


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 	lowerCAmelCase__			(      a__:		Optional[int] ,  a__:		int ,  a__:		Tuple ,  a__:		List[str] ,  a__:		Dict=True ,  a__:		Optional[int]="pt"	)  ->		Any:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		List[Any] ,  a__:		Dict ,  a__:		Union[str, Any]=None ,  )  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=      input_ids.ne(a__	).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 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE="train"      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=""      ,   )			->	Dict:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      Path(_SCREAMING_SNAKE_CASE						).joinpath(type_path + '.source'						)
									_UpperCAmelCase					=      Path(_SCREAMING_SNAKE_CASE						).joinpath(type_path + '.target'						)
									_UpperCAmelCase					=      self.get_char_lens(self.src_file						)
									_UpperCAmelCase					=      max_source_length
									_UpperCAmelCase					=      max_target_length
									assert min(self.src_lens						) > 0, f'''found empty line in {self.src_file}'''
									_UpperCAmelCase					=      tokenizer
									_UpperCAmelCase					=      prefix
									if n_obs is not None:
																_UpperCAmelCase					=      self.src_lens[:n_obs]
									_UpperCAmelCase					=      src_lang
									_UpperCAmelCase					=      tgt_lang
		def __len__(      self						)			->	Union[str, Any]:




									"""simple docstring"""





									return len(self.src_lens						)
		def __getitem__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Dict[str, torch.Tensor]:




									"""simple docstring"""





									_UpperCAmelCase					=      index + 1  # linecache starts at 1
									_UpperCAmelCase					=      self.prefix + linecache.getline(str(self.src_file						)      ,   _SCREAMING_SNAKE_CASE						).rstrip('\n'						)
									_UpperCAmelCase					=      linecache.getline(str(self.tgt_file						)      ,   _SCREAMING_SNAKE_CASE						).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      ,   _SCREAMING_SNAKE_CASE						):
																source_line += self.tokenizer.eos_token
																tgt_line += self.tokenizer.eos_token

									# Pad source and target to the right
									_UpperCAmelCase					=      (
									    self.tokenizer.question_encoder if isinstance(self.tokenizer      ,   _SCREAMING_SNAKE_CASE						) else self.tokenizer
									)
									_UpperCAmelCase					=      self.tokenizer.generator if isinstance(self.tokenizer      ,   _SCREAMING_SNAKE_CASE						) else self.tokenizer

									_UpperCAmelCase					=      encode_line(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   self.max_source_length      ,   'right'						)
									_UpperCAmelCase					=      encode_line(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   self.max_target_length      ,   'right'						)

									_UpperCAmelCase					=      source_inputs['input_ids'].squeeze()
									_UpperCAmelCase					=      target_inputs['input_ids'].squeeze()
									_UpperCAmelCase					=      source_inputs['attention_mask'].squeeze()
									return {
									    "input_ids": source_ids,
									    "attention_mask": src_mask,
									    "decoder_input_ids": target_ids,
									}
		@staticmethod
		def      UpperCAmelCase__    (      _SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





									return [len(_SCREAMING_SNAKE_CASE						) for x in Path(_SCREAMING_SNAKE_CASE						).open().readlines()]






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Dict[str, torch.Tensor]:




									"""simple docstring"""





									_UpperCAmelCase					=      torch.stack([x['input_ids'] for x in batch]						)
									_UpperCAmelCase					=      torch.stack([x['attention_mask'] for x in batch]						)
									_UpperCAmelCase					=      torch.stack([x['decoder_input_ids'] for x in batch]						)
									_UpperCAmelCase					=      (
									    self.tokenizer.generator.pad_token_id
									    if isinstance(self.tokenizer      ,   _SCREAMING_SNAKE_CASE						)
									    else self.tokenizer.pad_token_id
									)
									_UpperCAmelCase					=      (
									    self.tokenizer.question_encoder.pad_token_id
									    if isinstance(self.tokenizer      ,   _SCREAMING_SNAKE_CASE						)
									    else self.tokenizer.pad_token_id
									)
									_UpperCAmelCase					=      trim_batch(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase		,	_UpperCAmelCase					=      trim_batch(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      {
									    'input_ids': source_ids,
									    'attention_mask': source_mask,
									    'decoder_input_ids': y,
									}
									return batch



lowerCAmelCase__  :Tuple             =							getLogger(__name__)

def 	lowerCAmelCase__			(      a__:		List[List]	)  ->		Any:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		str	)  ->		None:


