



'''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

UpperCamelCase__    :     Any			  =   logging.get_logger(__name__)



def     UpperCAmelCase     (    )   ->       Optional[Any]:



   """simple docstring"""






   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	(  __A							):


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase  )    ->    str:
    # set global weakref object
    set_hf_deepspeed_config(self  )
    dep_version_check("""accelerate"""  )
    dep_version_check("""deepspeed"""  )
    super().__init__(_lowerCamelCase  )


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase  )    ->    Tuple:
    super().__init__(_lowerCamelCase  )
    A_  :		Union[str, Any]						=					None
    A_  :		Optional[Any]						=					[]







 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    if self._dtype is None:
       raise ValueError("""trainer_config_process() wasn't called yet to tell dtype"""  )
    return self._dtype







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    int:
    A_  :		int						=					self.get_value(_lowerCamelCase  )
    if val is None:
       return False
    else:
       return val == "auto"







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase=None					,    _lowerCamelCase=True  )    ->    List[Any]:
    A_						,  A_  :		Tuple						=					self.find_config_node(_lowerCamelCase  )
    if config is None:
       return

    if config.get(_lowerCamelCase  ) == "auto":
       A_  :		Tuple						=					hf_val
       return

    if not must_match:
       return

    A_  :		Optional[Any]						=					config.get(_lowerCamelCase  )
    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}"  )

 lowerCamelCase									=     partialmethod(__A,							must_match=__A							)







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    int:
    A_  :		Any						=					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"""					,    _lowerCamelCase					,    """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:
       A_  :		Tuple						=					"""apex""" if args.fpaa_backend == """apex""" else """amp"""
    else:
       A_  :		Any						=					None

    if args.save_on_each_node:
       # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
       A_  :		List[Any]						=					self.config.get("""checkpoint"""					,    {}  )
       A_  :		Optional[int]						=					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"""  ):
       A_  :		Any						=					torch.bfloataa
    elif self.is_false("""fp16.enabled"""  ):
       A_  :		Optional[Any]						=					torch.floataa
    else:
       A_  :		Optional[Any]						=					torch.floataa







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    int:
    A_  :		str						=					[
        """zero_optimization.reduce_bucket_size""",
        """zero_optimization.stage3_prefetch_bucket_size""",
        """zero_optimization.stage3_param_persistence_threshold""",
    ]
    A_  :		str						=					[x for x in hidden_size_based_keys if self.is_auto(_lowerCamelCase  )]

    if len(_lowerCamelCase  ) > 0:
       if hasattr(model.config					,    """hidden_size"""  ):
          A_  :		Union[str, Any]						=					model.config.hidden_size
       elif hasattr(model.config					,    """hidden_sizes"""  ):
          # if there are many hidden sizes pick the largest one
          A_  :		Dict						=					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"""					,    _lowerCamelCase					,    """num_training_steps (calculated)"""  )
    self.fill_match("""scheduler.params.warmup_num_steps"""					,    args.get_warmup_steps(_lowerCamelCase  )					,    """warmup_steps"""  )

    if len(self.mismatches  ) > 0:
       A_  :		Union[str, Any]						=					"""\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'."  )

UpperCamelCase__    :     List[str]			  =   None



def     UpperCAmelCase     (    a_		)   ->       Tuple:



   """simple docstring"""






   global _hf_deepspeed_config_weak_ref
   # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
   A_  :		List[str]						=					weakref.ref(a_		)



def     UpperCAmelCase     (    )   ->       Any:



   """simple docstring"""






   global _hf_deepspeed_config_weak_ref
   A_  :		Tuple						=					None



def     UpperCAmelCase     (    )   ->       Any:



   """simple docstring"""






   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     UpperCAmelCase     (    )   ->       Union[str, Any]:



   """simple docstring"""






   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     UpperCAmelCase     (    a_      ,		a_      ,		a_      ,		a_      ,		a_		)   ->       List[Any]:



   """simple docstring"""






   from accelerate.utils import DummyOptim, DummyScheduler

   A_  :		Union[str, Any]						=					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)

   A_  :		List[Any]						=					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."""		)
      A_  :		Tuple						=					DummyOptim(params=a_		)
   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.
      A_  :		Optional[Any]						=					trainer.create_optimizer()
      # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
      A_  :		List[Any]						=					True

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

   return optimizer, lr_scheduler



def     UpperCAmelCase     (    a_      ,		a_      ,		a_=False		)   ->       Optional[Any]:



   """simple docstring"""






   from deepspeed.utils import logger as ds_logger

   A_  :		int						=					trainer.model
   A_  :		List[str]						=					trainer.args

   A_  :		Optional[Any]						=					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(a_      ,		a_      ,		a_		)

   # 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"""		)
      A_						,  A_  :		Optional[Any]						=					None, None
      A_  :		List[Any]						=					None
   else:
      A_  :		Union[str, Any]						=					None  # important for when deepspeed_init is used as re-init
      A_  :		int						=					list(filter(lambda a_		: p.requires_grad      ,		model.parameters()		)		)
      A_						,  A_  :		List[Any]						=					deepspeed_optim_sched(
          a_      ,		a_      ,		a_      ,		a_      ,		a_		)

