

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	):



      """simple docstring"""







      __UpperCAmelCase     						= 'bigbird_pegasus'
      __UpperCAmelCase     						= ['past_key_values']
      __UpperCAmelCase     						= {
          'num_attention_heads': 'encoder_attention_heads',
          'hidden_size': 'd_model',
          'attention_probs_dropout_prob': 'attention_dropout',
      }
      def __init__( self	:				Any     ,snake_case	:				Optional[Any]=96103     ,snake_case	:				Optional[int]=4096     ,snake_case	:				Dict=16     ,snake_case	:				Dict=4096     ,snake_case	:				Optional[int]=16     ,snake_case	:				Any=16     ,snake_case	:				str=4096     ,snake_case	:				Union[str, Any]=16     ,snake_case	:				List[str]=0.0     ,snake_case	:				Optional[int]=0.0     ,snake_case	:				Optional[int]=True     ,snake_case	:				Optional[Any]=True     ,snake_case	:				Optional[int]="gelu_new"     ,snake_case	:				int=1024     ,snake_case	:				Optional[Any]=0.1     ,snake_case	:				Optional[Any]=0.0     ,snake_case	:				Any=0.0     ,snake_case	:				List[str]=0.02     ,snake_case	:				int=2     ,snake_case	:				Tuple=0.0     ,snake_case	:				List[Any]=True     ,snake_case	:				Any=0     ,snake_case	:				Tuple=2     ,snake_case	:				List[str]=1     ,snake_case	:				Optional[int]="block_sparse"     ,snake_case	:				Union[str, Any]=64     ,snake_case	:				Union[str, Any]=3     ,snake_case	:				Any=False     ,**snake_case	:				Tuple     ,):
           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=a__     ,bos_token_id=a__     ,eos_token_id=a__     ,is_encoder_decoder=a__     ,decoder_start_token_id=a__     ,**a__     ,)







class 						a_			(				_UpperCamelCase	):



      """simple docstring"""







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







           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_(a__     ,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		,    SCREAMING_SNAKE_CASE 		=self.num_layers
                     for i in range(a__							):
                          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	:				int							):







           if self.task in ["default", "seq2seq-lm"]:
                SCREAMING_SNAKE_CASE 		=super().outputs
           else:
                SCREAMING_SNAKE_CASE 		=super(a__     ,self							).outputs
                if self.use_past:
                     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=self.num_layers
                     for i in range(a__							):
                          SCREAMING_SNAKE_CASE 		={0: 'batch', 2: 'past_sequence + sequence'}
                          SCREAMING_SNAKE_CASE 		={0: 'batch', 2: 'past_sequence + sequence'}
           return common_outputs
      def 			_lowerCAmelCase	( self	:				int     ,snake_case	:				PreTrainedTokenizer     ,snake_case	:				int = -1     ,snake_case	:				int = -1     ,snake_case	:				bool = False     ,snake_case	:				Optional[TensorType] = None     ,):
           SCREAMING_SNAKE_CASE 		=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               a__     ,a__     ,a__     ,a__     ,a__							)

           # 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(
               a__     ,a__     ,a__     ,a__     ,a__							)
           SCREAMING_SNAKE_CASE 		={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
           SCREAMING_SNAKE_CASE 		=dict(**a__     ,**a__							)

           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		,    SCREAMING_SNAKE_CASE 		=common_inputs['input_ids'].shape
                SCREAMING_SNAKE_CASE 		=common_inputs['decoder_input_ids'].shape[1]
                SCREAMING_SNAKE_CASE		,    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(a__     ,a__							)]     ,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		,    SCREAMING_SNAKE_CASE 		=self.num_layers
                SCREAMING_SNAKE_CASE 		=min(a__     ,a__							)
                SCREAMING_SNAKE_CASE 		=max(a__     ,a__							) - min_num_layers
                SCREAMING_SNAKE_CASE 		='encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

                for _ in range(a__							):
                     common_inputs["past_key_values"].append(
                         (
                             torch.zeros(a__							),
                             torch.zeros(a__							),
                             torch.zeros(a__							),
                             torch.zeros(a__							),
                         )							)
                # TODO: test this.
                SCREAMING_SNAKE_CASE 		=encoder_shape if remaining_side_name == 'encoder' else decoder_shape
                for _ in range(a__     ,a__							):
                     common_inputs["past_key_values"].append((torch.zeros(a__							), torch.zeros(a__							))							)
           return common_inputs
      def 			_lowerCAmelCase	( self	:				Union[str, Any]     ,snake_case	:				PreTrainedTokenizer     ,snake_case	:				int = -1     ,snake_case	:				int = -1     ,snake_case	:				bool = False     ,snake_case	:				Optional[TensorType] = None     ,):
           SCREAMING_SNAKE_CASE 		=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               a__     ,a__     ,a__     ,a__     ,a__							)

           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		,    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		,    SCREAMING_SNAKE_CASE 		=self.num_layers
                SCREAMING_SNAKE_CASE		,    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(a__     ,a__     ,dtype=a__							)]     ,dim=1							)
                SCREAMING_SNAKE_CASE 		=[
                    (torch.zeros(a__							), torch.zeros(a__							)) for _ in range(a__							)
                ]
           return common_inputs
      def 			_lowerCAmelCase	( self	:				List[Any]     ,snake_case	:				PreTrainedTokenizer     ,snake_case	:				int = -1     ,snake_case	:				int = -1     ,snake_case	:				bool = False     ,snake_case	:				Optional[TensorType] = None     ,):
           SCREAMING_SNAKE_CASE 		=compute_effective_axis_dimension(
               a__     ,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(a__							)
           SCREAMING_SNAKE_CASE 		=compute_effective_axis_dimension(
               a__     ,fixed_dimension=OnnxConfig.default_fixed_sequence     ,num_token_to_add=a__							)

           # 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(a__     ,return_tensors=a__							)							)
           return common_inputs
      def 			_lowerCAmelCase	( self	:				Union[str, Any]     ,snake_case	:				PreTrainedTokenizer     ,snake_case	:				int = -1     ,snake_case	:				int = -1     ,snake_case	:				bool = False     ,snake_case	:				Optional[TensorType] = None     ,):







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

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

           return common_inputs


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







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





"""simple docstring"""


def   a__			(					__lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      1
				while b > 0:
								if b & 1:
												res *= a

								a *= a
								b >>= 1

				return res






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						Optional[Any]:
				_A       	=      1
				while b > 0:
								if b & 1:
												_A       	=      ((res % c) * (a % c)) % c

								a *= a
								b >>= 1

				return res