





"""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,
)


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


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





def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   Union[str, Any]      =       BertTokenizer.from_pretrained('''bert-base-uncased'''			,		do_lower_case=_lowercase  )
						_UpperCamelCase     :   Optional[int]      =       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     :   Dict      =       []
												_UpperCamelCase     :   Tuple      =       []

												import nltk
												import rouge

												nltk.download('''punkt'''  )
												_UpperCamelCase     :   List[Any]      =       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.
						_UpperCamelCase     :   List[Any]      =       ''''''
						_UpperCamelCase     :   Optional[Any]      =       ''''''

						_UpperCamelCase     :   Tuple      =       build_data_iterator(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   Optional[int]      =       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  ):
												_UpperCamelCase     :   Dict      =       predictor.translate_batch(_lowercase  )
												_UpperCamelCase     :   Union[str, Any]      =       predictor.from_batch(_lowercase  )
												_UpperCamelCase     :   List[Any]      =       [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:
												_UpperCamelCase     :   Optional[int]      =       rouge_evaluator.get_scores(_lowercase			,		_lowercase  )
												_UpperCamelCase     :   Optional[int]      =       format_rouge_scores(_lowercase  )
												save_rouge_scores(_lowercase  )
												print(_lowercase  )





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase  ):
						for summary, document_name in zip(_lowercase			,		_lowercase  ):
												# Prepare the summary file's name
												if "." in document_name:
																		_UpperCamelCase     :   Union[str, Any]      =       '''.'''.join(document_name.split('''.'''  )[:-1]  )
																		_UpperCamelCase     :   int      =       document_name.split('''.'''  )[-1]
																		_UpperCamelCase     :   List[str]      =       bare_document_name + '''_summary.''' + extension
												else:
																		_UpperCamelCase     :   Any      =       document_name + '''_summary'''

												_UpperCamelCase     :   List[str]      =       os.path.join(_lowercase			,		_lowercase  )
												with open(_lowercase			,		'''w'''  ) as output:
																		output.write(_lowercase  )





def 			a_							(				_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Optional[Any]      =       translation
						_UpperCamelCase     :   Any      =       (
						    raw_summary.replace('''[unused0]'''			,		''''''  )
						    .replace('''[unused3]'''			,		''''''  )
						    .replace('''[PAD]'''			,		''''''  )
						    .replace('''[unused1]'''			,		''''''  )
						    .replace(R''' +'''			,		''' '''  )
						    .replace(''' [unused2] '''			,		'''. '''  )
						    .replace('''[unused2]'''			,		''''''  )
						    .strip()
						)

						return summary





def 			a_							(				_lowercase  ):
						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 			a_							(				_lowercase  ):
						with open('''rouge_scores.txt'''			,		'''w'''  ) as output:
												output.write(_lowercase  )





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[int]      =       load_and_cache_examples(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   Tuple      =       SequentialSampler(_lowercase  )

						def collate_fn(_lowercase  ):
												return collate(_lowercase			,		_lowercase			,		block_size=512			,		device=args.device  )

						_UpperCamelCase     :   Dict      =       DataLoader(
						    _lowercase			,		sampler=_lowercase			,		batch_size=args.batch_size			,		collate_fn=_lowercase			,		)

						return iterator





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Union[str, Any]      =       CNNDMDataset(args.documents_dir  )
						return dataset





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Union[str, Any]      =       [x for x in data if not len(x[1]  ) == 0]  # remove empty_files
						_UpperCamelCase     :   Union[str, Any]      =       [name for name, _, _ in data]
						_UpperCamelCase     :   Optional[Any]      =       [''' '''.join(_lowercase  ) for _, _, summary_list in data]

						_UpperCamelCase     :   Any      =       [encode_for_summarization(_lowercase			,		_lowercase			,		_lowercase  ) for _, story, summary in data]
						_UpperCamelCase     :   List[Any]      =       torch.tensor(
						    [truncate_or_pad(_lowercase			,		_lowercase			,		tokenizer.pad_token_id  ) for story, _ in encoded_text]  )
						_UpperCamelCase     :   Any      =       compute_token_type_ids(_lowercase			,		tokenizer.cls_token_id  )
						_UpperCamelCase     :   Dict      =       build_mask(_lowercase			,		tokenizer.pad_token_id  )

