


'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__A	=logging.get_logger(__name__)







def     _UpperCamelCase      (		UpperCamelCase__					):





			if isinstance(SCREAMING_SNAKE_CASE__ ,      np.ndarray					):
						return list(tensor.shape					)

			UpperCAmelCase__  :					Optional[int]        =     tf.shape(SCREAMING_SNAKE_CASE__					)

			if tensor.shape == tf.TensorShape(SCREAMING_SNAKE_CASE__					):
						return dynamic

			UpperCAmelCase__  :					List[str]        =     tensor.shape.as_list()

			return [dynamic[i] if s is None else s for i, s in enumerate(SCREAMING_SNAKE_CASE__					)]







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ = None ,      UpperCamelCase__ = None					):
			return tf.nn.softmax(logits=logits + 1e-9 ,      axis=SCREAMING_SNAKE_CASE__ ,      name=SCREAMING_SNAKE_CASE__					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=1e-5 ,      UpperCamelCase__=-1					):





			if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__					):
						raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis."""					)

			# Get mean and variance on the axis to be normalized
			UpperCAmelCase__  :					str        =     tf.nn.moments(SCREAMING_SNAKE_CASE__ ,      axes=[axis] ,      keepdims=SCREAMING_SNAKE_CASE__					)

			if axis != -1:
						# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
						# on every dimension except axis
						UpperCAmelCase__  :					Optional[Any]        =     [1] * inputs.shape.rank
						UpperCAmelCase__  :					Any        =     shape_list(SCREAMING_SNAKE_CASE__					)[axis]
						UpperCAmelCase__  :					List[str]        =     tf.reshape(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__					)
						UpperCAmelCase__  :					List[Any]        =     tf.reshape(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__					)

			# Compute layer normalization using the batch_normalization
			# function.
			UpperCAmelCase__  :					List[Any]        =     tf.nn.batch_normalization(
			    SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      offset=SCREAMING_SNAKE_CASE__ ,      scale=SCREAMING_SNAKE_CASE__ ,      variance_epsilon=SCREAMING_SNAKE_CASE__ ,      )
			return outputs







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__=0 ,      UpperCamelCase__=-1					):





			if end_dim < 0:
						end_dim += input.shape.rank
			if start_dim < 0:
						start_dim += input.shape.rank

			if start_dim == end_dim:
						return input

			UpperCAmelCase__  :					int        =     tf.shape(SCREAMING_SNAKE_CASE__					)
			UpperCAmelCase__  :					str        =     tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]					)
			UpperCAmelCase__  :					List[str]        =     tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] ,      axis=0					)
			return tf.reshape(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__					)







def     _UpperCamelCase      (		UpperCamelCase__					):





			if not isinstance(SCREAMING_SNAKE_CASE__ ,      tf.Tensor					):
						UpperCAmelCase__  :					Optional[Any]        =     tf.convert_to_tensor(SCREAMING_SNAKE_CASE__					)  # Catches stray NumPy inputs
			if encoder_attention_mask.shape.rank == 3:
						UpperCAmelCase__  :					Optional[int]        =     encoder_attention_mask[:, None, :, :]
			if encoder_attention_mask.shape.rank == 2:
						UpperCAmelCase__  :					List[Any]        =     encoder_attention_mask[:, None, None, :]
			# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
			# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
			# /transformer/transformer_layers.py#L270
			# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
			# encoder_extended_attention_mask.transpose(-1, -2))
			UpperCAmelCase__  :					int        =     (
			    tf.cast(1 ,      encoder_attention_mask.dtype					) - encoder_extended_attention_mask
			) * encoder_extended_attention_mask.dtype.min

			return encoder_extended_attention_mask







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ = "input_ids"					):
			tf.debugging.assert_less(
			    SCREAMING_SNAKE_CASE__ ,      tf.cast(SCREAMING_SNAKE_CASE__ ,      dtype=tensor.dtype					) ,      message=(
			        f'''The maximum value of {tensor_name} ({tf.math.reduce_max(SCREAMING_SNAKE_CASE__					)}) must be smaller than the embedding '''
			        f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
			    ) ,      )







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     6_4_5_1_2
			# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
			# because in that case even chunking the array would not make the saving
			# possible.
			UpperCAmelCase__  :					Optional[int]        =     [x for x in data if len(SCREAMING_SNAKE_CASE__					) > HDF5_OBJECT_HEADER_LIMIT]

			# Expecting this to never be true.
			if bad_attributes:
						raise RuntimeError(
						    """The following attributes cannot be saved to HDF5 file because """
						    f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
						    f'''bytes: {bad_attributes}'''					)

			UpperCAmelCase__  :					str        =     np.asarray(SCREAMING_SNAKE_CASE__					)

			UpperCAmelCase__  :					Union[str, Any]        =     1
			UpperCAmelCase__  :					Optional[Any]        =     np.array_split(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__					)

			# This will never loop forever thanks to the test above.
			while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data					):
						num_chunks += 1
						UpperCAmelCase__  :					Any        =     np.array_split(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__					)

			if num_chunks > 1:
						for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE__					):
									UpperCAmelCase__  :					List[Any]        =     chunk_data
			else:
						UpperCAmelCase__  :					Union[str, Any]        =     data







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):





			if name in group.attrs:
						UpperCAmelCase__  :					Tuple        =     [n.decode("""utf8"""					) if hasattr(SCREAMING_SNAKE_CASE__ ,      """decode"""					) else n for n in group.attrs[name]]
			else:
						UpperCAmelCase__  :					Tuple        =     []
						UpperCAmelCase__  :					Optional[Any]        =     0
						while "%s%d" % (name, chunk_id) in group.attrs:
									data.extend(
									    [n.decode("""utf8"""					) if hasattr(SCREAMING_SNAKE_CASE__ ,      """decode"""					) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]]					)
									chunk_id += 1
			return data







def     _UpperCamelCase      (		UpperCamelCase__					):





			def _expand_single_ad_tensor(UpperCamelCase__					):
						if isinstance(SCREAMING_SNAKE_CASE__ ,      tf.Tensor					) and t.shape.rank == 1:
									return tf.expand_dims(SCREAMING_SNAKE_CASE__ ,      axis=-1					)
						return t

			return tf.nest.map_structure(_expand_single_ad_tensor ,      SCREAMING_SNAKE_CASE__					)



'''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
		)