





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


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

lowercase     :			Dict     						=	{
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}

class 			__A(    __UpperCAmelCase			):
     __A							=							"blenderbot"
     __A							=							["past_key_values"]
     __A							=							{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

     def __init__(   self,							A=8008,							A=128,							A=2,							A=1_0240,							A=32,							A=24,							A=1_0240,							A=32,							A=0.0,							A=0.0,							A=True,							A=True,							A="gelu",							A=2560,							A=0.1,							A=0.0,							A=0.0,							A=0.02,							A=1,							A=False,							A=0,							A=1,							A=2,							A=3,							A=2,							**A,							):





            """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														=    use_cache
            _UpperCamelCase														=    encoder_layers
            _UpperCamelCase														=    scale_embedding  # scale factor will be sqrt(d_model) if True

            super().__init__(
                pad_token_id=A,							bos_token_id=A,							eos_token_id=A,							is_encoder_decoder=A,							decoder_start_token_id=A,							encoder_no_repeat_ngram_size=A,							forced_eos_token_id=A,							**A,							)






class 			__A(    __UpperCAmelCase			):

     @property
     def 						_UpperCamelCase  (   self   ):





            """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_(A,							direction='''inputs'''   )
            elif self.task == "causal-lm":
                   _UpperCamelCase														=    OrderedDict(
                       [
                           ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                           ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ]   )
                   if self.use_past:
                          _UpperCamelCase							,		_UpperCamelCase														=    self.num_layers
                          for i in range(A   ):
                                 _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
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self,							A,							A = -1,							A = -1,							A = False,							A = None,							):





            """simple docstring"""

            _UpperCamelCase														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                A,							A,							A,							A,							A   )
            # Generate decoder inputs
            _UpperCamelCase														=    seq_length if not self.use_past else 1
            _UpperCamelCase														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                A,							A,							A,							A,							A   )
            _UpperCamelCase														=    {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
            _UpperCamelCase														=    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
                   _UpperCamelCase							,		_UpperCamelCase														=    common_inputs['''input_ids'''].shape
                   _UpperCamelCase														=    common_inputs['''decoder_input_ids'''].shape[1]
                   _UpperCamelCase							,		_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
                   _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(A,							A   )],							dim=1   )
                   _UpperCamelCase														=    []
                   _UpperCamelCase							,		_UpperCamelCase														=    self.num_layers

                   for _ in range(A   ):
                          common_inputs["past_key_values"].append(
                              (
                                  torch.zeros(A   ),
                                  torch.zeros(A   ),
                                  torch.zeros(A   ),
                                  torch.zeros(A   ),
                              )   )
            return common_inputs

     def 						_UpperCamelCase  (   self,							A,							A = -1,							A = -1,							A = False,							A = None,							):





            """simple docstring"""

            _UpperCamelCase														=    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
                   _UpperCamelCase							,		_UpperCamelCase														=    common_inputs['''input_ids'''].shape
                   _UpperCamelCase														=    seqlen
                   _UpperCamelCase							,		_UpperCamelCase														=    self.num_layers
                   _UpperCamelCase							,		_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(A,							A,							dtype=A   )],							dim=1   )
                   _UpperCamelCase														=    [
                       (torch.zeros(A   ), torch.zeros(A   )) for _ in range(A   )
                   ]
            return common_inputs

     def 						_UpperCamelCase  (   self,							A,							A = -1,							A = -1,							A = False,							A = None,							):





            """simple docstring"""

            _UpperCamelCase														=    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
            _UpperCamelCase														=    tokenizer.num_special_tokens_to_add(A   )
            _UpperCamelCase														=    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
            _UpperCamelCase														=    [''' '''.join([tokenizer.unk_token]   ) * seq_length] * batch_size
            _UpperCamelCase														=    dict(tokenizer(A,							return_tensors=A   )   )
            return common_inputs

     def 						_UpperCamelCase  (   self,							A,							A = -1,							A = -1,							A = False,							A = None,							):





            """simple docstring"""

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





            """simple docstring"""

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



     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            if direction not in ["inputs", "outputs"]:
                   raise ValueError(F'''direction must either be "inputs" or "outputs", but {direction} was given'''   )

            _UpperCamelCase														=    '''past_key_values''' if direction == '''inputs''' else '''present'''
            _UpperCamelCase							,		_UpperCamelCase														=    self.num_layers

            _UpperCamelCase														=    '''past_encoder_sequence'''
            _UpperCamelCase														=    '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

            for i in range(A   ):
                   _UpperCamelCase														=    {0: '''batch''', 2: decoder_sequence}
                   _UpperCamelCase														=    {0: '''batch''', 2: decoder_sequence}
                   _UpperCamelCase														=    {0: '''batch''', 2: encoder_sequence}
                   _UpperCamelCase														=    {0: '''batch''', 2: encoder_sequence}













import math
from decimal import Decimal, getcontext


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase = 4_000_000       ):

       try:
              _UpperCamelCase														=    int(lowerCAmelCase       )
       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.'''       )
       _UpperCamelCase														=    100
       _UpperCamelCase														=    (Decimal(5       ) ** Decimal(0.5       ) + 1) / Decimal(2       )

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


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






