

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__		 =     logging.get_logger(__name__)


UpperCamelCase__		 =     {
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							"codegen"
       __SCREAMING_SNAKE_CASE				    =							{
           "max_position_embeddings": "n_positions",
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }


       def __init__(		self      , _UpperCamelCase=5_04_00      , _UpperCamelCase=20_48      , _UpperCamelCase=20_48      , _UpperCamelCase=40_96      , _UpperCamelCase=28      , _UpperCamelCase=16      , _UpperCamelCase=64      , _UpperCamelCase=None      , _UpperCamelCase="gelu_new"      , _UpperCamelCase=0.0      , _UpperCamelCase=0.0      , _UpperCamelCase=0.0      , _UpperCamelCase=1E-5      , _UpperCamelCase=0.02      , _UpperCamelCase=True      , _UpperCamelCase=5_02_56      , _UpperCamelCase=5_02_56      , _UpperCamelCase=False      , **_UpperCamelCase      , )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							vocab_size
              __snake_case       	=							n_ctx
              __snake_case       	=							n_positions
              __snake_case       	=							n_embd
              __snake_case       	=							n_layer
              __snake_case       	=							n_head
              __snake_case       	=							n_inner
              __snake_case       	=							rotary_dim
              __snake_case       	=							activation_function
              __snake_case       	=							resid_pdrop
              __snake_case       	=							embd_pdrop
              __snake_case       	=							attn_pdrop
              __snake_case       	=							layer_norm_epsilon
              __snake_case       	=							initializer_range
              __snake_case       	=							use_cache

              __snake_case       	=							bos_token_id
              __snake_case       	=							eos_token_id

              super().__init__(
                  bos_token_id=_UpperCamelCase      , eos_token_id=_UpperCamelCase      , tie_word_embeddings=_UpperCamelCase      , **_UpperCamelCase   )






class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase = "default"      , _UpperCamelCase = None      , _UpperCamelCase = False      , )      ->							int:






              """simple docstring"""


              super().__init__(_UpperCamelCase      , task=_UpperCamelCase      , patching_specs=_UpperCamelCase      , use_past=_UpperCamelCase   )
              if not getattr(self._config      , """pad_token_id"""      , _UpperCamelCase   ):
                     # TODO: how to do that better?
                     __snake_case       	=							0


       @property
       def 			a		(		self   )      ->							Mapping[str, Mapping[int, str]]:






              """simple docstring"""


              __snake_case       	=							OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}   )
              if self.use_past:
                     self.fill_with_past_key_values_(_UpperCamelCase      , direction="""inputs"""   )
                     __snake_case       	=							{0: """batch""", 1: """past_sequence + sequence"""}
              else:
                     __snake_case       	=							{0: """batch""", 1: """sequence"""}

              return common_inputs


       @property
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              return self._config.n_layer


       @property
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              return self._config.n_head


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = -1      , _UpperCamelCase = -1      , _UpperCamelCase = False      , _UpperCamelCase = None      , )      ->							Mapping[str, Any]:






              """simple docstring"""


              __snake_case       	=							super(_UpperCamelCase      , self   ).generate_dummy_inputs(
                  _UpperCamelCase      , batch_size=_UpperCamelCase      , seq_length=_UpperCamelCase      , is_pair=_UpperCamelCase      , framework=_UpperCamelCase   )

              # We need to order the input in the way they appears in the forward()
              __snake_case       	=							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

                            __snake_case	,    __snake_case       	=							common_inputs["""input_ids"""].shape
                            # Not using the same length for past_key_values
                            __snake_case       	=							seqlen + 2
                            __snake_case       	=							(
                                batch,
                                self.num_attention_heads,
                                past_key_values_length,
                                self._config.hidden_size // self.num_attention_heads,
                            )
                            __snake_case       	=							[
                                (torch.zeros(_UpperCamelCase   ), torch.zeros(_UpperCamelCase   )) for _ in range(self.num_layers   )
                            ]

              __snake_case       	=							common_inputs["""attention_mask"""]
              if self.use_past:
                     __snake_case       	=							ordered_inputs["""attention_mask"""].dtype
                     __snake_case       	=							torch.cat(
                         [ordered_inputs["""attention_mask"""], torch.ones(_UpperCamelCase      , _UpperCamelCase      , dtype=_UpperCamelCase   )]      , dim=1   )

              return ordered_inputs






       @property
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              return 13




from math import isqrt, loga







def 						lowerCamelCase__      ( __A    :int		):


       """simple docstring"""




       __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	,__A	,__A		):
                            __snake_case       	=							False

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







def 						lowerCamelCase__      ( __A    :int = 8_0_0_8_0_0	,__A    :int = 8_0_0_8_0_0		):


       """simple docstring"""




       __snake_case       	=							degree * loga(__A		)
       __snake_case       	=							int(__A		)
       __snake_case       	=							calculate_prime_numbers(__A		)

       __snake_case       	=							0
       __snake_case       	=							0
       __snake_case       	=							len(__A		) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]		)
                  + prime_numbers[left] * loga(prime_numbers[right]		)
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


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

