




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 		_A(       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			str				,       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			List[str]				,       UpperCamelCase__							:			Union[str, Any]=True				,       UpperCamelCase__							:			Tuple="pt"				)    -> int:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			List[Any]				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Tuple=None				,       )    -> Optional[int]:
							'''simple docstring'''


							__lowercase      =    input_ids.ne(UpperCamelCase__				).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 (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __init__( self :     List[Any]				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     Any				,							lowerCamelCase__ :     Optional[Any]="train"				,							lowerCamelCase__ :     Any=None				,							lowerCamelCase__ :     int=None				,							lowerCamelCase__ :     List[str]=None				,							lowerCamelCase__ :     Dict=""				,							)    ->						str:
													"""simple docstring"""


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






						def __len__( self :     Dict				)    ->						Dict:
													"""simple docstring"""


													return len(self.src_lens				)






						def __getitem__( self :     List[Any]				,							lowerCamelCase__ :     Union[str, Any]				)    ->						Dict[str, torch.Tensor]:
													"""simple docstring"""


													__lowercase      =    index + 1  # linecache starts at 1
													__lowercase      =    self.prefix + linecache.getline(str(self.src_file				)				,							lowerCamelCase__				).rstrip('''\n'''				)
													__lowercase      =    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
													__lowercase      =    (
													    self.tokenizer.question_encoder if isinstance(self.tokenizer				,							lowerCamelCase__				) else self.tokenizer
													)
													__lowercase      =    self.tokenizer.generator if isinstance(self.tokenizer				,							lowerCamelCase__				) else self.tokenizer

													__lowercase      =    encode_line(lowerCamelCase__				,							lowerCamelCase__				,							self.max_source_length				,							'''right'''				)
													__lowercase      =    encode_line(lowerCamelCase__				,							lowerCamelCase__				,							self.max_target_length				,							'''right'''				)

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






						@staticmethod
						def     UpperCAmelCase_      ( lowerCamelCase__ :     List[str]				)    ->						Any:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     str				)    ->						Dict[str, torch.Tensor]:
													"""simple docstring"""


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





UpperCAmelCase__				     = getLogger(__name__)







def 		_A(       UpperCamelCase__							:			List[List]				)    -> List[Any]:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			str				)    -> None:
							'''simple docstring'''


							__lowercase      =    get_git_info()
							save_json(UpperCamelCase__				,       os.path.join(UpperCamelCase__				,       '''git_log.json'''				)				)







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			str=4				,       **UpperCamelCase__							:			Optional[Any]				)    -> Union[str, Any]:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			Any				)    -> Optional[int]:
							'''simple docstring'''


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







def 		_A(       )    -> str:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			Callable				,       UpperCamelCase__							:			Iterable				)    -> List:
							'''simple docstring'''


							return list(map(UpperCamelCase__				,       UpperCamelCase__				)				)







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Optional[int]				)    -> List[str]:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			Optional[Any]				)    -> List[str]:
							'''simple docstring'''


							def remove_articles(UpperCamelCase__							:			int				):
														return re.sub(r'''\b(a|an|the)\b'''				,       ''' '''				,       UpperCamelCase__				)

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

							def remove_punc(UpperCamelCase__							:			Tuple				):
														__lowercase      =    set(string.punctuation				)
														return "".join(ch for ch in text if ch not in exclude				)

							def lower(UpperCamelCase__							:			Optional[int]				):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__				)				)				)				)







def 		_A(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			Any				)    -> List[str]:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			List[Any]				,       UpperCamelCase__							:			str				)    -> Any:
							'''simple docstring'''


							return normalize_answer(UpperCamelCase__				) == normalize_answer(UpperCamelCase__				)







def 		_A(       UpperCamelCase__							:			List[str]				,       UpperCamelCase__							:			List[str]				)    -> Dict:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			Union[str, Any]				)    -> Dict:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Tuple				)    -> int:
							'''simple docstring'''


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












import cva
import numpy as np







class a :
						"""simple docstring"""

						def __init__( self :     Dict				,							lowerCamelCase__ :     float				,							lowerCamelCase__ :     int				)    ->						Dict:
													"""simple docstring"""


													if k in (0.0_4, 0.0_6):
																				__lowercase      =    k
																				__lowercase      =    window_size
													else:
																				raise ValueError('''invalid k value'''				)






						def __str__( self :     int				)    ->						str:
													"""simple docstring"""


													return str(self.k				)






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     str				)    ->						tuple[cva.Mat, list[list[int]]]:
													"""simple docstring"""


													__lowercase      =    cva.imread(lowerCamelCase__				,							0				)
													__lowercase     ,     __lowercase      =    img.shape
													__lowercase      =    []
													__lowercase      =    img.copy()
													__lowercase      =    cva.cvtColor(lowerCamelCase__				,							cva.COLOR_GRAY2RGB				)
													__lowercase     ,     __lowercase      =    np.gradient(lowerCamelCase__				)
													__lowercase      =    dx**2
													__lowercase      =    dy**2
													__lowercase      =    dx * dy
													__lowercase      =    0.0_4
													__lowercase      =    self.window_size // 2
													for y in range(lowerCamelCase__				,							h - offset				):
																				for x in range(lowerCamelCase__				,							w - offset				):
																											__lowercase      =    ixx[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()
																											__lowercase      =    iyy[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()
																											__lowercase      =    ixy[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()

																											__lowercase      =    (wxx * wyy) - (wxy**2)
																											__lowercase      =    wxx + wyy
																											__lowercase      =    det - k * (trace**2)
																											# Can change the value
																											if r > 0.5:
																																		corner_list.append([x, y, r]				)
																																		color_img.itemset((y, x, 0)				,							0				)
																																		color_img.itemset((y, x, 1)				,							0				)
																																		color_img.itemset((y, x, 2)				,							255				)
													return color_img, corner_list


if __name__ == "__main__":
			UpperCAmelCase__				     = HarrisCorner(0.04, 3)
			UpperCAmelCase__     ,							UpperCAmelCase__				     = edge_detect.detect("path_to_image")
			cva.imwrite("detect.png", color_img)






