





'''simple docstring'''

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCAmelCase							:     Optional[Any]		     ="\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__lowerCAmelCase							:     Union[str, Any]		     ="\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


__lowerCAmelCase							:     str		     ="\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"





def 						UpperCamelCase    (							_lowerCamelCase : List[Any]	):

				def remove_articles(_lowerCamelCase : Dict	):
								A__    =      re.compile(r"\b(a|an|the)\b"						,	re.UNICODE	)
								return re.sub(_lowerCamelCase						,	" "						,	_lowerCamelCase	)

				def white_space_fix(_lowerCamelCase : Tuple	):
								return " ".join(text.split()	)

				def remove_punc(_lowerCamelCase : int	):
								A__    =      set(string.punctuation	)
								return "".join(ch for ch in text if ch not in exclude	)

				def lower(_lowerCamelCase : Optional[int]	):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase	)	)	)	)





def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : List[str]	):
				return int(normalize_answer(_lowerCamelCase	) == normalize_answer(_lowerCamelCase	)	)





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : List[Any]	):
				A__    =      [any(compute_exact(_lowerCamelCase						,	_lowerCamelCase	) for ref in refs	) for pred, refs in zip(_lowerCamelCase						,	_lowerCamelCase	)]
				return (sum(_lowerCamelCase	) / len(_lowerCamelCase	)) * 1_00





def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : Union[str, Any]						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : str	):
				A__    =      [rgram for rgrams in rgramslist for rgram in rgrams]
				A__    =      Counter(_lowerCamelCase	)

				A__    =      Counter(_lowerCamelCase	)
				A__    =      Counter()
				for sgram, scount in sgramcounter.items():
								A__    =      scount * numref

				A__    =      Counter(_lowerCamelCase	)
				A__    =      Counter()
				for cgram, ccount in cgramcounter.items():
								A__    =      ccount * numref

				# KEEP
				A__    =      sgramcounter_rep & cgramcounter_rep
				A__    =      keepgramcounter_rep & rgramcounter
				A__    =      sgramcounter_rep & rgramcounter

				A__    =      0
				A__    =      0
				for keepgram in keepgramcountergood_rep:
								keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
								# Fix an alleged bug [2] in the keep score computation.
								# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
								keeptmpscorea += keepgramcountergood_rep[keepgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				#      a target exactly.
				A__    =      1
				A__    =      1
				if len(_lowerCamelCase	) > 0:
								A__    =      keeptmpscorea / len(_lowerCamelCase	)
				if len(_lowerCamelCase	) > 0:
								# Fix an alleged bug [2] in the keep score computation.
								# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
								A__    =      keeptmpscorea / sum(keepgramcounterall_rep.values()	)
				A__    =      0
				if keepscore_precision > 0 or keepscore_recall > 0:
								A__    =      2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

				# DELETION
				A__    =      sgramcounter_rep - cgramcounter_rep
				A__    =      delgramcounter_rep - rgramcounter
				A__    =      sgramcounter_rep - rgramcounter
				A__    =      0
				A__    =      0
				for delgram in delgramcountergood_rep:
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				A__    =      1
				if len(_lowerCamelCase	) > 0:
								A__    =      deltmpscorea / len(_lowerCamelCase	)

				# ADDITION
				A__    =      set(_lowerCamelCase	) - set(_lowerCamelCase	)
				A__    =      set(_lowerCamelCase	) & set(_lowerCamelCase	)
				A__    =      set(_lowerCamelCase	) - set(_lowerCamelCase	)

				A__    =      0
				for addgram in addgramcountergood:
								addtmpscore += 1

				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				A__    =      1
				A__    =      1
				if len(_lowerCamelCase	) > 0:
								A__    =      addtmpscore / len(_lowerCamelCase	)
				if len(_lowerCamelCase	) > 0:
								A__    =      addtmpscore / len(_lowerCamelCase	)
				A__    =      0
				if addscore_precision > 0 or addscore_recall > 0:
								A__    =      2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

				return (keepscore, delscore_precision, addscore)





def 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Union[str, Any]	):
				A__    =      len(_lowerCamelCase	)

				A__    =      ssent.split(" "	)
				A__    =      csent.split(" "	)
				A__    =      []
				A__    =      []
				A__    =      []
				A__    =      []
				A__    =      []
				A__    =      []

