




'''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()
a  :				Any									=  logging.get_logger(__name__)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[Any]:

					'''simple docstring'''

					snake_case_									=				DPTConfig()

					if "large" in checkpoint_url:
										snake_case_									=				1024
										snake_case_									=				4096
										snake_case_									=				24
										snake_case_									=				16
										snake_case_									=				[5, 11, 17, 23]
										snake_case_									=				[256, 512, 1024, 1024]
										snake_case_									=				(1, 384, 384)

					if "ade" in checkpoint_url:
										snake_case_									=				True

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

					return config, expected_shape


def 		__magic_name__    (	__UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_									=				['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
					for k in ignore_keys:
										state_dict.pop(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					if (
					    "pretrained.model" in name
					    and "cls_token" not in name
					    and "pos_embed" not in name
					    and "patch_embed" not in name
					):
										snake_case_									=				name.replace('''pretrained.model''',      '''dpt.encoder'''	)
					if "pretrained.model" in name:
										snake_case_									=				name.replace('''pretrained.model''',      '''dpt.embeddings'''	)
					if "patch_embed" in name:
										snake_case_									=				name.replace('''patch_embed''',      '''patch_embeddings'''	)
					if "pos_embed" in name:
										snake_case_									=				name.replace('''pos_embed''',      '''position_embeddings'''	)
					if "attn.proj" in name:
										snake_case_									=				name.replace('''attn.proj''',      '''attention.output.dense'''	)
					if "proj" in name and "project" not in name:
										snake_case_									=				name.replace('''proj''',      '''projection'''	)
					if "blocks" in name:
										snake_case_									=				name.replace('''blocks''',      '''layer'''	)
					if "mlp.fc1" in name:
										snake_case_									=				name.replace('''mlp.fc1''',      '''intermediate.dense'''	)
					if "mlp.fc2" in name:
										snake_case_									=				name.replace('''mlp.fc2''',      '''output.dense'''	)
					if "norm1" in name:
										snake_case_									=				name.replace('''norm1''',      '''layernorm_before'''	)
					if "norm2" in name:
										snake_case_									=				name.replace('''norm2''',      '''layernorm_after'''	)
					if "scratch.output_conv" in name:
										snake_case_									=				name.replace('''scratch.output_conv''',      '''head'''	)
					if "scratch" in name:
										snake_case_									=				name.replace('''scratch''',      '''neck'''	)
					if "layer1_rn" in name:
										snake_case_									=				name.replace('''layer1_rn''',      '''convs.0'''	)
					if "layer2_rn" in name:
										snake_case_									=				name.replace('''layer2_rn''',      '''convs.1'''	)
					if "layer3_rn" in name:
										snake_case_									=				name.replace('''layer3_rn''',      '''convs.2'''	)
					if "layer4_rn" in name:
										snake_case_									=				name.replace('''layer4_rn''',      '''convs.3'''	)
					if "refinenet" in name:
										snake_case_									=				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
										snake_case_									=				name.replace(F"refinenet{layer_idx}",      F"fusion_stage.layers.{abs(layer_idx-4	)}"	)
					if "out_conv" in name:
										snake_case_									=				name.replace('''out_conv''',      '''projection'''	)
					if "resConfUnit1" in name:
										snake_case_									=				name.replace('''resConfUnit1''',      '''residual_layer1'''	)
					if "resConfUnit2" in name:
										snake_case_									=				name.replace('''resConfUnit2''',      '''residual_layer2'''	)
					if "conv1" in name:
										snake_case_									=				name.replace('''conv1''',      '''convolution1'''	)
					if "conv2" in name:
										snake_case_									=				name.replace('''conv2''',      '''convolution2'''	)
					# readout blocks
					if "pretrained.act_postprocess1.0.project.0" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess1.0.project.0''',      '''neck.reassemble_stage.readout_projects.0.0'''	)
					if "pretrained.act_postprocess2.0.project.0" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess2.0.project.0''',      '''neck.reassemble_stage.readout_projects.1.0'''	)
					if "pretrained.act_postprocess3.0.project.0" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess3.0.project.0''',      '''neck.reassemble_stage.readout_projects.2.0'''	)
					if "pretrained.act_postprocess4.0.project.0" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess4.0.project.0''',      '''neck.reassemble_stage.readout_projects.3.0'''	)
					# resize blocks
					if "pretrained.act_postprocess1.3" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess1.3''',      '''neck.reassemble_stage.layers.0.projection'''	)
					if "pretrained.act_postprocess1.4" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess1.4''',      '''neck.reassemble_stage.layers.0.resize'''	)
					if "pretrained.act_postprocess2.3" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess2.3''',      '''neck.reassemble_stage.layers.1.projection'''	)
					if "pretrained.act_postprocess2.4" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess2.4''',      '''neck.reassemble_stage.layers.1.resize'''	)
					if "pretrained.act_postprocess3.3" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess3.3''',      '''neck.reassemble_stage.layers.2.projection'''	)
					if "pretrained.act_postprocess4.3" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess4.3''',      '''neck.reassemble_stage.layers.3.projection'''	)
					if "pretrained.act_postprocess4.4" in name:
										snake_case_									=				name.replace('''pretrained.act_postprocess4.4''',      '''neck.reassemble_stage.layers.3.resize'''	)
					if "pretrained" in name:
										snake_case_									=				name.replace('''pretrained''',      '''dpt'''	)
					if "bn" in name:
										snake_case_									=				name.replace('''bn''',      '''batch_norm'''	)
					if "head" in name:
										snake_case_									=				name.replace('''head''',      '''head.head'''	)
					if "encoder.norm" in name:
										snake_case_									=				name.replace('''encoder.norm''',      '''layernorm'''	)
					if "auxlayer" in name:
										snake_case_									=				name.replace('''auxlayer''',      '''auxiliary_head.head'''	)

