



"""simple docstring"""






def     _lowercase       (						__snake_case = "The quick brown fox jumps over the lazy dog"      ,):
			__lowerCAmelCase   :						str				= set()

			# Replace all the whitespace in our sentence
			__lowerCAmelCase   :						Optional[int]				= input_str.replace(" "      ,""			)
			for alpha in input_str:
						if "a" <= alpha.lower() <= "z":
									frequency.add(alpha.lower()			)
			return len(__snake_case			) == 26


def     _lowercase       (						__snake_case = "The quick brown fox jumps over the lazy dog"      ,):
			__lowerCAmelCase   :						str				= [False] * 26
			for char in input_str:
						if char.islower():
									__lowerCAmelCase   :						str				= True
						elif char.isupper():
									__lowerCAmelCase   :						Union[str, Any]				= True
			return all(__snake_case			)


def     _lowercase       (						__snake_case = "The quick brown fox jumps over the lazy dog"      ,):
			return len({char for char in input_str.lower() if char.isalpha()}			) == 26


def     _lowercase       (						):
			from timeit import timeit

			__lowerCAmelCase   :						Tuple				= '''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
			print(timeit("is_pangram()"      ,setup=__snake_case			)			)
			print(timeit("is_pangram_faster()"      ,setup=__snake_case			)			)
			print(timeit("is_pangram_fastest()"      ,setup=__snake_case			)			)
			# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
			# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()




"""simple docstring"""






import torch
from torch import nn



class     A__     (						nn.Module							):




	'''simple docstring'''





	def __init__(  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=1 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=False)       -> Dict:

				"""simple docstring"""

				super().__init__()

				__lowerCAmelCase   :						Optional[int]				= n_token
				__lowerCAmelCase   :						int				= d_embed
				__lowerCAmelCase   :						str				= d_proj

				__lowerCAmelCase   :						Optional[Any]				= cutoffs + [n_token]
				__lowerCAmelCase   :						Any				= [0] + self.cutoffs
				__lowerCAmelCase   :						Tuple				= div_val

				__lowerCAmelCase   :						Optional[Any]				= self.cutoffs[0]
				__lowerCAmelCase   :						Union[str, Any]				= len(self.cutoffs) - 1
				__lowerCAmelCase   :						List[str]				= self.shortlist_size + self.n_clusters

				if self.n_clusters > 0:
							__lowerCAmelCase   :						Any				= nn.Parameter(torch.zeros(self.n_clusters ,    self.d_embed))
							__lowerCAmelCase   :						Dict				= nn.Parameter(torch.zeros(self.n_clusters))

				__lowerCAmelCase   :						List[Any]				= nn.ModuleList()
				__lowerCAmelCase   :						Any				= nn.ParameterList()

				if div_val == 1:
							for i in range(len(self.cutoffs)):
										if d_proj != d_embed:
													self.out_projs.append(nn.Parameter(torch.FloatTensor(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)))
										else:
													self.out_projs.append(_SCREAMING_SNAKE_CASE)

							self.out_layers.append(nn.Linear(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE))
				else:
							for i in range(len(self.cutoffs)):
										__lowerCAmelCase		,			__lowerCAmelCase   :						Tuple				= self.cutoff_ends[i], self.cutoff_ends[i + 1]
										__lowerCAmelCase   :						str				= d_embed // (div_val**i)

										self.out_projs.append(nn.Parameter(torch.FloatTensor(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)))

										self.out_layers.append(nn.Linear(_SCREAMING_SNAKE_CASE ,    r_idx - l_idx))

				__lowerCAmelCase   :						str				= keep_order

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	Dict)       -> int:

				"""simple docstring"""

				if proj is None:
							__lowerCAmelCase   :						Dict				= nn.functional.linear(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    bias=_SCREAMING_SNAKE_CASE)
				else:
							# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
							__lowerCAmelCase   :						Optional[Any]				= nn.functional.linear(_SCREAMING_SNAKE_CASE ,    proj.t().contiguous())
							__lowerCAmelCase   :						List[Any]				= nn.functional.linear(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    bias=_SCREAMING_SNAKE_CASE)
							# else:
							#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
							#     if bias is not None:
							#         logit = logit + bias

				return logit

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	Tuple=None ,    _SCREAMING_SNAKE_CASE:	int=False)       -> List[str]:

