



"""simple docstring"""






import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					def __init__(			self				:    Optional[Any],    lowerCamelCase				:    List[Any],    lowerCamelCase				:    Dict,    lowerCamelCase				:    str=False,    lowerCamelCase				:    Any=1024,    lowerCamelCase				:    Optional[Any]=1024,    lowerCamelCase				:    Any=3.6,    lowerCamelCase				:    Union[str, Any]=False,    )->   List[Any]:
								lowerCamelCase__						:						List[Any]						      =tokenizer
								lowerCamelCase__						:						str						      =tokenizer.bos_token_id
								lowerCamelCase__						:						Tuple						      =dataset
								lowerCamelCase__						:						List[Any]						      =seq_length
								lowerCamelCase__						:						Dict						      =0
								lowerCamelCase__						:						int						      =infinite
								lowerCamelCase__						:						int						      =0
								lowerCamelCase__						:						int						      =tokenized

								if self.tokenized:
											lowerCamelCase__						:						int						      =seq_length * num_of_sequences
											lowerCamelCase__						:						List[Any]						      ='''input_ids'''
								else:
											lowerCamelCase__						:						List[Any]						      =seq_length * chars_per_token * num_of_sequences
											lowerCamelCase__						:						Tuple						      ='''content'''



					def __iter__(			self				:    List[str]	)->   Any:
								lowerCamelCase__						:						Optional[Any]						      =iter(self.dataset	)
								lowerCamelCase__						:						Tuple						      =True
								while more_examples:
											lowerCamelCase__      ,  lowerCamelCase__						:						str						      =[], 0
											while True:
														if buffer_len >= self.max_buffer_size:
																	break
														try:
																	buffer.append(next(lowerCamelCase	)[self.content_field]	)
																	buffer_len += len(buffer[-1]	)
														except StopIteration:
																	if self.infinite:
																				lowerCamelCase__						:						Optional[Any]						      =iter(self.dataset	)
																				self.epoch += 1
																				logger.info(F'''Dataset epoch: {self.epoch}'''	)
																	else:
																				lowerCamelCase__						:						List[str]						      =False
																				break
											if self.tokenized:
														lowerCamelCase__						:						Union[str, Any]						      =buffer
											else:
														lowerCamelCase__						:						Any						      =self.tokenizer(lowerCamelCase,    truncation=lowerCamelCase	)['''input_ids''']
											lowerCamelCase__						:						Optional[int]						      =[]
											for tokenized_input in tokenized_inputs:
														all_token_ids.extend(tokenized_input + [self.concat_token_id]	)
											for i in range(0,    len(lowerCamelCase	),    self.seq_length	):
														lowerCamelCase__						:						Tuple						      =all_token_ids[i : i + self.seq_length]
														if len(lowerCamelCase	) == self.seq_length:
																	self.current_size += 1
																	yield torch.tensor(lowerCamelCase	)




					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    Optional[int]=1000	)->   Optional[int]:
								return ShufflerIterDataPipe(self,    buffer_size=lowerCamelCase	)






def 	snake_case__  (						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			lowerCamelCase__						:						Optional[int]						      =args.model_ckpt.split('''/'''      )[-1]
			lowerCamelCase__						:						Tuple						      =logging.getLogger(__name__      )
			lowerCamelCase__						:						Dict						      =Path(args.save_dir      ) / '''log/'''
			log_dir.mkdir(exist_ok=__lowerCamelCase      )
			lowerCamelCase__						:						str						      =f'''debug_{accelerator.process_index}.log'''
			logging.basicConfig(
			    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''				,						datefmt='''%m/%d/%Y %H:%M:%S'''				,						level=logging.INFO				,						handlers=[logging.FileHandler(log_dir / filename      ), logging.StreamHandler()]				,						)
			if accelerator.is_main_process:  # we only want to setup logging once
						accelerator.init_trackers(__lowerCamelCase				,						vars(__lowerCamelCase      )      )
						lowerCamelCase__						:						Any						      =accelerator.trackers[0].run.name
						logger.setLevel(logging.INFO      )
						datasets.utils.logging.set_verbosity_info()
						transformers.utils.logging.set_verbosity_info()
			else:
						lowerCamelCase__						:						List[str]						      =''''''
						logger.setLevel(logging.ERROR      )
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()
			return logger, run_name






