


'''simple docstring'''



from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__A	='\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n'

__A	='\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n'

__A	='\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for \'record\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'prediction_text\': the predicted answer text\n        - for \'multirc\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question-answer pair as specified by the dataset\n            - \'prediction\': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for \'record\': list of question-answers dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'answers\': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for \'record\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1\': F1 score\n    - for \'multirc\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1_m\': Per-question macro-F1 score\n        - \'f1_a\': Average F1 score over all answers\n    - for \'axb\':\n        \'matthews_correlation\': Matthew Correlation\n    - for \'cb\':\n        - \'accuracy\': Accuracy\n        - \'f1\': F1 score\n    - for all others:\n        - \'accuracy\': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')\n    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]\n    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')\n    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			return float((preds == labels).mean()					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__="binary"					):
			UpperCAmelCase__  :					Optional[int]        =     simple_accuracy(UpperCamelCase__ ,      UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     float(fa_score(y_true=UpperCamelCase__ ,      y_pred=UpperCamelCase__ ,      average=UpperCamelCase__					)					)
			return {
			    "accuracy": acc,
			    "f1": fa,
			}







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     {}
			for id_pred, label in zip(UpperCamelCase__ ,      UpperCamelCase__					):
						UpperCAmelCase__  :					Optional[Any]        =     f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
						UpperCAmelCase__  :					Optional[int]        =     id_pred["""prediction"""]
						if question_id in question_map:
									question_map[question_id].append((pred, label)					)
						else:
									UpperCAmelCase__  :					Dict        =     [(pred, label)]
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[Any]        =     [], []
			for question, preds_labels in question_map.items():
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Union[str, Any]        =     zip(*UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[int]        =     fa_score(y_true=UpperCamelCase__ ,      y_pred=UpperCamelCase__ ,      average="""macro"""					)
						fas.append(UpperCamelCase__					)
						UpperCAmelCase__  :					List[str]        =     int(sum(pred == label for pred, label in preds_labels					) == len(UpperCamelCase__					)					)
						ems.append(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     float(sum(UpperCamelCase__					) / len(UpperCamelCase__					)					)
			UpperCAmelCase__  :					Optional[int]        =     sum(UpperCamelCase__					) / len(UpperCamelCase__					)
			UpperCAmelCase__  :					str        =     float(fa_score(y_true=UpperCamelCase__ ,      y_pred=[id_pred["""prediction"""] for id_pred in ids_preds]					)					)
			return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,   _KWARGS_DESCRIPTION	)
class 		_snake_case					(     datasets.Metric	):




					def 				snake_case__						(      self):
								if self.config_name not in [
								    "boolq",
								    "cb",
								    "copa",
								    "multirc",
								    "record",
								    "rte",
								    "wic",
								    "wsc",
								    "wsc.fixed",
								    "axb",
								    "axg",
								]:
											raise KeyError(
											    """You should supply a configuration name selected in """
											    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""")
								return datasets.MetricInfo(
								    description=_DESCRIPTION							,							citation=_CITATION							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(self._get_feature_types())							,							codebase_urls=[]							,							reference_urls=[]							,							format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None							,							)




					def 				snake_case__						(      self):
								if self.config_name == "record":
											return {
											    "predictions": {
											        "idx": {
											            "passage": datasets.Value("""int64"""),
											            "query": datasets.Value("""int64"""),
											        },
											        "prediction_text": datasets.Value("""string"""),
											    },
											    "references": {
											        "idx": {
											            "passage": datasets.Value("""int64"""),
											            "query": datasets.Value("""int64"""),
											        },
											        "answers": datasets.Sequence(datasets.Value("""string""")),
											    },
											}
								elif self.config_name == "multirc":
											return {
											    "predictions": {
											        "idx": {
											            "answer": datasets.Value("""int64"""),
											            "paragraph": datasets.Value("""int64"""),
											            "question": datasets.Value("""int64"""),
											        },
											        "prediction": datasets.Value("""int64"""),
											    },
											    "references": datasets.Value("""int64"""),
											}
								else:
											return {
											    "predictions": datasets.Value("""int64"""),
											    "references": datasets.Value("""int64"""),
											}




