




'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A_ 			=		logging.get_logger(__name__)

A_ 			=		{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Any:
      for attribute in key.split('.'		):
            snake_case__					:      List[Any]  			=						getattr(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

      if weight_type is not None:
            snake_case__					:      Optional[int]  			=						getattr(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		).shape
      else:
            snake_case__					:      str  			=						hf_pointer.shape

      assert hf_shape == value.shape, (
          f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
          f" {value.shape} for {full_name}"
      )

      if weight_type == "weight":
            snake_case__					:      str  			=						value
      elif weight_type == "weight_g":
            snake_case__					:      int  			=						value
      elif weight_type == "weight_v":
            snake_case__					:      Tuple  			=						value
      elif weight_type == "bias":
            snake_case__					:      List[str]  			=						value
      else:
            snake_case__					:      Tuple  			=						value

      logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Union[str, Any]:
      snake_case__					:      str  			=						[]
      snake_case__					:      List[Any]  			=						fairseq_model.state_dict()

      snake_case__					:      Optional[int]  			=						hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            snake_case__					:      int  			=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      __SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			hf_model.config.feat_extract_norm == 'group'							,			)
                  snake_case__					:      List[str]  			=						True
            else:
                  for key, mapped_key in MAPPING.items():
                        snake_case__					:      int  			=						'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

                        if key in name or key.split('w2v_model.'		)[-1] == name.split('.'		)[0]:
                              snake_case__					:      Union[str, Any]  			=						True
                              if "*" in mapped_key:
                                    snake_case__					:      Optional[int]  			=						name.split(__SCREAMING_SNAKE_CASE		)[0].split('.'		)[-2]
                                    if not layer_index.isnumeric():
                                          continue
                                    snake_case__					:      List[Any]  			=						mapped_key.replace('*'							,			__SCREAMING_SNAKE_CASE		)
                              if "weight_g" in name:
                                    snake_case__					:      Optional[Any]  			=						'weight_g'
                              elif "weight_v" in name:
                                    snake_case__					:      Optional[Any]  			=						'weight_v'
                              elif "weight" in name:
                                    snake_case__					:      Optional[Any]  			=						'weight'
                              elif "bias" in name:
                                    snake_case__					:      int  			=						'bias'
                              else:
                                    snake_case__					:      Any  			=						None
                              set_recursively(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
                        continue
            if not is_used:
                  unused_weights.append(__SCREAMING_SNAKE_CASE		)

      logger.warning(f"Unused weights: {unused_weights}"		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      List[str]:
      snake_case__					:      Dict  			=						full_name.split('conv_layers.'		)[-1]
      snake_case__					:      Tuple  			=						name.split('.'		)
      snake_case__					:      str  			=						int(items[0]		)
      snake_case__					:      List[Any]  			=						int(items[1]		)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
                  )
                  snake_case__					:      str  			=						value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
                  )
                  snake_case__					:      List[str]  			=						value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                      " found."
                  )
                  snake_case__					:      str  			=						value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
                  )
                  snake_case__					:      Any  			=						value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
      else:
            unused_weights.append(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      List[str]:
      snake_case__					:      Tuple  			=						SEWDConfig()
      if is_finetuned:
            snake_case__					:      Optional[Any]  			=						model.wav_encoder.wav_model.cfg
      else:
            snake_case__					:      Dict  			=						model.cfg

      snake_case__					:      List[str]  			=						fs_config.conv_bias
      snake_case__					:      List[str]  			=						eval(fs_config.conv_feature_layers		)
      snake_case__					:      Union[str, Any]  			=						[x[0] for x in conv_layers]
      snake_case__					:      Any  			=						[x[1] for x in conv_layers]
      snake_case__					:      int  			=						[x[2] for x in conv_layers]
      snake_case__					:      List[Any]  			=						'gelu'
      snake_case__					:      Optional[Any]  			=						'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
      snake_case__					:      Optional[int]  			=						0.0
      snake_case__					:      Optional[Any]  			=						fs_config.activation_fn.name
      snake_case__					:      Union[str, Any]  			=						fs_config.encoder_embed_dim
      snake_case__					:      Union[str, Any]  			=						0.02
      snake_case__					:      List[Any]  			=						fs_config.encoder_ffn_embed_dim
      snake_case__					:      Optional[Any]  			=						1E-5
      snake_case__					:      Any  			=						fs_config.encoder_layerdrop
      snake_case__					:      Tuple  			=						fs_config.encoder_attention_heads
      snake_case__					:      Optional[int]  			=						fs_config.conv_pos_groups
      snake_case__					:      Optional[int]  			=						fs_config.conv_pos
      snake_case__					:      Tuple  			=						len(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      int  			=						fs_config.encoder_layers
      snake_case__					:      str  			=						fs_config.squeeze_factor
      # DeBERTa-specific parameters:
      snake_case__					:      Any  			=						fs_config.max_position_embeddings
      snake_case__					:      Dict  			=						fs_config.position_buckets
      snake_case__					:      Dict  			=						fs_config.share_att_key
      snake_case__					:      List[str]  			=						fs_config.relative_attention
      snake_case__					:      Dict  			=						fs_config.position_biased_input
      snake_case__					:      List[str]  			=						tuple(fs_config.pos_att_type.split('|'		)		)
      snake_case__					:      str  			=						fs_config.norm_rel_ebd

