



'''simple docstring'''







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


SCREAMING_SNAKE_CASE__     : int				=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     : Dict				=   {
    """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 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		int		=      'bigbird_pegasus'
			__snake_case  :		Tuple		=      ['past_key_values']
			__snake_case  :		Any		=      {
			    'num_attention_heads': 'encoder_attention_heads',
			    'hidden_size': 'd_model',
			    'attention_probs_dropout_prob': 'attention_dropout',
			}






			def __init__(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=96_103      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=4_096      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=16      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=4_096      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=16      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=16      ,       SCREAMING_SNAKE_CASE  :							List[Any]=4_096      ,       SCREAMING_SNAKE_CASE  :							str=16      ,       SCREAMING_SNAKE_CASE  :							Dict=0.0      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=0.0      ,       SCREAMING_SNAKE_CASE  :							Tuple=True      ,       SCREAMING_SNAKE_CASE  :							Dict=True      ,       SCREAMING_SNAKE_CASE  :							List[str]="gelu_new"      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=1_024      ,       SCREAMING_SNAKE_CASE  :							Dict=0.1      ,       SCREAMING_SNAKE_CASE  :							Any=0.0      ,       SCREAMING_SNAKE_CASE  :							List[Any]=0.0      ,       SCREAMING_SNAKE_CASE  :							List[Any]=0.02      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=2      ,       SCREAMING_SNAKE_CASE  :							Any=0.0      ,       SCREAMING_SNAKE_CASE  :							int=True      ,       SCREAMING_SNAKE_CASE  :							List[str]=0      ,       SCREAMING_SNAKE_CASE  :							List[str]=2      ,       SCREAMING_SNAKE_CASE  :							int=1      ,       SCREAMING_SNAKE_CASE  :							int="block_sparse"      ,       SCREAMING_SNAKE_CASE  :							List[str]=64      ,       SCREAMING_SNAKE_CASE  :							Tuple=3      ,       SCREAMING_SNAKE_CASE  :							int=False      ,       **SCREAMING_SNAKE_CASE  :							int      ,       ):




					"""simple docstring"""







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

					# extra config
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      attention_type
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      block_size
					SCREAMING_SNAKE_CASE_						:int										=      num_random_blocks
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      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 				__lowerCAmelCase(							lowerCAmelCase__      ):






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




					"""simple docstring"""







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

							if self.use_past:
									SCREAMING_SNAKE_CASE_						:List[str]										=      {0: 'batch'}
									SCREAMING_SNAKE_CASE_						:Tuple										=      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
									SCREAMING_SNAKE_CASE_						:List[str]										=      {0: 'batch', 1: 'decoder_sequence'}
									SCREAMING_SNAKE_CASE_						:List[str]										=      {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.
							SCREAMING_SNAKE_CASE_						:int										=      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_						:Dict										=      self.num_layers
									for i in range(SCREAMING_SNAKE_CASE	):
											SCREAMING_SNAKE_CASE_						:Any										=      {0: 'batch', 2: 'past_sequence + sequence'}
											SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      {0: 'batch', 2: 'past_sequence + sequence'}
					else:
							SCREAMING_SNAKE_CASE_						: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 				_lowercase	(   self  :							str	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							PreTrainedTokenizer      ,       SCREAMING_SNAKE_CASE  :							int = -1      ,       SCREAMING_SNAKE_CASE  :							int = -1      ,       SCREAMING_SNAKE_CASE  :							bool = False      ,       SCREAMING_SNAKE_CASE  :							Optional[TensorType] = None      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						: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
					SCREAMING_SNAKE_CASE_						:List[str]										=      seq_length if not self.use_past else 1
					SCREAMING_SNAKE_CASE_						:List[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	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
					SCREAMING_SNAKE_CASE_						:Tuple										=      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
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[Any]										=      common_inputs['input_ids'].shape
							SCREAMING_SNAKE_CASE_						:Tuple										=      common_inputs['decoder_input_ids'].shape[1]
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.num_attention_heads
							SCREAMING_SNAKE_CASE_						:Tuple										=      (
							    batch,
							    num_encoder_attention_heads,
							    encoder_seq_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)
							SCREAMING_SNAKE_CASE_						:List[str]										=      decoder_seq_length + 3
							SCREAMING_SNAKE_CASE_						:int										=      (
							    batch,
							    num_decoder_attention_heads,
							    decoder_past_length,
							    self._config.hidden_size // num_decoder_attention_heads,
							)

