




'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import MobileNetVaImageProcessor






class 					lowercase_			(	unittest.TestCase       ):





     def __init__(    self    :				int    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				List[str]=7    ,       __lowerCamelCase    :				List[str]=3    ,       __lowerCamelCase    :				Tuple=18    ,       __lowerCamelCase    :				Dict=30    ,       __lowerCamelCase    :				Union[str, Any]=400    ,       __lowerCamelCase    :				str=True    ,       __lowerCamelCase    :				List[str]=None    ,       __lowerCamelCase    :				Any=True    ,       __lowerCamelCase    :				Dict=None    ,       ):
           snake_case__					:      List[Any]  			=						size if size is not None else {"shortest_edge": 20}
           snake_case__					:      Tuple  			=						crop_size if crop_size is not None else {"height": 18, "width": 18}
           snake_case__					:      Optional[Any]  			=						parent
           snake_case__					:      int  			=						batch_size
           snake_case__					:      List[str]  			=						num_channels
           snake_case__					:      Any  			=						image_size
           snake_case__					:      List[str]  			=						min_resolution
           snake_case__					:      List[Any]  			=						max_resolution
           snake_case__					:      Any  			=						do_resize
           snake_case__					:      List[str]  			=						size
           snake_case__					:      Tuple  			=						do_center_crop
           snake_case__					:      List[str]  			=						crop_size





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
           }






@require_torch
@require_vision
class 					lowercase_			(	UpperCAmelCase__	,					unittest.TestCase       ):
     A_       							= MobileNetVaImageProcessor if is_vision_available() else None





     def  _lowerCAmelCase			(    self    :				Tuple ):
           snake_case__					:      Optional[Any]  			=						MobileNetVaImageProcessingTester(self )





     @property
     def  _lowerCAmelCase			(    self    :				Tuple ):
           return self.image_processor_tester.prepare_image_processor_dict()





     def  _lowerCAmelCase			(    self    :				Tuple ):
           snake_case__					:      Union[str, Any]  			=						self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(lowerCamelCase__    ,       'do_resize' ) )
           self.assertTrue(hasattr(lowerCamelCase__    ,       'size' ) )
           self.assertTrue(hasattr(lowerCamelCase__    ,       'do_center_crop' ) )
           self.assertTrue(hasattr(lowerCamelCase__    ,       'center_crop' ) )





     def  _lowerCAmelCase			(    self    :				int ):
           snake_case__					:      Dict  			=						self.image_processing_class.from_dict(self.image_processor_dict )
           self.assertEqual(image_processor.size    ,       {'shortest_edge': 20} )
           self.assertEqual(image_processor.crop_size    ,       {'height': 18, 'width': 18} )

           snake_case__					:      List[Any]  			=						self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42    ,       crop_size=84 )
           self.assertEqual(image_processor.size    ,       {'shortest_edge': 42} )
           self.assertEqual(image_processor.crop_size    ,       {'height': 84, 'width': 84} )





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           pass





     def  _lowerCAmelCase			(    self    :				Dict ):
           # Initialize image_processing
           snake_case__					:      Dict  			=						self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           snake_case__					:      Optional[int]  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=lowerCamelCase__ )
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__    ,       Image.Image )

           # Test not batched input
           snake_case__					:      Tuple  			=						image_processing(image_inputs[0]    ,       return_tensors='pt' ).pixel_values
           self.assertEqual(
               encoded_images.shape    ,       (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,       )

           # Test batched
           snake_case__					:      str  			=						image_processing(lowerCamelCase__    ,       return_tensors='pt' ).pixel_values
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,       )





     def  _lowerCAmelCase			(    self    :				Any ):
           # Initialize image_processing
           snake_case__					:      Optional[int]  			=						self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           snake_case__					:      List[str]  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=lowerCamelCase__    ,       numpify=lowerCamelCase__ )
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__    ,       np.ndarray )

           # Test not batched input
           snake_case__					:      List[str]  			=						image_processing(image_inputs[0]    ,       return_tensors='pt' ).pixel_values
           self.assertEqual(
               encoded_images.shape    ,       (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,       )

           # Test batched
           snake_case__					:      Union[str, Any]  			=						image_processing(lowerCamelCase__    ,       return_tensors='pt' ).pixel_values
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,       )





