



'''simple docstring'''







import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
      import torch

lowercase__  :	List[Any]            =      logging.get_logger(__name__)





def     snake_case__			():
    return importlib.util.find_spec("deepspeed"						) is not None


if is_accelerate_available() and is_deepspeed_available():
      from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
      # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
      # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
      from builtins import object as DeepSpeedConfig

class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase			):
       # set global weakref object
       set_hf_deepspeed_config(self			)
       dep_version_check("accelerate"			)
       dep_version_check("deepspeed"			)
       super().__init__(__UpperCamelCase			)

class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase			):
       super().__init__(__UpperCamelCase			)
       UpperCamelCase__	     =      None
       UpperCamelCase__	     =      []


   def 						_UpperCamelCase							(	self			):
       if self._dtype is None:
           raise ValueError("trainer_config_process() wasn't called yet to tell dtype"			)
       return self._dtype


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self.get_value(__UpperCamelCase			)
       if val is None:
           return False
       else:
           return val == "auto"


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase=None  ,    __UpperCamelCase=True			):
       UpperCamelCase__     ,	UpperCamelCase__	     =      self.find_config_node(__UpperCamelCase			)
       if config is None:
           return

       if config.get(__UpperCamelCase			) == "auto":
           UpperCamelCase__	     =      hf_val
           return

       if not must_match:
           return

       UpperCamelCase__	     =      config.get(__UpperCamelCase			)
       if ds_val is not None and ds_val != hf_val:
           self.mismatches.append(f'- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}'			)

   A			:  Optional[Any]					=						partialmethod(lowerCAmelCase_       ,     must_match=lowerCAmelCase_	)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
       self.fill_match(
           "train_micro_batch_size_per_gpu"  ,    args.per_device_train_batch_size  ,    "per_device_train_batch_size"			)
       self.fill_match("gradient_accumulation_steps"  ,    args.gradient_accumulation_steps  ,    "gradient_accumulation_steps"			)
       self.fill_match("train_batch_size"  ,    __UpperCamelCase  ,    "train_batch_size (calculated)"			)
       self.fill_match("gradient_clipping"  ,    args.max_grad_norm  ,    "max_grad_norm"			)

       self.fill_match("optimizer.params.lr"  ,    args.learning_rate  ,    "learning_rate"			)
       self.fill_match("optimizer.params.betas"  ,    [args.adam_betaa, args.adam_betaa]  ,    "adam_beta1+adam_beta2"			)
       self.fill_match("optimizer.params.eps"  ,    args.adam_epsilon  ,    "adam_epsilon"			)
       self.fill_match("optimizer.params.weight_decay"  ,    args.weight_decay  ,    "weight_decay"			)

       self.fill_only("scheduler.params.warmup_min_lr"  ,    0			)  # not a trainer arg
       self.fill_match("scheduler.params.warmup_max_lr"  ,    args.learning_rate  ,    "learning_rate"			)
       # total_num_steps - will get set in trainer_config_finalize

       # fp16
       if args.fpaa or args.fpaa_full_eval:
           UpperCamelCase__	     =      "apex" if args.fpaa_backend == "apex" else "amp"
       else:
           UpperCamelCase__	     =      None

       if args.save_on_each_node:
           # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
           UpperCamelCase__	     =      self.config.get("checkpoint"  ,    {}			)
           UpperCamelCase__	     =      args.save_on_each_node

       # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
       # any here unless the user did the work
       self.fill_match(
           "fp16.enabled"  ,    ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == "amp")  ,    "fp16|fp16_full_eval+fp16_backend(amp)"  ,    )

       # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
       # ZeRO features
       self.fill_match("amp.enabled"  ,    fpaa_backend == "apex"  ,    "fp16+fp16_backend(apex)"			)
       self.fill_match("amp.opt_level"  ,    args.fpaa_opt_level  ,    "fp16_opt_level"			)

       self.fill_match("bf16.enabled"  ,    (args.bfaa or args.bfaa_full_eval)  ,    "bf16|bf16_full_eval"			)

