






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_	):







     '''simple docstring'''




     @register_to_config
     def __init__(					self							:      str			,						UpperCamelCase_							:      int = 128			,						UpperCamelCase_							:      int = 256			,						UpperCamelCase_							:      float = 2000.0			,						UpperCamelCase_							:      int = 768			,						UpperCamelCase_							:      int = 12			,						UpperCamelCase_							:      int = 12			,						UpperCamelCase_							:      int = 64			,						UpperCamelCase_							:      int = 2_048			,						UpperCamelCase_							:      float = 0.1			,						):


         """simple docstring"""



         super().__init__()

         __A       =						nn.Sequential(
             nn.Linear(UpperCamelCase_			,						d_model * 4			,						bias=UpperCamelCase_					)			,						nn.SiLU()			,						nn.Linear(d_model * 4			,						d_model * 4			,						bias=UpperCamelCase_					)			,						nn.SiLU()			,						)

         __A       =						nn.Embedding(UpperCamelCase_			,						UpperCamelCase_					)
         __A       =						False

         __A       =						nn.Linear(UpperCamelCase_			,						UpperCamelCase_			,						bias=UpperCamelCase_					)

         __A       =						nn.Dropout(p=UpperCamelCase_					)

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

         __A       =						TaLayerNorm(UpperCamelCase_					)

         __A       =						nn.Dropout(p=UpperCamelCase_					)
         __A       =						nn.Linear(UpperCamelCase_			,						UpperCamelCase_			,						bias=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      List[str]					):


         """simple docstring"""



         __A       =						torch.mul(query_input.unsqueeze(-1					)			,						key_input.unsqueeze(-2					)					)
         return mask.unsqueeze(-3					)





     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      List[Any]					):


         """simple docstring"""



         __A     ,						__A     ,						__A       =						decoder_input_tokens.shape
         assert decoder_noise_time.shape == (batch,)

         # decoder_noise_time is in [0, 1), so rescale to expected timing range.
         __A       =						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       =						self.conditioning_emb(UpperCamelCase_					).unsqueeze(1					)

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

         __A       =						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       =						torch.broadcast_to(
             torch.arange(UpperCamelCase_			,						device=decoder_input_tokens.device					)			,						(batch, seq_length)			,						)

         __A       =						self.position_encoding(UpperCamelCase_					)

         __A       =						self.continuous_inputs_projection(UpperCamelCase_					)
         inputs += position_encodings
         __A       =						self.dropout(UpperCamelCase_					)

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

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

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

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

         __A       =						self.decoder_norm(UpperCamelCase_					)
         __A       =						self.post_dropout(UpperCamelCase_					)

         __A       =						self.spec_out(UpperCamelCase_					)
         return spec_out


class    __lowercase   (						nn.Module	):







     '''simple docstring'''




     def __init__(					self							:      int			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Any=1e-6					):


         """simple docstring"""



         super().__init__()
         __A       =						nn.ModuleList()

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

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

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





     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      List[str]=None			,						UpperCamelCase_							:      Union[str, Any]=None			,						UpperCamelCase_							:      Optional[Any]=None			,						UpperCamelCase_							:      str=None			,						UpperCamelCase_							:      str=None			,						):


         """simple docstring"""



         __A       =						self.layer[0](
             UpperCamelCase_			,						conditioning_emb=UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						)

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

             __A       =						self.layer[1](
                 UpperCamelCase_			,						key_value_states=UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						)

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

         return (hidden_states,)


class    __lowercase   (						nn.Module	):







     '''simple docstring'''




     def __init__(					self							:      Tuple			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Union[str, Any]					):


         """simple docstring"""



         super().__init__()
         __A       =						TaLayerNorm(UpperCamelCase_					)
         __A       =						TaFiLMLayer(in_features=d_model * 4			,						out_features=UpperCamelCase_					)
         __A       =						Attention(query_dim=UpperCamelCase_			,						heads=UpperCamelCase_			,						dim_head=UpperCamelCase_			,						out_bias=UpperCamelCase_			,						scale_qk=UpperCamelCase_					)
         __A       =						nn.Dropout(UpperCamelCase_					)





     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      List[str]=None			,						UpperCamelCase_							:      Optional[int]=None			,						):


         """simple docstring"""



         __A       =						self.layer_norm(UpperCamelCase_					)

         if conditioning_emb is not None:
             __A       =						self.FiLMLayer(UpperCamelCase_			,						UpperCamelCase_					)

