
"""simple docstring"""


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


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    str      =    '''gptj'''
						_lowerCamelCase:    Tuple      =    {
						    '''max_position_embeddings''': '''n_positions''',
						    '''hidden_size''': '''n_embd''',
						    '''num_attention_heads''': '''n_head''',
						    '''num_hidden_layers''': '''n_layer''',
						}


						def __init__( self     :      str  ,A_     :      Tuple=5_0400  ,A_     :      Union[str, Any]=2048  ,A_     :      Tuple=4096  ,A_     :      Any=28  ,A_     :      List[str]=16  ,A_     :      Tuple=64  ,A_     :      List[str]=None  ,A_     :      Optional[int]="gelu_new"  ,A_     :      Tuple=0.0  ,A_     :      Union[str, Any]=0.0  ,A_     :      Tuple=0.0  ,A_     :      Optional[int]=1e-5  ,A_     :      str=0.02  ,A_     :      Optional[Any]=True  ,A_     :      Optional[Any]=5_0256  ,A_     :      List[Any]=5_0256  ,A_     :      Any=False  ,**A_     :      int  ,)     ->   int:
							A						   =   vocab_size
							A						   =   n_positions
							A						   =   n_embd
							A						   =   n_layer
							A						   =   n_head
							A						   =   n_inner
							A						   =   rotary_dim
							A						   =   activation_function
							A						   =   resid_pdrop
							A						   =   embd_pdrop
							A						   =   attn_pdrop
							A						   =   layer_norm_epsilon
							A						   =   initializer_range
							A						   =   use_cache

							A						   =   bos_token_id
							A						   =   eos_token_id

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







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


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


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

							return common_inputs


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							return self._config.n_layer


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      PreTrainedTokenizer  ,A_     :      int = -1  ,A_     :      int = -1  ,A_     :      bool = False  ,A_     :      Optional[TensorType] = None  ,)     ->   Mapping[str, Any]:
							A						   =   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()
							A						   =   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						   =   common_inputs['input_ids'].shape
									# Not using the same length for past_key_values
									A						   =   seqlen + 2
									A						   =   (
									    batch,
									    self.num_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // self.num_attention_heads,
									)
									A						   =   [
									    (torch.zeros(A_      ), torch.zeros(A_      )) for _ in range(self.num_layers      )
									]

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

							return ordered_inputs




						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   int:
							return 13

"""simple docstring"""


from math import pi, sqrt






def       _snake_case				(		snake_case__   :     float     ):
	if num <= 0:
		raise ValueError('math domain error'     )
	if num > 171.5:
		raise OverflowError('math range error'     )
	elif num - int(snake_case__     ) not in (0, 0.5):
		raise NotImplementedError('num must be an integer or a half-integer'     )
	elif num == 0.5:
		return sqrt(snake_case__     )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1     )






def       _snake_case				(		):
	assert gamma(0.5     ) == sqrt(snake_case__     )
	assert gamma(1     ) == 1.0
	assert gamma(2     ) == 1.0


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		_lowercase							   =				1.0
		while num:
				_lowercase							   =				float(input('''Gamma of: '''))
				print(F"""gamma({num}) = {gamma(num)}""")
				print('''\nEnter 0 to exit...''')