





'''simple docstring'''






import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__lowerCamelCase						: Optional[int]		=   os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
      from run_translation import main  # noqa


set_seed(42)
__lowerCamelCase						: Optional[Any]		=   'sshleifer/student_marian_en_ro_6_1'
__lowerCamelCase						: int		=   'sshleifer/tiny-mbart'







@require_torch
class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					List[str]=False							,	UpperCamelCase__    :					str=None							,	UpperCamelCase__    :					Optional[Any]=True							,	UpperCamelCase__    :					Optional[Any]=True							,	UpperCamelCase__    :					Dict=True							,	UpperCamelCase__    :					Tuple=True							,	)     ->						Union[str, Any]:
    _UpperCamelCase       			=self.run_trainer(
        eval_steps=1							,	max_len=12							,	model_name=UpperCamelCase__							,	num_train_epochs=1							,	distributed=UpperCamelCase__							,	extra_args_str=UpperCamelCase__							,	predict_with_generate=UpperCamelCase__							,	do_train=UpperCamelCase__							,	do_eval=UpperCamelCase__							,	do_predict=UpperCamelCase__							,	)
    _UpperCamelCase       			=TrainerState.load_from_json(os.path.join(UpperCamelCase__							,	'''trainer_state.json'''  )  ).log_history

    if not do_eval:
      return

    _UpperCamelCase       			=[log for log in logs if '''eval_loss''' in log.keys()]

    _UpperCamelCase       			=eval_metrics[0]
    if predict_with_generate:
      assert "eval_bleu" in first_step_stats

      _UpperCamelCase       			=eval_metrics[-1]
      assert isinstance(last_step_stats['''eval_bleu''']							,	UpperCamelCase__  )
      assert not math.isnan(float(last_step_stats['''eval_loss''']  )  ), "eval_loss must not be `nan`"


  @require_torch_non_multi_gpu
  def   UpperCamelCase__      (				self    :					Any  )     ->						List[Any]:
    self.run_seqaseq_quick()


  @require_torch_multi_gpu
  def   UpperCamelCase__      (				self    :					Any  )     ->						Optional[int]:
    self.run_seqaseq_quick(distributed=UpperCamelCase__  )


  @require_torch_multi_gpu
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Tuple:
    self.run_seqaseq_quick(distributed=UpperCamelCase__  )


  @unittest.skip('''Requires an update of the env running those tests'''  )
  @require_torch_multi_gpu
  @require_fairscale
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						List[Any]:
    self.run_seqaseq_quick(distributed=UpperCamelCase__							,	extra_args_str='''--sharded_ddp simple'''  )


  @unittest.skip('''Requires an update of the env running those tests'''  )
  @require_torch_multi_gpu
  @require_fairscale
  def   UpperCamelCase__      (				self    :					Dict  )     ->						Union[str, Any]:
    self.run_seqaseq_quick(distributed=UpperCamelCase__							,	extra_args_str='''--sharded_ddp simple --fp16'''  )


  @unittest.skip('''Requires an update of the env running those tests'''  )
  @require_torch_multi_gpu
  @require_fairscale
  def   UpperCamelCase__      (				self    :					str  )     ->						Tuple:
    self.run_seqaseq_quick(distributed=UpperCamelCase__							,	extra_args_str='''--sharded_ddp zero_dp_2'''							,	predict_with_generate=UpperCamelCase__  )


  @unittest.skip('''Requires an update of the env running those tests'''  )
  @require_torch_multi_gpu
  @require_fairscale
  def   UpperCamelCase__      (				self    :					int  )     ->						List[str]:
    self.run_seqaseq_quick(
        distributed=UpperCamelCase__							,	extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''							,	predict_with_generate=UpperCamelCase__  )


  @require_apex
  @require_torch_gpu
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						int:
    # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
    # program and it breaks other tests that run from the same pytest worker, therefore until this is
    # sorted out it must be run only in an external program, that is distributed=True in this
    # test and only under one or more gpus - if we want cpu will need to make a special test
    #
    # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
    # 2nd main() call it botches the future eval.
    #
    self.run_seqaseq_quick(distributed=UpperCamelCase__							,	extra_args_str='''--fp16 --fp16_backend=apex'''  )
    # test 2nd time - was getting eval_loss': nan'
    # to reproduce the problem set distributed=False
    self.run_seqaseq_quick(distributed=UpperCamelCase__							,	extra_args_str='''--fp16 --fp16_backend=apex'''  )


  @parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']  )
  @require_torch_multi_gpu
  def   UpperCamelCase__      (				self    :					Any							,	UpperCamelCase__    :					List[Any]  )     ->						Optional[int]:
    # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
    _UpperCamelCase       			={
        # test with the default log_level - should be info and thus log info once
        '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
        # test with low log_level and log_level_replica - should be noisy on all processes
        # now the info string should appear twice on 2 processes
        '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
        # test with high log_level and low log_level_replica
        # now the info string should appear once only on the replica
        '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
        # test with high log_level and log_level_replica - should be quiet on all processes
        '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
    }

    _UpperCamelCase       			=experiments[experiment_id]
    _UpperCamelCase       			={'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
    _UpperCamelCase       			='''Running training'''
    with CaptureStderr() as cl:
      self.run_seqaseq_quick(**UpperCamelCase__							,	extra_args_str=data['''extra_args_str''']  )
    _UpperCamelCase       			=len(re.findall(UpperCamelCase__							,	cl.err  )  )
    self.assertEqual(UpperCamelCase__							,	data['''n_matches''']  )


