


import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

__snake_case            =   logging.getLogger(__name__)




@dataclass
class    UpperCAmelCase					:
			lowercase						= field(
			    default=1_2_8      , metadata={
			        """help""": (
			            """The maximum total input sequence length after tokenization. Sequences longer """
			            """than this will be truncated, sequences shorter will be padded."""
			        )
			    }      , )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}    )
			lowercase						= field(
			    default=__snake_case      , 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."""
			        )
			    }      , )
			lowercase						= field(
			    default=__snake_case      , metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of training examples to this """
			            """value if set."""
			        )
			    }      , )
			lowercase						= field(
			    default=__snake_case      , metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
			            """value if set."""
			        )
			    }      , )
			lowercase						= field(
			    default=__snake_case      , metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of prediction examples to this """
			            """value if set."""
			        )
			    }      , )




@dataclass
class    UpperCAmelCase					:
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Train language if it is different from the evaluation language."""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      , )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""}      , )
			lowercase						= field(
			    default=__snake_case      , 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=__snake_case      , metadata={
			        """help""": (
			            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
			            """with private models)."""
			        )
			    }      , )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}      , )






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
			UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry("""run_xnli"""       ,  SCREAMING_SNAKE_CASE_	)

			# 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	)]       ,  )

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			UpperCamelCase         =					training_args.get_process_log_level()
			logger.setLevel(SCREAMING_SNAKE_CASE_	)
			datasets.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE_	)
			transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE_	)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# 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}'	)
			logger.info(f'Training/evaluation parameters {training_args}'	)

			# 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."""	)

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

			# In distributed training, the load_dataset function guarantees that only one local process can concurrently
			# download the dataset.
			# Downloading and loading xnli dataset from the hub.
			if training_args.do_train:
						if model_args.train_language is None:
									UpperCamelCase         =					load_dataset(
									    """xnli"""       ,  model_args.language       ,  split="""train"""       ,  cache_dir=model_args.cache_dir       ,  use_auth_token=True if model_args.use_auth_token else None       ,  )
						else:
									UpperCamelCase         =					load_dataset(
									    """xnli"""       ,  model_args.train_language       ,  split="""train"""       ,  cache_dir=model_args.cache_dir       ,  use_auth_token=True if model_args.use_auth_token else None       ,  )
						UpperCamelCase         =					train_dataset.features["""label"""].names

			if training_args.do_eval:
						UpperCamelCase         =					load_dataset(
						    """xnli"""       ,  model_args.language       ,  split="""validation"""       ,  cache_dir=model_args.cache_dir       ,  use_auth_token=True if model_args.use_auth_token else None       ,  )
						UpperCamelCase         =					eval_dataset.features["""label"""].names

			if training_args.do_predict:
						UpperCamelCase         =					load_dataset(
						    """xnli"""       ,  model_args.language       ,  split="""test"""       ,  cache_dir=model_args.cache_dir       ,  use_auth_token=True if model_args.use_auth_token else None       ,  )
						UpperCamelCase         =					predict_dataset.features["""label"""].names

			# Labels
			UpperCamelCase         =					len(SCREAMING_SNAKE_CASE_	)

			# Load pretrained model and tokenizer
			# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			UpperCamelCase         =					AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,  num_labels=SCREAMING_SNAKE_CASE_       ,  idalabel={str(SCREAMING_SNAKE_CASE_	): label for i, label in enumerate(SCREAMING_SNAKE_CASE_	)}       ,  labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE_	)}       ,  finetuning_task="""xnli"""       ,  cache_dir=model_args.cache_dir       ,  revision=model_args.model_revision       ,  use_auth_token=True if model_args.use_auth_token else None       ,  )
			UpperCamelCase         =					AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,  do_lower_case=model_args.do_lower_case       ,  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       ,  )
			UpperCamelCase         =					AutoModelForSequenceClassification.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       ,  ignore_mismatched_sizes=model_args.ignore_mismatched_sizes       ,  )

			# Preprocessing the datasets
			# Padding strategy
			if data_args.pad_to_max_length:
						UpperCamelCase         =					"""max_length"""
			else:
						# We will pad later, dynamically at batch creation, to the max sequence length in each batch
						UpperCamelCase         =					False

			def preprocess_function(SCREAMING_SNAKE_CASE_   :		Any	):
						# Tokenize the texts
						return tokenizer(
						    examples["""premise"""]       ,  examples["""hypothesis"""]       ,  padding=SCREAMING_SNAKE_CASE_       ,  max_length=data_args.max_seq_length       ,  truncation=SCREAMING_SNAKE_CASE_       ,  )

			if training_args.do_train:
						if data_args.max_train_samples is not None:
									UpperCamelCase         =					min(len(SCREAMING_SNAKE_CASE_	)       ,  data_args.max_train_samples	)
									UpperCamelCase         =					train_dataset.select(range(SCREAMING_SNAKE_CASE_	)	)
						with training_args.main_process_first(desc="""train dataset map pre-processing"""	):
									UpperCamelCase         =					train_dataset.map(
									    SCREAMING_SNAKE_CASE_       ,  batched=SCREAMING_SNAKE_CASE_       ,  load_from_cache_file=not data_args.overwrite_cache       ,  desc="""Running tokenizer on train dataset"""       ,  )
						# Log a few random samples from the training set:
						for index in random.sample(range(len(SCREAMING_SNAKE_CASE_	)	)       ,  3	):
									logger.info(f'Sample {index} of the training set: {train_dataset[index]}.'	)

