

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__					: Optional[Any]								=      logging.get_logger(__name__)

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

  def __init__(   self  ,						_A=96103  ,						_A=4096  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=0.0  ,						_A=0.0  ,						_A=True  ,						_A=True  ,						_A="gelu_new"  ,						_A=1024  ,						_A=0.1  ,						_A=0.0  ,						_A=0.0  ,						_A=0.0_2  ,						_A=2  ,						_A=0.0  ,						_A=True  ,						_A=0  ,						_A=2  ,						_A=1  ,						_A="block_sparse"  ,						_A=64  ,						_A=3  ,						_A=False  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  vocab_size
      SCREAMING_SNAKE_CASE_													=  max_position_embeddings
      SCREAMING_SNAKE_CASE_													=  d_model
      SCREAMING_SNAKE_CASE_													=  encoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  encoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  decoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  decoder_layers
      SCREAMING_SNAKE_CASE_													=  decoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  dropout
      SCREAMING_SNAKE_CASE_													=  attention_dropout
      SCREAMING_SNAKE_CASE_													=  activation_dropout
      SCREAMING_SNAKE_CASE_													=  activation_function
      SCREAMING_SNAKE_CASE_													=  init_std
      SCREAMING_SNAKE_CASE_													=  encoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  decoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  classifier_dropout
      SCREAMING_SNAKE_CASE_													=  use_cache
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  scale_embedding  # scale factor will be sqrt(d_model) if True

      # extra config
      SCREAMING_SNAKE_CASE_													=  attention_type
      SCREAMING_SNAKE_CASE_													=  block_size
      SCREAMING_SNAKE_CASE_													=  num_random_blocks
      SCREAMING_SNAKE_CASE_													=  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  __snake_case					(      UpperCamelCase_    ):

  @property
  def   lowerCAmelCase__       (   self):




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

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




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

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = -1  ,						_A = -1  ,						_A = False  ,						_A = None  ,						):
      SCREAMING_SNAKE_CASE_													=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowercase_  ,						lowercase_  ,						lowercase_  ,						lowercase_  ,						lowercase_)

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

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

          SCREAMING_SNAKE_CASE_													=  []
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          SCREAMING_SNAKE_CASE_													=  self.num_layers
          SCREAMING_SNAKE_CASE_													=  min(lowercase_  ,						lowercase_)
          SCREAMING_SNAKE_CASE_													=  max(lowercase_  ,						lowercase_) - min_num_layers
          SCREAMING_SNAKE_CASE_													=  '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.
          SCREAMING_SNAKE_CASE_													=  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  ,						_A  ,						_A = -1  ,						_A = -1  ,						_A = False  ,						_A = None  ,						):
      SCREAMING_SNAKE_CASE_													=  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
          SCREAMING_SNAKE_CASE_													=  common_inputs['input_ids'].shape
          # Not using the same length for past_key_values
          SCREAMING_SNAKE_CASE_													=  seqlen + 2
          SCREAMING_SNAKE_CASE_													=  self.num_layers
          SCREAMING_SNAKE_CASE_													=  self.num_attention_heads
          SCREAMING_SNAKE_CASE_													=  (
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )

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

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = -1  ,						_A = -1  ,						_A = False  ,						_A = None  ,						):
      SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  tokenizer.num_special_tokens_to_add(lowercase_)
      SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  [' '.join([tokenizer.unk_token]) * seq_length] * batch_size
      SCREAMING_SNAKE_CASE_													=  dict(tokenizer(lowercase_  ,						return_tensors=lowercase_))
      return common_inputs

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = -1  ,						_A = -1  ,						_A = False  ,						_A = None  ,						):




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




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





def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    if power < 0:
        return -1
    base %= mod
    SCREAMING_SNAKE_CASE_													=  1

    while power > 0:
        if power & 1:
            SCREAMING_SNAKE_CASE_													=  (result * base) % mod
        SCREAMING_SNAKE_CASE_													=  power >> 1
        SCREAMING_SNAKE_CASE_													=  (base * base) % mod

    return result


def    _UpperCAmelCase    (					):





    """simple docstring"""


    print(modular_exponential(3	,				200	,				13    )    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       main()


