




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__            =     logging.get_logger(__name__)


UpperCamelCase__            =     {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCamelCase__            =     {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
}

UpperCamelCase__            =     {
    """RUCAIBox/mvp""": 1024,
}







@lru_cache()
def       _a (		):
					__lowerCAmelCase							  =			(
					    list(range(ord("!"	) , ord("~"	) + 1	)	) + list(range(ord("¡"	) , ord("¬"	) + 1	)	) + list(range(ord("®"	) , ord("ÿ"	) + 1	)	)
					)
					__lowerCAmelCase							  =			bs[:]
					__lowerCAmelCase							  =			0
					for b in range(2**8	):
										if b not in bs:
															bs.append(SCREAMING_SNAKE_CASE_	)
															cs.append(2**8 + n	)
															n += 1
					__lowerCAmelCase							  =			[chr(SCREAMING_SNAKE_CASE_	) for n in cs]
					return dict(zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)	)







def       _a (		SCREAMING_SNAKE_CASE_				:							List[Any]	):
					__lowerCAmelCase							  =			set()
					__lowerCAmelCase							  =			word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)	)
										__lowerCAmelCase							  =			char
					return pairs


class      a__       (   snake_case__ ):
						_a				:       Tuple   =       VOCAB_FILES_NAMES
						_a				:       List[str]   =       PRETRAINED_VOCAB_FILES_MAP
						_a				:       List[str]   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a				:       Dict   =       ["""input_ids""", """attention_mask"""]






						def __init__(  self						,     _A						,     _A						,     _A="replace"						,     _A="<s>"						,     _A="</s>"						,     _A="</s>"						,     _A="<s>"						,     _A="<unk>"						,     _A="<pad>"						,     _A="<mask>"						,     _A=False						,     **_A						,     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			AddedToken(_A						,     lstrip=_A						,     rstrip=_A     ) if isinstance(_A						,     _A     ) else bos_token
											__lowerCAmelCase							  =			AddedToken(_A						,     lstrip=_A						,     rstrip=_A     ) if isinstance(_A						,     _A     ) else eos_token
											__lowerCAmelCase							  =			AddedToken(_A						,     lstrip=_A						,     rstrip=_A     ) if isinstance(_A						,     _A     ) else sep_token
											__lowerCAmelCase							  =			AddedToken(_A						,     lstrip=_A						,     rstrip=_A     ) if isinstance(_A						,     _A     ) else cls_token
											__lowerCAmelCase							  =			AddedToken(_A						,     lstrip=_A						,     rstrip=_A     ) if isinstance(_A						,     _A     ) else unk_token
											__lowerCAmelCase							  =			AddedToken(_A						,     lstrip=_A						,     rstrip=_A     ) if isinstance(_A						,     _A     ) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											__lowerCAmelCase							  =			AddedToken(_A						,     lstrip=_A						,     rstrip=_A     ) if isinstance(_A						,     _A     ) else mask_token

											super().__init__(
											    errors=_A						,     bos_token=_A						,     eos_token=_A						,     unk_token=_A						,     sep_token=_A						,     cls_token=_A						,     pad_token=_A						,     mask_token=_A						,     add_prefix_space=_A						,     **_A						,     )
											with open(_A						,     encoding="utf-8"     ) as vocab_handle:
																__lowerCAmelCase							  =			json.load(_A     )
											__lowerCAmelCase							  =			{v: k for k, v in self.encoder.items()}
											__lowerCAmelCase							  =			errors  # how to handle errors in decoding
											__lowerCAmelCase							  =			bytes_to_unicode()
											__lowerCAmelCase							  =			{v: k for k, v in self.byte_encoder.items()}
											with open(_A						,     encoding="utf-8"     ) as merges_handle:
																__lowerCAmelCase							  =			merges_handle.read().split("\n"     )[1:-1]
											__lowerCAmelCase							  =			[tuple(merge.split()     ) for merge in bpe_merges]
											__lowerCAmelCase							  =			dict(zip(_A						,     range(len(_A     )     )     )     )
											__lowerCAmelCase							  =			{}
											__lowerCAmelCase							  =			add_prefix_space

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											__lowerCAmelCase							  =			re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"     )






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return len(self.encoder     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return dict(self.encoder						,     **self.added_tokens_encoder     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A     ):




