"""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 		__A							(				SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_	):






     @register_to_config
     def __init__(	self       ,  a__ = 128       ,  a__ = 256       ,  a__ = 2_0_0_0.0       ,  a__ = 768       ,  a__ = 12       ,  a__ = 12       ,  a__ = 64       ,  a__ = 2048       ,  a__ = 0.1       ,  ):
       super().__init__()

       _lowerCAmelCase		: str						    =  nn.Sequential(
           nn.Linear(a__       ,  d_model * 4       ,  bias=a__						)       ,  nn.SiLU()       ,  nn.Linear(d_model * 4       ,  d_model * 4       ,  bias=a__						)       ,  nn.SiLU()       ,  )

       _lowerCAmelCase		: Optional[Any]						    =  nn.Embedding(a__       ,  a__						)
       _lowerCAmelCase		: str						    =  False

       _lowerCAmelCase		: Optional[int]						    =  nn.Linear(a__       ,  a__       ,  bias=a__						)

       _lowerCAmelCase		: int						    =  nn.Dropout(p=a__						)

       _lowerCAmelCase		: str						    =  nn.ModuleList()
       for lyr_num in range(a__						):
         # FiLM conditional T5 decoder
         _lowerCAmelCase		: Optional[int]						    =  DecoderLayer(d_model=a__       ,  d_kv=a__       ,  num_heads=a__       ,  d_ff=a__       ,  dropout_rate=a__						)
         self.decoders.append(a__						)

       _lowerCAmelCase		: Union[str, Any]						    =  TaLayerNorm(a__						)

       _lowerCAmelCase		: List[str]						    =  nn.Dropout(p=a__						)
       _lowerCAmelCase		: Dict						    =  nn.Linear(a__       ,  a__       ,  bias=a__						)






     def 					__A						(	self       ,  a__       ,  a__						):
       _lowerCAmelCase		: int						    =  torch.mul(query_input.unsqueeze(-1						)       ,  key_input.unsqueeze(-2						)						)
       return mask.unsqueeze(-3						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  decoder_input_tokens.shape
       assert decoder_noise_time.shape == (batch,)

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

       _lowerCAmelCase		: List[Any]						    =  self.conditioning_emb(a__						).unsqueeze(1						)

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

       _lowerCAmelCase		: Tuple						    =  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.
       _lowerCAmelCase		: str						    =  torch.broadcast_to(
           torch.arange(a__       ,  device=decoder_input_tokens.device						)       ,  (batch, seq_length)       ,  )

       _lowerCAmelCase		: List[Any]						    =  self.position_encoding(a__						)

       _lowerCAmelCase		: Dict						    =  self.continuous_inputs_projection(a__						)
       inputs += position_encodings
       _lowerCAmelCase		: int						    =  self.dropout(a__						)

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

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

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

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

       _lowerCAmelCase		: List[str]						    =  self.decoder_norm(a__						)
       _lowerCAmelCase		: Optional[int]						    =  self.post_dropout(a__						)

       _lowerCAmelCase		: int						    =  self.spec_out(a__						)
       return spec_out







class 		__A							(				nn.Module	):






     def __init__(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__=1e-6						):
       super().__init__()
       _lowerCAmelCase		: Any						    =  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 					__A						(	self       ,  a__       ,  a__=None       ,  a__=None       ,  a__=None       ,  a__=None       ,  a__=None       ,  ):
       _lowerCAmelCase		: List[Any]						    =  self.layer[0](
           a__       ,  conditioning_emb=a__       ,  attention_mask=a__       ,  )

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

         _lowerCAmelCase		: str						    =  self.layer[1](
             a__       ,  key_value_states=a__       ,  attention_mask=a__       ,  )

       # Apply Film Conditional Feed Forward layer
       _lowerCAmelCase		: Optional[Any]						    =  self.layer[-1](a__       ,  a__						)

       return (hidden_states,)







class 		__A							(				nn.Module	):






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






     def 					__A						(	self       ,  a__       ,  a__=None       ,  a__=None       ,  ):
       # pre_self_attention_layer_norm
       _lowerCAmelCase		: List[str]						    =  self.layer_norm(a__						)

       if conditioning_emb is not None:
         _lowerCAmelCase		: Tuple						    =  self.FiLMLayer(a__       ,  a__						)

