


'''simple docstring'''





import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__snake_case            =   logging.getLogger(__name__)
__snake_case            =   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__snake_case            =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class    UpperCAmelCase					:
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={
			        """help""": (
			            """The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch."""
			        )
			    }      , )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_lowerCAmelCase    )}      , )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={
			        """help""": (
			            """Override some existing default config settings when a model is trained from scratch. Example: """
			            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
			        )
			    }      , )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      , )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}      , )
			lowercase						= field(
			    default="""main"""      , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}      , )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={
			        """help""": (
			            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
			            """with private models)."""
			        )
			    }      , )




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
									raise ValueError(
									    """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""						)




@dataclass
class    UpperCAmelCase					:
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}    )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
			lowercase						= field(default=_lowerCAmelCase      , metadata={"""help""": """The input training data file (a text file)."""}    )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}      , )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}      , )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}      , )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )
			lowercase						= field(
			    default=5      , metadata={
			        """help""": """The percentage of the train set used as validation set in case there\'s no validation split"""
			    }      , )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={
			        """help""": (
			            """The maximum total input sequence length after tokenization. Sequences longer """
			            """than this will be truncated. Default to the max input length of the model."""
			        )
			    }      , )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={"""help""": """The number of processes to use for the preprocessing."""}      , )
			lowercase						= field(
			    default=0.15      , metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}    )
			lowercase						= field(
			    default=_lowerCAmelCase      , metadata={
			        """help""": (
			            """Whether to pad all samples to `max_seq_length`. """
			            """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
			        )
			    }      , )




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						if self.train_file is not None:
									UpperCamelCase         =					self.train_file.split("""."""						)[-1]
									assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
						if self.validation_file is not None:
									UpperCamelCase         =					self.validation_file.split("""."""						)[-1]
									assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




			with open(SCREAMING_SNAKE_CASE_       ,  """r"""       ,  encoding="""utf-8"""	) as f:
						UpperCamelCase         =					[json.loads(SCREAMING_SNAKE_CASE_	) for line in f.read().splitlines() if (len(SCREAMING_SNAKE_CASE_	) > 0 and not line.isspace())]
			assert len(SCREAMING_SNAKE_CASE_	) == len(SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					{c: dataset[c] for c in dataset.column_names}
			UpperCamelCase         =					refs
			return Dataset.from_dict(SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
			if len(sys.argv	) == 2 and sys.argv[1].endswith(""".json"""	):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
			else:
						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					parser.parse_args_into_dataclasses()

			# Detecting last checkpoint.
			UpperCamelCase         =					None
			if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
						UpperCamelCase         =					get_last_checkpoint(training_args.output_dir	)
						if last_checkpoint is None and len(os.listdir(training_args.output_dir	)	) > 0:
									raise ValueError(
									    f'Output directory ({training_args.output_dir}) already exists and is not empty. '
									    """Use --overwrite_output_dir to overcome."""	)
						elif last_checkpoint is not None:
									logger.info(
									    f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
									    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""	)

    # Setup logging
			logging.basicConfig(
			    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""       ,  datefmt="""%m/%d/%Y %H:%M:%S"""       ,  handlers=[logging.StreamHandler(sys.stdout	)]       ,  )
			logger.setLevel(logging.INFO if is_main_process(training_args.local_rank	) else logging.WARN	)

			# Log on each process the small summary:
			logger.warning(
			    f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
			    + f'distributed training: {bool(training_args.local_rank != -1	)}, 16-bits training: {training_args.fpaa}'	)
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank	):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info("""Training/evaluation parameters %s"""       ,  SCREAMING_SNAKE_CASE_	)