def 	snake_case__  (						__lowerCamelCase							:       List[Any]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Optional[int]						      ={'''streaming''': True}
			lowerCamelCase__						:						Any						      =load_dataset(args.dataset_name_train				,						split='''train'''				,						**__lowerCamelCase      )
			lowerCamelCase__						:						Dict						      =train_data.shuffle(buffer_size=args.shuffle_buffer				,						seed=args.seed      )
			lowerCamelCase__						:						Tuple						      =load_dataset(args.dataset_name_valid				,						split='''train'''				,						**__lowerCamelCase      )
			lowerCamelCase__						:						List[Any]						      =ConstantLengthDataset(
			    __lowerCamelCase				,						__lowerCamelCase				,						infinite=__lowerCamelCase				,						seq_length=args.seq_length				,						tokenized=args.tokenized      )
			lowerCamelCase__						:						List[Any]						      =ConstantLengthDataset(
			    __lowerCamelCase				,						__lowerCamelCase				,						infinite=__lowerCamelCase				,						seq_length=args.seq_length				,						tokenized=args.tokenized      )
			lowerCamelCase__						:						str						      =train_dataset.shuffle(buffer_size=args.shuffle_buffer      )
			lowerCamelCase__						:						Union[str, Any]						      =DataLoader(__lowerCamelCase				,						batch_size=args.train_batch_size				,						shuffle=__lowerCamelCase      )
			lowerCamelCase__						:						List[Any]						      =DataLoader(__lowerCamelCase				,						batch_size=args.valid_batch_size      )
			return train_dataloader, eval_dataloader






def 	snake_case__  (						__lowerCamelCase							:       Dict				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       int=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]      ):
			"""simple docstring"""
			lowerCamelCase__      ,  lowerCamelCase__						:						Dict						      =[], []
			for n, p in model.named_parameters():
						if any(nd in n for nd in no_decay      ):
									params_without_wd.append(__lowerCamelCase      )
						else:
									params_with_wd.append(__lowerCamelCase      )
			return [
			    {"params": params_with_wd, "weight_decay": args.weight_decay},
			    {"params": params_without_wd, "weight_decay": 0.0},
			]






def 	snake_case__  (						__lowerCamelCase							:       Tuple				,						__lowerCamelCase							:       Optional[int]      ):
			"""simple docstring"""
			logger.info(f'''Step {step}: {metrics}'''      )
			if accelerator.is_main_process:
						accelerator.log(__lowerCamelCase				,						__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Dict      ):
			"""simple docstring"""
			# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
			lowerCamelCase__						:						Tuple						      =accelerator.unwrap_model(__lowerCamelCase      ).config
			lowerCamelCase__						:						Any						      =4 if args.gradient_checkpointing else 3
			lowerCamelCase__						:						Any						      =args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
			lowerCamelCase__						:						str						      =24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
			lowerCamelCase__						:						Dict						      =factor * (
			    1.0
			    + (args.seq_length / (6.0 * config_model.n_embd))
			    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
			)
			lowerCamelCase__						:						Dict						      =flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
			return tflops






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]      ):
			"""simple docstring"""
			model.eval()
			lowerCamelCase__						:						str						      =[]
			for step, batch in enumerate(__lowerCamelCase      ):
						with torch.no_grad():
									lowerCamelCase__						:						Optional[Any]						      =model(__lowerCamelCase				,						labels=__lowerCamelCase      )
						lowerCamelCase__						:						int						      =outputs.loss.repeat(args.valid_batch_size      )
						losses.append(accelerator.gather(__lowerCamelCase      )      )
						if args.max_eval_steps > 0 and step >= args.max_eval_steps:
									break
			lowerCamelCase__						:						str						      =torch.cat(__lowerCamelCase      )
			lowerCamelCase__						:						str						      =losses[: eval_dataloader.dataset.current_size].mean()
			try:
						lowerCamelCase__						:						Optional[Any]						      =torch.exp(__lowerCamelCase      )
			except OverflowError:
						lowerCamelCase__						:						Dict						      =float('''inf'''      )
			return loss.item(), perplexity.item()