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

   return optimizer, lr_scheduler







def     UpperCAmelCase     (    a_      ,		a_		)   ->       List[str]:



   """simple docstring"""






   import glob

   A_  :		Optional[int]						=					sorted(glob.glob(F"{checkpoint_path}/global_step*"		)		)

   if len(a_		) > 0:
      logger.info(F"Attempting to resume from {checkpoint_path}"		)
      # this magically updates self.optimizer and self.lr_scheduler
      A_						,  A_  :		Union[str, Any]						=					deepspeed_engine.load_checkpoint(
          a_      ,		load_optimizer_states=a_      ,		load_lr_scheduler_states=a_		)
      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 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
  from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Tuple						=					self.config_class(**self.inputs_dict  )
    self.parent.assertTrue(hasattr(_lowerCamelCase					,    """hidden_sizes"""  )  )
    self.parent.assertTrue(hasattr(_lowerCamelCase					,    """num_attention_heads"""  )  )
    self.parent.assertTrue(hasattr(_lowerCamelCase					,    """num_encoder_blocks"""  )  )


class 							_lowerCAmelCase	:


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase=13					,    _lowerCamelCase=64					,    _lowerCamelCase=3					,    _lowerCamelCase=4					,    _lowerCamelCase=[2, 2, 2, 2]					,    _lowerCamelCase=[8, 4, 2, 1]					,    _lowerCamelCase=[16, 32, 64, 128]					,    _lowerCamelCase=[1, 4, 8, 16]					,    _lowerCamelCase=[1, 2, 4, 8]					,    _lowerCamelCase=True					,    _lowerCamelCase=True					,    _lowerCamelCase="gelu"					,    _lowerCamelCase=0.1					,    _lowerCamelCase=0.1					,    _lowerCamelCase=0.02					,    _lowerCamelCase=16					,    _lowerCamelCase=3					,    _lowerCamelCase=None					,    )    ->    Optional[int]:
    A_  :		int						=					parent
    A_  :		int						=					batch_size
    A_  :		Optional[int]						=					image_size
    A_  :		str						=					num_channels
    A_  :		Optional[Any]						=					num_encoder_blocks
    A_  :		List[str]						=					sr_ratios
    A_  :		int						=					depths
    A_  :		str						=					hidden_sizes
    A_  :		Optional[int]						=					downsampling_rates
    A_  :		Any						=					num_attention_heads
    A_  :		List[str]						=					is_training
    A_  :		Optional[Any]						=					use_labels
    A_  :		List[str]						=					hidden_act
    A_  :		Tuple						=					hidden_dropout_prob
    A_  :		Optional[int]						=					attention_probs_dropout_prob
    A_  :		Optional[int]						=					initializer_range
    A_  :		str						=					decoder_hidden_size
    A_  :		Optional[Any]						=					num_labels
    A_  :		List[Any]						=					scope







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    A_  :		List[Any]						=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

    A_  :		Dict						=					None
    if self.use_labels:
       A_  :		Optional[Any]						=					ids_tensor([self.batch_size, self.image_size, self.image_size]					,    self.num_labels  )

    A_  :		Any						=					self.get_config()
    return config, pixel_values, labels







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    return GLPNConfig(
        image_size=self.image_size					,    num_channels=self.num_channels					,    num_encoder_blocks=self.num_encoder_blocks					,    depths=self.depths					,    hidden_sizes=self.hidden_sizes					,    num_attention_heads=self.num_attention_heads					,    hidden_act=self.hidden_act					,    hidden_dropout_prob=self.hidden_dropout_prob					,    attention_probs_dropout_prob=self.attention_probs_dropout_prob					,    initializer_range=self.initializer_range					,    decoder_hidden_size=self.decoder_hidden_size					,    )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    str:
    A_  :		Tuple						=					GLPNModel(config=_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()
    A_  :		Optional[Any]						=					model(_lowerCamelCase  )
    A_  :		Optional[int]						=					self.image_size // (self.downsampling_rates[-1] * 2)
    self.parent.assertEqual(
        result.last_hidden_state.shape					,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    Optional[int]:
    A_  :		Tuple						=					self.num_labels
    A_  :		Any						=					GLPNForDepthEstimation(_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()
    A_  :		Dict						=					model(_lowerCamelCase  )
    self.parent.assertEqual(result.predicted_depth.shape					,    (self.batch_size, self.image_size, self.image_size)  )
    A_  :		Optional[int]						=					model(_lowerCamelCase					,    labels=_lowerCamelCase  )
    self.parent.assertEqual(result.predicted_depth.shape					,    (self.batch_size, self.image_size, self.image_size)  )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_  :		Optional[Any]						=					self.prepare_config_and_inputs()
    A_						,  A_						,  A_  :		Tuple						=					config_and_inputs
    A_  :		Dict						=					{"""pixel_values""": pixel_values}
    return config, inputs_dict


@require_torch
class 							_lowerCAmelCase	(  __A,							__A,							unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 lowerCamelCase									=     (
     {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
 )

 lowerCamelCase									=     False
 lowerCamelCase									=     False
 lowerCamelCase									=     False







