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						:    Optional[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    lowercase_    (     _UpperCamelCase		):
							_lowerCamelCase						=		'bigbird_pegasus'
							_lowerCamelCase						=		['past_key_values']
							_lowerCamelCase						=		{
							    'num_attention_heads': 'encoder_attention_heads',
							    'hidden_size': 'd_model',
							    'attention_probs_dropout_prob': 'attention_dropout',
							}



							def __init__(						self      ,				lowercase_=96_103      ,				lowercase_=4_096      ,				lowercase_=16      ,				lowercase_=4_096      ,				lowercase_=16      ,				lowercase_=16      ,				lowercase_=4_096      ,				lowercase_=16      ,				lowercase_=0.0      ,				lowercase_=0.0      ,				lowercase_=True      ,				lowercase_=True      ,				lowercase_="gelu_new"      ,				lowercase_=1_024      ,				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_      ,				):
													_snake_case						:     List[Any]				=    vocab_size
													_snake_case						:     str				=    max_position_embeddings
													_snake_case						:     int				=    d_model
													_snake_case						:     List[Any]				=    encoder_ffn_dim
													_snake_case						:     Tuple				=    encoder_layers
													_snake_case						:     Union[str, Any]				=    encoder_attention_heads
													_snake_case						:     Optional[int]				=    decoder_ffn_dim
													_snake_case						:     Tuple				=    decoder_layers
													_snake_case						:     str				=    decoder_attention_heads
													_snake_case						:     str				=    dropout
													_snake_case						:     Optional[Any]				=    attention_dropout
													_snake_case						:     Tuple				=    activation_dropout
													_snake_case						:     Optional[int]				=    activation_function
													_snake_case						:     List[Any]				=    init_std
													_snake_case						:     List[Any]				=    encoder_layerdrop
													_snake_case						:     Dict				=    decoder_layerdrop
													_snake_case						:     List[str]				=    classifier_dropout
													_snake_case						:     Tuple				=    use_cache
													_snake_case						:     Union[str, Any]				=    encoder_layers
													_snake_case						:     List[str]				=    scale_embedding  # scale factor will be sqrt(d_model) if True

													# extra config
													_snake_case						:     Any				=    attention_type
													_snake_case						:     str				=    block_size
													_snake_case						:     Optional[Any]				=    num_random_blocks
													_snake_case						:     Optional[Any]				=    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    lowercase_    (     _UpperCamelCase		):



							@property
							def        UpperCamelCase       (						self				):







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

																			if self.use_past:
																									_snake_case						:     str				=    {0: "batch"}
																									_snake_case						:     Any				=    {0: "batch", 1: "past_decoder_sequence + sequence"}
																			else:
																									_snake_case						:     List[Any]				=    {0: "batch", 1: "decoder_sequence"}
																									_snake_case						:     str				=    {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.
																			_snake_case						:     Dict				=    OrderedDict(
																			    [
																			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																			    ]				)
																			if self.use_past:
																									_snake_case				,_snake_case						:     int				=    self.num_layers
																									for i in range(a__				):
																															_snake_case						:     List[str]				=    {0: "batch", 2: "past_sequence + sequence"}
																															_snake_case						:     str				=    {0: "batch", 2: "past_sequence + sequence"}
													else:
																			_snake_case						:     Optional[Any]				=    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				):







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



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_ = -1      ,				lowercase_ = -1      ,				lowercase_ = False      ,				lowercase_ = None      ,				):
													_snake_case						:     str				=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    a__      ,				a__      ,				a__      ,				a__      ,				a__				)

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

																			_snake_case						:     int				=    torch.cat(
																			    [common_inputs["decoder_attention_mask"], torch.ones(a__      ,				a__				)]      ,				dim=1				)

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

																			_snake_case						:     Optional[int]				=    common_inputs["attention_mask"].dtype
																			_snake_case						:     Any				=    torch.cat(
																			    [common_inputs["attention_mask"], torch.ones(a__      ,				a__      ,				dtype=a__				)]      ,				dim=1				)
																			_snake_case						:     List[Any]				=    [
																			    (torch.zeros(a__				), torch.zeros(a__				)) for _ in range(a__				)
																			]
													return common_inputs



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_ = -1      ,				lowercase_ = -1      ,				lowercase_ = False      ,				lowercase_ = None      ,				):
													_snake_case						:     List[Any]				=    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
													_snake_case						:     Tuple				=    tokenizer.num_special_tokens_to_add(a__				)
													_snake_case						:     str				=    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
													_snake_case						:     str				=    [" ".join([tokenizer.unk_token]				) * seq_length] * batch_size
													_snake_case						:     Optional[Any]				=    dict(tokenizer(a__      ,				return_tensors=a__				)				)
													return common_inputs



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_ = -1      ,				lowercase_ = -1      ,				lowercase_ = False      ,				lowercase_ = None      ,				):







													if self.task in ["default", "seq2seq-lm"]:
																			_snake_case						:     int				=    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":
																			_snake_case						:     Optional[int]				=    self._generate_dummy_inputs_for_causal_lm(
																			    a__      ,				batch_size=a__      ,				seq_length=a__      ,				is_pair=a__      ,				framework=a__				)
													else:
																			_snake_case						:     Dict				=    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        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_				):







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



"""simple docstring"""


import math
from decimal import Decimal, getcontext






def    a__       ( __lowercase = 400_0000      )		->		int:

				try:
								_A         =	int(__lowercase      )
				except (TypeError, ValueError):
								raise TypeError("Parameter n must be int or castable to int."      )
				if n <= 0:
								raise ValueError("Parameter n must be greater than or equal to one."      )
				_A         =	100
				_A         =	(Decimal(5      ) ** Decimal(0.5      ) + 1) / Decimal(2      )

				_A         =	(math.floor(math.log(n * (phi + 2)   ,	__lowercase      ) - 1      ) // 3) * 3 + 2
				_A         =	Decimal(round(phi ** Decimal(index + 1      )      )      ) / (phi + 2)
				_A         =	num // 2
				return int(__lowercase      )


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