





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,
)


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


__lowercase													=			namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase			=				BertTokenizer.from_pretrained('''bert-base-uncased'''     ,							do_lower_case=lowercase_)
  __lowerCAmelCase			=				BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization''')
  model.to(args.device)
  model.eval()

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

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

    import nltk
    import rouge

    nltk.download('''punkt''')
    __lowerCAmelCase			=				rouge.Rouge(
        metrics=['''rouge-n''', '''rouge-l''']     ,							max_n=2     ,							limit_length=lowercase_     ,							length_limit=args.beam_size     ,							length_limit_type='''words'''     ,							apply_avg=lowercase_     ,							apply_best=lowercase_     ,							alpha=0.5     ,							weight_factor=1.2     ,							stemming=lowercase_     ,							)

  # these (unused) arguments are defined to keep the compatibility
  # with the legacy code and will be deleted in a next iteration.
  __lowerCAmelCase			=				''
  __lowerCAmelCase			=				''

  __lowerCAmelCase			=				build_data_iterator(lowercase_     ,							lowercase_)
  __lowerCAmelCase			=				build_predictor(lowercase_     ,							lowercase_     ,							lowercase_     ,							lowercase_)

  logger.info('''***** Running evaluation *****''')
  logger.info('''  Number examples = %d'''     ,							len(data_iterator.dataset))
  logger.info('''  Batch size = %d'''     ,							args.batch_size)
  logger.info('''''')
  logger.info('''***** Beam Search parameters *****''')
  logger.info('''  Beam size = %d'''     ,							args.beam_size)
  logger.info('''  Minimum length = %d'''     ,							args.min_length)
  logger.info('''  Maximum length = %d'''     ,							args.max_length)
  logger.info('''  Alpha (length penalty) = %.2f'''     ,							args.alpha)
  logger.info('''  Trigrams %s be blocked'''     ,							('''will''' if args.block_trigram else '''will NOT'''))

  for batch in tqdm(lowercase_):
    __lowerCAmelCase			=				predictor.translate_batch(lowercase_)
    __lowerCAmelCase			=				predictor.from_batch(lowercase_)
    __lowerCAmelCase			=				[format_summary(lowercase_) for t in translations]
    save_summaries(lowercase_     ,							args.summaries_output_dir     ,							batch.document_names)

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

  if args.compute_rouge:
    __lowerCAmelCase			=				rouge_evaluator.get_scores(lowercase_     ,							lowercase_)
    __lowerCAmelCase			=				format_rouge_scores(lowercase_)
    save_rouge_scores(lowercase_)
    print(lowercase_)


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		List[Any]     ,							__lowercase:		Union[str, Any]     ,							__lowercase:		List[str]):



  '''simple docstring'''






  for summary, document_name in zip(lowercase_     ,							lowercase_):
    # Prepare the summary file's name
    if "." in document_name:
      __lowerCAmelCase			=				'.'.join(document_name.split('''.''')[:-1])
      __lowerCAmelCase			=				document_name.split('''.''')[-1]
      __lowerCAmelCase			=				bare_document_name + '_summary.' + extension
    else:
      __lowerCAmelCase			=				document_name + '_summary'

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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase			=				translation
  __lowerCAmelCase			=				(
      raw_summary.replace('''[unused0]'''     ,							'''''')
      .replace('''[unused3]'''     ,							'''''')
      .replace('''[PAD]'''     ,							'''''')
      .replace('''[unused1]'''     ,							'''''')
      .replace(R''' +'''     ,							''' ''')
      .replace(''' [unused2] '''     ,							'''. ''')
      .replace('''[unused2]'''     ,							'''''')
      .strip()
  )

  return summary


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Any):



  '''simple docstring'''






  return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
      scores['''rouge-1''']['''f''']     ,							scores['''rouge-1''']['''p''']     ,							scores['''rouge-1''']['''r''']     ,							scores['''rouge-2''']['''f''']     ,							scores['''rouge-2''']['''p''']     ,							scores['''rouge-2''']['''r''']     ,							scores['''rouge-l''']['''f''']     ,							scores['''rouge-l''']['''p''']     ,							scores['''rouge-l''']['''r''']     ,							)


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		int):



  '''simple docstring'''






