

import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase__							=			16
lowerCAmelCase__							=			32
def   _lowerCAmelCase(						__A      ):
  return int(x / 2**20      )






class     __magic_name__     :


 def __enter__(    self		:			Dict     )     ->   Dict:
   gc.collect()
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
   UpperCAmelCase          =	torch.cuda.memory_allocated()
   UpperCAmelCase          =	psutil.Process()

   UpperCAmelCase          =	self.cpu_mem_used()
   UpperCAmelCase          =	True
   UpperCAmelCase          =	threading.Thread(target=self.peak_monitor_func     )
   UpperCAmelCase          =	True
   peak_monitor_thread.start()
   return self


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   str:
   return self.process.memory_info().rss


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   Any:
   UpperCAmelCase          =	-1

   while True:
     UpperCAmelCase          =	max(self.cpu_mem_used()      ,    self.cpu_peak     )

     # can't sleep or will not catch the peak right (this comment is here on purpose)
     # time.sleep(0.001) # 1msec

     if not self.peak_monitoring:
       break


 def __exit__(    self		:			Union[str, Any]      ,    *lowerCAmelCase__		:			int     )     ->   Tuple:
   UpperCAmelCase          =	False

   gc.collect()
   torch.cuda.empty_cache()
   UpperCAmelCase          =	torch.cuda.memory_allocated()
   UpperCAmelCase          =	torch.cuda.max_memory_allocated()
   UpperCAmelCase          =	bamb(self.end - self.begin     )
   UpperCAmelCase          =	bamb(self.peak - self.begin     )

   UpperCAmelCase          =	self.cpu_mem_used()
   UpperCAmelCase          =	bamb(self.cpu_end - self.cpu_begin     )
   UpperCAmelCase          =	bamb(self.cpu_peak - self.cpu_begin     )
   # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      lowerCAmelCase__							=			mocked_dataloaders  # noqa: F811
def   _lowerCAmelCase(						__A ,							__A      ):
  # For testing only
  if os.environ.get("TESTING_MOCKED_DATALOADERS" ,							__A      ) == "1":
    UpperCAmelCase          =	2

  # New Code #
  # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  UpperCAmelCase          =	FullyShardedDataParallelPlugin(
      state_dict_config=FullStateDictConfig(offload_to_cpu=__A ,							ranka_only=__A      ) ,							optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=__A ,							ranka_only=__A      ) ,							)

  # Initialize accelerator
  if args.with_tracking:
    UpperCAmelCase          =	Accelerator(
        cpu=args.cpu ,							mixed_precision=args.mixed_precision ,							log_with="wandb" ,							project_dir=args.logging_dir ,							fsdp_plugin=__A ,							)
  else:
    UpperCAmelCase          =	Accelerator(fsdp_plugin=__A      )
  accelerator.print(accelerator.distributed_type      )

  if hasattr(args.checkpointing_steps ,							"isdigit"      ):
    if args.checkpointing_steps == "epoch":
      UpperCAmelCase          =	args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      UpperCAmelCase          =	int(args.checkpointing_steps      )
    else:
      raise ValueError(
          F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."      )
  else:
    UpperCAmelCase          =	None
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  UpperCAmelCase          =	config["lr"]
  UpperCAmelCase          =	int(config["num_epochs"]      )
  UpperCAmelCase          =	int(config["seed"]      )
  UpperCAmelCase          =	int(config["batch_size"]      )

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    UpperCAmelCase          =	vars(__A      )
    accelerator.init_trackers("fsdp_glue_no_trainer" ,							__A      )

  UpperCAmelCase          =	AutoTokenizer.from_pretrained(args.model_name_or_path      )
  UpperCAmelCase          =	load_dataset("glue" ,							"mrpc"      )
  UpperCAmelCase          =	evaluate.load("glue" ,							"mrpc"      )

  def tokenize_function(__A      ):
    # max_length=None => use the model max length (it's actually the default)
    UpperCAmelCase          =	tokenizer(examples["sentence1"] ,							examples["sentence2"] ,							truncation=__A ,							max_length=__A      )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    UpperCAmelCase          =	datasets.map(
        __A ,							batched=__A ,							remove_columns=["idx", "sentence1", "sentence2"] ,							)

