




'''simple docstring'''



import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


UpperCamelCase_       =							get_logger(__name__)


UpperCamelCase_       =							Path(__file__).parent / "model_card_template.md"
UpperCamelCase_       =							uuida().hex
UpperCamelCase_       =							os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase_       =							os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase_       =							HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"




def    lowercase__(       __UpperCamelCase: Optional[Any] = None  ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	str   =		f"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
      if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
            return ua + "; telemetry/off"
      if is_torch_available():
            ua += f"; torch/{_torch_version}"
      if is_flax_available():
            ua += f"; jax/{_jax_version}"
            ua += f"; flax/{_flax_version}"
      if is_onnx_available():
            ua += f"; onnxruntime/{_onnxruntime_version}"
      # CI will set this value to True
      if os.environ.get('DIFFUSERS_IS_CI'	,''  ).upper() in ENV_VARS_TRUE_VALUES:
            ua += "; is_ci/true"
      if isinstance(__lowerCAmelCase	,__lowerCAmelCase  ):
            ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items()  )
      elif isinstance(__lowerCAmelCase	,__lowerCAmelCase  ):
            ua += "; " + user_agent
      return ua




def    lowercase__(       __UpperCamelCase: Optional[Any]	,__UpperCamelCase: List[str] = None	,__UpperCamelCase: str = None  ):

      """simple docstring"""






      if token is None:
            SCREAMING_SNAKE_CASE					:	List[str]   =		HfFolder.get_token()
      if organization is None:
            SCREAMING_SNAKE_CASE					:	int   =		whoami(__lowerCAmelCase  )["name"]
            return f"{username}/{model_id}"
      else:
            return f"{organization}/{model_id}"




def    lowercase__(       __UpperCamelCase: Optional[int]	,__UpperCamelCase: Optional[Any]  ):

      """simple docstring"""






      if not is_jinja_available():
            raise ValueError(
                'Modelcard rendering is based on Jinja templates.'
                ' Please make sure to have `jinja` installed before using `create_model_card`.'
                ' To install it, please run `pip install Jinja2`.'  )

      if hasattr(__lowerCAmelCase	,'local_rank'  ) and args.local_rank not in [-1, 0]:
            return

      SCREAMING_SNAKE_CASE					:	str   =		args.hub_token if hasattr(__lowerCAmelCase	,'hub_token'  ) else None
      SCREAMING_SNAKE_CASE					:	Tuple   =		get_full_repo_name(__lowerCAmelCase	,token=__lowerCAmelCase  )

      SCREAMING_SNAKE_CASE					:	int   =		ModelCard.from_template(
          card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
              language='en'	,license='apache-2.0'	,library_name='diffusers'	,tags=[]	,datasets=args.dataset_name	,metrics=[]	,)	,template_path=__lowerCAmelCase	,model_name=__lowerCAmelCase	,repo_name=__lowerCAmelCase	,dataset_name=args.dataset_name if hasattr(__lowerCAmelCase	,'dataset_name'  ) else None	,learning_rate=args.learning_rate	,train_batch_size=args.train_batch_size	,eval_batch_size=args.eval_batch_size	,gradient_accumulation_steps=(
              args.gradient_accumulation_steps if hasattr(__lowerCAmelCase	,'gradient_accumulation_steps'  ) else None
          )	,adam_betaa=args.adam_betaa if hasattr(__lowerCAmelCase	,'adam_beta1'  ) else None	,adam_betaa=args.adam_betaa if hasattr(__lowerCAmelCase	,'adam_beta2'  ) else None	,adam_weight_decay=args.adam_weight_decay if hasattr(__lowerCAmelCase	,'adam_weight_decay'  ) else None	,adam_epsilon=args.adam_epsilon if hasattr(__lowerCAmelCase	,'adam_epsilon'  ) else None	,lr_scheduler=args.lr_scheduler if hasattr(__lowerCAmelCase	,'lr_scheduler'  ) else None	,lr_warmup_steps=args.lr_warmup_steps if hasattr(__lowerCAmelCase	,'lr_warmup_steps'  ) else None	,ema_inv_gamma=args.ema_inv_gamma if hasattr(__lowerCAmelCase	,'ema_inv_gamma'  ) else None	,ema_power=args.ema_power if hasattr(__lowerCAmelCase	,'ema_power'  ) else None	,ema_max_decay=args.ema_max_decay if hasattr(__lowerCAmelCase	,'ema_max_decay'  ) else None	,mixed_precision=args.mixed_precision	,)

