


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()
_lowercase      :      Union[str, Any]            =					logging.get_logger(__name__)


def 				lowerCamelCase__				(	A       :      Optional[int]					):




						'''simple docstring'''






						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(lowerCamelCase_					)-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(lowerCamelCase_					)-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(lowerCamelCase_					)-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(lowerCamelCase_					)-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__				(	A       :      Tuple				,       A       :      Optional[int]					):




						'''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)
																		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__				(	):




						'''simple docstring'''






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

						return image


@torch.no_grad()
def 				lowerCamelCase__				(	A       :      Optional[int]				,       A       :      List[Any]				,       A       :      str=False				,       A       :      List[str]=None					):




						'''simple docstring'''






						UpperCAmelCase					    =     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)
						UpperCAmelCase					    =     GLPNImageProcessor()

						# prepare image
						UpperCAmelCase					    =     prepare_img()
						UpperCAmelCase					    =     image_processor(images=lowerCamelCase_				,       return_tensors='''pt'''					).pixel_values

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

						# load original state dict
						UpperCAmelCase					    =     torch.load(lowerCamelCase_				,       map_location=torch.device('''cpu'''					)					)

						# rename keys
						UpperCAmelCase					    =     rename_keys(lowerCamelCase_					)

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

						# create HuggingFace model and load state dict
						UpperCAmelCase					    =     GLPNForDepthEstimation(lowerCamelCase_					)
						model.load_state_dict(lowerCamelCase_					)
						model.eval()

						# forward pass
						UpperCAmelCase					    =     model(lowerCamelCase_					)
						UpperCAmelCase					    =     outputs.predicted_depth

						# verify output
						if model_name is not None:
												if "nyu" in model_name:
																		UpperCAmelCase					    =     torch.tensor(
																		    [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]					)
												elif "kitti" in model_name:
																		UpperCAmelCase					    =     torch.tensor(
																		    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]					)
												else:
																		raise ValueError(f"""Unknown model name: {model_name}"""					)

												UpperCAmelCase					    =     torch.Size([1, 4_80, 6_40]					)

												assert predicted_depth.shape == expected_shape
												assert torch.allclose(predicted_depth[0, :3, :3]				,       lowerCamelCase_				,       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(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__":
	_lowercase      :      Any            =					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.""",
	)
	_lowercase      :      List[str]            =					parser.parse_args()
	convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 	UpperCamelCase__(       lowerCAmelCase						,    lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				List[str]  		=	StableDiffusionSAGPipeline
				__magic_name__    :				str  		=	TEXT_TO_IMAGE_PARAMS
				__magic_name__    :				Any  		=	TEXT_TO_IMAGE_BATCH_PARAMS
				__magic_name__    :				Optional[int]  		=	TEXT_TO_IMAGE_IMAGE_PARAMS
				__magic_name__    :				List[Any]  		=	TEXT_TO_IMAGE_IMAGE_PARAMS
				__magic_name__    :				str  		=	False




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

										"""simple docstring"""




										torch.manual_seed(0				)
										UpperCAmelCase					    =     UNetaDConditionModel(
										    block_out_channels=(32, 64)				,   layers_per_block=2				,   sample_size=32				,   in_channels=4				,   out_channels=4				,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,   cross_attention_dim=32				,   )
										UpperCAmelCase					    =     DDIMScheduler(
										    beta_start=0.00085				,   beta_end=0.012				,   beta_schedule='''scaled_linear'''				,   clip_sample=lowerCAmelCase				,   set_alpha_to_one=lowerCAmelCase				,   )
										torch.manual_seed(0				)
										UpperCAmelCase					    =     AutoencoderKL(
										    block_out_channels=[32, 64]				,   in_channels=3				,   out_channels=3				,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,   latent_channels=4				,   )
										torch.manual_seed(0				)
										UpperCAmelCase					    =     CLIPTextConfig(
										    bos_token_id=0				,   eos_token_id=2				,   hidden_size=32				,   intermediate_size=37				,   layer_norm_eps=1E-05				,   num_attention_heads=4				,   num_hidden_layers=5				,   pad_token_id=1				,   vocab_size=1000				,   )
										UpperCAmelCase					    =     CLIPTextModel(lowerCAmelCase				)
										UpperCAmelCase					    =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

										UpperCAmelCase					    =     {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components




				def 					a__(		self      :			Optional[Any]				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Tuple=0				)->					str:

										"""simple docstring"""




										if str(lowerCAmelCase				).startswith('''mps'''				):
																UpperCAmelCase					    =     torch.manual_seed(lowerCAmelCase				)
										else:
																UpperCAmelCase					    =     torch.Generator(device=lowerCAmelCase				).manual_seed(lowerCAmelCase				)
										UpperCAmelCase					    =     {
										    '''prompt''': '''.''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 1.0,
										    '''sag_scale''': 1.0,
										    '''output_type''': '''numpy''',
										}
										return inputs





				def 					a__(		self      :			Any				)->					List[str]:

										"""simple docstring"""




										super().test_inference_batch_single_identical(expected_max_diff=3E-3				)







@slow
@require_torch_gpu
class 	UpperCamelCase__(       unittest.TestCase	):




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

										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




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

										"""simple docstring"""




										UpperCAmelCase					    =     StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''				)
										UpperCAmelCase					    =     sag_pipe.to(lowerCAmelCase				)
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     '''.'''
										UpperCAmelCase					    =     torch.manual_seed(0				)
										UpperCAmelCase					    =     sag_pipe(
										    [prompt]				,   generator=lowerCAmelCase				,   guidance_scale=7.5				,   sag_scale=1.0				,   num_inference_steps=20				,   output_type='''np'''				)

										UpperCAmelCase					    =     output.images

										UpperCAmelCase					    =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										UpperCAmelCase					    =     np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2




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

										"""simple docstring"""




										UpperCAmelCase					    =     StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
										UpperCAmelCase					    =     sag_pipe.to(lowerCAmelCase				)
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     '''.'''
										UpperCAmelCase					    =     torch.manual_seed(0				)
										UpperCAmelCase					    =     sag_pipe(
										    [prompt]				,   generator=lowerCAmelCase				,   guidance_scale=7.5				,   sag_scale=1.0				,   num_inference_steps=20				,   output_type='''np'''				)

										UpperCAmelCase					    =     output.images

										UpperCAmelCase					    =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										UpperCAmelCase					    =     np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2





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

										"""simple docstring"""




										UpperCAmelCase					    =     StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
										UpperCAmelCase					    =     sag_pipe.to(lowerCAmelCase				)
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     '''.'''
										UpperCAmelCase					    =     torch.manual_seed(0				)
										UpperCAmelCase					    =     sag_pipe(
										    [prompt]				,   width=768				,   height=512				,   generator=lowerCAmelCase				,   guidance_scale=7.5				,   sag_scale=1.0				,   num_inference_steps=20				,   output_type='''np'''				,   )

										UpperCAmelCase					    =     output.images

										assert image.shape == (1, 512, 768, 3)