     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           # Initialize image_processing
           snake_case__					:      List[str]  			=						self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           snake_case__					:      Union[str, Any]  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=lowerCamelCase__    ,       torchify=lowerCamelCase__ )
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__    ,       torch.Tensor )

           # Test not batched input
           snake_case__					:      Any  			=						image_processing(image_inputs[0]    ,       return_tensors='pt' ).pixel_values
           self.assertEqual(
               encoded_images.shape    ,       (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,       )

           # Test batched
           snake_case__					:      str  			=						image_processing(lowerCamelCase__    ,       return_tensors='pt' ).pixel_values
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,       )








'''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		)						->      Dict:
      snake_case__					:      Dict  			=						BertTokenizer.from_pretrained('bert-base-uncased'							,			do_lower_case=__SCREAMING_SNAKE_CASE		)
      snake_case__					:      List[Any]  			=						BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'		)
      model.to(args.device		)
      model.eval()

      snake_case__					:      Optional[int]  			=						{
          'BOS': tokenizer.vocab['[unused0]'],
          'EOS': tokenizer.vocab['[unused1]'],
          'PAD': tokenizer.vocab['[PAD]'],
      }

      if args.compute_rouge:
            snake_case__					:      Any  			=						[]
            snake_case__					:      Any  			=						[]

            import nltk
            import rouge

            nltk.download('punkt'		)
            snake_case__					:      str  			=						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__					:      List[Any]  			=						''
      snake_case__					:      int  			=						''

      snake_case__					:      str  			=						build_data_iterator(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Optional[Any]  			=						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__					:      Dict  			=						predictor.translate_batch(__SCREAMING_SNAKE_CASE		)
            snake_case__					:      Optional[Any]  			=						predictor.from_batch(__SCREAMING_SNAKE_CASE		)
            snake_case__					:      Union[str, 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__					:      int  			=						rouge_evaluator.get_scores(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
            snake_case__					:      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		)						->      Optional[int]:
      for summary, document_name in zip(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		):
            # Prepare the summary file's name
            if "." in document_name:
                  snake_case__					:      str  			=						'.'.join(document_name.split('.'		)[:-1]		)
                  snake_case__					:      Optional[int]  			=						document_name.split('.'		)[-1]
                  snake_case__					:      Tuple  			=						bare_document_name + '_summary.' + extension
            else:
                  snake_case__					:      Union[str, Any]  			=						document_name + '_summary'

            snake_case__					:      Union[str, Any]  			=						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		)						->      List[Any]:
      snake_case__			,       snake_case__			,       snake_case__					:      Any  			=						translation
      snake_case__					:      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		)						->      Optional[int]:
      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		)						->      Optional[Any]:
      with open('rouge_scores.txt'							,			'w'		) as output:
            output.write(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Any:
      snake_case__					:      Optional[Any]  			=						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=512							,			device=args.device		)

      snake_case__					:      List[Any]  			=						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		)						->      Dict:
      snake_case__					:      Any  			=						CNNDMDataset(args.documents_dir		)
      return dataset






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      List[Any]:
      snake_case__					:      Optional[int]  			=						[x for x in data if not len(x[1]		) == 0]  # remove empty_files
      snake_case__					:      List[str]  			=						[name for name, _, _ in data]
      snake_case__					:      List[str]  			=						[' '.join(__SCREAMING_SNAKE_CASE		) for _, _, summary_list in data]

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

      snake_case__					:      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		)						->      Optional[int]:
      snake_case__					:      Optional[Any]  			=						summary_tokens.to('cpu'		).numpy()
      snake_case__					:      Dict  			=						tokenizer.decode(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Optional[int]  			=						summary.split('.'		)
      snake_case__					:      Dict  			=						[s + '.' for s in sentences]
      return sentences






def 		UpperCamelCase__			(		)						->      Any:
      snake_case__					:      Optional[int]  			=						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=200							,			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__					:      str  			=						parser.parse_args()

      # Select device (distibuted not available)
      snake_case__					:      str  			=						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__					:      Union[str, Any]  			=						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		)						->      Dict:
      if not os.path.exists(__SCREAMING_SNAKE_CASE		):
            return False

      snake_case__					:      int  			=						os.listdir(__SCREAMING_SNAKE_CASE		)
      if len(__SCREAMING_SNAKE_CASE		) == 0:
            return False

      return True


if __name__ == "__main__":
      main()