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase):
								if self.config_name == "axb":
											return {"matthews_correlation": matthews_corrcoef(_lowerCamelCase							,							_lowerCamelCase)}
								elif self.config_name == "cb":
											return acc_and_fa(_lowerCamelCase							,							_lowerCamelCase							,							fa_avg="""macro""")
								elif self.config_name == "record":
											UpperCAmelCase__  :					int        =     [
											    {
											        """qas""": [
											            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
											            for ref in references
											        ]
											    }
											]
											UpperCAmelCase__  :					Optional[Any]        =     {pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
											return evaluate_record(_lowerCamelCase							,							_lowerCamelCase)[0]
								elif self.config_name == "multirc":
											return evaluate_multirc(_lowerCamelCase							,							_lowerCamelCase)
								elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
											return {"accuracy": simple_accuracy(_lowerCamelCase							,							_lowerCamelCase)}
								else:
											raise KeyError(
											    """You should supply a configuration name selected in """
											    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""")



'''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()
__A	=logging.get_logger(__name__)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     DPTConfig(embedding_type="""hybrid"""					)

			if "large" in checkpoint_url:
						UpperCAmelCase__  :					List[str]        =     1_0_2_4
						UpperCAmelCase__  :					List[str]        =     4_0_9_6
						UpperCAmelCase__  :					Optional[int]        =     2_4
						UpperCAmelCase__  :					str        =     1_6
						UpperCAmelCase__  :					Optional[Any]        =     [5, 1_1, 1_7, 2_3]
						UpperCAmelCase__  :					Any        =     [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
						UpperCAmelCase__  :					Union[str, Any]        =     (1, 3_8_4, 3_8_4)

			if "nyu" or "midas" in checkpoint_url:
						UpperCAmelCase__  :					List[str]        =     7_6_8
						UpperCAmelCase__  :					Union[str, Any]        =     [1, 1, 1, 0.5]
						UpperCAmelCase__  :					Union[str, Any]        =     [2_5_6, 5_1_2, 7_6_8, 7_6_8]
						UpperCAmelCase__  :					int        =     1_5_0
						UpperCAmelCase__  :					Any        =     1_6
						UpperCAmelCase__  :					Optional[Any]        =     (1, 3_8_4, 3_8_4)
						UpperCAmelCase__  :					Optional[int]        =     False
						UpperCAmelCase__  :					int        =     """project"""

			if "ade" in checkpoint_url:
						UpperCAmelCase__  :					Any        =     True
						UpperCAmelCase__  :					str        =     7_6_8
						UpperCAmelCase__  :					Optional[int]        =     [1, 1, 1, 0.5]
						UpperCAmelCase__  :					str        =     1_5_0
						UpperCAmelCase__  :					str        =     1_6
						UpperCAmelCase__  :					List[Any]        =     """huggingface/label-files"""
						UpperCAmelCase__  :					Optional[int]        =     """ade20k-id2label.json"""
						UpperCAmelCase__  :					Optional[int]        =     json.load(open(cached_download(hf_hub_url(UpperCamelCase__ ,      UpperCamelCase__ ,      repo_type="""dataset"""					)					) ,      """r"""					)					)
						UpperCAmelCase__  :					List[str]        =     {int(UpperCamelCase__					): v for k, v in idalabel.items()}
						UpperCAmelCase__  :					List[str]        =     idalabel
						UpperCAmelCase__  :					List[Any]        =     {v: k for k, v in idalabel.items()}
						UpperCAmelCase__  :					Optional[Any]        =     [1, 1_5_0, 4_8_0, 4_8_0]

