"""simple docstring"""






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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=True       ,    __UpperCAmelCase="pt" )  ->  int:
   lowercase__:   List[Any]					=      {'''add_prefix_space''': True} if isinstance(lowerCAmelCase__       ,    lowerCAmelCase__ ) and not line.startswith(''' ''' ) else {}
   lowercase__:   Dict					=      padding_side
   return tokenizer(
       [line]       ,    max_length=lowerCAmelCase__       ,    padding='''max_length''' if pad_to_max_length else None       ,    truncation=lowerCAmelCase__       ,    return_tensors=lowerCAmelCase__       ,    add_special_tokens=lowerCAmelCase__       ,    **lowerCAmelCase__       ,    )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=None       ,    )  ->  Optional[Any]:
   lowercase__:   Tuple					=      input_ids.ne(lowerCAmelCase__ ).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     UpperCAmelCase						(__lowercase  ):



    """simple docstring"""

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






    def __len__(   self							):
       return len(self.src_lens							)






    def __getitem__(   self    ,			_UpperCAmelCase							):
       lowercase__:   Tuple					=      index + 1  # linecache starts at 1
       lowercase__:   List[Any]					=      self.prefix + linecache.getline(str(self.src_file							)    ,			snake_case_							).rstrip('''\n'''							)
       lowercase__:   int					=      linecache.getline(str(self.tgt_file							)    ,			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    ,			snake_case_							):
          source_line += self.tokenizer.eos_token
          tgt_line += self.tokenizer.eos_token

       # Pad source and target to the right
       lowercase__:   int					=      (
           self.tokenizer.question_encoder if isinstance(self.tokenizer    ,			snake_case_							) else self.tokenizer
       )
       lowercase__:   Any					=      self.tokenizer.generator if isinstance(self.tokenizer    ,			snake_case_							) else self.tokenizer

       lowercase__:   List[str]					=      encode_line(snake_case_    ,			snake_case_    ,			self.max_source_length    ,			'''right'''							)
       lowercase__:   Tuple					=      encode_line(snake_case_    ,			snake_case_    ,			self.max_target_length    ,			'''right'''							)

       lowercase__:   Tuple					=      source_inputs['''input_ids'''].squeeze()
       lowercase__:   Any					=      target_inputs['''input_ids'''].squeeze()
       lowercase__:   Optional[Any]					=      source_inputs['''attention_mask'''].squeeze()
       return {
           "input_ids": source_ids,
           "attention_mask": src_mask,
           "decoder_input_ids": target_ids,
       }






    @staticmethod
    def _snake_case			(   _UpperCAmelCase							):
       return [len(snake_case_							) for x in Path(snake_case_							).open().readlines()]






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Optional[int]					=      torch.stack([x['''input_ids'''] for x in batch]							)
       lowercase__:   Optional[Any]					=      torch.stack([x['''attention_mask'''] for x in batch]							)
       lowercase__:   Optional[int]					=      torch.stack([x['''decoder_input_ids'''] for x in batch]							)
       lowercase__:   Optional[Any]					=      (
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer    ,			snake_case_							)
           else self.tokenizer.pad_token_id
       )
       lowercase__:   List[str]					=      (
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer    ,			snake_case_							)
           else self.tokenizer.pad_token_id
       )
       lowercase__:   int					=      trim_batch(snake_case_    ,			snake_case_							)
       lowercase__:   List[str]					=      trim_batch(snake_case_    ,			snake_case_    ,			attention_mask=snake_case_							)
       lowercase__:   Dict					=      {
           '''input_ids''': source_ids,
           '''attention_mask''': source_mask,
           '''decoder_input_ids''': y,
       }
       return batch



