





'''simple docstring'''






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


snake_case_     =	logging.get_logger(__name__)

snake_case_     =	{
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}


class   _lowercase						(							a				):
   _UpperCamelCase       		=   """marian"""
   _UpperCamelCase       		=   ["""past_key_values"""]
   _UpperCamelCase       		=   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


   def __init__( self    ,   _UpperCAmelCase=58_101    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=1_024    ,   _UpperCAmelCase=12    ,   _UpperCAmelCase=4_096    ,   _UpperCAmelCase=16    ,   _UpperCAmelCase=12    ,   _UpperCAmelCase=4_096    ,   _UpperCAmelCase=16    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase="gelu"    ,   _UpperCAmelCase=1_024    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=0.02    ,   _UpperCAmelCase=58_100    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=58_100    ,   _UpperCAmelCase=0    ,   _UpperCAmelCase=0    ,   _UpperCAmelCase=True    ,   **_UpperCAmelCase    ,   ):
       A       :       Dict							=						vocab_size
       A       :       Optional[int]							=						decoder_vocab_size or vocab_size
       A       :       int							=						max_position_embeddings
       A       :       Union[str, Any]							=						d_model
       A       :       Dict							=						encoder_ffn_dim
       A       :       Dict							=						encoder_layers
       A       :       Tuple							=						encoder_attention_heads
       A       :       int							=						decoder_ffn_dim
       A       :       List[Any]							=						decoder_layers
       A       :       Union[str, Any]							=						decoder_attention_heads
       A       :       Optional[int]							=						dropout
       A       :       Optional[Any]							=						attention_dropout
       A       :       int							=						activation_dropout
       A       :       Dict							=						activation_function
       A       :       Union[str, Any]							=						init_std
       A       :       List[str]							=						encoder_layerdrop
       A       :       Optional[Any]							=						decoder_layerdrop
       A       :       List[str]							=						use_cache
       A       :       Tuple							=						encoder_layers
       A       :       int							=						scale_embedding  # scale factor will be sqrt(d_model) if True
       A       :       Tuple							=						share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=_UpperCAmelCase    ,   eos_token_id=_UpperCAmelCase    ,   is_encoder_decoder=_UpperCAmelCase    ,   decoder_start_token_id=_UpperCAmelCase    ,   forced_eos_token_id=_UpperCAmelCase    ,   **_UpperCAmelCase    ,   )







class   _lowercase						(							a				):


   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
   def 					snake_case				( self					):
       if self.task in ["default", "seq2seq-lm"]:
           A       :       Dict							=						OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ]					)

           if self.use_past:
               A       :       str							=						{0: '''batch'''}
               A       :       Optional[int]							=						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
           else:
               A       :       Union[str, Any]							=						{0: '''batch''', 1: '''decoder_sequence'''}
               A       :       List[Any]							=						{0: '''batch''', 1: '''decoder_sequence'''}

           if self.use_past:
               self.fill_with_past_key_values_(_UpperCAmelCase    ,   direction='''inputs'''					)
       elif self.task == "causal-lm":
           # TODO: figure this case out.
           A       :       Union[str, Any]							=						OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ]					)
           if self.use_past:
               A,	A       :       Tuple							=						self.num_layers
               for i in range(_UpperCAmelCase					):
                   A       :       Any							=						{0: '''batch''', 2: '''past_sequence + sequence'''}
                   A       :       str							=						{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 					snake_case				( self					):
       if self.task in ["default", "seq2seq-lm"]:
           A       :       Union[str, Any]							=						super().outputs
       else:
           A       :       Union[str, Any]							=						super(_UpperCAmelCase    ,   self					).outputs
           if self.use_past:
               A,	A       :       Tuple							=						self.num_layers
               for i in range(_UpperCAmelCase					):
                   A       :       Union[str, Any]							=						{0: '''batch''', 2: '''past_sequence + sequence'''}
                   A       :       Any							=						{0: '''batch''', 2: '''past_sequence + sequence'''}
       return common_outputs


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = -1    ,   _UpperCAmelCase = -1    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = None    ,   ):
       A       :       Dict							=						self._generate_dummy_inputs_for_encoder_and_decoder(
           _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)

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

           A       :       Dict							=						torch.cat(
               [common_inputs['''decoder_attention_mask'''], torch.ones(_UpperCAmelCase    ,   _UpperCAmelCase					)]    ,   dim=1					)