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            snake_case__					:      List[str]  			=						model.cfg
            snake_case__					:      Tuple  			=						fs_config.final_dropout
            snake_case__					:      Optional[int]  			=						fs_config.layerdrop
      snake_case__					:      Optional[int]  			=						fs_config.activation_dropout
      snake_case__					:      List[str]  			=						fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      snake_case__					:      Union[str, Any]  			=						fs_config.attention_dropout
      snake_case__					:      Dict  			=						fs_config.dropout_input
      snake_case__					:      List[str]  			=						fs_config.dropout
      snake_case__					:      Optional[int]  			=						fs_config.mask_channel_length
      snake_case__					:      Optional[Any]  			=						fs_config.mask_channel_prob
      snake_case__					:      Tuple  			=						fs_config.mask_length
      snake_case__					:      Tuple  			=						fs_config.mask_prob

      snake_case__					:      str  			=						'Wav2Vec2FeatureExtractor'
      snake_case__					:      Union[str, Any]  			=						'Wav2Vec2CTCTokenizer'

      return config






@torch.no_grad()
def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=True		)						->      Any:

      if is_finetuned:
            snake_case__			,       snake_case__			,       snake_case__					:      Tuple  			=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]							,			arg_overrides={'data': '/'.join(dict_path.split('/'		)[:-1]		)}		)
      else:
            snake_case__			,       snake_case__			,       snake_case__					:      List[Any]  			=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

      if config_path is not None:
            snake_case__					:      Optional[int]  			=						SEWDConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
      else:
            snake_case__					:      str  			=						convert_config(model[0]							,			__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Optional[Any]  			=						model[0].eval()

      snake_case__					:      Dict  			=						True if config.feat_extract_norm == 'layer' else False
      snake_case__					:      Any  			=						WavaVecaFeatureExtractor(
          feature_size=1							,			sampling_rate=1_6000							,			padding_value=0							,			do_normalize=__SCREAMING_SNAKE_CASE							,			return_attention_mask=__SCREAMING_SNAKE_CASE							,			)

      if is_finetuned:
            if dict_path:
                  snake_case__					:      Dict  			=						Dictionary.load(__SCREAMING_SNAKE_CASE		)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  snake_case__					:      int  			=						target_dict.pad_index
                  snake_case__					:      Any  			=						target_dict.bos_index
                  snake_case__					:      Tuple  			=						target_dict.pad_index
                  snake_case__					:      Tuple  			=						target_dict.bos_index
                  snake_case__					:      List[str]  			=						target_dict.eos_index
                  snake_case__					:      Optional[int]  			=						len(target_dict.symbols		)
                  snake_case__					:      Optional[int]  			=						os.path.join(__SCREAMING_SNAKE_CASE							,			'vocab.json'		)
                  if not os.path.isdir(__SCREAMING_SNAKE_CASE		):
                        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__SCREAMING_SNAKE_CASE		)		)
                        return
                  os.makedirs(__SCREAMING_SNAKE_CASE							,			exist_ok=__SCREAMING_SNAKE_CASE		)
                  with open(__SCREAMING_SNAKE_CASE							,			'w'							,			encoding='utf-8'		) as vocab_handle:
                        json.dump(target_dict.indices							,			__SCREAMING_SNAKE_CASE		)
                  snake_case__					:      List[Any]  			=						WavaVecaCTCTokenizer(
                      __SCREAMING_SNAKE_CASE							,			unk_token=target_dict.unk_word							,			pad_token=target_dict.pad_word							,			bos_token=target_dict.bos_word							,			eos_token=target_dict.eos_word							,			word_delimiter_token='|'							,			do_lower_case=__SCREAMING_SNAKE_CASE							,			)
                  snake_case__					:      List[Any]  			=						WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE							,			tokenizer=__SCREAMING_SNAKE_CASE		)
                  processor.save_pretrained(__SCREAMING_SNAKE_CASE		)

            snake_case__					:      Union[str, Any]  			=						SEWDForCTC(__SCREAMING_SNAKE_CASE		)
      else:
            snake_case__					:      Tuple  			=						SEWDModel(__SCREAMING_SNAKE_CASE		)
            feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE		)

      recursively_load_weights(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

      hf_model.save_pretrained(__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      A_ 			=		argparse.ArgumentParser()
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
      parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      parser.add_argument(
          "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
      )
      A_ 			=		parser.parse_args()
      convert_sew_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
      )






'''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()
