






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 (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case=True   ,      __snake_case="pt" )				->						Union[str, Any]:
       _UpperCAmelCase							=    {"""add_prefix_space""": True} if isinstance(__snake_case   ,      __snake_case ) and not line.startswith(""" """ ) else {}
       _UpperCAmelCase							=    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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case=None   ,      )				->						Union[str, Any]:
       _UpperCAmelCase							=    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 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			int="train"       ,				lowerCamelCase		:			Any=None       ,				lowerCamelCase		:			Union[str, Any]=None       ,				lowerCamelCase		:			Optional[Any]=None       ,				lowerCamelCase		:			int=""       ,				)						->	List[Any]:


         """simple docstring"""
         super().__init__()
         _UpperCAmelCase							=    Path(lowerCamelCase							).joinpath(type_path + """.source"""							)
         _UpperCAmelCase							=    Path(lowerCamelCase							).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		:			Optional[int]							)						->	List[Any]:


         """simple docstring"""
         return len(self.src_lens							)


  def __getitem__(      self		:			int       ,				lowerCamelCase		:			Tuple							)						->	Dict[str, torch.Tensor]:


         """simple docstring"""
         _UpperCAmelCase							=    index + 1  # linecache starts at 1
         _UpperCAmelCase							=    self.prefix + linecache.getline(str(self.src_file							)       ,				lowerCamelCase							).rstrip("""\n"""							)
         _UpperCAmelCase							=    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
         _UpperCAmelCase							=    (
             self.tokenizer.question_encoder if isinstance(self.tokenizer       ,				lowerCamelCase							) else self.tokenizer
         )
         _UpperCAmelCase							=    self.tokenizer.generator if isinstance(self.tokenizer       ,				lowerCamelCase							) else self.tokenizer

         _UpperCAmelCase							=    encode_line(lowerCamelCase       ,				lowerCamelCase       ,				self.max_source_length       ,				"""right"""							)
         _UpperCAmelCase							=    encode_line(lowerCamelCase       ,				lowerCamelCase       ,				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  lowerCamelCase       (      lowerCamelCase		:			Union[str, Any]							)						->	str:


         """simple docstring"""
         return [len(lowerCamelCase							) for x in Path(lowerCamelCase							).open().readlines()]





  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			int							)						->	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       ,				lowerCamelCase							)
             else self.tokenizer.pad_token_id
         )
         _UpperCAmelCase							=    (
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer       ,				lowerCamelCase							)
             else self.tokenizer.pad_token_id
         )
         _UpperCAmelCase							=    trim_batch(lowerCamelCase       ,				lowerCamelCase							)
         _UpperCAmelCase	,			_UpperCAmelCase							=    trim_batch(lowerCamelCase       ,				lowerCamelCase       ,				attention_mask=lowerCamelCase							)
         _UpperCAmelCase							=    {
             """input_ids""": source_ids,
             """attention_mask""": source_mask,
             """decoder_input_ids""": y,
         }
         return batch



__a:     str   					=					getLogger(__name__)




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						int:
       return list(itertools.chain.from_iterable(__snake_case ) )




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						None:
       _UpperCAmelCase							=    get_git_info()
       save_json(__snake_case   ,      os.path.join(__snake_case   ,      """git_log.json""" ) )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case=4   ,      **__snake_case )				->						List[Any]:
       with open(__snake_case   ,      """w""" ) as f:
              json.dump(__snake_case   ,      __snake_case   ,      indent=__snake_case   ,      **__snake_case )




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




def       _SCREAMING_SNAKE_CASE (							)				->						Union[str, Any]:
       _UpperCAmelCase							=    git.Repo(search_parent_directories=__snake_case )
       _UpperCAmelCase							=    {
           """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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						List:
       return list(map(__snake_case   ,      __snake_case ) )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Any:
       with open(__snake_case   ,      """wb""" ) as f:
              return pickle.dump(__snake_case   ,      __snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Dict:

       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 ):
              _UpperCAmelCase							=    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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Tuple:
       _UpperCAmelCase							=    normalize_answer(__snake_case ).split()
       _UpperCAmelCase							=    normalize_answer(__snake_case ).split()
       _UpperCAmelCase							=    Counter(__snake_case ) & Counter(__snake_case )
       _UpperCAmelCase							=    sum(common.values() )
       if num_same == 0:
              return 0
       _UpperCAmelCase							=    1.0 * num_same / len(__snake_case )
       _UpperCAmelCase							=    1.0 * num_same / len(__snake_case )
       _UpperCAmelCase							=    (2 * precision * recall) / (precision + recall)
       return fa




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Dict:
       return normalize_answer(__snake_case ) == normalize_answer(__snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Dict:
       assert len(__snake_case ) == len(__snake_case )
       _UpperCAmelCase							=    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       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Any:
       return model_prefix.startswith("""rag""" )





def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						List[Any]:
       _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(__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
                     _UpperCAmelCase							=    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 glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__a:     List[Any]   					=					(720, 1280)  # Height, Width
__a:     str   					=					(0.4, 0.6)  # if height or width lower than this scale, drop it.
__a:     Optional[Any]   					=					1 / 100
__a:     Optional[Any]   					=					''''''
__a:     Tuple   					=					''''''
__a:     List[Any]   					=					''''''
__a:     Union[str, Any]   					=					250




def       _SCREAMING_SNAKE_CASE (							)				->						None:
       _UpperCAmelCase	,			_UpperCAmelCase							=    get_dataset(__snake_case   ,      __snake_case )
       for index in range(__snake_case ):
              _UpperCAmelCase							=    random.sample(range(len(__snake_case ) )   ,      4 )
              _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    update_image_and_anno(
                  __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      filter_scale=__snake_case   ,      )