					return name


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

					for i in range(config.num_hidden_layers	):
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										snake_case_									=				state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"	)
										snake_case_									=				state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias"	)
										# next, add query, keys and values (in that order) to the state dict
										snake_case_									=				in_proj_weight[: config.hidden_size, :]
										snake_case_									=				in_proj_bias[: config.hidden_size]
										snake_case_									=				in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										snake_case_									=				in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										snake_case_									=				in_proj_weight[
										    -config.hidden_size :, :
										]
										snake_case_									=				in_proj_bias[-config.hidden_size :]


def 		__magic_name__    (	)		->					Any:

					'''simple docstring'''

					snake_case_									=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					snake_case_									=				Image.open(requests.get(__UpperCAmelCase,      stream=__UpperCAmelCase	).raw	)
					return im


@torch.no_grad()
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_			,snake_case_									=				get_dpt_config(__UpperCAmelCase	)
					# load original state_dict from URL
					snake_case_									=				torch.hub.load_state_dict_from_url(__UpperCAmelCase,      map_location='''cpu'''	)
					# remove certain keys
					remove_ignore_keys_(__UpperCAmelCase	)
					# rename keys
					for key in state_dict.copy().keys():
										snake_case_									=				state_dict.pop(__UpperCAmelCase	)
										snake_case_									=				val
					# read in qkv matrices
					read_in_q_k_v(__UpperCAmelCase,      __UpperCAmelCase	)

					# load HuggingFace model
					snake_case_									=				DPTForSemanticSegmentation(__UpperCAmelCase	) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__UpperCAmelCase	)
					model.load_state_dict(__UpperCAmelCase	)
					model.eval()

					# Check outputs on an image
					snake_case_									=				480 if '''ade''' in checkpoint_url else 384
					snake_case_									=				DPTImageProcessor(size=__UpperCAmelCase	)

					snake_case_									=				prepare_img()
					snake_case_									=				image_processor(__UpperCAmelCase,      return_tensors='''pt'''	)

					# forward pass
					snake_case_									=				model(**__UpperCAmelCase	).logits if '''ade''' in checkpoint_url else model(**__UpperCAmelCase	).predicted_depth

					# Assert logits
					snake_case_									=				torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]	)
					if "ade" in checkpoint_url:
										snake_case_									=				torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]	)
					assert outputs.shape == torch.Size(__UpperCAmelCase	)
					assert (
					    torch.allclose(outputs[0, 0, :3, :3],      __UpperCAmelCase,      atol=1e-4	)
					    if "ade" in checkpoint_url
					    else torch.allclose(outputs[0, :3, :3],      __UpperCAmelCase	)
					)

					Path(__UpperCAmelCase	).mkdir(exist_ok=__UpperCAmelCase	)
					print(F"Saving model to {pytorch_dump_folder_path}"	)
					model.save_pretrained(__UpperCAmelCase	)
					print(F"Saving image processor to {pytorch_dump_folder_path}"	)
					image_processor.save_pretrained(__UpperCAmelCase	)

					if push_to_hub:
										print('''Pushing model to 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__":
							a  :				Tuple									=  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.',
							)

							a  :				List[Any]									=  parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







