




import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_=None					)				->		List[Any]:

    """simple docstring"""


    __UpperCamelCase     						=  None
    # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
    if str(weight.dtype					) == "torch.bfloat16":
        # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
        __UpperCamelCase     						=  weight.view(torch.intaa					)
        __UpperCamelCase     						=  '''bfloat16'''
    __UpperCamelCase     						=  weight.cpu().numpy()
    __UpperCamelCase     						=  os.path.join(lowercase_    ,   F"{weight_name}.dat"					)
    if index is not None:
        if dtype is None:
            __UpperCamelCase     						=  str(array.dtype					)
        __UpperCamelCase     						=  {'''dtype''': dtype, '''shape''': list(array.shape					)}
    if array.ndim == 0:
        __UpperCamelCase     						=  array[None]
    __UpperCamelCase     						=  np.memmap(lowercase_    ,   dtype=array.dtype    ,   mode='''w+'''    ,   shape=array.shape					)
    __UpperCamelCase     						=  array[:]
    file_array.flush()
    return index




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		List[Any]:

    """simple docstring"""


    __UpperCamelCase     						=  tuple(weight_info['''shape''']					)
    if shape == ():
        # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
        __UpperCamelCase     						=  (1,)

    __UpperCamelCase     						=  weight_info['''dtype''']
    if dtype == "bfloat16":
        # NumPy does not support bfloat16 so this was saved as a int16
        __UpperCamelCase     						=  '''int16'''

    __UpperCamelCase     						=  np.memmap(lowercase_    ,   dtype=lowercase_    ,   shape=lowercase_    ,   mode='''r'''					)

    if len(weight_info['''shape''']					) == 0:
        __UpperCamelCase     						=  weight[0]
    __UpperCamelCase     						=  torch.tensor(lowercase_					)
    if weight_info["dtype"] == "bfloat16":
        __UpperCamelCase     						=  weight.view(torch.bfloataa					)

    return weight




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		Dict:

    """simple docstring"""


    if index is None or len(lowercase_					) == 0:
        # Nothing to save
        return

    __UpperCamelCase     						=  os.path.join(lowercase_    ,   '''index.json'''					)
    if os.path.isfile(lowercase_					):
        with open(lowercase_    ,   '''r'''    ,   encoding='''utf-8'''					) as f:
            __UpperCamelCase     						=  json.load(lowercase_					)
    else:
        __UpperCamelCase     						=  {}
    current_index.update(lowercase_					)

    with open(lowercase_    ,   '''w'''    ,   encoding='''utf-8'''					) as f:
        json.dump(lowercase_    ,   lowercase_    ,   indent=2					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		Tuple:

    """simple docstring"""


    os.makedirs(lowercase_    ,   exist_ok=lowercase_					)
    __UpperCamelCase     						=  {}
    for name, parameter in state_dict.items():
        __UpperCamelCase     						=  offload_weight(lowercase_    ,   lowercase_    ,   lowercase_    ,   index=lowercase_					)

    # Update index
    save_offload_index(lowercase_    ,   lowercase_					)

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      def __init__(   self    :       int       ,				snake_case    :       Mapping       ,				snake_case    :       str ):
          __UpperCamelCase     						=  dataset
          __UpperCamelCase     						=  prefix







      def __getitem__(   self    :       Any       ,				snake_case    :       Optional[Any] ):
          return self.dataset[F"{self.prefix}{key}"]







      def __iter__(   self    :       Tuple ):
          return iter([key for key in self.dataset if key.startswith(self.prefix )] )







      def __len__(   self    :       List[Any] ):
          return len(self.dataset )







class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      def __init__(   self    :       Optional[int]       ,				snake_case    :       Dict[str, torch.Tensor] = None       ,				snake_case    :       Optional[Union[str, os.PathLike]] = None       ,				snake_case    :       Mapping = None       ,				snake_case    :       Union[str, Any]=None       ,				):
          if state_dict is None and save_folder is None:
              raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.''' )

          __UpperCamelCase     						=  {} if state_dict is None else state_dict
          __UpperCamelCase     						=  save_folder
          if index is None and save_folder is not None:
              with open(os.path.join(snake_case       ,				'''index.json''' ) ) as f:
                  __UpperCamelCase     						=  json.load(snake_case )
          __UpperCamelCase     						=  {} if index is None else index
          __UpperCamelCase     						=  list(self.state_dict.keys() )
          self.all_keys.extend([key for key in self.index if key not in self.all_keys] )
          __UpperCamelCase     						=  device







      def __getitem__(   self    :       List[Any]       ,				snake_case    :       str ):
          # State dict gets priority
          if key in self.state_dict:
              return self.state_dict[key]
          __UpperCamelCase     						=  self.index[key]
          if weight_info.get('''safetensors_file''' ) is not None:
              if not is_safetensors_available():
                  raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.''' )

              from safetensors import safe_open

              __UpperCamelCase     						=  '''cpu''' if self.device is None else self.device
              with safe_open(weight_info['''safetensors_file''']       ,				framework='''pt'''       ,				device=snake_case ) as f:
                  __UpperCamelCase     						=  f.get_tensor(weight_info.get('''weight_name'''       ,				snake_case ) )

              if "dtype" in weight_info:
                  return tensor.to(getattr(snake_case       ,				weight_info['''dtype'''] ) )
              else:
                  return tensor

