.TestCase       ):
	# FIXME: add fast tests
	pass






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



	@property
	def        a  (  self     :       List[str]						):
		return (
		    "CUDAExecutionProvider",
		    {
		        "gpu_mem_limit": "15000000000",  # 15GB
		        "arena_extend_strategy": "kSameAsRequested",
		    },
		)



	@property
	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= ort.SessionOptions()
		__UpperCAmelCase      		= False
		return options



	def        a  (  self     :       Any						):
		__UpperCAmelCase      		= load_image(
		    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
		    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''						)
		__UpperCAmelCase      		= load_image(
		    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
		    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''						)
		__UpperCAmelCase      		= OnnxStableDiffusionInpaintPipeline.from_pretrained(
		    '''runwayml/stable-diffusion-inpainting'''			,    revision='''onnx'''			,    safety_checker=_lowercase			,    feature_extractor=_lowercase			,    provider=self.gpu_provider			,    sess_options=self.gpu_options			,    )
		pipe.set_progress_bar_config(disable=_lowercase						)

		__UpperCAmelCase      		= '''A red cat sitting on a park bench'''

		__UpperCAmelCase      		= np.random.RandomState(0						)
		__UpperCAmelCase      		= pipe(
		    prompt=_lowercase			,    image=_lowercase			,    mask_image=_lowercase			,    guidance_scale=7.5			,    num_inference_steps=10			,    generator=_lowercase			,    output_type='''np'''			,    )
		__UpperCAmelCase      		= output.images
		__UpperCAmelCase      		= images[0, 2_55:2_58, 2_55:2_58, -1]

		assert images.shape == (1, 5_12, 5_12, 3)
		__UpperCAmelCase      		= np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]						)

		assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3



	def        a  (  self     :       Optional[int]						):
		__UpperCAmelCase      		= load_image(
		    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
		    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''						)
		__UpperCAmelCase      		= load_image(
		    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
		    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''						)
		__UpperCAmelCase      		= LMSDiscreteScheduler.from_pretrained(
		    '''runwayml/stable-diffusion-inpainting'''			,    subfolder='''scheduler'''			,    revision='''onnx'''						)
		__UpperCAmelCase      		= OnnxStableDiffusionInpaintPipeline.from_pretrained(
		    '''runwayml/stable-diffusion-inpainting'''			,    revision='''onnx'''			,    scheduler=_lowercase			,    safety_checker=_lowercase			,    feature_extractor=_lowercase			,    provider=self.gpu_provider			,    sess_options=self.gpu_options			,    )
		pipe.set_progress_bar_config(disable=_lowercase						)

		__UpperCAmelCase      		= '''A red cat sitting on a park bench'''

		__UpperCAmelCase      		= np.random.RandomState(0						)
		__UpperCAmelCase      		= pipe(
		    prompt=_lowercase			,    image=_lowercase			,    mask_image=_lowercase			,    guidance_scale=7.5			,    num_inference_steps=20			,    generator=_lowercase			,    output_type='''np'''			,    )
		__UpperCAmelCase      		= output.images
		__UpperCAmelCase      		= images[0, 2_55:2_58, 2_55:2_58, -1]

		assert images.shape == (1, 5_12, 5_12, 3)
		__UpperCAmelCase      		= np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]						)

		assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3


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()
_snake_case      = logging.get_logger(__name__)

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

     return new_state_dict

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Union[str, Any]:
     # 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)
               UpperCamelCase						    =					state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'			)
               UpperCamelCase						    =					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
               UpperCamelCase						    =					kv_weight[
                   : config.hidden_sizes[i], :
               ]
               UpperCamelCase						    =					kv_bias[: config.hidden_sizes[i]]
               UpperCamelCase						    =					kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               UpperCamelCase						    =					kv_bias[config.hidden_sizes[i] :]

def  __lowerCamelCase					( )       ->      Any:
     UpperCamelCase						    =					'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCamelCase						    =					Image.open(requests.get(_lowercase						,     stream=_lowercase			).raw			)

     return image

@torch.no_grad()
def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase=False						,     _lowercase=None			)       ->      Tuple:
     UpperCamelCase						    =					GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,     decoder_hidden_size=64						,     depths=[3, 8, 27, 3]			)

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

     # prepare image
     UpperCamelCase						    =					prepare_img()
     UpperCamelCase						    =					image_processor(images=_lowercase						,     return_tensors='pt'			).pixel_values

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

     # load original state dict
     UpperCamelCase						    =					torch.load(_lowercase						,     map_location=torch.device('cpu'			)			)

     # rename keys
     UpperCamelCase						    =					rename_keys(_lowercase			)

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

     # create HuggingFace model and load state dict
     UpperCamelCase						    =					GLPNForDepthEstimation(_lowercase			)
     model.load_state_dict(_lowercase			)
     model.eval()

     # forward pass
     UpperCamelCase						    =					model(_lowercase			)
     UpperCamelCase						    =					outputs.predicted_depth

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

          UpperCamelCase						    =					torch.Size([1, 480, 640]			)

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


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