'''simple docstring'''

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				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		CycleDiffusionPipeline
				snake_case_      =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    "negative_prompt",
				    "height",
				    "width",
				    "negative_prompt_embeds",
				}
				snake_case_      =		PipelineTesterMixin.required_optional_params - {"latents"}
				snake_case_      =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}     )
				snake_case_      =		IMAGE_TO_IMAGE_IMAGE_PARAMS
				snake_case_      =		IMAGE_TO_IMAGE_IMAGE_PARAMS


				def   A_ (		self    :					Tuple					):
									torch.manual_seed(0					)
									snake_case_									=				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       ,				)
									snake_case_									=				DDIMScheduler(
									    beta_start=0.0_0085       ,				beta_end=0.012       ,				beta_schedule='''scaled_linear'''       ,				num_train_timesteps=1000       ,				clip_sample=lowercase_       ,				set_alpha_to_one=lowercase_       ,				)
									torch.manual_seed(0					)
									snake_case_									=				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					)
									snake_case_									=				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       ,				)
									snake_case_									=				CLIPTextModel(lowercase_					)
									snake_case_									=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

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


				def   A_ (		self    :					Any       ,				lowercase_    :					int       ,				lowercase_    :					Optional[Any]=0					):
									snake_case_									=				floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(lowercase_					)					).to(lowercase_					)
									snake_case_									=				image / 2 + 0.5
									if str(lowercase_					).startswith('''mps'''					):
														snake_case_									=				torch.manual_seed(lowercase_					)
									else:
														snake_case_									=				torch.Generator(device=lowercase_					).manual_seed(lowercase_					)
									snake_case_									=				{
									    '''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   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

									snake_case_									=				self.get_dummy_components()
									snake_case_									=				CycleDiffusionPipeline(**lowercase_					)
									snake_case_									=				pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				self.get_dummy_inputs(lowercase_					)
									snake_case_									=				pipe(**lowercase_					)
									snake_case_									=				output.images

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

									assert images.shape == (1, 32, 32, 3)
									snake_case_									=				np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]					)

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


				@unittest.skipIf(torch_device != '''cuda'''       ,				'''This test requires a GPU'''					)
				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				self.get_dummy_components()
									for name, module in components.items():
														if hasattr(lowercase_       ,				'''half'''					):
																			snake_case_									=				module.half()
									snake_case_									=				CycleDiffusionPipeline(**lowercase_					)
									snake_case_									=				pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				self.get_dummy_inputs(lowercase_					)
									snake_case_									=				pipe(**lowercase_					)
									snake_case_									=				output.images

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

									assert images.shape == (1, 32, 32, 3)
									snake_case_									=				np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]					)

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


				@skip_mps
				def   A_ (		self    :					Optional[int]					):
									return super().test_save_load_local()


				@unittest.skip('''non-deterministic pipeline'''					)
				def   A_ (		self    :					List[Any]					):
									return super().test_inference_batch_single_identical()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_dict_tuple_outputs_equivalent()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_save_load_optional_components()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_attention_slicing_forward_pass()







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


				def   A_ (		self    :					List[Any]					):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/cycle-diffusion/black_colored_car.png'''					)
									snake_case_									=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''					)
									snake_case_									=				init_image.resize((512, 512)					)

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

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

									snake_case_									=				'''A black colored car'''
									snake_case_									=				'''A blue colored car'''

									snake_case_									=				torch.manual_seed(0					)
									snake_case_									=				pipe(
									    prompt=lowercase_       ,				source_prompt=lowercase_       ,				image=lowercase_       ,				num_inference_steps=100       ,				eta=0.1       ,				strength=0.85       ,				guidance_scale=3       ,				source_guidance_scale=1       ,				generator=lowercase_       ,				output_type='''np'''       ,				)
									snake_case_									=				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   A_ (		self    :					List[str]					):
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/cycle-diffusion/black_colored_car.png'''					)
									snake_case_									=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''					)
									snake_case_									=				init_image.resize((512, 512)					)

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

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

									snake_case_									=				'''A black colored car'''
									snake_case_									=				'''A blue colored car'''

									snake_case_									=				torch.manual_seed(0					)
									snake_case_									=				pipe(
									    prompt=lowercase_       ,				source_prompt=lowercase_       ,				image=lowercase_       ,				num_inference_steps=100       ,				eta=0.1       ,				strength=0.85       ,				guidance_scale=3       ,				source_guidance_scale=1       ,				generator=lowercase_       ,				output_type='''np'''       ,				)
									snake_case_									=				output.images

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