       # deepspeed's default mode is fp16 unless there is a config that says differently
       if self.is_true("bf16.enabled"			):
           UpperCamelCase__	     =      torch.bfloataa
       elif self.is_false("fp16.enabled"			):
           UpperCamelCase__	     =      torch.floataa
       else:
           UpperCamelCase__	     =      torch.floataa







   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      [
           "zero_optimization.reduce_bucket_size",
           "zero_optimization.stage3_prefetch_bucket_size",
           "zero_optimization.stage3_param_persistence_threshold",
       ]
       UpperCamelCase__	     =      [x for x in hidden_size_based_keys if self.is_auto(__UpperCamelCase			)]

       if len(__UpperCamelCase			) > 0:
           if hasattr(model.config  ,    "hidden_size"			):
               UpperCamelCase__	     =      model.config.hidden_size
           elif hasattr(model.config  ,    "hidden_sizes"			):
               # if there are many hidden sizes pick the largest one
               UpperCamelCase__	     =      max(model.config.hidden_sizes			)
           else:
               raise ValueError(
                   "The model's config file has neither `hidden_size` nor `hidden_sizes` entry, "
                   "therefore it's not possible to automatically fill out the following `auto` entries "
                   f'in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '
                   "`auto` values for these keys with an integer value of your choice."			)

           self.fill_only("zero_optimization.reduce_bucket_size"  ,    hidden_size * hidden_size			)
           if self.is_zeroa():
               # automatically assign the optimal config values based on model config
               self.fill_only("zero_optimization.stage3_prefetch_bucket_size"  ,    0.9 * hidden_size * hidden_size			)
               self.fill_only("zero_optimization.stage3_param_persistence_threshold"  ,    10 * hidden_size			)

        # scheduler
       self.fill_match("scheduler.params.total_num_steps"  ,    __UpperCamelCase  ,    "num_training_steps (calculated)"			)
       self.fill_match("scheduler.params.warmup_num_steps"  ,    args.get_warmup_steps(__UpperCamelCase			)  ,    "warmup_steps"			)

       if len(self.mismatches			) > 0:
           UpperCamelCase__	     =      "\n".join(self.mismatches			)
           raise ValueError(
               "Please correct the following DeepSpeed config values that mismatch TrainingArguments"
               f' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.'			)

lowercase__  :	Optional[int]            =      None





def     snake_case__			(lowerCAmelCase_						):
    # this is a special weakref global object to allow us to get to Deepspeed config from APIs
    # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
    global _hf_deepspeed_config_weak_ref
    # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
    UpperCamelCase__	     =      weakref.ref(lowerCAmelCase_						)





def     snake_case__			():
    # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
    global _hf_deepspeed_config_weak_ref
    UpperCamelCase__	     =      None





def     snake_case__			():
    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().is_zeroa()
    else:
        return False





def     snake_case__			():
    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().config
    else:
        return None





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						):
    from accelerate.utils import DummyOptim, DummyScheduler

    UpperCamelCase__	     =      hf_deepspeed_config.config

    # Optimizer + Scheduler
    # Currently supported combos:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Yes
    # 3. DS scheduler + HF optimizer: Yes
    # 4. HF scheduler + DS optimizer: No
    #
    # Unless Offload is enabled in which case it's:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Mostly*
    # 3. DS scheduler + HF optimizer: Mostly*
    # 4. HF scheduler + DS optimizer: No
    #
    # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

    UpperCamelCase__	     =      None
    if "optimizer" in config:
        if args.adafactor:
            raise ValueError(
                "--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. "
                "Only one optimizer can be configured."						)
        UpperCamelCase__	     =      DummyOptim(params=lowerCAmelCase_						)
    else:
        if hf_deepspeed_config.is_offload():
            logger.info(
                "Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"
                " custom optimizer has both CPU and GPU implementation (except LAMB)"						)

        # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
        # But trainer uses AdamW by default.
        UpperCamelCase__	     =      trainer.create_optimizer()
        # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
        UpperCamelCase__	     =      True