											"""simple docstring"""



											if token in self.cache:
																return self.cache[token]
											__lowerCAmelCase							  =			tuple(_A     )
											__lowerCAmelCase							  =			get_pairs(_A     )

											if not pairs:
																return token

											while True:
																__lowerCAmelCase							  =			min(_A						,     key=lambda _A     : self.bpe_ranks.get(_A						,     float("inf"     )     )     )
																if bigram not in self.bpe_ranks:
																					break
																__lowerCAmelCase  ,				__lowerCAmelCase							  =			bigram
																__lowerCAmelCase							  =			[]
																__lowerCAmelCase							  =			0
																while i < len(_A     ):
																					try:
																										__lowerCAmelCase							  =			word.index(_A						,     _A     )
																					except ValueError:
																										new_word.extend(word[i:]     )
																										break
																					else:
																										new_word.extend(word[i:j]     )
																										__lowerCAmelCase							  =			j

																					if word[i] == first and i < len(_A     ) - 1 and word[i + 1] == second:
																										new_word.append(first + second     )
																										i += 2
																					else:
																										new_word.append(word[i]     )
																										i += 1
																__lowerCAmelCase							  =			tuple(_A     )
																__lowerCAmelCase							  =			new_word
																if len(_A     ) == 1:
																					break
																else:
																					__lowerCAmelCase							  =			get_pairs(_A     )
											__lowerCAmelCase							  =			" ".join(_A     )
											__lowerCAmelCase							  =			word
											return word






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			[]
											for token in re.findall(self.pat						,     _A     ):
																__lowerCAmelCase							  =			"".join(
																    self.byte_encoder[b] for b in token.encode("utf-8"     )     )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A     ).split(" "     )     )
											return bpe_tokens






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A     ):




											"""simple docstring"""



											return self.encoder.get(_A						,     self.encoder.get(self.unk_token     )     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A     ):




											"""simple docstring"""



											return self.decoder.get(_A     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			"".join(_A     )
											__lowerCAmelCase							  =			bytearray([self.byte_decoder[c] for c in text]     ).decode("utf-8"						,     errors=self.errors     )
											return text






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = None     ):




											"""simple docstring"""



											if not os.path.isdir(_A     ):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
																return
											__lowerCAmelCase							  =			os.path.join(
											    _A						,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
											__lowerCAmelCase							  =			os.path.join(
											    _A						,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]     )

											with open(_A						,     "w"						,     encoding="utf-8"     ) as f:
																f.write(json.dumps(self.encoder						,     indent=2						,     sort_keys=_A						,     ensure_ascii=_A     ) + "\n"     )

											__lowerCAmelCase							  =			0
											with open(_A						,     "w"						,     encoding="utf-8"     ) as writer:
																writer.write("#version: 0.2\n"     )
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,     key=lambda _A     : kv[1]     ):
																					if index != token_index:
																										logger.warning(
																										    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																										    " Please check that the tokenizer is not corrupted!"     )
																										__lowerCAmelCase							  =			token_index
																					writer.write(" ".join(_A     ) + "\n"     )
																					index += 1

											return vocab_file, merge_file






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = None     ):




											"""simple docstring"""



											if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											__lowerCAmelCase							  =			[self.cls_token_id]
											__lowerCAmelCase							  =			[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = None						,     _A = False     ):




											"""simple docstring"""



											if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_A						,     token_ids_a=_A						,     already_has_special_tokens=_A     )

											if token_ids_a is None:
																return [1] + ([0] * len(_A     )) + [1]
											return [1] + ([0] * len(_A     )) + [1, 1] + ([0] * len(_A     )) + [1]






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = None     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			[self.sep_token_id]
											__lowerCAmelCase							  =			[self.cls_token_id]

											if token_ids_a is None:
																return len(cls + token_ids_a + sep     ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A=False						,     **_A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			kwargs.pop("add_prefix_space"						,     self.add_prefix_space     )
											if (is_split_into_words or add_prefix_space) and (len(_A     ) > 0 and not text[0].isspace()):
																__lowerCAmelCase							  =			" " + text
											return (text, kwargs)










import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


UpperCamelCase__            =     logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


UpperCamelCase__            =     namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])







def       _a (		SCREAMING_SNAKE_CASE_				:							int	):
					__lowerCAmelCase							  =			BertTokenizer.from_pretrained("bert-base-uncased" , do_lower_case=SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"	)
					model.to(args.device	)
					model.eval()

