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


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    """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 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						Tuple 			=   'blenderbot'
   UpperCamelCase_				:						List[str] 			=   ['past_key_values']
   UpperCamelCase_				:						Optional[Any] 			=   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
   def __init__(    self							,     lowerCAmelCase_=80_08							,     lowerCAmelCase_=1_28							,     lowerCAmelCase_=2							,     lowerCAmelCase_=1_02_40							,     lowerCAmelCase_=32							,     lowerCAmelCase_=24							,     lowerCAmelCase_=1_02_40							,     lowerCAmelCase_=32							,     lowerCAmelCase_=0.0							,     lowerCAmelCase_=0.0							,     lowerCAmelCase_=True							,     lowerCAmelCase_=True							,     lowerCAmelCase_="gelu"							,     lowerCAmelCase_=25_60							,     lowerCAmelCase_=0.1							,     lowerCAmelCase_=0.0							,     lowerCAmelCase_=0.0							,     lowerCAmelCase_=0.02							,     lowerCAmelCase_=1							,     lowerCAmelCase_=False							,     lowerCAmelCase_=0							,     lowerCAmelCase_=1							,     lowerCAmelCase_=2							,     lowerCAmelCase_=3							,     lowerCAmelCase_=2							,     **lowerCAmelCase_							,     )	->List[str]:
        a  :    Optional[Any]				=						vocab_size
        a  :    Any				=						max_position_embeddings
        a  :    Dict				=						d_model
        a  :    str				=						encoder_ffn_dim
        a  :    Optional[int]				=						encoder_layers
        a  :    Optional[int]				=						encoder_attention_heads
        a  :    Union[str, Any]				=						decoder_ffn_dim
        a  :    Tuple				=						decoder_layers
        a  :    Union[str, Any]				=						decoder_attention_heads
        a  :    Optional[int]				=						dropout
        a  :    Dict				=						attention_dropout
        a  :    Optional[Any]				=						activation_dropout
        a  :    Union[str, Any]				=						activation_function
        a  :    List[Any]				=						init_std
        a  :    Tuple				=						encoder_layerdrop
        a  :    Optional[Any]				=						decoder_layerdrop
        a  :    List[Any]				=						use_cache
        a  :    str				=						encoder_layers
        a  :    Optional[int]				=						scale_embedding  # scale factor will be sqrt(d_model) if True

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






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   @property
   def _lowercase(    self    )	->Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
             a  :    str				=						OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]    )
             if self.use_past:
                  a  :    Any				=						{0: '''batch'''}
                  a  :    int				=						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
             else:
                  a  :    List[Any]				=						{0: '''batch''', 1: '''decoder_sequence'''}
                  a  :    Optional[int]				=						{0: '''batch''', 1: '''decoder_sequence'''}
             if self.use_past:
                  self.fill_with_past_key_values_(lowerCAmelCase_							,     direction='''inputs'''    )
        elif self.task == "causal-lm":
             a  :    List[Any]				=						OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]    )
             if self.use_past:
                  a			,							a  :    Optional[Any]				=						self.num_layers
                  for i in range(lowerCAmelCase_    ):
                       a  :    Union[str, Any]				=						{0: '''batch''', 2: '''past_sequence + sequence'''}
                       a  :    Any				=						{0: '''batch''', 2: '''past_sequence + sequence'''}
        else:
             a  :    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
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def _lowercase(    self    )	->Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
             a  :    Tuple				=						super().outputs
        else:
             a  :    Union[str, Any]				=						super(lowerCAmelCase_							,     self    ).outputs
             if self.use_past:
                  a			,							a  :    List[str]				=						self.num_layers
                  for i in range(lowerCAmelCase_    ):
                       a  :    Optional[int]				=						{0: '''batch''', 2: '''past_sequence + sequence'''}
                       a  :    Tuple				=						{0: '''batch''', 2: '''past_sequence + sequence'''}
        return common_outputs
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = -1							,     lowerCAmelCase_ = -1							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = None							,     )	->Mapping[str, Any]:
        a  :    Dict				=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )
        # Generate decoder inputs
        a  :    Union[str, Any]				=						seq_length if not self.use_past else 1
        a  :    int				=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )
        a  :    Optional[int]				=						{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
        a  :    int				=						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
             a			,							a  :    List[Any]				=						common_inputs['''input_ids'''].shape
             a  :    List[str]				=						common_inputs['''decoder_input_ids'''].shape[1]
             a			,							a  :    Optional[Any]				=						self.num_attention_heads
             a  :    Optional[Any]				=						(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             a  :    List[Any]				=						decoder_seq_length
             a  :    int				=						(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )
             a  :    str				=						torch.cat(
                 [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCAmelCase_							,     lowerCAmelCase_    )]							,     dim=1    )
             a  :    Union[str, Any]				=						[]
             a			,							a  :    Any				=						self.num_layers

