
"""simple docstring"""







from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
							import tensorflow as tf

							from ..tf_utils import stable_softmax


if is_torch_available():
							import torch


lowerCamelCase__   					=		logging.get_logger(__name__)




@add_end_docstrings(
    _lowerCamelCase       ,     R'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '       ,     )
class  A__       (	_lowerCamelCase):

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			if self.framework == "tf":
				__lowerCAmelCase			:						List[Any]									=      tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
			elif self.framework == "pt":
				__lowerCAmelCase			:						Tuple									=      torch.nonzero(input_ids == self.tokenizer.mask_token_id       ,		as_tuple=_SCREAMING_SNAKE_CASE )
			else:
				raise ValueError('Unsupported framework' )
			return masked_index

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Union[str, Any]									=      self.get_masked_index(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Dict									=      np.prod(masked_index.shape )
			if numel < 1:
				raise PipelineException(
				    'fill-mask'       ,		self.model.base_model_prefix       ,		f"No mask_token ({self.tokenizer.mask_token}) found on the input"       ,		)

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				for model_input in model_inputs:
					self._ensure_exactly_one_mask_token(model_input['input_ids'][0] )
			else:
				for input_ids in model_inputs["input_ids"]:
					self._ensure_exactly_one_mask_token(_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=None       ,		**_SCREAMING_SNAKE_CASE ):
			if return_tensors is None:
				__lowerCAmelCase			:						Dict									=      self.framework
			__lowerCAmelCase			:						List[Any]									=      self.tokenizer(_SCREAMING_SNAKE_CASE       ,		return_tensors=_SCREAMING_SNAKE_CASE )
			self.ensure_exactly_one_mask_token(_SCREAMING_SNAKE_CASE )
			return model_inputs

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Union[str, Any]									=      self.model(**_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[Any]									=      model_inputs['input_ids']
			return model_outputs

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=5       ,		_SCREAMING_SNAKE_CASE=None ):
			# Cap top_k if there are targets
			if target_ids is not None and target_ids.shape[0] < top_k:
				__lowerCAmelCase			:						Dict									=      target_ids.shape[0]
			__lowerCAmelCase			:						Tuple									=      model_outputs['input_ids'][0]
			__lowerCAmelCase			:						Union[str, Any]									=      model_outputs['logits']

			if self.framework == "tf":
				__lowerCAmelCase			:						List[str]									=      tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]

				__lowerCAmelCase			:						Optional[int]									=      outputs.numpy()

				__lowerCAmelCase			:						Dict									=      outputs[0, masked_index, :]
				__lowerCAmelCase			:						str									=      stable_softmax(_SCREAMING_SNAKE_CASE       ,		axis=-1 )
				if target_ids is not None:
					__lowerCAmelCase			:						Optional[Any]									=      tf.gather_nd(tf.squeeze(_SCREAMING_SNAKE_CASE       ,		0 )       ,		target_ids.reshape(-1       ,		1 ) )
					__lowerCAmelCase			:						Dict									=      tf.expand_dims(_SCREAMING_SNAKE_CASE       ,		0 )

				__lowerCAmelCase			:						Tuple									=      tf.math.top_k(_SCREAMING_SNAKE_CASE       ,		k=_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			,   __lowerCAmelCase			:						Optional[int]									=      topk.values.numpy(), topk.indices.numpy()
			else:
				__lowerCAmelCase			:						List[Any]									=      torch.nonzero(input_ids == self.tokenizer.mask_token_id       ,		as_tuple=_SCREAMING_SNAKE_CASE ).squeeze(-1 )
				# Fill mask pipeline supports only one ${mask_token} per sample

				__lowerCAmelCase			:						List[str]									=      outputs[0, masked_index, :]
				__lowerCAmelCase			:						Tuple									=      logits.softmax(dim=-1 )
				if target_ids is not None:
					__lowerCAmelCase			:						Dict									=      probs[..., target_ids]

