





'''simple docstring'''






import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel


def 					_a   (__SCREAMING_SNAKE_CASE=32  ,      __SCREAMING_SNAKE_CASE=10  ,      __SCREAMING_SNAKE_CASE=100  ,      __SCREAMING_SNAKE_CASE=1026  ,      __SCREAMING_SNAKE_CASE=True  ,      __SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"  ,      __SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"  ,      ):


  """simple docstring"""

  set_seed(3   )
  # generate train_data and objective_set
  _UpperCamelCase			,     _UpperCamelCase       			=generate_datasets(
      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      number=__SCREAMING_SNAKE_CASE  ,      min_len=1026  ,      trim=__SCREAMING_SNAKE_CASE   )
  # keeps model same across runs
  set_seed(4   )
  # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
  # can we train on GPU?
  _UpperCamelCase       			=torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''   )

  # load pretrained model
  _UpperCamelCase       			=load_gpta('''gpt2'''   ).to(__SCREAMING_SNAKE_CASE   )
  print('''computing perplexity on objective set'''   )
  _UpperCamelCase       			=compute_perplexity(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ).item()
  print('''perplexity on objective set:'''  ,      __SCREAMING_SNAKE_CASE   )

  # collect igf pairs and save to file demo.jbl
  collect_objective_set(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  # clean up, delete model and data we don't need anymore
  del model, train_data, objective_set
  torch.cuda.empty_cache()


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=15  ,      __SCREAMING_SNAKE_CASE=128  ,      __SCREAMING_SNAKE_CASE=100  ,      __SCREAMING_SNAKE_CASE="igf_model.pt"  ,      ):


  """simple docstring"""

  set_seed(42   )

  # Load pre-trained model
  _UpperCamelCase       			=GPTaLMHeadModel.from_pretrained('''gpt2'''   )

  # Initialize secondary learner to use embedding weights of model
  _UpperCamelCase       			=SecondaryLearner(__SCREAMING_SNAKE_CASE   )

  # Train secondary learner
  _UpperCamelCase       			=train_secondary_learner(
      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      max_epochs=__SCREAMING_SNAKE_CASE  ,      batch_size=__SCREAMING_SNAKE_CASE  ,      eval_freq=100  ,      igf_model_path=__SCREAMING_SNAKE_CASE  ,      )

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=32  ,      __SCREAMING_SNAKE_CASE=1000  ,      __SCREAMING_SNAKE_CASE=16  ,      __SCREAMING_SNAKE_CASE=1.0  ,      __SCREAMING_SNAKE_CASE=recopy_gpta  ,      __SCREAMING_SNAKE_CASE=None  ,      __SCREAMING_SNAKE_CASE=10  ,      __SCREAMING_SNAKE_CASE="gpt2_finetuned.pt"  ,      ):


  """simple docstring"""

  _UpperCamelCase       			=torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''   )
  _UpperCamelCase       			=RandomSampler(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=DataLoader(__SCREAMING_SNAKE_CASE  ,      sampler=__SCREAMING_SNAKE_CASE   )

  _UpperCamelCase       			=max_steps // (len(__SCREAMING_SNAKE_CASE   )) + 1
  _UpperCamelCase       			=0
  _UpperCamelCase       			=torch.zeros((1, context_len)  ,      dtype=torch.long  ,      device=__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=recopy_model(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(__SCREAMING_SNAKE_CASE   )
    secondary_learner.eval()
  _UpperCamelCase       			=[]
  _UpperCamelCase       			=0

  _UpperCamelCase       			=[]
  _UpperCamelCase       			=[]

