

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  ,   A_ = 128  ,   A_ = 256  ,   A_ = 20_00.0  ,   A_ = 768  ,   A_ = 12  ,   A_ = 12  ,   A_ = 64  ,   A_ = 2048  ,   A_ = 0.1  ,   ):







										'''simple docstring'''


										super().__init__()

										_UpperCAmelCase    :						Optional[Any]							=      nn.Sequential(
										    nn.Linear(A_  ,   d_model * 4  ,   bias=A_		)  ,   nn.SiLU()  ,   nn.Linear(d_model * 4  ,   d_model * 4  ,   bias=A_		)  ,   nn.SiLU()  ,   )

										_UpperCAmelCase    :						Optional[Any]							=      nn.Embedding(A_  ,   A_		)
										_UpperCAmelCase    :						Tuple							=      False

										_UpperCAmelCase    :						List[str]							=      nn.Linear(A_  ,   A_  ,   bias=A_		)

										_UpperCAmelCase    :						Any							=      nn.Dropout(p=A_		)

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

										_UpperCAmelCase    :						Optional[int]							=      TaLayerNorm(A_		)

										_UpperCAmelCase    :						Any							=      nn.Dropout(p=A_		)
										_UpperCAmelCase    :						int							=      nn.Linear(A_  ,   A_  ,   bias=A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      torch.mul(query_input.unsqueeze(-1		)  ,   key_input.unsqueeze(-2		)		)
										return mask.unsqueeze(-3		)





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Union[str, Any]							=      decoder_input_tokens.shape
										assert decoder_noise_time.shape == (batch,)

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

										_UpperCAmelCase    :						List[Any]							=      self.conditioning_emb(A_		).unsqueeze(1		)

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

										_UpperCAmelCase    :						Optional[int]							=      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    :						List[Any]							=      torch.broadcast_to(
										    torch.arange(A_  ,   device=decoder_input_tokens.device		)  ,   (batch, seq_length)  ,   )

										_UpperCAmelCase    :						List[str]							=      self.position_encoding(A_		)

										_UpperCAmelCase    :						int							=      self.continuous_inputs_projection(A_		)
										inputs += position_encodings
										_UpperCAmelCase    :						Any							=      self.dropout(A_		)

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

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

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

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

										_UpperCAmelCase    :						Dict							=      self.decoder_norm(A_		)
										_UpperCAmelCase    :						List[Any]							=      self.post_dropout(A_		)

										_UpperCAmelCase    :						List[str]							=      self.spec_out(A_		)
										return spec_out







class 			a					(		nn.Module			):


						def __init__(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_=1e-6		):







										'''simple docstring'''


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

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

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

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





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_=None  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      self.layer[0](
										    A_  ,   conditioning_emb=A_  ,   attention_mask=A_  ,   )

										if encoder_hidden_states is not None:
														_UpperCAmelCase    :						Optional[Any]							=      torch.where(encoder_attention_mask > 0  ,   0  ,   -1e10		).to(
														    encoder_hidden_states.dtype		)

														_UpperCAmelCase    :						Tuple							=      self.layer[1](
														    A_  ,   key_value_states=A_  ,   attention_mask=A_  ,   )

										# Apply Film Conditional Feed Forward layer
										_UpperCAmelCase    :						Optional[int]							=      self.layer[-1](A_  ,   A_		)

										return (hidden_states,)







class 			a					(		nn.Module			):


						def __init__(    self  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						str							=      TaLayerNorm(A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      TaFiLMLayer(in_features=d_model * 4  ,   out_features=A_		)
										_UpperCAmelCase    :						Any							=      Attention(query_dim=A_  ,   heads=A_  ,   dim_head=A_  ,   out_bias=A_  ,   scale_qk=A_		)
										_UpperCAmelCase    :						List[str]							=      nn.Dropout(A_		)





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_=None  ,   A_=None  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.layer_norm(A_		)

