'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

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


enable_full_determinism()


class 			lowerCamelCase_  (     UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		List[str]      			=   StableUnCLIPImgaImgPipeline
    a__			:		Optional[Any]      			=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    a__			:		Optional[int]      			=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a__			:		str      			=   frozenset(
        []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    a__			:		List[Any]      			=   frozenset([]				)







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Optional[Any]    						=	32
        __UpperCamelCase  :int    						=	embedder_hidden_size

        # image encoding components

        __UpperCamelCase  :Dict    						=	CLIPImageProcessor(crop_size=32				,				size=32)

        torch.manual_seed(0)
        __UpperCamelCase  :Tuple    						=	CLIPVisionModelWithProjection(
            CLIPVisionConfig(
                hidden_size=__lowercase				,				projection_dim=__lowercase				,				num_hidden_layers=5				,				num_attention_heads=4				,				image_size=32				,				intermediate_size=37				,				patch_size=1				,				))

        # regular denoising components

        torch.manual_seed(0)
        __UpperCamelCase  :Union[str, Any]    						=	StableUnCLIPImageNormalizer(embedding_dim=__lowercase)
        __UpperCamelCase  :Any    						=	DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')

        torch.manual_seed(0)
        __UpperCamelCase  :List[Any]    						=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        torch.manual_seed(0)
        __UpperCamelCase  :str    						=	CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0				,				eos_token_id=2				,				hidden_size=__lowercase				,				projection_dim=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				,				))

        torch.manual_seed(0)
        __UpperCamelCase  :Any    						=	UNetaDConditionModel(
            sample_size=32				,				in_channels=4				,				out_channels=4				,				down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')				,				up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')				,				block_out_channels=(32, 64)				,				attention_head_dim=(2, 4)				,				class_embed_type='''projection'''				,				projection_class_embeddings_input_dim=embedder_projection_dim * 2				,				cross_attention_dim=__lowercase				,				layers_per_block=1				,				upcast_attention=__lowercase				,				use_linear_projection=__lowercase				,				)

        torch.manual_seed(0)
        __UpperCamelCase  :Dict    						=	DDIMScheduler(
            beta_schedule='''scaled_linear'''				,				beta_start=0.0_00_85				,				beta_end=0.0_12				,				prediction_type='''v_prediction'''				,				set_alpha_to_one=__lowercase				,				steps_offset=1				,				)

        torch.manual_seed(0)
        __UpperCamelCase  :Any    						=	AutoencoderKL()

        __UpperCamelCase  :Dict    						=	{
            # image encoding components
            '''feature_extractor''': feature_extractor,
            '''image_encoder''': image_encoder.eval(),
            # image noising components
            '''image_normalizer''': image_normalizer.eval(),
            '''image_noising_scheduler''': image_noising_scheduler,
            # regular denoising components
            '''tokenizer''': tokenizer,
            '''text_encoder''': text_encoder.eval(),
            '''unet''': unet.eval(),
            '''scheduler''': scheduler,
            '''vae''': vae.eval(),
        }

        return components







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=0				,				__lowercase=True) ->			Tuple:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase  :int    						=	torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase  :int    						=	torch.Generator(device=__lowercase).manual_seed(__lowercase)

        __UpperCamelCase  :Tuple    						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(__lowercase)).to(__lowercase)

        if pil_image:
            __UpperCamelCase  :Tuple    						=	input_image * 0.5 + 0.5
            __UpperCamelCase  :Any    						=	input_image.clamp(0				,				1)
            __UpperCamelCase  :Any    						=	input_image.cpu().permute(0				,				2				,				3				,				1).float().numpy()
            __UpperCamelCase  :int    						=	DiffusionPipeline.numpy_to_pil(__lowercase)[0]

        return {
            "prompt": "An anime racoon running a marathon",
            "image": input_image,
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "np",
        }







    @skip_mps
    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase  :Dict    						=	self.get_dummy_components()
        __UpperCamelCase  :Dict    						=	StableUnCLIPImgaImgPipeline(**__lowercase)
        __UpperCamelCase  :int    						=	sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :Optional[int]    						=	self.get_dummy_inputs(__lowercase)
        inputs.update({'''image_embeds''': None})
        __UpperCamelCase  :Any    						=	sd_pipe(**__lowercase).images
        __UpperCamelCase  :int    						=	image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __UpperCamelCase  :Union[str, Any]    						=	np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Union[str, Any]    						=	torch_device in ['''cpu''', '''mps''']

        self._test_attention_slicing_forward_pass(test_max_difference=__lowercase)







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[Any]    						=	torch_device in ['''cpu''', '''mps''']

        self._test_inference_batch_single_identical(test_max_difference=__lowercase)







    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available()				,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,				)
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__lowercase)


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






    '''simple docstring'''






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







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :Union[str, Any]    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase  :str    						=	load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase  :Optional[int]    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-l-img2img'''				,				torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :Union[str, Any]    						=	torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase  :str    						=	pipe(__lowercase				,				'''anime turle'''				,				generator=__lowercase				,				output_type='''np''')

