
"""simple docstring"""

import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_A            = logging.getLogger(__name__)
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		lowerCamelCase     :			List[str]		=    p * torch.log(a_			)
		lowerCamelCase     :			int		=    0
		return -plogp.sum(dim=-1			)
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		logger.info('lv, h >\t' + '\t'.join(F"""{x + 1}""" for x in range(len(a_			)			)			)			)
		for row in range(len(a_			)			):
				if tensor.dtype != torch.long:
						logger.info(F"""layer {row + 1}:\t""" + '\t'.join(F"""{x:.5f}""" for x in tensor[row].cpu().data			)			)
				else:
						logger.info(F"""layer {row + 1}:\t""" + '\t'.join(F"""{x:d}""" for x in tensor[row].cpu().data			)			)
def 		UpperCAmelCase					(		a_,						a_,						a_,						a_=True,						a_=True,						a_=None,						a_=False			):





		'''simple docstring'''


		lowerCamelCase	,    lowerCamelCase     :			str		=    model.config.num_hidden_layers, model.config.num_attention_heads
		lowerCamelCase     :			Tuple		=    torch.zeros(a_,						a_			).to(args.device			)
		lowerCamelCase     :			int		=    torch.zeros(a_,						a_			).to(args.device			)

		if head_mask is None:
				lowerCamelCase     :			Any		=    torch.ones(a_,						a_			).to(args.device			)

		head_mask.requires_grad_(requires_grad=a_			)
		# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
		if actually_pruned:
				lowerCamelCase     :			List[str]		=    None

		lowerCamelCase     :			Optional[int]		=    None
		lowerCamelCase     :			Any		=    None
		lowerCamelCase     :			Optional[Any]		=    0.0

		for step, inputs in enumerate(tqdm(a_,						desc='Iteration',						disable=args.local_rank not in [-1, 0]			)			):
				for k, v in inputs.items():
						lowerCamelCase     :			Optional[Any]		=    v.to(args.device			)

				# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
				lowerCamelCase     :			Optional[Any]		=    model(**a_,						head_mask=a_			)
				lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			Tuple		=    (
				    outputs[0],
				    outputs[1],
				    outputs[-1],
				)  # Loss and logits are the first, attention the last
				loss.backward()  # Backpropagate to populate the gradients in the head mask

				if compute_entropy:
						for layer, attn in enumerate(a_			):
								lowerCamelCase     :			int		=    entropy(attn.detach()			) * inputs['attention_mask'].float().unsqueeze(1			)
								attn_entropy[layer] += masked_entropy.sum(-1			).sum(0			).detach()

				if compute_importance:
						head_importance += head_mask.grad.abs().detach()

				# Also store our logits/labels if we want to compute metrics afterwards
				if preds is None:
						lowerCamelCase     :			Optional[Any]		=    logits.detach().cpu().numpy()
						lowerCamelCase     :			Dict		=    inputs['labels'].detach().cpu().numpy()
				else:
						lowerCamelCase     :			str		=    np.append(a_,						logits.detach().cpu().numpy(),						axis=0			)
						lowerCamelCase     :			int		=    np.append(a_,						inputs['labels'].detach().cpu().numpy(),						axis=0			)

				tot_tokens += inputs["attention_mask"].float().detach().sum().data

		# Normalize
		attn_entropy /= tot_tokens
		head_importance /= tot_tokens
		# Layerwise importance normalization
		if not args.dont_normalize_importance_by_layer:
				lowerCamelCase     :			str		=    2
				lowerCamelCase     :			Dict		=    torch.pow(torch.pow(a_,						a_			).sum(-1			),						1 / exponent			)
				head_importance /= norm_by_layer.unsqueeze(-1			) + 1E-20

		if not args.dont_normalize_global_importance:
				lowerCamelCase     :			Any		=    (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

