






import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case=True,  snake_case="pt" ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      {'''add_prefix_space''': True} if isinstance(snake_case,  snake_case ) and not line.startswith(''' ''' ) else {}
				__magic_name__		:Optional[int]        =      padding_side
				return tokenizer(
				    [line],  max_length=snake_case,  padding='''max_length''' if pad_to_max_length else None,  truncation=snake_case,  return_tensors=snake_case,  add_special_tokens=snake_case,  **snake_case,  )

def      __lowercase					( snake_case,  snake_case,  snake_case=None,  ):







				"""simple docstring"""






				__magic_name__		:int        =      input_ids.ne(snake_case ).any(dim=0 )
				if attention_mask is None:
								return input_ids[:, keep_column_mask]
				else:
								return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class 						lowerCamelCase_				(      lowerCamelCase ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase="train"       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=""       ,					):
								"""simple docstring"""
								super().__init__()
								__magic_name__		:str        =      Path(__lowerCAmelCase     ).joinpath(type_path + '''.source'''     )
								__magic_name__		:Optional[Any]        =      Path(__lowerCAmelCase     ).joinpath(type_path + '''.target'''     )
								__magic_name__		:Optional[int]        =      self.get_char_lens(self.src_file     )
								__magic_name__		:int        =      max_source_length
								__magic_name__		:Optional[Any]        =      max_target_length
								assert min(self.src_lens     ) > 0, F'''found empty line in {self.src_file}'''
								__magic_name__		:str        =      tokenizer
								__magic_name__		:Optional[Any]        =      prefix
								if n_obs is not None:
												__magic_name__		:str        =      self.src_lens[:n_obs]
								__magic_name__		:str        =      src_lang
								__magic_name__		:Union[str, Any]        =      tgt_lang







				def __len__(						self     ):
								"""simple docstring"""
								return len(self.src_lens     )







				def __getitem__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:str        =      index + 1  # linecache starts at 1
								__magic_name__		:List[str]        =      self.prefix + linecache.getline(str(self.src_file     )       ,					__lowerCAmelCase     ).rstrip('''\n'''     )
								__magic_name__		:Any        =      linecache.getline(str(self.tgt_file     )       ,					__lowerCAmelCase     ).rstrip('''\n'''     )
								assert source_line, F'''empty source line for index {index}'''
								assert tgt_line, F'''empty tgt line for index {index}'''

								# Need to add eos token manually for T5
								if isinstance(self.tokenizer       ,					__lowerCAmelCase     ):
												source_line += self.tokenizer.eos_token
												tgt_line += self.tokenizer.eos_token

								# Pad source and target to the right
								__magic_name__		:str        =      (
								    self.tokenizer.question_encoder if isinstance(self.tokenizer       ,					__lowerCAmelCase     ) else self.tokenizer
								)
								__magic_name__		:Union[str, Any]        =      self.tokenizer.generator if isinstance(self.tokenizer       ,					__lowerCAmelCase     ) else self.tokenizer

								__magic_name__		:str        =      encode_line(__lowerCAmelCase       ,					__lowerCAmelCase       ,					self.max_source_length       ,					'''right'''     )
								__magic_name__		:Tuple        =      encode_line(__lowerCAmelCase       ,					__lowerCAmelCase       ,					self.max_target_length       ,					'''right'''     )

								__magic_name__		:List[Any]        =      source_inputs['''input_ids'''].squeeze()
								__magic_name__		:Dict        =      target_inputs['''input_ids'''].squeeze()
								__magic_name__		:Union[str, Any]        =      source_inputs['''attention_mask'''].squeeze()
								return {
								    "input_ids": source_ids,
								    "attention_mask": src_mask,
								    "decoder_input_ids": target_ids,
								}







				@staticmethod
				def 			A	(						__lowerCAmelCase     ):
								"""simple docstring"""
								return [len(__lowerCAmelCase     ) for x in Path(__lowerCAmelCase     ).open().readlines()]