				A__    =      []
				A__    =      []
				A__    =      []
				A__    =      []
				for rsent in rsents:
								A__    =      rsent.split(" "	)
								A__    =      []
								A__    =      []
								A__    =      []
								ragramslist.append(_lowerCamelCase	)
								for i in range(0						,	len(_lowerCamelCase	) - 1	):
												if i < len(_lowerCamelCase	) - 1:
																A__    =      ragrams[i] + " " + ragrams[i + 1]
																ragrams.append(_lowerCamelCase	)
												if i < len(_lowerCamelCase	) - 2:
																A__    =      ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
																ragrams.append(_lowerCamelCase	)
												if i < len(_lowerCamelCase	) - 3:
																A__    =      ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
																ragrams.append(_lowerCamelCase	)
								ragramslist.append(_lowerCamelCase	)
								ragramslist.append(_lowerCamelCase	)
								ragramslist.append(_lowerCamelCase	)

				for i in range(0						,	len(_lowerCamelCase	) - 1	):
								if i < len(_lowerCamelCase	) - 1:
												A__    =      sagrams[i] + " " + sagrams[i + 1]
												sagrams.append(_lowerCamelCase	)
								if i < len(_lowerCamelCase	) - 2:
												A__    =      sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
												sagrams.append(_lowerCamelCase	)
								if i < len(_lowerCamelCase	) - 3:
												A__    =      sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
												sagrams.append(_lowerCamelCase	)

				for i in range(0						,	len(_lowerCamelCase	) - 1	):
								if i < len(_lowerCamelCase	) - 1:
												A__    =      cagrams[i] + " " + cagrams[i + 1]
												cagrams.append(_lowerCamelCase	)
								if i < len(_lowerCamelCase	) - 2:
												A__    =      cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
												cagrams.append(_lowerCamelCase	)
								if i < len(_lowerCamelCase	) - 3:
												A__    =      cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
												cagrams.append(_lowerCamelCase	)

				((A__),     (A__),     (A__))    =      SARIngram(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
				((A__),     (A__),     (A__))    =      SARIngram(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
				((A__),     (A__),     (A__))    =      SARIngram(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
				((A__),     (A__),     (A__))    =      SARIngram(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
				A__    =      sum([keepascore, keepascore, keepascore, keepascore]	) / 4
				A__    =      sum([delascore, delascore, delascore, delascore]	) / 4
				A__    =      sum([addascore, addascore, addascore, addascore]	) / 4
				A__    =      (avgkeepscore + avgdelscore + avgaddscore) / 3
				return finalscore





def 						UpperCamelCase    (							_lowerCamelCase : Tuple						,	_lowerCamelCase : bool = True						,	_lowerCamelCase : str = "13a"						,	_lowerCamelCase : bool = True	):
				# Normalization is requried for the ASSET dataset (one of the primary
				# datasets in sentence simplification) to allow using space
				# to split the sentence. Even though Wiki-Auto and TURK datasets,
				# do not require normalization, we do it for consistency.
				# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
				# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

				if lowercase:
								A__    =      sentence.lower()

				if tokenizer in ["13a", "intl"]:
								if version.parse(sacrebleu.__version__	).major >= 2:
												A__    =      sacrebleu.metrics.bleu._get_tokenizer(_lowerCamelCase	)()(_lowerCamelCase	)
								else:
												A__    =      sacrebleu.TOKENIZERS[tokenizer]()(_lowerCamelCase	)
				elif tokenizer == "moses":
								A__    =      sacremoses.MosesTokenizer().tokenize(_lowerCamelCase						,	return_str=_lowerCamelCase						,	escape=_lowerCamelCase	)
				elif tokenizer == "penn":
								A__    =      sacremoses.MosesTokenizer().penn_tokenize(_lowerCamelCase						,	return_str=_lowerCamelCase	)
				else:
								A__    =      sentence

				if not return_str:
								A__    =      normalized_sent.split()

				return normalized_sent





def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : int						,	_lowerCamelCase : Union[str, Any]	):
				if not (len(_lowerCamelCase	) == len(_lowerCamelCase	) == len(_lowerCamelCase	)):
								raise ValueError("Sources length must match predictions and references lengths."	)
				A__    =      0
				for src, pred, refs in zip(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	):
								sari_score += SARIsent(normalize(_lowerCamelCase	)						,	normalize(_lowerCamelCase	)						,	[normalize(_lowerCamelCase	) for sent in refs]	)
				A__    =      sari_score / len(_lowerCamelCase	)
				return 1_00 * sari_score





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : Dict						,	_lowerCamelCase : List[Any]="exp"						,	_lowerCamelCase : int=None						,	_lowerCamelCase : str=False						,	_lowerCamelCase : List[str]=False						,	_lowerCamelCase : Dict=False						,	):
				A__    =      len(references[0]	)
				if any(len(_lowerCamelCase	) != references_per_prediction for refs in references	):
								raise ValueError("Sacrebleu requires the same number of references for each prediction"	)
				A__    =      [[refs[i] for refs in references] for i in range(_lowerCamelCase	)]
				A__    =      sacrebleu.corpus_bleu(
				    _lowerCamelCase						,	_lowerCamelCase						,	smooth_method=_lowerCamelCase						,	smooth_value=_lowerCamelCase						,	force=_lowerCamelCase						,	lowercase=_lowerCamelCase						,	use_effective_order=_lowerCamelCase						,	)
				return output.score






