
"""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,
)


__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 		_snake_case			(      lowercase__						:    int						)    ->							Optional[int]:


			'''simple docstring'''





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

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

			if args.compute_rouge:
						lowerCAmelCase_       :Dict     	=    []
						lowerCAmelCase_       :Dict     	=    []

						import nltk
						import rouge

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

			# these (unused) arguments are defined to keep the compatibility
			# with the legacy code and will be deleted in a next iteration.
			lowerCAmelCase_       :Tuple     	=    """"""
			lowerCAmelCase_       :Union[str, Any]     	=    """"""

			lowerCAmelCase_       :int     	=    build_data_iterator(lowercase__ ,	lowercase__						)
			lowerCAmelCase_       :str     	=    build_predictor(lowercase__ ,	lowercase__ ,	lowercase__ ,	lowercase__						)

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

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

			if args.compute_rouge:
						lowerCAmelCase_       :Tuple     	=    rouge_evaluator.get_scores(lowercase__ ,	lowercase__						)
						lowerCAmelCase_       :int     	=    format_rouge_scores(lowercase__						)
						save_rouge_scores(lowercase__						)
						print(lowercase__						)






def 		_snake_case			(      lowercase__						:    Optional[Any] ,	lowercase__						:    str ,	lowercase__						:    Any						)    ->							Tuple:


			'''simple docstring'''





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

						lowerCAmelCase_       :int     	=    os.path.join(lowercase__ ,	lowercase__						)
						with open(lowercase__ ,	"""w"""						) as output:
									output.write(lowercase__						)






def 		_snake_case			(      lowercase__						:    List[Any]						)    ->							Optional[Any]:


			'''simple docstring'''





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

			return summary






def 		_snake_case			(      lowercase__						:    int						)    ->							List[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 		_snake_case			(      lowercase__						:    Dict						)    ->							Dict:


			'''simple docstring'''





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






def 		_snake_case			(      lowercase__						:    Optional[Any] ,	lowercase__						:    List[str]						)    ->							int:


			'''simple docstring'''





			lowerCAmelCase_       :str     	=    load_and_cache_examples(lowercase__ ,	lowercase__						)
			lowerCAmelCase_       :List[Any]     	=    SequentialSampler(lowercase__						)

			def collate_fn(lowercase__						:    Optional[int]						):
						return collate(lowercase__ ,	lowercase__ ,	block_size=5_1_2 ,	device=args.device						)

			lowerCAmelCase_       :List[str]     	=    DataLoader(
			    lowercase__ ,	sampler=lowercase__ ,	batch_size=args.batch_size ,	collate_fn=lowercase__ ,	)

			return iterator






def 		_snake_case			(      lowercase__						:    Tuple ,	lowercase__						:    Optional[int]						)    ->							List[Any]:


			'''simple docstring'''





			lowerCAmelCase_       :Optional[int]     	=    CNNDMDataset(args.documents_dir						)
			return dataset






def 		_snake_case			(      lowercase__						:    Tuple ,	lowercase__						:    Optional[Any] ,	lowercase__						:    Dict ,	lowercase__						:    Any						)    ->							Optional[int]:


			'''simple docstring'''





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

			lowerCAmelCase_       :Dict     	=    [encode_for_summarization(lowercase__ ,	lowercase__ ,	lowercase__						) for _, story, summary in data]
			lowerCAmelCase_       :List[Any]     	=    torch.tensor(
			    [truncate_or_pad(lowercase__ ,	lowercase__ ,	tokenizer.pad_token_id						) for story, _ in encoded_text]						)
			lowerCAmelCase_       :Union[str, Any]     	=    compute_token_type_ids(lowercase__ ,	tokenizer.cls_token_id						)
			lowerCAmelCase_       :List[Any]     	=    build_mask(lowercase__ ,	tokenizer.pad_token_id						)

			lowerCAmelCase_       :List[str]     	=    Batch(
			    document_names=lowercase__ ,	batch_size=len(lowercase__						) ,	src=encoded_stories.to(lowercase__						) ,	segs=encoder_token_type_ids.to(lowercase__						) ,	mask_src=encoder_mask.to(lowercase__						) ,	tgt_str=lowercase__ ,	)

			return batch






def 		_snake_case			(      lowercase__						:    Optional[Any] ,	lowercase__						:    Any						)    ->							Dict:


			'''simple docstring'''





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






def 		_snake_case			(      )    ->							List[str]:


			'''simple docstring'''





