'''simple docstring'''


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


snake_case__     :			List[str]      			=  logging.get_logger(__name__)

snake_case__     :			Optional[int]      			=  {
    '''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						(       lowerCamelCase_       ):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						    =		"""blenderbot"""
  lowerCamelCase_		:Optional[Any]						    =		["""past_key_values"""]
  lowerCamelCase_		:Any						    =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


  def __init__( self      ,		snake_case_=8_0_0_8      ,		snake_case_=1_2_8      ,		snake_case_=2      ,		snake_case_=1_0_2_4_0      ,		snake_case_=3_2      ,		snake_case_=2_4      ,		snake_case_=1_0_2_4_0      ,		snake_case_=3_2      ,		snake_case_=0.0      ,		snake_case_=0.0      ,		snake_case_=True      ,		snake_case_=True      ,		snake_case_="gelu"      ,		snake_case_=2_5_6_0      ,		snake_case_=0.1      ,		snake_case_=0.0      ,		snake_case_=0.0      ,		snake_case_=0.02      ,		snake_case_=1      ,		snake_case_=False      ,		snake_case_=0      ,		snake_case_=1      ,		snake_case_=2      ,		snake_case_=3      ,		snake_case_=2      ,		**snake_case_      ,		):




       '''simple docstring'''

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

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







class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  @property
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       if self.task in ["default", "seq2seq-lm"]:
            UpperCAmelCase_							:   Dict   =      OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]						)
            if self.use_past:
                 UpperCAmelCase_							:   Optional[int]   =      {0: 'batch'}
                 UpperCAmelCase_							:   List[Any]   =      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                 UpperCAmelCase_							:   List[Any]   =      {0: 'batch', 1: 'decoder_sequence'}
                 UpperCAmelCase_							:   Optional[int]   =      {0: 'batch', 1: 'decoder_sequence'}
            if self.use_past:
                 self.fill_with_past_key_values_(_UpperCamelCase      ,		direction='inputs'						)
       elif self.task == "causal-lm":
            UpperCAmelCase_							:   List[Any]   =      OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]						)
            if self.use_past:
                 UpperCAmelCase_   ,			UpperCAmelCase_							:   Union[str, Any]   =      self.num_layers
                 for i in range(_UpperCamelCase						):
                      UpperCAmelCase_							:   Dict   =      {0: 'batch', 2: 'past_sequence + sequence'}
                      UpperCAmelCase_							:   Optional[int]   =      {0: 'batch', 2: 'past_sequence + sequence'}
       else:
            UpperCAmelCase_							:   Tuple   =      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_							:   Tuple   =      super().outputs
       else:
            UpperCAmelCase_							:   List[str]   =      super(_UpperCamelCase      ,		self						).outputs
            if self.use_past:
                 UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[Any]   =      self.num_layers
                 for i in range(_UpperCamelCase						):
                      UpperCAmelCase_							:   List[Any]   =      {0: 'batch', 2: 'past_sequence + sequence'}
                      UpperCAmelCase_							:   Union[str, Any]   =      {0: 'batch', 2: 'past_sequence + sequence'}
       return common_outputs


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




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase						)
       # Generate decoder inputs
       UpperCAmelCase_							:   Optional[int]   =      seq_length if not self.use_past else 1
       UpperCAmelCase_							:   int   =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase						)
       UpperCAmelCase_							:   Any   =      {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       UpperCAmelCase_							:   int   =      dict(**_UpperCamelCase      ,		**_UpperCamelCase						)

       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_							:   int   =      common_inputs['input_ids'].shape
            UpperCAmelCase_							:   List[str]   =      common_inputs['decoder_input_ids'].shape[1]
            UpperCAmelCase_   ,			UpperCAmelCase_							:   Union[str, Any]   =      self.num_attention_heads
            UpperCAmelCase_							:   Union[str, Any]   =      (
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            UpperCAmelCase_							:   List[Any]   =      decoder_seq_length
            UpperCAmelCase_							:   Optional[int]   =      (
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )
            UpperCAmelCase_							:   Optional[Any]   =      torch.cat(
                [common_inputs['decoder_attention_mask'], torch.ones(_UpperCamelCase      ,		_UpperCamelCase						)]      ,		dim=1						)
            UpperCAmelCase_							:   Union[str, Any]   =      []
            UpperCAmelCase_   ,			UpperCAmelCase_							:   List[str]   =      self.num_layers

