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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCAmelCase     :   Union[str, Any]					=				logging.get_logger(__name__)

UpperCAmelCase     :   Any					=				{
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}




class    __lowerCAmelCase  (       a_						):



 '''simple docstring'''
 _lowerCamelCase						:	Any				    = 'gpt2'
 _lowerCamelCase						:	Tuple				    = ['past_key_values']
 _lowerCamelCase						:	Tuple				    = {
     'hidden_size': 'n_embd',
     'max_position_embeddings': 'n_positions',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }

 def __init__(			self       :		str       ,			lowerCamelCase       :		Optional[int]=5_0_2_5_7       ,			lowerCamelCase       :		Tuple=1_0_2_4       ,			lowerCamelCase       :		Union[str, Any]=7_6_8       ,			lowerCamelCase       :		Any=1_2       ,			lowerCamelCase       :		Tuple=1_2       ,			lowerCamelCase       :		Dict=None       ,			lowerCamelCase       :		Optional[int]="gelu_new"       ,			lowerCamelCase       :		Optional[int]=0.1       ,			lowerCamelCase       :		List[str]=0.1       ,			lowerCamelCase       :		List[Any]=0.1       ,			lowerCamelCase       :		Tuple=1e-5       ,			lowerCamelCase       :		List[Any]=0.0_2       ,			lowerCamelCase       :		Optional[int]="cls_index"       ,			lowerCamelCase       :		str=True       ,			lowerCamelCase       :		Dict=None       ,			lowerCamelCase       :		List[str]=True       ,			lowerCamelCase       :		str=0.1       ,			lowerCamelCase       :		Optional[int]=True       ,			lowerCamelCase       :		List[str]=True       ,			lowerCamelCase       :		List[Any]=5_0_2_5_6       ,			lowerCamelCase       :		Dict=5_0_2_5_6       ,			lowerCamelCase       :		Any=False       ,			lowerCamelCase       :		Dict=False       ,			**lowerCamelCase       :		List[Any]       ,			) ->							Union[str, Any]:
        _UpperCamelCase     :       int  						=  vocab_size
        _UpperCamelCase     :       int  						=  n_positions
        _UpperCamelCase     :       Tuple  						=  n_embd
        _UpperCamelCase     :       List[str]  						=  n_layer
        _UpperCamelCase     :       str  						=  n_head
        _UpperCamelCase     :       List[Any]  						=  n_inner
        _UpperCamelCase     :       int  						=  activation_function
        _UpperCamelCase     :       Dict  						=  resid_pdrop
        _UpperCamelCase     :       List[Any]  						=  embd_pdrop
        _UpperCamelCase     :       Tuple  						=  attn_pdrop
        _UpperCamelCase     :       Union[str, Any]  						=  layer_norm_epsilon
        _UpperCamelCase     :       str  						=  initializer_range
        _UpperCamelCase     :       Union[str, Any]  						=  summary_type
        _UpperCamelCase     :       str  						=  summary_use_proj
        _UpperCamelCase     :       int  						=  summary_activation
        _UpperCamelCase     :       Any  						=  summary_first_dropout
        _UpperCamelCase     :       str  						=  summary_proj_to_labels
        _UpperCamelCase     :       Optional[int]  						=  scale_attn_weights
        _UpperCamelCase     :       List[Any]  						=  use_cache
        _UpperCamelCase     :       Optional[int]  						=  scale_attn_by_inverse_layer_idx
        _UpperCamelCase     :       List[str]  						=  reorder_and_upcast_attn

        _UpperCamelCase     :       Any  						=  bos_token_id
        _UpperCamelCase     :       Union[str, Any]  						=  eos_token_id

        super().__init__(bos_token_id=lowerCamelCase       ,			eos_token_id=lowerCamelCase       ,			**lowerCamelCase  )







class    __lowerCAmelCase  (       a_						):



 '''simple docstring'''

 def __init__(			self       :		Dict       ,			lowerCamelCase       :		PretrainedConfig       ,			lowerCamelCase       :		str = "default"       ,			lowerCamelCase       :		List[PatchingSpec] = None       ,			lowerCamelCase       :		bool = False       ,			) ->							List[Any]:
        super().__init__(lowerCamelCase       ,			task=lowerCamelCase       ,			patching_specs=lowerCamelCase       ,			use_past=lowerCamelCase  )
        if not getattr(self._config       ,			'pad_token_id'       ,			lowerCamelCase  ):
               # TODO: how to do that better?
               _UpperCamelCase     :       Optional[Any]  						=  0

 @property
 def 					__snake_case					(			self       :		Union[str, Any]  ) ->							Mapping[str, Mapping[int, str]]:
        _UpperCamelCase     :       int  						=  OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}  )
        if self.use_past:
               self.fill_with_past_key_values_(lowerCamelCase       ,			direction='inputs'  )
               _UpperCamelCase     :       Any  						=  {0: 'batch', 1: 'past_sequence + sequence'}
        else:
               _UpperCamelCase     :       Tuple  						=  {0: 'batch', 1: 'sequence'}

        return common_inputs

 @property
 def 					__snake_case					(			self       :		Tuple  ) ->							int:
        return self._config.n_layer

 @property
 def 					__snake_case					(			self       :		str  ) ->							int:
        return self._config.n_head

 def 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		PreTrainedTokenizer       ,			lowerCamelCase       :		int = -1       ,			lowerCamelCase       :		int = -1       ,			lowerCamelCase       :		bool = False       ,			lowerCamelCase       :		Optional[TensorType] = None       ,			) ->							Mapping[str, Any]:
        _UpperCamelCase     :       Union[str, Any]  						=  super(lowerCamelCase       ,			self  ).generate_dummy_inputs(
            lowerCamelCase       ,			batch_size=lowerCamelCase       ,			seq_length=lowerCamelCase       ,			is_pair=lowerCamelCase       ,			framework=lowerCamelCase  )

        # We need to order the input in the way they appears in the forward()
        _UpperCamelCase     :       Optional[Any]  						=  OrderedDict({'input_ids': common_inputs['input_ids']}  )

        # Need to add the past_keys
        if self.use_past:
               if not is_torch_available():
                      raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'  )
               else:
                      import torch

                      _UpperCamelCase     :       str  						=  common_inputs['input_ids'].shape
                      # Not using the same length for past_key_values
                      _UpperCamelCase     :       List[str]  						=  seqlen + 2
                      _UpperCamelCase     :       int  						=  (
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      _UpperCamelCase     :       Optional[int]  						=  [
                          (torch.zeros(lowerCamelCase  ), torch.zeros(lowerCamelCase  )) for _ in range(self.num_layers  )
                      ]

        _UpperCamelCase     :       List[str]  						=  common_inputs['attention_mask']
        if self.use_past:
               _UpperCamelCase     :       Any  						=  ordered_inputs['attention_mask'].dtype
               _UpperCamelCase     :       List[str]  						=  torch.cat(
                   [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase       ,			lowerCamelCase       ,			dtype=lowerCamelCase  )]       ,			dim=1  )

        return ordered_inputs

 @property
 def 					__snake_case					(			self       :		Optional[int]  ) ->							int:
        return 1_3




import math
from collections.abc import Generator


def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
       _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)				))
       for i in (n for n in numbers if n > 1):
              for j in range(2		,						lowercase_				):
                     if (i % j) == 0:
                            break
              else:
                     yield i


def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
       _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)				))
       for i in (n for n in numbers if n > 1):
              # only need to check for factors up to sqrt(i)
              _UpperCamelCase     :       Any  						=  int(math.sqrt(lowercase_				)				) + 1
              for j in range(2		,						lowercase_				):
                     if (i % j) == 0:
                            break
              else:
                     yield i


def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
       _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)		,						2				))
       # It's useless to test even numbers as they will not be prime
       if max_n > 2:
              yield 2  # Because 2 will not be tested, it's necessary to yield it now
       for i in (n for n in numbers if n > 1):
              _UpperCamelCase     :       Tuple  						=  int(math.sqrt(lowercase_				)				) + 1
              for j in range(3		,						lowercase_		,						2				):
                     # As we removed the even numbers, we don't need them now
                     if (i % j) == 0:
                            break
              else:
                     yield i


def SCREAMING_SNAKE_CASE				(     ):
       from timeit import timeit

       _UpperCamelCase     :       Optional[Any]  						=  'from __main__ import slow_primes, primes, fast_primes'
       print(timeit('slow_primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
       print(timeit('primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
       print(timeit('fast_primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)


if __name__ == "__main__":
 UpperCAmelCase					=				int(input('''Calculate primes up to:\n>> ''').strip())
 for ret in primes(number):
  print(ret)
 benchmark()



