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


def A (__lowerCamelCase :List[Any]						):
						_lowerCAmelCase    		=					DPTConfig(embedding_type="""hybrid"""						)

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

						if "nyu" or "midas" in checkpoint_url:
												_lowerCAmelCase    		=					768
												_lowerCAmelCase    		=					[1, 1, 1, 0.5]
												_lowerCAmelCase    		=					[256, 512, 768, 768]
												_lowerCAmelCase    		=					150
												_lowerCAmelCase    		=					16
												_lowerCAmelCase    		=					(1, 384, 384)
												_lowerCAmelCase    		=					False
												_lowerCAmelCase    		=					"""project"""

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

						return config, expected_shape


def A (__lowerCamelCase :str						):
						_lowerCAmelCase    		=					["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
						for k in ignore_keys:
												state_dict.pop(__lowerCamelCase					,      __lowerCamelCase						)


def A (__lowerCamelCase :Union[str, Any]						):
						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												_lowerCAmelCase    		=					name.replace("""pretrained.model"""					,      """dpt.encoder"""						)
						if "pretrained.model" in name:
												_lowerCAmelCase    		=					name.replace("""pretrained.model"""					,      """dpt.embeddings"""						)
						if "patch_embed" in name:
												_lowerCAmelCase    		=					name.replace("""patch_embed"""					,      """"""						)
						if "pos_embed" in name:
												_lowerCAmelCase    		=					name.replace("""pos_embed"""					,      """position_embeddings"""						)
						if "attn.proj" in name:
												_lowerCAmelCase    		=					name.replace("""attn.proj"""					,      """attention.output.dense"""						)
						if "proj" in name and "project" not in name:
												_lowerCAmelCase    		=					name.replace("""proj"""					,      """projection"""						)
						if "blocks" in name:
												_lowerCAmelCase    		=					name.replace("""blocks"""					,      """layer"""						)
						if "mlp.fc1" in name:
												_lowerCAmelCase    		=					name.replace("""mlp.fc1"""					,      """intermediate.dense"""						)
						if "mlp.fc2" in name:
												_lowerCAmelCase    		=					name.replace("""mlp.fc2"""					,      """output.dense"""						)
						if "norm1" in name and "backbone" not in name:
												_lowerCAmelCase    		=					name.replace("""norm1"""					,      """layernorm_before"""						)
						if "norm2" in name and "backbone" not in name:
												_lowerCAmelCase    		=					name.replace("""norm2"""					,      """layernorm_after"""						)
						if "scratch.output_conv" in name:
												_lowerCAmelCase    		=					name.replace("""scratch.output_conv"""					,      """head"""						)
						if "scratch" in name:
												_lowerCAmelCase    		=					name.replace("""scratch"""					,      """neck"""						)
						if "layer1_rn" in name:
												_lowerCAmelCase    		=					name.replace("""layer1_rn"""					,      """convs.0"""						)
						if "layer2_rn" in name:
												_lowerCAmelCase    		=					name.replace("""layer2_rn"""					,      """convs.1"""						)
						if "layer3_rn" in name:
												_lowerCAmelCase    		=					name.replace("""layer3_rn"""					,      """convs.2"""						)
						if "layer4_rn" in name:
												_lowerCAmelCase    		=					name.replace("""layer4_rn"""					,      """convs.3"""						)
						if "refinenet" in name:
												_lowerCAmelCase    		=					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
												_lowerCAmelCase    		=					name.replace(f'refinenet{layer_idx}'					,      f'fusion_stage.layers.{abs(layer_idx-4						)}'						)
						if "out_conv" in name:
												_lowerCAmelCase    		=					name.replace("""out_conv"""					,      """projection"""						)
						if "resConfUnit1" in name:
												_lowerCAmelCase    		=					name.replace("""resConfUnit1"""					,      """residual_layer1"""						)
						if "resConfUnit2" in name:
												_lowerCAmelCase    		=					name.replace("""resConfUnit2"""					,      """residual_layer2"""						)
						if "conv1" in name:
												_lowerCAmelCase    		=					name.replace("""conv1"""					,      """convolution1"""						)
						if "conv2" in name:
												_lowerCAmelCase    		=					name.replace("""conv2"""					,      """convolution2"""						)
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												_lowerCAmelCase    		=					name.replace("""pretrained.act_postprocess1.0.project.0"""					,      """neck.reassemble_stage.readout_projects.0.0"""						)
						if "pretrained.act_postprocess2.0.project.0" in name:
												_lowerCAmelCase    		=					name.replace("""pretrained.act_postprocess2.0.project.0"""					,      """neck.reassemble_stage.readout_projects.1.0"""						)
						if "pretrained.act_postprocess3.0.project.0" in name:
												_lowerCAmelCase    		=					name.replace("""pretrained.act_postprocess3.0.project.0"""					,      """neck.reassemble_stage.readout_projects.2.0"""						)
						if "pretrained.act_postprocess4.0.project.0" in name:
												_lowerCAmelCase    		=					name.replace("""pretrained.act_postprocess4.0.project.0"""					,      """neck.reassemble_stage.readout_projects.3.0"""						)

