



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_					( a     ,      a     ,      a):
							lowerCamelCase__         =	[r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']







							@register_to_config
							def __init__(     self,    __a,    __a,    __a = None,    __a = 5_0257,    __a = 1024,    __a = 768,    __a = 12,    __a = 12,    __a = None,    __a = "gelu_new",    __a = 0.1,    __a = 0.1,    __a = 0.1,    __a = 1E-5,    __a = 0.02,    __a = True,    __a = True,    __a = False,    __a = False,    ):

														'''simple docstring'''

														super().__init__()

														_lowerCAmelCase  :							Tuple				     =					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.")

														_lowerCAmelCase  :							int				     =					prefix_inner_dim
														_lowerCAmelCase  :							List[str]				     =					prefix_hidden_dim

														_lowerCAmelCase  :							List[Any]				     =					(
														    nn.Linear(self.prefix_inner_dim,    self.prefix_hidden_dim)
														    if self.prefix_hidden_dim is not None
														    else nn.Identity()
														)
														_lowerCAmelCase  :							Union[str, Any]				     =					(
														    nn.Linear(self.prefix_hidden_dim,    __a) if self.prefix_hidden_dim is not None else nn.Identity()
														)

														_lowerCAmelCase  :							Union[str, Any]				     =					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,    )
														_lowerCAmelCase  :							List[str]				     =					GPTaLMHeadModel(__a)







							def      snake_case__   (     self,    __a,    __a,    __a = None,    __a = None,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					self.transformer.transformer.wte(__a)
														_lowerCAmelCase  :							Any				     =					self.encode_prefix(__a)
														_lowerCAmelCase  :							List[Any]				     =					self.decode_prefix(__a)
														_lowerCAmelCase  :							int				     =					torch.cat((prefix_embeds, embedding_text),    dim=1)

														if labels is not None:
																					_lowerCAmelCase  :							Optional[int]				     =					self.get_dummy_token(input_ids.shape[0],    input_ids.device)
																					_lowerCAmelCase  :							Union[str, Any]				     =					torch.cat((dummy_token, input_ids),    dim=1)
														_lowerCAmelCase  :							Any				     =					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      snake_case__   (     self,    __a,    __a):

														'''simple docstring'''

														return torch.zeros(__a,    self.prefix_length,    dtype=torch.intaa,    device=__a)







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														return self.encode_prefix(__a)







							@torch.no_grad()
							def      snake_case__   (     self,    __a,    __a,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					torch.split(__a,    1,    dim=0)
														_lowerCAmelCase  :							int				     =					[]
														_lowerCAmelCase  :							List[str]				     =					[]
														for feature in features:
																					_lowerCAmelCase  :							Dict				     =					self.decode_prefix(feature.to(__a))  # back to the clip feature
																					# Only support beam search for now
																					_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					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])
														_lowerCAmelCase  :							List[Any]				     =					torch.stack(__a)
														_lowerCAmelCase  :							Any				     =					torch.stack(__a)
														return generated_tokens, generated_seq_lengths







							@torch.no_grad()
							def      snake_case__   (     self,    __a=None,    __a=None,    __a=None,    __a = 5,    __a = 67,    __a = 1.0,    __a = None,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					eos_token_id
														_lowerCAmelCase  :							List[str]				     =					None
														_lowerCAmelCase  :							List[Any]				     =					None
														_lowerCAmelCase  :							Union[str, Any]				     =					torch.ones(__a,    device=__a,    dtype=torch.int)
														_lowerCAmelCase  :							Dict				     =					torch.zeros(__a,    device=__a,    dtype=torch.bool)

														if input_embeds is not None:
																					_lowerCAmelCase  :							int				     =					input_embeds
														else:
																					_lowerCAmelCase  :							Any				     =					self.transformer.transformer.wte(__a)

														for i in range(__a):
																					_lowerCAmelCase  :							List[Any]				     =					self.transformer(inputs_embeds=__a)
																					_lowerCAmelCase  :							Dict				     =					outputs.logits
																					_lowerCAmelCase  :							Optional[Any]				     =					logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
																					_lowerCAmelCase  :							int				     =					logits.softmax(-1).log()