    UpperCamelCase__	     =      None
    if "scheduler" in config:
        UpperCamelCase__	     =      DummyScheduler(lowerCAmelCase_						)
    else:
        if isinstance(lowerCAmelCase_       ,	lowerCAmelCase_						):
            raise ValueError(
                "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
                "Please configure a scheduler in the DeepSpeed config."						)
        UpperCamelCase__	     =      trainer.create_scheduler(num_training_steps=lowerCAmelCase_       ,	optimizer=lowerCAmelCase_						)

    return optimizer, lr_scheduler





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_=False						):
    from deepspeed.utils import logger as ds_logger

    UpperCamelCase__	     =      trainer.model
    UpperCamelCase__	     =      trainer.args

    UpperCamelCase__	     =      trainer.accelerator.state.deepspeed_plugin.hf_ds_config

    # resume config update - some bits like `model` and `num_training_steps` only become available during train
    hf_deepspeed_config.trainer_config_finalize(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						)

    # set the Deepspeed log level consistent with the Trainer
    ds_logger.setLevel(args.get_process_log_level()						)

    if inference:
        # only Z3 makes sense for the inference
        if not hf_deepspeed_config.is_zeroa():
            raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config"						)

        # in case the training config is re-used for inference
        hf_deepspeed_config.del_config_sub_tree("optimizer"						)
        hf_deepspeed_config.del_config_sub_tree("lr_scheduler"						)
        UpperCamelCase__     ,	UpperCamelCase__	     =      None, None
        UpperCamelCase__	     =      None
    else:
        UpperCamelCase__	     =      None  # important for when deepspeed_init is used as re-init
        UpperCamelCase__	     =      list(filter(lambda lowerCAmelCase_						: p.requires_grad       ,	model.parameters()						)						)
        UpperCamelCase__     ,	UpperCamelCase__	     =      deepspeed_optim_sched(
            lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						)

    # keep for quick debug:
    # from pprint import pprint; pprint(config)

    return optimizer, lr_scheduler





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    # it's possible that the user is trying to resume from model_path, which doesn't necessarily
    # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
    # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
    # path contains what looks like a deepspeed checkpoint
    import glob

    UpperCamelCase__	     =      sorted(glob.glob(F'{checkpoint_path}/global_step*'						)						)

    if len(lowerCAmelCase_						) > 0:
        logger.info(F'Attempting to resume from {checkpoint_path}'						)
        # this magically updates self.optimizer and self.lr_scheduler
        UpperCamelCase__     ,	UpperCamelCase__	     =      deepspeed_engine.load_checkpoint(
            lowerCAmelCase_       ,	load_optimizer_states=lowerCAmelCase_       ,	load_lr_scheduler_states=lowerCAmelCase_						)
        if load_path is None:
            raise ValueError(F'[deepspeed] failed to resume from checkpoint {checkpoint_path}'						)
    else:
        raise ValueError(F'Can\'t find a valid checkpoint at {checkpoint_path}'						)









'''simple docstring'''







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_				:






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=2  ,    __UpperCamelCase=32  ,    __UpperCamelCase=16  ,    __UpperCamelCase=3  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=32  ,    __UpperCamelCase=4  ,    __UpperCamelCase=[0, 1, 2, 3]  ,    __UpperCamelCase=4  ,    __UpperCamelCase=37  ,    __UpperCamelCase="gelu"  ,    __UpperCamelCase=0.1  ,    __UpperCamelCase=0.1  ,    __UpperCamelCase=0.02  ,    __UpperCamelCase=3  ,    __UpperCamelCase=False  ,    __UpperCamelCase=None  ,    ):
       UpperCamelCase__	     =      parent
       UpperCamelCase__	     =      batch_size
       UpperCamelCase__	     =      image_size
       UpperCamelCase__	     =      patch_size
       UpperCamelCase__	     =      num_channels
       UpperCamelCase__	     =      is_training
       UpperCamelCase__	     =      use_labels
       UpperCamelCase__	     =      hidden_size
       UpperCamelCase__	     =      num_hidden_layers
       UpperCamelCase__	     =      backbone_out_indices
       UpperCamelCase__	     =      num_attention_heads
       UpperCamelCase__	     =      intermediate_size
       UpperCamelCase__	     =      hidden_act
       UpperCamelCase__	     =      hidden_dropout_prob
       UpperCamelCase__	     =      attention_probs_dropout_prob
       UpperCamelCase__	     =      initializer_range
       UpperCamelCase__	     =      num_labels
       UpperCamelCase__	     =      scope
       UpperCamelCase__	     =      is_hybrid
       # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
       UpperCamelCase__	     =      (image_size // patch_size) ** 2
       UpperCamelCase__	     =      num_patches + 1


