
'''simple docstring'''
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     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     str        =    BertTokenizer.from_pretrained("bert-base-uncased",		do_lower_case=__SCREAMING_SNAKE_CASE							)
    snake_case_			:     Optional[Any]        =    BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"							)
    model.to(args.device							)
    model.eval()

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

    if args.compute_rouge:
        snake_case_			:     Optional[int]        =    []
        snake_case_			:     Dict        =    []

        import nltk
        import rouge

        nltk.download("punkt"							)
        snake_case_			:     Optional[int]        =    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.
    snake_case_			:     str        =    ""
    snake_case_			:     List[Any]        =    ""

    snake_case_			:     str        =    build_data_iterator(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)
    snake_case_			:     Tuple        =    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							):
        snake_case_			:     str        =    predictor.translate_batch(__SCREAMING_SNAKE_CASE							)
        snake_case_			:     Dict        =    predictor.from_batch(__SCREAMING_SNAKE_CASE							)
        snake_case_			:     Any        =    [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:
        snake_case_			:     Optional[Any]        =    rouge_evaluator.get_scores(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)
        snake_case_			:     List[str]        =    format_rouge_scores(__SCREAMING_SNAKE_CASE							)
        save_rouge_scores(__SCREAMING_SNAKE_CASE							)
        print(__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






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

        snake_case_			:     Tuple        =    os.path.join(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)
        with open(__SCREAMING_SNAKE_CASE,		"w"							) as output:
            output.write(__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






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

    return summary

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    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     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    with open("rouge_scores.txt",		"w"							) as output:
        output.write(__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     str        =    load_and_cache_examples(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)
    snake_case_			:     Union[str, Any]        =    SequentialSampler(__SCREAMING_SNAKE_CASE							)

    def collate_fn(__SCREAMING_SNAKE_CASE							):
        return collate(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		block_size=5_1_2,		device=args.device							)

    snake_case_			:     int        =    DataLoader(
        __SCREAMING_SNAKE_CASE,		sampler=__SCREAMING_SNAKE_CASE,		batch_size=args.batch_size,		collate_fn=__SCREAMING_SNAKE_CASE,		)

    return iterator

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     List[str]        =    CNNDMDataset(args.documents_dir							)
    return dataset

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     List[Any]        =    [x for x in data if not len(x[1]							) == 0]  # remove empty_files
    snake_case_			:     str        =    [name for name, _, _ in data]
    snake_case_			:     int        =    [" ".join(__SCREAMING_SNAKE_CASE							) for _, _, summary_list in data]

    snake_case_			:     str        =    [encode_for_summarization(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							) for _, story, summary in data]
    snake_case_			:     int        =    torch.tensor(
        [truncate_or_pad(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		tokenizer.pad_token_id							) for story, _ in encoded_text]							)
    snake_case_			:     Optional[int]        =    compute_token_type_ids(__SCREAMING_SNAKE_CASE,		tokenizer.cls_token_id							)
    snake_case_			:     List[Any]        =    build_mask(__SCREAMING_SNAKE_CASE,		tokenizer.pad_token_id							)

    snake_case_			:     Optional[int]        =    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     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     int        =    summary_tokens.to("cpu"							).numpy()
    snake_case_			:     int        =    tokenizer.decode(__SCREAMING_SNAKE_CASE							)
    snake_case_			:     List[str]        =    summary.split("."							)
    snake_case_			:     Optional[Any]        =    [s + "." for s in sentences]
    return sentences

def     UpperCamelCase_  (						):
    """simple docstring"""






    snake_case_			:     Optional[Any]        =    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=5_0,		type=__SCREAMING_SNAKE_CASE,		help="Minimum number of tokens for the summaries.",		)
    parser.add_argument(
        "--max_length",		default=2_0_0,		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.",		)
    snake_case_			:     Optional[Any]        =    parser.parse_args()

    # Select device (distibuted not available)
    snake_case_			:     int        =    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:
        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=__SCREAMING_SNAKE_CASE							)

    evaluate(__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if not os.path.exists(__SCREAMING_SNAKE_CASE							):
        return False

    snake_case_			:     str        =    os.listdir(__SCREAMING_SNAKE_CASE							)
    if len(__SCREAMING_SNAKE_CASE							) == 0:
        return False

    return True


if __name__ == "__main__":
  main()






'''simple docstring'''
import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Optional[Any]        =    json.load(open(__SCREAMING_SNAKE_CASE,		"r"							)							)
    snake_case_			:     Optional[int]        =    GPTNeoConfig(
        hidden_size=config_json["n_embd"],		num_layers=config_json["n_layer"],		num_heads=config_json["n_head"],		attention_types=config_json["attention_types"],		max_position_embeddings=config_json["n_positions"],		resid_dropout=config_json["res_dropout"],		embed_dropout=config_json["embed_dropout"],		attention_dropout=config_json["attn_dropout"],		)
    print(f'Building PyTorch model from configuration: {config}'							)
    snake_case_			:     Optional[Any]        =    GPTNeoForCausalLM(__SCREAMING_SNAKE_CASE							)

    # Load weights from tf checkpoint
    load_tf_weights_in_gpt_neo(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'							)
    model.save_pretrained(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
  a_           =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained mesh-tf model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  a_           =    parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




