


"""simple docstring"""







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

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


__lowercase    					=      logging.get_logger(__name__)

__lowercase    					=      {
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							Tuple	=       """gpt_neo"""
    UpperCAmelCase       :							str	=       ["""past_key_values"""]
    UpperCAmelCase       :							Optional[Any]	=       {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






    def __init__(					self				:    Optional[Any]				, __UpperCAmelCase				:    Optional[Any]=50257				, __UpperCAmelCase				:    int=2048				, __UpperCAmelCase				:    List[Any]=2048				, __UpperCAmelCase				:    int=24				, __UpperCAmelCase				:    Optional[Any]=[[["global", "local"], 12]]				, __UpperCAmelCase				:    Optional[int]=16				, __UpperCAmelCase				:    Dict=None				, __UpperCAmelCase				:    Optional[int]=256				, __UpperCAmelCase				:    Union[str, Any]="gelu_new"				, __UpperCAmelCase				:    Dict=0.0				, __UpperCAmelCase				:    Dict=0.0				, __UpperCAmelCase				:    Any=0.0				, __UpperCAmelCase				:    Optional[Any]=0.1				, __UpperCAmelCase				:    str=1e-5				, __UpperCAmelCase				:    List[Any]=0.02				, __UpperCAmelCase				:    Tuple=True				, __UpperCAmelCase				:    List[Any]=50256				, __UpperCAmelCase				:    Optional[Any]=50256				, **__UpperCAmelCase				:    List[str]				, ):
      a						:		List[Any]      							=			vocab_size
      a						:		Optional[int]      							=			max_position_embeddings
      a						:		Tuple      							=			hidden_size
      a						:		Optional[Any]      							=			num_layers
      a						:		Optional[int]      							=			num_heads
      a						:		Optional[int]      							=			intermediate_size
      a						:		List[str]      							=			window_size
      a						:		Union[str, Any]      							=			activation_function
      a						:		Union[str, Any]      							=			resid_dropout
      a						:		List[Any]      							=			embed_dropout
      a						:		Any      							=			attention_dropout
      a						:		List[str]      							=			classifier_dropout
      a						:		Any      							=			layer_norm_epsilon
      a						:		Union[str, Any]      							=			initializer_range
      a						:		Dict      							=			use_cache

      a						:		Any      							=			bos_token_id
      a						:		List[str]      							=			eos_token_id

      a						:		Any      							=			attention_types
      a						:		Dict      							=			self.expand_attention_types_params(__UpperCAmelCase)

      if len(self.attention_layers) != self.num_layers:
        raise ValueError(
            "Configuration for convolutional module is incorrect. "
            "It is required that `len(config.attention_layers)` == `config.num_layers` "
            f'''but is `len(config.attention_layers) = {len(self.attention_layers)}`, '''
            f'''`config.num_layers = {self.num_layers}`. '''
            "`config.attention_layers` is prepared using `config.attention_types`. "
            "Please verify the value of `config.attention_types` argument.")

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






    @staticmethod
    def 	__snake_case							(					__UpperCAmelCase				:    Tuple):
      a						:		List[str]      							=			[]
      for item in attention_types:
        for _ in range(item[1]):
          attentions.extend(item[0])
      return attentions






def      lowercase			(				A_  ,   A_  ,   A_  ,   A_       )->      str:
  '''simple docstring'''







  import torch

  a						:		Dict      							=			input.size()
  a						:		Any      							=			len(A_       )
  a						:		Optional[int]      							=			shape[dimension]

  a						:		Optional[Any]      							=			torch.arange(0  ,   A_  ,   A_       )
  a						:		Tuple      							=			torch.div(sizedim - size  ,   A_  ,   rounding_mode="floor"       ) + 1
  a						:		Optional[Any]      							=			torch.arange(A_       ) + low_indices[:min_length][:, None]

  a						:		List[str]      							=			[slice(A_       )] * rank
  a						:		Union[str, Any]      							=			indices
  a						:		str      							=			input[s]

  a						:		int      							=			list(range(0  ,   rank + 1       )       )
  perm.append(perm.pop(dimension + 1       )       )

  return sliced.permute(A_       )


def      lowercase			(				A_  ,   A_       )->      str:
  '''simple docstring'''







  import torch

  a						:		Tuple      							=			torch.arange(1  ,   A_       )
  a						:		str      							=			torch.remainder(A_  ,   A_       )
  a						:		Optional[Any]      							=			remainders == 0
  a						:		Tuple      							=			candidates[divisor_indices]
  a						:		List[Any]      							=			torch.max(A_       )
  return largest_divisor, torch.div(A_  ,   A_  ,   rounding_mode="floor"       )







class 						_A  (  _a  ):







    """simple docstring"""




    @property
    def 	__snake_case							(					self				:    Tuple):
      a						:		str      							=			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
      if self.use_past:
        self.fill_with_past_key_values_(__UpperCAmelCase				, direction="inputs")
        a						:		List[Any]      							=			{0: "batch", 1: "past_sequence + sequence"}
      else:
        a						:		Union[str, Any]      							=			{0: "batch", 1: "sequence"}

      return common_inputs






    @property
    def 	__snake_case							(					self				:    Any):
      return self._config.num_heads






    def 	__snake_case							(					self				:    Optional[Any]				, __UpperCAmelCase				:    PreTrainedTokenizer				, __UpperCAmelCase				:    int = -1				, __UpperCAmelCase				:    int = -1				, __UpperCAmelCase				:    bool = False				, __UpperCAmelCase				:    Optional[TensorType] = None				, ):
      a						:		Optional[int]      							=			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()
      a						:		Dict      							=			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

          a							,		a						:		Dict      							=			common_inputs["input_ids"].shape
          # Not using the same length for past_key_values
          a						:		Tuple      							=			seqlen + 2
          a						:		Tuple      							=			(
              batch,
              self.num_attention_heads,
              past_key_values_length,
              self._config.hidden_size // self.num_attention_heads,
          )
          a						:		Union[str, Any]      							=			[
              (torch.zeros(__UpperCAmelCase), torch.zeros(__UpperCAmelCase)) for _ in range(self.num_layers)
          ]

      a						:		List[Any]      							=			common_inputs["attention_mask"]
      if self.use_past:
        a						:		Optional[int]      							=			ordered_inputs["attention_mask"].dtype
        a						:		Tuple      							=			torch.cat(
            [ordered_inputs["attention_mask"], torch.ones(__UpperCAmelCase				, __UpperCAmelCase				, dtype=__UpperCAmelCase)]				, dim=1)

      return ordered_inputs






    @property
    def 	__snake_case							(					self				:    str):
      return 13













from math import ceil, sqrt





def      lowerCAmelCase_ (       __a = 1000000		)     ->		int:







  """simple docstring"""





  lowerCamelCase__:      Optional[int]			  =0

  for outer_width in range(3	,    (limit // 4) + 2		):
    if outer_width**2 > limit:
      lowerCamelCase__:      Dict			  =max(ceil(sqrt(outer_width**2 - limit		)		)	,    1		)
    else:
      lowerCamelCase__:      str			  =1
    if (outer_width - hole_width_lower_bound) % 2:
      hole_width_lower_bound += 1

    answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

  return answer


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