# Settings
_lowercase					:					Dict 		=		HfArgumentParser(TrainingArguments)
_lowercase					:					Optional[Any] 		=		parser.parse_args()

# Accelerator
_lowercase					:					Optional[int] 		=		ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
_lowercase					:					str 		=		Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
_lowercase					:					List[str] 		=		{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

_lowercase					:					Union[str, Any] 		=		Namespace(**vars(args), **acc_state)
_lowercase					:					List[str] 		=		accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
		_lowercase					:					List[Any] 		=		Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
_lowercase		,      _lowercase					:					Optional[Any] 		=		setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
		hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
_lowercase					:					int 		=		AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
_lowercase					:					List[Any] 		=		AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_lowercase		,      _lowercase					:					Union[str, Any] 		=		create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_lowercase					:					Tuple 		=		AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_lowercase					:					List[str] 		=		get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)






def 	snake_case__  (						):
			"""simple docstring"""
			return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
_lowercase		,      _lowercase		,      _lowercase		,      _lowercase					:					int 		=		accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# 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)
				_lowercase					:					Dict 		=		os.path.basename(args.resume_from_checkpoint)
		else:
				# Get the most recent checkpoint
				_lowercase					:					Optional[Any] 		=		[f.name for f in os.scandir(args.save_dir) if f.is_dir() and "step" in str(f)]
				dirs.sort(key=os.path.getctime)
				_lowercase					:					int 		=		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract the step of the checkpoint to continue from there
		_lowercase					:					int 		=		os.path.splitext(path)[0]
		_lowercase					:					List[str] 		=		int(training_difference.replace("step_", ""))

# Train model
model.train()
_lowercase					:					Optional[int] 		=		0
_lowercase					:					List[Any] 		=		time.time()
_lowercase					:					Dict 		=		0
for step, batch in enumerate(train_dataloader, start=1):
		if args.resume_from_checkpoint and step < resume_step:
				continue  # we need to skip steps until we reach the resumed step
		_lowercase					:					int 		=		model(batch, labels=batch, use_cache=False).loss
		_lowercase					:					Union[str, Any] 		=		accelerator.gather(loss.repeat(args.train_batch_size)).mean()
		loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
		log_metrics(step, {"samples": step * samples_per_step, "loss_per_step/train": loss.item()})
		_lowercase					:					Dict 		=		loss / args.gradient_accumulation_steps
		if step % args.gradient_accumulation_steps != 0:
				# Prevent backward from doing gradient all_reduce in every step
				if accelerator.distributed_type == DistributedType.MULTI_GPU:
						with model.no_sync():
								accelerator.backward(loss)
				else:
						accelerator.backward(loss)
		else:
				_lowercase					:					List[Any] 		=		get_lr()
				accelerator.backward(loss)
				accelerator.clip_grad_norm_(model.parameters(), 1.0)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
				_lowercase					:					str 		=		time.time() - t_start
				_lowercase					:					List[str] 		=		compute_tflops(elapsed_time, accelerator, args)
				log_metrics(
				    step,
				    {
				        "steps": completed_steps,
				        "loss/train": loss_tracking,
				        "lr": lr,
				        "tflops": tflops,
				        "time_per_iteration": elapsed_time,
				    },
				)
				_lowercase					:					List[Any] 		=		time.time()
				_lowercase					:					Union[str, Any] 		=		0
				completed_steps += 1
		if step % args.save_checkpoint_steps == 0:
				logger.info("Evaluating and saving model checkpoint")
				_lowercase		,      _lowercase					:					Union[str, Any] 		=		evaluate(args)
				log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
				accelerator.wait_for_everyone()
				_lowercase					:					Union[str, Any] 		=		os.path.join(args.save_dir, f'step_{step}')
				accelerator.save_state(save_dir)
				if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message=f'step {step}')
				model.train()
		if completed_steps >= args.max_train_steps:
				break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
_lowercase		,      _lowercase					:					List[str] 		=		evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
_lowercase					:					List[str] 		=		accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
_lowercase					:					Optional[int] 		=		os.path.join(args.save_dir, f'step_{step}')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
		hf_repo.push_to_hub(commit_message="final model")









'''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  )  )
