


import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin


class        _a							(			UpperCamelCase__     ,	UpperCamelCase__			):

							@register_to_config
							def __init__(							self:	Optional[Any]    ,						UpperCamelCase_:	int = 128    ,						UpperCamelCase_:	int = 256    ,						UpperCamelCase_:	float = 2000.0    ,						UpperCamelCase_:	int = 768    ,						UpperCamelCase_:	int = 12    ,						UpperCamelCase_:	int = 12    ,						UpperCamelCase_:	int = 64    ,						UpperCamelCase_:	int = 2_048    ,						UpperCamelCase_:	float = 0.1    ,						)			->   Any:





									"""simple docstring"""







									super().__init__()

									lowercase__            =      nn.Sequential(
									    nn.Linear(UpperCamelCase_    ,						d_model * 4    ,						bias=UpperCamelCase_							)    ,						nn.SiLU()    ,						nn.Linear(d_model * 4    ,						d_model * 4    ,						bias=UpperCamelCase_							)    ,						nn.SiLU()    ,						)

									lowercase__            =      nn.Embedding(UpperCamelCase_    ,						UpperCamelCase_							)
									lowercase__            =      False

									lowercase__            =      nn.Linear(UpperCamelCase_    ,						UpperCamelCase_    ,						bias=UpperCamelCase_							)

									lowercase__            =      nn.Dropout(p=UpperCamelCase_							)

									lowercase__            =      nn.ModuleList()
									for lyr_num in range(UpperCamelCase_							):
											# FiLM conditional T5 decoder
											lowercase__            =      DecoderLayer(d_model=UpperCamelCase_    ,						d_kv=UpperCamelCase_    ,						num_heads=UpperCamelCase_    ,						d_ff=UpperCamelCase_    ,						dropout_rate=UpperCamelCase_							)
											self.decoders.append(UpperCamelCase_							)

									lowercase__            =      TaLayerNorm(UpperCamelCase_							)

									lowercase__            =      nn.Dropout(p=UpperCamelCase_							)
									lowercase__            =      nn.Linear(UpperCamelCase_    ,						UpperCamelCase_    ,						bias=UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Optional[Any]							)			->   Any:





									"""simple docstring"""







									lowercase__            =      torch.mul(query_input.unsqueeze(-1							)    ,						key_input.unsqueeze(-2							)							)
									return mask.unsqueeze(-3							)


							def       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Tuple    ,						UpperCamelCase_:	List[str]							)			->   List[Any]:





									"""simple docstring"""







									lowercase__     ,		lowercase__     ,		lowercase__            =      decoder_input_tokens.shape
									assert decoder_noise_time.shape == (batch,)

									# decoder_noise_time is in [0, 1), so rescale to expected timing range.
									lowercase__            =      get_timestep_embedding(
									    decoder_noise_time * self.config.max_decoder_noise_time    ,						embedding_dim=self.config.d_model    ,						max_period=self.config.max_decoder_noise_time    ,						).to(dtype=self.dtype							)

									lowercase__            =      self.conditioning_emb(UpperCamelCase_							).unsqueeze(1							)

									assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

									lowercase__            =      decoder_input_tokens.shape[1]

									# If we want to use relative positions for audio context, we can just offset
									# this sequence by the length of encodings_and_masks.
									lowercase__            =      torch.broadcast_to(
									    torch.arange(UpperCamelCase_    ,						device=decoder_input_tokens.device							)    ,						(batch, seq_length)    ,						)

									lowercase__            =      self.position_encoding(UpperCamelCase_							)

									lowercase__            =      self.continuous_inputs_projection(UpperCamelCase_							)
									inputs += position_encodings
									lowercase__            =      self.dropout(UpperCamelCase_							)

									# decoder: No padding present.
									lowercase__            =      torch.ones(
									    decoder_input_tokens.shape[:2]    ,						device=decoder_input_tokens.device    ,						dtype=inputs.dtype							)

									# Translate encoding masks to encoder-decoder masks.
									lowercase__            =      [(x, self.encoder_decoder_mask(UpperCamelCase_    ,						UpperCamelCase_							)) for x, y in encodings_and_masks]