        __UpperCamelCase  :List[str]    						=	output.images[0]

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

        assert_mean_pixel_difference(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :Dict    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase  :str    						=	load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase  :Tuple    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img'''				,				torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :int    						=	torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase  :int    						=	pipe(__lowercase				,				'''anime turle'''				,				generator=__lowercase				,				output_type='''np''')

        __UpperCamelCase  :List[str]    						=	output.images[0]

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

        assert_mean_pixel_difference(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :List[Any]    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __UpperCamelCase  :Any    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img'''				,				torch_dtype=torch.floataa)
        __UpperCamelCase  :Any    						=	pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :Tuple    						=	pipe(
            __lowercase				,				'''anime turtle'''				,				num_inference_steps=2				,				output_type='''np'''				,				)

        __UpperCamelCase  :str    						=	torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9






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 lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	DPTConfig(embedding_type='''hybrid'''			)

    if "large" in checkpoint_url:
        __UpperCamelCase  :Dict    						=	1_024
        __UpperCamelCase  :Optional[int]    						=	4_096
        __UpperCamelCase  :int    						=	24
        __UpperCamelCase  :List[str]    						=	16
        __UpperCamelCase  :List[str]    						=	[5, 11, 17, 23]
        __UpperCamelCase  :Optional[int]    						=	[256, 512, 1_024, 1_024]
        __UpperCamelCase  :str    						=	(1, 384, 384)

    if "nyu" or "midas" in checkpoint_url:
        __UpperCamelCase  :str    						=	768
        __UpperCamelCase  :Dict    						=	[1, 1, 1, 0.5]
        __UpperCamelCase  :List[str]    						=	[256, 512, 768, 768]
        __UpperCamelCase  :int    						=	150
        __UpperCamelCase  :Union[str, Any]    						=	16
        __UpperCamelCase  :str    						=	(1, 384, 384)
        __UpperCamelCase  :List[str]    						=	False
        __UpperCamelCase  :List[Any]    						=	'''project'''

    if "ade" in checkpoint_url:
        __UpperCamelCase  :List[Any]    						=	True
        __UpperCamelCase  :Optional[Any]    						=	768
        __UpperCamelCase  :List[Any]    						=	[1, 1, 1, 0.5]
        __UpperCamelCase  :List[str]    						=	150
        __UpperCamelCase  :Optional[int]    						=	16
        __UpperCamelCase  :Optional[int]    						=	'''huggingface/label-files'''
        __UpperCamelCase  :Dict    						=	'''ade20k-id2label.json'''
        __UpperCamelCase  :int    						=	json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    repo_type='''dataset'''			)			)						,    '''r'''			)			)
        __UpperCamelCase  :Any    						=	{int(SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
        __UpperCamelCase  :Tuple    						=	idalabel
        __UpperCamelCase  :Dict    						=	{v: k for k, v in idalabel.items()}
        __UpperCamelCase  :Optional[int]    						=	[1, 150, 480, 480]

    return config, expected_shape



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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



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

    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        __UpperCamelCase  :Tuple    						=	name.replace('''pretrained.act_postprocess1.3'''						,    '''neck.reassemble_stage.layers.0.projection'''			)
    if "pretrained.act_postprocess1.4" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''pretrained.act_postprocess1.4'''						,    '''neck.reassemble_stage.layers.0.resize'''			)
    if "pretrained.act_postprocess2.3" in name:
        __UpperCamelCase  :Tuple    						=	name.replace('''pretrained.act_postprocess2.3'''						,    '''neck.reassemble_stage.layers.1.projection'''			)
    if "pretrained.act_postprocess2.4" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''pretrained.act_postprocess2.4'''						,    '''neck.reassemble_stage.layers.1.resize'''			)
    if "pretrained.act_postprocess3.3" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''pretrained.act_postprocess3.3'''						,    '''neck.reassemble_stage.layers.2.projection'''			)
    if "pretrained.act_postprocess4.3" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''pretrained.act_postprocess4.3'''						,    '''neck.reassemble_stage.layers.3.projection'''			)
    if "pretrained.act_postprocess4.4" in name:
        __UpperCamelCase  :List[str]    						=	name.replace('''pretrained.act_postprocess4.4'''						,    '''neck.reassemble_stage.layers.3.resize'''			)
    if "pretrained" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''pretrained'''						,    '''dpt'''			)
    if "bn" in name:
        __UpperCamelCase  :List[str]    						=	name.replace('''bn'''						,    '''batch_norm'''			)
    if "head" in name:
        __UpperCamelCase  :Tuple    						=	name.replace('''head'''						,    '''head.head'''			)
    if "encoder.norm" in name:
        __UpperCamelCase  :List[str]    						=	name.replace('''encoder.norm'''						,    '''layernorm'''			)
    if "auxlayer" in name:
        __UpperCamelCase  :Optional[Any]    						=	name.replace('''auxlayer'''						,    '''auxiliary_head.head'''			)
    if "backbone" in name:
        __UpperCamelCase  :int    						=	name.replace('''backbone'''						,    '''backbone.bit.encoder'''			)

    if ".." in name:
        __UpperCamelCase  :Tuple    						=	name.replace('''..'''						,    '''.'''			)

    if "stem.conv" in name:
        __UpperCamelCase  :List[Any]    						=	name.replace('''stem.conv'''						,    '''bit.embedder.convolution'''			)
    if "blocks" in name:
        __UpperCamelCase  :Tuple    						=	name.replace('''blocks'''						,    '''layers'''			)
    if "convolution" in name and "backbone" in name:
        __UpperCamelCase  :List[Any]    						=	name.replace('''convolution'''						,    '''conv'''			)
    if "layer" in name and "backbone" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''layer'''						,    '''layers'''			)
    if "backbone.bit.encoder.bit" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''backbone.bit.encoder.bit'''						,    '''backbone.bit'''			)
    if "embedder.conv" in name:
        __UpperCamelCase  :Optional[Any]    						=	name.replace('''embedder.conv'''						,    '''embedder.convolution'''			)
    if "backbone.bit.encoder.stem.norm" in name:
        __UpperCamelCase  :List[Any]    						=	name.replace('''backbone.bit.encoder.stem.norm'''						,    '''backbone.bit.embedder.norm'''			)
    return name



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''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)
        __UpperCamelCase  :Any    						=	state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""			)
        __UpperCamelCase  :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
        __UpperCamelCase  :Union[str, Any]    						=	in_proj_weight[: config.hidden_size, :]
        __UpperCamelCase  :Dict    						=	in_proj_bias[: config.hidden_size]
        __UpperCamelCase  :Union[str, Any]    						=	in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        __UpperCamelCase  :Dict    						=	in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        __UpperCamelCase  :str    						=	in_proj_weight[
            -config.hidden_size :, :
        ]
        __UpperCamelCase  :Union[str, Any]    						=	in_proj_bias[-config.hidden_size :]



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __UpperCamelCase  :List[Any]    						=	Image.open(requests.get(SCREAMING_SNAKE_CASE						,    stream=SCREAMING_SNAKE_CASE			).raw			)
    return im



@torch.no_grad()
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase						, __UpperCamelCase  :Union[str, Any]    						=	get_dpt_config(SCREAMING_SNAKE_CASE			)
    # load original state_dict from URL
    # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
    __UpperCamelCase  :Optional[Any]    						=	torch.load(SCREAMING_SNAKE_CASE						,    map_location='''cpu'''			)
    # remove certain keys
    remove_ignore_keys_(SCREAMING_SNAKE_CASE			)
    # rename keys
    for key in state_dict.copy().keys():
        __UpperCamelCase  :Any    						=	state_dict.pop(SCREAMING_SNAKE_CASE			)
        __UpperCamelCase  :Optional[int]    						=	val
    # read in qkv matrices
    read_in_q_k_v(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    # load HuggingFace model
    __UpperCamelCase  :Union[str, Any]    						=	DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE			) if '''ade''' in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE			)
    model.load_state_dict(SCREAMING_SNAKE_CASE			)
    model.eval()

    # Check outputs on an image
    __UpperCamelCase  :Any    						=	480 if '''ade''' in checkpoint_url else 384
    __UpperCamelCase  :Optional[Any]    						=	DPTImageProcessor(size=SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Dict    						=	prepare_img()
    __UpperCamelCase  :Any    						=	image_processor(SCREAMING_SNAKE_CASE						,    return_tensors='''pt'''			)

    # forward pass
    __UpperCamelCase  :Optional[Any]    						=	model(**SCREAMING_SNAKE_CASE			).logits if '''ade''' in checkpoint_url else model(**SCREAMING_SNAKE_CASE			).predicted_depth

    if show_prediction:
        __UpperCamelCase  :str    						=	(
            torch.nn.functional.interpolate(
                outputs.unsqueeze(1			)						,    size=(image.size[1], image.size[0])						,    mode='''bicubic'''						,    align_corners=SCREAMING_SNAKE_CASE						,    )
            .squeeze()
            .cpu()
            .numpy()
        )

        Image.fromarray((prediction / prediction.max()) * 255			).show()

    if pytorch_dump_folder_path is not None:
        Path(SCREAMING_SNAKE_CASE			).mkdir(exist_ok=SCREAMING_SNAKE_CASE			)
        print(f"""Saving model to {pytorch_dump_folder_path}"""			)
        model.save_pretrained(SCREAMING_SNAKE_CASE			)
        print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
        image_processor.save_pretrained(SCREAMING_SNAKE_CASE			)

    if push_to_hub:
        model.push_to_hub('''ybelkada/dpt-hybrid-midas'''			)
        image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''			)


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=False,
        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.''',
    )
    parser.add_argument(
        '''--show_prediction''',
        action='''store_true''',
    )

    __lowercase											=      parser.parse_args()
    convert_dpt_checkpoint(
        args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
    )





