


import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowerCamelCase						 =	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[Any]					):
       '''simple docstring'''





       if "token" in model_name_or_path:
              return "rag_token"
       if "sequence" in model_name_or_path:
              return "rag_sequence"
       if "bart" in model_name_or_path:
              return "bart"
       return None




def      a_				(   SCREAMING_SNAKE_CASE__ :				List[str]		,      SCREAMING_SNAKE_CASE__ :				List[str]		,      SCREAMING_SNAKE_CASE__ :				int					):
       '''simple docstring'''





       return max(metric_fn(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					) for gt in ground_truths					)




def      a_				(   SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				List[Any]		,      SCREAMING_SNAKE_CASE__ :				Optional[Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		int          =[line.strip() for line in open(SCREAMING_SNAKE_CASE__		,      'r'					).readlines()]
       _lowerCamelCase	:		List[str]          =[]

       if args.gold_data_mode == "qa":
              _lowerCamelCase	:		Optional[int]          =pd.read_csv(SCREAMING_SNAKE_CASE__		,      sep='\t'		,      header=SCREAMING_SNAKE_CASE__					)
              for answer_list in data[1]:
                     _lowerCamelCase	:		List[Any]          =ast.literal_eval(SCREAMING_SNAKE_CASE__					)
                     answers.append(SCREAMING_SNAKE_CASE__					)
       else:
              _lowerCamelCase	:		str          =[line.strip() for line in open(SCREAMING_SNAKE_CASE__		,      'r'					).readlines()]
              _lowerCamelCase	:		int          =[[reference] for reference in references]

       _lowerCamelCase	:		Tuple          =0
       for prediction, ground_truths in zip(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					):
              total += 1
              em += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
              fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		int          =1_00.0 * em / total
       _lowerCamelCase	:		List[Any]          =1_00.0 * fa / total

       logger.info(F'''F1: {fa:.2f}'''					)
       logger.info(F'''EM: {em:.2f}'''					)




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[Any]		,      SCREAMING_SNAKE_CASE__ :				List[Any]		,      SCREAMING_SNAKE_CASE__ :				Tuple					):
       '''simple docstring'''





       _lowerCamelCase	:		List[Any]          =args.k
       _lowerCamelCase	:		Union[str, Any]          =[line.strip() for line in open(SCREAMING_SNAKE_CASE__		,      'r'					).readlines()]
       _lowerCamelCase	:		Any          =[line.strip() for line in open(SCREAMING_SNAKE_CASE__		,      'r'					).readlines()]

       _lowerCamelCase	:		Any          =0
       for hypo, reference in zip(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					):
              _lowerCamelCase	:		Any          =set(hypo.split('\t'					)[:k]					)
              _lowerCamelCase	:		List[str]          =set(reference.split('\t'					)					)
              total += 1
              em += len(hypo_provenance & ref_provenance					) / k

       _lowerCamelCase	:		Tuple          =1_00.0 * em / total
       logger.info(F'''Precision@{k}: {em: .2f}'''					)




def      a_				(   SCREAMING_SNAKE_CASE__ :				Dict		,      SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				Dict					):
       '''simple docstring'''





       def strip_title(SCREAMING_SNAKE_CASE__ :				str					):
              if title.startswith('"'					):
                     _lowerCamelCase	:		List[Any]          =title[1:]
              if title.endswith('"'					):
                     _lowerCamelCase	:		str          =title[:-1]
              return title

       _lowerCamelCase	:		Any          =rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           SCREAMING_SNAKE_CASE__		,      return_tensors='pt'		,      padding=SCREAMING_SNAKE_CASE__		,      truncation=SCREAMING_SNAKE_CASE__		,      )['input_ids'].to(args.device					)

       _lowerCamelCase	:		Optional[int]          =rag_model.rag.question_encoder(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		List[str]          =question_enc_outputs[0]

       _lowerCamelCase	:		Dict          =rag_model.retriever(
           SCREAMING_SNAKE_CASE__		,      question_enc_pool_output.cpu().detach().to(torch.floataa					).numpy()		,      prefix=rag_model.rag.generator.config.prefix		,      n_docs=rag_model.config.n_docs		,      return_tensors='pt'		,      )
       _lowerCamelCase	:		Union[str, Any]          =rag_model.retriever.index.get_doc_dicts(result.doc_ids					)
       _lowerCamelCase	:		Tuple          =[]
       for docs in all_docs:
              _lowerCamelCase	:		List[Any]          =[strip_title(SCREAMING_SNAKE_CASE__					) for title in docs['title']]
              provenance_strings.append('\t'.join(SCREAMING_SNAKE_CASE__					)					)
       return provenance_strings