		# Print/save matrices
		np.save(os.path.join(args.output_dir,						'attn_entropy.npy'			),						attn_entropy.detach().cpu().numpy()			)
		np.save(os.path.join(args.output_dir,						'head_importance.npy'			),						head_importance.detach().cpu().numpy()			)

		logger.info('Attention entropies'			)
		print_ad_tensor(a_			)
		logger.info('Head importance scores'			)
		print_ad_tensor(a_			)
		logger.info('Head ranked by importance scores'			)
		lowerCamelCase     :			str		=    torch.zeros(head_importance.numel(),						dtype=torch.long,						device=args.device			)
		lowerCamelCase     :			List[str]		=    torch.arange(
		    head_importance.numel(),						device=args.device			)
		lowerCamelCase     :			List[str]		=    head_ranks.view_as(a_			)
		print_ad_tensor(a_			)

		return attn_entropy, head_importance, preds, labels
def 		UpperCAmelCase					(		a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			int		=    compute_heads_importance(a_,						a_,						a_,						compute_entropy=a_			)
		lowerCamelCase     :			Any		=    np.argmax(a_,						axis=1			) if args.output_mode == 'classification' else np.squeeze(a_			)
		lowerCamelCase     :			List[Any]		=    glue_compute_metrics(args.task_name,						a_,						a_			)[args.metric_name]
		logger.info('Pruning: original score: %f, threshold: %f',						a_,						original_score * args.masking_threshold			)

		lowerCamelCase     :			Dict		=    torch.ones_like(a_			)
		lowerCamelCase     :			Tuple		=    max(1,						int(new_head_mask.numel() * args.masking_amount			)			)

		lowerCamelCase     :			Union[str, Any]		=    original_score
		while current_score >= original_score * args.masking_threshold:
				lowerCamelCase     :			Dict		=    new_head_mask.clone()  # save current head mask
				# heads from least important to most - keep only not-masked heads
				lowerCamelCase     :			Optional[Any]		=    float('Inf'			)
				lowerCamelCase     :			List[str]		=    head_importance.view(-1			).sort()[1]

				if len(a_			) <= num_to_mask:
						break

				# mask heads
				lowerCamelCase     :			Tuple		=    current_heads_to_mask[:num_to_mask]
				logger.info('Heads to mask: %s',						str(current_heads_to_mask.tolist()			)			)
				lowerCamelCase     :			Any		=    new_head_mask.view(-1			)
				lowerCamelCase     :			Tuple		=    0.0
				lowerCamelCase     :			str		=    new_head_mask.view_as(a_			)
				lowerCamelCase     :			Dict		=    new_head_mask.clone().detach()
				print_ad_tensor(a_			)

				# Compute metric and head importance again
				lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			str		=    compute_heads_importance(
				    a_,						a_,						a_,						compute_entropy=a_,						head_mask=a_			)
				lowerCamelCase     :			Tuple		=    np.argmax(a_,						axis=1			) if args.output_mode == 'classification' else np.squeeze(a_			)
				lowerCamelCase     :			Optional[Any]		=    glue_compute_metrics(args.task_name,						a_,						a_			)[args.metric_name]
				logger.info(
				    'Masking: current score: %f, remaining heads %d (%.1f percents)',						a_,						new_head_mask.sum(),						new_head_mask.sum() / new_head_mask.numel() * 100,						)

		logger.info('Final head mask'			)
		print_ad_tensor(a_			)
		np.save(os.path.join(args.output_dir,						'head_mask.npy'			),						head_mask.detach().cpu().numpy()			)

		return head_mask
def 		UpperCAmelCase					(		a_,						a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			Optional[Any]		=    datetime.now()
		lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			Dict		=    compute_heads_importance(
		    a_,						a_,						a_,						compute_entropy=a_,						compute_importance=a_,						head_mask=a_			)
		lowerCamelCase     :			Optional[int]		=    np.argmax(a_,						axis=1			) if args.output_mode == 'classification' else np.squeeze(a_			)
		lowerCamelCase     :			int		=    glue_compute_metrics(args.task_name,						a_,						a_			)[args.metric_name]
		lowerCamelCase     :			int		=    datetime.now() - before_time

		lowerCamelCase     :			Union[str, Any]		=    sum(p.numel() for p in model.parameters()			)
		lowerCamelCase     :			Any		=    {
		    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(a_			)			)
		}

		assert sum(len(a_			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
		model.prune_heads(a_			)
		lowerCamelCase     :			List[str]		=    sum(p.numel() for p in model.parameters()			)