       # Self-attention block
       _lowerCAmelCase		: List[str]						    =  self.attention(a__						)

       _lowerCAmelCase		: str						    =  hidden_states + self.dropout(a__						)

       return hidden_states







class 		__A							(				nn.Module	):






     def __init__(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__						):
       super().__init__()
       _lowerCAmelCase		: Tuple						    =  Attention(query_dim=a__       ,  heads=a__       ,  dim_head=a__       ,  out_bias=a__       ,  scale_qk=a__						)
       _lowerCAmelCase		: Any						    =  TaLayerNorm(a__       ,  eps=a__						)
       _lowerCAmelCase		: Tuple						    =  nn.Dropout(a__						)






     def 					__A						(	self       ,  a__       ,  a__=None       ,  a__=None       ,  ):
       _lowerCAmelCase		: int						    =  self.layer_norm(a__						)
       _lowerCAmelCase		: List[str]						    =  self.attention(
           a__       ,  encoder_hidden_states=a__       ,  attention_mask=attention_mask.squeeze(1						)       ,  )
       _lowerCAmelCase		: Dict						    =  hidden_states + self.dropout(a__						)
       return layer_output







class 		__A							(				nn.Module	):






     def __init__(	self       ,  a__       ,  a__       ,  a__       ,  a__						):
       super().__init__()
       _lowerCAmelCase		: Optional[int]						    =  TaDenseGatedActDense(d_model=a__       ,  d_ff=a__       ,  dropout_rate=a__						)
       _lowerCAmelCase		: str						    =  TaFiLMLayer(in_features=d_model * 4       ,  out_features=a__						)
       _lowerCAmelCase		: Dict						    =  TaLayerNorm(a__       ,  eps=a__						)
       _lowerCAmelCase		: int						    =  nn.Dropout(a__						)






     def 					__A						(	self       ,  a__       ,  a__=None						):
       _lowerCAmelCase		: Optional[int]						    =  self.layer_norm(a__						)
       if conditioning_emb is not None:
         _lowerCAmelCase		: Any						    =  self.film(a__       ,  a__						)

       _lowerCAmelCase		: Optional[int]						    =  self.DenseReluDense(a__						)
       _lowerCAmelCase		: Tuple						    =  hidden_states + self.dropout(a__						)
       return hidden_states







class 		__A							(				nn.Module	):






     def __init__(	self       ,  a__       ,  a__       ,  a__						):
       super().__init__()
       _lowerCAmelCase		: int						    =  nn.Linear(a__       ,  a__       ,  bias=a__						)
       _lowerCAmelCase		: List[str]						    =  nn.Linear(a__       ,  a__       ,  bias=a__						)
       _lowerCAmelCase		: Any						    =  nn.Linear(a__       ,  a__       ,  bias=a__						)
       _lowerCAmelCase		: Dict						    =  nn.Dropout(a__						)
       _lowerCAmelCase		: Any						    =  NewGELUActivation()






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Tuple						    =  self.act(self.wi_a(a__						)						)
       _lowerCAmelCase		: Dict						    =  self.wi_a(a__						)
       _lowerCAmelCase		: Any						    =  hidden_gelu * hidden_linear
       _lowerCAmelCase		: Optional[Any]						    =  self.dropout(a__						)

       _lowerCAmelCase		: List[str]						    =  self.wo(a__						)
       return hidden_states







class 		__A							(				nn.Module	):






     def __init__(	self       ,  a__       ,  a__=1e-6						):
       super().__init__()
       _lowerCAmelCase		: Optional[Any]						    =  nn.Parameter(torch.ones(a__						)						)
       _lowerCAmelCase		: str						    =  eps






     def 					__A						(	self       ,  a__						):
       # 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

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

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

       return self.weight * hidden_states







class 		__A							(				nn.Module	):






     def 					__A						(	self       ,  a__						):
       return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi						) * (input + 0.0_4_4_7_1_5 * torch.pow(a__       ,  3.0						))						))







class 		__A							(				nn.Module	):






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






     def 					__A						(	self       ,  a__       ,  a__						):
       _lowerCAmelCase		: Optional[int]						    =  self.scale_bias(a__						)
       _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  torch.chunk(a__       ,  2       ,  -1						)
       _lowerCAmelCase		: str						    =  x * (1 + scale) + shift
       return x





"""simple docstring"""




_a   :	Optional[Any] 					=     '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_a   :	List[str] 					=     [{'type': 'code', 'content': INSTALL_CONTENT}]
_a   :	str 					=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




