'''simple docstring'''

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						(						__lowerCamelCase ,				__lowerCamelCase ):


   @register_to_config
   def __init__(   self				:	List[str]   ,		__lowerCAmelCase				:	int = 128   ,		__lowerCAmelCase				:	int = 256   ,		__lowerCAmelCase				:	float = 2000.0   ,		__lowerCAmelCase				:	int = 768   ,		__lowerCAmelCase				:	int = 12   ,		__lowerCAmelCase				:	int = 12   ,		__lowerCAmelCase				:	int = 64   ,		__lowerCAmelCase				:	int = 2048   ,		__lowerCAmelCase				:	float = 0.1   ,		)      ->					Dict:
         super().__init__()

         lowercase_     =      nn.Sequential(
             nn.Linear(__lowerCAmelCase   ,		d_model * 4   ,		bias=__lowerCAmelCase)   ,		nn.SiLU()   ,		nn.Linear(d_model * 4   ,		d_model * 4   ,		bias=__lowerCAmelCase)   ,		nn.SiLU()   ,		)

         lowercase_     =      nn.Embedding(__lowerCAmelCase   ,		__lowerCAmelCase)
         lowercase_     =      False

         lowercase_     =      nn.Linear(__lowerCAmelCase   ,		__lowerCAmelCase   ,		bias=__lowerCAmelCase)

         lowercase_     =      nn.Dropout(p=__lowerCAmelCase)

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

         lowercase_     =      TaLayerNorm(__lowerCAmelCase)

         lowercase_     =      nn.Dropout(p=__lowerCAmelCase)
         lowercase_     =      nn.Linear(__lowerCAmelCase   ,		__lowerCAmelCase   ,		bias=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	Optional[Any])      ->					Tuple:
         lowercase_     =      torch.mul(query_input.unsqueeze(-1)   ,		key_input.unsqueeze(-2))
         return mask.unsqueeze(-3)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Optional[Any])      ->					Any:
         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(__lowerCAmelCase).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(__lowerCAmelCase   ,		device=decoder_input_tokens.device)   ,		(batch, seq_length)   ,		)

         lowercase_     =      self.position_encoding(__lowerCAmelCase)

         lowercase_     =      self.continuous_inputs_projection(__lowerCAmelCase)
         inputs += position_encodings
         lowercase_     =      self.dropout(__lowerCAmelCase)

         # 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(__lowerCAmelCase   ,		__lowerCAmelCase)) 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(
                   __lowerCAmelCase   ,		conditioning_emb=__lowerCAmelCase   ,		encoder_hidden_states=__lowerCAmelCase   ,		encoder_attention_mask=__lowerCAmelCase   ,		)[0]

         lowercase_     =      self.decoder_norm(__lowerCAmelCase)
         lowercase_     =      self.post_dropout(__lowerCAmelCase)

         lowercase_     =      self.spec_out(__lowerCAmelCase)
         return spec_out





class 							lowercase						(						nn.Module ):


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Union[str, Any]=1e-6)      ->					List[Any]:
         super().__init__()
         lowercase_     =      nn.ModuleList()

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

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

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


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Union[str, Any]=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	List[Any]=None   ,		__lowerCAmelCase				:	Tuple=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		)      ->					Optional[int]:
         lowercase_     =      self.layer[0](
             __lowerCAmelCase   ,		conditioning_emb=__lowerCAmelCase   ,		attention_mask=__lowerCAmelCase   ,		)

         if encoder_hidden_states is not None:
               lowercase_     =      torch.where(encoder_attention_mask > 0   ,		0   ,		-1e10).to(
                   encoder_hidden_states.dtype)

               lowercase_     =      self.layer[1](
                   __lowerCAmelCase   ,		key_value_states=__lowerCAmelCase   ,		attention_mask=__lowerCAmelCase   ,		)

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

         return (hidden_states,)





class 							lowercase						(						nn.Module ):


   def __init__(   self				:	Tuple   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	Any)      ->					str:
         super().__init__()
         lowercase_     =      TaLayerNorm(__lowerCAmelCase)
         lowercase_     =      TaFiLMLayer(in_features=d_model * 4   ,		out_features=__lowerCAmelCase)
         lowercase_     =      Attention(query_dim=__lowerCAmelCase   ,		heads=__lowerCAmelCase   ,		dim_head=__lowerCAmelCase   ,		out_bias=__lowerCAmelCase   ,		scale_qk=__lowerCAmelCase)
         lowercase_     =      nn.Dropout(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Any=None   ,		__lowerCAmelCase				:	List[Any]=None   ,		)      ->					Dict:
         # pre_self_attention_layer_norm
         lowercase_     =      self.layer_norm(__lowerCAmelCase)

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

         # Self-attention block
         lowercase_     =      self.attention(__lowerCAmelCase)

         lowercase_     =      hidden_states + self.dropout(__lowerCAmelCase)

         return hidden_states





class 							lowercase						(						nn.Module ):


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	List[str])      ->					List[Any]:
         super().__init__()
         lowercase_     =      Attention(query_dim=__lowerCAmelCase   ,		heads=__lowerCAmelCase   ,		dim_head=__lowerCAmelCase   ,		out_bias=__lowerCAmelCase   ,		scale_qk=__lowerCAmelCase)
         lowercase_     =      TaLayerNorm(__lowerCAmelCase   ,		eps=__lowerCAmelCase)
         lowercase_     =      nn.Dropout(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	str=None   ,		)      ->					List[Any]:
         lowercase_     =      self.layer_norm(__lowerCAmelCase)
         lowercase_     =      self.attention(
             __lowerCAmelCase   ,		encoder_hidden_states=__lowerCAmelCase   ,		attention_mask=attention_mask.squeeze(1)   ,		)
         lowercase_     =      hidden_states + self.dropout(__lowerCAmelCase)
         return layer_output





class 							lowercase						(						nn.Module ):