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

       UpperCamelCase__	     =      self.get_config()

       return config, pixel_values, labels


   def 						_UpperCamelCase							(	self			):
       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=__UpperCamelCase  ,    initializer_range=self.initializer_range  ,    is_hybrid=self.is_hybrid  ,    )


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      DPTModel(config=__UpperCamelCase			)
       model.to(__UpperCamelCase			)
       model.eval()
       UpperCamelCase__	     =      model(__UpperCamelCase			)
       self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, self.seq_length, self.hidden_size)			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self.num_labels
       UpperCamelCase__	     =      DPTForDepthEstimation(__UpperCamelCase			)
       model.to(__UpperCamelCase			)
       model.eval()
       UpperCamelCase__	     =      model(__UpperCamelCase			)
       self.parent.assertEqual(result.predicted_depth.shape  ,    (self.batch_size, self.image_size, self.image_size)			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self.num_labels
       UpperCamelCase__	     =      DPTForSemanticSegmentation(__UpperCamelCase			)
       model.to(__UpperCamelCase			)
       model.eval()
       UpperCamelCase__	     =      model(__UpperCamelCase  ,    labels=__UpperCamelCase			)
       self.parent.assertEqual(
           result.logits.shape  ,    (self.batch_size, self.num_labels, self.image_size, self.image_size)			)







   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.prepare_config_and_inputs()
       UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__	     =      config_and_inputs
       UpperCamelCase__	     =      {"pixel_values": pixel_values}
       return config, inputs_dict

@require_torch
class    lowerCamelCase_				(						lowerCAmelCase_       ,     lowerCAmelCase_       ,     unittest.TestCase	):






   '''simple docstring'''

   A			:  int					=						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
   A			:  Any					=						(
       {
           "depth-estimation": DPTForDepthEstimation,
           "feature-extraction": DPTModel,
           "image-segmentation": DPTForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   A			:  int					=						False
   A			:  List[str]					=						False
   A			:  int					=						False


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      DPTModelTester(self			)
       UpperCamelCase__	     =      ConfigTester(self  ,    config_class=__UpperCamelCase  ,    has_text_modality=__UpperCamelCase  ,    hidden_size=37			)


   def 						_UpperCamelCase							(	self			):
       self.config_tester.run_common_tests()


   @unittest.skip(reason="DPT does not use inputs_embeds"			)
   def 						_UpperCamelCase							(	self			):
       pass


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__     ,	UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__	     =      model_class(__UpperCamelCase			)
           self.assertIsInstance(model.get_input_embeddings()  ,    (nn.Module)			)
           UpperCamelCase__	     =      model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__UpperCamelCase  ,    nn.Linear			)			)


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__     ,	UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase__	     =      ["pixel_values"]
           self.assertListEqual(arg_names[:1]  ,    __UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       for model_class in self.all_model_classes:
           if model_class.__name__ == "DPTForDepthEstimation":
               continue

           UpperCamelCase__     ,	UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs_for_common()
           UpperCamelCase__	     =      True

           if model_class in get_values(__UpperCamelCase			):
               continue

           UpperCamelCase__	     =      model_class(__UpperCamelCase			)
           model.to(__UpperCamelCase			)
           model.train()
           UpperCamelCase__	     =      self._prepare_for_class(__UpperCamelCase  ,    __UpperCamelCase  ,    return_labels=__UpperCamelCase			)
           UpperCamelCase__	     =      model(**__UpperCamelCase			).loss
           loss.backward()


   def 						_UpperCamelCase							(	self			):
       for model_class in self.all_model_classes:
           if model_class.__name__ == "DPTForDepthEstimation":
               continue

