

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 				lowercase       ( lowercase_			,						lowercase_    ):




				@register_to_config
				def __init__(      self   ,     snake_case = 128   ,     snake_case = 256   ,     snake_case = 20_00.0   ,     snake_case = 768   ,     snake_case = 12   ,     snake_case = 12   ,     snake_case = 64   ,     snake_case = 2048   ,     snake_case = 0.1   ,     ):
											super().__init__()

											snake_case_           =					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()   ,     )

											snake_case_           =					nn.Embedding(snake_case   ,     snake_case )
											snake_case_           =					False

											snake_case_           =					nn.Linear(snake_case   ,     snake_case   ,     bias=snake_case )

											snake_case_           =					nn.Dropout(p=snake_case )

											snake_case_           =					nn.ModuleList()
											for lyr_num in range(snake_case ):
																		# FiLM conditional T5 decoder
																		snake_case_           =					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 )

											snake_case_           =					TaLayerNorm(snake_case )

											snake_case_           =					nn.Dropout(p=snake_case )
											snake_case_           =					nn.Linear(snake_case   ,     snake_case   ,     bias=snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					torch.mul(query_input.unsqueeze(-1 )   ,     key_input.unsqueeze(-2 ) )
											return mask.unsqueeze(-3 )




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_       ,					snake_case_       ,					snake_case_           =					decoder_input_tokens.shape
											assert decoder_noise_time.shape == (batch,)

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

											snake_case_           =					self.conditioning_emb(snake_case ).unsqueeze(1 )

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

											snake_case_           =					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.
											snake_case_           =					torch.broadcast_to(
											    torch.arange(snake_case   ,     device=decoder_input_tokens.device )   ,     (batch, seq_length)   ,     )

											snake_case_           =					self.position_encoding(snake_case )

											snake_case_           =					self.continuous_inputs_projection(snake_case )
											inputs += position_encodings
											snake_case_           =					self.dropout(snake_case )

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

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

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

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

											snake_case_           =					self.decoder_norm(snake_case )
											snake_case_           =					self.post_dropout(snake_case )

											snake_case_           =					self.spec_out(snake_case )
											return spec_out







class 				lowercase       ( nn.Module    ):




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case=1e-6 ):
											super().__init__()
											snake_case_           =					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 					a							(      self   ,     snake_case   ,     snake_case=None   ,     snake_case=None   ,     snake_case=None   ,     snake_case=None   ,     snake_case=None   ,     ):
											snake_case_           =					self.layer[0](
											    snake_case   ,     conditioning_emb=snake_case   ,     attention_mask=snake_case   ,     )

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

																		snake_case_           =					self.layer[1](
																		    snake_case   ,     key_value_states=snake_case   ,     attention_mask=snake_case   ,     )

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

											return (hidden_states,)







class 				lowercase       ( nn.Module    ):




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case ):
											super().__init__()
											snake_case_           =					TaLayerNorm(snake_case )
											snake_case_           =					TaFiLMLayer(in_features=d_model * 4   ,     out_features=snake_case )
											snake_case_           =					Attention(query_dim=snake_case   ,     heads=snake_case   ,     dim_head=snake_case   ,     out_bias=snake_case   ,     scale_qk=snake_case )
											snake_case_           =					nn.Dropout(snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case=None   ,     snake_case=None   ,     ):
											# pre_self_attention_layer_norm
											snake_case_           =					self.layer_norm(snake_case )

											if conditioning_emb is not None:
																		snake_case_           =					self.FiLMLayer(snake_case   ,     snake_case )

											# Self-attention block
											snake_case_           =					self.attention(snake_case )

											snake_case_           =					hidden_states + self.dropout(snake_case )

											return hidden_states







class 				lowercase       ( nn.Module    ):




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case ):
											super().__init__()
											snake_case_           =					Attention(query_dim=snake_case   ,     heads=snake_case   ,     dim_head=snake_case   ,     out_bias=snake_case   ,     scale_qk=snake_case )
											snake_case_           =					TaLayerNorm(snake_case   ,     eps=snake_case )
											snake_case_           =					nn.Dropout(snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case=None   ,     snake_case=None   ,     ):
											snake_case_           =					self.layer_norm(snake_case )
											snake_case_           =					self.attention(
											    snake_case   ,     encoder_hidden_states=snake_case   ,     attention_mask=attention_mask.squeeze(1 )   ,     )
											snake_case_           =					hidden_states + self.dropout(snake_case )
											return layer_output







class 				lowercase       ( nn.Module    ):