		lowerCamelCase     :			List[str]		=    datetime.now()
		lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			Tuple		=    compute_heads_importance(
		    a_,						a_,						a_,						compute_entropy=a_,						compute_importance=a_,						head_mask=a_,						actually_pruned=a_,						)
		lowerCamelCase     :			Dict		=    np.argmax(a_,						axis=1			) if args.output_mode == 'classification' else np.squeeze(a_			)
		lowerCamelCase     :			Any		=    glue_compute_metrics(args.task_name,						a_,						a_			)[args.metric_name]
		lowerCamelCase     :			Dict		=    datetime.now() - before_time

		logger.info(
		    'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)',						a_,						a_,						pruned_num_params / original_num_params * 100,						)
		logger.info('Pruning: score with masking: %f score with pruning: %f',						a_,						a_			)
		logger.info('Pruning: speed ratio (new timing / original timing): %f percents',						original_time / new_time * 100			)
def 		UpperCAmelCase					(		):





		'''simple docstring'''


		lowerCamelCase     :			str		=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--data_dir',						default=a_,						type=a_,						required=a_,						help='The input data dir. Should contain the .tsv files (or other data files) for the task.',						)
		parser.add_argument(
		    '--model_name_or_path',						default=a_,						type=a_,						required=a_,						help='Path to pretrained model or model identifier from huggingface.co/models',						)
		parser.add_argument(
		    '--task_name',						default=a_,						type=a_,						required=a_,						help='The name of the task to train selected in the list: ' + ', '.join(glue_processors.keys()			),						)
		parser.add_argument(
		    '--output_dir',						default=a_,						type=a_,						required=a_,						help='The output directory where the model predictions and checkpoints will be written.',						)

		# Other parameters
		parser.add_argument(
		    '--config_name',						default='',						type=a_,						help='Pretrained config name or path if not the same as model_name_or_path',						)
		parser.add_argument(
		    '--tokenizer_name',						default='',						type=a_,						help='Pretrained tokenizer name or path if not the same as model_name_or_path',						)
		parser.add_argument(
		    '--cache_dir',						default=a_,						type=a_,						help='Where do you want to store the pre-trained models downloaded from huggingface.co',						)
		parser.add_argument(
		    '--data_subset',						type=a_,						default=-1,						help='If > 0: limit the data to a subset of data_subset instances.'			)
		parser.add_argument(
		    '--overwrite_output_dir',						action='store_true',						help='Whether to overwrite data in output directory'			)
		parser.add_argument(
		    '--overwrite_cache',						action='store_true',						help='Overwrite the cached training and evaluation sets'			)

		parser.add_argument(
		    '--dont_normalize_importance_by_layer',						action='store_true',						help='Don\'t normalize importance score by layers'			)
		parser.add_argument(
		    '--dont_normalize_global_importance',						action='store_true',						help='Don\'t normalize all importance scores between 0 and 1',						)

		parser.add_argument(
		    '--try_masking',						action='store_true',						help='Whether to try to mask head until a threshold of accuracy.'			)
		parser.add_argument(
		    '--masking_threshold',						default=0.9,						type=a_,						help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).',						)
		parser.add_argument(
		    '--masking_amount',						default=0.1,						type=a_,						help='Amount to heads to masking at each masking step.'			)
		parser.add_argument('--metric_name',						default='acc',						type=a_,						help='Metric to use for head masking.'			)

		parser.add_argument(
		    '--max_seq_length',						default=128,						type=a_,						help=(
		        'The maximum total input sequence length after WordPiece tokenization. \n'
		        'Sequences longer than this will be truncated, sequences shorter padded.'
		    ),						)
		parser.add_argument('--batch_size',						default=1,						type=a_,						help='Batch size.'			)

		parser.add_argument('--seed',						type=a_,						default=42			)
		parser.add_argument('--local_rank',						type=a_,						default=-1,						help='local_rank for distributed training on gpus'			)
		parser.add_argument('--no_cuda',						action='store_true',						help='Whether not to use CUDA when available'			)
		parser.add_argument('--server_ip',						type=a_,						default='',						help='Can be used for distant debugging.'			)
		parser.add_argument('--server_port',						type=a_,						default='',						help='Can be used for distant debugging.'			)
		lowerCamelCase     :			Union[str, Any]		=    parser.parse_args()

		if args.server_ip and args.server_port:
				# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
				import ptvsd

				print('Waiting for debugger attach'			)
				ptvsd.enable_attach(address=(args.server_ip, args.server_port),						redirect_output=a_			)
				ptvsd.wait_for_attach()