						_UpperCamelCase     :   Dict      =       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 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Tuple      =       summary_tokens.to('''cpu'''  ).numpy()
						_UpperCamelCase     :   Any      =       tokenizer.decode(_lowercase  )
						_UpperCamelCase     :   List[str]      =       summary.split('''.'''  )
						_UpperCamelCase     :   str      =       [s + '''.''' for s in sentences]
						return sentences





def 			a_							(				):
						_UpperCamelCase     :   List[Any]      =       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=200			,		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.'''			,		)
						_UpperCamelCase     :   int      =       parser.parse_args()

						# Select device (distibuted not available)
						_UpperCamelCase     :   List[Any]      =       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     :   List[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=_lowercase  )

						evaluate(_lowercase  )





def 			a_							(				_lowercase  ):
						if not os.path.exists(_lowercase  ):
												return False

						_UpperCamelCase     :   Optional[int]      =       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


UpperCAmelCase_			      =  logging.get_logger(__name__)

UpperCAmelCase_			      =  {
    '''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 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  'bigbird_pegasus'
				SCREAMING_SNAKE_CASE_ 				=  ['past_key_values']
				SCREAMING_SNAKE_CASE_ 				=  {
				    'num_attention_heads': 'encoder_attention_heads',
				    'hidden_size': 'd_model',
				    'attention_probs_dropout_prob': 'attention_dropout',
				}







				def __init__(				self						,	SCREAMING_SNAKE_CASE__=9_61_03						,	SCREAMING_SNAKE_CASE__=40_96						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=40_96						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=40_96						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__="gelu_new"						,	SCREAMING_SNAKE_CASE__=10_24						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.02						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__="block_sparse"						,	SCREAMING_SNAKE_CASE__=64						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=False						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					_snake_case					:							Dict					     =   vocab_size
					_snake_case					:							Any					     =   max_position_embeddings
					_snake_case					:							Any					     =   d_model
					_snake_case					:							Dict					     =   encoder_ffn_dim
					_snake_case					:							Union[str, Any]					     =   encoder_layers
					_snake_case					:							Optional[int]					     =   encoder_attention_heads
					_snake_case					:							Optional[int]					     =   decoder_ffn_dim
					_snake_case					:							List[Any]					     =   decoder_layers
					_snake_case					:							str					     =   decoder_attention_heads
					_snake_case					:							Optional[Any]					     =   dropout
					_snake_case					:							Optional[Any]					     =   attention_dropout
					_snake_case					:							int					     =   activation_dropout
					_snake_case					:							Any					     =   activation_function
					_snake_case					:							Optional[Any]					     =   init_std
					_snake_case					:							Tuple					     =   encoder_layerdrop
					_snake_case					:							List[Any]					     =   decoder_layerdrop
					_snake_case					:							Optional[Any]					     =   classifier_dropout
					_snake_case					:							List[Any]					     =   use_cache
					_snake_case					:							Union[str, Any]					     =   encoder_layers
					_snake_case					:							Union[str, Any]					     =   scale_embedding  # scale factor will be sqrt(d_model) if True

					# extra config
					_snake_case					:							str					     =   attention_type
					_snake_case					:							Tuple					     =   block_size
					_snake_case					:							List[Any]					     =   num_random_blocks
					_snake_case					:							List[str]					     =   use_bias

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





class 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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

						if self.use_past:
							_snake_case					:							Any					     =   {0: """batch"""}
							_snake_case					:							str					     =   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
						else:
							_snake_case					:							int					     =   {0: """batch""", 1: """decoder_sequence"""}
							_snake_case					:							Optional[Any]					     =   {0: """batch""", 1: """decoder_sequence"""}

						if self.use_past:
							self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__						,	direction="""inputs"""      )
					elif self.task == "causal-lm":
						# TODO: figure this case out.
						_snake_case					:							Any					     =   OrderedDict(
						    [
						        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
						        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
						    ]      )
						if self.use_past:
							_snake_case     , _snake_case					:							Tuple					     =   self.num_layers
							for i in range(SCREAMING_SNAKE_CASE__      ):
								_snake_case					:							Dict					     =   {0: """batch""", 2: """past_sequence + sequence"""}
								_snake_case					:							int					     =   {0: """batch""", 2: """past_sequence + sequence"""}
					else:
						_snake_case					:							Dict					     =   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 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					if self.task in ["default", "seq2seq-lm"]:
						_snake_case					:							str					     =   super().outputs
					else:
						_snake_case					:							List[Any]					     =   super(SCREAMING_SNAKE_CASE__						,	self      ).outputs
						if self.use_past:
							_snake_case     , _snake_case					:							List[str]					     =   self.num_layers
							for i in range(SCREAMING_SNAKE_CASE__      ):
								_snake_case					:							Optional[Any]					     =   {0: """batch""", 2: """past_sequence + sequence"""}
								_snake_case					:							List[str]					     =   {0: """batch""", 2: """past_sequence + sequence"""}
					return common_outputs







