






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, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


SCREAMING_SNAKE_CASE__      :			str							=		logging.getLogger(__name__)

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if os.path.exists(snake_case ):
								if os.path.exists(os.path.join(snake_case,  '''config.json''' ) ) and os.path.isfile(
								    os.path.join(snake_case,  '''config.json''' ) ):
												os.remove(os.path.join(snake_case,  '''config.json''' ) )
								if os.path.exists(os.path.join(snake_case,  '''pytorch_model.bin''' ) ) and os.path.isfile(
								    os.path.join(snake_case,  '''pytorch_model.bin''' ) ):
												os.remove(os.path.join(snake_case,  '''pytorch_model.bin''' ) )
				else:
								os.makedirs(snake_case )
				model.save_pretrained(snake_case )

def      __lowercase					( snake_case,  snake_case=False ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      2
				if unlogit:
								__magic_name__		:Tuple        =      torch.pow(snake_case,  snake_case )
				__magic_name__		:List[Any]        =      p * torch.log(snake_case )
				__magic_name__		:Any        =      0
				return -plogp.sum(dim=-1 )

def      __lowercase					( snake_case ):







				"""simple docstring"""






				logger.info('''lv, h >\t''' + '''\t'''.join(f'''{x + 1}''' for x in range(len(snake_case ) ) ) )
				for row in range(len(snake_case ) ):
								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      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case=True,  snake_case=True,  snake_case=None,  snake_case=False ):







				"""simple docstring"""






				__magic_name__     ,				__magic_name__		:Optional[int]        =      model.config.num_hidden_layers, model.config.num_attention_heads
				__magic_name__		:List[Any]        =      torch.zeros(snake_case,  snake_case ).to(args.device )
				__magic_name__		:Union[str, Any]        =      torch.zeros(snake_case,  snake_case ).to(args.device )

				if head_mask is None:
								__magic_name__		:List[Any]        =      torch.ones(snake_case,  snake_case ).to(args.device )

				head_mask.requires_grad_(requires_grad=snake_case )
				# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
				if actually_pruned:
								__magic_name__		:int        =      None

				__magic_name__		:str        =      0.0
				__magic_name__		:List[str]        =      0.0
				for step, inputs in enumerate(tqdm(snake_case,  desc='''Iteration''',  disable=args.local_rank not in [-1, 0] ) ):
								__magic_name__		:str        =      tuple(t.to(args.device ) for t in inputs )
								((__magic_name__)     ,				)		:List[str]        =      inputs

								# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
								__magic_name__		:str        =      model(snake_case,  labels=snake_case,  head_mask=snake_case )
								#  (loss), lm_logits, presents, (all hidden_states), (attentions)
								__magic_name__     ,				__magic_name__     ,				__magic_name__		:List[Any]        =      (
								    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
								total_loss += loss.detach().cpu().numpy()
								if compute_entropy:
												for layer, attn in enumerate(snake_case ):
																__magic_name__		:Any        =      entropy(attn.detach(),  snake_case )
																attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()

								if compute_importance:
												head_importance += head_mask.grad.abs().detach()
								tot_tokens += torch.ones_like(snake_case ).float().detach().sum().data

				# Normalize
				attn_entropy /= tot_tokens
				head_importance /= tot_tokens
				# Layerwise importance normalization
				if not args.dont_normalize_importance_by_layer:
								__magic_name__		:List[str]        =      2
								__magic_name__		:Optional[Any]        =      torch.pow(torch.pow(snake_case,  snake_case ).sum(-1 ),  1 / exponent )
								head_importance /= norm_by_layer.unsqueeze(-1 ) + 1E-2_0

				if not args.dont_normalize_global_importance:
								__magic_name__		:Tuple        =      (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

				# Print matrices
				if compute_entropy:
								logger.info('''Attention entropies''' )
								print_ad_tensor(snake_case )
				if compute_importance:
								logger.info('''Head importance scores''' )
								print_ad_tensor(snake_case )
				logger.info('''Head ranked by importance scores''' )
				__magic_name__		:int        =      torch.zeros(head_importance.numel(),  dtype=torch.long,  device=args.device )
				__magic_name__		:Dict        =      torch.arange(
				    head_importance.numel(),  device=args.device )
				__magic_name__		:int        =      head_ranks.view_as(snake_case )
				print_ad_tensor(snake_case )
				return attn_entropy, head_importance, total_loss