  @slow
  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						List[str]:
    _UpperCamelCase       			=self.run_trainer(
        eval_steps=2							,	max_len=128							,	model_name=UpperCamelCase__							,	learning_rate=3E-4							,	num_train_epochs=10							,	distributed=UpperCamelCase__							,	)

    # Check metrics
    _UpperCamelCase       			=TrainerState.load_from_json(os.path.join(UpperCamelCase__							,	'''trainer_state.json'''  )  ).log_history
    _UpperCamelCase       			=[log for log in logs if '''eval_loss''' in log.keys()]
    _UpperCamelCase       			=eval_metrics[0]
    _UpperCamelCase       			=eval_metrics[-1]

    assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
    assert isinstance(last_step_stats['''eval_bleu''']							,	UpperCamelCase__  )

    # test if do_predict saves generations and metrics
    _UpperCamelCase       			=os.listdir(UpperCamelCase__  )
    _UpperCamelCase       			={os.path.basename(UpperCamelCase__  ) for p in contents}
    assert "generated_predictions.txt" in contents
    assert "predict_results.json" in contents


  @slow
  @require_bitsandbytes
  def   UpperCamelCase__      (				self    :					int  )     ->						List[str]:
    from transformers.training_args import OptimizerNames

    def train_and_return_metrics(UpperCamelCase__    :					str  ) -> Tuple[int, float]:
      _UpperCamelCase       			='''--skip_memory_metrics 0'''

      _UpperCamelCase       			=self.run_trainer(
          max_len=128							,	model_name=UpperCamelCase__							,	learning_rate=3E-4							,	num_train_epochs=1							,	optim=UpperCamelCase__							,	distributed=UpperCamelCase__							,	extra_args_str=UpperCamelCase__							,	do_eval=UpperCamelCase__							,	do_predict=UpperCamelCase__							,	n_gpus_to_use=1							,	)

      # Check metrics
      _UpperCamelCase       			=TrainerState.load_from_json(Path(UpperCamelCase__							,	'''trainer_state.json'''  )  ).log_history
      _UpperCamelCase       			=int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20  )
      _UpperCamelCase       			=int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20  )

      _UpperCamelCase       			=logs[0]['''train_loss''']
      return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

    _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value  )
    _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=train_and_return_metrics(OptimizerNames.ADAMW_BNB.value  )

    _UpperCamelCase       			=gpu_alloc_mem_orig - gpu_alloc_mem_bnb

    _UpperCamelCase       			=gpu_peak_mem_orig + gpu_alloc_mem_orig
    _UpperCamelCase       			=gpu_peak_mem_bnb + gpu_alloc_mem_bnb
    _UpperCamelCase       			=gpu_total_mem_orig - gpu_total_mem_bnb

    # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
    # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
    # in 2 bytes and the diff in optim memory usage is derived as so:
    #
    # - normal 25*8=~200MB (8 bytes per param)
    # - bnb    25*2= ~50MB (2 bytes per param)
    #
    # Thus we should expect ~150MB total memory saved.
    #
    # Peak memory should be the same - the total should be different by about that same margin
    #
    # After leaving a small margin to accommodate for differences between gpus let's check
    # that we have at least 120MB in savings
    _UpperCamelCase       			=120

    # uncomment the following if this test starts failing - requires py38 for a new print feature
    # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
    # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
    # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
    # print(f"{gpu_alloc_mem_diff=}MB")
    # print(f"{gpu_peak_mem_diff=}MB")
    # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
    # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

    self.assertGreater(
        UpperCamelCase__							,	UpperCamelCase__							,	'''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
        F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
        F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''							,	)

    self.assertGreater(
        UpperCamelCase__							,	UpperCamelCase__							,	'''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
        F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
        F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''							,	)

    self.assertEqual(
        UpperCamelCase__							,	UpperCamelCase__							,	F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''  )




  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					int							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					int							,	UpperCamelCase__    :					float = 3E-3							,	UpperCamelCase__    :					str = "adafactor"							,	UpperCamelCase__    :					bool = False							,	UpperCamelCase__    :					str = None							,	UpperCamelCase__    :					int = 0							,	UpperCamelCase__    :					bool = True							,	UpperCamelCase__    :					bool = True							,	UpperCamelCase__    :					bool = True							,	UpperCamelCase__    :					bool = True							,	UpperCamelCase__    :					int = None							,	)     ->						Optional[Any]:
    _UpperCamelCase       			=self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
    _UpperCamelCase       			=self.get_auto_remove_tmp_dir()
    _UpperCamelCase       			=F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(UpperCamelCase__  )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCamelCase__  )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

    _UpperCamelCase       			=F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(UpperCamelCase__  )}
        '''.split()

    _UpperCamelCase       			='''
            --do_predict
        '''.split()

    _UpperCamelCase       			=[]
    if do_train:
      args += args_train

    if do_eval:
      args += args_eval

    if do_predict:
      args += args_predict

    if predict_with_generate:
      args += "--predict_with_generate".split()

    if do_train:
      if optim == "adafactor":
        args += "--adafactor".split()
      else:
        args += F'''--optim {optim}'''.split()

    if extra_args_str is not None:
      args += extra_args_str.split()

    if distributed:
      if n_gpus_to_use is None:
        _UpperCamelCase       			=get_gpu_count()
      _UpperCamelCase       			=get_torch_dist_unique_port()
      _UpperCamelCase       			=F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
      _UpperCamelCase       			=[sys.executable] + distributed_args + args
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
      execute_subprocess_async(UpperCamelCase__							,	env=self.get_env()  )
    else:
      _UpperCamelCase       			=['''run_translation.py'''] + args
      with patch.object(UpperCamelCase__							,	'''argv'''							,	UpperCamelCase__  ):
        main()

    return output_dir













'''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
      )