					__lowerCAmelCase							  =			{
					    "BOS": tokenizer.vocab["[unused0]"],
					    "EOS": tokenizer.vocab["[unused1]"],
					    "PAD": tokenizer.vocab["[PAD]"],
					}

					if args.compute_rouge:
										__lowerCAmelCase							  =			[]
										__lowerCAmelCase							  =			[]

										import nltk
										import rouge

										nltk.download("punkt"	)
										__lowerCAmelCase							  =			rouge.Rouge(
										    metrics=["rouge-n", "rouge-l"] , max_n=2 , limit_length=SCREAMING_SNAKE_CASE_ , length_limit=args.beam_size , length_limit_type="words" , apply_avg=SCREAMING_SNAKE_CASE_ , apply_best=SCREAMING_SNAKE_CASE_ , alpha=0.5 , weight_factor=1.2 , stemming=SCREAMING_SNAKE_CASE_ , )

					# these (unused) arguments are defined to keep the compatibility
					# with the legacy code and will be deleted in a next iteration.
					__lowerCAmelCase							  =			""
					__lowerCAmelCase							  =			""

					__lowerCAmelCase							  =			build_data_iterator(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			build_predictor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)

					logger.info("***** Running evaluation *****"	)
					logger.info("  Number examples = %d" , len(data_iterator.dataset	)	)
					logger.info("  Batch size = %d" , args.batch_size	)
					logger.info(""	)
					logger.info("***** Beam Search parameters *****"	)
					logger.info("  Beam size = %d" , args.beam_size	)
					logger.info("  Minimum length = %d" , args.min_length	)
					logger.info("  Maximum length = %d" , args.max_length	)
					logger.info("  Alpha (length penalty) = %.2f" , args.alpha	)
					logger.info("  Trigrams %s be blocked" , ("will" if args.block_trigram else "will NOT")	)

					for batch in tqdm(SCREAMING_SNAKE_CASE_	):
										__lowerCAmelCase							  =			predictor.translate_batch(SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			predictor.from_batch(SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			[format_summary(SCREAMING_SNAKE_CASE_	) for t in translations]
										save_summaries(SCREAMING_SNAKE_CASE_ , args.summaries_output_dir , batch.document_names	)

										if args.compute_rouge:
															reference_summaries += batch.tgt_str
															generated_summaries += summaries

					if args.compute_rouge:
										__lowerCAmelCase							  =			rouge_evaluator.get_scores(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			format_rouge_scores(SCREAMING_SNAKE_CASE_	)
										save_rouge_scores(SCREAMING_SNAKE_CASE_	)
										print(SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							List[str] , SCREAMING_SNAKE_CASE_				:							Optional[Any] , SCREAMING_SNAKE_CASE_				:							Dict	):
					for summary, document_name in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	):
										# Prepare the summary file's name
										if "." in document_name:
															__lowerCAmelCase							  =			".".join(document_name.split("."	)[:-1]	)
															__lowerCAmelCase							  =			document_name.split("."	)[-1]
															__lowerCAmelCase							  =			bare_document_name + "_summary." + extension
										else:
															__lowerCAmelCase							  =			document_name + "_summary"

										__lowerCAmelCase							  =			os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
										with open(SCREAMING_SNAKE_CASE_ , "w"	) as output:
															output.write(SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					__lowerCAmelCase  ,				__lowerCAmelCase  ,				__lowerCAmelCase							  =			translation
					__lowerCAmelCase							  =			(
					    raw_summary.replace("[unused0]" , ""	)
					    .replace("[unused3]" , ""	)
					    .replace("[PAD]" , ""	)
					    .replace("[unused1]" , ""	)
					    .replace(R" +" , " "	)
					    .replace(" [unused2] " , ". "	)
					    .replace("[unused2]" , ""	)
					    .strip()
					)

					return summary







def       _a (		SCREAMING_SNAKE_CASE_				:							Union[str, Any]	):
					return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
					    scores["rouge-1"]["f"] , scores["rouge-1"]["p"] , scores["rouge-1"]["r"] , scores["rouge-2"]["f"] , scores["rouge-2"]["p"] , scores["rouge-2"]["r"] , scores["rouge-l"]["f"] , scores["rouge-l"]["p"] , scores["rouge-l"]["r"] , )







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					with open("rouge_scores.txt" , "w"	) as output:
										output.write(SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					__lowerCAmelCase							  =			load_and_cache_examples(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			SequentialSampler(SCREAMING_SNAKE_CASE_	)

					def collate_fn(SCREAMING_SNAKE_CASE_				:							List[Any]	):
										return collate(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , block_size=5_12 , device=args.device	)

					__lowerCAmelCase							  =			DataLoader(
					    SCREAMING_SNAKE_CASE_ , sampler=SCREAMING_SNAKE_CASE_ , batch_size=args.batch_size , collate_fn=SCREAMING_SNAKE_CASE_ , )

					return iterator







def       _a (		SCREAMING_SNAKE_CASE_				:							Union[str, Any] , SCREAMING_SNAKE_CASE_				:							List[Any]	):
					__lowerCAmelCase							  =			CNNDMDataset(args.documents_dir	)
					return dataset







