
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       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase=True    ,       lowercase="pt" )   ->  Optional[Any]:
		'''simple docstring'''

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


def       A       (					lowercase    ,       lowercase    ,       lowercase=None    ,       )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     input_ids.ne(lowercase ).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 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_				,      A_				,      A_				,      A_="train"				,      A_=None				,      A_=None				,      A_=None				,      A_=""				,      )  ->					List[str]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase		=     Path(A_       ).joinpath(type_path + '.source'       )
				UpperCamelCase		=     Path(A_       ).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       )  ->					int:
				"""simple docstring"""
				return len(self.src_lens       )



		def __getitem__( self				,      A_       )  ->					Dict[str, torch.Tensor]:
				"""simple docstring"""
				UpperCamelCase		=     index + 1  # linecache starts at 1
				UpperCamelCase		=     self.prefix + linecache.getline(str(self.src_file       )				,      A_       ).rstrip('\n'       )
				UpperCamelCase		=     linecache.getline(str(self.tgt_file       )				,      A_       ).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				,      A_       ):
						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				,      A_       ) else self.tokenizer
				)
				UpperCamelCase		=     self.tokenizer.generator if isinstance(self.tokenizer				,      A_       ) else self.tokenizer

				UpperCamelCase		=     encode_line(A_				,      A_				,      self.max_source_length				,      'right'       )
				UpperCamelCase		=     encode_line(A_				,      A_				,      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 ( A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				return [len(A_       ) for x in Path(A_       ).open().readlines()]



		def __UpperCamelCase ( self				,      A_       )  ->					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				,      A_       )
				    else self.tokenizer.pad_token_id
				)
				UpperCamelCase		=     (
				    self.tokenizer.question_encoder.pad_token_id
				    if isinstance(self.tokenizer				,      A_       )
				    else self.tokenizer.pad_token_id
				)
				UpperCamelCase		=     trim_batch(A_				,      A_       )
				UpperCamelCase	,  UpperCamelCase		=     trim_batch(A_				,      A_				,      attention_mask=A_       )
				UpperCamelCase		=     {
				    'input_ids': source_ids,
				    'attention_mask': source_mask,
				    'decoder_input_ids': y,
				}
				return batch






_UpperCAmelCase					: Any      =       getLogger(__name__)


def       A       (					lowercase )   ->  Optional[int]:
		'''simple docstring'''

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


def       A       (					lowercase )   ->  None:
		'''simple docstring'''

		UpperCamelCase		=     get_git_info()
		save_json(lowercase    ,       os.path.join(lowercase    ,       'git_log.json' ) )


def       A       (					lowercase    ,       lowercase    ,       lowercase=4    ,       **lowercase )   ->  Tuple:
		'''simple docstring'''

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


def       A       (					lowercase )   ->  Union[str, Any]:
		'''simple docstring'''

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


def       A       (					)   ->  List[str]:
		'''simple docstring'''

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


def       A       (					lowercase    ,       lowercase )   ->  List:
		'''simple docstring'''

		return list(map(lowercase    ,       lowercase ) )


def       A       (					lowercase    ,       lowercase )   ->  Union[str, Any]:
		'''simple docstring'''

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


def       A       (					lowercase )   ->  Optional[int]:
		'''simple docstring'''

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

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

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

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

		return white_space_fix(remove_articles(remove_punc(lower(lowercase ) ) ) )


def       A       (					lowercase    ,       lowercase )   ->  Union[str, Any]:
		'''simple docstring'''

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


def       A       (					lowercase    ,       lowercase )   ->  str:
		'''simple docstring'''

		return normalize_answer(lowercase ) == normalize_answer(lowercase )


def       A       (					lowercase    ,       lowercase )   ->  Dict:
		'''simple docstring'''

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


def       A       (					lowercase )   ->  Optional[Any]:
		'''simple docstring'''

		return model_prefix.startswith('rag' )


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  Optional[int]:
		'''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(lowercase    ,       lowercase    ,       lowercase ):
						if not hasattr(lowercase    ,       lowercase ) and not hasattr(lowercase    ,       equivalent_param[p] ):
								logger.info('config doesn\'t have a `{}` attribute'.format(lowercase ) )
								delattr(lowercase    ,       lowercase )
								continue
						UpperCamelCase		=     p if hasattr(lowercase    ,       lowercase ) else equivalent_param[p]
						setattr(lowercase    ,       lowercase    ,       getattr(lowercase    ,       lowercase ) )
						delattr(lowercase    ,       lowercase )
		return hparams, config


from __future__ import annotations

import os
from collections.abc import Mapping

_UpperCAmelCase					: Tuple      =       tuple[int, int]


class 				lowercase					:



		def __init__( self				,      A_				,      A_       )  ->					None:
				"""simple docstring"""
				UpperCamelCase		=     vertices
				UpperCamelCase		=     {
				    (min(A_       ), max(A_       )): weight for edge, weight in edges.items()
				}



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					None:
				"""simple docstring"""
				self.vertices.add(edge[0]       )
				self.vertices.add(edge[1]       )
				UpperCamelCase		=     weight



		def __UpperCamelCase ( self       )  ->					Graph:
				"""simple docstring"""
				UpperCamelCase		=     Graph({min(self.vertices       )}				,      {}       )
				UpperCamelCase		=     42
				UpperCamelCase		=     42
				UpperCamelCase		=     42
				UpperCamelCase		=     42

				while len(subgraph.vertices       ) < len(self.vertices       ):
						UpperCamelCase		=     max(self.edges.values()       ) + 1
						for edge, weight in self.edges.items():
								if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
										if weight < min_weight:
												UpperCamelCase		=     edge
												UpperCamelCase		=     weight

						subgraph.add_edge(A_				,      A_       )

				return subgraph






def       A       (					lowercase = "p107_network.txt" )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     os.path.abspath(os.path.dirname(lowercase ) )
		UpperCamelCase		=     os.path.join(lowercase    ,       lowercase )
		UpperCamelCase		=     {}
		UpperCamelCase		=     42
		UpperCamelCase		=     42
		UpperCamelCase		=     42

		with open(lowercase ) as f:
				UpperCamelCase		=     f.read().strip().split('\n' )

		UpperCamelCase		=     [line.split(',' ) for line in data]

		for edgea in range(1    ,       len(lowercase ) ):
				for edgea in range(lowercase ):
						if adjaceny_matrix[edgea][edgea] != "-":
								UpperCamelCase		=     int(adjaceny_matrix[edgea][edgea] )

		UpperCamelCase		=     Graph(set(range(len(lowercase ) ) )    ,       lowercase )

		UpperCamelCase		=     graph.prims_algorithm()

		UpperCamelCase		=     sum(graph.edges.values() )
		UpperCamelCase		=     sum(subgraph.edges.values() )

		return initial_total - optimal_total


if __name__ == "__main__":
					print(F'''{solution() = }''')