				__lowerCAmelCase			,   __lowerCAmelCase			:						Any									=      probs.topk(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Tuple									=      []
			__lowerCAmelCase			:						int									=      values.shape[0] == 1
			for i, (_values, _predictions) in enumerate(zip(values.tolist()       ,		predictions.tolist() ) ):
				__lowerCAmelCase			:						Union[str, Any]									=      []
				for v, p in zip(_values       ,		_predictions ):
					# Copy is important since we're going to modify this array in place
					__lowerCAmelCase			:						Optional[Any]									=      input_ids.numpy().copy()
					if target_ids is not None:
						__lowerCAmelCase			:						int									=      target_ids[p].tolist()

					__lowerCAmelCase			:						List[Any]									=      p
					# Filter padding out:
					__lowerCAmelCase			:						Optional[Any]									=      tokens[np.where(tokens != self.tokenizer.pad_token_id )]
					# Originally we skip special tokens to give readable output.
					# For multi masks though, the other [MASK] would be removed otherwise
					# making the output look odd, so we add them back
					__lowerCAmelCase			:						int									=      self.tokenizer.decode(_SCREAMING_SNAKE_CASE       ,		skip_special_tokens=_SCREAMING_SNAKE_CASE )
					__lowerCAmelCase			:						Optional[int]									=      {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p] ), 'sequence': sequence}
					row.append(_SCREAMING_SNAKE_CASE )
				result.append(_SCREAMING_SNAKE_CASE )
			if single_mask:
				return result[0]
			return result

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=None ):
			if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Union[str, Any]									=      [targets]
			try:
				__lowerCAmelCase			:						Dict									=      self.tokenizer.get_vocab()
			except Exception:
				__lowerCAmelCase			:						str									=      {}
			__lowerCAmelCase			:						Any									=      []
			for target in targets:
				__lowerCAmelCase			:						Dict									=      vocab.get(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
				if id_ is None:
					__lowerCAmelCase			:						List[Any]									=      self.tokenizer(
					    _SCREAMING_SNAKE_CASE       ,		add_special_tokens=_SCREAMING_SNAKE_CASE       ,		return_attention_mask=_SCREAMING_SNAKE_CASE       ,		return_token_type_ids=_SCREAMING_SNAKE_CASE       ,		max_length=1       ,		truncation=_SCREAMING_SNAKE_CASE       ,		)['input_ids']
					if len(_SCREAMING_SNAKE_CASE ) == 0:
						logger.warning(
						    f"The specified target token `{target}` does not exist in the model vocabulary. "
						    'We cannot replace it with anything meaningful, ignoring it' )
						continue
					__lowerCAmelCase			:						Tuple									=      input_ids[0]
					# XXX: If users encounter this pass
					# it becomes pretty slow, so let's make sure
					# The warning enables them to fix the input to
					# get faster performance.
					logger.warning(
					    f"The specified target token `{target}` does not exist in the model vocabulary. "
					    f"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`." )
				target_ids.append(id_ )
			__lowerCAmelCase			:						List[str]									=      list(set(_SCREAMING_SNAKE_CASE ) )
			if len(_SCREAMING_SNAKE_CASE ) == 0:
				raise ValueError('At least one target must be provided when passed.' )
			__lowerCAmelCase			:						List[str]									=      np.array(_SCREAMING_SNAKE_CASE )
			return target_ids

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=None ):
			__lowerCAmelCase			:						List[Any]									=      {}

			if targets is not None:
				__lowerCAmelCase			:						Optional[int]									=      self.get_target_ids(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						Union[str, Any]									=      target_ids

			if top_k is not None:
				__lowerCAmelCase			:						Tuple									=      top_k

			if self.tokenizer.mask_token_id is None:
				raise PipelineException(
				    'fill-mask'       ,		self.model.base_model_prefix       ,		'The tokenizer does not define a `mask_token`.' )
			return {}, {}, postprocess_params







		def __call__(      self       ,		_SCREAMING_SNAKE_CASE       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Tuple									=      super().__call__(_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )
			if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ) and len(_SCREAMING_SNAKE_CASE ) == 1:
				return outputs[0]
			return outputs




'''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()
UpperCamelCase__:   Optional[int]								=			logging.get_logger(__name__)