      SCREAMING_SNAKE_CASE					:	int   =		os.path.join(args.output_dir	,'README.md'  )
      model_card.save(__lowerCAmelCase  )




def    lowercase__(       __UpperCamelCase: Union[str, Any]	,__UpperCamelCase: str = None  ):

      """simple docstring"""






      if resolved_file is None or commit_hash is not None:
            return commit_hash
      SCREAMING_SNAKE_CASE					:	str   =		str(Path(__lowerCAmelCase  ).as_posix()  )
      SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		re.search(r'snapshots/([^/]+)/'	,__lowerCAmelCase  )
      if search is None:
            return None
      SCREAMING_SNAKE_CASE					:	Any   =		search.groups()[0]
      return commit_hash if REGEX_COMMIT_HASH.match(__lowerCAmelCase  ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
UpperCamelCase_       =							os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
UpperCamelCase_       =							os.path.join(hf_cache_home, "diffusers")




def    lowercase__(       __UpperCamelCase: Union[str, Any] = None	,__UpperCamelCase: List[str] = None  ):

      """simple docstring"""






      if new_cache_dir is None:
            SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		DIFFUSERS_CACHE
      if old_cache_dir is None:
            SCREAMING_SNAKE_CASE					:	Any   =		old_diffusers_cache

      SCREAMING_SNAKE_CASE					:	Optional[int]   =		Path(__lowerCAmelCase  ).expanduser()
      SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		Path(__lowerCAmelCase  ).expanduser()
      for old_blob_path in old_cache_dir.glob('**/blobs/*'  ):
            if old_blob_path.is_file() and not old_blob_path.is_symlink():
                  SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		new_cache_dir / old_blob_path.relative_to(__lowerCAmelCase  )
                  new_blob_path.parent.mkdir(parents=__lowerCAmelCase	,exist_ok=__lowerCAmelCase  )
                  os.replace(__lowerCAmelCase	,__lowerCAmelCase  )
                  try:
                        os.symlink(__lowerCAmelCase	,__lowerCAmelCase  )
                  except OSError:
                        logger.warning(
                            'Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'  )
      # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


UpperCamelCase_       =							os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
      UpperCamelCase_       =							0
else:
      with open(cache_version_file) as f:
            try:
                  UpperCamelCase_       =							int(f.read())
            except ValueError:
                  UpperCamelCase_       =							0

if cache_version < 1:
      UpperCamelCase_       =							os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
      if old_cache_is_not_empty:
            logger.warning(
                "The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
                "existing cached models. This is a one-time operation, you can interrupt it or run it "
                "later by calling `diffusers.utils.hub_utils.move_cache()`."
            )
            try:
                  move_cache()
            except Exception as e:
                  UpperCamelCase_       =							"\n".join(traceback.format_tb(e.__traceback__))
                  logger.error(
                      F"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
                      "file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
                      "message and we will do our best to help."
                  )

if cache_version < 1:
      try:
            os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
            with open(cache_version_file, "w") as f:
                  f.write("1")
      except Exception:
            logger.warning(
                F"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
                "the directory exists and can be written to."
            )




def    lowercase__(       __UpperCamelCase: List[str]	,__UpperCamelCase: Optional[Any] = None  ):

      """simple docstring"""






      if variant is not None:
            SCREAMING_SNAKE_CASE					:	str   =		weights_name.split('.'  )
            SCREAMING_SNAKE_CASE					:	int   =		splits[:-1] + [variant] + splits[-1:]
            SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		".".join(__lowerCAmelCase  )