																					if scores is None:
																												_lowerCAmelCase     ,					_lowerCAmelCase  :							int				     =					logits.topk(__a,    -1)
																												_lowerCAmelCase  :							List[Any]				     =					generated.expand(__a,    *generated.shape[1:])
																												_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					next_tokens.permute(1,    0), scores.squeeze(0)
																												if tokens is None:
																																			_lowerCAmelCase  :							Union[str, Any]				     =					next_tokens
																												else:
																																			_lowerCAmelCase  :							int				     =					tokens.expand(__a,    *tokens.shape[1:])
																																			_lowerCAmelCase  :							Union[str, Any]				     =					torch.cat((tokens, next_tokens),    dim=1)
																					else:
																												_lowerCAmelCase  :							str				     =					-float(np.inf)
																												_lowerCAmelCase  :							Optional[Any]				     =					0
																												_lowerCAmelCase  :							int				     =					scores[:, None] + logits
																												seq_lengths[~is_stopped] += 1
																												_lowerCAmelCase  :							Any				     =					scores_sum / seq_lengths[:, None]
																												_lowerCAmelCase     ,					_lowerCAmelCase  :							Union[str, Any]				     =					scores_sum_average.view(-1).topk(__a,    -1)
																												_lowerCAmelCase  :							Optional[Any]				     =					next_tokens // scores_sum.shape[1]
																												_lowerCAmelCase  :							Tuple				     =					seq_lengths[next_tokens_source]
																												_lowerCAmelCase  :							List[Any]				     =					next_tokens % scores_sum.shape[1]
																												_lowerCAmelCase  :							List[Any]				     =					next_tokens.unsqueeze(1)
																												_lowerCAmelCase  :							Any				     =					tokens[next_tokens_source]
																												_lowerCAmelCase  :							str				     =					torch.cat((tokens, next_tokens),    dim=1)
																												_lowerCAmelCase  :							List[Any]				     =					generated[next_tokens_source]
																												_lowerCAmelCase  :							Dict				     =					scores_sum_average * seq_lengths
																												_lowerCAmelCase  :							Any				     =					is_stopped[next_tokens_source]

																					_lowerCAmelCase  :							int				     =					self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0],    1,    -1)
																					_lowerCAmelCase  :							Dict				     =					torch.cat((generated, next_token_embed),    dim=1)
																					_lowerCAmelCase  :							List[str]				     =					is_stopped + next_tokens.eq(__a).squeeze()
																					if is_stopped.all():
																												break

														_lowerCAmelCase  :							Union[str, Any]				     =					scores / seq_lengths
														_lowerCAmelCase  :							Union[str, Any]				     =					scores.argsort(descending=__a)
														# tokens tensors are already padded to max_seq_length
														_lowerCAmelCase  :							Optional[int]				     =					[tokens[i] for i in order]
														_lowerCAmelCase  :							Dict				     =					torch.stack(__a,    dim=0)
														_lowerCAmelCase  :							Optional[int]				     =					torch.tensor([seq_lengths[i] for i in order],    dtype=seq_lengths.dtype)
														return output_texts, seq_lengths





import logging

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

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






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




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

							return covariance_sum / features.shape[1]






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




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

							return covariance_sum / features.shape[1]






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




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

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							assert classes > dimensions

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

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






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[int]				     =					np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]						)
							_lowerCAmelCase  :							List[Any]				     =					np.array([0, 0, 0, 1, 1]						)
							_lowerCAmelCase  :							List[Any]				     =					2
							_lowerCAmelCase  :							Union[str, Any]				     =					2

							# Assert that the function raises an AssertionError if dimensions > classes
							with pytest.raises(_lowerCamelCase						) as error_info:
														_lowerCAmelCase  :							Union[str, Any]				     =					linear_discriminant_analysis(
														    _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)
														if isinstance(_lowerCamelCase       ,				np.ndarray						):
																					raise AssertionError(
																					    "Did not raise AssertionError for dimensions > classes"						)
														assert error_info.type is AssertionError






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]						)
							_lowerCAmelCase  :							List[str]				     =					2
							_lowerCAmelCase  :							List[Any]				     =					np.array([[6.92_82_03_23, 8.66_02_54_04, 10.39_23_04_85], [3.0, 3.0, 3.0]]						)

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()