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  UpperCAmelCase          =	tokenized_datasets.rename_column("label" ,							"labels"      )

  # If the batch size is too big we use gradient accumulation
  UpperCAmelCase          =	1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    UpperCAmelCase          =	batch_size // MAX_GPU_BATCH_SIZE
    UpperCAmelCase          =	MAX_GPU_BATCH_SIZE

  def collate_fn(__A      ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    UpperCAmelCase          =	128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      UpperCAmelCase          =	16
    elif accelerator.mixed_precision != "no":
      UpperCAmelCase          =	8
    else:
      UpperCAmelCase          =	None

    return tokenizer.pad(
        __A ,							padding="longest" ,							max_length=__A ,							pad_to_multiple_of=__A ,							return_tensors="pt" ,							)

  # Instantiate dataloaders.
  UpperCAmelCase          =	DataLoader(
      tokenized_datasets["train"] ,							shuffle=__A ,							collate_fn=__A ,							batch_size=__A      )
  UpperCAmelCase          =	DataLoader(
      tokenized_datasets["validation"] ,							shuffle=__A ,							collate_fn=__A ,							batch_size=__A      )

  set_seed(__A      )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  UpperCAmelCase          =	AutoModelForSequenceClassification.from_pretrained(
      args.model_name_or_path ,							return_dict=__A ,							low_cpu_mem_usage=__A      )

  # New Code #
  # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
  UpperCAmelCase          =	accelerator.prepare(__A      )
  accelerator.print(__A      )

  # Instantiate optimizer
  # New Code #
  # For FSDP feature, at present it doesn't support multiple parameter groups,
  # so we need to create a single parameter group for the whole model
  UpperCAmelCase          =	torch.optim.AdamW(params=model.parameters() ,							lr=__A ,							weight_decay=2E-4      )

  # Instantiate scheduler
  UpperCAmelCase          =	get_linear_schedule_with_warmup(
      optimizer=__A ,							num_warmup_steps=10 ,							num_training_steps=(len(__A      ) * num_epochs) // gradient_accumulation_steps ,							)

  # New Code #
  # For FSDP feature, prepare everything except the model as we have already prepared the model
  # before creating the optimizer
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  UpperCAmelCase          =	accelerator.prepare(
      __A ,							__A ,							__A ,							__A      )

  UpperCAmelCase          =	0

  # Potentially load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"      )
      accelerator.load_state(args.resume_from_checkpoint      )
      UpperCAmelCase          =	os.path.basename(args.resume_from_checkpoint      )
    else:
      # Get the most recent checkpoint
      UpperCAmelCase          =	[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
      dirs.sort(key=os.path.getctime      )
      UpperCAmelCase          =	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    UpperCAmelCase          =	os.path.splitext(__A      )[0]

    if "epoch" in training_difference:
      num_epochs -= int(training_difference.replace("epoch_" ,							""      )      )
      UpperCAmelCase          =	None
    else:
      UpperCAmelCase          =	int(training_difference.replace("step_" ,							""      )      )
      num_epochs -= resume_step // len(__A      )
      # If resuming by step, we also need to know exactly how far into the DataLoader we went
      UpperCAmelCase          =	(num_epochs * len(__A      )) - resume_step