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	):






					"""simple docstring"""

					_snake_case					:							Optional[Any]					     =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					# Generate decoder inputs
					_snake_case					:							Union[str, Any]					     =   seq_length if not self.use_past else 1
					_snake_case					:							Tuple					     =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Union[str, Any]					     =   {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
					_snake_case					:							Optional[int]					     =   dict(**SCREAMING_SNAKE_CASE__						,	**SCREAMING_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
						_snake_case     , _snake_case					:							int					     =   common_inputs["""input_ids"""].shape
						_snake_case					:							List[Any]					     =   common_inputs["""decoder_input_ids"""].shape[1]
						_snake_case     , _snake_case					:							List[Any]					     =   self.num_attention_heads
						_snake_case					:							Any					     =   (
						    batch,
						    num_encoder_attention_heads,
						    encoder_seq_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						_snake_case					:							int					     =   decoder_seq_length + 3
						_snake_case					:							List[Any]					     =   (
						    batch,
						    num_decoder_attention_heads,
						    decoder_past_length,
						    self._config.hidden_size // num_decoder_attention_heads,
						)

						_snake_case					:							List[str]					     =   torch.cat(
						    [common_inputs["""decoder_attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )]						,	dim=1      )

						_snake_case					:							Dict					     =   []
						# If the number of encoder and decoder layers are present in the model configuration, both are considered
						_snake_case     , _snake_case					:							str					     =   self.num_layers
						_snake_case					:							Any					     =   min(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
						_snake_case					:							Any					     =   max(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ) - min_num_layers
						_snake_case					:							Union[str, Any]					     =   """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	):






					"""simple docstring"""

					_snake_case					:							Tuple					     =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_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
						_snake_case     , _snake_case					:							Optional[Any]					     =   common_inputs["""input_ids"""].shape
						# Not using the same length for past_key_values
						_snake_case					:							Dict					     =   seqlen + 2
						_snake_case     , _snake_case					:							Optional[int]					     =   self.num_layers
						_snake_case     , _snake_case					:							List[Any]					     =   self.num_attention_heads
						_snake_case					:							str					     =   (
						    batch,
						    num_encoder_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)

						_snake_case					:							Optional[int]					     =   common_inputs["""attention_mask"""].dtype
						_snake_case					:							str					     =   torch.cat(
						    [common_inputs["""attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	dtype=SCREAMING_SNAKE_CASE__      )]						,	dim=1      )
						_snake_case					:							Tuple					     =   [
						    (torch.zeros(SCREAMING_SNAKE_CASE__      ), torch.zeros(SCREAMING_SNAKE_CASE__      )) for _ in range(SCREAMING_SNAKE_CASE__      )
						]
					return common_inputs







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   compute_effective_axis_dimension(
					    SCREAMING_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
					_snake_case					:							List[str]					     =   tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   compute_effective_axis_dimension(
					    SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_sequence						,	num_token_to_add=SCREAMING_SNAKE_CASE__      )

					# Generate dummy inputs according to compute batch and sequence
					_snake_case					:							str					     =   [""" """.join([tokenizer.unk_token]      ) * seq_length] * batch_size
					_snake_case					:							Optional[int]					     =   dict(tokenizer(SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__      )      )
					return common_inputs







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	):






					"""simple docstring"""

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

					elif self.task == "causal-lm":
						_snake_case					:							List[Any]					     =   self._generate_dummy_inputs_for_causal_lm(
						    SCREAMING_SNAKE_CASE__						,	batch_size=SCREAMING_SNAKE_CASE__						,	seq_length=SCREAMING_SNAKE_CASE__						,	is_pair=SCREAMING_SNAKE_CASE__						,	framework=SCREAMING_SNAKE_CASE__      )
					else:
						_snake_case					:							Dict					     =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    SCREAMING_SNAKE_CASE__						,	batch_size=SCREAMING_SNAKE_CASE__						,	seq_length=SCREAMING_SNAKE_CASE__						,	is_pair=SCREAMING_SNAKE_CASE__						,	framework=SCREAMING_SNAKE_CASE__      )

					return common_inputs







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					if self.task in ["default", "seq2seq-lm"]:
						_snake_case					:							Union[str, Any]					     =   super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					else:
						_snake_case					:							int					     =   super(SCREAMING_SNAKE_CASE__						,	self      )._flatten_past_key_values_(
						    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )





