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,
)


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


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





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  int:






      '''simple docstring'''





      UpperCAmelCase    : Optional[int]      =BertTokenizer.from_pretrained('''bert-base-uncased'''		,					do_lower_case=__lowerCAmelCase						)
      UpperCAmelCase    : Union[str, Any]      =BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''						)
      model.to(args.device						)
      model.eval()

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

      if args.compute_rouge:
            UpperCAmelCase    : Any      =[]
            UpperCAmelCase    : List[Any]      =[]

            import nltk
            import rouge

            nltk.download('''punkt'''						)
            UpperCAmelCase    : Optional[int]      =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.
      UpperCAmelCase    : str      =''''''
      UpperCAmelCase    : List[Any]      =''''''

      UpperCAmelCase    : Any      =build_data_iterator(__lowerCAmelCase		,					__lowerCAmelCase						)
      UpperCAmelCase    : Any      =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						):
            UpperCAmelCase    : Optional[Any]      =predictor.translate_batch(__lowerCAmelCase						)
            UpperCAmelCase    : Tuple      =predictor.from_batch(__lowerCAmelCase						)
            UpperCAmelCase    : Any      =[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:
            UpperCAmelCase    : str      =rouge_evaluator.get_scores(__lowerCAmelCase		,					__lowerCAmelCase						)
            UpperCAmelCase    : int      =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:
                  UpperCAmelCase    : Any      ='''.'''.join(document_name.split('''.'''						)[:-1]						)
                  UpperCAmelCase    : int      =document_name.split('''.'''						)[-1]
                  UpperCAmelCase    : Optional[int]      =bare_document_name + '''_summary.''' + extension
            else:
                  UpperCAmelCase    : List[Any]      =document_name + '''_summary'''

            UpperCAmelCase    : Optional[int]      =os.path.join(__lowerCAmelCase		,					__lowerCAmelCase						)
            with open(__lowerCAmelCase		,					'''w'''						) as output:
                  output.write(__lowerCAmelCase						)





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  str:






      '''simple docstring'''





      UpperCAmelCase  ,						UpperCAmelCase  ,						UpperCAmelCase    : Dict      =translation
      UpperCAmelCase    : str      =(
          raw_summary.replace('''[unused0]'''		,					''''''						)
          .replace('''[unused3]'''		,					''''''						)
          .replace('''[PAD]'''		,					''''''						)
          .replace('''[unused1]'''		,					''''''						)
          .replace(R''' +'''		,					''' '''						)
          .replace(''' [unused2] '''		,					'''. '''						)
          .replace('''[unused2]'''		,					''''''						)
          .strip()
      )

      return summary





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  Optional[int]:






      '''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						)->  Optional[Any]:






      '''simple docstring'''





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





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  List[Any]:






      '''simple docstring'''





      UpperCAmelCase    : Optional[Any]      =load_and_cache_examples(__lowerCAmelCase		,					__lowerCAmelCase						)
      UpperCAmelCase    : List[Any]      =SequentialSampler(__lowerCAmelCase						)

      def collate_fn(__lowerCAmelCase						):
            return collate(__lowerCAmelCase		,					__lowerCAmelCase		,					block_size=5_12		,					device=args.device						)

      UpperCAmelCase    : Dict      =DataLoader(
          __lowerCAmelCase		,					sampler=__lowerCAmelCase		,					batch_size=args.batch_size		,					collate_fn=__lowerCAmelCase		,					)

      return iterator





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  Tuple:






      '''simple docstring'''





      UpperCAmelCase    : int      =CNNDMDataset(args.documents_dir						)
      return dataset





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)->  Dict:






      '''simple docstring'''





      UpperCAmelCase    : Union[str, Any]      =[x for x in data if not len(x[1]						) == 0]  # remove empty_files
      UpperCAmelCase    : Tuple      =[name for name, _, _ in data]
      UpperCAmelCase    : List[Any]      =[''' '''.join(__lowerCAmelCase						) for _, _, summary_list in data]

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

      UpperCAmelCase    : Union[str, Any]      =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[int]:






      '''simple docstring'''





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





def   lowerCAmelCase_ (				)->  str:






      '''simple docstring'''





      UpperCAmelCase    : Optional[Any]      =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=2_00		,					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.'''		,					)
      UpperCAmelCase    : int      =parser.parse_args()