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case ):
											super().__init__()
											snake_case_           =					TaDenseGatedActDense(d_model=snake_case   ,     d_ff=snake_case   ,     dropout_rate=snake_case )
											snake_case_           =					TaFiLMLayer(in_features=d_model * 4   ,     out_features=snake_case )
											snake_case_           =					TaLayerNorm(snake_case   ,     eps=snake_case )
											snake_case_           =					nn.Dropout(snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case=None ):
											snake_case_           =					self.layer_norm(snake_case )
											if conditioning_emb is not None:
																		snake_case_           =					self.film(snake_case   ,     snake_case )

											snake_case_           =					self.DenseReluDense(snake_case )
											snake_case_           =					hidden_states + self.dropout(snake_case )
											return hidden_states







class 				lowercase       ( nn.Module    ):




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											super().__init__()
											snake_case_           =					nn.Linear(snake_case   ,     snake_case   ,     bias=snake_case )
											snake_case_           =					nn.Linear(snake_case   ,     snake_case   ,     bias=snake_case )
											snake_case_           =					nn.Linear(snake_case   ,     snake_case   ,     bias=snake_case )
											snake_case_           =					nn.Dropout(snake_case )
											snake_case_           =					NewGELUActivation()




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					self.act(self.wi_a(snake_case ) )
											snake_case_           =					self.wi_a(snake_case )
											snake_case_           =					hidden_gelu * hidden_linear
											snake_case_           =					self.dropout(snake_case )

											snake_case_           =					self.wo(snake_case )
											return hidden_states







class 				lowercase       ( nn.Module    ):




				def __init__(      self   ,     snake_case   ,     snake_case=1e-6 ):
											super().__init__()
											snake_case_           =					nn.Parameter(torch.ones(snake_case ) )
											snake_case_           =					eps




				def 					a							(      self   ,     snake_case ):
											# 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

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

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

											return self.weight * hidden_states







class 				lowercase       ( nn.Module    ):




				def 					a							(      self   ,     snake_case ):
											return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.04_47_15 * torch.pow(snake_case   ,     3.0 )) ))







class 				lowercase       ( nn.Module    ):




				def __init__(      self   ,     snake_case   ,     snake_case ):
											super().__init__()
											snake_case_           =					nn.Linear(snake_case   ,     out_features * 2   ,     bias=snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					self.scale_bias(snake_case )
											snake_case_       ,					snake_case_           =					torch.chunk(snake_case   ,     2   ,     -1 )
											snake_case_           =					x * (1 + scale) + shift
											return x







from __future__ import annotations







class 				lowercase       :




				def __init__(      self   ,     snake_case ):
											snake_case_           =					TypeError(
											    'Matrices must be formed from a list of zero or more lists containing at '
											    'least one and the same number of values, each of which must be of type '
											    'int or float.' )
											if len(snake_case ) != 0:
																		snake_case_           =					len(rows[0] )
																		if cols == 0:
																									raise error
																		for row in rows:
																									if len(snake_case ) != cols:
																																raise error
																									for value in row:
																																if not isinstance(snake_case   ,     (int, float) ):
																																							raise error
																		snake_case_           =					rows
											else:
																		snake_case_           =					[]




				def 					a							(      self ):
											return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]




				@property
				def 					a							(      self ):
											return len(self.rows )




				@property
				def 					a							(      self ):
											return len(self.rows[0] )




				@property
				def 					a							(      self ):
											return (self.num_rows, self.num_columns)




				@property
				def 					a							(      self ):
											return self.order[0] == self.order[1]




				def 					a							(      self ):
											snake_case_           =					[
											    [0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
											    for row_num in range(self.num_rows )
											]
											return Matrix(snake_case )




				def 					a							(      self ):
											if not self.is_square:
																		return 0
											if self.order == (0, 0):
																		return 1
											if self.order == (1, 1):
																		return int(self.rows[0][0] )
											if self.order == (2, 2):
																		return int(
																		    (self.rows[0][0] * self.rows[1][1])
																		    - (self.rows[0][1] * self.rows[1][0]) )
											else:
																		return sum(
																		    self.rows[0][column] * self.cofactors().rows[0][column]
																		    for column in range(self.num_columns ) )




				def 					a							(      self ):
											return bool(self.determinant() )




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					[
											    [
											        self.rows[other_row][other_column]
											        for other_column in range(self.num_columns )
											        if other_column != column
											    ]
											    for other_row in range(self.num_rows )
											    if other_row != row
											]
											return Matrix(snake_case ).determinant()




