


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      ,   _SCREAMING_SNAKE_CASE = 128      ,   _SCREAMING_SNAKE_CASE = 256      ,   _SCREAMING_SNAKE_CASE = 2000.0      ,   _SCREAMING_SNAKE_CASE = 768      ,   _SCREAMING_SNAKE_CASE = 12      ,   _SCREAMING_SNAKE_CASE = 12      ,   _SCREAMING_SNAKE_CASE = 64      ,   _SCREAMING_SNAKE_CASE = 2048      ,   _SCREAMING_SNAKE_CASE = 0.1      ,   )			->	int:




									"""simple docstring"""





									super().__init__()

									_UpperCAmelCase					=      nn.Sequential(
									    nn.Linear(_SCREAMING_SNAKE_CASE      ,   d_model * 4      ,   bias=_SCREAMING_SNAKE_CASE						)      ,   nn.SiLU()      ,   nn.Linear(d_model * 4      ,   d_model * 4      ,   bias=_SCREAMING_SNAKE_CASE						)      ,   nn.SiLU()      ,   )

									_UpperCAmelCase					=      nn.Embedding(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      False

									_UpperCAmelCase					=      nn.Linear(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   bias=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      nn.Dropout(p=_SCREAMING_SNAKE_CASE						)

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

									_UpperCAmelCase					=      TaLayerNorm(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      nn.Dropout(p=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      nn.Linear(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   bias=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





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






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




									"""simple docstring"""





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

									# decoder_noise_time is in [0, 1), so rescale to expected timing range.
									_UpperCAmelCase					=      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						)

									_UpperCAmelCase					=      self.conditioning_emb(_SCREAMING_SNAKE_CASE						).unsqueeze(1						)

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

									_UpperCAmelCase					=      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.
									_UpperCAmelCase					=      torch.broadcast_to(
									    torch.arange(_SCREAMING_SNAKE_CASE      ,   device=decoder_input_tokens.device						)      ,   (batch, seq_length)      ,   )

									_UpperCAmelCase					=      self.position_encoding(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.continuous_inputs_projection(_SCREAMING_SNAKE_CASE						)
									inputs += position_encodings
									_UpperCAmelCase					=      self.dropout(_SCREAMING_SNAKE_CASE						)

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

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

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

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

									_UpperCAmelCase					=      self.decoder_norm(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.post_dropout(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.spec_out(_SCREAMING_SNAKE_CASE						)
									return spec_out



class 		__a				(    nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=1e-6						)			->	List[Any]:




									"""simple docstring"""





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

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

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

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






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   )			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      self.layer[0](
									    _SCREAMING_SNAKE_CASE      ,   conditioning_emb=_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   )

									if encoder_hidden_states is not None:
																_UpperCAmelCase					=      torch.where(encoder_attention_mask > 0      ,   0      ,   -1e1_0						).to(
																    encoder_hidden_states.dtype						)

																_UpperCAmelCase					=      self.layer[1](
																    _SCREAMING_SNAKE_CASE      ,   key_value_states=_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   )

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

									return (hidden_states,)



class 		__a				(    nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





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






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   )			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      self.layer_norm(_SCREAMING_SNAKE_CASE						)

									if conditioning_emb is not None:
																_UpperCAmelCase					=      self.FiLMLayer(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

									# Self-attention block
									_UpperCAmelCase					=      self.attention(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      hidden_states + self.dropout(_SCREAMING_SNAKE_CASE						)

									return hidden_states



class 		__a				(    nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




									"""simple docstring"""





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






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   )			->	Tuple:




									"""simple docstring"""





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



class 		__a				(    nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





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






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      self.layer_norm(_SCREAMING_SNAKE_CASE						)
									if conditioning_emb is not None:
																_UpperCAmelCase					=      self.film(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.DenseReluDense(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      hidden_states + self.dropout(_SCREAMING_SNAKE_CASE						)
									return hidden_states



class 		__a				(    nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      nn.Linear(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   bias=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      nn.Linear(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   bias=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      nn.Linear(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   bias=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      nn.Dropout(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      NewGELUActivation()






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




									"""simple docstring"""





									_UpperCAmelCase					=      self.act(self.wi_a(_SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      self.wi_a(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      hidden_gelu * hidden_linear
									_UpperCAmelCase					=      self.dropout(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.wo(_SCREAMING_SNAKE_CASE						)
									return hidden_states



class 		__a				(    nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=1e-6						)			->	Optional[int]:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      nn.Parameter(torch.ones(_SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      eps






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





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

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

									return self.weight * hidden_states



class 		__a				(    nn.Module      ):
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_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(_SCREAMING_SNAKE_CASE      ,   3.0						))						))



class 		__a				(    nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	int:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      nn.Linear(_SCREAMING_SNAKE_CASE      ,   out_features * 2      ,   bias=_SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





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




from __future__ import annotations

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

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]      ,  a__:		NDArray[floataa]      ,  a__:		list[int]      ,  a__:		int      ,  )  ->		list[float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      coefficient_matrix.shape
							_UpperCAmelCase		,	_UpperCAmelCase					=      constant_matrix.shape

							if rowsa != colsa:
														_UpperCAmelCase					=      F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if colsa != 1:
														_UpperCAmelCase					=      F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if rowsa != rowsa:
														_UpperCAmelCase					=      (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
														)
														raise ValueError(a__	)

							if len(a__	) != rowsa:
														_UpperCAmelCase					=      (
														    'Number of initial values must be equal to number of rows in coefficient '
														    F'''matrix but received {len(a__	)} and {rowsa}'''
														)
														raise ValueError(a__	)

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

							_UpperCAmelCase					=      np.concatenate(
							    (coefficient_matrix, constant_matrix)      ,  axis=1	)

							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							strictly_diagonally_dominant(a__	)

							# Iterates the whole matrix for given number of times
							for _ in range(a__	):
														_UpperCAmelCase					=      []
														for row in range(a__	):
																					_UpperCAmelCase					=      0
																					for col in range(a__	):
																												if col == row:
																																			_UpperCAmelCase					=      table[row][col]
																												elif col == cols - 1:
																																			_UpperCAmelCase					=      table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					_UpperCAmelCase					=      (temp + val) / denom
																					new_val.append(a__	)
														_UpperCAmelCase					=      new_val

							return [float(a__	) for i in new_val]

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]	)  ->		bool:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							_UpperCAmelCase					=      True

							for i in range(0      ,  a__	):
														_UpperCAmelCase					=      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()
