





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     UpperCamelCase__					(     lowerCamelCase__				, lowerCamelCase__				):






		'''simple docstring'''






		@register_to_config
		def __init__(							self,   snake_case__ = 1_28,   snake_case__ = 2_56,   snake_case__ = 2000.0,   snake_case__ = 7_68,   snake_case__ = 12,   snake_case__ = 12,   snake_case__ = 64,   snake_case__ = 20_48,   snake_case__ = 0.1,   )       -> Tuple:






									"""simple docstring"""
									super().__init__()

									lowercase_	:   str  				=     nn.Sequential(
									    nn.Linear(snake_case__,   d_model * 4,   bias=snake_case__  ),   nn.SiLU(),   nn.Linear(d_model * 4,   d_model * 4,   bias=snake_case__  ),   nn.SiLU(),   )

									lowercase_	:   Optional[Any]  				=     nn.Embedding(snake_case__,   snake_case__  )
									lowercase_	:   Optional[int]  				=     False

									lowercase_	:   int  				=     nn.Linear(snake_case__,   snake_case__,   bias=snake_case__  )

									lowercase_	:   List[str]  				=     nn.Dropout(p=snake_case__  )

									lowercase_	:   Optional[int]  				=     nn.ModuleList()
									for lyr_num in range(snake_case__  ):
																# FiLM conditional T5 decoder
																lowercase_	:   List[Any]  				=     DecoderLayer(d_model=snake_case__,   d_kv=snake_case__,   num_heads=snake_case__,   d_ff=snake_case__,   dropout_rate=snake_case__  )
																self.decoders.append(snake_case__  )

									lowercase_	:   Union[str, Any]  				=     TaLayerNorm(snake_case__  )

									lowercase_	:   str  				=     nn.Dropout(p=snake_case__  )
									lowercase_	:   Tuple  				=     nn.Linear(snake_case__,   snake_case__,   bias=snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									lowercase_	:   int  				=     torch.mul(query_input.unsqueeze(-1  ),   key_input.unsqueeze(-2  )  )
									return mask.unsqueeze(-3  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									lowercase_				,		lowercase_				,		lowercase_	:   int  				=     decoder_input_tokens.shape
									assert decoder_noise_time.shape == (batch,)

									# decoder_noise_time is in [0, 1), so rescale to expected timing range.
									lowercase_	:   Union[str, Any]  				=     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_	:   str  				=     self.conditioning_emb(snake_case__  ).unsqueeze(1  )

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

									lowercase_	:   List[Any]  				=     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_	:   Optional[int]  				=     torch.broadcast_to(
									    torch.arange(snake_case__,   device=decoder_input_tokens.device  ),   (batch, seq_length),   )

									lowercase_	:   int  				=     self.position_encoding(snake_case__  )

									lowercase_	:   Union[str, Any]  				=     self.continuous_inputs_projection(snake_case__  )
									inputs += position_encodings
									lowercase_	:   Optional[int]  				=     self.dropout(snake_case__  )

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

									# Translate encoding masks to encoder-decoder masks.
									lowercase_	:   Tuple  				=     [(x, self.encoder_decoder_mask(snake_case__,   snake_case__  )) for x, y in encodings_and_masks]

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

									for lyr in self.decoders:
																lowercase_	:   int  				=     lyr(
																    snake_case__,   conditioning_emb=snake_case__,   encoder_hidden_states=snake_case__,   encoder_attention_mask=snake_case__,   )[0]

									lowercase_	:   str  				=     self.decoder_norm(snake_case__  )
									lowercase_	:   Dict  				=     self.post_dropout(snake_case__  )

									lowercase_	:   str  				=     self.spec_out(snake_case__  )
									return spec_out




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__=1E-6  )       -> Tuple:






									"""simple docstring"""
									super().__init__()
									lowercase_	:   Dict  				=     nn.ModuleList()

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

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

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






		def 						snake_case__				(							self,   snake_case__,   snake_case__=None,   snake_case__=None,   snake_case__=None,   snake_case__=None,   snake_case__=None,   )       -> Any:






									"""simple docstring"""
									lowercase_	:   Optional[int]  				=     self.layer[0](
									    snake_case__,   conditioning_emb=snake_case__,   attention_mask=snake_case__,   )

									if encoder_hidden_states is not None:
																lowercase_	:   int  				=     torch.where(encoder_attention_mask > 0,   0,   -1E10  ).to(
																    encoder_hidden_states.dtype  )

																lowercase_	:   Optional[Any]  				=     self.layer[1](
																    snake_case__,   key_value_states=snake_case__,   attention_mask=snake_case__,   )

									# Apply Film Conditional Feed Forward layer
									lowercase_	:   List[str]  				=     self.layer[-1](snake_case__,   snake_case__  )

									return (hidden_states,)




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__  )       -> List[str]:






									"""simple docstring"""
									super().__init__()
									lowercase_	:   Optional[int]  				=     TaLayerNorm(snake_case__  )
									lowercase_	:   str  				=     TaFiLMLayer(in_features=d_model * 4,   out_features=snake_case__  )
									lowercase_	:   Optional[int]  				=     Attention(query_dim=snake_case__,   heads=snake_case__,   dim_head=snake_case__,   out_bias=snake_case__,   scale_qk=snake_case__  )
									lowercase_	:   Dict  				=     nn.Dropout(snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__=None,   snake_case__=None,   )       -> Dict:






									"""simple docstring"""
									# pre_self_attention_layer_norm
									lowercase_	:   Union[str, Any]  				=     self.layer_norm(snake_case__  )

									if conditioning_emb is not None:
																lowercase_	:   Any  				=     self.FiLMLayer(snake_case__,   snake_case__  )

									# Self-attention block
									lowercase_	:   str  				=     self.attention(snake_case__  )

									lowercase_	:   List[str]  				=     hidden_states + self.dropout(snake_case__  )

									return hidden_states




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__  )       -> Dict:






									"""simple docstring"""
									super().__init__()
									lowercase_	:   List[str]  				=     Attention(query_dim=snake_case__,   heads=snake_case__,   dim_head=snake_case__,   out_bias=snake_case__,   scale_qk=snake_case__  )
									lowercase_	:   Any  				=     TaLayerNorm(snake_case__,   eps=snake_case__  )
									lowercase_	:   int  				=     nn.Dropout(snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__=None,   snake_case__=None,   )       -> List[str]:






									"""simple docstring"""
									lowercase_	:   int  				=     self.layer_norm(snake_case__  )
									lowercase_	:   Optional[Any]  				=     self.attention(
									    snake_case__,   encoder_hidden_states=snake_case__,   attention_mask=attention_mask.squeeze(1  ),   )
									lowercase_	:   Any  				=     hidden_states + self.dropout(snake_case__  )
									return layer_output




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__  )       -> Tuple:






									"""simple docstring"""
									super().__init__()
									lowercase_	:   Optional[Any]  				=     TaDenseGatedActDense(d_model=snake_case__,   d_ff=snake_case__,   dropout_rate=snake_case__  )
									lowercase_	:   List[Any]  				=     TaFiLMLayer(in_features=d_model * 4,   out_features=snake_case__  )
									lowercase_	:   str  				=     TaLayerNorm(snake_case__,   eps=snake_case__  )
									lowercase_	:   Optional[int]  				=     nn.Dropout(snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__=None  )       -> int:






									"""simple docstring"""
									lowercase_	:   Any  				=     self.layer_norm(snake_case__  )
									if conditioning_emb is not None:
																lowercase_	:   Any  				=     self.film(snake_case__,   snake_case__  )

									lowercase_	:   str  				=     self.DenseReluDense(snake_case__  )
									lowercase_	:   int  				=     hidden_states + self.dropout(snake_case__  )
									return hidden_states




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__  )       -> Any:






									"""simple docstring"""
									super().__init__()
									lowercase_	:   Optional[int]  				=     nn.Linear(snake_case__,   snake_case__,   bias=snake_case__  )
									lowercase_	:   str  				=     nn.Linear(snake_case__,   snake_case__,   bias=snake_case__  )
									lowercase_	:   Tuple  				=     nn.Linear(snake_case__,   snake_case__,   bias=snake_case__  )
									lowercase_	:   List[str]  				=     nn.Dropout(snake_case__  )
									lowercase_	:   str  				=     NewGELUActivation()






		def 						snake_case__				(							self,   snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									lowercase_	:   str  				=     self.act(self.wi_a(snake_case__  )  )
									lowercase_	:   Dict  				=     self.wi_a(snake_case__  )
									lowercase_	:   Tuple  				=     hidden_gelu * hidden_linear
									lowercase_	:   List[Any]  				=     self.dropout(snake_case__  )

									lowercase_	:   Optional[Any]  				=     self.wo(snake_case__  )
									return hidden_states