									# cross attend style: concat encodings
									lowercase__            =      torch.cat([x[0] for x in encodings_and_encdec_masks]    ,						dim=1							)
									lowercase__            =      torch.cat([x[1] for x in encodings_and_encdec_masks]    ,						dim=-1							)

									for lyr in self.decoders:
											lowercase__            =      lyr(
											    UpperCamelCase_    ,						conditioning_emb=UpperCamelCase_    ,						encoder_hidden_states=UpperCamelCase_    ,						encoder_attention_mask=UpperCamelCase_    ,						)[0]

									lowercase__            =      self.decoder_norm(UpperCamelCase_							)
									lowercase__            =      self.post_dropout(UpperCamelCase_							)

									lowercase__            =      self.spec_out(UpperCamelCase_							)
									return spec_out


class        _a							(			nn.Module			):

							def __init__(							self:	int    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	Dict    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Optional[int]    ,						UpperCamelCase_:	Dict    ,						UpperCamelCase_:	Optional[Any]=1E-6							)			->   Optional[int]:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      nn.ModuleList()

									# cond self attention: layer 0
									self.layer.append(
									    TaLayerSelfAttentionCond(d_model=UpperCamelCase_    ,						d_kv=UpperCamelCase_    ,						num_heads=UpperCamelCase_    ,						dropout_rate=UpperCamelCase_							)							)

									# cross attention: layer 1
									self.layer.append(
									    TaLayerCrossAttention(
									        d_model=UpperCamelCase_    ,						d_kv=UpperCamelCase_    ,						num_heads=UpperCamelCase_    ,						dropout_rate=UpperCamelCase_    ,						layer_norm_epsilon=UpperCamelCase_    ,						)							)

									# Film Cond MLP + dropout: last layer
									self.layer.append(
									    TaLayerFFCond(d_model=UpperCamelCase_    ,						d_ff=UpperCamelCase_    ,						dropout_rate=UpperCamelCase_    ,						layer_norm_epsilon=UpperCamelCase_							)							)


							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	Tuple    ,						UpperCamelCase_:	Any=None    ,						UpperCamelCase_:	Any=None    ,						UpperCamelCase_:	Tuple=None    ,						UpperCamelCase_:	Union[str, Any]=None    ,						UpperCamelCase_:	List[str]=None    ,						)			->   Any:





									"""simple docstring"""







									lowercase__            =      self.layer[0](
									    UpperCamelCase_    ,						conditioning_emb=UpperCamelCase_    ,						attention_mask=UpperCamelCase_    ,						)

									if encoder_hidden_states is not None:
											lowercase__            =      torch.where(encoder_attention_mask > 0    ,						0    ,						-1E1_0							).to(
											    encoder_hidden_states.dtype							)

											lowercase__            =      self.layer[1](
											    UpperCamelCase_    ,						key_value_states=UpperCamelCase_    ,						attention_mask=UpperCamelCase_    ,						)

									# Apply Film Conditional Feed Forward layer
									lowercase__            =      self.layer[-1](UpperCamelCase_    ,						UpperCamelCase_							)

									return (hidden_states,)


class        _a							(			nn.Module			):

							def __init__(							self:	Dict    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	Union[str, Any]    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	Union[str, Any]							)			->   Dict:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      TaLayerNorm(UpperCamelCase_							)
									lowercase__            =      TaFiLMLayer(in_features=d_model * 4    ,						out_features=UpperCamelCase_							)
									lowercase__            =      Attention(query_dim=UpperCamelCase_    ,						heads=UpperCamelCase_    ,						dim_head=UpperCamelCase_    ,						out_bias=UpperCamelCase_    ,						scale_qk=UpperCamelCase_							)
									lowercase__            =      nn.Dropout(UpperCamelCase_							)


							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	Any=None    ,						UpperCamelCase_:	Any=None    ,						)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      self.layer_norm(UpperCamelCase_							)

									if conditioning_emb is not None:
											lowercase__            =      self.FiLMLayer(UpperCamelCase_    ,						UpperCamelCase_							)