    # Now we train the model
  for epoch in range(__A      ):
    # New Code #
    # context manager to track the peak memory usage during the training epoch
    with TorchTracemalloc() as tracemalloc:
      model.train()
      if args.with_tracking:
        UpperCAmelCase          =	0
      for step, batch in enumerate(__A      ):
        # We need to skip steps until we reach the resumed step
        if args.resume_from_checkpoint and epoch == 0:
          if resume_step is not None and step < resume_step:
            pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device      )
        UpperCAmelCase          =	model(**__A      )
        UpperCAmelCase          =	outputs.loss
        UpperCAmelCase          =	loss / gradient_accumulation_steps
        # We keep track of the loss at each epoch
        if args.with_tracking:
          total_loss += loss.detach().float()
        accelerator.backward(__A      )
        if step % gradient_accumulation_steps == 0:
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()
          # accelerator.print(lr_scheduler.get_lr())

        overall_step += 1

        if isinstance(__A ,							__A      ):
          UpperCAmelCase          =	F"step_{overall_step}"
          if overall_step % checkpointing_steps == 0:
            if args.output_dir is not None:
              UpperCAmelCase          =	os.path.join(args.output_dir ,							__A      )
            accelerator.save_state(__A      )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
    accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin      )      )      )
    accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used      )      )
    accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked      )      )
    accelerator.print(
        "Total Peak Memory consumed during the train (max): {}".format(
            tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
    # Logging the peak memory usage of the GPU to the tracker
    if args.with_tracking:
      accelerator.log(
          {
              "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin      ),
          } ,							step=__A ,							)

    # New Code #
    # context manager to track the peak memory usage during the evaluation
    with TorchTracemalloc() as tracemalloc:
      model.eval()
      for step, batch in enumerate(__A      ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device      )
        with torch.no_grad():
          UpperCAmelCase          =	model(**__A      )
        UpperCAmelCase          =	outputs.logits.argmax(dim=-1      )
        UpperCAmelCase          =	accelerator.gather_for_metrics((predictions, batch["labels"])      )
        metric.add_batch(
            predictions=__A ,							references=__A ,							)

      UpperCAmelCase          =	metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"epoch {epoch}:" ,							__A      )
      if args.with_tracking:
        accelerator.log(
            {
                "accuracy": eval_metric["accuracy"],
                "f1": eval_metric["f1"],
                "train_loss": total_loss.item() / len(__A      ),
            } ,							step=__A ,							)

      if checkpointing_steps == "epoch":
        UpperCAmelCase          =	F"epoch_{epoch}"
        if args.output_dir is not None:
          UpperCAmelCase          =	os.path.join(args.output_dir ,							__A      )
        accelerator.save_state(__A      )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
    accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin      )      )      )
    accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used      )      )
    accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked      )      )
    accelerator.print(
        "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
    # Logging the peak memory usage of the GPU to the tracker
    if args.with_tracking:
      accelerator.log(
          {
              "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin      ),
          } ,							step=__A ,							)

  if args.with_tracking:
    accelerator.end_training()
def   _lowerCAmelCase(						):
  UpperCAmelCase          =	argparse.ArgumentParser(description="Simple example of training script."      )
  parser.add_argument(
      "--mixed_precision" ,							type=__A ,							default=__A ,							choices=["no", "fp16", "bf16", "fp8"] ,							help="Whether to use mixed precision. Choose"
      "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
      "and an Nvidia Ampere GPU." ,							)
  parser.add_argument("--cpu" ,							action="store_true" ,							help="If passed, will train on the CPU."      )
  parser.add_argument(
      "--checkpointing_steps" ,							type=__A ,							default=__A ,							help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch." ,							)
  parser.add_argument(
      "--resume_from_checkpoint" ,							type=__A ,							default=__A ,							help="If the training should continue from a checkpoint folder." ,							)
  parser.add_argument(
      "--with_tracking" ,							action="store_true" ,							help="Whether to load in all available experiment trackers from the environment and use them for logging." ,							)
  parser.add_argument(
      "--output_dir" ,							type=__A ,							default="." ,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." ,							)
  parser.add_argument(
      "--logging_dir" ,							type=__A ,							default="logs" ,							help="Location on where to store experiment tracking logs`" ,							)
  parser.add_argument(
      "--model_name_or_path" ,							type=__A ,							help="Path to pretrained model or model identifier from huggingface.co/models." ,							required=__A ,							)
  UpperCAmelCase          =	parser.parse_args()
  UpperCAmelCase          =	{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(__A ,							__A      )