      return weights_name





def    lowercase__(       __UpperCamelCase: Optional[int]	,*,
    __UpperCamelCase: Optional[int]	,__UpperCamelCase: Union[str, Any]	,__UpperCamelCase: Any	,__UpperCamelCase: Tuple	,__UpperCamelCase: List[Any]	,__UpperCamelCase: List[Any]	,__UpperCamelCase: Optional[Any]	,__UpperCamelCase: Tuple	,__UpperCamelCase: Any	,__UpperCamelCase: Tuple	,__UpperCamelCase: int=None	,):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	str   =		str(__lowerCAmelCase  )
      if os.path.isfile(__lowerCAmelCase  ):
            return pretrained_model_name_or_path
      elif os.path.isdir(__lowerCAmelCase  ):
            if os.path.isfile(os.path.join(__lowerCAmelCase	,__lowerCAmelCase  )  ):
                  # Load from a PyTorch checkpoint
                  SCREAMING_SNAKE_CASE					:	str   =		os.path.join(__lowerCAmelCase	,__lowerCAmelCase  )
                  return model_file
            elif subfolder is not None and os.path.isfile(
                os.path.join(__lowerCAmelCase	,__lowerCAmelCase	,__lowerCAmelCase  )  ):
                  SCREAMING_SNAKE_CASE					:	Tuple   =		os.path.join(__lowerCAmelCase	,__lowerCAmelCase	,__lowerCAmelCase  )
                  return model_file
            else:
                  raise EnvironmentError(
                      f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."  )
      else:
            # 1. First check if deprecated way of loading from branches is used
            if (
                revision in DEPRECATED_REVISION_ARGS
                and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
                and version.parse(version.parse(__lowerCAmelCase  ).base_version  ) >= version.parse('0.20.0'  )
            ):
                  try:
                        SCREAMING_SNAKE_CASE					:	List[str]   =		hf_hub_download(
                            __lowerCAmelCase	,filename=_add_variant(__lowerCAmelCase	,__lowerCAmelCase  )	,cache_dir=__lowerCAmelCase	,force_download=__lowerCAmelCase	,proxies=__lowerCAmelCase	,resume_download=__lowerCAmelCase	,local_files_only=__lowerCAmelCase	,use_auth_token=__lowerCAmelCase	,user_agent=__lowerCAmelCase	,subfolder=__lowerCAmelCase	,revision=revision or commit_hash	,)
                        warnings.warn(
                            f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."	,__lowerCAmelCase	,)
                        return model_file
                  except:  # noqa: E722
                        warnings.warn(
                            f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(__lowerCAmelCase	,__lowerCAmelCase  )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(__lowerCAmelCase	,__lowerCAmelCase  )}' so that the correct variant file can be added."	,__lowerCAmelCase	,)
            try:
                  # 2. Load model file as usual
                  SCREAMING_SNAKE_CASE					:	int   =		hf_hub_download(
                      __lowerCAmelCase	,filename=__lowerCAmelCase	,cache_dir=__lowerCAmelCase	,force_download=__lowerCAmelCase	,proxies=__lowerCAmelCase	,resume_download=__lowerCAmelCase	,local_files_only=__lowerCAmelCase	,use_auth_token=__lowerCAmelCase	,user_agent=__lowerCAmelCase	,subfolder=__lowerCAmelCase	,revision=revision or commit_hash	,)
                  return model_file

            except RepositoryNotFoundError:
                  raise EnvironmentError(
                      f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
                      'listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '
                      'token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '
                      'login`.'  )
            except RevisionNotFoundError:
                  raise EnvironmentError(
                      f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
                      'this model name. Check the model page at '
                      f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."  )
            except EntryNotFoundError:
                  raise EnvironmentError(
                      f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."  )
            except HTTPError as err:
                  raise EnvironmentError(
                      f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"  )
            except ValueError:
                  raise EnvironmentError(
                      f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
                      f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
                      f" directory containing a file named {weights_name} or"
                      ' \nCheckout your internet connection or see how to run the library in'
                      ' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'  )
            except EnvironmentError:
                  raise EnvironmentError(
                      f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
                      '\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '
                      f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
                      f"containing a file named {weights_name}"  )






'''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__					=   logging.get_logger(__name__)






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					DPTConfig()

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

		if "ade" in checkpoint_url:
				_UpperCAmelCase						:				Optional[int]	    =					True