		# Setup devices and distributed training
		if args.local_rank == -1 or args.no_cuda:
				lowerCamelCase     :			Dict		=    torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'			)
				lowerCamelCase     :			Optional[Any]		=    0 if args.no_cuda else torch.cuda.device_count()
		else:
				torch.cuda.set_device(args.local_rank			)
				lowerCamelCase     :			Optional[int]		=    torch.device('cuda',						args.local_rank			)
				lowerCamelCase     :			List[str]		=    1
				torch.distributed.init_process_group(backend='nccl'			)  # Initializes the distributed backend

		# Setup logging
		logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN			)
		logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device,						args.n_gpu,						bool(args.local_rank != -1			)			)			)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(args.local_rank			):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

		# Set seeds
		set_seed(args.seed			)

		# Prepare GLUE task
		lowerCamelCase     :			Union[str, Any]		=    args.task_name.lower()
		if args.task_name not in glue_processors:
				raise ValueError('Task not found: %s' % (args.task_name)			)
		lowerCamelCase     :			int		=    glue_processors[args.task_name]()
		lowerCamelCase     :			str		=    glue_output_modes[args.task_name]
		lowerCamelCase     :			int		=    processor.get_labels()
		lowerCamelCase     :			Any		=    len(a_			)

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		lowerCamelCase     :			int		=    AutoConfig.from_pretrained(
		    args.config_name if args.config_name else args.model_name_or_path,						num_labels=a_,						finetuning_task=args.task_name,						output_attentions=a_,						cache_dir=args.cache_dir,						)
		lowerCamelCase     :			Optional[int]		=    AutoTokenizer.from_pretrained(
		    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,						cache_dir=args.cache_dir,						)
		lowerCamelCase     :			Tuple		=    AutoModelForSequenceClassification.from_pretrained(
		    args.model_name_or_path,						from_tf=bool('.ckpt' in args.model_name_or_path			),						config=a_,						cache_dir=args.cache_dir,						)

		# Distributed and parallel training
		model.to(args.device			)
		if args.local_rank != -1:
				lowerCamelCase     :			Tuple		=    nn.parallel.DistributedDataParallel(
				    a_,						device_ids=[args.local_rank],						output_device=args.local_rank,						find_unused_parameters=a_			)
		elif args.n_gpu > 1:
				lowerCamelCase     :			Union[str, Any]		=    nn.DataParallel(a_			)

		# Print/save training arguments
		os.makedirs(args.output_dir,						exist_ok=a_			)
		torch.save(a_,						os.path.join(args.output_dir,						'run_args.bin'			)			)
		logger.info('Training/evaluation parameters %s',						a_			)

		# Prepare dataset for the GLUE task
		lowerCamelCase     :			Optional[int]		=    GlueDataset(a_,						tokenizer=a_,						mode='dev'			)
		if args.data_subset > 0:
				lowerCamelCase     :			Union[str, Any]		=    Subset(a_,						list(range(min(args.data_subset,						len(a_			)			)			)			)			)
		lowerCamelCase     :			Dict		=    SequentialSampler(a_			) if args.local_rank == -1 else DistributedSampler(a_			)
		lowerCamelCase     :			int		=    DataLoader(
		    a_,						sampler=a_,						batch_size=args.batch_size,						collate_fn=a_			)

		# Compute head entropy and importance score
		compute_heads_importance(a_,						a_,						a_			)

		# Try head masking (set heads to zero until the score goes under a threshole)
		# and head pruning (remove masked heads and see the effect on the network)
		if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
				lowerCamelCase     :			Union[str, Any]		=    mask_heads(a_,						a_,						a_			)
				prune_heads(a_,						a_,						a_,						a_			)


if __name__ == "__main__":
					main()








"""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        _lowercase   :