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		Optional[Any]						=					GLPNModelTester(self  )
    A_  :		Dict						=					GLPNConfigTester(self					,    config_class=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    self.config_tester.run_common_tests()







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		int						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Dict						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*_lowerCamelCase  )







 @unittest.skip("""GLPN does not use inputs_embeds"""  )
 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    pass







 @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""  )
 def       UpperCAmelCase_       (      self  )    ->    Any:
    pass







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_						,  A_  :		Union[str, Any]						=					self.model_tester.prepare_config_and_inputs_for_common()

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

       A_  :		Dict						=					["""pixel_values"""]
       self.assertListEqual(arg_names[:1]					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_						,  A_  :		List[str]						=					self.model_tester.prepare_config_and_inputs_for_common()
    A_  :		Optional[Any]						=					True

    for model_class in self.all_model_classes:
       A_  :		int						=					True
       A_  :		str						=					False
       A_  :		Any						=					True
       A_  :		Dict						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
          A_  :		List[str]						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )
       A_  :		Union[str, Any]						=					outputs.attentions

       A_  :		List[str]						=					sum(self.model_tester.depths  )
       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       A_  :		Dict						=					True
       A_  :		List[str]						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
          A_  :		Any						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )
       A_  :		str						=					outputs.attentions

       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )

       # verify the first attentions (first block, first layer)
       A_  :		List[Any]						=					(self.model_tester.image_size // 4) ** 2
       A_  :		str						=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(attentions[0].shape[-3:]  )					,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,    )

       # verify the last attentions (last block, last layer)
       A_  :		Optional[Any]						=					(self.model_tester.image_size // 32) ** 2
       A_  :		Optional[Any]						=					(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
       self.assertListEqual(
           list(attentions[-1].shape[-3:]  )					,    [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]					,    )
       A_  :		str						=					len(_lowerCamelCase  )

       # Check attention is always last and order is fine
       A_  :		Union[str, Any]						=					True
       A_  :		str						=					True
       A_  :		Tuple						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
          A_  :		Dict						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )

       self.assertEqual(out_len + 1					,    len(_lowerCamelCase  )  )

       A_  :		Any						=					outputs.attentions

       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )
       # verify the first attentions (first block, first layer)
       A_  :		List[Any]						=					(self.model_tester.image_size // 4) ** 2
       A_  :		Optional[int]						=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(self_attentions[0].shape[-3:]  )					,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,    )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    def check_hidden_states_output(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  ):
       A_  :		List[Any]						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()

       with torch.no_grad():
          A_  :		Tuple						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )

       A_  :		List[str]						=					outputs.hidden_states

       A_  :		Dict						=					self.model_tester.num_encoder_blocks
       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )

       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-3:]  )					,    [
               self.model_tester.hidden_sizes[0],
               self.model_tester.image_size // 4,
               self.model_tester.image_size // 4,
           ]					,    )

    A_						,  A_  :		Optional[int]						=					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       A_  :		Any						=					True
       check_hidden_states_output(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       A_  :		Optional[Any]						=					True

       check_hidden_states_output(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    if not self.model_tester.is_training:
       return

    A_						,  A_  :		Any						=					self.model_tester.prepare_config_and_inputs_for_common()
    A_  :		Union[str, Any]						=					True

    for model_class in self.all_model_classes:
       if model_class in get_values(_lowerCamelCase  ):
          continue
       # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       # this can then be incorporated into _prepare_for_class in test_modeling_common.py
       if model_class.__name__ == "GLPNForDepthEstimation":
          A_						,  A_						,  A_						,  A_  :		Any						=					inputs_dict["""pixel_values"""].shape
          A_  :		List[Any]						=					torch.zeros(
              [self.model_tester.batch_size, height, width]					,    device=_lowerCamelCase  ).long()
       A_  :		Union[str, Any]						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.train()
       A_  :		List[str]						=					self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase					,    return_labels=_lowerCamelCase  )
       A_  :		Dict						=					model(**_lowerCamelCase  ).loss
       loss.backward()







 @slow
 def       UpperCAmelCase_       (      self  )    ->    Dict:
    for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       A_  :		Union[str, Any]						=					GLPNModel.from_pretrained(_lowerCamelCase  )
       self.assertIsNotNone(_lowerCamelCase  )



def     UpperCAmelCase     (    )   ->       int:



   """simple docstring"""






   A_  :		int						=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
   return image







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


 """simple docstring"""

 @slow
 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		Tuple						=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
    A_  :		List[Any]						=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(_lowerCamelCase  )

    A_  :		str						=					prepare_img()
    A_  :		int						=					image_processor(images=_lowerCamelCase					,    return_tensors="""pt"""  ).to(_lowerCamelCase  )

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

    # verify the predicted depth
    A_  :		List[Any]						=					torch.Size([1, 480, 640]  )
    self.assertEqual(outputs.predicted_depth.shape					,    _lowerCamelCase  )

    A_  :		Union[str, Any]						=					torch.tensor(
        [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]  ).to(_lowerCamelCase  )

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