"""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 			lowerCamelCase_ (					__lowerCAmelCase				)							->					Tuple:




						'''simple docstring'''



						lowerCamelCase__					=BertTokenizer.from_pretrained("bert-base-uncased"   ,    do_lower_case=__lowerCAmelCase				)
						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=__lowerCAmelCase   ,    length_limit=args.beam_size   ,    length_limit_type="words"   ,    apply_avg=__lowerCAmelCase   ,    apply_best=__lowerCAmelCase   ,    alpha=0.5   ,    weight_factor=1.2   ,    stemming=__lowerCAmelCase   ,    )

						# 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(__lowerCAmelCase   ,    __lowerCAmelCase				)
						lowerCamelCase__					=build_predictor(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)

						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(__lowerCAmelCase				):
												lowerCamelCase__					=predictor.translate_batch(__lowerCAmelCase				)
												lowerCamelCase__					=predictor.from_batch(__lowerCAmelCase				)
												lowerCamelCase__					=[format_summary(__lowerCAmelCase				) for t in translations]
												save_summaries(__lowerCAmelCase   ,    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(__lowerCAmelCase   ,    __lowerCAmelCase				)
												lowerCamelCase__					=format_rouge_scores(__lowerCAmelCase				)
												save_rouge_scores(__lowerCAmelCase				)
												print(__lowerCAmelCase				)





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[Any]:




						'''simple docstring'''



						for summary, document_name in zip(__lowerCAmelCase   ,    __lowerCAmelCase				):
												# 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(__lowerCAmelCase   ,    __lowerCAmelCase				)
												with open(__lowerCAmelCase   ,    "w"				) as output:
																		output.write(__lowerCAmelCase				)





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					List[Any]:




						'''simple docstring'''



						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 			lowerCamelCase_ (					__lowerCAmelCase				)							->					Optional[Any]:




						'''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 			lowerCamelCase_ (					__lowerCAmelCase				)							->					List[Any]:




						'''simple docstring'''



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





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__					=load_and_cache_examples(__lowerCAmelCase   ,    __lowerCAmelCase				)
						lowerCamelCase__					=SequentialSampler(__lowerCAmelCase				)

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

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

						return iterator





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Tuple:




						'''simple docstring'''



						lowerCamelCase__					=CNNDMDataset(args.documents_dir				)
						return dataset





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					=[x for x in data if not len(x[1]				) == 0]  # remove empty_files
						lowerCamelCase__					=[name for name, _, _ in data]
						lowerCamelCase__					=[" ".join(__lowerCAmelCase				) for _, _, summary_list in data]

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

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

						return batch





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[Any]:




						'''simple docstring'''



						lowerCamelCase__					=summary_tokens.to("cpu"				).numpy()
						lowerCamelCase__					=tokenizer.decode(__lowerCAmelCase				)
						lowerCamelCase__					=summary.split("."				)
						lowerCamelCase__					=[s + "." for s in sentences]
						return sentences





def 			lowerCamelCase_ (					)							->					List[Any]:




						'''simple docstring'''



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

						evaluate(__lowerCAmelCase				)





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					List[str]:




						'''simple docstring'''



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

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

						return True


if __name__ == "__main__":
	main()










from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase     :							Any	     =			logging.get_logger(__name__)

lowercase     :							int	     =			{
    # pcqm4mv1 now deprecated
    '''graphormer-base''': '''https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json''',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						A   :      Optional[int]						 =    'graphormer'
						A   :      List[str]						 =    ['past_key_values']






						def __init__(		self						, _SCREAMING_SNAKE_CASE = 1						, _SCREAMING_SNAKE_CASE = 512 * 9						, _SCREAMING_SNAKE_CASE = 512 * 3						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 128						, _SCREAMING_SNAKE_CASE = 5						, _SCREAMING_SNAKE_CASE = 1024						, _SCREAMING_SNAKE_CASE = "multi_hop"						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 12						, _SCREAMING_SNAKE_CASE = 768						, _SCREAMING_SNAKE_CASE = 768						, _SCREAMING_SNAKE_CASE = 32						, _SCREAMING_SNAKE_CASE = 0.1						, _SCREAMING_SNAKE_CASE = 0.1						, _SCREAMING_SNAKE_CASE = 0.0						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = "gelu"						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 0						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 0.0						, _SCREAMING_SNAKE_CASE = 8						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = True						, _SCREAMING_SNAKE_CASE = True						, _SCREAMING_SNAKE_CASE=0						, _SCREAMING_SNAKE_CASE=1						, _SCREAMING_SNAKE_CASE=2						, **_SCREAMING_SNAKE_CASE						, )       -> List[str]:
								snake_case_ :							str         =				num_classes
								snake_case_ :							Union[str, Any]         =				num_atoms
								snake_case_ :							Dict         =				num_in_degree
								snake_case_ :							Union[str, Any]         =				num_out_degree
								snake_case_ :							List[Any]         =				num_edges
								snake_case_ :							Union[str, Any]         =				num_spatial
								snake_case_ :							int         =				num_edge_dis
								snake_case_ :							List[Any]         =				edge_type
								snake_case_ :							Any         =				multi_hop_max_dist
								snake_case_ :							Tuple         =				spatial_pos_max
								snake_case_ :							Union[str, Any]         =				max_nodes
								snake_case_ :							str         =				num_hidden_layers
								snake_case_ :							str         =				embedding_dim
								snake_case_ :							List[str]         =				embedding_dim
								snake_case_ :							Dict         =				ffn_embedding_dim
								snake_case_ :							Any         =				num_attention_heads
								snake_case_ :							List[str]         =				dropout
								snake_case_ :							List[str]         =				attention_dropout
								snake_case_ :							Optional[Any]         =				layerdrop
								snake_case_ :							Optional[int]         =				encoder_normalize_before
								snake_case_ :							Union[str, Any]         =				pre_layernorm
								snake_case_ :							int         =				apply_graphormer_init
								snake_case_ :							List[Any]         =				activation_fn
								snake_case_ :							str         =				embed_scale
								snake_case_ :							int         =				freeze_embeddings
								snake_case_ :							Optional[Any]         =				num_trans_layers_to_freeze
								snake_case_ :							int         =				share_input_output_embed
								snake_case_ :							int         =				traceable
								snake_case_ :							Tuple         =				q_noise
								snake_case_ :							Tuple         =				qn_block_size

								# These parameters are here for future extensions
								# atm, the model only supports self attention
								snake_case_ :							List[Any]         =				kdim
								snake_case_ :							Tuple         =				vdim
								snake_case_ :							Any         =				self_attention
								snake_case_ :							Any         =				bias

								super().__init__(
								    pad_token_id=_SCREAMING_SNAKE_CASE						, bos_token_id=_SCREAMING_SNAKE_CASE						, eos_token_id=_SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE						, )
