


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 		_snake_case  (				_snake_case						,   _snake_case							):




 @register_to_config
 def __init__(		self ,  _lowerCamelCase = 128 ,  _lowerCamelCase = 256 ,  _lowerCamelCase = 2000.0 ,  _lowerCamelCase = 768 ,  _lowerCamelCase = 12 ,  _lowerCamelCase = 12 ,  _lowerCamelCase = 64 ,  _lowerCamelCase = 2048 ,  _lowerCamelCase = 0.1 ,  ):
       super().__init__()

       a						:Union[str, Any]										=	nn.Sequential(
           nn.Linear(_lowerCamelCase ,  d_model * 4 ,  bias=_lowerCamelCase   ) ,  nn.SiLU() ,  nn.Linear(d_model * 4 ,  d_model * 4 ,  bias=_lowerCamelCase   ) ,  nn.SiLU() ,  )

       a						:Union[str, Any]										=	nn.Embedding(_lowerCamelCase ,  _lowerCamelCase   )
       a						:Tuple										=	False

       a						:Union[str, Any]										=	nn.Linear(_lowerCamelCase ,  _lowerCamelCase ,  bias=_lowerCamelCase   )

       a						:Any										=	nn.Dropout(p=_lowerCamelCase   )

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

       a						:Union[str, Any]										=	TaLayerNorm(_lowerCamelCase   )

       a						:Optional[int]										=	nn.Dropout(p=_lowerCamelCase   )
       a						:str										=	nn.Linear(_lowerCamelCase ,  _lowerCamelCase ,  bias=_lowerCamelCase   )




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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a							,    a							,    a						:int										=	decoder_input_tokens.shape
       assert decoder_noise_time.shape == (batch,)

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

       a						:Any										=	self.conditioning_emb(_lowerCamelCase   ).unsqueeze(1   )

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

       a						:str										=	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.
       a						:Any										=	torch.broadcast_to(
           torch.arange(_lowerCamelCase ,  device=decoder_input_tokens.device   ) ,  (batch, seq_length) ,  )

       a						:Tuple										=	self.position_encoding(_lowerCamelCase   )

       a						:Any										=	self.continuous_inputs_projection(_lowerCamelCase   )
       inputs += position_encodings
       a						:int										=	self.dropout(_lowerCamelCase   )

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

       # Translate encoding masks to encoder-decoder masks.
       a						:List[Any]										=	[(x, self.encoder_decoder_mask(_lowerCamelCase ,  _lowerCamelCase   )) for x, y in encodings_and_masks]

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

       for lyr in self.decoders:
             a						:List[str]										=	lyr(
                 _lowerCamelCase ,  conditioning_emb=_lowerCamelCase ,  encoder_hidden_states=_lowerCamelCase ,  encoder_attention_mask=_lowerCamelCase ,  )[0]

       a						:Tuple										=	self.decoder_norm(_lowerCamelCase   )
       a						:str										=	self.post_dropout(_lowerCamelCase   )

       a						:Tuple										=	self.spec_out(_lowerCamelCase   )
       return spec_out







class 		_snake_case  (				nn.Module							):




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase=1e-6   ):
       super().__init__()
       a						:List[str]										=	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 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  ):
       a						:str										=	self.layer[0](
           _lowerCamelCase ,  conditioning_emb=_lowerCamelCase ,  attention_mask=_lowerCamelCase ,  )

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

             a						:Any										=	self.layer[1](
                 _lowerCamelCase ,  key_value_states=_lowerCamelCase ,  attention_mask=_lowerCamelCase ,  )

       # Apply Film Conditional Feed Forward layer
       a						:int										=	self.layer[-1](_lowerCamelCase ,  _lowerCamelCase   )

       return (hidden_states,)







class 		_snake_case  (				nn.Module							):




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       super().__init__()
       a						:Any										=	TaLayerNorm(_lowerCamelCase   )
       a						:Union[str, Any]										=	TaFiLMLayer(in_features=d_model * 4 ,  out_features=_lowerCamelCase   )
       a						:str										=	Attention(query_dim=_lowerCamelCase ,  heads=_lowerCamelCase ,  dim_head=_lowerCamelCase ,  out_bias=_lowerCamelCase ,  scale_qk=_lowerCamelCase   )
       a						:Optional[Any]										=	nn.Dropout(_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  ):
       # pre_self_attention_layer_norm
       a						:int										=	self.layer_norm(_lowerCamelCase   )

       if conditioning_emb is not None:
             a						:Dict										=	self.FiLMLayer(_lowerCamelCase ,  _lowerCamelCase   )

       # Self-attention block
       a						:int										=	self.attention(_lowerCamelCase   )

       a						:str										=	hidden_states + self.dropout(_lowerCamelCase   )

       return hidden_states







class 		_snake_case  (				nn.Module							):