__A			   = getLogger(__name__)



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Optional[int]:
   return list(itertools.chain.from_iterable(lowerCAmelCase__ ) )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   lowercase__:   Tuple					=      get_git_info()
   save_json(lowerCAmelCase__       ,    os.path.join(lowerCAmelCase__       ,    '''git_log.json''' ) )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=4       ,    **__UpperCAmelCase )  ->  Optional[int]:
   with open(lowerCAmelCase__       ,    '''w''' ) as f:
      json.dump(lowerCAmelCase__       ,    lowerCAmelCase__       ,    indent=lowerCAmelCase__       ,    **lowerCAmelCase__ )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Union[str, Any]:
   with open(lowerCAmelCase__ ) as f:
      return json.load(lowerCAmelCase__ )



def SCREAMING_SNAKE_CASE__			(   )  ->  int:
   lowercase__:   Optional[int]					=      git.Repo(search_parent_directories=lowerCAmelCase__ )
   lowercase__:   List[str]					=      {
       '''repo_id''': str(lowerCAmelCase__ ),
       '''repo_sha''': str(repo.head.object.hexsha ),
       '''repo_branch''': str(repo.active_branch ),
       '''hostname''': str(socket.gethostname() ),
   }
   return repo_infos



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[str]:
   return list(map(lowerCAmelCase__       ,    lowerCAmelCase__ ) )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Dict:
   with open(lowerCAmelCase__       ,    '''wb''' ) as f:
      return pickle.dump(lowerCAmelCase__       ,    lowerCAmelCase__ )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Optional[int]:

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

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

   def remove_punc(__UpperCAmelCase ):
      lowercase__:   Dict					=      set(string.punctuation )
      return "".join(ch for ch in text if ch not in exclude )

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

   return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase__ ) ) ) )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Optional[int]:
   lowercase__:   str					=      normalize_answer(lowerCAmelCase__ ).split()
   lowercase__:   Dict					=      normalize_answer(lowerCAmelCase__ ).split()
   lowercase__:   str					=      Counter(lowerCAmelCase__ ) & Counter(lowerCAmelCase__ )
   lowercase__:   Optional[int]					=      sum(common.values() )
   if num_same == 0:
      return 0
   lowercase__:   Union[str, Any]					=      1.0 * num_same / len(lowerCAmelCase__ )
   lowercase__:   Dict					=      1.0 * num_same / len(lowerCAmelCase__ )
   lowercase__:   str					=      (2 * precision * recall) / (precision + recall)
   return fa



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  int:
   return normalize_answer(lowerCAmelCase__ ) == normalize_answer(lowerCAmelCase__ )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Tuple:
   assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )
   lowercase__:   List[str]					=      0
   for hypo, pred in zip(lowerCAmelCase__       ,    lowerCAmelCase__ ):
      em += exact_match_score(lowerCAmelCase__       ,    lowerCAmelCase__ )
   if len(lowerCAmelCase__ ) > 0:
      em /= len(lowerCAmelCase__ )
   return {"em": em}



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Union[str, Any]:
   return model_prefix.startswith('''rag''' )




def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Optional[Any]:
   lowercase__:   Optional[int]					=      {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   lowercase__:   int					=      '''dropout_rate'''
   for p in extra_params:
      if getattr(lowerCAmelCase__       ,    lowerCAmelCase__       ,    lowerCAmelCase__ ):
         if not hasattr(lowerCAmelCase__       ,    lowerCAmelCase__ ) and not hasattr(lowerCAmelCase__       ,    equivalent_param[p] ):
            logger.info('''config doesn\'t have a `{}` attribute'''.format(lowerCAmelCase__ ) )
            delattr(lowerCAmelCase__       ,    lowerCAmelCase__ )
            continue
         lowercase__:   Optional[int]					=      p if hasattr(lowerCAmelCase__       ,    lowerCAmelCase__ ) else equivalent_param[p]
         setattr(lowerCAmelCase__       ,    lowerCAmelCase__       ,    getattr(lowerCAmelCase__       ,    lowerCAmelCase__ ) )
         delattr(lowerCAmelCase__       ,    lowerCAmelCase__ )
   return hparams, config