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__     ,				__magic_name__     ,				__magic_name__		:Optional[Any]        =      compute_heads_importance(snake_case,  snake_case,  snake_case,  compute_entropy=snake_case )
				__magic_name__		:int        =      1 / loss  # instead of downsteam score use the LM loss
				logger.info('''Pruning: original score: %f, threshold: %f''',  snake_case,  original_score * args.masking_threshold )

				__magic_name__		:int        =      torch.ones_like(snake_case )
				__magic_name__		:Optional[Any]        =      max(1,  int(new_head_mask.numel() * args.masking_amount ) )

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

								if len(snake_case ) <= num_to_mask:
												print('''BREAK BY num_to_mask''' )
												break

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

								# Compute metric and head importance again
								__magic_name__     ,				__magic_name__     ,				__magic_name__		:Tuple        =      compute_heads_importance(
								    snake_case,  snake_case,  snake_case,  compute_entropy=snake_case,  head_mask=snake_case )
								__magic_name__		:Optional[int]        =      1 / loss
								logger.info(
								    '''Masking: current score: %f, remaining heads %d (%.1f percents)''',  snake_case,  new_head_mask.sum(),  new_head_mask.sum() / new_head_mask.numel() * 1_0_0,  )

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

				return head_mask

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      datetime.now()
				__magic_name__     ,				__magic_name__     ,				__magic_name__		:Tuple        =      compute_heads_importance(
				    snake_case,  snake_case,  snake_case,  compute_entropy=snake_case,  compute_importance=snake_case,  head_mask=snake_case )
				__magic_name__		:int        =      1 / loss
				__magic_name__		:str        =      datetime.now() - before_time

				__magic_name__		:str        =      sum(p.numel() for p in model.parameters() )
				__magic_name__		:Dict        =      {
				    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(snake_case ) )
				}

				for k, v in heads_to_prune.items():
								if isinstance(snake_case,  snake_case ):
												__magic_name__		:Union[str, Any]        =      [
												    v,
												]

				assert sum(len(snake_case ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
				model.prune_heads(snake_case )
				__magic_name__		:str        =      sum(p.numel() for p in model.parameters() )

				__magic_name__		:List[Any]        =      datetime.now()
				__magic_name__     ,				__magic_name__     ,				__magic_name__		:Dict        =      compute_heads_importance(
				    snake_case,  snake_case,  snake_case,  compute_entropy=snake_case,  compute_importance=snake_case,  head_mask=snake_case,  actually_pruned=snake_case,  )

				__magic_name__		:int        =      1 / loss
				__magic_name__		:Any        =      datetime.now() - before_time

				logger.info(
				    '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''',  snake_case,  snake_case,  pruned_num_params / original_num_params * 1_0_0,  )
				logger.info('''Pruning: score with masking: %f score with pruning: %f''',  snake_case,  snake_case )
				logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''',  original_time / new_time * 1_0_0 )
				save_model(snake_case,  args.output_dir )

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Optional[Any]        =      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--data_dir''',  default=snake_case,  type=snake_case,  required=snake_case,  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=snake_case,  type=snake_case,  required=snake_case,  help='''Path to pretrained model or model identifier from huggingface.co/models''',  )
				parser.add_argument(
				    '''--output_dir''',  default=snake_case,  type=snake_case,  required=snake_case,  help='''The output directory where the model predictions and checkpoints will be written.''',  )

				# Other parameters
				parser.add_argument(
				    '''--config_name''',  default='''''',  type=snake_case,  help='''Pretrained config name or path if not the same as model_name_or_path''',  )
				parser.add_argument(
				    '''--tokenizer_name''',  default='''''',  type=snake_case,  help='''Pretrained tokenizer name or path if not the same as model_name_or_path''',  )
				parser.add_argument(
				    '''--cache_dir''',  default=snake_case,  type=snake_case,  help='''Where do you want to store the pre-trained models downloaded from s3''',  )
				parser.add_argument(
				    '''--data_subset''',  type=snake_case,  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=snake_case,  help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''',  )
				parser.add_argument(
				    '''--masking_amount''',  default=0.1,  type=snake_case,  help='''Amount to heads to masking at each masking step.''' )
				parser.add_argument('''--metric_name''',  default='''acc''',  type=snake_case,  help='''Metric to use for head masking.''' )

				parser.add_argument(
				    '''--max_seq_length''',  default=1_2_8,  type=snake_case,  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=snake_case,  help='''Batch size.''' )

				parser.add_argument('''--seed''',  type=snake_case,  default=4_2 )
				parser.add_argument('''--local_rank''',  type=snake_case,  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=snake_case,  default='''''',  help='''Can be used for distant debugging.''' )
				parser.add_argument('''--server_port''',  type=snake_case,  default='''''',  help='''Can be used for distant debugging.''' )
				__magic_name__		: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=snake_case )
								ptvsd.wait_for_attach()

