



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_						:     List[Any]	     =		logging.get_logger(__name__)

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		DPTConfig(embedding_type="hybrid" )

      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 "nyu" or "midas" in checkpoint_url:
            lowerCamelCase								=		768
            lowerCamelCase								=		[1, 1, 1, 0.5]
            lowerCamelCase								=		[256, 512, 768, 768]
            lowerCamelCase								=		150
            lowerCamelCase								=		16
            lowerCamelCase								=		(1, 384, 384)
            lowerCamelCase								=		False
            lowerCamelCase								=		"project"

      if "ade" in checkpoint_url:
            lowerCamelCase								=		True
            lowerCamelCase								=		768
            lowerCamelCase								=		[1, 1, 1, 0.5]
            lowerCamelCase								=		150
            lowerCamelCase								=		16
            lowerCamelCase								=		"huggingface/label-files"
            lowerCamelCase								=		"ade20k-id2label.json"
            lowerCamelCase								=		json.load(open(cached_download(hf_hub_url(UpperCAmelCase__   ,   UpperCAmelCase__   ,   repo_type="dataset" ) )   ,   "r" ) )
            lowerCamelCase								=		{int(UpperCAmelCase__ ): 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     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



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

def     __lowercase(	UpperCAmelCase__ ):


      """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"   ,   "" )
      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 and "backbone" not in name:
            lowerCamelCase								=		name.replace("norm1"   ,   "layernorm_before" )
      if "norm2" in name and "backbone" not 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" )
      if "backbone" in name:
            lowerCamelCase								=		name.replace("backbone"   ,   "backbone.bit.encoder" )

      if ".." in name:
            lowerCamelCase								=		name.replace(".."   ,   "." )

      if "stem.conv" in name:
            lowerCamelCase								=		name.replace("stem.conv"   ,   "bit.embedder.convolution" )
      if "blocks" in name:
            lowerCamelCase								=		name.replace("blocks"   ,   "layers" )
      if "convolution" in name and "backbone" in name:
            lowerCamelCase								=		name.replace("convolution"   ,   "conv" )
      if "layer" in name and "backbone" in name:
            lowerCamelCase								=		name.replace("layer"   ,   "layers" )
      if "backbone.bit.encoder.bit" in name:
            lowerCamelCase								=		name.replace("backbone.bit.encoder.bit"   ,   "backbone.bit" )
      if "embedder.conv" in name:
            lowerCamelCase								=		name.replace("embedder.conv"   ,   "embedder.convolution" )
      if "backbone.bit.encoder.stem.norm" in name:
            lowerCamelCase								=		name.replace("backbone.bit.encoder.stem.norm"   ,   "backbone.bit.embedder.norm" )
      return name

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """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     __lowercase(	):


      """simple docstring"""



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

@torch.no_grad()
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



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

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

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

      lowerCamelCase								=		prepare_img()
      lowerCamelCase								=		image_processor(UpperCAmelCase__   ,   return_tensors="pt" )

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

      if show_prediction:
            lowerCamelCase								=		(
                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__":
     a_						:     Union[str, Any]	     =		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',
     )

     a_						:     List[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
     )




import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard





@slow
@require_flax
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           super().tearDown()
           gc.collect()







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/sd2-inpaint/init_image.png"    )
           lowerCamelCase								=		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"    )

           lowerCamelCase								=		"xvjiarui/stable-diffusion-2-inpainting"
           lowerCamelCase			,							lowerCamelCase								=		FlaxStableDiffusionInpaintPipeline.from_pretrained(__a				,			safety_checker=__a    )

           lowerCamelCase								=		"Face of a yellow cat, high resolution, sitting on a park bench"

           lowerCamelCase								=		jax.random.PRNGKey(0    )
           lowerCamelCase								=		50

           lowerCamelCase								=		jax.device_count()
           lowerCamelCase								=		num_samples * [prompt]
           lowerCamelCase								=		num_samples * [init_image]
           lowerCamelCase								=		num_samples * [mask_image]
           lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		pipeline.prepare_inputs(__a				,			__a				,			__a    )

           # shard inputs and rng
           lowerCamelCase								=		replicate(__a    )
           lowerCamelCase								=		jax.random.split(__a				,			jax.device_count()    )
           lowerCamelCase								=		shard(__a    )
           lowerCamelCase								=		shard(__a    )
           lowerCamelCase								=		shard(__a    )

           lowerCamelCase								=		pipeline(
               __a				,			__a				,			__a				,			__a				,			__a				,			__a				,			jit=__a    )

           lowerCamelCase								=		output.images.reshape(__a				,			5_12				,			5_12				,			3    )

           lowerCamelCase								=		images[0, 2_53:2_56, 2_53:2_56, -1]

           lowerCamelCase								=		jnp.asarray(jax.device_get(image_slice.flatten()    )    )
           lowerCamelCase								=		jnp.array(
               [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]    )
           print(F"""output_slice: {output_slice}"""    )
           assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2