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ):
 # vision encoder
 if "img_encoder.pos_embed" in name:
  __a :  Any						=  name.replace('''img_encoder.pos_embed'''  ,   '''vision_model.embeddings.position_embeddings'''  )
 if "img_encoder.patch_embed.proj" in name:
  __a :  str						=  name.replace('''img_encoder.patch_embed.proj'''  ,   '''vision_model.embeddings.patch_embeddings.projection'''  )
 if "img_encoder.patch_embed.norm" in name:
  __a :  int						=  name.replace('''img_encoder.patch_embed.norm'''  ,   '''vision_model.embeddings.layernorm'''  )
 if "img_encoder.layers" in name:
  __a :  Union[str, Any]						=  name.replace('''img_encoder.layers'''  ,   '''vision_model.encoder.stages'''  )
 if "blocks" in name and "res" not in name:
  __a :  List[Any]						=  name.replace('''blocks'''  ,   '''layers'''  )
 if "attn" in name and "pre_assign" not in name:
  __a :  Tuple						=  name.replace('''attn'''  ,   '''self_attn'''  )
 if "proj" in name and "self_attn" in name and "text" not in name:
  __a :  List[Any]						=  name.replace('''proj'''  ,   '''out_proj'''  )
 if "pre_assign_attn.attn.proj" in name:
  __a :  Any						=  name.replace('''pre_assign_attn.attn.proj'''  ,   '''pre_assign_attn.attn.out_proj'''  )
 if "norm1" in name:
  __a :  Union[str, Any]						=  name.replace('''norm1'''  ,   '''layer_norm1'''  )
 if "norm2" in name and "pre_assign" not in name:
  __a :  Optional[int]						=  name.replace('''norm2'''  ,   '''layer_norm2'''  )
 if "img_encoder.norm" in name:
  __a :  Union[str, Any]						=  name.replace('''img_encoder.norm'''  ,   '''vision_model.layernorm'''  )
 # text encoder
 if "text_encoder.token_embedding" in name:
  __a :  List[Any]						=  name.replace('''text_encoder.token_embedding'''  ,   '''text_model.embeddings.token_embedding'''  )
 if "text_encoder.positional_embedding" in name:
  __a :  Any						=  name.replace('''text_encoder.positional_embedding'''  ,   '''text_model.embeddings.position_embedding.weight'''  )
 if "text_encoder.transformer.resblocks." in name:
  __a :  Any						=  name.replace('''text_encoder.transformer.resblocks.'''  ,   '''text_model.encoder.layers.'''  )
 if "ln_1" in name:
  __a :  str						=  name.replace('''ln_1'''  ,   '''layer_norm1'''  )
 if "ln_2" in name:
  __a :  Union[str, Any]						=  name.replace('''ln_2'''  ,   '''layer_norm2'''  )
 if "c_fc" in name:
  __a :  Union[str, Any]						=  name.replace('''c_fc'''  ,   '''fc1'''  )
 if "c_proj" in name:
  __a :  Union[str, Any]						=  name.replace('''c_proj'''  ,   '''fc2'''  )
 if "text_encoder" in name:
  __a :  Optional[int]						=  name.replace('''text_encoder'''  ,   '''text_model'''  )
 if "ln_final" in name:
  __a :  str						=  name.replace('''ln_final'''  ,   '''final_layer_norm'''  )
 # projection layers
 if "img_projector.linear_hidden." in name:
  __a :  List[str]						=  name.replace('''img_projector.linear_hidden.'''  ,   '''visual_projection.'''  )
 if "img_projector.linear_out." in name:
  __a :  str						=  name.replace('''img_projector.linear_out.'''  ,   '''visual_projection.3.'''  )
 if "text_projector.linear_hidden" in name:
  __a :  int						=  name.replace('''text_projector.linear_hidden'''  ,   '''text_projection'''  )
 if "text_projector.linear_out" in name:
  __a :  List[str]						=  name.replace('''text_projector.linear_out'''  ,   '''text_projection.3'''  )

 return name




def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				Tuple  ):
 for key in orig_state_dict.copy().keys():
  __a :  List[Any]						=  orig_state_dict.pop(lowerCAmelCase__  )

