
"""simple docstring"""




import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format='%(message)s')


def 						UpperCAmelCase      (     a_						)		->     List[str]:







				"""simple docstring"""



				return input_array.reshape((input_array.size, 1)						)


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     str:







				"""simple docstring"""



				__A             =			np.nan
				for i in range(_a						):
								__A             =			features[:, labels == i]
								__A             =			data.mean(1						)
								# Centralize the data of class i
								__A             =			data - column_reshape(_a						)
								if i > 0:
												# If covariance_sum is not None
												covariance_sum += np.dot(_a		,       centered_data.T						)
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												__A             =			np.dot(_a		,       centered_data.T						)

				return covariance_sum / features.shape[1]


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Any:







				"""simple docstring"""



				__A             =			features.mean(1						)
				__A             =			np.nan
				for i in range(_a						):
								__A             =			features[:, labels == i]
								__A             =			data.shape[1]
								__A             =			data.mean(1						)
								if i > 0:
												# If covariance_sum is not None
												covariance_sum += device_data * np.dot(
												    column_reshape(_a						) - column_reshape(_a						)		,       (column_reshape(_a						) - column_reshape(_a						)).T		,       )
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												__A             =			device_data * np.dot(
												    column_reshape(_a						) - column_reshape(_a						)		,       (column_reshape(_a						) - column_reshape(_a						)).T		,       )

				return covariance_sum / features.shape[1]


def 						UpperCAmelCase      (     a_		,       a_						)		->     List[str]:







				"""simple docstring"""



				if features.any():
								__A             =			features.mean(1						)
								# Center the dataset
								__A             =			features - np.reshape(_a		,       (data_mean.size, 1)						)
								__A             =			np.dot(_a		,       centered_data.T						) / features.shape[1]
								__A             =			np.linalg.eigh(_a						)
								# Take all the columns in the reverse order (-1), and then takes only the first
								__A             =			eigenvectors[:, ::-1][:, 0:dimensions]
								# Project the database on the new space
								__A             =			np.dot(filtered_eigenvectors.T		,       _a						)
								logging.info("Principal Component Analysis computed"						)

								return projected_data
				else:
								logging.basicConfig(level=logging.ERROR		,       format="%(message)s"		,       force=_a						)
								logging.error("Dataset empty"						)
								raise AssertionError


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_						)		->     List[str]:







				"""simple docstring"""



				assert classes > dimensions

				# Check if features have been already loaded
				if features.any:
								__A             =			eigh(
								    covariance_between_classes(_a		,       _a		,       _a						)		,       covariance_within_classes(_a		,       _a		,       _a						)		,       )
								__A             =			eigenvectors[:, ::-1][:, :dimensions]
								__A             =			np.linalg.svd(_a						)
								__A             =			svd_matrix[:, 0:dimensions]
								__A             =			np.dot(filtered_svd_matrix.T		,       _a						)
								logging.info("Linear Discriminant Analysis computed"						)

								return projected_data
				else:
								logging.basicConfig(level=logging.ERROR		,       format="%(message)s"		,       force=_a						)
								logging.error("Dataset empty"						)
								raise AssertionError


def 						UpperCAmelCase      (     )		->     Any:







				"""simple docstring"""



				__A             =			np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]						)
				__A             =			np.array([0, 0, 0, 1, 1]						)
				__A             =			2
				__A             =			2

				# Assert that the function raises an AssertionError if dimensions > classes
				with pytest.raises(_a						) as error_info:
								__A             =			linear_discriminant_analysis(
								    _a		,       _a		,       _a		,       _a						)
								if isinstance(_a		,       np.ndarray						):
												raise AssertionError(
												    "Did not raise AssertionError for dimensions > classes"						)
								assert error_info.type is AssertionError


def 						UpperCAmelCase      (     )		->     List[Any]:







				"""simple docstring"""



				__A             =			np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]						)
				__A             =			2
				__A             =			np.array([[6.92_820_323, 8.66_025_404, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]						)

				with pytest.raises(_a						) as error_info:
								__A             =			principal_component_analysis(_a		,       _a						)
								if not np.allclose(_a		,       _a						):
												raise AssertionError
								assert error_info.type is AssertionError


if __name__ == "__main__":
					import doctest

					doctest.testmod()


from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]






	@register_to_config
	def __init__(	self :			Tuple							,A :			int							,A :			int							,A :			Optional[int] = None							,A :			int = 5_02_57							,A :			int = 10_24							,A :			int = 7_68							,A :			int = 12							,A :			int = 12							,A :			Optional[int] = None							,A :			str = "gelu_new"							,A :			float = 0.1							,A :			float = 0.1							,A :			float = 0.1							,A :			float = 1E-5							,A :			float = 0.02							,A :			bool = True							,A :			bool = True							,A :			bool = False							,A :			bool = False							,):
					super().__init__()

					__A             =			prefix_length

					if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
									raise ValueError(
									    f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
									    f''' `n_embd`: {n_embd} are not equal.'''			)

					__A             =			prefix_inner_dim
					__A             =			prefix_hidden_dim

					__A             =			(
					    nn.Linear(self.prefix_inner_dim							,self.prefix_hidden_dim			)
					    if self.prefix_hidden_dim is not None
					    else nn.Identity()
					)
					__A             =			(
					    nn.Linear(self.prefix_hidden_dim							,A			) if self.prefix_hidden_dim is not None else nn.Identity()
					)