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       super().__init__()
       a						:Optional[Any]										=	Attention(query_dim=_lowerCamelCase ,  heads=_lowerCamelCase ,  dim_head=_lowerCamelCase ,  out_bias=_lowerCamelCase ,  scale_qk=_lowerCamelCase   )
       a						:List[Any]										=	TaLayerNorm(_lowerCamelCase ,  eps=_lowerCamelCase   )
       a						:str										=	nn.Dropout(_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  ):
       a						:Optional[int]										=	self.layer_norm(_lowerCamelCase   )
       a						:int										=	self.attention(
           _lowerCamelCase ,  encoder_hidden_states=_lowerCamelCase ,  attention_mask=attention_mask.squeeze(1   ) ,  )
       a						:Optional[int]										=	hidden_states + self.dropout(_lowerCamelCase   )
       return layer_output







class 		_snake_case  (				nn.Module							):




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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None   ):
       a						:str										=	self.layer_norm(_lowerCamelCase   )
       if conditioning_emb is not None:
             a						:Union[str, Any]										=	self.film(_lowerCamelCase ,  _lowerCamelCase   )

       a						:Any										=	self.DenseReluDense(_lowerCamelCase   )
       a						:Any										=	hidden_states + self.dropout(_lowerCamelCase   )
       return hidden_states







class 		_snake_case  (				nn.Module							):




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       super().__init__()
       a						:int										=	nn.Linear(_lowerCamelCase ,  _lowerCamelCase ,  bias=_lowerCamelCase   )
       a						:Optional[Any]										=	nn.Linear(_lowerCamelCase ,  _lowerCamelCase ,  bias=_lowerCamelCase   )
       a						:Union[str, Any]										=	nn.Linear(_lowerCamelCase ,  _lowerCamelCase ,  bias=_lowerCamelCase   )
       a						:Dict										=	nn.Dropout(_lowerCamelCase   )
       a						:Optional[Any]										=	NewGELUActivation()




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:List[Any]										=	self.act(self.wi_a(_lowerCamelCase   )   )
       a						:Any										=	self.wi_a(_lowerCamelCase   )
       a						:Union[str, Any]										=	hidden_gelu * hidden_linear
       a						:str										=	self.dropout(_lowerCamelCase   )

       a						:Dict										=	self.wo(_lowerCamelCase   )
       return hidden_states







class 		_snake_case  (				nn.Module							):




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase=1e-6   ):
       super().__init__()
       a						:str										=	nn.Parameter(torch.ones(_lowerCamelCase   )   )
       a						:Dict										=	eps




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       # 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

       a						:Any										=	hidden_states.to(torch.floataa   ).pow(2   ).mean(-1 ,  keepdim=_lowerCamelCase   )
       a						:List[Any]										=	hidden_states * torch.rsqrt(variance + self.variance_epsilon   )

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

       return self.weight * hidden_states







class 		_snake_case  (				nn.Module							):




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.04_4715 * torch.pow(_lowerCamelCase ,  3.0   ))   ))







class 		_snake_case  (				nn.Module							):




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
       super().__init__()
       a						:List[Any]										=	nn.Linear(_lowerCamelCase ,  out_features * 2 ,  bias=_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:str										=	self.scale_bias(_lowerCamelCase   )
       a							,    a						:Any										=	torch.chunk(_lowerCamelCase ,  2 ,  -1   )
       a						:Optional[int]										=	x * (1 + scale) + shift
       return x









from ...configuration_utils import PretrainedConfig







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'bert-generation'




 def __init__(		self ,  _lowerCamelCase=5_0358 ,  _lowerCamelCase=1024 ,  _lowerCamelCase=24 ,  _lowerCamelCase=16 ,  _lowerCamelCase=4096 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=512 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-12 ,  _lowerCamelCase=0 ,  _lowerCamelCase=2 ,  _lowerCamelCase=1 ,  _lowerCamelCase="absolute" ,  _lowerCamelCase=True ,  **_lowerCamelCase ,  ):
       super().__init__(pad_token_id=_lowerCamelCase ,  bos_token_id=_lowerCamelCase ,  eos_token_id=_lowerCamelCase ,  **_lowerCamelCase   )

       a						:Optional[int]										=	vocab_size
       a						:Tuple										=	hidden_size
       a						:Any										=	num_hidden_layers
       a						:Any										=	num_attention_heads
       a						:List[Any]										=	hidden_act
       a						:Tuple										=	intermediate_size
       a						:Any										=	hidden_dropout_prob
       a						:int										=	attention_probs_dropout_prob
       a						:Dict										=	max_position_embeddings
       a						:int										=	initializer_range
       a						:Union[str, Any]										=	layer_norm_eps
       a						:str										=	position_embedding_type
       a						:int										=	use_cache





