'''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()
__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   int					)       ->      List[str]:

		'''simple docstring'''


		lowerCAmelCase     =    DPTConfig()

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

		if "ade" in checkpoint_url:
				lowerCAmelCase     =    True

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

		return config, expected_shape






def 							snake_case__				(     _A:   Union[str, Any]					)       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
		for k in ignore_keys:
				state_dict.pop(_A ,  _A					)






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

		return name






def 							snake_case__				(     _A:   Dict ,  _A:   Tuple					)       ->      Tuple:

		'''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     =    state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"					)
				lowerCAmelCase     =    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     =    in_proj_weight[: config.hidden_size, :]
				lowerCAmelCase     =    in_proj_bias[: config.hidden_size]
				lowerCAmelCase     =    in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCAmelCase     =    in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowerCAmelCase     =    in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCAmelCase     =    in_proj_bias[-config.hidden_size :]






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

		'''simple docstring'''


		lowerCAmelCase     =    """http://images.cocodataset.org/val2017/000000039769.jpg"""
		lowerCAmelCase     =    Image.open(requests.get(_A ,  stream=_A					).raw					)
		return im






@torch.no_grad()
def 							snake_case__				(     _A:   List[str] ,  _A:   List[Any] ,  _A:   Any ,  _A:   List[str]					)       ->      Tuple:

		'''simple docstring'''


		lowerCAmelCase   ,	lowerCAmelCase     =    get_dpt_config(_A					)
		# load original state_dict from URL
		lowerCAmelCase     =    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     =    state_dict.pop(_A					)
				lowerCAmelCase     =    val
		# read in qkv matrices
		read_in_q_k_v(_A ,  _A					)

		# load HuggingFace model
		lowerCAmelCase     =    DPTForSemanticSegmentation(_A					) if """ade""" in checkpoint_url else DPTForDepthEstimation(_A					)
		model.load_state_dict(_A					)
		model.eval()

		# Check outputs on an image
		lowerCAmelCase     =    480 if """ade""" in checkpoint_url else 384
		lowerCAmelCase     =    DPTImageProcessor(size=_A					)

		lowerCAmelCase     =    prepare_img()
		lowerCAmelCase     =    image_processor(_A ,  return_tensors="""pt"""					)

		# forward pass
		lowerCAmelCase     =    model(**_A					).logits if """ade""" in checkpoint_url else model(**_A					).predicted_depth

		# Assert logits
		lowerCAmelCase     =    torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]					)
		if "ade" in checkpoint_url:
				lowerCAmelCase     =    torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]					)
		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__":
						__lowercase        =    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.''',
						)

						__lowercase        =    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 PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 							a__(   lowerCAmelCase__					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			KandinskyImgaImgPipeline
			UpperCAmelCase_     :	Union[str, Any]						=			['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
			UpperCAmelCase_     :	List[str]						=			[
			    '''prompt''',
			    '''negative_prompt''',
			    '''image_embeds''',
			    '''negative_image_embeds''',
			    '''image''',
			]
			UpperCAmelCase_     :	Dict						=			[
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''strength''',
			    '''guidance_scale''',
			    '''negative_prompt''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCAmelCase_     :	str						=			False



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 32



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 32



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return self.time_input_dim



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return self.time_input_dim * 4



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 100



			@property
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""")
					return tokenizer



			@property
			def 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase     =    MCLIPConfig(
					    numDims=self.cross_attention_dim			,       transformerDimensions=self.text_embedder_hidden_size			,       hidden_size=self.text_embedder_hidden_size			,       intermediate_size=37			,       num_attention_heads=4			,       num_hidden_layers=5			,       vocab_size=1005			,       )

					lowerCAmelCase     =    MultilingualCLIP(__lowerCAmelCase)
					lowerCAmelCase     =    text_encoder.eval()

					return text_encoder



			@property
			def 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)

					lowerCAmelCase     =    {
					    """in_channels""": 4,
					    # Out channels is double in channels because predicts mean and variance
					    """out_channels""": 8,
					    """addition_embed_type""": """text_image""",
					    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
					    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
					    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
					    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
					    """layers_per_block""": 1,
					    """encoder_hid_dim""": self.text_embedder_hidden_size,
					    """encoder_hid_dim_type""": """text_image_proj""",
					    """cross_attention_dim""": self.cross_attention_dim,
					    """attention_head_dim""": 4,
					    """resnet_time_scale_shift""": """scale_shift""",
					    """class_embed_type""": None,
					}

					lowerCAmelCase     =    UNetaDConditionModel(**__lowerCAmelCase)
					return model