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

										# Self-attention block
										_UpperCAmelCase    :						Optional[Any]							=      self.attention(A_		)

										_UpperCAmelCase    :						Union[str, Any]							=      hidden_states + self.dropout(A_		)

										return hidden_states







class 			a					(		nn.Module			):


						def __init__(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						List[Any]							=      Attention(query_dim=A_  ,   heads=A_  ,   dim_head=A_  ,   out_bias=A_  ,   scale_qk=A_		)
										_UpperCAmelCase    :						List[str]							=      TaLayerNorm(A_  ,   eps=A_		)
										_UpperCAmelCase    :						Any							=      nn.Dropout(A_		)





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_=None  ,   A_=None  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      self.layer_norm(A_		)
										_UpperCAmelCase    :						List[Any]							=      self.attention(
										    A_  ,   encoder_hidden_states=A_  ,   attention_mask=attention_mask.squeeze(1		)  ,   )
										_UpperCAmelCase    :						List[Any]							=      hidden_states + self.dropout(A_		)
										return layer_output







class 			a					(		nn.Module			):


						def __init__(    self  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						List[str]							=      TaDenseGatedActDense(d_model=A_  ,   d_ff=A_  ,   dropout_rate=A_		)
										_UpperCAmelCase    :						Tuple							=      TaFiLMLayer(in_features=d_model * 4  ,   out_features=A_		)
										_UpperCAmelCase    :						List[Any]							=      TaLayerNorm(A_  ,   eps=A_		)
										_UpperCAmelCase    :						str							=      nn.Dropout(A_		)





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_=None		):







										'''simple docstring'''


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

										_UpperCAmelCase    :						int							=      self.DenseReluDense(A_		)
										_UpperCAmelCase    :						List[Any]							=      hidden_states + self.dropout(A_		)
										return hidden_states







class 			a					(		nn.Module			):


						def __init__(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						Any							=      nn.Linear(A_  ,   A_  ,   bias=A_		)
										_UpperCAmelCase    :						List[Any]							=      nn.Linear(A_  ,   A_  ,   bias=A_		)
										_UpperCAmelCase    :						Any							=      nn.Linear(A_  ,   A_  ,   bias=A_		)
										_UpperCAmelCase    :						Optional[int]							=      nn.Dropout(A_		)
										_UpperCAmelCase    :						Any							=      NewGELUActivation()





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.act(self.wi_a(A_		)		)
										_UpperCAmelCase    :						str							=      self.wi_a(A_		)
										_UpperCAmelCase    :						Any							=      hidden_gelu * hidden_linear
										_UpperCAmelCase    :						Tuple							=      self.dropout(A_		)

										_UpperCAmelCase    :						List[str]							=      self.wo(A_		)
										return hidden_states







class 			a					(		nn.Module			):


						def __init__(    self  ,   A_  ,   A_=1e-6		):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						Optional[Any]							=      nn.Parameter(torch.ones(A_		)		)
										_UpperCAmelCase    :						Optional[int]							=      eps





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


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

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

										return self.weight * hidden_states







class 			a					(		nn.Module			):


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.04_47_15 * torch.pow(A_  ,   3.0		))		))







class 			a					(		nn.Module			):


						def __init__(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						Optional[int]							=      nn.Linear(A_  ,   out_features * 2  ,   bias=A_		)





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      self.scale_bias(A_		)
										_UpperCAmelCase						,							_UpperCAmelCase    :						str							=      torch.chunk(A_  ,   2  ,   -1		)
										_UpperCAmelCase    :						Tuple							=      x * (1 + scale) + shift
										return x





from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload

class 			a					:


						def __init__(    self  ,   A_ = None		):







										'''simple docstring'''


										if components is None:
														_UpperCAmelCase    :						Dict							=      []
										_UpperCAmelCase    :						Dict							=      list(A_		)


						def __len__(    self		):







										'''simple docstring'''


										return len(self.__components		)


						def __str__(    self		):







										'''simple docstring'''


										return "(" + ",".join(map(A_  ,   self.__components		)		) + ")"


						def __add__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      len(self		)
										if size == len(A_		):
														_UpperCAmelCase    :						Union[str, Any]							=      [self.__components[i] + other.component(A_		) for i in range(A_		)]
														return Vector(A_		)
										else:
														raise Exception("must have the same size"		)


						def __sub__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      len(self		)
										if size == len(A_		):
														_UpperCAmelCase    :						Optional[Any]							=      [self.__components[i] - other.component(A_		) for i in range(A_		)]
														return Vector(A_		)
										else:  # error case
														raise Exception("must have the same size"		)


						@overload
						def __mul__(    self  ,   A_		):







										'''simple docstring'''


										...


