




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()
UpperCAmelCase__				     = logging.get_logger(__name__)







def 		_A(       UpperCamelCase__							:			Union[str, Any]				)    -> Union[str, Any]:
							'''simple docstring'''


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

							return new_state_dict







def 		_A(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			Any				)    -> Dict:
							'''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)
																					__lowercase      =    state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'				)
																					__lowercase      =    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
																					__lowercase      =    kv_weight[
																					    : config.hidden_sizes[i], :
																					]
																					__lowercase      =    kv_bias[: config.hidden_sizes[i]]
																					__lowercase      =    kv_weight[
																					    config.hidden_sizes[i] :, :
																					]
																					__lowercase      =    kv_bias[config.hidden_sizes[i] :]







def 		_A(       )    -> Optional[Any]:
							'''simple docstring'''


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

							return image







@torch.no_grad()
def 		_A(       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Dict				,       UpperCamelCase__							:			Optional[Any]=False				,       UpperCamelCase__							:			Tuple=None				)    -> str:
							'''simple docstring'''


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

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

							# prepare image
							__lowercase      =    prepare_img()
							__lowercase      =    image_processor(images=UpperCamelCase__				,       return_tensors='''pt'''				).pixel_values

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

							# load original state dict
							__lowercase      =    torch.load(UpperCamelCase__				,       map_location=torch.device('''cpu'''				)				)

							# rename keys
							__lowercase      =    rename_keys(UpperCamelCase__				)

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

							# create HuggingFace model and load state dict
							__lowercase      =    GLPNForDepthEstimation(UpperCamelCase__				)
							model.load_state_dict(UpperCamelCase__				)
							model.eval()

							# forward pass
							__lowercase      =    model(UpperCamelCase__				)
							__lowercase      =    outputs.predicted_depth

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

														__lowercase      =    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__":
			UpperCAmelCase__				     = 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.",
			)
			UpperCAmelCase__				     = parser.parse_args()
			convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)












import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class a (     __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       Any       	=		CycleDiffusionPipeline
						UpperCamelCase_	:       List[str]       	=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    'negative_prompt',
						    'height',
						    'width',
						    'negative_prompt_embeds',
						}
						UpperCamelCase_	:       int       	=		PipelineTesterMixin.required_optional_params - {'latents'}
						UpperCamelCase_	:       Dict       	=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}  )
						UpperCamelCase_	:       Dict       	=		IMAGE_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase_	:       Optional[Any]       	=		IMAGE_TO_IMAGE_IMAGE_PARAMS






						def     UpperCAmelCase_      ( self :     int				)    ->						Dict:
													"""simple docstring"""


													torch.manual_seed(0				)
													__lowercase      =    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				,							)
													__lowercase      =    DDIMScheduler(
													    beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule='''scaled_linear'''				,							num_train_timesteps=1_000				,							clip_sample=lowerCamelCase__				,							set_alpha_to_one=lowerCamelCase__				,							)
													torch.manual_seed(0				)
													__lowercase      =    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				)
													__lowercase      =    CLIPTextConfig(
													    bos_token_id=0				,							eos_token_id=2				,							hidden_size=32				,							intermediate_size=37				,							layer_norm_eps=1e-0_5				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1_000				,							)
													__lowercase      =    CLIPTextModel(lowerCamelCase__				)
													__lowercase      =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     List[str]=0				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    floats_tensor((1, 3, 32, 32)				,							rng=random.Random(lowerCamelCase__				)				).to(lowerCamelCase__				)
													__lowercase      =    image / 2 + 0.5
													if str(lowerCamelCase__				).startswith('''mps'''				):
																				__lowercase      =    torch.manual_seed(lowerCamelCase__				)
													else:
																				__lowercase      =    torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)
													__lowercase      =    {
													    '''prompt''': '''An astronaut riding an elephant''',
													    '''source_prompt''': '''An astronaut riding a horse''',
													    '''image''': image,
													    '''generator''': generator,
													    '''num_inference_steps''': 2,
													    '''eta''': 0.1,
													    '''strength''': 0.8,
													    '''guidance_scale''': 3,
													    '''source_guidance_scale''': 1,
													    '''output_type''': '''numpy''',
													}
													return inputs