			# Set seed before initializing model.
			set_seed(training_args.seed	)

			# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
			# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
			# (the dataset will be downloaded automatically from the datasets Hub).
			#
			# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
			# 'text' is found. You can easily tweak this behavior (see below).
			#
			# In distributed training, the load_dataset function guarantee that only one local process can concurrently
			# download the dataset.
			if data_args.dataset_name is not None:
						# Downloading and loading a dataset from the hub.
						UpperCamelCase         =					load_dataset(data_args.dataset_name       ,  data_args.dataset_config_name	)
						if "validation" not in datasets.keys():
									UpperCamelCase         =					load_dataset(
									    data_args.dataset_name       ,  data_args.dataset_config_name       ,  split=f'train[:{data_args.validation_split_percentage}%]'       ,  )
									UpperCamelCase         =					load_dataset(
									    data_args.dataset_name       ,  data_args.dataset_config_name       ,  split=f'train[{data_args.validation_split_percentage}%:]'       ,  )
			else:
						UpperCamelCase         =					{}
						if data_args.train_file is not None:
									UpperCamelCase         =					data_args.train_file
						if data_args.validation_file is not None:
									UpperCamelCase         =					data_args.validation_file
						UpperCamelCase         =					data_args.train_file.split("""."""	)[-1]
						if extension == "txt":
									UpperCamelCase         =					"""text"""
						UpperCamelCase         =					load_dataset(SCREAMING_SNAKE_CASE_       ,  data_files=SCREAMING_SNAKE_CASE_	)
			# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
			# https://huggingface.co/docs/datasets/loading_datasets.html.

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			UpperCamelCase         =					{
			    """cache_dir""": model_args.cache_dir,
			    """revision""": model_args.model_revision,
			    """use_auth_token""": True if model_args.use_auth_token else None,
			}
			if model_args.config_name:
						UpperCamelCase         =					AutoConfig.from_pretrained(model_args.config_name       ,  **SCREAMING_SNAKE_CASE_	)
			elif model_args.model_name_or_path:
						UpperCamelCase         =					AutoConfig.from_pretrained(model_args.model_name_or_path       ,  **SCREAMING_SNAKE_CASE_	)
			else:
						UpperCamelCase         =					CONFIG_MAPPING[model_args.model_type]()
						logger.warning("""You are instantiating a new config instance from scratch."""	)
						if model_args.config_overrides is not None:
									logger.info(f'Overriding config: {model_args.config_overrides}'	)
									config.update_from_string(model_args.config_overrides	)
									logger.info(f'New config: {config}'	)

			UpperCamelCase         =					{
			    """cache_dir""": model_args.cache_dir,
			    """use_fast""": model_args.use_fast_tokenizer,
			    """revision""": model_args.model_revision,
			    """use_auth_token""": True if model_args.use_auth_token else None,
			}
			if model_args.tokenizer_name:
						UpperCamelCase         =					AutoTokenizer.from_pretrained(model_args.tokenizer_name       ,  **SCREAMING_SNAKE_CASE_	)
			elif model_args.model_name_or_path:
						UpperCamelCase         =					AutoTokenizer.from_pretrained(model_args.model_name_or_path       ,  **SCREAMING_SNAKE_CASE_	)
			else:
						raise ValueError(
						    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
						    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""	)

			if model_args.model_name_or_path:
						UpperCamelCase         =					AutoModelForMaskedLM.from_pretrained(
						    model_args.model_name_or_path       ,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path	)       ,  config=SCREAMING_SNAKE_CASE_       ,  cache_dir=model_args.cache_dir       ,  revision=model_args.model_revision       ,  use_auth_token=True if model_args.use_auth_token else None       ,  )
			else:
						logger.info("""Training new model from scratch"""	)
						UpperCamelCase         =					AutoModelForMaskedLM.from_config(SCREAMING_SNAKE_CASE_	)

			model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE_	)	)

			# Preprocessing the datasets.
			# First we tokenize all the texts.
			if training_args.do_train:
						UpperCamelCase         =					datasets["""train"""].column_names
			else:
						UpperCamelCase         =					datasets["""validation"""].column_names
			UpperCamelCase         =					"""text""" if """text""" in column_names else column_names[0]

