

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 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      int        =  'bigbird_pegasus'
   lowerCamelCase__					:      Tuple        =  ['past_key_values']
   lowerCamelCase__					:      List[Any]        =  {
       'num_attention_heads': 'encoder_attention_heads',
       'hidden_size': 'd_model',
       'attention_probs_dropout_prob': 'attention_dropout',
   }



   def __init__(							self,       lowercase_=96103,       lowercase_=4096,       lowercase_=16,       lowercase_=4096,       lowercase_=16,       lowercase_=16,       lowercase_=4096,       lowercase_=16,       lowercase_=0.0,       lowercase_=0.0,       lowercase_=True,       lowercase_=True,       lowercase_="gelu_new",       lowercase_=1024,       lowercase_=0.1,       lowercase_=0.0,       lowercase_=0.0,       lowercase_=0.02,       lowercase_=2,       lowercase_=0.0,       lowercase_=True,       lowercase_=0,       lowercase_=2,       lowercase_=1,       lowercase_="block_sparse",       lowercase_=64,       lowercase_=3,       lowercase_=False,       **lowercase_,       )				->       int:






     """simple docstring"""






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

     # extra config
     a__    =attention_type
     a__    =block_size
     a__    =num_random_blocks
     a__    =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 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   @property
   def 					_UpperCAmelCase					(							self			)				->       Mapping[str, Mapping[int, str]]:






     """simple docstring"""






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

       if self.use_past:
         a__    ={0: '''batch'''}
         a__    ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
         a__    ={0: '''batch''', 1: '''decoder_sequence'''}
         a__    ={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.
       a__    =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]			)
       if self.use_past:
         a__, a__    =self.num_layers
         for i in range(lowercase_			):
           a__    ={0: '''batch''', 2: '''past_sequence + sequence'''}
           a__    ={0: '''batch''', 2: '''past_sequence + sequence'''}
     else:
       a__    =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 					_UpperCAmelCase					(							self			)				->       Mapping[str, Mapping[int, str]]:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = -1,       lowercase_ = -1,       lowercase_ = False,       lowercase_ = None,       )				->       Mapping[str, Any]:






     """simple docstring"""






     a__    =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_			)

     # Generate decoder inputs
     a__    =seq_length if not self.use_past else 1
     a__    =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_			)
     a__    ={F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
     a__    =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
       a__, a__    =common_inputs['''input_ids'''].shape
       a__    =common_inputs['''decoder_input_ids'''].shape[1]
       a__, a__    =self.num_attention_heads
       a__    =(
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       a__    =decoder_seq_length + 3
       a__    =(
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       a__    =torch.cat(
           [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_,       lowercase_			)],       dim=1			)

       a__    =[]
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       a__, a__    =self.num_layers
       a__    =min(lowercase_,       lowercase_			)
       a__    =max(lowercase_,       lowercase_			) - min_num_layers
       a__    ='''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.
       a__    =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 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = -1,       lowercase_ = -1,       lowercase_ = False,       lowercase_ = None,       )				->       Mapping[str, Any]:






     """simple docstring"""






     a__    =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
       a__, a__    =common_inputs['''input_ids'''].shape
       # Not using the same length for past_key_values
       a__    =seqlen + 2
       a__, a__    =self.num_layers
       a__, a__    =self.num_attention_heads
       a__    =(
           batch,
           num_encoder_attention_heads,
           past_key_values_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )

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



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = -1,       lowercase_ = -1,       lowercase_ = False,       lowercase_ = None,       )				->       Mapping[str, Any]:






     """simple docstring"""






     a__    =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
     a__    =tokenizer.num_special_tokens_to_add(lowercase_			)
     a__    =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
     a__    =[''' '''.join([tokenizer.unk_token]			) * seq_length] * batch_size
     a__    =dict(tokenizer(lowercase_,       return_tensors=lowercase_			)			)
     return common_inputs



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = -1,       lowercase_ = -1,       lowercase_ = False,       lowercase_ = None,       )				->       Mapping[str, Any]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__    =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":
       a__    =self._generate_dummy_inputs_for_causal_lm(
           lowercase_,       batch_size=lowercase_,       seq_length=lowercase_,       is_pair=lowercase_,       framework=lowercase_			)
     else:
       a__    =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 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_,       lowercase_			)				->       str:






     """simple docstring"""






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



def      UpperCAmelCase__							(     _A :					Optional[int]      , _A :					Optional[int]				):





  '''simple docstring'''







  sequence.append(_A				)

  a__    =0
  while sequence[index] != target:
    index += 1

  sequence.pop()

  if index == len(_A				):
    return None

  return index


if __name__ == "__main__":
       lowerCamelCase 	=   input('''Enter numbers separated by comma:\n''').strip()
       lowerCamelCase 	=   [int(item) for item in user_input.split(''',''')]

       lowerCamelCase 	=   input('''Enter a single number to be found in the list:\n''')
       lowerCamelCase 	=   int(target_input)
       lowerCamelCase 	=   sentinel_linear_search(sequence, target)
       if result is not None:
              print(f"""{target} found at positions: {result}""")
       else:
              print('''Not found''')
