





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" )  ->				str:







			'''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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=None			,			)  ->				Optional[int]:







			'''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_       (a_			):
	def __init__(    self							,					_A							,					_A							,					_A							,					_A							,					_A="train"							,					_A=None							,					_A=None							,					_A=None							,					_A=""							,					):






				'''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		):






				'''simple docstring'''







				return len(self.src_lens		)
	def __getitem__(    self							,					_A		):






				'''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      _lowercase				(    _A		):






				'''simple docstring'''







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







	def      _lowercase				(    self							,					_A		):






				'''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
__A				:    str       =   getLogger(__name__)





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Optional[Any]:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Dict:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=4			,			**UpperCamelCase__ )  ->				Tuple:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Any:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (				)  ->				Tuple:







			'''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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Dict:







			'''simple docstring'''

			return list(map(a__			,			a__ ) )





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Tuple:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

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

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

			def remove_punc(UpperCamelCase__ ):
						UpperCAmelCase   		=				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(a__ ) ) ) )





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Any:







			'''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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				List[Any]:







			'''simple docstring'''

			return normalize_answer(a__ ) == normalize_answer(a__ )





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				str:







			'''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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				int:







			'''simple docstring'''

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






def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				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






'''simple docstring'''



from itertools import count







def    a__       (  a__ = 50						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      [1] * min_block_length

     for n in count(a__						):
          fill_count_functions.append(1						)

          for block_length in range(a__      ,    n + 1						):
               for block_start in range(n - block_length						):
                    fill_count_functions[n] += fill_count_functions[
                        n - block_start - block_length - 1
                    ]

               fill_count_functions[n] += 1

          if fill_count_functions[n] > 1_00_00_00:
               break

     return n


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