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[Any]		,      SCREAMING_SNAKE_CASE__ :				List[str]		,      SCREAMING_SNAKE_CASE__ :				Tuple					):
       '''simple docstring'''





       with torch.no_grad():
              _lowerCamelCase	:		Tuple          =rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  SCREAMING_SNAKE_CASE__		,      return_tensors='pt'		,      padding=SCREAMING_SNAKE_CASE__		,      truncation=SCREAMING_SNAKE_CASE__					)

              _lowerCamelCase	:		Dict          =inputs_dict.input_ids.to(args.device					)
              _lowerCamelCase	:		str          =inputs_dict.attention_mask.to(args.device					)
              _lowerCamelCase	:		Any          =rag_model.generate(  # rag_model overwrites generate
                  SCREAMING_SNAKE_CASE__		,      attention_mask=SCREAMING_SNAKE_CASE__		,      num_beams=args.num_beams		,      min_length=args.min_length		,      max_length=args.max_length		,      early_stopping=SCREAMING_SNAKE_CASE__		,      num_return_sequences=1		,      bad_words_ids=[[0, 0]]		,      )
              _lowerCamelCase	:		Tuple          =rag_model.retriever.generator_tokenizer.batch_decode(SCREAMING_SNAKE_CASE__		,      skip_special_tokens=SCREAMING_SNAKE_CASE__					)

              if args.print_predictions:
                     for q, a in zip(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					):
                            logger.info('Q: {} - A: {}'.format(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)

              return answers




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		List[Any]          =argparse.ArgumentParser()
       parser.add_argument(
           '--model_type'		,      choices=['rag_sequence', 'rag_token', 'bart']		,      type=SCREAMING_SNAKE_CASE__		,      help=(
               'RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'
               ' model_name_or_path'
           )		,      )
       parser.add_argument(
           '--index_name'		,      default=SCREAMING_SNAKE_CASE__		,      choices=['exact', 'compressed', 'legacy']		,      type=SCREAMING_SNAKE_CASE__		,      help='RAG model retriever type'		,      )
       parser.add_argument(
           '--index_path'		,      default=SCREAMING_SNAKE_CASE__		,      type=SCREAMING_SNAKE_CASE__		,      help='Path to the retrieval index'		,      )
       parser.add_argument('--n_docs'		,      default=5		,      type=SCREAMING_SNAKE_CASE__		,      help='Number of retrieved docs'					)
       parser.add_argument(
           '--model_name_or_path'		,      default=SCREAMING_SNAKE_CASE__		,      type=SCREAMING_SNAKE_CASE__		,      required=SCREAMING_SNAKE_CASE__		,      help='Path to pretrained checkpoints or model identifier from huggingface.co/models'		,      )
       parser.add_argument(
           '--eval_mode'		,      choices=['e2e', 'retrieval']		,      default='e2e'		,      type=SCREAMING_SNAKE_CASE__		,      help=(
               'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
               ' precision@k.'
           )		,      )
       parser.add_argument('--k'		,      default=1		,      type=SCREAMING_SNAKE_CASE__		,      help='k for the precision@k calculation'					)
       parser.add_argument(
           '--evaluation_set'		,      default=SCREAMING_SNAKE_CASE__		,      type=SCREAMING_SNAKE_CASE__		,      required=SCREAMING_SNAKE_CASE__		,      help='Path to a file containing evaluation samples'		,      )
       parser.add_argument(
           '--gold_data_path'		,      default=SCREAMING_SNAKE_CASE__		,      type=SCREAMING_SNAKE_CASE__		,      required=SCREAMING_SNAKE_CASE__		,      help='Path to a tab-separated file with gold samples'		,      )
       parser.add_argument(
           '--gold_data_mode'		,      default='qa'		,      type=SCREAMING_SNAKE_CASE__		,      choices=['qa', 'ans']		,      help=(
               'Format of the gold data file'
               'qa - a single line in the following format: question [tab] answer_list'
               'ans - a single line of the gold file contains the expected answer string'
           )		,      )
       parser.add_argument(
           '--predictions_path'		,      type=SCREAMING_SNAKE_CASE__		,      default='predictions.txt'		,      help='Name of the predictions file, to be stored in the checkpoints directory'		,      )
       parser.add_argument(
           '--eval_all_checkpoints'		,      action='store_true'		,      help='Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'		,      )
       parser.add_argument(
           '--eval_batch_size'		,      default=8		,      type=SCREAMING_SNAKE_CASE__		,      help='Batch size per GPU/CPU for evaluation.'		,      )
       parser.add_argument(
           '--recalculate'		,      help='Recalculate predictions even if the prediction file exists'		,      action='store_true'		,      )
       parser.add_argument(
           '--num_beams'		,      default=4		,      type=SCREAMING_SNAKE_CASE__		,      help='Number of beams to be used when generating answers'		,      )
       parser.add_argument('--min_length'		,      default=1		,      type=SCREAMING_SNAKE_CASE__		,      help='Min length of the generated answers'					)
       parser.add_argument('--max_length'		,      default=50		,      type=SCREAMING_SNAKE_CASE__		,      help='Max length of the generated answers'					)