  # Compute the performance of the transformer model at the beginning
  _UpperCamelCase       			=compute_perplexity(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  test_perps.append(__SCREAMING_SNAKE_CASE   )
  print('''Test perplexity, step'''  ,      __SCREAMING_SNAKE_CASE  ,      ''':'''  ,      __SCREAMING_SNAKE_CASE   )
  for epoch in range(int(__SCREAMING_SNAKE_CASE   )   ):
    for step, example in enumerate(__SCREAMING_SNAKE_CASE   ):
      torch.cuda.empty_cache()
      _UpperCamelCase       			=random.randint(0  ,      example.size(2   ) - context_len - 1   )
      _UpperCamelCase       			=example[0, 0, start : start + context_len]
      lm_optimizer.zero_grad()
      _UpperCamelCase       			=model(__SCREAMING_SNAKE_CASE  ,      labels=__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=True

      if secondary_learner is not None:
        _UpperCamelCase       			=secondary_learner.forward(
            torch.tensor(__SCREAMING_SNAKE_CASE  ,      dtype=torch.long  ,      device=__SCREAMING_SNAKE_CASE   ).unsqueeze(0   )   )[0].item()
        observed_qs.append(float(__SCREAMING_SNAKE_CASE   )   )

        # Here we implement the simple non-constant threshold for the predicted IG(X) value
        # We will decay the selectivity of our secondary learner filter from
        # 1 standard deviation above average to 1 below average after 10 batches.

        if global_step == 10:
          _UpperCamelCase       			=-1
        if predicted_q < threshold:
          _UpperCamelCase       			=False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()   )   )
        _UpperCamelCase       			=outputs[0]
        lm_loss.backward()
        examples += 1

      del outputs

      # Once the batch is filled with enough contexts, backprop on the batch.
      if examples == batch_size:
        torch.cuda.empty_cache()
        _UpperCamelCase       			=0
        # Do LM backprop
        torch.nn.utils.clip_grad_norm_(model.parameters()  ,      3.0   )
        lm_optimizer.step()
        lm_scheduler.step()  # Update learning rate schedule
        global_step += 1
        # Compute the performance of the transformer model at this batch
        if global_step % eval_interval == 0:
          _UpperCamelCase       			=compute_perplexity(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
          test_perps.append(__SCREAMING_SNAKE_CASE   )

          print('''Test perplexity, step'''  ,      __SCREAMING_SNAKE_CASE  ,      ''':'''  ,      __SCREAMING_SNAKE_CASE   )
            # Break out of the loop after 60 batches
      if max_steps > 0 and global_step > 60:
        break
    if max_steps > 0 and global_step > 60:
      break

    # save finetuned transformer model
  torch.save(model.state_dict()  ,      __SCREAMING_SNAKE_CASE   )
  torch.cuda.empty_cache()
  # Do some cleaning up so we can reinitialize for the next run of this function
  del lm_optimizer
  del lm_scheduler
  return model


def 					_a   ():


  """simple docstring"""

  _UpperCamelCase       			=argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''   )

  # Required parameters
  parser.add_argument(
      '''--data_dir'''  ,      default=__SCREAMING_SNAKE_CASE  ,      type=__SCREAMING_SNAKE_CASE  ,      required=__SCREAMING_SNAKE_CASE  ,      help='''The input data dir. Should contain data files for WikiText.'''  ,      )
  parser.add_argument(
      '''--model_name_or_path'''  ,      default=__SCREAMING_SNAKE_CASE  ,      type=__SCREAMING_SNAKE_CASE  ,      required=__SCREAMING_SNAKE_CASE  ,      help='''Path to pretrained model or model identifier from huggingface.co/models'''  ,      )
  parser.add_argument(
      '''--data_file'''  ,      type=__SCREAMING_SNAKE_CASE  ,      default=__SCREAMING_SNAKE_CASE  ,      help=(
          '''A jbl file containing tokenized data which can be split as objective dataset, '''
          '''train_dataset and test_dataset.'''
      )  ,      )

  parser.add_argument(
      '''--igf_data_file'''  ,      type=__SCREAMING_SNAKE_CASE  ,      default=__SCREAMING_SNAKE_CASE  ,      help='''A jbl file containing the context and information gain pairs to train secondary learner.'''  ,      )

  parser.add_argument(
      '''--output_dir'''  ,      default=__SCREAMING_SNAKE_CASE  ,      type=__SCREAMING_SNAKE_CASE  ,      required=__SCREAMING_SNAKE_CASE  ,      help='''The output directory where the final fine-tuned model is stored.'''  ,      )

  parser.add_argument(
      '''--tokenizer_name'''  ,      default=__SCREAMING_SNAKE_CASE  ,      type=__SCREAMING_SNAKE_CASE  ,      help='''Pretrained tokenizer name or path if not the same as model_name'''  ,      )
  parser.add_argument('''--seed'''  ,      type=__SCREAMING_SNAKE_CASE  ,      default=__SCREAMING_SNAKE_CASE  ,      help='''A seed for reproducible training.'''   )

  parser.add_argument(
      '''--context_len'''  ,      default=32  ,      type=__SCREAMING_SNAKE_CASE  ,      help=(
          '''The maximum total input sequence length after tokenization. Sequences longer '''
          '''than this will be truncated, sequences shorter will be padded.'''
      )  ,      )

  parser.add_argument(
      '''--size_objective_set'''  ,      default=100  ,      type=__SCREAMING_SNAKE_CASE  ,      help='''number of articles that are long enough to be used as our objective set'''  ,      )
  parser.add_argument(
      '''--eval_freq'''  ,      default=100  ,      type=__SCREAMING_SNAKE_CASE  ,      help='''secondary model evaluation is triggered at eval_freq'''   )

  parser.add_argument('''--max_steps'''  ,      default=1000  ,      type=__SCREAMING_SNAKE_CASE  ,      help='''To calculate training epochs'''   )

  parser.add_argument(
      '''--secondary_learner_batch_size'''  ,      default=128  ,      type=__SCREAMING_SNAKE_CASE  ,      help='''batch size of training data for secondary learner'''  ,      )

  parser.add_argument(
      '''--batch_size'''  ,      default=16  ,      type=__SCREAMING_SNAKE_CASE  ,      help='''batch size of training data of language model(gpt2) '''   )

  parser.add_argument(
      '''--eval_interval'''  ,      default=10  ,      type=__SCREAMING_SNAKE_CASE  ,      help=(
          '''decay the selectivity of our secondary learner filter from'''
          '''1 standard deviation above average to 1 below average after 10 batches'''
      )  ,      )

  parser.add_argument(
      '''--number'''  ,      default=100  ,      type=__SCREAMING_SNAKE_CASE  ,      help='''The number of examples split to be used as objective_set/test_data'''   )

  parser.add_argument(
      '''--min_len'''  ,      default=1026  ,      type=__SCREAMING_SNAKE_CASE  ,      help='''The minimum length of the article to be used as objective set'''   )

  parser.add_argument(
      '''--secondary_learner_max_epochs'''  ,      default=15  ,      type=__SCREAMING_SNAKE_CASE  ,      help='''number of epochs to train secondary learner'''   )

  parser.add_argument('''--trim'''  ,      default=__SCREAMING_SNAKE_CASE  ,      type=__SCREAMING_SNAKE_CASE  ,      help='''truncate the example if it exceeds context length'''   )

  parser.add_argument(
      '''--threshold'''  ,      default=1.0  ,      type=__SCREAMING_SNAKE_CASE  ,      help=(
          '''The threshold value used by secondary learner to filter the train_data and allow only'''
          ''' informative data as input to the model'''
      )  ,      )

  parser.add_argument('''--finetuned_model_name'''  ,      default='''gpt2_finetuned.pt'''  ,      type=__SCREAMING_SNAKE_CASE  ,      help='''finetuned_model_name'''   )

  parser.add_argument(
      '''--recopy_model'''  ,      default=__SCREAMING_SNAKE_CASE  ,      type=__SCREAMING_SNAKE_CASE  ,      help='''Reset the model to the original pretrained GPT-2 weights after each iteration'''  ,      )