	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		, )		->							List[str]:
			lowerCamelCase     :			List[str]		=    parent
			lowerCamelCase     :			Optional[int]		=    batch_size
			lowerCamelCase     :			Union[str, Any]		=    image_size
			lowerCamelCase     :			str		=    patch_size
			lowerCamelCase     :			Optional[Any]		=    num_channels
			lowerCamelCase     :			Tuple		=    is_training
			lowerCamelCase     :			int		=    use_labels
			lowerCamelCase     :			Union[str, Any]		=    hidden_size
			lowerCamelCase     :			List[Any]		=    num_hidden_layers
			lowerCamelCase     :			List[str]		=    backbone_out_indices
			lowerCamelCase     :			str		=    num_attention_heads
			lowerCamelCase     :			str		=    intermediate_size
			lowerCamelCase     :			str		=    hidden_act
			lowerCamelCase     :			Union[str, Any]		=    hidden_dropout_prob
			lowerCamelCase     :			Tuple		=    attention_probs_dropout_prob
			lowerCamelCase     :			List[Any]		=    initializer_range
			lowerCamelCase     :			Any		=    num_labels
			lowerCamelCase     :			Optional[Any]		=    scope
			lowerCamelCase     :			List[str]		=    is_hybrid
			# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
			lowerCamelCase     :			Optional[int]		=    (image_size // patch_size) ** 2
			lowerCamelCase     :			Tuple		=    num_patches + 1




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Optional[Any]		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

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

			return config, pixel_values, labels




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			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_					)		->							Union[str, Any]:
			lowerCamelCase     :			Dict		=    DPTModel(config=UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			List[Any]		=    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_					)		->							List[Any]:
			lowerCamelCase     :			str		=    self.num_labels
			lowerCamelCase     :			Optional[int]		=    DPTForDepthEstimation(UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			str		=    model(UpperCAmelCase_					)
			self.parent.assertEqual(result.predicted_depth.shape		, (self.batch_size, self.image_size, self.image_size)					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Tuple:
			lowerCamelCase     :			Optional[Any]		=    self.num_labels
			lowerCamelCase     :			str		=    DPTForSemanticSegmentation(UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			List[Any]		=    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					)		->							Dict:
			lowerCamelCase     :			int		=    self.prepare_config_and_inputs()
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			List[str]		=    config_and_inputs
			lowerCamelCase     :			int		=    {'pixel_values': pixel_values}
			return config, inputs_dict






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

	lowercase_        =	False
	lowercase_        =	False
	lowercase_        =	False




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Optional[Any]		=    DPTModelTester(self					)
			lowerCamelCase     :			Optional[int]		=    ConfigTester(self		, config_class=UpperCAmelCase_		, has_text_modality=UpperCAmelCase_		, hidden_size=37					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			self.config_tester.run_common_tests()




	@unittest.skip(reason='DPT does not use inputs_embeds'					)
	def 			_UpperCamelCase    ( self					)		->							int:
			pass




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase	,    lowerCamelCase     :			Optional[Any]		=    self.model_tester.prepare_config_and_inputs_for_common()

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




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase	,    lowerCamelCase     :			str		=    self.model_tester.prepare_config_and_inputs_for_common()

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

					lowerCamelCase     :			Any		=    ['pixel_values']
					self.assertListEqual(arg_names[:1]		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			str		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Tuple		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_depth_estimation(*UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			int		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase_					)




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

					lowerCamelCase	,    lowerCamelCase     :			str		=    self.model_tester.prepare_config_and_inputs_for_common()
					lowerCamelCase     :			Optional[int]		=    True

					if model_class in get_values(UpperCAmelCase_					):
							continue

					lowerCamelCase     :			Tuple		=    model_class(UpperCAmelCase_					)
					model.to(UpperCAmelCase_					)
					model.train()
					lowerCamelCase     :			Tuple		=    self._prepare_for_class(UpperCAmelCase_		, UpperCAmelCase_		, return_labels=UpperCAmelCase_					)
					lowerCamelCase     :			int		=    model(**UpperCAmelCase_					).loss
					loss.backward()