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											if (row + column) % 2 == 0:
																		return self.get_minor(snake_case   ,     snake_case )
											return -1 * self.get_minor(snake_case   ,     snake_case )




				def 					a							(      self ):
											return Matrix(
											    [
											        [self.get_minor(snake_case   ,     snake_case ) for column in range(self.num_columns )]
											        for row in range(self.num_rows )
											    ] )




				def 					a							(      self ):
											return Matrix(
											    [
											        [
											            self.minors().rows[row][column]
											            if (row + column) % 2 == 0
											            else self.minors().rows[row][column] * -1
											            for column in range(self.minors().num_columns )
											        ]
											        for row in range(self.minors().num_rows )
											    ] )




				def 					a							(      self ):
											snake_case_           =					[
											    [self.cofactors().rows[column][row] for column in range(self.num_columns )]
											    for row in range(self.num_rows )
											]
											return Matrix(snake_case )




				def 					a							(      self ):
											snake_case_           =					self.determinant()
											if not determinant:
																		raise TypeError('Only matrices with a non-zero determinant have an inverse' )
											return self.adjugate() * (1 / determinant)




				def __repr__(      self ):
											return str(self.rows )




				def __str__(      self ):
											if self.num_rows == 0:
																		return "[]"
											if self.num_rows == 1:
																		return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
											return (
											    "["
											    + "\n ".join(
											        [
											            '[' + '. '.join([str(snake_case ) for value in row] ) + '.]'
											            for row in self.rows
											        ] )
											    + "]"
											)




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					TypeError('Row must be a list containing all ints and/or floats' )
											if not isinstance(snake_case   ,     snake_case ):
																		raise type_error
											for value in row:
																		if not isinstance(snake_case   ,     (int, float) ):
																									raise type_error
											if len(snake_case ) != self.num_columns:
																		raise ValueError(
																		    'Row must be equal in length to the other rows in the matrix' )
											if position is None:
																		self.rows.append(snake_case )
											else:
																		snake_case_           =					self.rows[0:position] + [row] + self.rows[position:]




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					TypeError(
											    'Column must be a list containing all ints and/or floats' )
											if not isinstance(snake_case   ,     snake_case ):
																		raise type_error
											for value in column:
																		if not isinstance(snake_case   ,     (int, float) ):
																									raise type_error
											if len(snake_case ) != self.num_rows:
																		raise ValueError(
																		    'Column must be equal in length to the other columns in the matrix' )
											if position is None:
																		snake_case_           =					[self.rows[i] + [column[i]] for i in range(self.num_rows )]
											else:
																		snake_case_           =					[
																		    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
																		    for i in range(self.num_rows )
																		]




				def __eq__(      self   ,     snake_case ):
											if not isinstance(snake_case   ,     snake_case ):
																		return NotImplemented
											return self.rows == other.rows




				def __ne__(      self   ,     snake_case ):
											return not self == other




				def __neg__(      self ):
											return self * -1




				def __add__(      self   ,     snake_case ):
											if self.order != other.order:
																		raise ValueError('Addition requires matrices of the same order' )
											return Matrix(
											    [
											        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
											        for i in range(self.num_rows )
											    ] )




				def __sub__(      self   ,     snake_case ):
											if self.order != other.order:
																		raise ValueError('Subtraction requires matrices of the same order' )
											return Matrix(
											    [
											        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
											        for i in range(self.num_rows )
											    ] )




				def __mul__(      self   ,     snake_case ):
											if isinstance(snake_case   ,     (int, float) ):
																		return Matrix(
																		    [[int(element * other ) for element in row] for row in self.rows] )
											elif isinstance(snake_case   ,     snake_case ):
																		if self.num_columns != other.num_rows:
																									raise ValueError(
																									    'The number of columns in the first matrix must '
																									    'be equal to the number of rows in the second' )
																		return Matrix(
																		    [
																		        [Matrix.dot_product(snake_case   ,     snake_case ) for column in other.columns()]
																		        for row in self.rows
																		    ] )
											else:
																		raise TypeError(
																		    'A Matrix can only be multiplied by an int, float, or another matrix' )




				def __pow__(      self   ,     snake_case ):
											if not isinstance(snake_case   ,     snake_case ):
																		raise TypeError('A Matrix can only be raised to the power of an int' )
											if not self.is_square:
																		raise ValueError('Only square matrices can be raised to a power' )
											if other == 0:
																		return self.identity()
											if other < 0:
																		if self.is_invertable():
																									return self.inverse() ** (-other)
																		raise ValueError(
																		    'Only invertable matrices can be raised to a negative power' )
											snake_case_           =					self
											for _ in range(other - 1 ):
																		result *= self
											return result




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     snake_case ):
											return sum(row[i] * column[i] for i in range(len(snake_case ) ) )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




