




'''simple docstring'''


import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, 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


enable_full_determinism()







class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

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






      @property
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Tuple     						=  1
           a_     :							Optional[int]     						=  3
           a_     :							List[str]     						=  (32, 32)

           a_     :							Optional[Any]     						=  floats_tensor((batch_size, num_channels) + sizes						,						rng=random.Random(0       )       ).to(lowercase__       )
           return image






      @property
      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           torch.manual_seed(0       )
           a_     :							Tuple     						=  UNetaDConditionModel(
               block_out_channels=(32, 32, 64)						,						layers_per_block=2						,						sample_size=32						,						in_channels=7						,						out_channels=4						,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""")						,						up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""")						,						cross_attention_dim=32						,						attention_head_dim=8						,						use_linear_projection=lowercase__						,						only_cross_attention=(True, True, False)						,						num_class_embeds=100						,						)
           return model






      @property
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           torch.manual_seed(0       )
           a_     :							Tuple     						=  AutoencoderKL(
               block_out_channels=[32, 32, 64]						,						in_channels=3						,						out_channels=3						,						down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,						latent_channels=4						,						)
           return model






      @property
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           torch.manual_seed(0       )
           a_     :							int     						=  CLIPTextConfig(
               bos_token_id=0						,						eos_token_id=2						,						hidden_size=32						,						intermediate_size=37						,						layer_norm_eps=1e-0_5						,						num_attention_heads=4						,						num_hidden_layers=5						,						pad_token_id=1						,						vocab_size=1000						,						hidden_act="""gelu"""						,						projection_dim=512						,						)
           return CLIPTextModel(lowercase__       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							str     						=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
           a_     :							Optional[Any]     						=  self.dummy_cond_unet_upscale
           a_     :							Dict     						=  DDPMScheduler()
           a_     :							List[str]     						=  DDIMScheduler(prediction_type="""v_prediction"""       )
           a_     :							Optional[int]     						=  self.dummy_vae
           a_     :							Tuple     						=  self.dummy_text_encoder
           a_     :							Optional[Any]     						=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

           a_     :							List[Any]     						=  self.dummy_image.cpu().permute(0						,						2						,						3						,						1       )[0]
           a_     :							str     						=  Image.fromarray(np.uinta(lowercase__       )       ).convert("""RGB"""       ).resize((64, 64)       )

           # make sure here that pndm scheduler skips prk
           a_     :							List[Any]     						=  StableDiffusionUpscalePipeline(
               unet=lowercase__						,						low_res_scheduler=lowercase__						,						scheduler=lowercase__						,						vae=lowercase__						,						text_encoder=lowercase__						,						tokenizer=lowercase__						,						max_noise_level=350						,						)
           a_     :							int     						=  sd_pipe.to(lowercase__       )
           sd_pipe.set_progress_bar_config(disable=lowercase__       )

           a_     :							str     						=  """A painting of a squirrel eating a burger"""
           a_     :							int     						=  torch.Generator(device=lowercase__       ).manual_seed(0       )
           a_     :							Optional[int]     						=  sd_pipe(
               [prompt]						,						image=lowercase__						,						generator=lowercase__						,						guidance_scale=6.0						,						noise_level=20						,						num_inference_steps=2						,						output_type="""np"""						,						)

           a_     :							Optional[Any]     						=  output.images

           a_     :							int     						=  torch.Generator(device=lowercase__       ).manual_seed(0       )
           a_     :							str     						=  sd_pipe(
               [prompt]						,						image=lowercase__						,						generator=lowercase__						,						guidance_scale=6.0						,						noise_level=20						,						num_inference_steps=2						,						output_type="""np"""						,						return_dict=lowercase__						,						)[0]

           a_     :							int     						=  image[0, -3:, -3:, -1]
           a_     :							List[Any]     						=  image_from_tuple[0, -3:, -3:, -1]

           a_     :							Tuple     						=  low_res_image.size[0] * 4
           assert image.shape == (1, expected_height_width, expected_height_width, 3)
           a_     :							List[str]     						=  np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]       )

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






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							str     						=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
           a_     :							Dict     						=  self.dummy_cond_unet_upscale
           a_     :							List[Any]     						=  DDPMScheduler()
           a_     :							Union[str, Any]     						=  DDIMScheduler(prediction_type="""v_prediction"""       )
           a_     :							Union[str, Any]     						=  self.dummy_vae
           a_     :							Union[str, Any]     						=  self.dummy_text_encoder
           a_     :							Union[str, Any]     						=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

           a_     :							Any     						=  self.dummy_image.cpu().permute(0						,						2						,						3						,						1       )[0]
           a_     :							Dict     						=  Image.fromarray(np.uinta(lowercase__       )       ).convert("""RGB"""       ).resize((64, 64)       )

