



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__  =			logging.get_logger(__name__)

UpperCAmelCase__  =			{
    "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       (     snake_case_		):
			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  :    Union[str, Any]					,						A  :    Union[str, Any]=9_61_03					,						A  :    Optional[Any]=40_96					,						A  :    Optional[Any]=16					,						A  :    List[str]=40_96					,						A  :    Optional[Any]=16					,						A  :    Optional[Any]=16					,						A  :    Any=40_96					,						A  :    List[Any]=16					,						A  :    Tuple=0.0					,						A  :    int=0.0					,						A  :    Optional[Any]=True					,						A  :    Any=True					,						A  :    Optional[int]="gelu_new"					,						A  :    str=10_24					,						A  :    Union[str, Any]=0.1					,						A  :    int=0.0					,						A  :    List[str]=0.0					,						A  :    Union[str, Any]=0.0_2					,						A  :    Union[str, Any]=2					,						A  :    List[Any]=0.0					,						A  :    Optional[Any]=True					,						A  :    Optional[Any]=0					,						A  :    str=2					,						A  :    Optional[int]=1					,						A  :    str="block_sparse"					,						A  :    int=64					,						A  :    Dict=3					,						A  :    Union[str, Any]=False					,						**A  :    Union[str, Any]					,						)  ->       Union[str, Any]:



						"""simple docstring"""






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

						# extra config
						_UpperCAmelCase									=  attention_type
						_UpperCAmelCase									=  block_size
						_UpperCAmelCase									=  num_random_blocks
						_UpperCAmelCase									=  use_bias

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







class      __lowerCAmelCase       (     snake_case_		):



			@property
			def        _lowerCamelCase			(		self  :    Union[str, Any])  ->       Union[str, Any]:



						"""simple docstring"""






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

									if self.use_past:
												_UpperCAmelCase									=  {0: 'batch'}
												_UpperCAmelCase									=  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
									else:
												_UpperCAmelCase									=  {0: 'batch', 1: 'decoder_sequence'}
												_UpperCAmelCase									=  {0: 'batch', 1: 'decoder_sequence'}

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



						"""simple docstring"""






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



			def        _lowerCamelCase			(		self  :    Optional[Any]					,						A  :    List[str]					,						A  :    Tuple = -1					,						A  :    Union[str, Any] = -1					,						A  :    Dict = False					,						A  :    str = None					,						)  ->       Any:



						"""simple docstring"""






						_UpperCAmelCase									=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_)

						# Generate decoder inputs
						_UpperCAmelCase									=  seq_length if not self.use_past else 1
						_UpperCAmelCase									=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_)
						_UpperCAmelCase									=  {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
						_UpperCAmelCase									=  dict(**lowerCamelCase_					,						**lowerCamelCase_)

						if self.use_past:
									if not is_torch_available():
												raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
									else:
												import torch
									_UpperCAmelCase									=  common_inputs['input_ids'].shape
									_UpperCAmelCase									=  common_inputs['decoder_input_ids'].shape[1]
									_UpperCAmelCase									=  self.num_attention_heads
									_UpperCAmelCase									=  (
									    batch,
									    num_encoder_attention_heads,
									    encoder_seq_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									_UpperCAmelCase									=  decoder_seq_length + 3
									_UpperCAmelCase									=  (
									    batch,
									    num_decoder_attention_heads,
									    decoder_past_length,
									    self._config.hidden_size // num_decoder_attention_heads,
									)

									_UpperCAmelCase									=  torch.cat(
									    [common_inputs['decoder_attention_mask'], torch.ones(lowerCamelCase_					,						lowerCamelCase_)]					,						dim=1)

									_UpperCAmelCase									=  []
									# If the number of encoder and decoder layers are present in the model configuration, both are considered
									_UpperCAmelCase									=  self.num_layers
									_UpperCAmelCase									=  min(lowerCamelCase_					,						lowerCamelCase_)
									_UpperCAmelCase									=  max(lowerCamelCase_					,						lowerCamelCase_) - min_num_layers
									_UpperCAmelCase									=  'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

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



			def        _lowerCamelCase			(		self  :    Any					,						A  :    List[Any]					,						A  :    List[str] = -1					,						A  :    Any = -1					,						A  :    Dict = False					,						A  :    Any = None					,						)  ->       Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase									=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_)

						if self.use_past:
									if not is_torch_available():
												raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
									else:
												import torch
									_UpperCAmelCase									=  common_inputs['input_ids'].shape
									# Not using the same length for past_key_values
									_UpperCAmelCase									=  seqlen + 2
									_UpperCAmelCase									=  self.num_layers
									_UpperCAmelCase									=  self.num_attention_heads
									_UpperCAmelCase									=  (
									    batch,
									    num_encoder_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)

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



			def        _lowerCamelCase			(		self  :    Dict					,						A  :    Optional[int]					,						A  :    str = -1					,						A  :    Union[str, Any] = -1					,						A  :    List[str] = False					,						A  :    Optional[int] = None					,						)  ->       Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase									=  compute_effective_axis_dimension(
						    lowerCamelCase_					,						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
						_UpperCAmelCase									=  tokenizer.num_special_tokens_to_add(lowerCamelCase_)
						_UpperCAmelCase									=  compute_effective_axis_dimension(
						    lowerCamelCase_					,						fixed_dimension=OnnxConfig.default_fixed_sequence					,						num_token_to_add=lowerCamelCase_)

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



			def        _lowerCamelCase			(		self  :    List[str]					,						A  :    Optional[Any]					,						A  :    Optional[Any] = -1					,						A  :    List[str] = -1					,						A  :    Dict = False					,						A  :    Any = None					,						)  ->       Dict:



						"""simple docstring"""






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

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

						return common_inputs



			def        _lowerCamelCase			(		self  :    Dict					,						A  :    Union[str, Any]					,						A  :    List[Any]					,						A  :    Union[str, Any]					,						A  :    List[str])  ->       List[Any]:



						"""simple docstring"""






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








"""simple docstring"""
from itertools import combinations, product






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							):
 if xa == ya == 0 or xa == ya == 0:
  return False
 lowerCamelCase__	:     Tuple								=		xa * xa + ya * ya
 lowerCamelCase__	:     Union[str, Any]								=		xa * xa + ya * ya
 lowerCamelCase__	:     Optional[Any]								=		(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
 return (
     a_square + b_square == c_square
     or a_square + c_square == b_square
     or b_square + c_square == a_square
 )






def 			lowerCamelCase_    (		_lowerCamelCase = 50							):
 return sum(
     1
     for pta, pta in combinations(product(range(limit + 1							)     ,  repeat=2							)     ,  2							)
     if is_right(*_lowerCamelCase     ,  *_lowerCamelCase							)							)


if __name__ == "__main__":
      print(f"{solution() = }")