       parser.add_argument(
           '--print_predictions'		,      action='store_true'		,      help='If True, prints predictions while evaluating.'		,      )
       parser.add_argument(
           '--print_docs'		,      action='store_true'		,      help='If True, prints docs retried while generating.'		,      )
       _lowerCamelCase	:		List[str]          =parser.parse_args()
       _lowerCamelCase	:		Dict          =torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)
       return args




def      a_				(   SCREAMING_SNAKE_CASE__ :				Tuple					):
       '''simple docstring'''





       _lowerCamelCase	:		Any          ={}
       if args.model_type is None:
              _lowerCamelCase	:		Tuple          =infer_model_type(args.model_name_or_path					)
              assert args.model_type is not None
       if args.model_type.startswith('rag'					):
              _lowerCamelCase	:		Any          =RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
              _lowerCamelCase	:		Tuple          =args.n_docs
              if args.index_name is not None:
                     _lowerCamelCase	:		List[str]          =args.index_name
              if args.index_path is not None:
                     _lowerCamelCase	:		str          =args.index_path
       else:
              _lowerCamelCase	:		List[Any]          =BartForConditionalGeneration

       _lowerCamelCase	:		Tuple          =(
           [f.path for f in os.scandir(args.model_name_or_path					) if f.is_dir()]
           if args.eval_all_checkpoints
           else [args.model_name_or_path]
       )

       logger.info('Evaluate the following checkpoints: %s'		,      SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		Dict          =get_scores if args.eval_mode == 'e2e' else get_precision_at_k
       _lowerCamelCase	:		List[str]          =evaluate_batch_eae if args.eval_mode == 'e2e' else evaluate_batch_retrieval

       for checkpoint in checkpoints:
              if os.path.exists(args.predictions_path					) and (not args.recalculate):
                     logger.info('Calculating metrics based on an existing predictions file: {}'.format(args.predictions_path					)					)
                     score_fn(SCREAMING_SNAKE_CASE__		,      args.predictions_path		,      args.gold_data_path					)
                     continue

              logger.info('***** Running evaluation for {} *****'.format(SCREAMING_SNAKE_CASE__					)					)
              logger.info('  Batch size = %d'		,      args.eval_batch_size					)
              logger.info('  Predictions will be stored under {}'.format(args.predictions_path					)					)

              if args.model_type.startswith('rag'					):
                     _lowerCamelCase	:		Union[str, Any]          =RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__		,      **SCREAMING_SNAKE_CASE__					)
                     _lowerCamelCase	:		Tuple          =model_class.from_pretrained(SCREAMING_SNAKE_CASE__		,      retriever=SCREAMING_SNAKE_CASE__		,      **SCREAMING_SNAKE_CASE__					)
                     model.retriever.init_retrieval()
              else:
                     _lowerCamelCase	:		List[str]          =model_class.from_pretrained(SCREAMING_SNAKE_CASE__		,      **SCREAMING_SNAKE_CASE__					)
              model.to(args.device					)

              with open(args.evaluation_set		,      'r'					) as eval_file, open(args.predictions_path		,      'w'					) as preds_file:
                     _lowerCamelCase	:		Optional[Any]          =[]
                     for line in tqdm(SCREAMING_SNAKE_CASE__					):
                            questions.append(line.strip()					)
                            if len(SCREAMING_SNAKE_CASE__					) == args.eval_batch_size:
                                   _lowerCamelCase	:		str          =evaluate_batch_fn(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
                                   preds_file.write('\n'.join(SCREAMING_SNAKE_CASE__					) + '\n'					)
                                   preds_file.flush()
                                   _lowerCamelCase	:		int          =[]
                     if len(SCREAMING_SNAKE_CASE__					) > 0:
                            _lowerCamelCase	:		Optional[int]          =evaluate_batch_fn(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
                            preds_file.write('\n'.join(SCREAMING_SNAKE_CASE__					)					)
                            preds_file.flush()

                     score_fn(SCREAMING_SNAKE_CASE__		,      args.predictions_path		,      args.gold_data_path					)


if __name__ == "__main__":
 lowerCamelCase						 =	get_args()
 main(args)