          __UpperCamelCase     						=  os.path.join(self.save_folder       ,				F"{key}.dat" )
          return load_offloaded_weight(snake_case       ,				snake_case )







      def __iter__(   self    :       Optional[Any] ):
          return iter(self.all_keys )







      def __len__(   self    :       List[Any] ):
          return len(self.all_keys )







def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		List[Any]:

    """simple docstring"""


    __UpperCamelCase     						=  {}
    for module_name in submodule_names:
        # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
        # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
        result.update(
            {
                key: param
                for key, param in state_dict.items()
                if key == module_name or key.startswith(module_name + '''.'''					)
            }					)
    return result













'''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,
)


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


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




def       _lowerCAmelCase  ( lowerCamelCase_	:     int ):
			__lowercase   =							BertTokenizer.from_pretrained('''bert-base-uncased'''						,				do_lower_case=lowerCamelCase_ )
			__lowercase   =							BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization''' )
			model.to(args.device )
			model.eval()

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

			if args.compute_rouge:
						__lowercase   =							[]
						__lowercase   =							[]

						import nltk
						import rouge

						nltk.download('''punkt''' )
						__lowercase   =							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.
			__lowercase   =							''''''
			__lowercase   =							''''''

			__lowercase   =							build_data_iterator(lowerCamelCase_						,				lowerCamelCase_ )
			__lowercase   =							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_ ):
						__lowercase   =							predictor.translate_batch(lowerCamelCase_ )
						__lowercase   =							predictor.from_batch(lowerCamelCase_ )
						__lowercase   =							[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:
						__lowercase   =							rouge_evaluator.get_scores(lowerCamelCase_						,				lowerCamelCase_ )
						__lowercase   =							format_rouge_scores(lowerCamelCase_ )
						save_rouge_scores(lowerCamelCase_ )
						print(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     str						,				lowerCamelCase_	:     Dict ):
			for summary, document_name in zip(lowerCamelCase_						,				lowerCamelCase_ ):
						# Prepare the summary file's name
						if "." in document_name:
									__lowercase   =							'''.'''.join(document_name.split('''.''' )[:-1] )
									__lowercase   =							document_name.split('''.''' )[-1]
									__lowercase   =							bare_document_name + '''_summary.''' + extension
						else:
									__lowercase   =							document_name + '''_summary'''

						__lowercase   =							os.path.join(lowerCamelCase_						,				lowerCamelCase_ )
						with open(lowerCamelCase_						,				'''w''' ) as output:
									output.write(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any] ):
			__lowercase  ,						__lowercase  ,						__lowercase   =							translation
			__lowercase   =							(
			    raw_summary.replace('''[unused0]'''						,				'''''' )
			    .replace('''[unused3]'''						,				'''''' )
			    .replace('''[PAD]'''						,				'''''' )
			    .replace('''[unused1]'''						,				'''''' )
			    .replace(r''' +'''						,				''' ''' )
			    .replace(''' [unused2] '''						,				'''. ''' )
			    .replace('''[unused2]'''						,				'''''' )
			    .strip()
			)

			return summary




def       _lowerCAmelCase  ( lowerCamelCase_	:     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       _lowerCAmelCase  ( lowerCamelCase_	:     Dict ):
			with open('''rouge_scores.txt'''						,				'''w''' ) as output:
						output.write(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							load_and_cache_examples(lowerCamelCase_						,				lowerCamelCase_ )
			__lowercase   =							SequentialSampler(lowerCamelCase_ )

			def collate_fn(lowerCamelCase_	:     List[str] ):
						return collate(lowerCamelCase_						,				lowerCamelCase_						,				block_size=5_1_2						,				device=args.device )

			__lowercase   =							DataLoader(
			    lowerCamelCase_						,				sampler=lowerCamelCase_						,				batch_size=args.batch_size						,				collate_fn=lowerCamelCase_						,				)

			return iterator




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							CNNDMDataset(args.documents_dir )
			return dataset




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     Any						,				lowerCamelCase_	:     Optional[Any] ):
			__lowercase   =							[x for x in data if not len(x[1] ) == 0]  # remove empty_files
			__lowercase   =							[name for name, _, _ in data]
			__lowercase   =							[''' '''.join(lowerCamelCase_ ) for _, _, summary_list in data]

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

			__lowercase   =							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_	:     List[Any]						,				lowerCamelCase_	:     Union[str, Any] ):
			__lowercase   =							summary_tokens.to('''cpu''' ).numpy()
			__lowercase   =							tokenizer.decode(lowerCamelCase_ )
			__lowercase   =							summary.split('''.''' )
			__lowercase   =							[s + '''.''' for s in sentences]
			return sentences




def       _lowerCAmelCase  ( ):
			__lowercase   =							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=5_0						,				type=lowerCamelCase_						,				help='''Minimum number of tokens for the summaries.'''						,				)
			parser.add_argument(
			    '''--max_length'''						,				default=2_0_0						,				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.'''						,				)
			__lowercase   =							parser.parse_args()

			# Select device (distibuted not available)
			__lowercase   =							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:
						__lowercase   =							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_	:     Optional[Any] ):
			if not os.path.exists(lowerCamelCase_ ):
						return False

			__lowercase   =							os.listdir(lowerCamelCase_ )
			if len(lowerCamelCase_ ) == 0:
						return False

			return True


if __name__ == "__main__":
	main()