							SCREAMING_SNAKE_CASE_						:List[Any]										=      torch.cat(
							    [common_inputs['decoder_attention_mask'], torch.ones(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)]      ,       dim=1	)

							SCREAMING_SNAKE_CASE_						:Optional[int]										=      []
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Any										=      self.num_layers
							SCREAMING_SNAKE_CASE_						:Tuple										=      min(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      max(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	) - min_num_layers
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      '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.
							SCREAMING_SNAKE_CASE_						:Dict										=      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 				_lowercase	(   self  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							PreTrainedTokenizer      ,       SCREAMING_SNAKE_CASE  :							int = -1      ,       SCREAMING_SNAKE_CASE  :							int = -1      ,       SCREAMING_SNAKE_CASE  :							bool = False      ,       SCREAMING_SNAKE_CASE  :							Optional[TensorType] = None      ,       ):




					"""simple docstring"""







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

							SCREAMING_SNAKE_CASE_						:Dict										=      common_inputs['attention_mask'].dtype
							SCREAMING_SNAKE_CASE_						:str										=      torch.cat(
							    [common_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       dtype=SCREAMING_SNAKE_CASE	)]      ,       dim=1	)
							SCREAMING_SNAKE_CASE_						:List[Any]										=      [
							    (torch.zeros(SCREAMING_SNAKE_CASE	), torch.zeros(SCREAMING_SNAKE_CASE	)) for _ in range(SCREAMING_SNAKE_CASE	)
							]
					return common_inputs






			def 				_lowercase	(   self  :							Dict      ,       SCREAMING_SNAKE_CASE  :							PreTrainedTokenizer      ,       SCREAMING_SNAKE_CASE  :							int = -1      ,       SCREAMING_SNAKE_CASE  :							int = -1      ,       SCREAMING_SNAKE_CASE  :							bool = False      ,       SCREAMING_SNAKE_CASE  :							Optional[TensorType] = None      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      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
					SCREAMING_SNAKE_CASE_						:List[Any]										=      tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      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
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      [' '.join([tokenizer.unk_token]	) * seq_length] * batch_size
					SCREAMING_SNAKE_CASE_						:Any										=      dict(tokenizer(SCREAMING_SNAKE_CASE      ,       return_tensors=SCREAMING_SNAKE_CASE	)	)
					return common_inputs






			def 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							PreTrainedTokenizer      ,       SCREAMING_SNAKE_CASE  :							int = -1      ,       SCREAMING_SNAKE_CASE  :							int = -1      ,       SCREAMING_SNAKE_CASE  :							bool = False      ,       SCREAMING_SNAKE_CASE  :							Optional[TensorType] = None      ,       ):




					"""simple docstring"""







					if self.task in ["default", "seq2seq-lm"]:
							SCREAMING_SNAKE_CASE_						:Any										=      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":
							SCREAMING_SNAKE_CASE_						: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:
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      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 				_lowercase	(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]	):




					"""simple docstring"""







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





'''simple docstring'''







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		for i, _ in enumerate(SCREAMING_SNAKE_CASE     ):
				if (i % 2 == 1) == (nums[i - 1] > nums[i]):
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Dict										=      nums[i], nums[i - 1]

		return nums


if __name__ == "__main__":
							print("""Enter the array elements:""")
							SCREAMING_SNAKE_CASE__     : Tuple				=   list(map(int, input().split()))
							print("""The unsorted array is:""")
							print(array)
							print("""Array after Wiggle sort:""")
							print(wiggle_sort(array))