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	int)      ->					Any:
         super().__init__()
         lowercase_     =      TaDenseGatedActDense(d_model=__lowerCAmelCase   ,		d_ff=__lowerCAmelCase   ,		dropout_rate=__lowerCAmelCase)
         lowercase_     =      TaFiLMLayer(in_features=d_model * 4   ,		out_features=__lowerCAmelCase)
         lowercase_     =      TaLayerNorm(__lowerCAmelCase   ,		eps=__lowerCAmelCase)
         lowercase_     =      nn.Dropout(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Optional[int]=None)      ->					Union[str, Any]:
         lowercase_     =      self.layer_norm(__lowerCAmelCase)
         if conditioning_emb is not None:
               lowercase_     =      self.film(__lowerCAmelCase   ,		__lowerCAmelCase)

         lowercase_     =      self.DenseReluDense(__lowerCAmelCase)
         lowercase_     =      hidden_states + self.dropout(__lowerCAmelCase)
         return hidden_states





class 							lowercase						(						nn.Module ):


   def __init__(   self				:	Tuple   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Any)      ->					Union[str, Any]:
         super().__init__()
         lowercase_     =      nn.Linear(__lowerCAmelCase   ,		__lowerCAmelCase   ,		bias=__lowerCAmelCase)
         lowercase_     =      nn.Linear(__lowerCAmelCase   ,		__lowerCAmelCase   ,		bias=__lowerCAmelCase)
         lowercase_     =      nn.Linear(__lowerCAmelCase   ,		__lowerCAmelCase   ,		bias=__lowerCAmelCase)
         lowercase_     =      nn.Dropout(__lowerCAmelCase)
         lowercase_     =      NewGELUActivation()


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	Optional[int])      ->					int:
         lowercase_     =      self.act(self.wi_a(__lowerCAmelCase))
         lowercase_     =      self.wi_a(__lowerCAmelCase)
         lowercase_     =      hidden_gelu * hidden_linear
         lowercase_     =      self.dropout(__lowerCAmelCase)

         lowercase_     =      self.wo(__lowerCAmelCase)
         return hidden_states





class 							lowercase						(						nn.Module ):


   def __init__(   self				:	List[Any]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Optional[int]=1e-6)      ->					Union[str, Any]:
         super().__init__()
         lowercase_     =      nn.Parameter(torch.ones(__lowerCAmelCase))
         lowercase_     =      eps


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	List[str])      ->					Tuple:
         # 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_     =      hidden_states.to(torch.floataa).pow(2).mean(-1   ,		keepdim=__lowerCAmelCase)
         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 							lowercase						(						nn.Module ):


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	torch.Tensor)      ->					torch.Tensor:
         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.04_4715 * torch.pow(__lowerCAmelCase   ,		3.0))))





class 							lowercase						(						nn.Module ):


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	Optional[int])      ->					Dict:
         super().__init__()
         lowercase_     =      nn.Linear(__lowerCAmelCase   ,		out_features * 2   ,		bias=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	List[str])      ->					Dict:
         lowercase_     =      self.scale_bias(__lowerCAmelCase)
         lowercase_ ,							lowercase_     =      torch.chunk(__lowerCAmelCase   ,		2   ,		-1)
         lowercase_     =      x * (1 + scale) + shift
         return x







'''simple docstring'''

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowerCAmelCase_ : Dict						  =      TypeVar("T")





class 							lowercase						(						Generic[T] ):


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	bool = True)      ->					None:
         lowercase_     =      {}  # dictionary of lists
         lowercase_     =      directed


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	T   ,		__lowerCAmelCase				:	T)      ->					GraphAdjacencyList[T]:

         if not self.directed:  # For undirected graphs
               # if both source vertex and destination vertex are both present in the
               # adjacency list, add destination vertex to source vertex list of adjacent
               # vertices and add source vertex to destination vertex list of adjacent
               # vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination vertex
               # to source vertex list of adjacent vertices, then create a new vertex with
               # destination vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [source_vertex]
               # if only destination vertex is present in adjacency list, add source vertex
               # to destination vertex list of adjacent vertices, then create a new vertex
               # with source vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif destination_vertex in self.adj_list:
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and assign a list
               # containing the destination vertex as it's first adjacent vertex also
               # create a new vertex with destination vertex as key and assign a list
               # containing the source vertex as it's first adjacent vertex.
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      [source_vertex]
         else:  # For directed graphs
               # if both source vertex and destination vertex are present in adjacency
               # list, add destination vertex to source vertex list of adjacent vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination
               # vertex to source vertex list of adjacent vertices and create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      []
               # if only destination vertex is present in adjacency list, create a new
               # vertex with source vertex as key and assign a list containing destination
               # vertex as first adjacent vertex
               elif destination_vertex in self.adj_list:
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and a list containing
               # destination vertex as it's first adjacent vertex. Then create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      []

         return self


   def __repr__(   self				:	str)      ->					str:
         return pformat(self.adj_list)