			lowerCAmelCase_       :str     	=    argparse.ArgumentParser()
			parser.add_argument(
			    """--documents_dir""" ,	default=lowercase__ ,	type=lowercase__ ,	required=lowercase__ ,	help="""The folder where the documents to summarize are located.""" ,	)
			parser.add_argument(
			    """--summaries_output_dir""" ,	default=lowercase__ ,	type=lowercase__ ,	required=lowercase__ ,	help="""The folder in wich the summaries should be written. Defaults to the folder where the documents are""" ,	)
			parser.add_argument(
			    """--compute_rouge""" ,	default=lowercase__ ,	type=lowercase__ ,	required=lowercase__ ,	help="""Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.""" ,	)
			# EVALUATION options
			parser.add_argument(
			    """--no_cuda""" ,	default=lowercase__ ,	type=lowercase__ ,	help="""Whether to force the execution on CPU.""" ,	)
			parser.add_argument(
			    """--batch_size""" ,	default=4 ,	type=lowercase__ ,	help="""Batch size per GPU/CPU for training.""" ,	)
			# BEAM SEARCH arguments
			parser.add_argument(
			    """--min_length""" ,	default=5_0 ,	type=lowercase__ ,	help="""Minimum number of tokens for the summaries.""" ,	)
			parser.add_argument(
			    """--max_length""" ,	default=2_0_0 ,	type=lowercase__ ,	help="""Maixmum number of tokens for the summaries.""" ,	)
			parser.add_argument(
			    """--beam_size""" ,	default=5 ,	type=lowercase__ ,	help="""The number of beams to start with for each example.""" ,	)
			parser.add_argument(
			    """--alpha""" ,	default=0.95 ,	type=lowercase__ ,	help="""The value of alpha for the length penalty in the beam search.""" ,	)
			parser.add_argument(
			    """--block_trigram""" ,	default=lowercase__ ,	type=lowercase__ ,	help="""Whether to block the existence of repeating trigrams in the text generated by beam search.""" ,	)
			lowerCAmelCase_       :Any     	=    parser.parse_args()

			# Select device (distibuted not available)
			lowerCAmelCase_       :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:
						lowerCAmelCase_       :str     	=    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=lowercase__						)

			evaluate(lowercase__						)






def 		_snake_case			(      lowercase__						:    List[str]						)    ->							Tuple:


			'''simple docstring'''





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

			lowerCAmelCase_       :Union[str, Any]     	=    os.listdir(lowercase__						)
			if len(lowercase__						) == 0:
						return False

			return True


if __name__ == "__main__":
				main()








"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


__UpperCAmelCase 					=    logging.get_logger(__name__)

__UpperCAmelCase 					=    {
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
				import detectrona




class      _SCREAMING_SNAKE_CASE   (     A__				):
			UpperCAmelCase_    :Optional[int]												=		"layoutlmv2"






			def __init__(   self			,  __A=3_0522			,  __A=768			,  __A=12			,  __A=12			,  __A=3072			,  __A="gelu"			,  __A=0.1			,  __A=0.1			,  __A=512			,  __A=2			,  __A=0.0_2			,  __A=1E-12			,  __A=0			,  __A=1024			,  __A=128			,  __A=32			,  __A=True			,  __A=256			,  __A=64			,  __A=True			,  __A=[7, 7, 256]			,  __A=128			,  __A=128			,  __A=True			,  __A=True			,  __A=False			,  __A=None			,  **__A			,  )     ->	List[str]:
						super().__init__(
						    vocab_size=__A			,  hidden_size=__A			,  num_hidden_layers=__A			,  num_attention_heads=__A			,  intermediate_size=__A			,  hidden_act=__A			,  hidden_dropout_prob=__A			,  attention_probs_dropout_prob=__A			,  max_position_embeddings=__A			,  type_vocab_size=__A			,  initializer_range=__A			,  layer_norm_eps=__A			,  pad_token_id=__A			,  **__A			,  )
						lowerCAmelCase_       :Optional[Any]     	=    max_ad_position_embeddings
						lowerCAmelCase_       :Dict     	=    max_rel_pos
						lowerCAmelCase_       :str     	=    rel_pos_bins
						lowerCAmelCase_       :List[str]     	=    fast_qkv
						lowerCAmelCase_       :Union[str, Any]     	=    max_rel_ad_pos
						lowerCAmelCase_       :List[str]     	=    rel_ad_pos_bins
						lowerCAmelCase_       :Any     	=    convert_sync_batchnorm
						lowerCAmelCase_       :Dict     	=    image_feature_pool_shape
						lowerCAmelCase_       :str     	=    coordinate_size
						lowerCAmelCase_       :Tuple     	=    shape_size
						lowerCAmelCase_       :Tuple     	=    has_relative_attention_bias
						lowerCAmelCase_       :Union[str, Any]     	=    has_spatial_attention_bias
						lowerCAmelCase_       :int     	=    has_visual_segment_embedding
						lowerCAmelCase_       :Any     	=    (
						    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
						)






			@classmethod
			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						return {
						    "MODEL.MASK_ON": True,
						    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
						    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
						    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
						    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
						    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
						    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
						    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
						    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
						    "MODEL.POST_NMS_TOPK_TEST": 1000,
						    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
						    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
						    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
						    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
						    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
						    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
						    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
						    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
						    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
						    "MODEL.RESNETS.DEPTH": 101,
						    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
						    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
						    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
						    "MODEL.RESNETS.NUM_GROUPS": 32,
						    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
						    "MODEL.RESNETS.STRIDE_IN_1X1": False,
						}






			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						lowerCAmelCase_       :Any     	=    detectrona.config.get_cfg()
						for k, v in self.detectrona_config_args.items():
									lowerCAmelCase_       :int     	=    k.split("""."""			)
									lowerCAmelCase_       :List[Any]     	=    detectrona_config
									for attribute in attributes[:-1]:
												lowerCAmelCase_       :List[Any]     	=    getattr(__A			,  __A			)
									setattr(__A			,  attributes[-1]			,  __A			)

						return detectrona_config