				_UpperCAmelCase						:				List[Any]	    =					150
				_UpperCAmelCase						:				Optional[Any]	    =					"huggingface/label-files"
				_UpperCAmelCase						:				Optional[int]	    =					"ade20k-id2label.json"
				_UpperCAmelCase						:				Tuple	    =					json.load(open(cached_download(hf_hub_url(__lowerCAmelCase					,       __lowerCAmelCase					,       repo_type="dataset"      )      )					,       "r"      )      )
				_UpperCAmelCase						:				str	    =					{int(__lowerCAmelCase      ): v for k, v in idalabel.items()}
				_UpperCAmelCase						:				int	    =					idalabel
				_UpperCAmelCase						:				Union[str, Any]	    =					{v: k for k, v in idalabel.items()}
				_UpperCAmelCase						:				int	    =					[1, 150, 480, 480]

		return config, expected_shape






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					["pretrained.model.head.weight", "pretrained.model.head.bias"]
		for k in ignore_keys:
				state_dict.pop(__lowerCAmelCase					,       __lowerCAmelCase      )






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

		return name






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		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						:				int	    =					state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""      )
				_UpperCAmelCase						:				str	    =					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						:				List[str]	    =					in_proj_weight[: config.hidden_size, :]
				_UpperCAmelCase						:				Dict	    =					in_proj_bias[: config.hidden_size]
				_UpperCAmelCase						:				List[str]	    =					in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				_UpperCAmelCase						:				int	    =					in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				_UpperCAmelCase						:				Union[str, Any]	    =					in_proj_weight[
				    -config.hidden_size :, :
				]
				_UpperCAmelCase						:				str	    =					in_proj_bias[-config.hidden_size :]






def        __lowerCAmelCase       ():
		_UpperCAmelCase						:				Optional[int]	    =					"http://images.cocodataset.org/val2017/000000039769.jpg"
		_UpperCAmelCase						:				Any	    =					Image.open(requests.get(__lowerCAmelCase					,       stream=__lowerCAmelCase      ).raw      )
		return im






@torch.no_grad()
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				Dict	    =					get_dpt_config(__lowerCAmelCase      )
		# load original state_dict from URL
		_UpperCAmelCase						:				List[Any]	    =					torch.hub.load_state_dict_from_url(__lowerCAmelCase					,       map_location="cpu"      )
		# remove certain keys
		remove_ignore_keys_(__lowerCAmelCase      )
		# rename keys
		for key in state_dict.copy().keys():
				_UpperCAmelCase						:				Tuple	    =					state_dict.pop(__lowerCAmelCase      )
				_UpperCAmelCase						:				List[Any]	    =					val
		# read in qkv matrices
		read_in_q_k_v(__lowerCAmelCase					,       __lowerCAmelCase      )

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

		# Check outputs on an image
		_UpperCAmelCase						:				Any	    =					480 if "ade" in checkpoint_url else 384
		_UpperCAmelCase						:				List[str]	    =					DPTImageProcessor(size=__lowerCAmelCase      )

		_UpperCAmelCase						:				Any	    =					prepare_img()
		_UpperCAmelCase						:				Dict	    =					image_processor(__lowerCAmelCase					,       return_tensors="pt"      )

		# forward pass
		_UpperCAmelCase						:				Tuple	    =					model(**__lowerCAmelCase      ).logits if "ade" in checkpoint_url else model(**__lowerCAmelCase      ).predicted_depth

		# Assert logits
		_UpperCAmelCase						:				Dict	    =					torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]      )
		if "ade" in checkpoint_url:
				_UpperCAmelCase						:				str	    =					torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]      )
		assert outputs.shape == torch.Size(__lowerCAmelCase      )
		assert (
		    torch.allclose(outputs[0, 0, :3, :3]					,       __lowerCAmelCase					,       atol=1e-4      )
		    if "ade" in checkpoint_url
		    else torch.allclose(outputs[0, :3, :3]					,       __lowerCAmelCase      )
		)

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

		if push_to_hub:
				print("Pushing model to hub..."      )
				model.push_to_hub(
				    repo_path_or_name=Path(__lowerCAmelCase					,       __lowerCAmelCase      )					,       organization="nielsr"					,       commit_message="Add model"					,       use_temp_dir=__lowerCAmelCase					,       )
				image_processor.push_to_hub(
				    repo_path_or_name=Path(__lowerCAmelCase					,       __lowerCAmelCase      )					,       organization="nielsr"					,       commit_message="Add image processor"					,       use_temp_dir=__lowerCAmelCase					,       )


if __name__ == "__main__":
						lowerCamelCase__					=   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.',
						)

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



