
import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
     from transformers.trainer import DEFAULT_CALLBACKS

     from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel



class    _snake_case (						UpperCAmelCase_				):


 def __init__(       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   []


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.events.append("""on_init_end""")


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.events.append("""on_train_begin""")


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.events.append("""on_train_end""")


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.events.append("""on_epoch_begin""")


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.events.append("""on_epoch_end""")


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.events.append("""on_step_begin""")


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.events.append("""on_step_end""")


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.events.append("""on_evaluate""")


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.events.append("""on_predict""")


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.events.append("""on_save""")


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.events.append("""on_log""")



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.events.append("""on_prediction_step""")



@require_torch
class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   tempfile.mkdtemp()


 def   lowercase__  (       self):


   '''simple docstring'''




   shutil.rmtree(self.output_dir)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_=0       ,	SCREAMING_SNAKE_CASE_=0       ,	SCREAMING_SNAKE_CASE_=64       ,	SCREAMING_SNAKE_CASE_=64       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=False       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Any     		=   RegressionDataset(length=SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[int]     		=   RegressionDataset(length=SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   RegressionModelConfig(a=SCREAMING_SNAKE_CASE_       ,	b=SCREAMING_SNAKE_CASE_)
   lowercase__	: Any     		=   RegressionPreTrainedModel(SCREAMING_SNAKE_CASE_)

   lowercase__	: Any     		=   TrainingArguments(self.output_dir       ,	disable_tqdm=SCREAMING_SNAKE_CASE_       ,	report_to=[]       ,	**SCREAMING_SNAKE_CASE_)
   return Trainer(
       SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	train_dataset=SCREAMING_SNAKE_CASE_       ,	eval_dataset=SCREAMING_SNAKE_CASE_       ,	callbacks=SCREAMING_SNAKE_CASE_       ,	)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.assertEqual(len(SCREAMING_SNAKE_CASE_)       ,	len(SCREAMING_SNAKE_CASE_))

   # Order doesn't matter
   lowercase__	: str     		=   sorted(SCREAMING_SNAKE_CASE_       ,	key=lambda SCREAMING_SNAKE_CASE_: cb.__name__ if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) else cb.__class__.__name__)
   lowercase__	: Tuple     		=   sorted(SCREAMING_SNAKE_CASE_       ,	key=lambda SCREAMING_SNAKE_CASE_: cb.__name__ if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) else cb.__class__.__name__)

   for cba, cba in zip(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) and isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
       self.assertEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
     elif isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) and not isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
       self.assertEqual(SCREAMING_SNAKE_CASE_       ,	cba.__class__)
     elif not isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) and isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
       self.assertEqual(cba.__class__       ,	SCREAMING_SNAKE_CASE_)
     else:
       self.assertEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: int     		=   ["""on_init_end""", """on_train_begin"""]
   lowercase__	: Union[str, Any]     		=   0
   lowercase__	: Union[str, Any]     		=   len(trainer.get_eval_dataloader())
   lowercase__	: Dict     		=   ["""on_prediction_step"""] * len(trainer.get_eval_dataloader()) + ["""on_log""", """on_evaluate"""]
   for _ in range(trainer.state.num_train_epochs):
     expected_events.append("""on_epoch_begin""")
     for _ in range(SCREAMING_SNAKE_CASE_):
       step += 1
       expected_events += ["on_step_begin", "on_step_end"]
       if step % trainer.args.logging_steps == 0:
         expected_events.append("""on_log""")
       if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
         expected_events += evaluation_events.copy()
       if step % trainer.args.save_steps == 0:
         expected_events.append("""on_save""")
     expected_events.append("""on_epoch_end""")
     if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
       expected_events += evaluation_events.copy()
   expected_events += ["on_log", "on_train_end"]
   return expected_events


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   self.get_trainer()
   lowercase__	: Union[str, Any]     		=   DEFAULT_CALLBACKS.copy() + [ProgressCallback]
   self.check_callbacks_equality(trainer.callback_handler.callbacks       ,	SCREAMING_SNAKE_CASE_)

   # Callbacks passed at init are added to the default callbacks
   lowercase__	: Any     		=   self.get_trainer(callbacks=[MyTestTrainerCallback])
   expected_callbacks.append(SCREAMING_SNAKE_CASE_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks       ,	SCREAMING_SNAKE_CASE_)

   # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
   lowercase__	: Any     		=   self.get_trainer(disable_tqdm=SCREAMING_SNAKE_CASE_)
   lowercase__	: Tuple     		=   DEFAULT_CALLBACKS.copy() + [PrinterCallback]
   self.check_callbacks_equality(trainer.callback_handler.callbacks       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   DEFAULT_CALLBACKS.copy() + [ProgressCallback]
   lowercase__	: Tuple     		=   self.get_trainer()

   # We can add, pop, or remove by class name
   trainer.remove_callback(SCREAMING_SNAKE_CASE_)
   expected_callbacks.remove(SCREAMING_SNAKE_CASE_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks       ,	SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[int]     		=   self.get_trainer()
   lowercase__	: List[Any]     		=   trainer.pop_callback(SCREAMING_SNAKE_CASE_)
   self.assertEqual(cb.__class__       ,	SCREAMING_SNAKE_CASE_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks       ,	SCREAMING_SNAKE_CASE_)

   trainer.add_callback(SCREAMING_SNAKE_CASE_)
   expected_callbacks.insert(0       ,	SCREAMING_SNAKE_CASE_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks       ,	SCREAMING_SNAKE_CASE_)

   # We can also add, pop, or remove by instance
   lowercase__	: Union[str, Any]     		=   self.get_trainer()
   lowercase__	: Optional[Any]     		=   trainer.callback_handler.callbacks[0]
   trainer.remove_callback(SCREAMING_SNAKE_CASE_)
   expected_callbacks.remove(SCREAMING_SNAKE_CASE_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks       ,	SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   self.get_trainer()
   lowercase__	: Optional[Any]     		=   trainer.callback_handler.callbacks[0]
   lowercase__	: Union[str, Any]     		=   trainer.pop_callback(SCREAMING_SNAKE_CASE_)
   self.assertEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks       ,	SCREAMING_SNAKE_CASE_)

   trainer.add_callback(SCREAMING_SNAKE_CASE_)
   expected_callbacks.insert(0       ,	SCREAMING_SNAKE_CASE_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks       ,	SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self):


   '''simple docstring'''




   import warnings

   # XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
   warnings.simplefilter(action="""ignore"""       ,	category=SCREAMING_SNAKE_CASE_)

   lowercase__	: Union[str, Any]     		=   self.get_trainer(callbacks=[MyTestTrainerCallback])
   trainer.train()
   lowercase__	: Union[str, Any]     		=   trainer.callback_handler.callbacks[-2].events
   self.assertEqual(SCREAMING_SNAKE_CASE_       ,	self.get_expected_events(SCREAMING_SNAKE_CASE_))

   # Independent log/save/eval
   lowercase__	: List[Any]     		=   self.get_trainer(callbacks=[MyTestTrainerCallback]       ,	logging_steps=5)
   trainer.train()
   lowercase__	: List[str]     		=   trainer.callback_handler.callbacks[-2].events
   self.assertEqual(SCREAMING_SNAKE_CASE_       ,	self.get_expected_events(SCREAMING_SNAKE_CASE_))

   lowercase__	: Optional[Any]     		=   self.get_trainer(callbacks=[MyTestTrainerCallback]       ,	save_steps=5)
   trainer.train()
   lowercase__	: Dict     		=   trainer.callback_handler.callbacks[-2].events
   self.assertEqual(SCREAMING_SNAKE_CASE_       ,	self.get_expected_events(SCREAMING_SNAKE_CASE_))

   lowercase__	: Any     		=   self.get_trainer(callbacks=[MyTestTrainerCallback]       ,	eval_steps=5       ,	evaluation_strategy="""steps""")
   trainer.train()
   lowercase__	: int     		=   trainer.callback_handler.callbacks[-2].events
   self.assertEqual(SCREAMING_SNAKE_CASE_       ,	self.get_expected_events(SCREAMING_SNAKE_CASE_))

   lowercase__	: Tuple     		=   self.get_trainer(callbacks=[MyTestTrainerCallback]       ,	evaluation_strategy="""epoch""")
   trainer.train()
   lowercase__	: Optional[int]     		=   trainer.callback_handler.callbacks[-2].events
   self.assertEqual(SCREAMING_SNAKE_CASE_       ,	self.get_expected_events(SCREAMING_SNAKE_CASE_))