           # make sure here that pndm scheduler skips prk
           a_     :							Tuple     						=  StableDiffusionUpscalePipeline(
               unet=lowercase__						,						low_res_scheduler=lowercase__						,						scheduler=lowercase__						,						vae=lowercase__						,						text_encoder=lowercase__						,						tokenizer=lowercase__						,						max_noise_level=350						,						)
           a_     :							Dict     						=  sd_pipe.to(lowercase__       )
           sd_pipe.set_progress_bar_config(disable=lowercase__       )

           a_     :							Optional[Any]     						=  """A painting of a squirrel eating a burger"""
           a_     :							Any     						=  sd_pipe(
               2 * [prompt]						,						image=2 * [low_res_image]						,						guidance_scale=6.0						,						noise_level=20						,						num_inference_steps=2						,						output_type="""np"""						,						)
           a_     :							Optional[Any]     						=  output.images
           assert image.shape[0] == 2

           a_     :							Union[str, Any]     						=  torch.Generator(device=lowercase__       ).manual_seed(0       )
           a_     :							Union[str, Any]     						=  sd_pipe(
               [prompt]						,						image=lowercase__						,						generator=lowercase__						,						num_images_per_prompt=2						,						guidance_scale=6.0						,						noise_level=20						,						num_inference_steps=2						,						output_type="""np"""						,						)
           a_     :							Optional[int]     						=  output.images
           assert image.shape[0] == 2






      @unittest.skipIf(torch_device != """cuda"""						,						"""This test requires a GPU"""       )
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							int     						=  self.dummy_cond_unet_upscale
           a_     :							Any     						=  DDPMScheduler()
           a_     :							Optional[int]     						=  DDIMScheduler(prediction_type="""v_prediction"""       )
           a_     :							int     						=  self.dummy_vae
           a_     :							Union[str, Any]     						=  self.dummy_text_encoder
           a_     :							Tuple     						=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

           a_     :							List[Any]     						=  self.dummy_image.cpu().permute(0						,						2						,						3						,						1       )[0]
           a_     :							int     						=  Image.fromarray(np.uinta(lowercase__       )       ).convert("""RGB"""       ).resize((64, 64)       )

           # put models in fp16, except vae as it overflows in fp16
           a_     :							Tuple     						=  unet.half()
           a_     :							int     						=  text_encoder.half()

           # make sure here that pndm scheduler skips prk
           a_     :							List[str]     						=  StableDiffusionUpscalePipeline(
               unet=lowercase__						,						low_res_scheduler=lowercase__						,						scheduler=lowercase__						,						vae=lowercase__						,						text_encoder=lowercase__						,						tokenizer=lowercase__						,						max_noise_level=350						,						)
           a_     :							Union[str, Any]     						=  sd_pipe.to(lowercase__       )
           sd_pipe.set_progress_bar_config(disable=lowercase__       )

           a_     :							List[Any]     						=  """A painting of a squirrel eating a burger"""
           a_     :							List[str]     						=  torch.manual_seed(0       )
           a_     :							List[str]     						=  sd_pipe(
               [prompt]						,						image=lowercase__						,						generator=lowercase__						,						num_inference_steps=2						,						output_type="""np"""						,						).images

           a_     :							str     						=  low_res_image.size[0] * 4
           assert image.shape == (1, expected_height_width, expected_height_width, 3)







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






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/sd2-upscale/low_res_cat.png"""       )
           a_     :							Union[str, Any]     						=  load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
               """/upsampled_cat.npy"""       )

           a_     :							Union[str, Any]     						=  """stabilityai/stable-diffusion-x4-upscaler"""
           a_     :							int     						=  StableDiffusionUpscalePipeline.from_pretrained(lowercase__       )
           pipe.to(lowercase__       )
           pipe.set_progress_bar_config(disable=lowercase__       )
           pipe.enable_attention_slicing()

           a_     :							Any     						=  """a cat sitting on a park bench"""

           a_     :							int     						=  torch.manual_seed(0       )
           a_     :							Union[str, Any]     						=  pipe(
               prompt=lowercase__						,						image=lowercase__						,						generator=lowercase__						,						output_type="""np"""						,						)
           a_     :							int     						=  output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image       ).max() < 1e-3






