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

from packaging import version


if TYPE_CHECKING:
      from ... import PreTrainedTokenizer, TensorType

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


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : List[str]      =			'bloom'
   __UpperCAmelCase : Dict      =			['past_key_values']
   __UpperCAmelCase : Union[str, Any]      =			{
       'num_hidden_layers': 'n_layer',
       'num_attention_heads': 'n_head',
   }


   def __init__(self     :						Dict       ,  __UpperCAmelCase     :						Tuple=2_5_0_8_8_0       ,  __UpperCAmelCase     :						Union[str, Any]=6_4       ,  __UpperCAmelCase     :						str=2       ,  __UpperCAmelCase     :						Tuple=8       ,  __UpperCAmelCase     :						str=1E-5       ,  __UpperCAmelCase     :						Tuple=0.02       ,  __UpperCAmelCase     :						Optional[int]=True       ,  __UpperCAmelCase     :						List[Any]=1       ,  __UpperCAmelCase     :						str=2       ,  __UpperCAmelCase     :						int=False       ,  __UpperCAmelCase     :						Any=0.0       ,  __UpperCAmelCase     :						Any=0.0       ,  __UpperCAmelCase     :						Tuple=1       ,  __UpperCAmelCase     :						str=False       ,  **__UpperCAmelCase     :						Union[str, Any]       ,  )						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  vocab_size
         # Backward compatibility with n_embed kwarg
         UpperCAmelCase__								=  kwargs.pop("n_embed"       ,  __UpperCAmelCase		)
         UpperCAmelCase__								=  hidden_size if n_embed is None else n_embed
         UpperCAmelCase__								=  n_layer
         UpperCAmelCase__								=  n_head
         UpperCAmelCase__								=  layer_norm_epsilon
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  use_cache
         UpperCAmelCase__								=  pretraining_tp
         UpperCAmelCase__								=  apply_residual_connection_post_layernorm
         UpperCAmelCase__								=  hidden_dropout
         UpperCAmelCase__								=  attention_dropout

         UpperCAmelCase__								=  bos_token_id
         UpperCAmelCase__								=  eos_token_id
         UpperCAmelCase__								=  slow_but_exact

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





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : List[str]      =			version.parse('1.12'     )


   def __init__(self     :						Dict       ,  __UpperCAmelCase     :						PretrainedConfig       ,  __UpperCAmelCase     :						str = "default"       ,  __UpperCAmelCase     :						List[PatchingSpec] = None       ,  __UpperCAmelCase     :						bool = False       ,  )						->				Optional[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?
               UpperCAmelCase__								=  0


   @property
   def 			lowercase_      (self     :						List[str]		)						->				Mapping[str, Mapping[int, str]]:
         """simple docstring"""



         UpperCAmelCase__								=  OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
         if self.use_past:
               # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
               self.fill_with_past_key_values_(__UpperCAmelCase       ,  direction="inputs"       ,  inverted_values_shape=__UpperCAmelCase		)
               UpperCAmelCase__								=  {0: "batch", 1: "past_sequence + sequence"}
         else:
               UpperCAmelCase__								=  {0: "batch", 1: "sequence"}

         return common_inputs


   @property
   def 			lowercase_      (self     :						List[str]		)						->				int:
         """simple docstring"""



         return self._config.n_layer


   @property
   def 			lowercase_      (self     :						Dict		)						->				int:
         """simple docstring"""



         return self._config.n_head


   @property
   def 			lowercase_      (self     :						Any		)						->				float:
         """simple docstring"""



         return 1E-3


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						"PreTrainedTokenizer"       ,  __UpperCAmelCase     :						int = -1       ,  __UpperCAmelCase     :						int = -1       ,  __UpperCAmelCase     :						bool = False       ,  __UpperCAmelCase     :						Optional["TensorType"] = None       ,  )						->				Mapping[str, Any]:
         """simple docstring"""



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

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

         return ordered_inputs





   @property
   def 			lowercase_      (self     :						Optional[Any]		)						->				int:
         """simple docstring"""



         return 1_3





'''simple docstring'''







from math import isqrt, loga
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Any													=       [True] * max_number
						for i in range(2      ,      isqrt(max_number - 1							) + 1							):
												if is_prime[i]:
																		for j in range(i**2      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							):
																								lowercase_	:     Optional[Any]													=       False

						return [i for i in range(2      ,      __SCREAMING_SNAKE_CASE							) if is_prime[i]]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int = 800800      ,      __SCREAMING_SNAKE_CASE					: int = 800800							):



						"""simple docstring"""





						lowercase_	:     Union[str, Any]													=       degree * loga(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Any													=       int(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Optional[Any]													=       calculate_prime_numbers(__SCREAMING_SNAKE_CASE							)

						lowercase_	:     Optional[Any]													=       0
						lowercase_	:     List[Any]													=       0
						lowercase_	:     Union[str, Any]													=       len(__SCREAMING_SNAKE_CASE							) - 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() = }""")