				"""simple docstring"""

				if labels is not None:
							# Shift so that tokens < n predict n
							__lowerCAmelCase   :						List[Any]				= hidden[..., :-1, :].contiguous()
							__lowerCAmelCase   :						Tuple				= labels[..., 1:].contiguous()
							__lowerCAmelCase   :						Union[str, Any]				= hidden.view(-1 ,    hidden.size(-1))
							__lowerCAmelCase   :						Optional[int]				= labels.view(-1)
							if hidden.size(0) != labels.size(0):
										raise RuntimeError("Input and labels should have the same size in the batch dimension.")
				else:
							__lowerCAmelCase   :						List[Any]				= hidden.view(-1 ,    hidden.size(-1))

				if self.n_clusters == 0:
							__lowerCAmelCase   :						Any				= self._compute_logit(_SCREAMING_SNAKE_CASE ,    self.out_layers[0].weight ,    self.out_layers[0].bias ,    self.out_projs[0])
							if labels is not None:
										__lowerCAmelCase   :						Any				= labels != -100
										__lowerCAmelCase   :						Tuple				= torch.zeros_like(_SCREAMING_SNAKE_CASE ,    dtype=hidden.dtype ,    device=hidden.device)
										__lowerCAmelCase   :						List[Any]				= (
										    -nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,    dim=-1)[mask].gather(1 ,    labels[mask].unsqueeze(1)).squeeze(1)
										)
							else:
										__lowerCAmelCase   :						List[Any]				= nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,    dim=-1)
				else:
							# construct weights and biases
							__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[Any]				= [], []
							for i in range(len(self.cutoffs)):
										if self.div_val == 1:
													__lowerCAmelCase		,			__lowerCAmelCase   :						int				= self.cutoff_ends[i], self.cutoff_ends[i + 1]
													__lowerCAmelCase   :						Optional[Any]				= self.out_layers[0].weight[l_idx:r_idx]
													__lowerCAmelCase   :						Optional[int]				= self.out_layers[0].bias[l_idx:r_idx]
										else:
													__lowerCAmelCase   :						int				= self.out_layers[i].weight
													__lowerCAmelCase   :						List[Any]				= self.out_layers[i].bias

										if i == 0:
													__lowerCAmelCase   :						int				= torch.cat([weight_i, self.cluster_weight] ,    dim=0)
													__lowerCAmelCase   :						Union[str, Any]				= torch.cat([bias_i, self.cluster_bias] ,    dim=0)

										weights.append(_SCREAMING_SNAKE_CASE)
										biases.append(_SCREAMING_SNAKE_CASE)

							__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						Dict				= weights[0], biases[0], self.out_projs[0]

							__lowerCAmelCase   :						Any				= self._compute_logit(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						Optional[Any]				= nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,    dim=1)

							if labels is None:
										__lowerCAmelCase   :						Optional[Any]				= hidden.new_empty((head_logit.size(0), self.n_token))
							else:
										__lowerCAmelCase   :						int				= torch.zeros_like(_SCREAMING_SNAKE_CASE ,    dtype=hidden.dtype ,    device=hidden.device)

							__lowerCAmelCase   :						str				= 0
							__lowerCAmelCase   :						List[str]				= [0] + self.cutoffs
							for i in range(len(_SCREAMING_SNAKE_CASE) - 1):
										__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= cutoff_values[i], cutoff_values[i + 1]

										if labels is not None:
													__lowerCAmelCase   :						List[str]				= (labels >= l_idx) & (labels < r_idx)
													__lowerCAmelCase   :						Any				= mask_i.nonzero().squeeze()

													if indices_i.numel() == 0:
																continue

													__lowerCAmelCase   :						Optional[Any]				= labels.index_select(0 ,    _SCREAMING_SNAKE_CASE) - l_idx
													__lowerCAmelCase   :						List[Any]				= head_logprob.index_select(0 ,    _SCREAMING_SNAKE_CASE)
													__lowerCAmelCase   :						Tuple				= hidden.index_select(0 ,    _SCREAMING_SNAKE_CASE)
										else:
													__lowerCAmelCase   :						List[str]				= hidden