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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		List[Any]     ,							__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase			=				load_and_cache_examples(lowercase_     ,							lowercase_)
  __lowerCAmelCase			=				SequentialSampler(lowercase_)

  def collate_fn(__lowercase:		Optional[int]):
    return collate(lowercase_     ,							lowercase_     ,							block_size=5_12     ,							device=args.device)

  __lowerCAmelCase			=				DataLoader(
      lowercase_     ,							sampler=lowercase_     ,							batch_size=args.batch_size     ,							collate_fn=lowercase_     ,							)

  return iterator


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Optional[Any]     ,							__lowercase:		int):



  '''simple docstring'''






  __lowerCAmelCase			=				CNNDMDataset(args.documents_dir)
  return dataset


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		List[str]     ,							__lowercase:		Any     ,							__lowercase:		int     ,							__lowercase:		int):



  '''simple docstring'''






  __lowerCAmelCase			=				[x for x in data if not len(x[1]) == 0]  # remove empty_files
  __lowerCAmelCase			=				[name for name, _, _ in data]
  __lowerCAmelCase			=				[' '.join(lowercase_) for _, _, summary_list in data]

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

  __lowerCAmelCase			=				Batch(
      document_names=lowercase_     ,							batch_size=len(lowercase_)     ,							src=encoded_stories.to(lowercase_)     ,							segs=encoder_token_type_ids.to(lowercase_)     ,							mask_src=encoder_mask.to(lowercase_)     ,							tgt_str=lowercase_     ,							)

  return batch


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Optional[int]     ,							__lowercase:		Any):



  '''simple docstring'''






  __lowerCAmelCase			=				summary_tokens.to('''cpu''').numpy()
  __lowerCAmelCase			=				tokenizer.decode(lowercase_)
  __lowerCAmelCase			=				summary.split('''.''')
  __lowerCAmelCase			=				[s + '.' for s in sentences]
  return sentences


def 					__SCREAMING_SNAKE_CASE  (							):



  '''simple docstring'''






  __lowerCAmelCase			=				argparse.ArgumentParser()
  parser.add_argument(
      '''--documents_dir'''     ,							default=lowercase_     ,							type=lowercase_     ,							required=lowercase_     ,							help='''The folder where the documents to summarize are located.'''     ,							)
  parser.add_argument(
      '''--summaries_output_dir'''     ,							default=lowercase_     ,							type=lowercase_     ,							required=lowercase_     ,							help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are'''     ,							)
  parser.add_argument(
      '''--compute_rouge'''     ,							default=lowercase_     ,							type=lowercase_     ,							required=lowercase_     ,							help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'''     ,							)
  # EVALUATION options
  parser.add_argument(
      '''--no_cuda'''     ,							default=lowercase_     ,							type=lowercase_     ,							help='''Whether to force the execution on CPU.'''     ,							)
  parser.add_argument(
      '''--batch_size'''     ,							default=4     ,							type=lowercase_     ,							help='''Batch size per GPU/CPU for training.'''     ,							)
  # BEAM SEARCH arguments
  parser.add_argument(
      '''--min_length'''     ,							default=50     ,							type=lowercase_     ,							help='''Minimum number of tokens for the summaries.'''     ,							)
  parser.add_argument(
      '''--max_length'''     ,							default=2_00     ,							type=lowercase_     ,							help='''Maixmum number of tokens for the summaries.'''     ,							)
  parser.add_argument(
      '''--beam_size'''     ,							default=5     ,							type=lowercase_     ,							help='''The number of beams to start with for each example.'''     ,							)
  parser.add_argument(
      '''--alpha'''     ,							default=0.95     ,							type=lowercase_     ,							help='''The value of alpha for the length penalty in the beam search.'''     ,							)
  parser.add_argument(
      '''--block_trigram'''     ,							default=lowercase_     ,							type=lowercase_     ,							help='''Whether to block the existence of repeating trigrams in the text generated by beam search.'''     ,							)
  __lowerCAmelCase			=				parser.parse_args()

  # Select device (distibuted not available)
  __lowerCAmelCase			=				torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')

  # Check the existence of directories
  if not args.summaries_output_dir:
    __lowerCAmelCase			=				args.documents_dir

  if not documents_dir_is_valid(args.documents_dir):
    raise FileNotFoundError(
        '''We could not find the directory you specified for the documents to summarize, or it was empty. Please'''
        ''' specify a valid path.''')
  os.makedirs(args.summaries_output_dir     ,							exist_ok=lowercase_)

  evaluate(lowercase_)