							'''simple docstring'''





							_UpperCAmelCase					=      get_git_info()
							save_json(a__ ,  os.path.join(a__ ,  'git_log.json'	)	)

def 	lowerCAmelCase__			(      a__:		Any ,  a__:		List[str] ,  a__:		Union[str, Any]=4 ,  **a__:		int	)  ->		Optional[int]:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		List[Any]	)  ->		str:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      )  ->		Dict:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		Callable ,  a__:		Iterable	)  ->		List:


							'''simple docstring'''





							return list(map(a__ ,  a__	)	)

def 	lowerCAmelCase__			(      a__:		Any ,  a__:		Dict	)  ->		Optional[int]:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		List[str]	)  ->		Dict:


							'''simple docstring'''





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

							def white_space_fix(a__:		Any	):
														return " ".join(text.split()	)

							def remove_punc(a__:		Any	):
														_UpperCAmelCase					=      set(string.punctuation	)
														return "".join(ch for ch in text if ch not in exclude	)

							def lower(a__:		Optional[Any]	):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(a__	)	)	)	)

def 	lowerCAmelCase__			(      a__:		Optional[Any] ,  a__:		List[str]	)  ->		Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=      normalize_answer(a__	).split()
							_UpperCAmelCase					=      normalize_answer(a__	).split()
							_UpperCAmelCase					=      Counter(a__	) & Counter(a__	)
							_UpperCAmelCase					=      sum(common.values()	)
							if num_same == 0:
														return 0
							_UpperCAmelCase					=      1.0 * num_same / len(a__	)
							_UpperCAmelCase					=      1.0 * num_same / len(a__	)
							_UpperCAmelCase					=      (2 * precision * recall) / (precision + recall)
							return fa

def 	lowerCAmelCase__			(      a__:		Optional[Any] ,  a__:		Union[str, Any]	)  ->		List[str]:


							'''simple docstring'''





							return normalize_answer(a__	) == normalize_answer(a__	)

def 	lowerCAmelCase__			(      a__:		List[str] ,  a__:		List[str]	)  ->		Dict:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		Union[str, Any]	)  ->		Optional[Any]:


							'''simple docstring'''





							return model_prefix.startswith('rag'	)







def 	lowerCAmelCase__			(      a__:		int ,  a__:		List[Any] ,  a__:		Union[str, Any]	)  ->		Union[str, Any]:


							'''simple docstring'''





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




import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)

def 	lowerCAmelCase__			(      a__:		str      ,  a__:		List[Any]	)  ->		Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=      'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
							_UpperCAmelCase					=      Image.open(requests.get(a__      ,  stream=a__	).raw	).convert('RGB'	)

							_UpperCAmelCase					=      transforms.Compose(
							    [
							        transforms.Resize((image_size, image_size)      ,  interpolation=InterpolationMode.BICUBIC	),
							        transforms.ToTensor(),
							        transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)      ,  (0.26_862_954, 0.26_130_258, 0.27_577_711)	),
							    ]	)
							_UpperCAmelCase					=      transform(a__	).unsqueeze(0	).to(a__	)
							return image

def 	lowerCAmelCase__			(      a__:		Optional[int]	)  ->		int:


							'''simple docstring'''





							if "visual_encoder" in key:
														_UpperCAmelCase					=      re.sub('visual_encoder*'      ,  'vision_model.encoder'      ,  a__	)
							if "blocks" in key:
														_UpperCAmelCase					=      re.sub(R'blocks'      ,  'layers'      ,  a__	)
							if "attn" in key:
														_UpperCAmelCase					=      re.sub(R'attn'      ,  'self_attn'      ,  a__	)
							if "norm1" in key:
														_UpperCAmelCase					=      re.sub(R'norm1'      ,  'layer_norm1'      ,  a__	)
							if "norm2" in key:
														_UpperCAmelCase					=      re.sub(R'norm2'      ,  'layer_norm2'      ,  a__	)
							if "encoder.norm" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.norm'      ,  'post_layernorm'      ,  a__	)
							if "encoder.patch_embed.proj" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.patch_embed.proj'      ,  'embeddings.patch_embedding'      ,  a__	)

							if "encoder.pos_embed" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.pos_embed'      ,  'embeddings.position_embedding'      ,  a__	)
							if "encoder.cls_token" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.cls_token'      ,  'embeddings.class_embedding'      ,  a__	)

							if "self_attn" in key:
														_UpperCAmelCase					=      re.sub(R'self_attn.proj'      ,  'self_attn.projection'      ,  a__	)

							return key

@torch.no_grad()
def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		List[str]=None	)  ->		Optional[Any]:


							'''simple docstring'''





							if config_path is not None:
														_UpperCAmelCase					=      BlipConfig.from_pretrained(a__	)
							else:
														_UpperCAmelCase					=      BlipConfig(projection_dim=5_1_2      ,  text_config={}      ,  vision_config={}	)

							_UpperCAmelCase					=      BlipForConditionalGeneration(a__	).eval()

							_UpperCAmelCase					=      'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

							_UpperCAmelCase					=      blip_decoder(pretrained=a__      ,  image_size=3_8_4      ,  vit='base'	)
							_UpperCAmelCase					=      pt_model.eval()

							_UpperCAmelCase					=      pt_model.state_dict()
							for key in modified_state_dict.copy():
														_UpperCAmelCase					=      modified_state_dict.pop(a__	)
														_UpperCAmelCase					=      rename_key(a__	)
														_UpperCAmelCase					=      value

							hf_model.load_state_dict(a__	)

							_UpperCAmelCase					=      3_8_4
							_UpperCAmelCase					=      load_demo_image(image_size=a__      ,  device='cpu'	)
							_UpperCAmelCase					=      BertTokenizer.from_pretrained('bert-base-uncased'	)
							_UpperCAmelCase					=      tokenizer(['a picture of']	).input_ids

							_UpperCAmelCase					=      hf_model.generate(a__      ,  a__	)

							assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

							_UpperCAmelCase					=      hf_model.generate(a__	)

							assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

							if pytorch_dump_folder_path is not None:
														hf_model.save_pretrained(a__	)

							# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
							_UpperCAmelCase					=      (
							    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
							)

							_UpperCAmelCase					=      blip_vqa(pretrained=a__      ,  image_size=a__      ,  vit='base'	)
							vqa_model.eval()

							_UpperCAmelCase					=      vqa_model.state_dict()
							for key in modified_state_dict.copy():
														_UpperCAmelCase					=      modified_state_dict.pop(a__	)
														_UpperCAmelCase					=      rename_key(a__	)
														_UpperCAmelCase					=      value

							_UpperCAmelCase					=      BlipForQuestionAnswering(a__	)

							hf_vqa_model.load_state_dict(a__	)

							_UpperCAmelCase					=      ['How many dogs are in this image?']
							_UpperCAmelCase					=      tokenizer(a__      ,  return_tensors='pt'	).input_ids

							_UpperCAmelCase					=      hf_vqa_model.generate(a__      ,  a__	)
							print(tokenizer.decode(answer[0]	)	)

							assert tokenizer.decode(answer[0]	) == "[UNK] 1 [SEP]"
							if pytorch_dump_folder_path is not None:
														hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa'	)

							_UpperCAmelCase					=      'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

							_UpperCAmelCase					=      blip_itm(pretrained=a__      ,  image_size=a__      ,  vit='base'	)
							itm_model.eval()

							_UpperCAmelCase					=      itm_model.state_dict()
							for key in modified_state_dict.copy():
														_UpperCAmelCase					=      modified_state_dict.pop(a__	)
														_UpperCAmelCase					=      rename_key(a__	)
														_UpperCAmelCase					=      value

							_UpperCAmelCase					=      BlipForImageTextRetrieval(a__	)

							_UpperCAmelCase					=      ['A picture of a woman with a dog sitting in a beach']
							_UpperCAmelCase					=      tokenizer(
							    a__      ,  return_tensors='pt'      ,  padding='max_length'      ,  truncation=a__      ,  max_length=3_5      ,  ).input_ids

							hf_itm_model.load_state_dict(a__	)
							hf_itm_model.eval()

							_UpperCAmelCase					=      hf_itm_model(a__      ,  a__      ,  use_itm_head=a__	)
							_UpperCAmelCase					=      hf_itm_model(a__      ,  a__      ,  use_itm_head=a__	)

							assert out[0].item() == 0.2_110_687_494_277_954
							assert torch.nn.functional.softmax(out_itm[0]      ,  dim=1	)[:, 1].item() == 0.45_698_845_386_505_127

							if pytorch_dump_folder_path is not None:
														hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'	)


if __name__ == "__main__":
		lowerCAmelCase__  :int             =							argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		lowerCAmelCase__  :List[Any]             =							parser.parse_args()

		convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