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

				__magic_name__		:Tuple        =      GPTaLMHeadModel.from_pretrained(args.model_name_or_path )

				# Distributed and parallel training
				model.to(args.device )
				if args.local_rank != -1:
								__magic_name__		:int        =      nn.parallel.DistributedDataParallel(
								    snake_case,  device_ids=[args.local_rank],  output_device=args.local_rank,  find_unused_parameters=snake_case )
				elif args.n_gpu > 1:
								__magic_name__		:int        =      nn.DataParallel(snake_case )

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

				# Prepare dataset
				__magic_name__		:int        =      np.concatenate(
				    [
				        np.loadtxt(args.data_dir,  dtype=np.intaa ),
				    ] )
				__magic_name__		:Optional[int]        =      (torch.from_numpy(snake_case ),)
				__magic_name__		:str        =      TensorDataset(*snake_case )
				__magic_name__		:Any        =      RandomSampler(snake_case )
				__magic_name__		:List[str]        =      DataLoader(snake_case,  sampler=snake_case,  batch_size=args.batch_size )

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

				# 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:
								__magic_name__		:Optional[Any]        =      mask_heads(snake_case,  snake_case,  snake_case )
								prune_heads(snake_case,  snake_case,  snake_case,  snake_case )


if __name__ == "__main__":
					main()














import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__      :			List[str]							=		logging.get_logger(__name__)

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[Any]        =      DPTConfig()

				if "large" in checkpoint_url:
								__magic_name__		:str        =      1_0_2_4
								__magic_name__		:Tuple        =      4_0_9_6
								__magic_name__		:Union[str, Any]        =      2_4
								__magic_name__		:int        =      1_6
								__magic_name__		:Dict        =      [5, 1_1, 1_7, 2_3]
								__magic_name__		:Optional[int]        =      [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
								__magic_name__		:Union[str, Any]        =      (1, 3_8_4, 3_8_4)

				if "ade" in checkpoint_url:
								__magic_name__		:Tuple        =      True

								__magic_name__		:int        =      1_5_0
								__magic_name__		:List[Any]        =      '''huggingface/label-files'''
								__magic_name__		:Dict        =      '''ade20k-id2label.json'''
								__magic_name__		:str        =      json.load(open(cached_download(hf_hub_url(snake_case,  snake_case,  repo_type='''dataset''' ) ),  '''r''' ) )
								__magic_name__		:Optional[int]        =      {int(snake_case ): v for k, v in idalabel.items()}
								__magic_name__		:Any        =      idalabel
								__magic_name__		:List[str]        =      {v: k for k, v in idalabel.items()}
								__magic_name__		:Optional[Any]        =      [1, 1_5_0, 4_8_0, 4_8_0]

				return config, expected_shape

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[Any]        =      ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
				for k in ignore_keys:
								state_dict.pop(snake_case,  snake_case )

def      __lowercase					( snake_case ):







				"""simple docstring"""