			UpperCamelCase         =					"""max_length""" if data_args.pad_to_max_length else False

			def tokenize_function(SCREAMING_SNAKE_CASE_   :		Union[str, Any]	):
						# Remove empty lines
						UpperCamelCase         =					[line for line in examples["""text"""] if len(SCREAMING_SNAKE_CASE_	) > 0 and not line.isspace()]
						return tokenizer(examples["""text"""]       ,  padding=SCREAMING_SNAKE_CASE_       ,  truncation=SCREAMING_SNAKE_CASE_       ,  max_length=data_args.max_seq_length	)

			UpperCamelCase         =					datasets.map(
			    SCREAMING_SNAKE_CASE_       ,  batched=SCREAMING_SNAKE_CASE_       ,  num_proc=data_args.preprocessing_num_workers       ,  remove_columns=[text_column_name]       ,  load_from_cache_file=not data_args.overwrite_cache       ,  )

			# Add the chinese references if provided
			if data_args.train_ref_file is not None:
						UpperCamelCase         =					add_chinese_references(tokenized_datasets["""train"""]       ,  data_args.train_ref_file	)
			if data_args.validation_ref_file is not None:
						UpperCamelCase         =					add_chinese_references(
						    tokenized_datasets["""validation"""]       ,  data_args.validation_ref_file	)
			# If we have ref files, need to avoid it removed by trainer
			UpperCamelCase         =					data_args.train_ref_file or data_args.validation_ref_file
			if has_ref:
						UpperCamelCase         =					False

			# Data collator
			# This one will take care of randomly masking the tokens.
			UpperCamelCase         =					DataCollatorForWholeWordMask(tokenizer=SCREAMING_SNAKE_CASE_       ,  mlm_probability=data_args.mlm_probability	)

			# Initialize our Trainer
			UpperCamelCase         =					Trainer(
			    model=SCREAMING_SNAKE_CASE_       ,  args=SCREAMING_SNAKE_CASE_       ,  train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None       ,  eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None       ,  tokenizer=SCREAMING_SNAKE_CASE_       ,  data_collator=SCREAMING_SNAKE_CASE_       ,  )

			# Training
			if training_args.do_train:
						if last_checkpoint is not None:
									UpperCamelCase         =					last_checkpoint
						elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path	):
									UpperCamelCase         =					model_args.model_name_or_path
						else:
									UpperCamelCase         =					None
						UpperCamelCase         =					trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE_	)
						trainer.save_model()  # Saves the tokenizer too for easy upload

						UpperCamelCase         =					os.path.join(training_args.output_dir       ,  """train_results.txt"""	)
						if trainer.is_world_process_zero():
									with open(SCREAMING_SNAKE_CASE_       ,  """w"""	) as writer:
												logger.info("""***** Train results *****"""	)
												for key, value in sorted(train_result.metrics.items()	):
															logger.info(f'  {key} = {value}'	)
															writer.write(f'{key} = {value}\n'	)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
									trainer.state.save_to_json(os.path.join(training_args.output_dir       ,  """trainer_state.json"""	)	)

    # Evaluation
			UpperCamelCase         =					{}
			if training_args.do_eval:
						logger.info("""*** Evaluate ***"""	)

						UpperCamelCase         =					trainer.evaluate()

						UpperCamelCase         =					math.exp(eval_output["""eval_loss"""]	)
						UpperCamelCase         =					perplexity

						UpperCamelCase         =					os.path.join(training_args.output_dir       ,  """eval_results_mlm_wwm.txt"""	)
						if trainer.is_world_process_zero():
									with open(SCREAMING_SNAKE_CASE_       ,  """w"""	) as writer:
												logger.info("""***** Eval results *****"""	)
												for key, value in sorted(results.items()	):
															logger.info(f'  {key} = {value}'	)
															writer.write(f'{key} = {value}\n'	)

			return results






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			main()


if __name__ == "__main__":
			main()








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()
__snake_case            =   logging.get_logger(__name__)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