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator

													__lowercase      =    self.get_dummy_components()
													__lowercase      =    CycleDiffusionPipeline(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				)
													__lowercase      =    pipe(**lowerCamelCase__				)
													__lowercase      =    output.images

													__lowercase      =    images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__lowercase      =    np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]				)

													assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2






						@unittest.skipIf(torch_device != '''cuda'''				,							'''This test requires a GPU'''				)
						def     UpperCAmelCase_      ( self :     str				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    self.get_dummy_components()
													for name, module in components.items():
																				if hasattr(lowerCamelCase__				,							'''half'''				):
																											__lowercase      =    module.half()
													__lowercase      =    CycleDiffusionPipeline(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				)
													__lowercase      =    pipe(**lowerCamelCase__				)
													__lowercase      =    output.images

													__lowercase      =    images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__lowercase      =    np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]				)

													assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2






						@skip_mps
						def     UpperCAmelCase_      ( self :     str				)    ->						int:
													"""simple docstring"""


													return super().test_save_load_local()






						@unittest.skip('''non-deterministic pipeline'''				)
						def     UpperCAmelCase_      ( self :     int				)    ->						Union[str, Any]:
													"""simple docstring"""


													return super().test_inference_batch_single_identical()






						@skip_mps
						def     UpperCAmelCase_      ( self :     int				)    ->						Optional[int]:
													"""simple docstring"""


													return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						List[str]:
													"""simple docstring"""


													return super().test_save_load_optional_components()






						@skip_mps
						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Tuple:
													"""simple docstring"""


													return super().test_attention_slicing_forward_pass()







@slow
@require_torch_gpu
class a (     unittest.TestCase  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/cycle-diffusion/black_colored_car.png'''				)
													__lowercase      =    load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''				)
													__lowercase      =    init_image.resize((512, 512)				)

													__lowercase      =    '''CompVis/stable-diffusion-v1-4'''
													__lowercase      =    DDIMScheduler.from_pretrained(lowerCamelCase__				,							subfolder='''scheduler'''				)
													__lowercase      =    CycleDiffusionPipeline.from_pretrained(
													    lowerCamelCase__				,							scheduler=lowerCamelCase__				,							safety_checker=lowerCamelCase__				,							torch_dtype=torch.floataa				,							revision='''fp16'''				)

													pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)
													pipe.enable_attention_slicing()

													__lowercase      =    '''A black colored car'''
													__lowercase      =    '''A blue colored car'''

													__lowercase      =    torch.manual_seed(0				)
													__lowercase      =    pipe(
													    prompt=lowerCamelCase__				,							source_prompt=lowerCamelCase__				,							image=lowerCamelCase__				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=lowerCamelCase__				,							output_type='''np'''				,							)
													__lowercase      =    output.images

													# the values aren't exactly equal, but the images look the same visually
													assert np.abs(image - expected_image				).max() < 5e-1






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/cycle-diffusion/black_colored_car.png'''				)
													__lowercase      =    load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''				)
													__lowercase      =    init_image.resize((512, 512)				)

													__lowercase      =    '''CompVis/stable-diffusion-v1-4'''
													__lowercase      =    DDIMScheduler.from_pretrained(lowerCamelCase__				,							subfolder='''scheduler'''				)
													__lowercase      =    CycleDiffusionPipeline.from_pretrained(lowerCamelCase__				,							scheduler=lowerCamelCase__				,							safety_checker=lowerCamelCase__				)

													pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)
													pipe.enable_attention_slicing()

													__lowercase      =    '''A black colored car'''
													__lowercase      =    '''A blue colored car'''

													__lowercase      =    torch.manual_seed(0				)
													__lowercase      =    pipe(
													    prompt=lowerCamelCase__				,							source_prompt=lowerCamelCase__				,							image=lowerCamelCase__				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=lowerCamelCase__				,							output_type='''np'''				,							)
													__lowercase      =    output.images

													assert np.abs(image - expected_image				).max() < 2e-2