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int] , SCREAMING_SNAKE_CASE_				:							Optional[Any] , SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							int	):
					__lowerCAmelCase							  =			[x for x in data if not len(x[1]	) == 0]  # remove empty_files
					__lowerCAmelCase							  =			[name for name, _, _ in data]
					__lowerCAmelCase							  =			[" ".join(SCREAMING_SNAKE_CASE_	) for _, _, summary_list in data]

					__lowerCAmelCase							  =			[encode_for_summarization(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	) for _, story, summary in data]
					__lowerCAmelCase							  =			torch.tensor(
					    [truncate_or_pad(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer.pad_token_id	) for story, _ in encoded_text]	)
					__lowerCAmelCase							  =			compute_token_type_ids(SCREAMING_SNAKE_CASE_ , tokenizer.cls_token_id	)
					__lowerCAmelCase							  =			build_mask(SCREAMING_SNAKE_CASE_ , tokenizer.pad_token_id	)

					__lowerCAmelCase							  =			Batch(
					    document_names=SCREAMING_SNAKE_CASE_ , batch_size=len(SCREAMING_SNAKE_CASE_	) , src=encoded_stories.to(SCREAMING_SNAKE_CASE_	) , segs=encoder_token_type_ids.to(SCREAMING_SNAKE_CASE_	) , mask_src=encoder_mask.to(SCREAMING_SNAKE_CASE_	) , tgt_str=SCREAMING_SNAKE_CASE_ , )

					return batch







def       _a (		SCREAMING_SNAKE_CASE_				:							Dict , SCREAMING_SNAKE_CASE_				:							Optional[Any]	):
					__lowerCAmelCase							  =			summary_tokens.to("cpu"	).numpy()
					__lowerCAmelCase							  =			tokenizer.decode(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			summary.split("."	)
					__lowerCAmelCase							  =			[s + "." for s in sentences]
					return sentences







def       _a (		):
					__lowerCAmelCase							  =			argparse.ArgumentParser()
					parser.add_argument(
					    "--documents_dir" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="The folder where the documents to summarize are located." , )
					parser.add_argument(
					    "--summaries_output_dir" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="The folder in wich the summaries should be written. Defaults to the folder where the documents are" , )
					parser.add_argument(
					    "--compute_rouge" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset." , )
					# EVALUATION options
					parser.add_argument(
					    "--no_cuda" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help="Whether to force the execution on CPU." , )
					parser.add_argument(
					    "--batch_size" , default=4 , type=SCREAMING_SNAKE_CASE_ , help="Batch size per GPU/CPU for training." , )
					# BEAM SEARCH arguments
					parser.add_argument(
					    "--min_length" , default=50 , type=SCREAMING_SNAKE_CASE_ , help="Minimum number of tokens for the summaries." , )
					parser.add_argument(
					    "--max_length" , default=2_00 , type=SCREAMING_SNAKE_CASE_ , help="Maixmum number of tokens for the summaries." , )
					parser.add_argument(
					    "--beam_size" , default=5 , type=SCREAMING_SNAKE_CASE_ , help="The number of beams to start with for each example." , )
					parser.add_argument(
					    "--alpha" , default=0.95 , type=SCREAMING_SNAKE_CASE_ , help="The value of alpha for the length penalty in the beam search." , )
					parser.add_argument(
					    "--block_trigram" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help="Whether to block the existence of repeating trigrams in the text generated by beam search." , )
					__lowerCAmelCase							  =			parser.parse_args()

					# Select device (distibuted not available)
					__lowerCAmelCase							  =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"	)

					# Check the existence of directories
					if not args.summaries_output_dir:
										__lowerCAmelCase							  =			args.documents_dir

					if not documents_dir_is_valid(args.documents_dir	):
										raise FileNotFoundError(
										    "We could not find the directory you specified for the documents to summarize, or it was empty. Please"
										    " specify a valid path."	)
					os.makedirs(args.summaries_output_dir , exist_ok=SCREAMING_SNAKE_CASE_	)

					evaluate(SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					if not os.path.exists(SCREAMING_SNAKE_CASE_	):
										return False

					__lowerCAmelCase							  =			os.listdir(SCREAMING_SNAKE_CASE_	)
					if len(SCREAMING_SNAKE_CASE_	) == 0:
										return False

					return True


if __name__ == "__main__":
					main()