  # function calls
  # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
  generate_n_pairs(
      context_len=32  ,      max_steps=10  ,      size_objective_set=100  ,      min_len=1026  ,      trim=__SCREAMING_SNAKE_CASE  ,      data_file='''data/tokenized_stories_train_wikitext103.jbl'''  ,      igf_data_file='''igf_context_pairs.jbl'''  ,      )

  # Load train data for secondary learner
  _UpperCamelCase       			=joblib.load('''data/IGF_values.jbl'''   )

  # Train secondary learner
  _UpperCamelCase       			=training_secondary_learner(
      __SCREAMING_SNAKE_CASE  ,      secondary_learner_max_epochs=15  ,      secondary_learner_batch_size=128  ,      eval_freq=100  ,      igf_model_path='''igf_model.pt'''  ,      )

  # load pretrained gpt2 model
  _UpperCamelCase       			=GPTaLMHeadModel.from_pretrained('''gpt2'''   )
  set_seed(42   )

  # Generate train and test data to train and evaluate gpt2 model
  _UpperCamelCase			,     _UpperCamelCase       			=generate_datasets(
      context_len=32  ,      file='''data/tokenized_stories_train_wikitext103.jbl'''  ,      number=100  ,      min_len=1026  ,      trim=__SCREAMING_SNAKE_CASE   )

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      context_len=32  ,      max_steps=1000  ,      batch_size=16  ,      threshold=1.0  ,      recopy_model=__SCREAMING_SNAKE_CASE  ,      secondary_learner=__SCREAMING_SNAKE_CASE  ,      eval_interval=10  ,      finetuned_model_name='''gpt2_finetuned.pt'''  ,      )


if __name__ == "__main__":
      main()













'''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()
__lowerCamelCase						: Optional[Any]		=   logging.get_logger(__name__)


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=DPTConfig(embedding_type='''hybrid'''   )

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

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

  if "ade" in checkpoint_url:
    _UpperCamelCase       			=True
    _UpperCamelCase       			=768
    _UpperCamelCase       			=[1, 1, 1, 0.5]
    _UpperCamelCase       			=150
    _UpperCamelCase       			=16
    _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 					_a   (__SCREAMING_SNAKE_CASE   ):


  """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 					_a   (__SCREAMING_SNAKE_CASE   ):


  """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'''  ,      ''''''   )
  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 and "backbone" not in name:
    _UpperCamelCase       			=name.replace('''norm1'''  ,      '''layernorm_before'''   )
  if "norm2" in name and "backbone" not 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'''   )
  if "backbone" in name:
    _UpperCamelCase       			=name.replace('''backbone'''  ,      '''backbone.bit.encoder'''   )

  if ".." in name:
    _UpperCamelCase       			=name.replace('''..'''  ,      '''.'''   )

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


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """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 					_a   ():


  """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 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase			,     _UpperCamelCase       			=get_dpt_config(__SCREAMING_SNAKE_CASE   )
  # load original state_dict from URL
  # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
  _UpperCamelCase       			=torch.load(__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

  if show_prediction:
    _UpperCamelCase       			=(
        torch.nn.functional.interpolate(
            outputs.unsqueeze(1   )  ,      size=(image.size[1], image.size[0])  ,      mode='''bicubic'''  ,      align_corners=__SCREAMING_SNAKE_CASE  ,      )
        .squeeze()
        .cpu()
        .numpy()
    )

    Image.fromarray((prediction / prediction.max()) * 255   ).show()

  if pytorch_dump_folder_path is not None:
    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:
    model.push_to_hub('''ybelkada/dpt-hybrid-midas'''   )
    image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''   )


if __name__ == "__main__":
      __lowerCamelCase						: List[str]		=   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',
      )

      __lowerCamelCase						: Dict		=   parser.parse_args()
      convert_dpt_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
      )