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

					lowerCamelCase	,    lowerCamelCase     :			List[Any]		=    self.model_tester.prepare_config_and_inputs_for_common()
					lowerCamelCase     :			List[str]		=    False
					lowerCamelCase     :			int		=    True

					if model_class in get_values(UpperCAmelCase_					) or not model_class.supports_gradient_checkpointing:
							continue
					lowerCamelCase     :			Optional[int]		=    model_class(UpperCAmelCase_					)
					model.to(UpperCAmelCase_					)
					model.gradient_checkpointing_enable()
					model.train()
					lowerCamelCase     :			Optional[Any]		=    self._prepare_for_class(UpperCAmelCase_		, UpperCAmelCase_		, return_labels=UpperCAmelCase_					)
					lowerCamelCase     :			str		=    model(**UpperCAmelCase_					).loss
					loss.backward()




	def 			_UpperCamelCase    ( self					)		->							Dict:
			lowerCamelCase	,    lowerCamelCase     :			Optional[Any]		=    self.model_tester.prepare_config_and_inputs_for_common()

			lowerCamelCase     :			Union[str, Any]		=    _config_zero_init(UpperCAmelCase_					)
			for model_class in self.all_model_classes:
					lowerCamelCase     :			List[str]		=    model_class(config=UpperCAmelCase_					)
					# Skip the check for the backbone
					lowerCamelCase     :			Optional[int]		=    []
					for name, module in model.named_modules():
							if module.__class__.__name__ == "DPTViTHybridEmbeddings":
									lowerCamelCase     :			Optional[int]		=    [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					)		->							Dict:
			pass




	@slow
	def 			_UpperCamelCase    ( self					)		->							str:
			for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase     :			List[str]		=    DPTModel.from_pretrained(UpperCAmelCase_					)
					self.assertIsNotNone(UpperCAmelCase_					)


def 		UpperCAmelCase					(		):





		'''simple docstring'''


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






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




	def 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			int		=    DPTImageProcessor.from_pretrained('Intel/dpt-large'					)
			lowerCamelCase     :			List[str]		=    DPTForDepthEstimation.from_pretrained('Intel/dpt-large'					).to(UpperCAmelCase_					)

			lowerCamelCase     :			str		=    prepare_img()
			lowerCamelCase     :			Tuple		=    image_processor(images=UpperCAmelCase_		, return_tensors='pt'					).to(UpperCAmelCase_					)

			# forward pass
			with torch.no_grad():
					lowerCamelCase     :			Dict		=    model(**UpperCAmelCase_					)
					lowerCamelCase     :			str		=    outputs.predicted_depth

			# verify the predicted depth
			lowerCamelCase     :			List[str]		=    torch.Size((1, 384, 384)					)
			self.assertEqual(predicted_depth.shape		, UpperCAmelCase_					)

			lowerCamelCase     :			Dict		=    torch.tensor(
			    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]					).to(UpperCAmelCase_					)

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




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			int		=    DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'					)
			lowerCamelCase     :			Optional[Any]		=    DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'					).to(UpperCAmelCase_					)

			lowerCamelCase     :			List[Any]		=    prepare_img()
			lowerCamelCase     :			Tuple		=    image_processor(images=UpperCAmelCase_		, return_tensors='pt'					).to(UpperCAmelCase_					)

			# forward pass
			with torch.no_grad():
					lowerCamelCase     :			List[str]		=    model(**UpperCAmelCase_					)

			# verify the logits
			lowerCamelCase     :			int		=    torch.Size((1, 150, 480, 480)					)
			self.assertEqual(outputs.logits.shape		, UpperCAmelCase_					)

			lowerCamelCase     :			Tuple		=    torch.tensor(
			    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]					).to(UpperCAmelCase_					)

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




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			Dict		=    DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'					)
			lowerCamelCase     :			str		=    DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'					).to(UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    prepare_img()
			lowerCamelCase     :			Any		=    image_processor(images=UpperCAmelCase_		, return_tensors='pt'					).to(UpperCAmelCase_					)

			# forward pass
			with torch.no_grad():
					lowerCamelCase     :			Dict		=    model(**UpperCAmelCase_					)

			lowerCamelCase     :			int		=    outputs.logits.detach().cpu()

			lowerCamelCase     :			int		=    image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_		, target_sizes=[(500, 300)]					)
			lowerCamelCase     :			Optional[Any]		=    torch.Size((500, 300)					)
			self.assertEqual(segmentation[0].shape		, UpperCAmelCase_					)

			lowerCamelCase     :			List[Any]		=    image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    torch.Size((480, 480)					)
			self.assertEqual(segmentation[0].shape		, UpperCAmelCase_					)






