

def 	lowerCamelCase__	(   _a = "mytext.txt"							,     _a = False):
	import socket

	SCREAMING_SNAKE_CASE				: str			       =    12312  # Reserve a port for your service.
	SCREAMING_SNAKE_CASE				: str			       =    socket.socket()  # Create a socket object
	SCREAMING_SNAKE_CASE				: int			       =    socket.gethostname()  # Get local machine name
	sock.bind((host, port))  # Bind to the port
	sock.listen(5)  # Now wait for client connection.

	print("Server listening....")

	while True:
		SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    sock.accept()  # Establish connection with client.
		print(f"Got connection from {addr}")
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    conn.recv(1024)
		print(f"Server received: {data = }")

		with open(_a							,     "rb") as in_file:
			SCREAMING_SNAKE_CASE				: Optional[int]			       =    in_file.read(1024)
			while data:
				conn.send(_a)
				print(f"Sent {data!r}")
				SCREAMING_SNAKE_CASE				: Tuple			       =    in_file.read(1024)

		print("Done sending")
		conn.close()
		if testing:  # Allow the test to complete
			break

	sock.shutdown(1)
	sock.close()


if __name__ == "__main__":
			send_file()


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,
)


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


a_         =      namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])

def 	lowerCamelCase__	(   _a):
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    BertTokenizer.from_pretrained("bert-base-uncased"							,     do_lower_case=_a)
	SCREAMING_SNAKE_CASE				: int			       =    BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization")
	model.to(args.device)
	model.eval()

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

	if args.compute_rouge:
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    []
		SCREAMING_SNAKE_CASE				: int			       =    []

		import nltk
		import rouge

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

	# these (unused) arguments are defined to keep the compatibility
	# with the legacy code and will be deleted in a next iteration.
	SCREAMING_SNAKE_CASE				: int			       =    ""
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    ""

	SCREAMING_SNAKE_CASE				: Tuple			       =    build_data_iterator(_a							,     _a)
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    build_predictor(_a							,     _a							,     _a							,     _a)

	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(_a):
		SCREAMING_SNAKE_CASE				: Tuple			       =    predictor.translate_batch(_a)
		SCREAMING_SNAKE_CASE				: List[Any]			       =    predictor.from_batch(_a)
		SCREAMING_SNAKE_CASE				: List[str]			       =    [format_summary(_a) for t in translations]
		save_summaries(_a							,     args.summaries_output_dir							,     batch.document_names)

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

	if args.compute_rouge:
		SCREAMING_SNAKE_CASE				: List[Any]			       =    rouge_evaluator.get_scores(_a							,     _a)
		SCREAMING_SNAKE_CASE				: Tuple			       =    format_rouge_scores(_a)
		save_rouge_scores(_a)
		print(_a)

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

		SCREAMING_SNAKE_CASE				: Dict			       =    os.path.join(_a							,     _a)
		with open(_a							,     "w") as output:
			output.write(_a)

def 	lowerCamelCase__	(   _a):
	SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Optional[Any]			       =    translation
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    (
	    raw_summary.replace("[unused0]"							,     "")
	    .replace("[unused3]"							,     "")
	    .replace("[PAD]"							,     "")
	    .replace("[unused1]"							,     "")
	    .replace(r" +"							,     " ")
	    .replace(" [unused2] "							,     ". ")
	    .replace("[unused2]"							,     "")
	    .strip()
	)

	return summary

def 	lowerCamelCase__	(   _a):
	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 	lowerCamelCase__	(   _a):
	with open("rouge_scores.txt"							,     "w") as output:
		output.write(_a)

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Any			       =    load_and_cache_examples(_a							,     _a)
	SCREAMING_SNAKE_CASE				: int			       =    SequentialSampler(_a)

	def collate_fn(_a):
		return collate(_a							,     _a							,     block_size=512							,     device=args.device)

