
"""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


SCREAMING_SNAKE_CASE_    					=						get_logger(__name__)


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




def     lowercase__						(					lowerCAmelCase				:					Union[Dict, str, None] = None ) ->      Tuple:






    """simple docstring"""




    UpperCAmelCase									=					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(__a ,				__a ):
        ua += "; " + "; ".join(F"{k}/{v}" for k, v in user_agent.items() )
    elif isinstance(__a ,				__a ):
        ua += "; " + user_agent
    return ua




def     lowercase__						(					lowerCAmelCase				:					str ,				lowerCAmelCase				:					Optional[str] = None ,				lowerCAmelCase				:					Optional[str] = None ) ->      Optional[int]:






    """simple docstring"""




    if token is None:
        UpperCAmelCase									=					HfFolder.get_token()
    if organization is None:
        UpperCAmelCase									=					whoami(__a )['name']
        return F"{username}/{model_id}"
    else:
        return F"{organization}/{model_id}"




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					List[str] ) ->      Optional[int]:






    """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(__a ,				'local_rank' ) and args.local_rank not in [-1, 0]:
        return

    UpperCAmelCase									=					args.hub_token if hasattr(__a ,				'hub_token' ) else None
    UpperCAmelCase									=					get_full_repo_name(__a ,				token=__a )

    UpperCAmelCase									=					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=__a ,				model_name=__a ,				repo_name=__a ,				dataset_name=args.dataset_name if hasattr(__a ,				'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(__a ,				'gradient_accumulation_steps' ) else None
        ) ,				adam_betaa=args.adam_betaa if hasattr(__a ,				'adam_beta1' ) else None ,				adam_betaa=args.adam_betaa if hasattr(__a ,				'adam_beta2' ) else None ,				adam_weight_decay=args.adam_weight_decay if hasattr(__a ,				'adam_weight_decay' ) else None ,				adam_epsilon=args.adam_epsilon if hasattr(__a ,				'adam_epsilon' ) else None ,				lr_scheduler=args.lr_scheduler if hasattr(__a ,				'lr_scheduler' ) else None ,				lr_warmup_steps=args.lr_warmup_steps if hasattr(__a ,				'lr_warmup_steps' ) else None ,				ema_inv_gamma=args.ema_inv_gamma if hasattr(__a ,				'ema_inv_gamma' ) else None ,				ema_power=args.ema_power if hasattr(__a ,				'ema_power' ) else None ,				ema_max_decay=args.ema_max_decay if hasattr(__a ,				'ema_max_decay' ) else None ,				mixed_precision=args.mixed_precision ,				)

    UpperCAmelCase									=					os.path.join(args.output_dir ,				'README.md' )
    model_card.save(__a )




def     lowercase__						(					lowerCAmelCase				:					Optional[str] ,				lowerCAmelCase				:					Optional[str] = None ) ->      Tuple:






    """simple docstring"""




    if resolved_file is None or commit_hash is not None:
        return commit_hash
    UpperCAmelCase									=					str(Path(__a ).as_posix() )
    UpperCAmelCase									=					re.search(r'snapshots/([^/]+)/' ,				__a )
    if search is None:
        return None
    UpperCAmelCase									=					search.groups()[0]
    return commit_hash if REGEX_COMMIT_HASH.match(__a ) 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.
SCREAMING_SNAKE_CASE_    					=						os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
SCREAMING_SNAKE_CASE_    					=						os.path.join(hf_cache_home, '''diffusers''')




def     lowercase__						(					lowerCAmelCase				:					Optional[str] = None ,				lowerCAmelCase				:					Optional[str] = None ) ->      Any:






    """simple docstring"""




    if new_cache_dir is None:
        UpperCAmelCase									=					DIFFUSERS_CACHE
    if old_cache_dir is None:
        UpperCAmelCase									=					old_diffusers_cache

    UpperCAmelCase									=					Path(__a ).expanduser()
    UpperCAmelCase									=					Path(__a ).expanduser()
    for old_blob_path in old_cache_dir.glob('**/blobs/*' ):
        if old_blob_path.is_file() and not old_blob_path.is_symlink():
            UpperCAmelCase									=					new_cache_dir / old_blob_path.relative_to(__a )
            new_blob_path.parent.mkdir(parents=__a ,				exist_ok=__a )
            os.replace(__a ,				__a )
            try:
                os.symlink(__a ,				__a )
            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).