if __name__ == "__main__":
      main()








import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
		from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import DPTImageProcessor


class 						lowerCamelCase__			:


						def __init__(			self    :	List[str]   ,							_a    :	Any   ,							_a    :	str=2   ,							_a    :	List[str]=3_2   ,							_a    :	str=1_6   ,							_a    :	Tuple=3   ,							_a    :	Any=True   ,							_a    :	Optional[Any]=True   ,							_a    :	Optional[Any]=3_2   ,							_a    :	Optional[Any]=4   ,							_a    :	str=[0, 1, 2, 3]   ,							_a    :	Any=4   ,							_a    :	List[Any]=3_7   ,							_a    :	Optional[Any]="gelu"   ,							_a    :	str=0.1   ,							_a    :	Optional[Any]=0.1   ,							_a    :	Optional[Any]=0.0_2   ,							_a    :	Optional[int]=3   ,							_a    :	List[str]=False   ,							_a    :	Dict=None   ,							):
													a__:							List[Any]									=parent
													a__:							List[str]									=batch_size
													a__:							Optional[Any]									=image_size
													a__:							Optional[Any]									=patch_size
													a__:							Dict									=num_channels
													a__:							Optional[int]									=is_training
													a__:							List[Any]									=use_labels
													a__:							List[str]									=hidden_size
													a__:							int									=num_hidden_layers
													a__:							List[Any]									=backbone_out_indices
													a__:							int									=num_attention_heads
													a__:							List[Any]									=intermediate_size
													a__:							int									=hidden_act
													a__:							int									=hidden_dropout_prob
													a__:							Union[str, Any]									=attention_probs_dropout_prob
													a__:							Tuple									=initializer_range
													a__:							Dict									=num_labels
													a__:							Tuple									=scope
													a__:							List[Any]									=is_hybrid
													# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
													a__:							Any									=(image_size // patch_size) ** 2
													a__:							Tuple									=num_patches + 1


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													a__:							str									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

													a__:							Dict									=None
													if self.use_labels:
																				a__:							Tuple									=ids_tensor([self.batch_size, self.image_size, self.image_size]   ,							self.num_labels      )

													a__:							Union[str, Any]									=self.get_config()

													return config, pixel_values, labels


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													return DPTConfig(
													    image_size=self.image_size   ,							patch_size=self.patch_size   ,							num_channels=self.num_channels   ,							hidden_size=self.hidden_size   ,							num_hidden_layers=self.num_hidden_layers   ,							backbone_out_indices=self.backbone_out_indices   ,							num_attention_heads=self.num_attention_heads   ,							intermediate_size=self.intermediate_size   ,							hidden_act=self.hidden_act   ,							hidden_dropout_prob=self.hidden_dropout_prob   ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,							is_decoder=_a   ,							initializer_range=self.initializer_range   ,							is_hybrid=self.is_hybrid   ,							)


						def 	_lowerCamelCase      (			self    :	Optional[int]   ,							_a    :	Dict   ,							_a    :	List[str]   ,							_a    :	int      ):
													a__:							Union[str, Any]									=DPTModel(config=_a      )
													model.to(_a      )
													model.eval()
													a__:							List[Any]									=model(_a      )
													self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)      )


						def 	_lowerCamelCase      (			self    :	int   ,							_a    :	Tuple   ,							_a    :	Union[str, Any]   ,							_a    :	List[Any]      ):
													a__:							Union[str, Any]									=self.num_labels
													a__:							List[str]									=DPTForDepthEstimation(_a      )
													model.to(_a      )
													model.eval()
													a__:							Optional[int]									=model(_a      )
													self.parent.assertEqual(result.predicted_depth.shape   ,							(self.batch_size, self.image_size, self.image_size)      )