   # A bit of everything
   lowercase__	: Any     		=   self.get_trainer(
       callbacks=[MyTestTrainerCallback]       ,	logging_steps=3       ,	save_steps=10       ,	eval_steps=5       ,	evaluation_strategy="""steps"""       ,	)
   trainer.train()
   lowercase__	: str     		=   trainer.callback_handler.callbacks[-2].events
   self.assertEqual(SCREAMING_SNAKE_CASE_       ,	self.get_expected_events(SCREAMING_SNAKE_CASE_))

   # warning should be emitted for duplicated callbacks
   with patch("""transformers.trainer_callback.logger.warning""") as warn_mock:
     lowercase__	: Dict     		=   self.get_trainer(
         callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]       ,	)
     assert str(SCREAMING_SNAKE_CASE_) in warn_mock.call_args[0][0]












'''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[int]				     =  logging.get_logger(__name__)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int]				):




     """simple docstring"""


     a_     :							List[Any]     						=  DPTConfig(embedding_type="""hybrid"""				)

     if "large" in checkpoint_url:
          a_     :							str     						=  1024
          a_     :							List[str]     						=  4096
          a_     :							int     						=  24
          a_     :							int     						=  16
          a_     :							Optional[Any]     						=  [5, 11, 17, 23]
          a_     :							Optional[int]     						=  [256, 512, 1024, 1024]
          a_     :							Union[str, Any]     						=  (1, 384, 384)

     if "nyu" or "midas" in checkpoint_url:
          a_     :							Dict     						=  768
          a_     :							str     						=  [1, 1, 1, 0.5]
          a_     :							Dict     						=  [256, 512, 768, 768]
          a_     :							int     						=  150
          a_     :							Any     						=  16
          a_     :							Optional[int]     						=  (1, 384, 384)
          a_     :							Optional[Any]     						=  False
          a_     :							Dict     						=  """project"""

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

     return config, expected_shape



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int]				):




     """simple docstring"""


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



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

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          a_     :							List[Any]     						=  name.replace("""pretrained.act_postprocess1.3""" ,    """neck.reassemble_stage.layers.0.projection"""				)
     if "pretrained.act_postprocess1.4" in name:
          a_     :							List[Any]     						=  name.replace("""pretrained.act_postprocess1.4""" ,    """neck.reassemble_stage.layers.0.resize"""				)
     if "pretrained.act_postprocess2.3" in name:
          a_     :							str     						=  name.replace("""pretrained.act_postprocess2.3""" ,    """neck.reassemble_stage.layers.1.projection"""				)
     if "pretrained.act_postprocess2.4" in name:
          a_     :							Optional[int]     						=  name.replace("""pretrained.act_postprocess2.4""" ,    """neck.reassemble_stage.layers.1.resize"""				)
     if "pretrained.act_postprocess3.3" in name:
          a_     :							Optional[Any]     						=  name.replace("""pretrained.act_postprocess3.3""" ,    """neck.reassemble_stage.layers.2.projection"""				)
     if "pretrained.act_postprocess4.3" in name:
          a_     :							List[Any]     						=  name.replace("""pretrained.act_postprocess4.3""" ,    """neck.reassemble_stage.layers.3.projection"""				)
     if "pretrained.act_postprocess4.4" in name:
          a_     :							Optional[Any]     						=  name.replace("""pretrained.act_postprocess4.4""" ,    """neck.reassemble_stage.layers.3.resize"""				)
     if "pretrained" in name:
          a_     :							int     						=  name.replace("""pretrained""" ,    """dpt"""				)
     if "bn" in name:
          a_     :							Any     						=  name.replace("""bn""" ,    """batch_norm"""				)
     if "head" in name:
          a_     :							int     						=  name.replace("""head""" ,    """head.head"""				)
     if "encoder.norm" in name:
          a_     :							Any     						=  name.replace("""encoder.norm""" ,    """layernorm"""				)
     if "auxlayer" in name:
          a_     :							Union[str, Any]     						=  name.replace("""auxlayer""" ,    """auxiliary_head.head"""				)
     if "backbone" in name:
          a_     :							Any     						=  name.replace("""backbone""" ,    """backbone.bit.encoder"""				)