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              _UpperCAmelCase							=    random_chars(3_2 )
              _UpperCAmelCase							=    path.split(os.sep )[-1].rsplit("""."""   ,      1 )[0]
              _UpperCAmelCase							=    f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
              cva.imwrite(f"""{file_root}.jpg"""   ,      __snake_case   ,      [cva.IMWRITE_JPEG_QUALITY, 8_5] )
              print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
              _UpperCAmelCase							=    []
              for anno in new_annos:
                     _UpperCAmelCase							=    anno[3] - anno[1]
                     _UpperCAmelCase							=    anno[4] - anno[2]
                     _UpperCAmelCase							=    anno[1] + width / 2
                     _UpperCAmelCase							=    anno[2] + height / 2
                     _UpperCAmelCase							=    f"""{anno[0]} {x_center} {y_center} {width} {height}"""
                     annos_list.append(__snake_case )
              with open(f"""{file_root}.txt"""   ,      """w""" ) as outfile:
                     outfile.write("""\n""".join(line for line in annos_list ) )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						tuple[list, list]:
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for label_file in glob.glob(os.path.join(__snake_case   ,      """*.txt""" ) ):
              _UpperCAmelCase							=    label_file.split(os.sep )[-1].rsplit("""."""   ,      1 )[0]
              with open(__snake_case ) as in_file:
                     _UpperCAmelCase							=    in_file.readlines()
              _UpperCAmelCase							=    os.path.join(__snake_case   ,      f"""{label_name}.jpg""" )

              _UpperCAmelCase							=    []
              for obj_list in obj_lists:
                     _UpperCAmelCase							=    obj_list.rstrip("""\n""" ).split(""" """ )
                     _UpperCAmelCase							=    float(obj[1] ) - float(obj[3] ) / 2
                     _UpperCAmelCase							=    float(obj[2] ) - float(obj[4] ) / 2
                     _UpperCAmelCase							=    float(obj[1] ) + float(obj[3] ) / 2
                     _UpperCAmelCase							=    float(obj[2] ) + float(obj[4] ) / 2

                     boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
              if not boxes:
                     continue
              img_paths.append(__snake_case )
              labels.append(__snake_case )
       return img_paths, labels




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case = 0.0   ,      )				->						tuple[list, list, str]:
       _UpperCAmelCase							=    np.zeros([output_size[0], output_size[1], 3]   ,      dtype=np.uinta )
       _UpperCAmelCase							=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       _UpperCAmelCase							=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       _UpperCAmelCase							=    int(scale_x * output_size[1] )
       _UpperCAmelCase							=    int(scale_y * output_size[0] )

       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for i, index in enumerate(__snake_case ):
              _UpperCAmelCase							=    all_img_list[index]
              path_list.append(__snake_case )
              _UpperCAmelCase							=    all_annos[index]
              _UpperCAmelCase							=    cva.imread(__snake_case )
              if i == 0:  # top-left
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (divid_point_x, divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    bbox[1] * scale_x
                            _UpperCAmelCase							=    bbox[2] * scale_y
                            _UpperCAmelCase							=    bbox[3] * scale_x
                            _UpperCAmelCase							=    bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              elif i == 1:  # top-right
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (output_size[1] - divid_point_x, divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    scale_x + bbox[1] * (1 - scale_x)
                            _UpperCAmelCase							=    bbox[2] * scale_y
                            _UpperCAmelCase							=    scale_x + bbox[3] * (1 - scale_x)
                            _UpperCAmelCase							=    bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              elif i == 2:  # bottom-left
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (divid_point_x, output_size[0] - divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    bbox[1] * scale_x
                            _UpperCAmelCase							=    scale_y + bbox[2] * (1 - scale_y)
                            _UpperCAmelCase							=    bbox[3] * scale_x
                            _UpperCAmelCase							=    scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              else:  # bottom-right
                     _UpperCAmelCase							=    cva.resize(
                         __snake_case   ,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    scale_x + bbox[1] * (1 - scale_x)
                            _UpperCAmelCase							=    scale_y + bbox[2] * (1 - scale_y)
                            _UpperCAmelCase							=    scale_x + bbox[3] * (1 - scale_x)
                            _UpperCAmelCase							=    scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )

    # Remove bounding box small than scale of filter
       if filter_scale > 0:
              _UpperCAmelCase							=    [
                  anno
                  for anno in new_anno
                  if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
              ]

       return output_img, new_anno, path_list[0]




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						str:
       assert number_char > 1, "The number of character should greater than 1"
       _UpperCAmelCase							=    ascii_lowercase + digits
       return "".join(random.choice(__snake_case ) for _ in range(__snake_case ) )


if __name__ == "__main__":
  main()
  print('''DONE ✅''')