	SCREAMING_SNAKE_CASE				: Tuple			       =    DataLoader(
	    _a							,     sampler=_a							,     batch_size=args.batch_size							,     collate_fn=_a							,     )

	return iterator

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Tuple			       =    CNNDMDataset(args.documents_dir)
	return dataset

def 	lowerCamelCase__	(   _a							,     _a							,     _a							,     _a):
	SCREAMING_SNAKE_CASE				: str			       =    [x for x in data if not len(x[1]) == 0]  # remove empty_files
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    [name for name, _, _ in data]
	SCREAMING_SNAKE_CASE				: Dict			       =    [" ".join(_a) for _, _, summary_list in data]

	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    [encode_for_summarization(_a							,     _a							,     _a) for _, story, summary in data]
	SCREAMING_SNAKE_CASE				: Dict			       =    torch.tensor(
	    [truncate_or_pad(_a							,     _a							,     tokenizer.pad_token_id) for story, _ in encoded_text])
	SCREAMING_SNAKE_CASE				: List[Any]			       =    compute_token_type_ids(_a							,     tokenizer.cls_token_id)
	SCREAMING_SNAKE_CASE				: Dict			       =    build_mask(_a							,     tokenizer.pad_token_id)

	SCREAMING_SNAKE_CASE				: Optional[int]			       =    Batch(
	    document_names=_a							,     batch_size=len(_a)							,     src=encoded_stories.to(_a)							,     segs=encoder_token_type_ids.to(_a)							,     mask_src=encoder_mask.to(_a)							,     tgt_str=_a							,     )

	return batch

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: int			       =    summary_tokens.to("cpu").numpy()
	SCREAMING_SNAKE_CASE				: Tuple			       =    tokenizer.decode(_a)
	SCREAMING_SNAKE_CASE				: List[str]			       =    summary.split(".")
	SCREAMING_SNAKE_CASE				: int			       =    [s + "." for s in sentences]
	return sentences

def 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: str			       =    argparse.ArgumentParser()
	parser.add_argument(
	    "--documents_dir"							,     default=_a							,     type=_a							,     required=_a							,     help="The folder where the documents to summarize are located."							,     )
	parser.add_argument(
	    "--summaries_output_dir"							,     default=_a							,     type=_a							,     required=_a							,     help="The folder in wich the summaries should be written. Defaults to the folder where the documents are"							,     )
	parser.add_argument(
	    "--compute_rouge"							,     default=_a							,     type=_a							,     required=_a							,     help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset."							,     )
	# EVALUATION options
	parser.add_argument(
	    "--no_cuda"							,     default=_a							,     type=_a							,     help="Whether to force the execution on CPU."							,     )
	parser.add_argument(
	    "--batch_size"							,     default=4							,     type=_a							,     help="Batch size per GPU/CPU for training."							,     )
	# BEAM SEARCH arguments
	parser.add_argument(
	    "--min_length"							,     default=50							,     type=_a							,     help="Minimum number of tokens for the summaries."							,     )
	parser.add_argument(
	    "--max_length"							,     default=200							,     type=_a							,     help="Maixmum number of tokens for the summaries."							,     )
	parser.add_argument(
	    "--beam_size"							,     default=5							,     type=_a							,     help="The number of beams to start with for each example."							,     )
	parser.add_argument(
	    "--alpha"							,     default=0.95							,     type=_a							,     help="The value of alpha for the length penalty in the beam search."							,     )
	parser.add_argument(
	    "--block_trigram"							,     default=_a							,     type=_a							,     help="Whether to block the existence of repeating trigrams in the text generated by beam search."							,     )
	SCREAMING_SNAKE_CASE				: Tuple			       =    parser.parse_args()

	# Select device (distibuted not available)
	SCREAMING_SNAKE_CASE				: Dict			       =    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:
		SCREAMING_SNAKE_CASE				: Dict			       =    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=_a)

	evaluate(_a)

def 	lowerCamelCase__	(   _a):
	if not os.path.exists(_a):
		return False

	SCREAMING_SNAKE_CASE				: Any			       =    os.listdir(_a)
	if len(_a) == 0:
		return False

	return True


if __name__ == "__main__":
			main()