      # Select device (distibuted not available)
      UpperCAmelCase    : Optional[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:
            UpperCAmelCase    : 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=__lowerCAmelCase						)

      evaluate(__lowerCAmelCase						)





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






      '''simple docstring'''





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

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

      return True


if __name__ == "__main__":
     main()

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


__snake_case			      =    os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
__snake_case			      =    os.path.join(hf_cache_home, '''accelerate''')
__snake_case			      =    os.path.join(cache_dir, '''default_config.yaml''')
__snake_case			      =    os.path.join(cache_dir, '''default_config.yaml''')

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
     __snake_case			      =    default_yaml_config_file
else:
     __snake_case			      =    default_json_config_file





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  Tuple:






      '''simple docstring'''





      if config_file is not None:
            if not os.path.isfile(__lowerCAmelCase						):
                  raise FileNotFoundError(
                      f'''The passed configuration file `{config_file}` does not exist. '''
                      '''Please pass an existing file to `accelerate launch`, or use the the default one '''
                      '''created through `accelerate config` and run `accelerate launch` '''
                      '''without the `--config_file` argument.'''						)
      else:
            UpperCAmelCase    : Union[str, Any]      =default_config_file
      with open(__lowerCAmelCase		,					'''r'''		,					encoding='''utf-8'''						) as f:
            if config_file.endswith('''.json'''						):
                  if (
                      json.load(__lowerCAmelCase						).get('''compute_environment'''		,					ComputeEnvironment.LOCAL_MACHINE						)
                      == ComputeEnvironment.LOCAL_MACHINE
                  ):
                        UpperCAmelCase    : List[str]      =ClusterConfig
                  else:
                        UpperCAmelCase    : List[Any]      =SageMakerConfig
                  return config_class.from_json_file(json_file=__lowerCAmelCase						)
            else:
                  if (
                      yaml.safe_load(__lowerCAmelCase						).get('''compute_environment'''		,					ComputeEnvironment.LOCAL_MACHINE						)
                      == ComputeEnvironment.LOCAL_MACHINE
                  ):
                        UpperCAmelCase    : Union[str, Any]      =ClusterConfig
                  else:
                        UpperCAmelCase    : List[Any]      =SageMakerConfig
                  return config_class.from_yaml_file(yaml_file=__lowerCAmelCase						)







@dataclass
class 			__snake_case   :
 __lowerCamelCase    :   ComputeEnvironment
 __lowerCamelCase    :   Union[DistributedType, SageMakerDistributedType]
 __lowerCamelCase    :   str
 __lowerCamelCase    :   bool
 def 	UpperCAmelCase__       (  self						)	->							List[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Union[str, Any]      =self.__dict__
       # For serialization, it's best to convert Enums to strings (or their underlying value type).
       for key, value in result.items():
             if isinstance(snake_case__		,   snake_case__						):
                   UpperCAmelCase    : Any      =value.value
             if isinstance(snake_case__		,   snake_case__						) and not bool(snake_case__						):
                   UpperCAmelCase    : Union[str, Any]      =None
       UpperCAmelCase    : Dict      ={k: v for k, v in result.items() if v is not None}
       return result
 @classmethod
 def 	UpperCAmelCase__       (  cls		,   snake_case__=None						)	->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase    : Tuple      =default_json_config_file if json_file is None else json_file
       with open(snake_case__		,   '''r'''		,   encoding='''utf-8'''						) as f:
             UpperCAmelCase    : Optional[int]      =json.load(snake_case__						)
       if "compute_environment" not in config_dict:
             UpperCAmelCase    : Optional[int]      =ComputeEnvironment.LOCAL_MACHINE
       if "mixed_precision" not in config_dict:
             UpperCAmelCase    : Optional[int]      ='''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
       if "fp16" in config_dict:  # Convert the config to the new format.
             del config_dict["fp16"]
       if "dynamo_backend" in config_dict:  # Convert the config to the new format.
             UpperCAmelCase    : Optional[Any]      =config_dict.pop('''dynamo_backend'''						)
             UpperCAmelCase    : List[Any]      ={} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
       if "use_cpu" not in config_dict:
             UpperCAmelCase    : Dict      =False
       return cls(**snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							int:
       '''simple docstring'''