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

if cache_version < 1:
    SCREAMING_SNAKE_CASE_    					=						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:
            SCREAMING_SNAKE_CASE_    					=						'''\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__						(					lowerCAmelCase				:					str ,				lowerCAmelCase				:					Optional[str] = None ) ->      Tuple:






    """simple docstring"""




    if variant is not None:
        UpperCAmelCase									=					weights_name.split('.' )
        UpperCAmelCase									=					splits[:-1] + [variant] + splits[-1:]
        UpperCAmelCase									=					'.'.join(__a )

    return weights_name







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






    """simple docstring"""




    UpperCAmelCase									=					str(__a )
    if os.path.isfile(__a ):
        return pretrained_model_name_or_path
    elif os.path.isdir(__a ):
        if os.path.isfile(os.path.join(__a ,				__a ) ):
            # Load from a PyTorch checkpoint
            UpperCAmelCase									=					os.path.join(__a ,				__a )
            return model_file
        elif subfolder is not None and os.path.isfile(
            os.path.join(__a ,				__a ,				__a ) ):
            UpperCAmelCase									=					os.path.join(__a ,				__a ,				__a )
            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(__a ).base_version ) >= version.parse('0.20.0' )
        ):
            try:
                UpperCAmelCase									=					hf_hub_download(
                    __a ,				filename=_add_variant(__a ,				__a ) ,				cache_dir=__a ,				force_download=__a ,				proxies=__a ,				resume_download=__a ,				local_files_only=__a ,				use_auth_token=__a ,				user_agent=__a ,				subfolder=__a ,				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." ,				__a ,				)
                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(__a ,				__a )} 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(__a ,				__a )}' so that the correct variant file can be added." ,				__a ,				)
        try:
            # 2. Load model file as usual
            UpperCAmelCase									=					hf_hub_download(
                __a ,				filename=__a ,				cache_dir=__a ,				force_download=__a ,				proxies=__a ,				resume_download=__a ,				local_files_only=__a ,				use_auth_token=__a ,				user_agent=__a ,				subfolder=__a ,				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()
SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)




def     lowercase__						(					lowerCAmelCase				:					int ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					DPTConfig()

    if "large" in checkpoint_url:
        UpperCAmelCase									=					1_024
        UpperCAmelCase									=					4_096
        UpperCAmelCase									=					24
        UpperCAmelCase									=					16
        UpperCAmelCase									=					[5, 11, 17, 23]
        UpperCAmelCase									=					[256, 512, 1_024, 1_024]
        UpperCAmelCase									=					(1, 384, 384)

    if "ade" in checkpoint_url:
        UpperCAmelCase									=					True

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

    return config, expected_shape




def     lowercase__						(					lowerCAmelCase				:					str ) ->      Any:






    """simple docstring"""




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




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      List[Any]:






    """simple docstring"""




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

    return name




def     lowercase__						(					lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					int ) ->      int:






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




def     lowercase__						(					) ->      Optional[Any]:






    """simple docstring"""




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




@torch.no_grad()
def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					List[str] ,				lowerCAmelCase				:					Union[str, Any] ) ->      Union[str, Any]:






    """simple docstring"""




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

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

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

    UpperCAmelCase									=					prepare_img()
    UpperCAmelCase									=					image_processor(lowerCAmelCase ,				return_tensors='pt' )

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

    # Assert logits
    UpperCAmelCase									=					torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
    if "ade" in checkpoint_url:
        UpperCAmelCase									=					torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
    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__":
   SCREAMING_SNAKE_CASE_    					=						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.''',
   )

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