     if ".." in name:
          a_     :							Any     						=  name.replace("""..""" ,    """."""				)

     if "stem.conv" in name:
          a_     :							Optional[int]     						=  name.replace("""stem.conv""" ,    """bit.embedder.convolution"""				)
     if "blocks" in name:
          a_     :							Dict     						=  name.replace("""blocks""" ,    """layers"""				)
     if "convolution" in name and "backbone" in name:
          a_     :							str     						=  name.replace("""convolution""" ,    """conv"""				)
     if "layer" in name and "backbone" in name:
          a_     :							Any     						=  name.replace("""layer""" ,    """layers"""				)
     if "backbone.bit.encoder.bit" in name:
          a_     :							Dict     						=  name.replace("""backbone.bit.encoder.bit""" ,    """backbone.bit"""				)
     if "embedder.conv" in name:
          a_     :							List[str]     						=  name.replace("""embedder.conv""" ,    """embedder.convolution"""				)
     if "backbone.bit.encoder.stem.norm" in name:
          a_     :							int     						=  name.replace("""backbone.bit.encoder.stem.norm""" ,    """backbone.bit.embedder.norm"""				)
     return name



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					str				):




     """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)
          a_     :							List[str]     						=  state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"				)
          a_     :							Optional[Any]     						=  state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias"				)
          # next, add query, keys and values (in that order) to the state dict
          a_     :							Any     						=  in_proj_weight[: config.hidden_size, :]
          a_     :							int     						=  in_proj_bias[: config.hidden_size]
          a_     :							List[Any]     						=  in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          a_     :							int     						=  in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          a_     :							Union[str, Any]     						=  in_proj_weight[
              -config.hidden_size :, :
          ]
          a_     :							str     						=  in_proj_bias[-config.hidden_size :]



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							Optional[Any]     						=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
     a_     :							Union[str, Any]     						=  Image.open(requests.get(UpperCamelCase__ ,    stream=UpperCamelCase__				).raw				)
     return im



@torch.no_grad()
def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Tuple ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					Optional[Any] ,    UpperCamelCase__    :					Any				):




     """simple docstring"""


     a_						,     a_     :							Optional[Any]     						=  get_dpt_config(UpperCamelCase__				)
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     a_     :							int     						=  torch.load(UpperCamelCase__ ,    map_location="""cpu"""				)
     # remove certain keys
     remove_ignore_keys_(UpperCamelCase__				)
     # rename keys
     for key in state_dict.copy().keys():
          a_     :							Any     						=  state_dict.pop(UpperCamelCase__				)
          a_     :							Tuple     						=  val
     # read in qkv matrices
     read_in_q_k_v(UpperCamelCase__ ,    UpperCamelCase__				)

     # load HuggingFace model
     a_     :							Any     						=  DPTForSemanticSegmentation(UpperCamelCase__				) if """ade""" in checkpoint_url else DPTForDepthEstimation(UpperCamelCase__				)
     model.load_state_dict(UpperCamelCase__				)
     model.eval()

     # Check outputs on an image
     a_     :							str     						=  480 if """ade""" in checkpoint_url else 384
     a_     :							Optional[int]     						=  DPTImageProcessor(size=UpperCamelCase__				)

     a_     :							Dict     						=  prepare_img()
     a_     :							Any     						=  image_processor(UpperCamelCase__ ,    return_tensors="""pt"""				)

     # forward pass
     a_     :							Dict     						=  model(**UpperCamelCase__				).logits if """ade""" in checkpoint_url else model(**UpperCamelCase__				).predicted_depth

     if show_prediction:
          a_     :							Any     						=  (
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1				) ,    size=(image.size[1], image.size[0]) ,    mode="""bicubic""" ,    align_corners=UpperCamelCase__ ,    )
              .squeeze()
              .cpu()
              .numpy()
          )

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

     if pytorch_dump_folder_path is not None:
          Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
          print(F"Saving model to {pytorch_dump_folder_path}"				)
          model.save_pretrained(UpperCamelCase__				)
          print(F"Saving image processor to {pytorch_dump_folder_path}"				)
          image_processor.save_pretrained(UpperCamelCase__				)

     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_      :  Dict				     =  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_      :  Optional[Any]				     =  parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )