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Dict):



  '''simple docstring'''






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

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

  return True


if __name__ == "__main__":
      main()











from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowercase													=			logging.get_logger(__name__)

__lowercase													=			{
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class   __snake_case				(			_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	"""bigbird_pegasus"""
    _lowerCAmelCase     =	["""past_key_values"""]
    _lowerCAmelCase     =	{
        """num_attention_heads""": """encoder_attention_heads""",
        """hidden_size""": """d_model""",
        """attention_probs_dropout_prob""": """attention_dropout""",
    }





    def __init__(   self						:		Any			,						snake_case						:		Optional[int]=96_103			,						snake_case						:		Tuple=4_096			,						snake_case						:		str=16			,						snake_case						:		Any=4_096			,						snake_case						:		Any=16			,						snake_case						:		Dict=16			,						snake_case						:		str=4_096			,						snake_case						:		Tuple=16			,						snake_case						:		Dict=0.0			,						snake_case						:		str=0.0			,						snake_case						:		Union[str, Any]=True			,						snake_case						:		List[Any]=True			,						snake_case						:		Optional[Any]="gelu_new"			,						snake_case						:		Union[str, Any]=1_024			,						snake_case						:		Union[str, Any]=0.1			,						snake_case						:		int=0.0			,						snake_case						:		Optional[Any]=0.0			,						snake_case						:		str=0.02			,						snake_case						:		int=2			,						snake_case						:		Any=0.0			,						snake_case						:		str=True			,						snake_case						:		Tuple=0			,						snake_case						:		Any=2			,						snake_case						:		int=1			,						snake_case						:		Any="block_sparse"			,						snake_case						:		List[Any]=64			,						snake_case						:		Dict=3			,						snake_case						:		Optional[Any]=False			,						**snake_case						:		Dict			,						):

      """simple docstring"""





      __lowerCAmelCase			=				vocab_size
      __lowerCAmelCase			=				max_position_embeddings
      __lowerCAmelCase			=				d_model
      __lowerCAmelCase			=				encoder_ffn_dim
      __lowerCAmelCase			=				encoder_layers
      __lowerCAmelCase			=				encoder_attention_heads
      __lowerCAmelCase			=				decoder_ffn_dim
      __lowerCAmelCase			=				decoder_layers
      __lowerCAmelCase			=				decoder_attention_heads
      __lowerCAmelCase			=				dropout
      __lowerCAmelCase			=				attention_dropout
      __lowerCAmelCase			=				activation_dropout
      __lowerCAmelCase			=				activation_function
      __lowerCAmelCase			=				init_std
      __lowerCAmelCase			=				encoder_layerdrop
      __lowerCAmelCase			=				decoder_layerdrop
      __lowerCAmelCase			=				classifier_dropout
      __lowerCAmelCase			=				use_cache
      __lowerCAmelCase			=				encoder_layers
      __lowerCAmelCase			=				scale_embedding  # scale factor will be sqrt(d_model) if True

      # extra config
      __lowerCAmelCase			=				attention_type
      __lowerCAmelCase			=				block_size
      __lowerCAmelCase			=				num_random_blocks
      __lowerCAmelCase			=				use_bias

      super().__init__(
          pad_token_id=snake_case			,						bos_token_id=snake_case			,						eos_token_id=snake_case			,						is_encoder_decoder=snake_case			,						decoder_start_token_id=snake_case			,						**snake_case			,						)


class   __snake_case				(			_a   ):






    '''simple docstring'''





    @property
    def 	A		(   self						:		List[str]							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ]							)

        if self.use_past:
          __lowerCAmelCase			=				{0: '''batch'''}
          __lowerCAmelCase			=				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
        else:
          __lowerCAmelCase			=				{0: '''batch''', 1: '''decoder_sequence'''}
          __lowerCAmelCase			=				{0: '''batch''', 1: '''decoder_sequence'''}

        if self.use_past:
          self.fill_with_past_key_values_(snake_case			,						direction='''inputs'''							)
      elif self.task == "causal-lm":
        # TODO: figure this case out.
        __lowerCAmelCase			=				OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ]							)
        if self.use_past:
          __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
          for i in range(snake_case							):
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
      else:
        __lowerCAmelCase			=				OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
            ]							)

      return common_inputs





    @property
    def 	A		(   self						:		Optional[int]							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				super().outputs
      else:
        __lowerCAmelCase			=				super(snake_case			,						self							).outputs
        if self.use_past:
          __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
          for i in range(snake_case							):
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
      return common_outputs





    def 	A		(   self						:		List[Any]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)

      # Generate decoder inputs
      __lowerCAmelCase			=				seq_length if not self.use_past else 1
      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)
      __lowerCAmelCase			=				{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      __lowerCAmelCase			=				dict(**snake_case			,						**snake_case							)

      if self.use_past:
        if not is_torch_available():
          raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
        else:
          import torch
        __lowerCAmelCase	,						__lowerCAmelCase			=				common_inputs['''input_ids'''].shape
        __lowerCAmelCase			=				common_inputs['''decoder_input_ids'''].shape[1]
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_attention_heads
        __lowerCAmelCase			=				(
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __lowerCAmelCase			=				decoder_seq_length + 3
        __lowerCAmelCase			=				(
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        __lowerCAmelCase			=				torch.cat(
            [common_inputs['''decoder_attention_mask'''], torch.ones(snake_case			,						snake_case							)]			,						dim=1							)

        __lowerCAmelCase			=				[]
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
        __lowerCAmelCase			=				min(snake_case			,						snake_case							)
        __lowerCAmelCase			=				max(snake_case			,						snake_case							) - min_num_layers
        __lowerCAmelCase			=				'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

        for _ in range(snake_case							):
          common_inputs["past_key_values"].append(
              (
                  torch.zeros(snake_case							),
                  torch.zeros(snake_case							),
                  torch.zeros(snake_case							),
                  torch.zeros(snake_case							),
              )							)
        # TODO: test this.
        __lowerCAmelCase			=				encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
        for _ in range(snake_case			,						snake_case							):
          common_inputs["past_key_values"].append((torch.zeros(snake_case							), torch.zeros(snake_case							))							)
      return common_inputs





    def 	A		(   self						:		Tuple			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)

      if self.use_past:
        if not is_torch_available():
          raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
        else:
          import torch
        __lowerCAmelCase	,						__lowerCAmelCase			=				common_inputs['''input_ids'''].shape
        # Not using the same length for past_key_values
        __lowerCAmelCase			=				seqlen + 2
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_attention_heads
        __lowerCAmelCase			=				(
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        __lowerCAmelCase			=				common_inputs['''attention_mask'''].dtype
        __lowerCAmelCase			=				torch.cat(
            [common_inputs['''attention_mask'''], torch.ones(snake_case			,						snake_case			,						dtype=snake_case							)]			,						dim=1							)
        __lowerCAmelCase			=				[
            (torch.zeros(snake_case							), torch.zeros(snake_case							)) for _ in range(snake_case							)
        ]
      return common_inputs





    def 	A		(   self						:		Optional[Any]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				compute_effective_axis_dimension(
          snake_case			,						fixed_dimension=OnnxConfig.default_fixed_batch			,						num_token_to_add=0							)

      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      __lowerCAmelCase			=				tokenizer.num_special_tokens_to_add(snake_case							)
      __lowerCAmelCase			=				compute_effective_axis_dimension(
          snake_case			,						fixed_dimension=OnnxConfig.default_fixed_sequence			,						num_token_to_add=snake_case							)

      # Generate dummy inputs according to compute batch and sequence
      __lowerCAmelCase			=				[''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
      __lowerCAmelCase			=				dict(tokenizer(snake_case			,						return_tensors=snake_case							)							)
      return common_inputs





    def 	A		(   self						:		List[str]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)

      elif self.task == "causal-lm":
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_causal_lm(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)
      else:
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)

      return common_inputs





    def 	A		(   self						:		int			,						snake_case						:		Optional[int]			,						snake_case						:		List[Any]			,						snake_case						:		str			,						snake_case						:		Union[str, Any]							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				super()._flatten_past_key_values_(snake_case			,						snake_case			,						snake_case			,						snake_case							)
      else:
        __lowerCAmelCase			=				super(snake_case			,						self							)._flatten_past_key_values_(
            snake_case			,						snake_case			,						snake_case			,						snake_case							)