			return config, expected_shape







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[int]        =     ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
			for k in ignore_keys:
						state_dict.pop(UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			if (
			    "pretrained.model" in name
			    and "cls_token" not in name
			    and "pos_embed" not in name
			    and "patch_embed" not in name
			):
						UpperCAmelCase__  :					int        =     name.replace("""pretrained.model""" ,      """dpt.encoder"""					)
			if "pretrained.model" in name:
						UpperCAmelCase__  :					int        =     name.replace("""pretrained.model""" ,      """dpt.embeddings"""					)
			if "patch_embed" in name:
						UpperCAmelCase__  :					Any        =     name.replace("""patch_embed""" ,      """"""					)
			if "pos_embed" in name:
						UpperCAmelCase__  :					Optional[int]        =     name.replace("""pos_embed""" ,      """position_embeddings"""					)
			if "attn.proj" in name:
						UpperCAmelCase__  :					Union[str, Any]        =     name.replace("""attn.proj""" ,      """attention.output.dense"""					)
			if "proj" in name and "project" not in name:
						UpperCAmelCase__  :					str        =     name.replace("""proj""" ,      """projection"""					)
			if "blocks" in name:
						UpperCAmelCase__  :					str        =     name.replace("""blocks""" ,      """layer"""					)
			if "mlp.fc1" in name:
						UpperCAmelCase__  :					Tuple        =     name.replace("""mlp.fc1""" ,      """intermediate.dense"""					)
			if "mlp.fc2" in name:
						UpperCAmelCase__  :					str        =     name.replace("""mlp.fc2""" ,      """output.dense"""					)
			if "norm1" in name and "backbone" not in name:
						UpperCAmelCase__  :					Optional[Any]        =     name.replace("""norm1""" ,      """layernorm_before"""					)
			if "norm2" in name and "backbone" not in name:
						UpperCAmelCase__  :					Dict        =     name.replace("""norm2""" ,      """layernorm_after"""					)
			if "scratch.output_conv" in name:
						UpperCAmelCase__  :					int        =     name.replace("""scratch.output_conv""" ,      """head"""					)
			if "scratch" in name:
						UpperCAmelCase__  :					Dict        =     name.replace("""scratch""" ,      """neck"""					)
			if "layer1_rn" in name:
						UpperCAmelCase__  :					List[Any]        =     name.replace("""layer1_rn""" ,      """convs.0"""					)
			if "layer2_rn" in name:
						UpperCAmelCase__  :					List[Any]        =     name.replace("""layer2_rn""" ,      """convs.1"""					)
			if "layer3_rn" in name:
						UpperCAmelCase__  :					Dict        =     name.replace("""layer3_rn""" ,      """convs.2"""					)
			if "layer4_rn" in name:
						UpperCAmelCase__  :					Any        =     name.replace("""layer4_rn""" ,      """convs.3"""					)
			if "refinenet" in name:
						UpperCAmelCase__  :					List[Any]        =     int(name[len("""neck.refinenet"""					) : len("""neck.refinenet"""					) + 1]					)
						# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
						UpperCAmelCase__  :					Union[str, Any]        =     name.replace(f'''refinenet{layer_idx}''' ,      f'''fusion_stage.layers.{abs(layer_idx-4					)}'''					)
			if "out_conv" in name:
						UpperCAmelCase__  :					Union[str, Any]        =     name.replace("""out_conv""" ,      """projection"""					)
			if "resConfUnit1" in name:
						UpperCAmelCase__  :					List[Any]        =     name.replace("""resConfUnit1""" ,      """residual_layer1"""					)
			if "resConfUnit2" in name:
						UpperCAmelCase__  :					Dict        =     name.replace("""resConfUnit2""" ,      """residual_layer2"""					)
			if "conv1" in name:
						UpperCAmelCase__  :					Union[str, Any]        =     name.replace("""conv1""" ,      """convolution1"""					)
			if "conv2" in name:
						UpperCAmelCase__  :					Tuple        =     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__  :					Tuple        =     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__  :					List[Any]        =     name.replace("""pretrained.act_postprocess4.0.project.0""" ,      """neck.reassemble_stage.readout_projects.3.0"""					)

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

			if ".." in name:
						UpperCAmelCase__  :					Dict        =     name.replace("""..""" ,      """."""					)

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







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			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__  :					Dict        =     state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''					)
						UpperCAmelCase__  :					Tuple        =     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__  :					Any        =     in_proj_weight[: config.hidden_size, :]
						UpperCAmelCase__  :					Optional[Any]        =     in_proj_bias[: config.hidden_size]
						UpperCAmelCase__  :					Optional[int]        =     in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCAmelCase__  :					List[str]        =     in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						UpperCAmelCase__  :					Optional[int]        =     in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCAmelCase__  :					Optional[Any]        =     in_proj_bias[-config.hidden_size :]







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







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

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

			# Check outputs on an image
			UpperCAmelCase__  :					Tuple        =     4_8_0 if """ade""" in checkpoint_url else 3_8_4
			UpperCAmelCase__  :					Tuple        =     DPTImageProcessor(size=UpperCamelCase__					)

			UpperCAmelCase__  :					Union[str, Any]        =     prepare_img()
			UpperCAmelCase__  :					Any        =     image_processor(UpperCamelCase__ ,      return_tensors="""pt"""					)

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

			if show_prediction:
						UpperCAmelCase__  :					str        =     (
						    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()) * 2_5_5					).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	=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	=parser.parse_args()
		convert_dpt_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
		)