					__A             =			GPTaConfig(
					    vocab_size=A							,n_positions=A							,n_embd=A							,n_layer=A							,n_head=A							,n_inner=A							,activation_function=A							,resid_pdrop=A							,embd_pdrop=A							,attn_pdrop=A							,layer_norm_epsilon=A							,initializer_range=A							,scale_attn_weights=A							,use_cache=A							,scale_attn_by_inverse_layer_idx=A							,reorder_and_upcast_attn=A							,)
					__A             =			GPTaLMHeadModel(A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			torch.Tensor							,A :			torch.Tensor							,A :			Optional[torch.Tensor] = None							,A :			Optional[torch.Tensor] = None							,):
					__A             =			self.transformer.transformer.wte(A			)
					__A             =			self.encode_prefix(A			)
					__A             =			self.decode_prefix(A			)
					__A             =			torch.cat((prefix_embeds, embedding_text)							,dim=1			)

					if labels is not None:
									__A             =			self.get_dummy_token(input_ids.shape[0]							,input_ids.device			)
									__A             =			torch.cat((dummy_token, input_ids)							,dim=1			)
					__A             =			self.transformer(inputs_embeds=A							,labels=A							,attention_mask=A			)
					if self.prefix_hidden_dim is not None:
									return out, hidden
					else:
									return out






	def     UpperCamelCase_  (	self :			int							,A :			int							,A :			torch.device			):
					return torch.zeros(A							,self.prefix_length							,dtype=torch.intaa							,device=A			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			List[Any]			):
					return self.encode_prefix(A			)






	@torch.no_grad()
	def     UpperCamelCase_  (	self :			str							,A :			List[str]							,A :			List[Any]							,A :			List[Any]			):
					__A             =			torch.split(A							,1							,dim=0			)
					__A             =			[]
					__A             =			[]
					for feature in features:
									__A             =			self.decode_prefix(feature.to(A			)			)  # back to the clip feature
									# Only support beam search for now
									__A      ,							__A             =			self.generate_beam(
									    input_embeds=A							,device=A							,eos_token_id=A			)
									generated_tokens.append(output_tokens[0]			)
									generated_seq_lengths.append(seq_lengths[0]			)
					__A             =			torch.stack(A			)
					__A             =			torch.stack(A			)
					return generated_tokens, generated_seq_lengths







	@torch.no_grad()
	def     UpperCamelCase_  (	self :			Tuple							,A :			List[str]=None							,A :			Dict=None							,A :			Optional[int]=None							,A :			int = 5							,A :			int = 67							,A :			float = 1.0							,A :			Optional[int] = None							,):
					__A             =			eos_token_id
					__A             =			None
					__A             =			None
					__A             =			torch.ones(A							,device=A							,dtype=torch.int			)
					__A             =			torch.zeros(A							,device=A							,dtype=torch.bool			)

					if input_embeds is not None:
									__A             =			input_embeds
					else:
									__A             =			self.transformer.transformer.wte(A			)

					for i in range(A			):
									__A             =			self.transformer(inputs_embeds=A			)
									__A             =			outputs.logits
									__A             =			logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
									__A             =			logits.softmax(-1			).log()

									if scores is None:
													__A      ,							__A             =			logits.topk(A							,-1			)
													__A             =			generated.expand(A							,*generated.shape[1:]			)
													__A      ,							__A             =			next_tokens.permute(1							,0			), scores.squeeze(0			)
													if tokens is None:
																	__A             =			next_tokens
													else:
																	__A             =			tokens.expand(A							,*tokens.shape[1:]			)
																	__A             =			torch.cat((tokens, next_tokens)							,dim=1			)
									else:
													__A             =			-float(np.inf			)
													__A             =			0
													__A             =			scores[:, None] + logits
													seq_lengths[~is_stopped] += 1
													__A             =			scores_sum / seq_lengths[:, None]
													__A      ,							__A             =			scores_sum_average.view(-1			).topk(A							,-1			)
													__A             =			next_tokens // scores_sum.shape[1]
													__A             =			seq_lengths[next_tokens_source]
													__A             =			next_tokens % scores_sum.shape[1]
													__A             =			next_tokens.unsqueeze(1			)
													__A             =			tokens[next_tokens_source]
													__A             =			torch.cat((tokens, next_tokens)							,dim=1			)
													__A             =			generated[next_tokens_source]
													__A             =			scores_sum_average * seq_lengths
													__A             =			is_stopped[next_tokens_source]

									__A             =			self.transformer.transformer.wte(next_tokens.squeeze()			).view(generated.shape[0]							,1							,-1			)
									__A             =			torch.cat((generated, next_token_embed)							,dim=1			)
									__A             =			is_stopped + next_tokens.eq(A			).squeeze()
									if is_stopped.all():
													break

					__A             =			scores / seq_lengths
					__A             =			scores.argsort(descending=A			)
					# tokens tensors are already padded to max_seq_length
					__A             =			[tokens[i] for i in order]
					__A             =			torch.stack(A							,dim=0			)
					__A             =			torch.tensor([seq_lengths[i] for i in order]							,dtype=seq_lengths.dtype			)
					return output_texts, seq_lengths