									# Self-attention block
									lowercase__            =      self.attention(UpperCamelCase_							)

									lowercase__            =      hidden_states + self.dropout(UpperCamelCase_							)

									return hidden_states


class        _a							(			nn.Module			):

							def __init__(							self:	Optional[Any]    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Union[str, Any]							)			->   str:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      Attention(query_dim=UpperCamelCase_    ,						heads=UpperCamelCase_    ,						dim_head=UpperCamelCase_    ,						out_bias=UpperCamelCase_    ,						scale_qk=UpperCamelCase_							)
									lowercase__            =      TaLayerNorm(UpperCamelCase_    ,						eps=UpperCamelCase_							)
									lowercase__            =      nn.Dropout(UpperCamelCase_							)


							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	Dict    ,						UpperCamelCase_:	List[Any]=None    ,						UpperCamelCase_:	List[str]=None    ,						)			->   Any:





									"""simple docstring"""







									lowercase__            =      self.layer_norm(UpperCamelCase_							)
									lowercase__            =      self.attention(
									    UpperCamelCase_    ,						encoder_hidden_states=UpperCamelCase_    ,						attention_mask=attention_mask.squeeze(1							)    ,						)
									lowercase__            =      hidden_states + self.dropout(UpperCamelCase_							)
									return layer_output


class        _a							(			nn.Module			):

							def __init__(							self:	Tuple    ,						UpperCamelCase_:	Optional[int]    ,						UpperCamelCase_:	Dict    ,						UpperCamelCase_:	Tuple    ,						UpperCamelCase_:	Union[str, Any]							)			->   List[Any]:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      TaDenseGatedActDense(d_model=UpperCamelCase_    ,						d_ff=UpperCamelCase_    ,						dropout_rate=UpperCamelCase_							)
									lowercase__            =      TaFiLMLayer(in_features=d_model * 4    ,						out_features=UpperCamelCase_							)
									lowercase__            =      TaLayerNorm(UpperCamelCase_    ,						eps=UpperCamelCase_							)
									lowercase__            =      nn.Dropout(UpperCamelCase_							)


							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	Tuple    ,						UpperCamelCase_:	Optional[Any]=None							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      self.layer_norm(UpperCamelCase_							)
									if conditioning_emb is not None:
											lowercase__            =      self.film(UpperCamelCase_    ,						UpperCamelCase_							)

									lowercase__            =      self.DenseReluDense(UpperCamelCase_							)
									lowercase__            =      hidden_states + self.dropout(UpperCamelCase_							)
									return hidden_states


class        _a							(			nn.Module			):

							def __init__(							self:	str    ,						UpperCamelCase_:	Tuple    ,						UpperCamelCase_:	Tuple    ,						UpperCamelCase_:	str							)			->   Dict:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      nn.Linear(UpperCamelCase_    ,						UpperCamelCase_    ,						bias=UpperCamelCase_							)
									lowercase__            =      nn.Linear(UpperCamelCase_    ,						UpperCamelCase_    ,						bias=UpperCamelCase_							)
									lowercase__            =      nn.Linear(UpperCamelCase_    ,						UpperCamelCase_    ,						bias=UpperCamelCase_							)
									lowercase__            =      nn.Dropout(UpperCamelCase_							)
									lowercase__            =      NewGELUActivation()


							def       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	Any							)			->   Any:





									"""simple docstring"""







									lowercase__            =      self.act(self.wi_a(UpperCamelCase_							)							)
									lowercase__            =      self.wi_a(UpperCamelCase_							)
									lowercase__            =      hidden_gelu * hidden_linear
									lowercase__            =      self.dropout(UpperCamelCase_							)

									lowercase__            =      self.wo(UpperCamelCase_							)
									return hidden_states


class        _a							(			nn.Module			):

							def __init__(							self:	Any    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Any=1E-6							)			->   Optional[Any]:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      nn.Parameter(torch.ones(UpperCamelCase_							)							)
									lowercase__            =      eps


							def       lowerCamelCase_ (							self:	Dict    ,						UpperCamelCase_:	Optional[Any]							)			->   List[Any]:





									"""simple docstring"""







									lowercase__            =      hidden_states.to(torch.floataa							).pow(2							).mean(-1    ,						keepdim=UpperCamelCase_							)
									lowercase__            =      hidden_states * torch.rsqrt(variance + self.variance_epsilon							)

									# convert into half-precision if necessary
									if self.weight.dtype in [torch.floataa, torch.bfloataa]:
											lowercase__            =      hidden_states.to(self.weight.dtype							)

									return self.weight * hidden_states


class        _a							(			nn.Module			):