						def 	_lowerCamelCase      (			self    :	List[Any]   ,							_a    :	str   ,							_a    :	Optional[int]   ,							_a    :	int      ):
													a__:							Any									=self.num_labels
													a__:							List[str]									=DPTForSemanticSegmentation(_a      )
													model.to(_a      )
													model.eval()
													a__:							Union[str, Any]									=model(_a   ,							labels=_a      )
													self.parent.assertEqual(
													    result.logits.shape   ,							(self.batch_size, self.num_labels, self.image_size, self.image_size)      )







						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__:							List[str]									=self.prepare_config_and_inputs()
													a__				,      a__				,      a__:							Union[str, Any]									=config_and_inputs
													a__:							Tuple									={"pixel_values": pixel_values}
													return config, inputs_dict


@require_torch
class 						lowerCamelCase__			(		_a					,       _a					,       unittest.TestCase ):
						_lowerCAmelCase             =			(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
						_lowerCAmelCase             =			(
						    {
						        '''depth-estimation''': DPTForDepthEstimation,
						        '''feature-extraction''': DPTModel,
						        '''image-segmentation''': DPTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_lowerCAmelCase             =			False
						_lowerCAmelCase             =			False
						_lowerCAmelCase             =			False


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							Dict									=DPTModelTester(self      )
													a__:							int									=ConfigTester(self   ,							config_class=_a   ,							has_text_modality=_a   ,							hidden_size=3_7      )


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													self.config_tester.run_common_tests()


						@unittest.skip(reason="DPT does not use inputs_embeds"      )
						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													pass


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__				,      a__:							Optional[Any]									=self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				a__:							Optional[Any]									=model_class(_a      )
																				self.assertIsInstance(model.get_input_embeddings()   ,							(nn.Module)      )
																				a__:							Optional[Any]									=model.get_output_embeddings()
																				self.assertTrue(x is None or isinstance(_a   ,							nn.Linear      )      )


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__				,      a__:							int									=self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				a__:							Any									=model_class(_a      )
																				a__:							Any									=inspect.signature(model.forward      )
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				a__:							List[str]									=[*signature.parameters.keys()]

																				a__:							List[str]									=["pixel_values"]
																				self.assertListEqual(arg_names[:1]   ,							_a      )


						def 	_lowerCamelCase      (			self    :	Any      ):
													a__:							List[str]									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_a      )


						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							Any									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_depth_estimation(*_a      )


						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							Any									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_semantic_segmentation(*_a      )


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													for model_class in self.all_model_classes:
																				if model_class.__name__ == "DPTForDepthEstimation":
																											continue

																				a__				,      a__:							List[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
																				a__:							Optional[int]									=True

																				if model_class in get_values(_a      ):
																											continue

																				a__:							Union[str, Any]									=model_class(_a      )
																				model.to(_a      )
																				model.train()
																				a__:							Optional[int]									=self._prepare_for_class(_a   ,							_a   ,							return_labels=_a      )
																				a__:							List[Any]									=model(**_a      ).loss
																				loss.backward()


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													for model_class in self.all_model_classes:
																				if model_class.__name__ == "DPTForDepthEstimation":
																											continue

																				a__				,      a__:							Optional[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
																				a__:							Union[str, Any]									=False
																				a__:							Any									=True

																				if model_class in get_values(_a      ) or not model_class.supports_gradient_checkpointing:
																											continue
																				a__:							Tuple									=model_class(_a      )
																				model.to(_a      )
																				model.gradient_checkpointing_enable()
																				model.train()
																				a__:							Dict									=self._prepare_for_class(_a   ,							_a   ,							return_labels=_a      )
																				a__:							Optional[Any]									=model(**_a      ).loss
																				loss.backward()