						@overload
						def __mul__(    self  ,   A_		):







										'''simple docstring'''


										...


						def __mul__(    self  ,   A_		):







										'''simple docstring'''


										if isinstance(A_  ,   (float, int)		):
														_UpperCAmelCase    :						str							=      [c * other for c in self.__components]
														return Vector(A_		)
										elif isinstance(A_  ,   A_		) and len(self		) == len(A_		):
														_UpperCAmelCase    :						int							=      len(self		)
														_UpperCAmelCase    :						Any							=      [self.__components[i] * other.component(A_		) for i in range(A_		)]
														return sum(A_		)
										else:  # error case
														raise Exception("invalid operand!"		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return Vector(self.__components		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										if isinstance(A_  ,   A_		) and -len(self.__components		) <= i < len(self.__components		):
														return self.__components[i]
										else:
														raise Exception("index out of range"		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										assert -len(self.__components		) <= pos < len(self.__components		)
										_UpperCAmelCase    :						Any							=      value


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if len(self.__components		) == 0:
														raise Exception("Vector is empty"		)
										_UpperCAmelCase    :						Dict							=      [c**2 for c in self.__components]
										return math.sqrt(sum(A_		)		)





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self * other
										_UpperCAmelCase    :						str							=      self.euclidean_length() * other.euclidean_length()
										if deg:
														return math.degrees(math.acos(num / den		)		)
										else:
														return math.acos(num / den		)







def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int	)				->    Vector:
				assert isinstance(lowerCAmelCase					, lowerCAmelCase	)
				return Vector([0] * dimension	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    Vector:
				assert isinstance(lowerCAmelCase					, lowerCAmelCase	) and (isinstance(lowerCAmelCase					, lowerCAmelCase	))
				_UpperCAmelCase    :						Tuple							=      [0] * dimension
				_UpperCAmelCase    :						Optional[Any]							=      1
				return Vector(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					float					, lowerCAmelCase:					Vector					, lowerCAmelCase:					Vector	)				->    Vector:
				assert (
				    isinstance(lowerCAmelCase					, lowerCAmelCase	)
				    and isinstance(lowerCAmelCase					, lowerCAmelCase	)
				    and (isinstance(lowerCAmelCase					, (int, float)	))
				)
				return x * scalar + y

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    Vector:
				random.seed(lowerCAmelCase	)
				_UpperCAmelCase    :						Optional[int]							=      [random.randint(lowerCAmelCase					, lowerCAmelCase	) for _ in range(lowerCAmelCase	)]
				return Vector(lowerCAmelCase	)

class 			a					:


						def __init__(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      matrix
										_UpperCAmelCase    :						Union[str, Any]							=      w
										_UpperCAmelCase    :						str							=      h


						def __str__(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      ""
										for i in range(self.__height		):
														ans += "|"
														for j in range(self.__width		):
																		if j < self.__width - 1:
																						ans += str(self.__matrix[i][j]		) + ","
																		else:
																						ans += str(self.__matrix[i][j]		) + "|\n"
										return ans


						def __add__(    self  ,   A_		):







										'''simple docstring'''


										if self.__width == other.width() and self.__height == other.height():
														_UpperCAmelCase    :						Optional[int]							=      []
														for i in range(self.__height		):
																		_UpperCAmelCase    :						List[str]							=      [
																		    self.__matrix[i][j] + other.component(A_  ,   A_		)
																		    for j in range(self.__width		)
																		]
																		matrix.append(A_		)
														return Matrix(A_  ,   self.__width  ,   self.__height		)
										else:
														raise Exception("matrix must have the same dimension!"		)


						def __sub__(    self  ,   A_		):