				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								__magic_name__		:int        =      name.replace('''pretrained.model''',  '''dpt.encoder''' )
				if "pretrained.model" in name:
								__magic_name__		:str        =      name.replace('''pretrained.model''',  '''dpt.embeddings''' )
				if "patch_embed" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''patch_embed''',  '''patch_embeddings''' )
				if "pos_embed" in name:
								__magic_name__		:List[Any]        =      name.replace('''pos_embed''',  '''position_embeddings''' )
				if "attn.proj" in name:
								__magic_name__		:List[Any]        =      name.replace('''attn.proj''',  '''attention.output.dense''' )
				if "proj" in name and "project" not in name:
								__magic_name__		:Dict        =      name.replace('''proj''',  '''projection''' )
				if "blocks" in name:
								__magic_name__		:str        =      name.replace('''blocks''',  '''layer''' )
				if "mlp.fc1" in name:
								__magic_name__		:Dict        =      name.replace('''mlp.fc1''',  '''intermediate.dense''' )
				if "mlp.fc2" in name:
								__magic_name__		:Tuple        =      name.replace('''mlp.fc2''',  '''output.dense''' )
				if "norm1" in name:
								__magic_name__		:List[Any]        =      name.replace('''norm1''',  '''layernorm_before''' )
				if "norm2" in name:
								__magic_name__		:Union[str, Any]        =      name.replace('''norm2''',  '''layernorm_after''' )
				if "scratch.output_conv" in name:
								__magic_name__		:Any        =      name.replace('''scratch.output_conv''',  '''head''' )
				if "scratch" in name:
								__magic_name__		:str        =      name.replace('''scratch''',  '''neck''' )
				if "layer1_rn" in name:
								__magic_name__		:Union[str, Any]        =      name.replace('''layer1_rn''',  '''convs.0''' )
				if "layer2_rn" in name:
								__magic_name__		:int        =      name.replace('''layer2_rn''',  '''convs.1''' )
				if "layer3_rn" in name:
								__magic_name__		:str        =      name.replace('''layer3_rn''',  '''convs.2''' )
				if "layer4_rn" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''layer4_rn''',  '''convs.3''' )
				if "refinenet" in name:
								__magic_name__		:int        =      int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
								# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
								__magic_name__		:List[Any]        =      name.replace(f'''refinenet{layer_idx}''',  f'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
				if "out_conv" in name:
								__magic_name__		:str        =      name.replace('''out_conv''',  '''projection''' )
				if "resConfUnit1" in name:
								__magic_name__		:Dict        =      name.replace('''resConfUnit1''',  '''residual_layer1''' )
				if "resConfUnit2" in name:
								__magic_name__		:int        =      name.replace('''resConfUnit2''',  '''residual_layer2''' )
				if "conv1" in name:
								__magic_name__		:str        =      name.replace('''conv1''',  '''convolution1''' )
				if "conv2" in name:
								__magic_name__		:Union[str, Any]        =      name.replace('''conv2''',  '''convolution2''' )
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								__magic_name__		:Optional[int]        =      name.replace('''pretrained.act_postprocess1.0.project.0''',  '''neck.reassemble_stage.readout_projects.0.0''' )
				if "pretrained.act_postprocess2.0.project.0" in name:
								__magic_name__		:int        =      name.replace('''pretrained.act_postprocess2.0.project.0''',  '''neck.reassemble_stage.readout_projects.1.0''' )
				if "pretrained.act_postprocess3.0.project.0" in name:
								__magic_name__		:Optional[int]        =      name.replace('''pretrained.act_postprocess3.0.project.0''',  '''neck.reassemble_stage.readout_projects.2.0''' )
				if "pretrained.act_postprocess4.0.project.0" in name:
								__magic_name__		:str        =      name.replace('''pretrained.act_postprocess4.0.project.0''',  '''neck.reassemble_stage.readout_projects.3.0''' )
				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								__magic_name__		:Any        =      name.replace('''pretrained.act_postprocess1.3''',  '''neck.reassemble_stage.layers.0.projection''' )
				if "pretrained.act_postprocess1.4" in name:
								__magic_name__		:Any        =      name.replace('''pretrained.act_postprocess1.4''',  '''neck.reassemble_stage.layers.0.resize''' )
				if "pretrained.act_postprocess2.3" in name:
								__magic_name__		:Dict        =      name.replace('''pretrained.act_postprocess2.3''',  '''neck.reassemble_stage.layers.1.projection''' )
				if "pretrained.act_postprocess2.4" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''pretrained.act_postprocess2.4''',  '''neck.reassemble_stage.layers.1.resize''' )
				if "pretrained.act_postprocess3.3" in name:
								__magic_name__		:List[str]        =      name.replace('''pretrained.act_postprocess3.3''',  '''neck.reassemble_stage.layers.2.projection''' )
				if "pretrained.act_postprocess4.3" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''pretrained.act_postprocess4.3''',  '''neck.reassemble_stage.layers.3.projection''' )
				if "pretrained.act_postprocess4.4" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''pretrained.act_postprocess4.4''',  '''neck.reassemble_stage.layers.3.resize''' )
				if "pretrained" in name:
								__magic_name__		:Any        =      name.replace('''pretrained''',  '''dpt''' )
				if "bn" in name:
								__magic_name__		:int        =      name.replace('''bn''',  '''batch_norm''' )
				if "head" in name:
								__magic_name__		:str        =      name.replace('''head''',  '''head.head''' )
				if "encoder.norm" in name:
								__magic_name__		:Union[str, Any]        =      name.replace('''encoder.norm''',  '''layernorm''' )
				if "auxlayer" in name:
								__magic_name__		:Dict        =      name.replace('''auxlayer''',  '''auxiliary_head.head''' )