  if "qkv" in key:
   # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
   # we need to split them up into separate matrices/vectors
   __a :  Tuple						=  key.split('''.'''  )
   __a				,       __a :  List[Any]						=  int(key_split[2]  ), int(key_split[4]  )
   __a :  List[Any]						=  config.vision_config.hidden_size
   if "weight" in key:
    __a :  int						=  val[:dim, :]
    __a :  List[str]						=  val[dim : dim * 2, :]
    __a :  List[Any]						=  val[-dim:, :]
   else:
    __a :  List[str]						=  val[:dim]
    __a :  int						=  val[dim : dim * 2]
    __a :  Any						=  val[-dim:]
  elif "in_proj" in key:
   # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
   # we need to split them up into separate matrices/vectors
   __a :  int						=  key.split('''.'''  )
   __a :  str						=  int(key_split[3]  )
   __a :  List[Any]						=  config.text_config.hidden_size
   if "weight" in key:
    __a :  List[str]						=  val[:dim, :]
    __a :  Any						=  val[
        dim : dim * 2, :
    ]
    __a :  Dict						=  val[-dim:, :]
   else:
    __a :  List[str]						=  val[:dim]
    __a :  Any						=  val[dim : dim * 2]
    __a :  Any						=  val[-dim:]
  else:
   __a :  Union[str, Any]						=  rename_key(lowerCAmelCase__  )
   # squeeze if necessary
   if (
       "text_projection.0" in new_name
       or "text_projection.3" in new_name
       or "visual_projection.0" in new_name
       or "visual_projection.3" in new_name
   ):
    __a :  List[Any]						=  val.squeeze_()
   else:
    __a :  Dict						=  val

 return orig_state_dict




def    __UpperCamelCase     (				):
 __a :  Optional[int]						=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __a :  str						=  Image.open(requests.get(lowerCAmelCase__  ,   stream=lowerCAmelCase__  ).raw  )
 return im




@torch.no_grad()
def    __UpperCamelCase     (				lowerCAmelCase__							:				List[Any]  ,   lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				Optional[Any]="groupvit-gcc-yfcc"  ,   lowerCAmelCase__							:				int=False  ):
 __a :  Union[str, Any]						=  GroupViTConfig()
 __a :  int						=  GroupViTModel(lowerCAmelCase__  ).eval()

 __a :  Any						=  torch.load(lowerCAmelCase__  ,   map_location='''cpu'''  )['''model''']
 __a :  Optional[Any]						=  convert_state_dict(lowerCAmelCase__  ,   lowerCAmelCase__  )
 __a				,       __a :  Dict						=  model.load_state_dict(lowerCAmelCase__  ,   strict=lowerCAmelCase__  )
 assert missing_keys == ["text_model.embeddings.position_ids"]
 assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowerCAmelCase__  ) == 0)

 # verify result
 __a :  Any						=  CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''  )
 __a :  Optional[Any]						=  prepare_img()
 __a :  Optional[int]						=  processor(text=['''a photo of a cat''', '''a photo of a dog''']  ,   images=lowerCAmelCase__  ,   padding=lowerCAmelCase__  ,   return_tensors='''pt'''  )

 with torch.no_grad():
  __a :  Tuple						=  model(**lowerCAmelCase__  )

 if model_name == "groupvit-gcc-yfcc":
  __a :  List[str]						=  torch.tensor([[13.35_23, 6.36_29]]  )
 elif model_name == "groupvit-gcc-redcaps":
  __a :  List[str]						=  torch.tensor([[16.18_73, 8.62_30]]  )
 else:
  raise ValueError(f"Model name {model_name} not supported."  )
 assert torch.allclose(outputs.logits_per_image  ,   lowerCAmelCase__  ,   atol=1e-3  )

 processor.save_pretrained(lowerCAmelCase__  )
 model.save_pretrained(lowerCAmelCase__  )
 print('''Successfully saved processor and model to'''  ,   lowerCAmelCase__  )

 if push_to_hub:
  print('''Pushing to the hub...'''  )
  processor.push_to_hub(lowerCAmelCase__  ,   organization='''nielsr'''  )
  model.push_to_hub(lowerCAmelCase__  ,   organization='''nielsr'''  )


if __name__ == "__main__":
      lowercase__       =argparse.ArgumentParser()
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
      )
      parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
      parser.add_argument(
          '--model_name',
          default='groupvit-gccy-fcc',
          type=str,
          help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
      )
      parser.add_argument(
          '--push_to_hub',
          action='store_true',
          help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
      )
      lowercase__       =parser.parse_args()

      convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