       with open(snake_case__		,   '''w'''		,   encoding='''utf-8'''						) as f:
             UpperCAmelCase    : Dict      =json.dumps(self.to_dict()		,   indent=2		,   sort_keys=snake_case__						) + '''\n'''
             f.write(snake_case__						)
 @classmethod
 def 	UpperCAmelCase__       (  cls		,   snake_case__=None						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =default_yaml_config_file if yaml_file is None else yaml_file
       with open(snake_case__		,   '''r'''		,   encoding='''utf-8'''						) as f:
             UpperCAmelCase    : List[str]      =yaml.safe_load(snake_case__						)
       if "compute_environment" not in config_dict:
             UpperCAmelCase    : str      =ComputeEnvironment.LOCAL_MACHINE

       if "mixed_precision" not in config_dict:
             UpperCAmelCase    : Optional[Any]      ='''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
       if isinstance(config_dict['''mixed_precision''']		,   snake_case__						) and not config_dict["mixed_precision"]:
             UpperCAmelCase    : Any      ='''no'''
       if "fp16" in config_dict:  # Convert the config to the new format.
             del config_dict["fp16"]
       if "dynamo_backend" in config_dict:  # Convert the config to the new format.
             UpperCAmelCase    : List[str]      =config_dict.pop('''dynamo_backend'''						)
             UpperCAmelCase    : str      ={} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
       if "use_cpu" not in config_dict:
             UpperCAmelCase    : Optional[int]      =False
       return cls(**snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							List[str]:
       '''simple docstring'''





       with open(snake_case__		,   '''w'''		,   encoding='''utf-8'''						) as f:
             yaml.safe_dump(self.to_dict()		,   snake_case__						)






 def 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





       if isinstance(self.compute_environment		,   snake_case__						):
             UpperCAmelCase    : Any      =ComputeEnvironment(self.compute_environment						)
       if isinstance(self.distributed_type		,   snake_case__						):
             if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
                   UpperCAmelCase    : int      =SageMakerDistributedType(self.distributed_type						)
             else:
                   UpperCAmelCase    : int      =DistributedType(self.distributed_type						)
       if self.dynamo_config is None:
             UpperCAmelCase    : Optional[int]      ={}







@dataclass
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   int
 __lowerCamelCase    :   int     =      0
 __lowerCamelCase    :   int     =      1
 __lowerCamelCase    :   Optional[str]     =      None
 __lowerCamelCase    :   Optional[str]     =      None
 __lowerCamelCase    :   Optional[int]     =      None
 __lowerCamelCase    :   Optional[str]     =      "static"
 __lowerCamelCase    :   Optional[bool]     =      False
 __lowerCamelCase    :   str     =      "main"

 # args for deepspeed_plugin
 __lowerCamelCase    :   dict     =      None
 # args for fsdp
 __lowerCamelCase    :   dict     =      None
 # args for megatron_lm
 __lowerCamelCase    :   dict     =      None
 # args for ipex
 __lowerCamelCase    :   dict     =      None
 # args for TPU
 __lowerCamelCase    :   bool     =      False

 # args for TPU pods
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   bool     =      False
 __lowerCamelCase    :   bool     =      False
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   List[str]     =      None
 __lowerCamelCase    :   List[str]     =      None
 __lowerCamelCase    :   List[str]     =      None

 # args for dynamo
 __lowerCamelCase    :   dict     =      None
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       if self.deepspeed_config is None:
             UpperCAmelCase    : Union[str, Any]      ={}
       if self.fsdp_config is None:
             UpperCAmelCase    : Any      ={}
       if self.megatron_lm_config is None:
             UpperCAmelCase    : List[Any]      ={}
       if self.ipex_config is None:
             UpperCAmelCase    : int      ={}
       return super().__post_init__()







@dataclass
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   str
 __lowerCamelCase    :   str
 __lowerCamelCase    :   Optional[str]     =      None
 __lowerCamelCase    :   Optional[str]     =      None
 __lowerCamelCase    :   str     =      "us-east-1"
 __lowerCamelCase    :   int     =      1
 __lowerCamelCase    :   str     =      "all"
 __lowerCamelCase    :   str     =      F'accelerate-sagemaker-{num_machines}'
 __lowerCamelCase    :   str     =      SAGEMAKER_PYTORCH_VERSION
 __lowerCamelCase    :   str     =      SAGEMAKER_TRANSFORMERS_VERSION
 __lowerCamelCase    :   str     =      SAGEMAKER_PYTHON_VERSION
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   dict     =      None
 __lowerCamelCase    :   dict     =      None