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__=1E-6  )       -> Any:






									"""simple docstring"""
									super().__init__()
									lowercase_	:   str  				=     nn.Parameter(torch.ones(snake_case__  )  )
									lowercase_	:   str  				=     eps






		def 						snake_case__				(							self,   snake_case__  )       -> Dict:






									"""simple docstring"""
									# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
									# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
									# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
									# half-precision inputs is done in fp32

									lowercase_	:   str  				=     hidden_states.to(torch.floataa  ).pow(2  ).mean(-1,   keepdim=snake_case__  )
									lowercase_	:   str  				=     hidden_states * torch.rsqrt(variance + self.variance_epsilon  )

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

									return self.weight * hidden_states




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def 						snake_case__				(							self,   snake_case__  )       -> torch.Tensor:






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




class     UpperCamelCase__					(     nn.Module				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__  )       -> List[str]:






									"""simple docstring"""
									super().__init__()
									lowercase_	:   str  				=     nn.Linear(snake_case__,   out_features * 2,   bias=snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> str:






									"""simple docstring"""
									lowercase_	:   List[Any]  				=     self.scale_bias(snake_case__  )
									lowercase_				,		lowercase_	:   List[Any]  				=     torch.chunk(snake_case__,   2,   -1  )
									lowercase_	:   Tuple  				=     x * (1 + scale) + shift
									return x






from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				,       )				-> list[float]:

							"""simple docstring"""



							lowercase_				,		lowercase_	:   List[str]  				=     coefficient_matrix.shape
							lowercase_				,		lowercase_	:   Any  				=     constant_matrix.shape

							if rowsa != colsa:
														lowercase_	:   List[Any]  				=     f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
														raise ValueError(lowercase				)

							if colsa != 1:
														lowercase_	:   Optional[int]  				=     f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
														raise ValueError(lowercase				)

							if rowsa != rowsa:
														lowercase_	:   Tuple  				=     (
														    """Coefficient and constant matrices dimensions must be nxn and nx1 but """
														    f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
														)
														raise ValueError(lowercase				)

							if len(lowercase				) != rowsa:
														lowercase_	:   int  				=     (
														    """Number of initial values must be equal to number of rows in coefficient """
														    f"""matrix but received {len(lowercase				)} and {rowsa}"""
														)
														raise ValueError(lowercase				)

							if iterations <= 0:
														raise ValueError("""Iterations must be at least 1"""				)

							lowercase_	:   NDArray[floataa]  				=     np.concatenate(
							    (coefficient_matrix, constant_matrix)				,       axis=1				)

							lowercase_				,		lowercase_	:   Dict  				=     table.shape

							strictly_diagonally_dominant(lowercase				)

							# Iterates the whole matrix for given number of times
							for _ in range(lowercase				):
														lowercase_	:   str  				=     []
														for row in range(lowercase				):
																					lowercase_	:   Dict  				=     0
																					for col in range(lowercase				):
																												if col == row:
																																			lowercase_	:   Optional[Any]  				=     table[row][col]
																												elif col == cols - 1:
																																			lowercase_	:   Tuple  				=     table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					lowercase_	:   List[Any]  				=     (temp + val) / denom
																					new_val.append(lowercase				)
														lowercase_	:   Optional[int]  				=     new_val

							return [float(lowercase				) for i in new_val]





def  __magic_name__  (				lowercase				)				-> bool:

							"""simple docstring"""



							lowercase_				,		lowercase_	:   str  				=     table.shape

							lowercase_	:   str  				=     True

							for i in range(0				,       lowercase				):
														lowercase_	:   Any  				=     0
														for j in range(0				,       cols - 1				):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""				)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
							import doctest

							doctest.testmod()