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/sd2-upscale/low_res_cat.png"""       )
           a_     :							Optional[int]     						=  load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
               """/upsampled_cat_fp16.npy"""       )

           a_     :							Dict     						=  """stabilityai/stable-diffusion-x4-upscaler"""
           a_     :							int     						=  StableDiffusionUpscalePipeline.from_pretrained(
               lowercase__						,						torch_dtype=torch.floataa						,						)
           pipe.to(lowercase__       )
           pipe.set_progress_bar_config(disable=lowercase__       )
           pipe.enable_attention_slicing()

           a_     :							Optional[Any]     						=  """a cat sitting on a park bench"""

           a_     :							Tuple     						=  torch.manual_seed(0       )
           a_     :							int     						=  pipe(
               prompt=lowercase__						,						image=lowercase__						,						generator=lowercase__						,						output_type="""np"""						,						)
           a_     :							Optional[int]     						=  output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image       ).max() < 5e-1






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

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

           a_     :							Union[str, Any]     						=  load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/sd2-upscale/low_res_cat.png"""       )

           a_     :							List[str]     						=  """stabilityai/stable-diffusion-x4-upscaler"""
           a_     :							Dict     						=  StableDiffusionUpscalePipeline.from_pretrained(
               lowercase__						,						torch_dtype=torch.floataa						,						)
           pipe.to(lowercase__       )
           pipe.set_progress_bar_config(disable=lowercase__       )
           pipe.enable_attention_slicing(1       )
           pipe.enable_sequential_cpu_offload()

           a_     :							List[Any]     						=  """a cat sitting on a park bench"""

           a_     :							Optional[int]     						=  torch.manual_seed(0       )
           a_     :							Dict     						=  pipe(
               prompt=lowercase__						,						image=lowercase__						,						generator=lowercase__						,						num_inference_steps=5						,						output_type="""np"""						,						)

           a_     :							int     						=  torch.cuda.max_memory_allocated()
           # make sure that less than 2.9 GB is allocated
           assert mem_bytes < 2.9 * 10**9






'''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()
lowerCAmelCase_      :  Optional[int]				     =  logging.get_logger(__name__)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int]				):




     """simple docstring"""


     a_     :							List[Any]     						=  DPTConfig(embedding_type="""hybrid"""				)

     if "large" in checkpoint_url:
          a_     :							str     						=  1024
          a_     :							List[str]     						=  4096
          a_     :							int     						=  24
          a_     :							int     						=  16
          a_     :							Optional[Any]     						=  [5, 11, 17, 23]
          a_     :							Optional[int]     						=  [256, 512, 1024, 1024]
          a_     :							Union[str, Any]     						=  (1, 384, 384)

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

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

     return config, expected_shape



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int]				):




     """simple docstring"""


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



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					str				):




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

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

     if ".." in name:
          a_     :							Any     						=  name.replace("""..""" ,    """."""				)

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



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					str				):




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



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							Optional[Any]     						=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
     a_     :							Union[str, Any]     						=  Image.open(requests.get(UpperCamelCase__ ,    stream=UpperCamelCase__				).raw				)
     return im



@torch.no_grad()
def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Tuple ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					Optional[Any] ,    UpperCamelCase__    :					Any				):




     """simple docstring"""


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

     # load HuggingFace model
     a_     :							Any     						=  DPTForSemanticSegmentation(UpperCamelCase__				) if """ade""" in checkpoint_url else DPTForDepthEstimation(UpperCamelCase__				)
     model.load_state_dict(UpperCamelCase__				)
     model.eval()

     # Check outputs on an image
     a_     :							str     						=  480 if """ade""" in checkpoint_url else 384
     a_     :							Optional[int]     						=  DPTImageProcessor(size=UpperCamelCase__				)

     a_     :							Dict     						=  prepare_img()
     a_     :							Any     						=  image_processor(UpperCamelCase__ ,    return_tensors="""pt"""				)

     # forward pass
     a_     :							Dict     						=  model(**UpperCamelCase__				).logits if """ade""" in checkpoint_url else model(**UpperCamelCase__				).predicted_depth

     if show_prediction:
          a_     :							Any     						=  (
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1				) ,    size=(image.size[1], image.size[0]) ,    mode="""bicubic""" ,    align_corners=UpperCamelCase__ ,    )
              .squeeze()
              .cpu()
              .numpy()
          )

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

     if pytorch_dump_folder_path is not None:
          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:
          model.push_to_hub("""ybelkada/dpt-hybrid-midas"""				)
          image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""				)


if __name__ == "__main__":
 lowerCAmelCase_      :  Dict				     =  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',
 )

 lowerCAmelCase_      :  Optional[Any]				     =  parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )