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


lowerCamelCase						 =	logging.get_logger(__name__)

lowerCamelCase						 =	{
    '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 A  (       UpperCamelCase_					):
      UpperCamelCase__							:  int				='bigbird_pegasus'
      UpperCamelCase__							:  Tuple				=['past_key_values']
      UpperCamelCase__							:  str				={
          'num_attention_heads': 'encoder_attention_heads',
          'hidden_size': 'd_model',
          'attention_probs_dropout_prob': 'attention_dropout',
      }






      def __init__( self						:					Tuple      ,       lowercase_						:					int=9_6103      ,       lowercase_						:					Optional[Any]=4096      ,       lowercase_						:					Tuple=16      ,       lowercase_						:					int=4096      ,       lowercase_						:					Any=16      ,       lowercase_						:					str=16      ,       lowercase_						:					List[str]=4096      ,       lowercase_						:					Optional[Any]=16      ,       lowercase_						:					Any=0.0      ,       lowercase_						:					List[Any]=0.0      ,       lowercase_						:					Optional[int]=True      ,       lowercase_						:					Tuple=True      ,       lowercase_						:					Dict="gelu_new"      ,       lowercase_						:					List[str]=1024      ,       lowercase_						:					Dict=0.1      ,       lowercase_						:					Union[str, Any]=0.0      ,       lowercase_						:					Any=0.0      ,       lowercase_						:					str=0.02      ,       lowercase_						:					List[str]=2      ,       lowercase_						:					Any=0.0      ,       lowercase_						:					int=True      ,       lowercase_						:					Union[str, Any]=0      ,       lowercase_						:					List[str]=2      ,       lowercase_						:					Optional[Any]=1      ,       lowercase_						:					Any="block_sparse"      ,       lowercase_						:					str=64      ,       lowercase_						:					Dict=3      ,       lowercase_						:					List[str]=False      ,       **lowercase_						:					List[str]      ,       ) ->						str:
             """simple docstring"""




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

             # extra config
             _lowerCamelCase	:		Tuple          =attention_type
             _lowerCamelCase	:		str          =block_size
             _lowerCamelCase	:		Optional[int]          =num_random_blocks
             _lowerCamelCase	:		Optional[int]          =use_bias

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




class A  (       UpperCamelCase_					):






      @property
      def 	lowerCamelCase	( self						:					Dict			) ->						Mapping[str, Mapping[int, str]]:
             """simple docstring"""




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

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

                    if self.use_past:
                           self.fill_with_past_key_values_(lowercase_      ,       direction='inputs'			)
             elif self.task == "causal-lm":
                    # TODO: figure this case out.
                    _lowerCamelCase	:		Union[str, Any]          =OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                        ]			)
                    if self.use_past:
                           _lowerCamelCase     ,					_lowerCamelCase	:		Union[str, Any]          =self.num_layers
                           for i in range(lowercase_			):
                                  _lowerCamelCase	:		Dict          ={0: 'batch', 2: 'past_sequence + sequence'}
                                  _lowerCamelCase	:		List[str]          ={0: 'batch', 2: 'past_sequence + sequence'}
             else:
                    _lowerCamelCase	:		Optional[int]          =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						:					Dict			) ->						Mapping[str, Mapping[int, str]]:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					PreTrainedTokenizer      ,       lowercase_						:					int = -1      ,       lowercase_						:					int = -1      ,       lowercase_						:					bool = False      ,       lowercase_						:					Optional[TensorType] = None      ,       ) ->						Mapping[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		int          =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)

             # Generate decoder inputs
             _lowerCamelCase	:		str          =seq_length if not self.use_past else 1
             _lowerCamelCase	:		List[Any]          =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)
             _lowerCamelCase	:		List[Any]          ={F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
             _lowerCamelCase	:		Union[str, Any]          =dict(**lowercase_      ,       **lowercase_			)