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,	_KWARGS_DESCRIPTION    )
class        UpperCAmelCase     (	datasets.Metric    ):
	def 					UpperCAmelCase_							(    self				:Any							)->							Union[str, Any]:
					return datasets.MetricInfo(
					    description=_DESCRIPTION						,	citation=_CITATION						,	inputs_description=_KWARGS_DESCRIPTION						,	features=datasets.Features(
					        {
					            "predictions": datasets.Value("string"						,	id="sequence"							),
					            "references": datasets.Sequence(datasets.Value("string"						,	id="sequence"							)						,	id="references"							),
					        }							)						,	codebase_urls=[
					        "https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
					        "https://github.com/cocoxu/simplification/blob/master/SARI.py",
					        "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
					        "https://github.com/mjpost/sacreBLEU",
					    ]						,	reference_urls=[
					        "https://www.aclweb.org/anthology/Q16-1029.pdf",
					        "https://github.com/mjpost/sacreBLEU",
					        "https://en.wikipedia.org/wiki/BLEU",
					        "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
					    ]						,	)



	def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Dict						,	lowercase_				:List[Any]						,	lowercase_				:int							)->							int:
					A__    =      {}
					result.update({"sari": compute_sari(sources=lowercase_						,	predictions=lowercase_						,	references=lowercase_							)}							)
					result.update({"sacrebleu": compute_sacrebleu(predictions=lowercase_						,	references=lowercase_							)}							)
					result.update({"exact": compute_em(predictions=lowercase_						,	references=lowercase_							)}							)
					return result










'''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							:     Tuple		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : Tuple	):
    A__    =      DPTConfig(embedding_type="hybrid"	)

    if "large" in checkpoint_url:
        A__    =      10_24
        A__    =      40_96
        A__    =      24
        A__    =      16
        A__    =      [5, 11, 17, 23]
        A__    =      [2_56, 5_12, 10_24, 10_24]
        A__    =      (1, 3_84, 3_84)

    if "nyu" or "midas" in checkpoint_url:
        A__    =      7_68
        A__    =      [1, 1, 1, 0.5]
        A__    =      [2_56, 5_12, 7_68, 7_68]
        A__    =      1_50
        A__    =      16
        A__    =      (1, 3_84, 3_84)
        A__    =      False
        A__    =      "project"

    if "ade" in checkpoint_url:
        A__    =      True
        A__    =      7_68
        A__    =      [1, 1, 1, 0.5]
        A__    =      1_50
        A__    =      16
        A__    =      "huggingface/label-files"
        A__    =      "ade20k-id2label.json"
        A__    =      json.load(open(cached_download(hf_hub_url(_lowerCamelCase						,	_lowerCamelCase						,	repo_type="dataset"	)	)						,	"r"	)	)
        A__    =      {int(_lowerCamelCase	): v for k, v in idalabel.items()}
        A__    =      idalabel
        A__    =      {v: k for k, v in idalabel.items()}
        A__    =      [1, 1_50, 4_80, 4_80]

    return config, expected_shape





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]	):
    A__    =      ["pretrained.model.head.weight", "pretrained.model.head.bias"]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase						,	_lowerCamelCase	)





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

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

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





def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : int	):
    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__    =      state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"	)
        A__    =      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__    =      in_proj_weight[: config.hidden_size, :]
        A__    =      in_proj_bias[: config.hidden_size]
        A__    =      in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        A__    =      in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        A__    =      in_proj_weight[
            -config.hidden_size :, :
        ]
        A__    =      in_proj_bias[-config.hidden_size :]





def 						UpperCamelCase    (							):
    A__    =      "http://images.cocodataset.org/val2017/000000039769.jpg"
    A__    =      Image.open(requests.get(_lowerCamelCase						,	stream=_lowerCamelCase	).raw	)
    return im





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

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

    # Check outputs on an image
    A__    =      4_80 if "ade" in checkpoint_url else 3_84
    A__    =      DPTImageProcessor(size=_lowerCamelCase	)

    A__    =      prepare_img()
    A__    =      image_processor(_lowerCamelCase						,	return_tensors="pt"	)

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

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

        Image.fromarray((prediction / prediction.max()) * 2_55	).show()

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


if __name__ == "__main__":
  __lowerCAmelCase							:     Tuple		     =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							:     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
  )