             for _ in range(lowerCAmelCase_    ):
                  common_inputs["past_key_values"].append(
                      (
                          torch.zeros(lowerCAmelCase_    ),
                          torch.zeros(lowerCAmelCase_    ),
                          torch.zeros(lowerCAmelCase_    ),
                          torch.zeros(lowerCAmelCase_    ),
                      )    )
        return common_inputs
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = -1							,     lowerCAmelCase_ = -1							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = None							,     )	->Mapping[str, Any]:
        a  :    str				=						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
             a			,							a  :    List[Any]				=						common_inputs['''input_ids'''].shape
             a  :    Tuple				=						seqlen
             a			,							a  :    Any				=						self.num_layers
             a			,							a  :    Optional[int]				=						self.num_attention_heads
             a  :    List[str]				=						(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             a  :    List[str]				=						common_inputs['''attention_mask'''].dtype
             a  :    str				=						torch.cat(
                 [common_inputs['''attention_mask'''], torch.ones(lowerCAmelCase_							,     lowerCAmelCase_							,     dtype=lowerCAmelCase_    )]							,     dim=1    )
             a  :    Dict				=						[
                 (torch.zeros(lowerCAmelCase_    ), torch.zeros(lowerCAmelCase_    )) for _ in range(lowerCAmelCase_    )
             ]
        return common_inputs
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = -1							,     lowerCAmelCase_ = -1							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = None							,     )	->Mapping[str, Any]:
        # Copied from OnnxConfig.generate_dummy_inputs
        # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        a  :    Dict				=						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
        a  :    Any				=						tokenizer.num_special_tokens_to_add(lowerCAmelCase_    )
        a  :    Any				=						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
        a  :    Tuple				=						[''' '''.join([tokenizer.unk_token]    ) * seq_length] * batch_size
        a  :    Any				=						dict(tokenizer(lowerCAmelCase_							,     return_tensors=lowerCAmelCase_    )    )
        return common_inputs
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = -1							,     lowerCAmelCase_ = -1							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = None							,     )	->Mapping[str, Any]:
        if self.task in ["default", "seq2seq-lm"]:
             a  :    Tuple				=						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":
             a  :    Optional[Any]				=						self._generate_dummy_inputs_for_causal_lm(
                 lowerCAmelCase_							,     batch_size=lowerCAmelCase_							,     seq_length=lowerCAmelCase_							,     is_pair=lowerCAmelCase_							,     framework=lowerCAmelCase_    )
        else:
             a  :    List[Any]				=						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 _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Optional[int]:
        if self.task in ["default", "seq2seq-lm"]:
             a  :    Any				=						super()._flatten_past_key_values_(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )
        else:
             a  :    Optional[int]				=						super(lowerCAmelCase_							,     self    )._flatten_past_key_values_(
                 lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )


   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->List[Any]:
        if direction not in ["inputs", "outputs"]:
             raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""    )

        a  :    Optional[int]				=						'''past_key_values''' if direction == '''inputs''' else '''present'''
        a			,							a  :    Optional[int]				=						self.num_layers

        a  :    List[Any]				=						'''past_encoder_sequence'''
        a  :    int				=						'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

        for i in range(lowerCAmelCase_    ):
             a  :    List[Any]				=						{0: '''batch''', 2: decoder_sequence}
             a  :    Dict				=						{0: '''batch''', 2: decoder_sequence}
             a  :    int				=						{0: '''batch''', 2: encoder_sequence}
             a  :    List[str]				=						{0: '''batch''', 2: encoder_sequence}
from __future__ import annotations







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )	->	list[float | int]:
     if not all((nth_term, start_term_a, common_ratio_r)       ):
          return []
     a  :    list[float | int]				=						[]
     a  :    str				=						1
     a  :    Dict				=						common_ratio_r
     for _ in range(int(lowerCAmelCase_       )       ):
          if not series:
               series.append(lowerCAmelCase_       )
          else:
               power += 1
               series.append(float(start_term_a * multiple       )       )
               a  :    Dict				=						pow(float(lowerCAmelCase_       )						,      lowerCAmelCase_       )
     return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase							   =      float(input("""Enter the last number (n term) of the Geometric Series"""))
  UpperCAmelCase							   =      float(input("""Enter the starting term (a) of the Geometric Series"""))
  UpperCAmelCase							   =      float(
      input("""Enter the common ratio between two terms (r) of the Geometric Series""")
  )
  print("""Formula of Geometric Series => a + ar + ar^2 ... +ar^n""")
  print(geometric_series(nth_term, start_term_a, common_ratio_r))