           A       :       Dict							=						[]
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           A,	A       :       Tuple							=						self.num_layers
           A       :       Tuple							=						min(_UpperCAmelCase    ,   _UpperCAmelCase					)
           A       :       Tuple							=						max(_UpperCAmelCase    ,   _UpperCAmelCase					) - min_num_layers
           A       :       List[Any]							=						'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

           for _ in range(_UpperCAmelCase					):
               common_inputs["past_key_values"].append(
                   (
                       torch.zeros(_UpperCAmelCase					),
                       torch.zeros(_UpperCAmelCase					),
                       torch.zeros(_UpperCAmelCase					),
                       torch.zeros(_UpperCAmelCase					),
                   )					)
           # TODO: test this.
           A       :       Union[str, Any]							=						encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
           for _ in range(_UpperCAmelCase    ,   _UpperCAmelCase					):
               common_inputs["past_key_values"].append((torch.zeros(_UpperCAmelCase					), torch.zeros(_UpperCAmelCase					))					)
       return common_inputs


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = -1    ,   _UpperCAmelCase = -1    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = None    ,   ):
       A       :       str							=						self._generate_dummy_inputs_for_encoder_and_decoder(
           _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
           A,	A       :       Union[str, Any]							=						common_inputs['''input_ids'''].shape
           # Not using the same length for past_key_values
           A       :       int							=						seqlen + 2
           A,	A       :       str							=						self.num_layers
           A,	A       :       List[Any]							=						self.num_attention_heads
           A       :       Optional[Any]							=						(
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )

           A       :       str							=						common_inputs['''attention_mask'''].dtype
           A       :       Optional[int]							=						torch.cat(
               [common_inputs['''attention_mask'''], torch.ones(_UpperCAmelCase    ,   _UpperCAmelCase    ,   dtype=_UpperCAmelCase					)]    ,   dim=1					)
           A       :       List[Any]							=						[
               (torch.zeros(_UpperCAmelCase					), torch.zeros(_UpperCAmelCase					)) for _ in range(_UpperCAmelCase					)
           ]
       return common_inputs


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = -1    ,   _UpperCAmelCase = -1    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = None    ,   ):
       # 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       :       int							=						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
       A       :       List[str]							=						tokenizer.num_special_tokens_to_add(_UpperCAmelCase					)
       A       :       List[Any]							=						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
       A       :       Union[str, Any]							=						[''' '''.join([tokenizer.unk_token]					) * seq_length] * batch_size
       A       :       str							=						dict(tokenizer(_UpperCAmelCase    ,   return_tensors=_UpperCAmelCase					)					)
       return common_inputs


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = -1    ,   _UpperCAmelCase = -1    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = None    ,   ):
       if self.task in ["default", "seq2seq-lm"]:
           A       :       int							=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               _UpperCAmelCase    ,   batch_size=_UpperCAmelCase    ,   seq_length=_UpperCAmelCase    ,   is_pair=_UpperCAmelCase    ,   framework=_UpperCAmelCase					)

       else:
           A       :       Dict							=						self._generate_dummy_inputs_for_causal_lm(
               _UpperCAmelCase    ,   batch_size=_UpperCAmelCase    ,   seq_length=_UpperCAmelCase    ,   is_pair=_UpperCAmelCase    ,   framework=_UpperCAmelCase					)

       return common_inputs


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       if self.task in ["default", "seq2seq-lm"]:
           A       :       List[Any]							=						super()._flatten_past_key_values_(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)
       else:
           A       :       Tuple							=						super(_UpperCAmelCase    ,   self					)._flatten_past_key_values_(
               _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)


   @property
   def 					snake_case				( self					):
       return 1E-4







'''simple docstring'''






from __future__ import annotations

import math
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  list[int]:

    if num <= 0:
        A       :       str							=						F'''{num}: Invalid input, please enter a positive integer.'''
        raise ValueError(UpperCamelCase__		)

    A       :       int							=						[True] * (num + 1)
    A       :       Dict							=						[]
    A       :       str							=						2
    A       :       Any							=						int(math.sqrt(UpperCamelCase__		)		)

    while start <= end:
        # If start is a prime
        if sieve[start] is True:
            prime.append(UpperCamelCase__		)

            # Set multiples of start be False
            for i in range(start * start    ,       num + 1    ,       UpperCamelCase__		):
                if sieve[i] is True:
                    A       :       Union[str, Any]							=						False

        start += 1

    for j in range(end + 1    ,       num + 1		):
        if sieve[j] is True:
            prime.append(UpperCamelCase__		)

    return prime


if __name__ == "__main__":
     print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