           UpperCamelCase__     ,	UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs_for_common()
           UpperCamelCase__	     =      False
           UpperCamelCase__	     =      True

           if model_class in get_values(__UpperCamelCase			) or not model_class.supports_gradient_checkpointing:
               continue
           UpperCamelCase__	     =      model_class(__UpperCamelCase			)
           model.to(__UpperCamelCase			)
           model.gradient_checkpointing_enable()
           model.train()
           UpperCamelCase__	     =      self._prepare_for_class(__UpperCamelCase  ,    __UpperCamelCase  ,    return_labels=__UpperCamelCase			)
           UpperCamelCase__	     =      model(**__UpperCamelCase			).loss
           loss.backward()


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__     ,	UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__	     =      _config_zero_init(__UpperCamelCase			)
       for model_class in self.all_model_classes:
           UpperCamelCase__	     =      model_class(config=__UpperCamelCase			)
           # Skip the check for the backbone
           UpperCamelCase__	     =      []
           for name, module in model.named_modules():
               if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                   UpperCamelCase__	     =      [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 						_UpperCamelCase							(	self			):
       pass







   @slow
   def 						_UpperCamelCase							(	self			):
       for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__	     =      DPTModel.from_pretrained(__UpperCamelCase			)
           self.assertIsNotNone(__UpperCamelCase			)





def     snake_case__			():
    UpperCamelCase__	     =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
    return image





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






   '''simple docstring'''

   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      DPTImageProcessor.from_pretrained("Intel/dpt-large"			)
       UpperCamelCase__	     =      DPTForDepthEstimation.from_pretrained("Intel/dpt-large"			).to(__UpperCamelCase			)

       UpperCamelCase__	     =      prepare_img()
       UpperCamelCase__	     =      image_processor(images=__UpperCamelCase  ,    return_tensors="pt"			).to(__UpperCamelCase			)

       # forward pass
       with torch.no_grad():
           UpperCamelCase__	     =      model(**__UpperCamelCase			)
           UpperCamelCase__	     =      outputs.predicted_depth

       # verify the predicted depth
       UpperCamelCase__	     =      torch.Size((1, 3_84, 3_84)			)
       self.assertEqual(predicted_depth.shape  ,    __UpperCamelCase			)

       UpperCamelCase__	     =      torch.tensor(
           [[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]			).to(__UpperCamelCase			)

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


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"			)
       UpperCamelCase__	     =      DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"			).to(__UpperCamelCase			)

       UpperCamelCase__	     =      prepare_img()
       UpperCamelCase__	     =      image_processor(images=__UpperCamelCase  ,    return_tensors="pt"			).to(__UpperCamelCase			)

       # forward pass
       with torch.no_grad():
           UpperCamelCase__	     =      model(**__UpperCamelCase			)

       # verify the logits
       UpperCamelCase__	     =      torch.Size((1, 1_50, 4_80, 4_80)			)
       self.assertEqual(outputs.logits.shape  ,    __UpperCamelCase			)

       UpperCamelCase__	     =      torch.tensor(
           [[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]			).to(__UpperCamelCase			)

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







   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"			)
       UpperCamelCase__	     =      DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"			).to(__UpperCamelCase			)

       UpperCamelCase__	     =      prepare_img()
       UpperCamelCase__	     =      image_processor(images=__UpperCamelCase  ,    return_tensors="pt"			).to(__UpperCamelCase			)

       # forward pass
       with torch.no_grad():
           UpperCamelCase__	     =      model(**__UpperCamelCase			)

       UpperCamelCase__	     =      outputs.logits.detach().cpu()

       UpperCamelCase__	     =      image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase  ,    target_sizes=[(5_00, 3_00)]			)
       UpperCamelCase__	     =      torch.Size((5_00, 3_00)			)
       self.assertEqual(segmentation[0].shape  ,    __UpperCamelCase			)

       UpperCamelCase__	     =      image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase			)
       UpperCamelCase__	     =      torch.Size((4_80, 4_80)			)
       self.assertEqual(segmentation[0].shape  ,    __UpperCamelCase			)