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:str        =      torch.stack([x['''input_ids'''] for x in batch]     )
								__magic_name__		:Tuple        =      torch.stack([x['''attention_mask'''] for x in batch]     )
								__magic_name__		:str        =      torch.stack([x['''decoder_input_ids'''] for x in batch]     )
								__magic_name__		:str        =      (
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer       ,					__lowerCAmelCase     )
								    else self.tokenizer.pad_token_id
								)
								__magic_name__		:Optional[Any]        =      (
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer       ,					__lowerCAmelCase     )
								    else self.tokenizer.pad_token_id
								)
								__magic_name__		:List[Any]        =      trim_batch(__lowerCAmelCase       ,					__lowerCAmelCase     )
								__magic_name__     ,				__magic_name__		:List[str]        =      trim_batch(__lowerCAmelCase       ,					__lowerCAmelCase       ,					attention_mask=__lowerCAmelCase     )
								__magic_name__		:Any        =      {
								    '''input_ids''': source_ids,
								    '''attention_mask''': source_mask,
								    '''decoder_input_ids''': y,
								}
								return batch







SCREAMING_SNAKE_CASE__      :			List[Any]							=		getLogger(__name__)

def      __lowercase					( snake_case ):







				"""simple docstring"""






				return list(itertools.chain.from_iterable(snake_case ) )

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Dict        =      get_git_info()
				save_json(snake_case,  os.path.join(snake_case,  '''git_log.json''' ) )

def      __lowercase					( snake_case,  snake_case,  snake_case=4,  **snake_case ):







				"""simple docstring"""






				with open(snake_case,  '''w''' ) as f:
								json.dump(snake_case,  snake_case,  indent=snake_case,  **snake_case )

def      __lowercase					( snake_case ):







				"""simple docstring"""






				with open(snake_case ) as f:
								return json.load(snake_case )

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Dict        =      git.Repo(search_parent_directories=snake_case )
				__magic_name__		:int        =      {
				    '''repo_id''': str(snake_case ),
				    '''repo_sha''': str(repo.head.object.hexsha ),
				    '''repo_branch''': str(repo.active_branch ),
				    '''hostname''': str(socket.gethostname() ),
				}
				return repo_infos

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				return list(map(snake_case,  snake_case ) )

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				with open(snake_case,  '''wb''' ) as f:
								return pickle.dump(snake_case,  snake_case )

def      __lowercase					( snake_case ):







				"""simple docstring"""






				def remove_articles(snake_case ):
								return re.sub(R'''\b(a|an|the)\b''',  ''' ''',  snake_case )

				def white_space_fix(snake_case ):
								return " ".join(text.split() )

				def remove_punc(snake_case ):
								__magic_name__		:int        =      set(string.punctuation )
								return "".join(ch for ch in text if ch not in exclude )

				def lower(snake_case ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(snake_case ) ) ) )

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:str        =      normalize_answer(snake_case ).split()
				__magic_name__		:int        =      normalize_answer(snake_case ).split()
				__magic_name__		:List[Any]        =      Counter(snake_case ) & Counter(snake_case )
				__magic_name__		:Any        =      sum(common.values() )
				if num_same == 0:
								return 0
				__magic_name__		:str        =      1.0 * num_same / len(snake_case )
				__magic_name__		:Optional[int]        =      1.0 * num_same / len(snake_case )
				__magic_name__		:int        =      (2 * precision * recall) / (precision + recall)
				return fa

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				return normalize_answer(snake_case ) == normalize_answer(snake_case )

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				assert len(snake_case ) == len(snake_case )
				__magic_name__		:str        =      0
				for hypo, pred in zip(snake_case,  snake_case ):
								em += exact_match_score(snake_case,  snake_case )
				if len(snake_case ) > 0:
								em /= len(snake_case )
				return {"em": em}

def      __lowercase					( snake_case ):







				"""simple docstring"""






				return model_prefix.startswith('''rag''' )

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      {p: p for p in extra_params}
				# T5 models don't have `dropout` param, they have `dropout_rate` instead
				__magic_name__		:Union[str, Any]        =      '''dropout_rate'''
				for p in extra_params:
								if getattr(snake_case,  snake_case,  snake_case ):
												if not hasattr(snake_case,  snake_case ) and not hasattr(snake_case,  equivalent_param[p] ):
																logger.info('''config doesn\'t have a `{}` attribute'''.format(snake_case ) )
																delattr(snake_case,  snake_case )
																continue
												__magic_name__		:List[Any]        =      p if hasattr(snake_case,  snake_case ) else equivalent_param[p]
												setattr(snake_case,  snake_case,  getattr(snake_case,  snake_case ) )
												delattr(snake_case,  snake_case )
				return hparams, config














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)






