




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


UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      {
    '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__   ( SCREAMING_SNAKE_CASE__				):
						_a							:   List[Any] =   """gpt_neo"""
						_a							:   Union[str, Any] =   ["""past_key_values"""]
						_a							:   Any =   {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






						def __init__(       self		,      _A=5_0_2_5_7		,      _A=2_0_4_8		,      _A=2_0_4_8		,      _A=2_4		,      _A=[[["global", "local"], 1_2]]		,      _A=1_6		,      _A=None		,      _A=2_5_6		,      _A="gelu_new"		,      _A=0.0		,      _A=0.0		,      _A=0.0		,      _A=0.1		,      _A=1E-5		,      _A=0.02		,      _A=True		,      _A=5_0_2_5_6		,      _A=5_0_2_5_6		,      **_A		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	vocab_size
											__lowerCAmelCase           =	max_position_embeddings
											__lowerCAmelCase           =	hidden_size
											__lowerCAmelCase           =	num_layers
											__lowerCAmelCase           =	num_heads
											__lowerCAmelCase           =	intermediate_size
											__lowerCAmelCase           =	window_size
											__lowerCAmelCase           =	activation_function
											__lowerCAmelCase           =	resid_dropout
											__lowerCAmelCase           =	embed_dropout
											__lowerCAmelCase           =	attention_dropout
											__lowerCAmelCase           =	classifier_dropout
											__lowerCAmelCase           =	layer_norm_epsilon
											__lowerCAmelCase           =	initializer_range
											__lowerCAmelCase           =	use_cache

											__lowerCAmelCase           =	bos_token_id
											__lowerCAmelCase           =	eos_token_id

											__lowerCAmelCase           =	attention_types
											__lowerCAmelCase           =	self.expand_attention_types_params(_A      )

											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=_A		,      eos_token_id=_A		,      **_A      )






						@staticmethod
						def     __SCREAMING_SNAKE_CASE(       _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	[]
											for item in attention_types:
																for _ in range(item[1]      ):
																					attentions.extend(item[0]      )
											return attentions







def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    Any					):
					import torch

					__lowerCAmelCase           =	input.size()
					__lowerCAmelCase           =	len(_UpperCAmelCase					)
					__lowerCAmelCase           =	shape[dimension]

					__lowerCAmelCase           =	torch.arange(0				,				_UpperCAmelCase				,				_UpperCAmelCase					)
					__lowerCAmelCase           =	torch.div(sizedim - size				,				_UpperCAmelCase				,				rounding_mode="floor"					) + 1
					__lowerCAmelCase           =	torch.arange(_UpperCAmelCase					) + low_indices[:min_length][:, None]

					__lowerCAmelCase           =	[slice(_UpperCAmelCase					)] * rank
					__lowerCAmelCase           =	indices
					__lowerCAmelCase           =	input[s]

					__lowerCAmelCase           =	list(range(0				,				rank + 1					)					)
					perm.append(perm.pop(dimension + 1					)					)

					return sliced.permute(_UpperCAmelCase					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					import torch

					__lowerCAmelCase           =	torch.arange(1				,				_UpperCAmelCase					)
					__lowerCAmelCase           =	torch.remainder(_UpperCAmelCase				,				_UpperCAmelCase					)
					__lowerCAmelCase           =	remainders == 0
					__lowerCAmelCase           =	candidates[divisor_indices]
					__lowerCAmelCase           =	torch.max(_UpperCAmelCase					)
					return largest_divisor, torch.div(_UpperCAmelCase				,				_UpperCAmelCase				,				rounding_mode="floor"					)


class       a__   ( SCREAMING_SNAKE_CASE__				):






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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

											return common_inputs






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return self._config.num_heads






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = -1		,      _A = -1		,      _A = False		,      _A = None		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	super(_A		,      self      ).generate_dummy_inputs(
											    _A		,      batch_size=_A		,      seq_length=_A		,      is_pair=_A		,      framework=_A      )

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

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

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

											return ordered_inputs






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return 1_3












import math







def      _a				(						SCREAMING_SNAKE_CASE_							:    int					):

					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or number % 2 == 0 or number % 3 == 0:
										# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
										return False

					# All primes number are in format of 6k +/- 1
					for i in range(5				,				int(math.sqrt(SCREAMING_SNAKE_CASE_					) + 1					)				,				6					):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True







def      _a				(						SCREAMING_SNAKE_CASE_							:    int = 1_00_01					):

					try:
										__lowerCAmelCase           =	int(SCREAMING_SNAKE_CASE_					)
					except (TypeError, ValueError):
										raise TypeError("Parameter nth must be int or castable to int."					) from None
					if nth <= 0:
										raise ValueError("Parameter nth must be greater than or equal to one."					)
					__lowerCAmelCase           =	[]
					__lowerCAmelCase           =	2
					while len(SCREAMING_SNAKE_CASE_					) < nth:
										if is_prime(SCREAMING_SNAKE_CASE_					):
															primes.append(SCREAMING_SNAKE_CASE_					)
															num += 1
										else:
															num += 1
					return primes[len(SCREAMING_SNAKE_CASE_					) - 1]


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




