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


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

__lowerCamelCase      :			Optional[Any]							       =		{
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							"blenderbot-small"
   lowerCAmelCase_														=							["past_key_values"]
   lowerCAmelCase_														=							{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




   def __init__(   self      :    Optional[int]	,     _lowercase      :    int=5_02_65	,     _lowercase      :    Union[str, Any]=5_12	,     _lowercase      :    int=8	,     _lowercase      :    Union[str, Any]=20_48	,     _lowercase      :    List[Any]=16	,     _lowercase      :    int=8	,     _lowercase      :    str=20_48	,     _lowercase      :    Dict=16	,     _lowercase      :    int=0.0	,     _lowercase      :    Dict=0.0	,     _lowercase      :    Optional[int]=True	,     _lowercase      :    Tuple=True	,     _lowercase      :    Any="gelu"	,     _lowercase      :    int=5_12	,     _lowercase      :    Optional[Any]=0.1	,     _lowercase      :    Tuple=0.0	,     _lowercase      :    Optional[int]=0.0	,     _lowercase      :    List[Any]=0.02	,     _lowercase      :    str=1	,     _lowercase      :    Optional[Any]=False	,     _lowercase      :    str=0	,     _lowercase      :    Tuple=1	,     _lowercase      :    str=2	,     _lowercase      :    Tuple=2	,     **_lowercase      :    str	,     ):



     """simple docstring"""






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

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







class __snake_case    (						lowerCamelCase_		):




   @property
   def      __a						(   self      :    str				):



     """simple docstring"""






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

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

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



     """simple docstring"""






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




   def      __a						(   self      :    Optional[int]	,     _lowercase      :    PreTrainedTokenizer	,     _lowercase      :    int = -1	,     _lowercase      :    int = -1	,     _lowercase      :    bool = False	,     _lowercase      :    Optional[TensorType] = None	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         _lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase				)

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

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

       SCREAMING_SNAKE_CASE__      							=				torch.cat(
           [common_inputs["""decoder_attention_mask"""], torch.ones(_lowercase	,     _lowercase				)]	,     dim=1				)

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

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




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    PreTrainedTokenizer	,     _lowercase      :    int = -1	,     _lowercase      :    int = -1	,     _lowercase      :    bool = False	,     _lowercase      :    Optional[TensorType] = None	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         _lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase				)

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

       SCREAMING_SNAKE_CASE__      							=				common_inputs["""attention_mask"""].dtype
       SCREAMING_SNAKE_CASE__      							=				torch.cat(
           [common_inputs["""attention_mask"""], torch.ones(_lowercase	,     _lowercase	,     dtype=_lowercase				)]	,     dim=1				)
       SCREAMING_SNAKE_CASE__      							=				[
           (torch.zeros(_lowercase				), torch.zeros(_lowercase				)) for _ in range(_lowercase				)
       ]
     return common_inputs




   def      __a						(   self      :    int	,     _lowercase      :    PreTrainedTokenizer	,     _lowercase      :    int = -1	,     _lowercase      :    int = -1	,     _lowercase      :    bool = False	,     _lowercase      :    Optional[TensorType] = None	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				compute_effective_axis_dimension(
         _lowercase	,     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
     SCREAMING_SNAKE_CASE__      							=				tokenizer.num_special_tokens_to_add(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				compute_effective_axis_dimension(
         _lowercase	,     fixed_dimension=OnnxConfig.default_fixed_sequence	,     num_token_to_add=_lowercase				)

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




   def      __a						(   self      :    List[Any]	,     _lowercase      :    PreTrainedTokenizer	,     _lowercase      :    int = -1	,     _lowercase      :    int = -1	,     _lowercase      :    bool = False	,     _lowercase      :    Optional[TensorType] = None	,     ):



     """simple docstring"""






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

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

     return common_inputs




   def      __a						(   self      :    int	,     _lowercase      :    List[Any]	,     _lowercase      :    List[Any]	,     _lowercase      :    int	,     _lowercase      :    Optional[int]				):



     """simple docstring"""






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

from math import isqrt



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> list[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[True] * max_number
  for i in range(2			,					isqrt(max_number - 1     ) + 1     ):
    if is_prime[i]:
      for j in range(i**2			,					__UpperCamelCase			,					__UpperCamelCase     ):
        SCREAMING_SNAKE_CASE__      							=				False

  return [i for i in range(2			,					__UpperCamelCase     ) if is_prime[i]]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int = 10**8     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				calculate_prime_numbers(max_number // 2     )

  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     ) - 1
  while left <= right:
    while prime_numbers[left] * prime_numbers[right] >= max_number:
      right -= 1
    semiprimes_count += right - left + 1
    left += 1

  return semiprimes_count


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