										'''simple docstring'''


										if self.__width == other.width() and self.__height == other.height():
														_UpperCAmelCase    :						Optional[Any]							=      []
														for i in range(self.__height		):
																		_UpperCAmelCase    :						Tuple							=      [
																		    self.__matrix[i][j] - other.component(A_  ,   A_		)
																		    for j in range(self.__width		)
																		]
																		matrix.append(A_		)
														return Matrix(A_  ,   self.__width  ,   self.__height		)
										else:
														raise Exception("matrices must have the same dimension!"		)


						@overload
						def __mul__(    self  ,   A_		):







										'''simple docstring'''


										...


						@overload
						def __mul__(    self  ,   A_		):







										'''simple docstring'''


										...


						def __mul__(    self  ,   A_		):







										'''simple docstring'''


										if isinstance(A_  ,   A_		):  # matrix-vector
														if len(A_		) == self.__width:
																		_UpperCAmelCase    :						Union[str, Any]							=      zero_vector(self.__height		)
																		for i in range(self.__height		):
																						_UpperCAmelCase    :						List[Any]							=      [
																						    self.__matrix[i][j] * other.component(A_		)
																						    for j in range(self.__width		)
																						]
																						ans.change_component(A_  ,   sum(A_		)		)
																		return ans
														else:
																		raise Exception(
																		    "vector must have the same size as the "
																		    "number of columns of the matrix!"		)
										elif isinstance(A_  ,   (int, float)		):  # matrix-scalar
														_UpperCAmelCase    :						Any							=      [
														    [self.__matrix[i][j] * other for j in range(self.__width		)]
														    for i in range(self.__height		)
														]
														return Matrix(A_  ,   self.__width  ,   self.__height		)
										return None


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.__height


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.__width


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if 0 <= x < self.__height and 0 <= y < self.__width:
														return self.__matrix[x][y]
										else:
														raise Exception("change_component: indices out of bounds"		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										if 0 <= x < self.__height and 0 <= y < self.__width:
														_UpperCAmelCase    :						Tuple							=      value
										else:
														raise Exception("change_component: indices out of bounds"		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if self.__height != self.__width:
														raise Exception("Matrix is not square"		)
										_UpperCAmelCase    :						Union[str, Any]							=      self.__matrix[:x] + self.__matrix[x + 1 :]
										for i in range(len(A_		)		):
														_UpperCAmelCase    :						Tuple							=      minor[i][:y] + minor[i][y + 1 :]
										return Matrix(A_  ,   self.__width - 1  ,   self.__height - 1		).determinant()


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if self.__height != self.__width:
														raise Exception("Matrix is not square"		)
										if 0 <= x < self.__height and 0 <= y < self.__width:
														return (-1) ** (x + y) * self.minor(A_  ,   A_		)
										else:
														raise Exception("Indices out of bounds"		)





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if self.__height != self.__width:
														raise Exception("Matrix is not square"		)
										if self.__height < 1:
														raise Exception("Matrix has no element"		)
										elif self.__height == 1:
														return self.__matrix[0][0]
										elif self.__height == 2:
														return (
														    self.__matrix[0][0] * self.__matrix[1][1]
														    - self.__matrix[0][1] * self.__matrix[1][0]
														)
										else:
														_UpperCAmelCase    :						Dict							=      [
														    self.__matrix[0][y] * self.cofactor(0  ,   A_		) for y in range(self.__width		)
														]
														return sum(A_		)







def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int	)				->    Matrix:
				_UpperCAmelCase    :						list[list[float]]							=      [[0] * n for _ in range(lowerCAmelCase	)]
				return Matrix(lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase	)





def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					int					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    Matrix:
				random.seed(lowerCAmelCase	)
				_UpperCAmelCase    :						list[list[float]]							=      [
				    [random.randint(lowerCAmelCase					, lowerCAmelCase	) for _ in range(lowerCAmelCase	)] for _ in range(lowerCAmelCase	)
				]
				return Matrix(lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase	)