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return {
					    "block_out_channels": [32, 64],
					    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
					    "in_channels": 3,
					    "latent_channels": 4,
					    "layers_per_block": 1,
					    "norm_num_groups": 8,
					    "norm_type": "spatial",
					    "num_vq_embeddings": 12,
					    "out_channels": 3,
					    "up_block_types": [
					        "AttnUpDecoderBlock2D",
					        "UpDecoderBlock2D",
					    ],
					    "vq_embed_dim": 4,
					}



			@property
			def 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase     =    VQModel(**self.dummy_movq_kwargs)
					return model



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.dummy_text_encoder
					lowerCAmelCase     =    self.dummy_tokenizer
					lowerCAmelCase     =    self.dummy_unet
					lowerCAmelCase     =    self.dummy_movq

					lowerCAmelCase     =    {
					    """num_train_timesteps""": 1000,
					    """beta_schedule""": """linear""",
					    """beta_start""": 0.00085,
					    """beta_end""": 0.012,
					    """clip_sample""": False,
					    """set_alpha_to_one""": False,
					    """steps_offset""": 0,
					    """prediction_type""": """epsilon""",
					    """thresholding""": False,
					}

					lowerCAmelCase     =    DDIMScheduler(**__lowerCAmelCase)

					lowerCAmelCase     =    {
					    """text_encoder""": text_encoder,
					    """tokenizer""": tokenizer,
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """movq""": movq,
					}

					return components



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=0):




					"""simple docstring"""


					lowerCAmelCase     =    floats_tensor((1, self.cross_attention_dim)			,       rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
					lowerCAmelCase     =    floats_tensor((1, self.cross_attention_dim)			,       rng=random.Random(seed + 1)).to(__lowerCAmelCase)
					# create init_image
					lowerCAmelCase     =    floats_tensor((1, 3, 64, 64)			,       rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
					lowerCAmelCase     =    image.cpu().permute(0			,       2			,       3			,       1)[0]
					lowerCAmelCase     =    Image.fromarray(np.uinta(__lowerCAmelCase)).convert("""RGB""").resize((256, 256))

					if str(__lowerCAmelCase).startswith("""mps"""):
							lowerCAmelCase     =    torch.manual_seed(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
					lowerCAmelCase     =    {
					    """prompt""": """horse""",
					    """image""": init_image,
					    """image_embeds""": image_embeds,
					    """negative_image_embeds""": negative_image_embeds,
					    """generator""": generator,
					    """height""": 64,
					    """width""": 64,
					    """num_inference_steps""": 10,
					    """guidance_scale""": 7.0,
					    """strength""": 0.2,
					    """output_type""": """np""",
					}
					return inputs







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    """cpu"""

					lowerCAmelCase     =    self.get_dummy_components()

					lowerCAmelCase     =    self.pipeline_class(**__lowerCAmelCase)
					lowerCAmelCase     =    pipe.to(__lowerCAmelCase)

					pipe.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase     =    pipe(**self.get_dummy_inputs(__lowerCAmelCase))
					lowerCAmelCase     =    output.images

					lowerCAmelCase     =    pipe(
					    **self.get_dummy_inputs(__lowerCAmelCase)			,       return_dict=__lowerCAmelCase			,       )[0]

					lowerCAmelCase     =    image[0, -3:, -3:, -1]
					lowerCAmelCase     =    image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)

					lowerCAmelCase     =    np.array(
					    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233])
					assert (
					    np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
					), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
					assert (
					    np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
					), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







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


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


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







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/kandinsky/kandinsky_img2img_frog.npy""")

					lowerCAmelCase     =    load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
					lowerCAmelCase     =    """A red cartoon frog, 4k"""

					lowerCAmelCase     =    KandinskyPriorPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1-prior"""			,       torch_dtype=torch.floataa)
					pipe_prior.to(__lowerCAmelCase)

					lowerCAmelCase     =    KandinskyImgaImgPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1"""			,       torch_dtype=torch.floataa)
					lowerCAmelCase     =    pipeline.to(__lowerCAmelCase)

					pipeline.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase     =    torch.Generator(device="""cpu""").manual_seed(0)
					lowerCAmelCase   ,	lowerCAmelCase     =    pipe_prior(
					    __lowerCAmelCase			,       generator=__lowerCAmelCase			,       num_inference_steps=5			,       negative_prompt=""""""			,       ).to_tuple()

					lowerCAmelCase     =    pipeline(
					    __lowerCAmelCase			,       image=__lowerCAmelCase			,       image_embeds=__lowerCAmelCase			,       negative_image_embeds=__lowerCAmelCase			,       generator=__lowerCAmelCase			,       num_inference_steps=100			,       height=768			,       width=768			,       strength=0.2			,       output_type="""np"""			,       )

					lowerCAmelCase     =    output.images[0]

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

					assert_mean_pixel_difference(__lowerCAmelCase			,       __lowerCAmelCase)




