
import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 							_UpperCamelCase					(       SCREAMING_SNAKE_CASE_  ,							SCREAMING_SNAKE_CASE_  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    str          =			IFInpaintingSuperResolutionPipeline
			__UpperCamelCase   :    str          =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
			__UpperCamelCase   :    int          =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}	)
			__UpperCamelCase   :    List[Any]          =			PipelineTesterMixin.required_optional_params - {"""latents"""}

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							return self._get_superresolution_dummy_components()

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Union[str, Any]    ,						snake_case_			:							Any=0   ):
							if str(snake_case_   ).startswith("""mps"""   ):
											UpperCamelCase_:				List[Any]						=    torch.manual_seed(snake_case_   )
							else:
											UpperCamelCase_:				Dict						=    torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )

							UpperCamelCase_:				List[str]						=    floats_tensor((1, 3, 16, 16)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				List[Any]						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				str						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )

							UpperCamelCase_:				Optional[int]						=    {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': image,
							    'original_image': original_image,
							    'mask_image': mask_image,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}

							return inputs

			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()    ,						reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,						)
			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_save_load_optional_components()

			@unittest.skipIf(torch_device != """cuda"""    ,						reason="""float16 requires CUDA"""   )
			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1   )

			def    lowerCAmelCase__						(							self			:							List[str]   ):
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_save_load_local()




			def    lowerCAmelCase__						(							self			:							Any   ):
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2    ,						)











"""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_							=    logging.get_logger(__name__)

def     UpperCAmelCase_					(				__a   :			Optional[Any]						):




			'''simple docstring'''



			_lowerCamelCase					:					int	       =    DPTConfig()

			if "large" in checkpoint_url:
						_lowerCamelCase					:					Optional[Any]	       =    10_24
						_lowerCamelCase					:					List[str]	       =    40_96
						_lowerCamelCase					:					Union[str, Any]	       =    24
						_lowerCamelCase					:					Any	       =    16
						_lowerCamelCase					:					Union[str, Any]	       =    [5, 11, 17, 23]
						_lowerCamelCase					:					Optional[int]	       =    [2_56, 5_12, 10_24, 10_24]
						_lowerCamelCase					:					List[str]	       =    (1, 3_84, 3_84)

			if "ade" in checkpoint_url:
						_lowerCamelCase					:					Any	       =    True

						_lowerCamelCase					:					List[str]	       =    1_50
						_lowerCamelCase					:					int	       =    'huggingface/label-files'
						_lowerCamelCase					:					Union[str, Any]	       =    'ade20k-id2label.json'
						_lowerCamelCase					:					Union[str, Any]	       =    json.load(open(cached_download(hf_hub_url(__a      ,							__a      ,							repo_type='dataset'						)						)      ,							'r'						)						)
						_lowerCamelCase					:					Optional[Any]	       =    {int(__a						): v for k, v in idalabel.items()}
						_lowerCamelCase					:					int	       =    idalabel
						_lowerCamelCase					:					Any	       =    {v: k for k, v in idalabel.items()}
						_lowerCamelCase					:					List[str]	       =    [1, 1_50, 4_80, 4_80]

			return config, expected_shape

def     UpperCAmelCase_					(				__a   :			Tuple						):




			'''simple docstring'''



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

def     UpperCAmelCase_					(				__a   :			Dict						):




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

			return name

def     UpperCAmelCase_					(				__a   :			Tuple      ,							__a   :			Any						):




			'''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)
						_lowerCamelCase					:					Any	       =    state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"						)
						_lowerCamelCase					:					Dict	       =    state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias"						)
						# next, add query, keys and values (in that order) to the state dict
						_lowerCamelCase					:					Tuple	       =    in_proj_weight[: config.hidden_size, :]
						_lowerCamelCase					:					Optional[int]	       =    in_proj_bias[: config.hidden_size]
						_lowerCamelCase					:					Optional[Any]	       =    in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						_lowerCamelCase					:					List[str]	       =    in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						_lowerCamelCase					:					Any	       =    in_proj_weight[
						    -config.hidden_size :, :
						]
						_lowerCamelCase					:					str	       =    in_proj_bias[-config.hidden_size :]

def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					Optional[int]	       =    'http://images.cocodataset.org/val2017/000000039769.jpg'
			_lowerCamelCase					:					Dict	       =    Image.open(requests.get(__a      ,							stream=__a						).raw						)
			return im

@torch.no_grad()
def     UpperCAmelCase_					(				__a   :			Optional[int]      ,							__a   :			int      ,							__a   :			str      ,							__a   :			List[str]						):




			'''simple docstring'''



			_lowerCamelCase       ,							_lowerCamelCase					:					List[str]	       =    get_dpt_config(__a						)
			# load original state_dict from URL
			_lowerCamelCase					:					List[Any]	       =    torch.hub.load_state_dict_from_url(__a      ,							map_location='cpu'						)
			# remove certain keys
			remove_ignore_keys_(__a						)
			# rename keys
			for key in state_dict.copy().keys():
						_lowerCamelCase					:					str	       =    state_dict.pop(__a						)
						_lowerCamelCase					:					str	       =    val
			# read in qkv matrices
			read_in_q_k_v(__a      ,							__a						)

			# load HuggingFace model
			_lowerCamelCase					:					List[str]	       =    DPTForSemanticSegmentation(__a						) if 'ade' in checkpoint_url else DPTForDepthEstimation(__a						)
			model.load_state_dict(__a						)
			model.eval()

			# Check outputs on an image
			_lowerCamelCase					:					str	       =    4_80 if 'ade' in checkpoint_url else 3_84
			_lowerCamelCase					:					Optional[Any]	       =    DPTImageProcessor(size=__a						)

			_lowerCamelCase					:					List[str]	       =    prepare_img()
			_lowerCamelCase					:					str	       =    image_processor(__a      ,							return_tensors='pt'						)

			# forward pass
			_lowerCamelCase					:					Tuple	       =    model(**__a						).logits if 'ade' in checkpoint_url else model(**__a						).predicted_depth

			# Assert logits
			_lowerCamelCase					:					Dict	       =    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:
						_lowerCamelCase					:					List[Any]	       =    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(__a						)
			assert (
			    torch.allclose(outputs[0, 0, :3, :3]      ,							__a      ,							atol=1E-4						)
			    if "ade" in checkpoint_url
			    else torch.allclose(outputs[0, :3, :3]      ,							__a						)
			)

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

			if push_to_hub:
						print('Pushing model to 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_							=    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_							=    parser.parse_args()
	convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