				return name

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				for i in range(config.num_hidden_layers ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								__magic_name__		:int        =      state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
								__magic_name__		:Union[str, Any]        =      state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
								# next, add query, keys and values (in that order) to the state dict
								__magic_name__		:Optional[Any]        =      in_proj_weight[: config.hidden_size, :]
								__magic_name__		:str        =      in_proj_bias[: config.hidden_size]
								__magic_name__		:List[str]        =      in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__magic_name__		:Optional[Any]        =      in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								__magic_name__		:int        =      in_proj_weight[
								    -config.hidden_size :, :
								]
								__magic_name__		:Union[str, Any]        =      in_proj_bias[-config.hidden_size :]

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__magic_name__		:Union[str, Any]        =      Image.open(requests.get(snake_case,  stream=snake_case ).raw )
				return im

@torch.no_grad()
def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__     ,				__magic_name__		:Any        =      get_dpt_config(snake_case )
				# load original state_dict from URL
				__magic_name__		:List[str]        =      torch.hub.load_state_dict_from_url(snake_case,  map_location='''cpu''' )
				# remove certain keys
				remove_ignore_keys_(snake_case )
				# rename keys
				for key in state_dict.copy().keys():
								__magic_name__		:int        =      state_dict.pop(snake_case )
								__magic_name__		:Tuple        =      val
				# read in qkv matrices
				read_in_q_k_v(snake_case,  snake_case )

				# load HuggingFace model
				__magic_name__		:int        =      DPTForSemanticSegmentation(snake_case ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(snake_case )
				model.load_state_dict(snake_case )
				model.eval()

				# Check outputs on an image
				__magic_name__		:Union[str, Any]        =      4_8_0 if '''ade''' in checkpoint_url else 3_8_4
				__magic_name__		:int        =      DPTImageProcessor(size=snake_case )

				__magic_name__		:Dict        =      prepare_img()
				__magic_name__		:List[str]        =      image_processor(snake_case,  return_tensors='''pt''' )

				# forward pass
				__magic_name__		:Dict        =      model(**snake_case ).logits if '''ade''' in checkpoint_url else model(**snake_case ).predicted_depth

				# Assert logits
				__magic_name__		:int        =      torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
				if "ade" in checkpoint_url:
								__magic_name__		:Any        =      torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
				assert outputs.shape == torch.Size(snake_case )
				assert (
				    torch.allclose(outputs[0, 0, :3, :3],  snake_case,  atol=1E-4 )
				    if "ade" in checkpoint_url
				    else torch.allclose(outputs[0, :3, :3],  snake_case )
				)

				Path(snake_case ).mkdir(exist_ok=snake_case )
				print(f'''Saving model to {pytorch_dump_folder_path}''' )
				model.save_pretrained(snake_case )
				print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
				image_processor.save_pretrained(snake_case )

				if push_to_hub:
								print('''Pushing model to hub...''' )
								model.push_to_hub(
								    repo_path_or_name=Path(snake_case,  snake_case ),  organization='''nielsr''',  commit_message='''Add model''',  use_temp_dir=snake_case,  )
								image_processor.push_to_hub(
								    repo_path_or_name=Path(snake_case,  snake_case ),  organization='''nielsr''',  commit_message='''Add image processor''',  use_temp_dir=snake_case,  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Tuple							=		argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
					    type=str,
					    help="""URL of the original DPT checkpoint you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    action="""store_true""",
					)
					parser.add_argument(
					    """--model_name""",
					    default="""dpt-large""",
					    type=str,
					    help="""Name of the model, in case you're pushing to the hub.""",
					)

					SCREAMING_SNAKE_CASE__      :			int							=		parser.parse_args()
					convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