						def 	_lowerCamelCase      (			self    :	Tuple      ):
													a__				,      a__:							str									=self.model_tester.prepare_config_and_inputs_for_common()

													a__:							Tuple									=_config_zero_init(_a      )
													for model_class in self.all_model_classes:
																				a__:							List[Any]									=model_class(config=_a      )
																				# Skip the check for the backbone
																				a__:							List[Any]									=[]
																				for name, module in model.named_modules():
																											if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																																		a__:							Optional[Any]									=[F"{name}.{key}" for key in module.state_dict().keys()]
																																		break

																				for name, param in model.named_parameters():
																											if param.requires_grad:
																																		if name in backbone_params:
																																									continue
																																		self.assertIn(
																																		    ((param.data.mean() * 1e9).round() / 1e9).item()   ,							[0.0, 1.0]   ,							msg=F"Parameter {name} of model {model_class} seems not properly initialized"   ,							)


						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."      )
						def 	_lowerCamelCase      (			self    :	Dict      ):
													pass







						@slow
						def 	_lowerCamelCase      (			self    :	str      ):
													for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				a__:							List[Any]									=DPTModel.from_pretrained(_a      )
																				self.assertIsNotNone(_a      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
							return image







@require_torch
@require_vision
@slow
class 						lowerCamelCase__			(		unittest.TestCase ):


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__:							Any									=DPTImageProcessor.from_pretrained("Intel/dpt-large"      )
													a__:							str									=DPTForDepthEstimation.from_pretrained("Intel/dpt-large"      ).to(_a      )

													a__:							Optional[int]									=prepare_img()
													a__:							List[Any]									=image_processor(images=_a   ,							return_tensors="pt"      ).to(_a      )

													# forward pass
													with torch.no_grad():
																				a__:							int									=model(**_a      )
																				a__:							List[str]									=outputs.predicted_depth

													# verify the predicted depth
													a__:							Optional[int]									=torch.Size((1, 3_8_4, 3_8_4)      )
													self.assertEqual(predicted_depth.shape   ,							_a      )

													a__:							Union[str, Any]									=torch.tensor(
													    [[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]      ).to(_a      )

													self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]   ,							_a   ,							atol=1e-4      )      )


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							Optional[Any]									=DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"      )
													a__:							Union[str, Any]									=DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"      ).to(_a      )

													a__:							Dict									=prepare_img()
													a__:							Optional[int]									=image_processor(images=_a   ,							return_tensors="pt"      ).to(_a      )

													# forward pass
													with torch.no_grad():
																				a__:							int									=model(**_a      )

													# verify the logits
													a__:							int									=torch.Size((1, 1_5_0, 4_8_0, 4_8_0)      )
													self.assertEqual(outputs.logits.shape   ,							_a      )

													a__:							Any									=torch.tensor(
													    [[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]      ).to(_a      )

													self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]   ,							_a   ,							atol=1e-4      )      )







						def 	_lowerCamelCase      (			self    :	int      ):
													a__:							Dict									=DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"      )
													a__:							List[Any]									=DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"      ).to(_a      )

													a__:							int									=prepare_img()
													a__:							Any									=image_processor(images=_a   ,							return_tensors="pt"      ).to(_a      )

													# forward pass
													with torch.no_grad():
																				a__:							Optional[int]									=model(**_a      )

													a__:							Union[str, Any]									=outputs.logits.detach().cpu()

													a__:							List[Any]									=image_processor.post_process_semantic_segmentation(outputs=_a   ,							target_sizes=[(5_0_0, 3_0_0)]      )
													a__:							Dict									=torch.Size((5_0_0, 3_0_0)      )
													self.assertEqual(segmentation[0].shape   ,							_a      )

													a__:							Optional[Any]									=image_processor.post_process_semantic_segmentation(outputs=_a      )
													a__:							List[str]									=torch.Size((4_8_0, 4_8_0)      )
													self.assertEqual(segmentation[0].shape   ,							_a      )