			if training_args.do_eval:
						if data_args.max_eval_samples is not None:
									UpperCamelCase         =					min(len(SCREAMING_SNAKE_CASE_	)       ,  data_args.max_eval_samples	)
									UpperCamelCase         =					eval_dataset.select(range(SCREAMING_SNAKE_CASE_	)	)
						with training_args.main_process_first(desc="""validation dataset map pre-processing"""	):
									UpperCamelCase         =					eval_dataset.map(
									    SCREAMING_SNAKE_CASE_       ,  batched=SCREAMING_SNAKE_CASE_       ,  load_from_cache_file=not data_args.overwrite_cache       ,  desc="""Running tokenizer on validation dataset"""       ,  )

			if training_args.do_predict:
						if data_args.max_predict_samples is not None:
									UpperCamelCase         =					min(len(SCREAMING_SNAKE_CASE_	)       ,  data_args.max_predict_samples	)
									UpperCamelCase         =					predict_dataset.select(range(SCREAMING_SNAKE_CASE_	)	)
						with training_args.main_process_first(desc="""prediction dataset map pre-processing"""	):
									UpperCamelCase         =					predict_dataset.map(
									    SCREAMING_SNAKE_CASE_       ,  batched=SCREAMING_SNAKE_CASE_       ,  load_from_cache_file=not data_args.overwrite_cache       ,  desc="""Running tokenizer on prediction dataset"""       ,  )

    # Get the metric function
			UpperCamelCase         =					evaluate.load("""xnli"""	)

			# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
			# predictions and label_ids field) and has to return a dictionary string to float.
			def compute_metrics(SCREAMING_SNAKE_CASE_   :		EvalPrediction	):
						UpperCamelCase         =					p.predictions[0] if isinstance(p.predictions       ,  SCREAMING_SNAKE_CASE_	) else p.predictions
						UpperCamelCase         =					np.argmax(SCREAMING_SNAKE_CASE_       ,  axis=1	)
						return metric.compute(predictions=SCREAMING_SNAKE_CASE_       ,  references=p.label_ids	)

			# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
			if data_args.pad_to_max_length:
						UpperCamelCase         =					default_data_collator
			elif training_args.fpaa:
						UpperCamelCase         =					DataCollatorWithPadding(SCREAMING_SNAKE_CASE_       ,  pad_to_multiple_of=8	)
			else:
						UpperCamelCase         =					None

			# Initialize our Trainer
			UpperCamelCase         =					Trainer(
			    model=SCREAMING_SNAKE_CASE_       ,  args=SCREAMING_SNAKE_CASE_       ,  train_dataset=train_dataset if training_args.do_train else None       ,  eval_dataset=eval_dataset if training_args.do_eval else None       ,  compute_metrics=SCREAMING_SNAKE_CASE_       ,  tokenizer=SCREAMING_SNAKE_CASE_       ,  data_collator=SCREAMING_SNAKE_CASE_       ,  )

			# Training
			if training_args.do_train:
						UpperCamelCase         =					None
						if training_args.resume_from_checkpoint is not None:
									UpperCamelCase         =					training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									UpperCamelCase         =					last_checkpoint
						UpperCamelCase         =					trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					train_result.metrics
						UpperCamelCase         =					(
						    data_args.max_train_samples if data_args.max_train_samples is not None else len(SCREAMING_SNAKE_CASE_	)
						)
						UpperCamelCase         =					min(SCREAMING_SNAKE_CASE_       ,  len(SCREAMING_SNAKE_CASE_	)	)

						trainer.save_model()  # Saves the tokenizer too for easy upload

						trainer.log_metrics("""train"""       ,  SCREAMING_SNAKE_CASE_	)
						trainer.save_metrics("""train"""       ,  SCREAMING_SNAKE_CASE_	)
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						logger.info("""*** Evaluate ***"""	)
						UpperCamelCase         =					trainer.evaluate(eval_dataset=SCREAMING_SNAKE_CASE_	)

						UpperCamelCase         =					data_args.max_eval_samples if data_args.max_eval_samples is not None else len(SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					min(SCREAMING_SNAKE_CASE_       ,  len(SCREAMING_SNAKE_CASE_	)	)

						trainer.log_metrics("""eval"""       ,  SCREAMING_SNAKE_CASE_	)
						trainer.save_metrics("""eval"""       ,  SCREAMING_SNAKE_CASE_	)

			# Prediction
			if training_args.do_predict:
						logger.info("""*** Predict ***"""	)
						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					trainer.predict(SCREAMING_SNAKE_CASE_       ,  metric_key_prefix="""predict"""	)

						UpperCamelCase         =					(
						    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(SCREAMING_SNAKE_CASE_	)
						)
						UpperCamelCase         =					min(SCREAMING_SNAKE_CASE_       ,  len(SCREAMING_SNAKE_CASE_	)	)

						trainer.log_metrics("""predict"""       ,  SCREAMING_SNAKE_CASE_	)
						trainer.save_metrics("""predict"""       ,  SCREAMING_SNAKE_CASE_	)

						UpperCamelCase         =					np.argmax(SCREAMING_SNAKE_CASE_       ,  axis=1	)
						UpperCamelCase         =					os.path.join(training_args.output_dir       ,  """predictions.txt"""	)
						if trainer.is_world_process_zero():
									with open(SCREAMING_SNAKE_CASE_       ,  """w"""	) as writer:
												writer.write("""index\tprediction\n"""	)
												for index, item in enumerate(SCREAMING_SNAKE_CASE_	):
															UpperCamelCase         =					label_list[item]
															writer.write(f'{index}\t{item}\n'	)


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)