			UpperCamelCase         =					DPTConfig()

			if "large" in checkpoint_url:
						UpperCamelCase         =					1_024
						UpperCamelCase         =					4_096
						UpperCamelCase         =					24
						UpperCamelCase         =					16
						UpperCamelCase         =					[5, 11, 17, 23]
						UpperCamelCase         =					[256, 512, 1_024, 1_024]
						UpperCamelCase         =					(1, 384, 384)

			if "ade" in checkpoint_url:
						UpperCamelCase         =					True

						UpperCamelCase         =					150
						UpperCamelCase         =					"""huggingface/label-files"""
						UpperCamelCase         =					"""ade20k-id2label.json"""
						UpperCamelCase         =					json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  repo_type="""dataset"""	)	)       ,  """r"""	)	)
						UpperCamelCase         =					{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
						UpperCamelCase         =					idalabel
						UpperCamelCase         =					{v: k for k, v in idalabel.items()}
						UpperCamelCase         =					[1, 150, 480, 480]

			return config, expected_shape






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]	):





			"""simple docstring"""




			UpperCamelCase         =					["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
			for k in ignore_keys:
						state_dict.pop(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




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

			return name






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Tuple	):





			"""simple docstring"""




			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         =					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'	)
						UpperCamelCase         =					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         =					in_proj_weight[: config.hidden_size, :]
						UpperCamelCase         =					in_proj_bias[: config.hidden_size]
						UpperCamelCase         =					in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCamelCase         =					in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						UpperCamelCase         =					in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCamelCase         =					in_proj_bias[-config.hidden_size :]






def 							_lowercase    (		):





			"""simple docstring"""




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






@torch.no_grad()
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




			UpperCamelCase  ,  UpperCamelCase         =					get_dpt_config(SCREAMING_SNAKE_CASE_	)
			# load original state_dict from URL
			UpperCamelCase         =					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_       ,  map_location="""cpu"""	)
			# remove certain keys
			remove_ignore_keys_(SCREAMING_SNAKE_CASE_	)
			# rename keys
			for key in state_dict.copy().keys():
						UpperCamelCase         =					state_dict.pop(SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					val
			# read in qkv matrices
			read_in_q_k_v(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

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

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

			UpperCamelCase         =					prepare_img()
			UpperCamelCase         =					image_processor(SCREAMING_SNAKE_CASE_       ,  return_tensors="""pt"""	)

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

			# Assert logits
			UpperCamelCase         =					torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]	)
			if "ade" in checkpoint_url:
						UpperCamelCase         =					torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]	)
			assert outputs.shape == torch.Size(SCREAMING_SNAKE_CASE_	)
			assert (
			    torch.allclose(outputs[0, 0, :3, :3]       ,  SCREAMING_SNAKE_CASE_       ,  atol=1e-4	)
			    if "ade" in checkpoint_url
			    else torch.allclose(outputs[0, :3, :3]       ,  SCREAMING_SNAKE_CASE_	)
			)

			Path(SCREAMING_SNAKE_CASE_	).mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)
			print(f'Saving model to {pytorch_dump_folder_path}'	)
			model.save_pretrained(SCREAMING_SNAKE_CASE_	)
			print(f'Saving image processor to {pytorch_dump_folder_path}'	)
			image_processor.save_pretrained(SCREAMING_SNAKE_CASE_	)

			if push_to_hub:
						print("""Pushing model to hub..."""	)
						model.push_to_hub(
						    repo_path_or_name=Path(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)       ,  organization="""nielsr"""       ,  commit_message="""Add model"""       ,  use_temp_dir=SCREAMING_SNAKE_CASE_       ,  )
						image_processor.push_to_hub(
						    repo_path_or_name=Path(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)       ,  organization="""nielsr"""       ,  commit_message="""Add image processor"""       ,  use_temp_dir=SCREAMING_SNAKE_CASE_       ,  )


if __name__ == "__main__":
			__snake_case            =   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=True,
			    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.",
			)

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