             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	:		int          =common_inputs['input_ids'].shape
                    _lowerCamelCase	:		List[Any]          =common_inputs['decoder_input_ids'].shape[1]
                    _lowerCamelCase     ,					_lowerCamelCase	:		Union[str, Any]          =self.num_attention_heads
                    _lowerCamelCase	:		Dict          =(
                        batch,
                        num_encoder_attention_heads,
                        encoder_seq_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )
                    _lowerCamelCase	:		List[Any]          =decoder_seq_length + 3
                    _lowerCamelCase	:		int          =(
                        batch,
                        num_decoder_attention_heads,
                        decoder_past_length,
                        self._config.hidden_size // num_decoder_attention_heads,
                    )

                    _lowerCamelCase	:		Union[str, Any]          =torch.cat(
                        [common_inputs['decoder_attention_mask'], torch.ones(lowercase_      ,       lowercase_			)]      ,       dim=1			)

                    _lowerCamelCase	:		Optional[Any]          =[]
                    # If the number of encoder and decoder layers are present in the model configuration, both are considered
                    _lowerCamelCase     ,					_lowerCamelCase	:		Tuple          =self.num_layers
                    _lowerCamelCase	:		Optional[int]          =min(lowercase_      ,       lowercase_			)
                    _lowerCamelCase	:		int          =max(lowercase_      ,       lowercase_			) - min_num_layers
                    _lowerCamelCase	:		str          ='encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

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






      def 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					PreTrainedTokenizer      ,       lowercase_						:					int = -1      ,       lowercase_						:					int = -1      ,       lowercase_						:					bool = False      ,       lowercase_						:					Optional[TensorType] = None      ,       ) ->						Mapping[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		str          =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)

             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	:		Optional[int]          =common_inputs['input_ids'].shape
                    # Not using the same length for past_key_values
                    _lowerCamelCase	:		Union[str, Any]          =seqlen + 2
                    _lowerCamelCase     ,					_lowerCamelCase	:		List[Any]          =self.num_layers
                    _lowerCamelCase     ,					_lowerCamelCase	:		Any          =self.num_attention_heads
                    _lowerCamelCase	:		List[str]          =(
                        batch,
                        num_encoder_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )

                    _lowerCamelCase	:		Tuple          =common_inputs['attention_mask'].dtype
                    _lowerCamelCase	:		Tuple          =torch.cat(
                        [common_inputs['attention_mask'], torch.ones(lowercase_      ,       lowercase_      ,       dtype=lowercase_			)]      ,       dim=1			)
                    _lowerCamelCase	:		Dict          =[
                        (torch.zeros(lowercase_			), torch.zeros(lowercase_			)) for _ in range(lowercase_			)
                    ]
             return common_inputs






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					PreTrainedTokenizer      ,       lowercase_						:					int = -1      ,       lowercase_						:					int = -1      ,       lowercase_						:					bool = False      ,       lowercase_						:					Optional[TensorType] = None      ,       ) ->						Mapping[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          =compute_effective_axis_dimension(
                 lowercase_      ,       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	:		Tuple          =tokenizer.num_special_tokens_to_add(lowercase_			)
             _lowerCamelCase	:		Any          =compute_effective_axis_dimension(
                 lowercase_      ,       fixed_dimension=OnnxConfig.default_fixed_sequence      ,       num_token_to_add=lowercase_			)

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






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					PreTrainedTokenizer      ,       lowercase_						:					int = -1      ,       lowercase_						:					int = -1      ,       lowercase_						:					bool = False      ,       lowercase_						:					Optional[TensorType] = None      ,       ) ->						Mapping[str, Any]:
             """simple docstring"""




             if self.task in ["default", "seq2seq-lm"]:
                    _lowerCamelCase	:		Union[str, Any]          =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                        lowercase_      ,       batch_size=lowercase_      ,       seq_length=lowercase_      ,       is_pair=lowercase_      ,       framework=lowercase_			)

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

             return common_inputs






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					Union[str, Any]      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					Dict      ,       lowercase_						:					Tuple			) ->						Dict:
             """simple docstring"""




             if self.task in ["default", "seq2seq-lm"]:
                    _lowerCamelCase	:		Union[str, Any]          =super()._flatten_past_key_values_(lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)
             else:
                    _lowerCamelCase	:		List[str]          =super(lowercase_      ,       self			)._flatten_past_key_values_(
                        lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)