										if i == 0:
													if labels is not None:
																__lowerCAmelCase   :						Optional[int]				= head_logprob_i.gather(1 ,    target_i[:, None]).squeeze(1)
													else:
																__lowerCAmelCase   :						Optional[Any]				= head_logprob[:, : self.cutoffs[0]]
										else:
													__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						List[str]				= weights[i], biases[i], self.out_projs[i]

													__lowerCAmelCase   :						Any				= self._compute_logit(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
													__lowerCAmelCase   :						Tuple				= nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,    dim=1)
													__lowerCAmelCase   :						int				= self.cutoffs[0] + i - 1  # No probability for the head cluster
													if labels is not None:
																__lowerCAmelCase   :						List[str]				= head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																    1 ,    target_i[:, None]).squeeze(1)
													else:
																__lowerCAmelCase   :						Optional[int]				= head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																__lowerCAmelCase   :						Optional[int]				= logprob_i

										if labels is not None:
													if (hasattr(self ,    "keep_order") and self.keep_order) or keep_order:
																out.index_copy_(0 ,    _SCREAMING_SNAKE_CASE ,    -logprob_i)
													else:
																out[offset : offset + logprob_i.size(0)].copy_(-logprob_i)
													offset += logprob_i.size(0)

				return out







	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	List[Any])       -> Optional[Any]:

				"""simple docstring"""

				if self.n_clusters == 0:
							__lowerCAmelCase   :						int				= self._compute_logit(_SCREAMING_SNAKE_CASE ,    self.out_layers[0].weight ,    self.out_layers[0].bias ,    self.out_projs[0])
							return nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,    dim=-1)
				else:
							# construct weights and biases
							__lowerCAmelCase		,			__lowerCAmelCase   :						Dict				= [], []
							for i in range(len(self.cutoffs)):
										if self.div_val == 1:
													__lowerCAmelCase		,			__lowerCAmelCase   :						int				= self.cutoff_ends[i], self.cutoff_ends[i + 1]
													__lowerCAmelCase   :						Optional[Any]				= self.out_layers[0].weight[l_idx:r_idx]
													__lowerCAmelCase   :						Union[str, Any]				= self.out_layers[0].bias[l_idx:r_idx]
										else:
													__lowerCAmelCase   :						Dict				= self.out_layers[i].weight
													__lowerCAmelCase   :						List[str]				= self.out_layers[i].bias

										if i == 0:
													__lowerCAmelCase   :						int				= torch.cat([weight_i, self.cluster_weight] ,    dim=0)
													__lowerCAmelCase   :						int				= torch.cat([bias_i, self.cluster_bias] ,    dim=0)

										weights.append(_SCREAMING_SNAKE_CASE)
										biases.append(_SCREAMING_SNAKE_CASE)

							__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= weights[0], biases[0], self.out_projs[0]
							__lowerCAmelCase   :						str				= self._compute_logit(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

							__lowerCAmelCase   :						Optional[Any]				= hidden.new_empty((head_logit.size(0), self.n_token))
							__lowerCAmelCase   :						Union[str, Any]				= nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,    dim=1)

							__lowerCAmelCase   :						Dict				= [0] + self.cutoffs
							for i in range(len(_SCREAMING_SNAKE_CASE) - 1):
										__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[Any]				= cutoff_values[i], cutoff_values[i + 1]

										if i == 0:
													__lowerCAmelCase   :						Union[str, Any]				= head_logprob[:, : self.cutoffs[0]]
										else:
													__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						Dict				= weights[i], biases[i], self.out_projs[i]

													__lowerCAmelCase   :						Any				= self._compute_logit(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
													__lowerCAmelCase   :						List[Any]				= nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,    dim=1)

													__lowerCAmelCase   :						int				= head_logprob[:, -i] + tail_logprob_i
													__lowerCAmelCase   :						Tuple				= logprob_i

							return out