						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												_lowerCAmelCase    		=					name.replace("""pretrained.act_postprocess1.3"""					,      """neck.reassemble_stage.layers.0.projection"""						)
						if "pretrained.act_postprocess1.4" in name:
												_lowerCAmelCase    		=					name.replace("""pretrained.act_postprocess1.4"""					,      """neck.reassemble_stage.layers.0.resize"""						)
						if "pretrained.act_postprocess2.3" in name:
												_lowerCAmelCase    		=					name.replace("""pretrained.act_postprocess2.3"""					,      """neck.reassemble_stage.layers.1.projection"""						)
						if "pretrained.act_postprocess2.4" in name:
												_lowerCAmelCase    		=					name.replace("""pretrained.act_postprocess2.4"""					,      """neck.reassemble_stage.layers.1.resize"""						)
						if "pretrained.act_postprocess3.3" in name:
												_lowerCAmelCase    		=					name.replace("""pretrained.act_postprocess3.3"""					,      """neck.reassemble_stage.layers.2.projection"""						)
						if "pretrained.act_postprocess4.3" in name:
												_lowerCAmelCase    		=					name.replace("""pretrained.act_postprocess4.3"""					,      """neck.reassemble_stage.layers.3.projection"""						)
						if "pretrained.act_postprocess4.4" in name:
												_lowerCAmelCase    		=					name.replace("""pretrained.act_postprocess4.4"""					,      """neck.reassemble_stage.layers.3.resize"""						)
						if "pretrained" in name:
												_lowerCAmelCase    		=					name.replace("""pretrained"""					,      """dpt"""						)
						if "bn" in name:
												_lowerCAmelCase    		=					name.replace("""bn"""					,      """batch_norm"""						)
						if "head" in name:
												_lowerCAmelCase    		=					name.replace("""head"""					,      """head.head"""						)
						if "encoder.norm" in name:
												_lowerCAmelCase    		=					name.replace("""encoder.norm"""					,      """layernorm"""						)
						if "auxlayer" in name:
												_lowerCAmelCase    		=					name.replace("""auxlayer"""					,      """auxiliary_head.head"""						)
						if "backbone" in name:
												_lowerCAmelCase    		=					name.replace("""backbone"""					,      """backbone.bit.encoder"""						)

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

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


def A (__lowerCamelCase :str					,      __lowerCamelCase :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)
												_lowerCAmelCase    		=					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'						)
												_lowerCAmelCase    		=					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'						)
												# next, add query, keys and values (in that order) to the state dict
												_lowerCAmelCase    		=					in_proj_weight[: config.hidden_size, :]
												_lowerCAmelCase    		=					in_proj_bias[: config.hidden_size]
												_lowerCAmelCase    		=					in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_lowerCAmelCase    		=					in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												_lowerCAmelCase    		=					in_proj_weight[
												    -config.hidden_size :, :
												]
												_lowerCAmelCase    		=					in_proj_bias[-config.hidden_size :]


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


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

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

						# Check outputs on an image
						_lowerCAmelCase    		=					480 if """ade""" in checkpoint_url else 384
						_lowerCAmelCase    		=					DPTImageProcessor(size=__lowerCamelCase						)

						_lowerCAmelCase    		=					prepare_img()
						_lowerCAmelCase    		=					image_processor(__lowerCamelCase					,      return_tensors="""pt"""						)

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

						if show_prediction:
												_lowerCAmelCase    		=					(
												    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__":
						_lowercase          =						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""",
						)

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



'''simple docstring'''



import importlib
import inspect
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase					:			Any       =	'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase					:			Optional[int]       =	importlib.util.spec_from_file_location(
    'transformers',
    os.path.join(PATH_TO_TRANSFORMERS, '__init__.py'),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
UpperCAmelCase					:			Dict       =	spec.loader.load_module()

UpperCAmelCase					:			Dict       =	transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
UpperCAmelCase					:			Dict       =	re.compile('\[(.+?)\]\((https://huggingface\.co/.+?)\)')


UpperCAmelCase					:			Any       =	{
    'CLIPConfigMixin',
    'DecisionTransformerConfigMixin',
    'EncoderDecoderConfigMixin',
    'RagConfigMixin',
    'SpeechEncoderDecoderConfigMixin',
    'VisionEncoderDecoderConfigMixin',
    'VisionTextDualEncoderConfigMixin',
}







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []

     for config_class in list(CONFIG_MAPPING.values()		):
          __SCREAMING_SNAKE_CASE      = False

          # source code of `config_class`
          __SCREAMING_SNAKE_CASE      = inspect.getsource(a__		)
          __SCREAMING_SNAKE_CASE      = _re_checkpoint.findall(a__		)

          for checkpoint in checkpoints:
               # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
               # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = checkpoint

               # verify the checkpoint name corresponds to the checkpoint link
               __SCREAMING_SNAKE_CASE      = F'https://huggingface.co/{ckpt_name}'
               if ckpt_link == ckpt_link_from_name:
                    __SCREAMING_SNAKE_CASE      = True
                    break

          __SCREAMING_SNAKE_CASE      = config_class.__name__
          if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
               configs_without_checkpoint.append(a__		)

     if len(a__		) > 0:
          __SCREAMING_SNAKE_CASE      = """\n""".join(sorted(a__		)		)
          raise ValueError(F'The following configurations don\'t contain any valid checkpoint:\n{message}'		)


if __name__ == "__main__":
  check_config_docstrings_have_checkpoints()