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]  )      ->   Optional[int]:
							UpperCAmelCase :       Tuple 							=		DPTConfig(embedding_type='''hybrid'''  )

							if "large" in checkpoint_url:
														UpperCAmelCase :       Tuple 							=		1024
														UpperCAmelCase :       List[Any] 							=		4096
														UpperCAmelCase :       str 							=		24
														UpperCAmelCase :       List[Any] 							=		16
														UpperCAmelCase :       str 							=		[5, 11, 17, 23]
														UpperCAmelCase :       List[Any] 							=		[256, 512, 1024, 1024]
														UpperCAmelCase :       Tuple 							=		(1, 384, 384)

							if "nyu" or "midas" in checkpoint_url:
														UpperCAmelCase :       Optional[Any] 							=		768
														UpperCAmelCase :       Tuple 							=		[1, 1, 1, 0.5]
														UpperCAmelCase :       int 							=		[256, 512, 768, 768]
														UpperCAmelCase :       Any 							=		150
														UpperCAmelCase :       Tuple 							=		16
														UpperCAmelCase :       Any 							=		(1, 384, 384)
														UpperCAmelCase :       Optional[Any] 							=		False
														UpperCAmelCase :       Tuple 							=		'''project'''

							if "ade" in checkpoint_url:
														UpperCAmelCase :       Any 							=		True
														UpperCAmelCase :       str 							=		768
														UpperCAmelCase :       Optional[int] 							=		[1, 1, 1, 0.5]
														UpperCAmelCase :       List[Any] 							=		150
														UpperCAmelCase :       List[Any] 							=		16
														UpperCAmelCase :       str 							=		'''huggingface/label-files'''
														UpperCAmelCase :       Tuple 							=		'''ade20k-id2label.json'''
														UpperCAmelCase :       Any 							=		json.load(open(cached_download(hf_hub_url(_lowerCAmelCase   ,      _lowerCAmelCase   ,      repo_type='''dataset'''  )  )   ,      '''r'''  )  )
														UpperCAmelCase :       Optional[Any] 							=		{int(_lowerCAmelCase  ): v for k, v in idalabel.items()}
														UpperCAmelCase :       List[Any] 							=		idalabel
														UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in idalabel.items()}
														UpperCAmelCase :       Union[str, Any] 							=		[1, 150, 480, 480]

							return config, expected_shape


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]  )      ->   int:
							UpperCAmelCase :       List[str] 							=		['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
							for k in ignore_keys:
														state_dict.pop(_lowerCAmelCase   ,      _lowerCAmelCase  )


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

							if ".." in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''..'''   ,      '''.'''  )

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


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						List[Any]  )      ->   Optional[Any]:
							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 :       Optional[int] 							=		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 :       Tuple 							=		in_proj_weight[: config.hidden_size, :]
														UpperCAmelCase :       int 							=		in_proj_bias[: config.hidden_size]
														UpperCAmelCase :       List[str] 							=		in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														UpperCAmelCase :       List[str] 							=		in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														UpperCAmelCase :       str 							=		in_proj_weight[
														    -config.hidden_size :, :
														]
														UpperCAmelCase :       Union[str, Any] 							=		in_proj_bias[-config.hidden_size :]


def  snake_case_     (					)      ->   List[str]:
							UpperCAmelCase :       Optional[int] 							=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							UpperCAmelCase :       Optional[int] 							=		Image.open(requests.get(_lowerCAmelCase   ,      stream=_lowerCAmelCase  ).raw  )
							return im


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

							# load HuggingFace model
							UpperCAmelCase :       Optional[Any] 							=		DPTForSemanticSegmentation(_lowerCAmelCase  ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase  )
							model.load_state_dict(_lowerCAmelCase  )
							model.eval()

							# Check outputs on an image
							UpperCAmelCase :       int 							=		480 if '''ade''' in checkpoint_url else 384
							UpperCAmelCase :       List[Any] 							=		DPTImageProcessor(size=_lowerCAmelCase  )

							UpperCAmelCase :       Dict 							=		prepare_img()
							UpperCAmelCase :       Optional[int] 							=		image_processor(_lowerCAmelCase   ,      return_tensors='''pt'''  )

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

							if show_prediction:
														UpperCAmelCase :       Dict 							=		(
														    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()) * 255  ).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__":
						UpperCamelCase__:   Dict								=			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",
						)

						UpperCamelCase__:   Tuple								=			parser.parse_args()
						convert_dpt_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
						)