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	torch.Tensor							)			->   torch.Tensor:





									"""simple docstring"""







									return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi							) * (input + 0.044715 * torch.pow(UpperCamelCase_    ,						3.0							))							))


class        _a							(			nn.Module			):

							def __init__(							self:	Tuple    ,						UpperCamelCase_:	Optional[int]    ,						UpperCamelCase_:	List[str]							)			->   str:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      nn.Linear(UpperCamelCase_    ,						out_features * 2    ,						bias=UpperCamelCase_							)


							def       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Any							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      self.scale_bias(UpperCamelCase_							)
									lowercase__     ,		lowercase__            =      torch.chunk(UpperCamelCase_    ,						2    ,						-1							)
									lowercase__            =      x * (1 + scale) + shift
									return x




import logging

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

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


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray    ):
       return input_array.reshape((input_array.size, 1)    )


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : int    ):
       snake_case       :   Any							=      np.nan
       for i in range(__lowerCamelCase    ):
              snake_case       :   List[str]							=      features[:, labels == i]
              snake_case       :   Dict							=      data.mean(1    )
              # Centralize the data of class i
              snake_case       :   Optional[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)
                     snake_case       :   Optional[Any]							=      np.dot(__lowerCamelCase				,					centered_data.T    )

       return covariance_sum / features.shape[1]


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : int    ):
       snake_case       :   Optional[Any]							=      features.mean(1    )
       snake_case       :   Tuple							=      np.nan
       for i in range(__lowerCamelCase    ):
              snake_case       :   Tuple							=      features[:, labels == i]
              snake_case       :   Tuple							=      data.shape[1]
              snake_case       :   List[str]							=      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)
                     snake_case       :   Optional[int]							=      device_data * np.dot(
                         column_reshape(__lowerCamelCase    ) - column_reshape(__lowerCamelCase    )				,					(column_reshape(__lowerCamelCase    ) - column_reshape(__lowerCamelCase    )).T				,					)

       return covariance_sum / features.shape[1]


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : int    ):

       # Check if the features have been loaded
       if features.any():
              snake_case       :   Tuple							=      features.mean(1    )
              # Center the dataset
              snake_case       :   List[str]							=      features - np.reshape(__lowerCamelCase				,					(data_mean.size, 1)    )
              snake_case       :   Optional[Any]							=      np.dot(__lowerCamelCase				,					centered_data.T    ) / features.shape[1]
              snake_case				,					snake_case       :   Dict							=      np.linalg.eigh(__lowerCamelCase    )
              # Take all the columns in the reverse order (-1), and then takes only the first
              snake_case       :   Optional[Any]							=      eigenvectors[:, ::-1][:, 0:dimensions]
              # Project the database on the new space
              snake_case       :   Union[str, 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 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : int				,					__lowerCamelCase     : int    ):
       assert classes > dimensions

       # Check if features have been already loaded
       if features.any:
              snake_case				,					snake_case       :   str							=      eigh(
                  covariance_between_classes(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )				,					covariance_within_classes(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )				,					)
              snake_case       :   str							=      eigenvectors[:, ::-1][:, :dimensions]
              snake_case				,					snake_case				,					snake_case       :   int							=      np.linalg.svd(__lowerCamelCase    )
              snake_case       :   List[Any]							=      svd_matrix[:, 0:dimensions]
              snake_case       :   Optional[Any]							=      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 			UpperCamelCase					(    ):
       # Create dummy dataset with 2 classes and 3 features
       snake_case       :   str							=      np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]    )
       snake_case       :   Union[str, Any]							=      np.array([0, 0, 0, 1, 1]    )
       snake_case       :   List[Any]							=      2
       snake_case       :   Any							=      2

       # Assert that the function raises an AssertionError if dimensions > classes
       with pytest.raises(__lowerCamelCase    ) as error_info:
              snake_case       :   str							=      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 			UpperCamelCase					(    ):
       snake_case       :   List[str]							=      np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]    )
       snake_case       :   List[str]							=      2
       snake_case       :   int							=      np.array([[6.9282_0323, 8.6602_5404, 10.3923_0485], [3.0, 3.0, 3.0]]    )

       with pytest.raises(__lowerCamelCase    ) as error_info:
              snake_case       :   Union[str, Any]							=      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()