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


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




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase						)

       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_							:   Any   =      common_inputs['input_ids'].shape
            UpperCAmelCase_							:   Union[str, Any]   =      seqlen
            UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[int]   =      self.num_layers
            UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[Any]   =      self.num_attention_heads
            UpperCAmelCase_							:   Union[str, Any]   =      (
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            UpperCAmelCase_							:   Optional[Any]   =      common_inputs['attention_mask'].dtype
            UpperCAmelCase_							:   Tuple   =      torch.cat(
                [common_inputs['attention_mask'], torch.ones(_UpperCamelCase      ,		_UpperCamelCase      ,		dtype=_UpperCamelCase						)]      ,		dim=1						)
            UpperCAmelCase_							:   List[str]   =      [
                (torch.zeros(_UpperCamelCase						), torch.zeros(_UpperCamelCase						)) for _ in range(_UpperCamelCase						)
            ]
       return common_inputs


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




       '''simple docstring'''

       UpperCAmelCase_							:   Dict   =      compute_effective_axis_dimension(
           _UpperCamelCase      ,		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_							:   str   =      tokenizer.num_special_tokens_to_add(_UpperCamelCase						)
       UpperCAmelCase_							:   int   =      compute_effective_axis_dimension(
           _UpperCamelCase      ,		fixed_dimension=OnnxConfig.default_fixed_sequence      ,		num_token_to_add=_UpperCamelCase						)

       # Generate dummy inputs according to compute batch and sequence
       UpperCAmelCase_							:   List[str]   =      [' '.join([tokenizer.unk_token]						) * seq_length] * batch_size
       UpperCAmelCase_							:   Optional[Any]   =      dict(tokenizer(_UpperCamelCase      ,		return_tensors=_UpperCamelCase						)						)
       return common_inputs


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




       '''simple docstring'''

       if self.task in ["default", "seq2seq-lm"]:
            UpperCAmelCase_							:   List[str]   =      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                _UpperCamelCase      ,		batch_size=_UpperCamelCase      ,		seq_length=_UpperCamelCase      ,		is_pair=_UpperCamelCase      ,		framework=_UpperCamelCase						)

       elif self.task == "causal-lm":
            UpperCAmelCase_							:   str   =      self._generate_dummy_inputs_for_causal_lm(
                _UpperCamelCase      ,		batch_size=_UpperCamelCase      ,		seq_length=_UpperCamelCase      ,		is_pair=_UpperCamelCase      ,		framework=_UpperCamelCase						)
       else:
            UpperCAmelCase_							:   Union[str, Any]   =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCamelCase      ,		batch_size=_UpperCamelCase      ,		seq_length=_UpperCamelCase      ,		is_pair=_UpperCamelCase      ,		framework=_UpperCamelCase						)

       return common_inputs


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

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






  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

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

       UpperCAmelCase_							:   Union[str, Any]   =      'past_key_values' if direction == 'inputs' else 'present'
       UpperCAmelCase_   ,			UpperCAmelCase_							:   Union[str, Any]   =      self.num_layers

       UpperCAmelCase_							:   Tuple   =      'past_encoder_sequence'
       UpperCAmelCase_							:   int   =      'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

       for i in range(_UpperCamelCase						):
            UpperCAmelCase_							:   Optional[int]   =      {0: 'batch', 2: decoder_sequence}
            UpperCAmelCase_							:   str   =      {0: 'batch', 2: decoder_sequence}
            UpperCAmelCase_							:   List[Any]   =      {0: 'batch', 2: encoder_sequence}
            UpperCAmelCase_							:   Optional[Any]   =      {0: 'batch', 2: encoder_sequence}







import math





def 		__lowerCamelCase							(UpperCAmelCase__   :							int     ):

					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or number % 2 == 0 or number % 3 == 0:
										# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
										return False

					# All primes number are in format of 6k +/- 1
					for i in range(5					,       int(math.sqrt(UpperCAmelCase__     ) + 1     )					,       6     ):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True





def 		__lowerCamelCase							(UpperCAmelCase__   :							int = 2_0_0_0_0_0_0     ):
					return sum(num for num in range(3					,       UpperCAmelCase__					,       2     ) if is_prime(UpperCAmelCase__     )     ) + 2 if n > 2 else 0


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