         # Self-attention block
         __A       =						self.attention(UpperCamelCase_					)

         __A       =						hidden_states + self.dropout(UpperCamelCase_					)

         return hidden_states


class    __lowercase   (						nn.Module	):







     '''simple docstring'''




     def __init__(					self							:      List[Any]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



         super().__init__()
         __A       =						Attention(query_dim=UpperCamelCase_			,						heads=UpperCamelCase_			,						dim_head=UpperCamelCase_			,						out_bias=UpperCamelCase_			,						scale_qk=UpperCamelCase_					)
         __A       =						TaLayerNorm(UpperCamelCase_			,						eps=UpperCamelCase_					)
         __A       =						nn.Dropout(UpperCamelCase_					)





     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      str=None			,						UpperCamelCase_							:      Dict=None			,						):


         """simple docstring"""



         __A       =						self.layer_norm(UpperCamelCase_					)
         __A       =						self.attention(
             UpperCamelCase_			,						encoder_hidden_states=UpperCamelCase_			,						attention_mask=attention_mask.squeeze(1					)			,						)
         __A       =						hidden_states + self.dropout(UpperCamelCase_					)
         return layer_output


class    __lowercase   (						nn.Module	):







     '''simple docstring'''




     def __init__(					self							:      str			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         super().__init__()
         __A       =						TaDenseGatedActDense(d_model=UpperCamelCase_			,						d_ff=UpperCamelCase_			,						dropout_rate=UpperCamelCase_					)
         __A       =						TaFiLMLayer(in_features=d_model * 4			,						out_features=UpperCamelCase_					)
         __A       =						TaLayerNorm(UpperCamelCase_			,						eps=UpperCamelCase_					)
         __A       =						nn.Dropout(UpperCamelCase_					)





     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[Any]=None					):


         """simple docstring"""



         __A       =						self.layer_norm(UpperCamelCase_					)
         if conditioning_emb is not None:
             __A       =						self.film(UpperCamelCase_			,						UpperCamelCase_					)

         __A       =						self.DenseReluDense(UpperCamelCase_					)
         __A       =						hidden_states + self.dropout(UpperCamelCase_					)
         return hidden_states


class    __lowercase   (						nn.Module	):







     '''simple docstring'''




     def __init__(					self							:      int			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         super().__init__()
         __A       =						nn.Linear(UpperCamelCase_			,						UpperCamelCase_			,						bias=UpperCamelCase_					)
         __A       =						nn.Linear(UpperCamelCase_			,						UpperCamelCase_			,						bias=UpperCamelCase_					)
         __A       =						nn.Linear(UpperCamelCase_			,						UpperCamelCase_			,						bias=UpperCamelCase_					)
         __A       =						nn.Dropout(UpperCamelCase_					)
         __A       =						NewGELUActivation()





     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         __A       =						self.act(self.wi_a(UpperCamelCase_					)					)
         __A       =						self.wi_a(UpperCamelCase_					)
         __A       =						hidden_gelu * hidden_linear
         __A       =						self.dropout(UpperCamelCase_					)

         __A       =						self.wo(UpperCamelCase_					)
         return hidden_states


class    __lowercase   (						nn.Module	):







     '''simple docstring'''




     def __init__(					self							:      List[str]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Any=1e-6					):


         """simple docstring"""



         super().__init__()
         __A       =						nn.Parameter(torch.ones(UpperCamelCase_					)					)
         __A       =						eps





     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						UpperCamelCase_							:      List[str]					):


         """simple docstring"""



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

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

         return self.weight * hidden_states


class    __lowercase   (						nn.Module	):







     '''simple docstring'''




     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      torch.Tensor					):


         """simple docstring"""



         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi					) * (input + 0.044715 * torch.pow(UpperCamelCase_			,						3.0					))					))


class    __lowercase   (						nn.Module	):







     '''simple docstring'''




     def __init__(					self							:      Optional[Any]			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      List[Any]					):


         """simple docstring"""



         super().__init__()
         __A       =						nn.Linear(UpperCamelCase_			,						out_features * 2			,						bias=UpperCamelCase_					)





     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         __A       =						self.scale_bias(UpperCamelCase_					)
         __A     ,						__A       =						torch.chunk(UpperCamelCase_			,						2			,						-1					)
         __A       =						x * (1 + scale) + shift
         return x












def       _SCREAMING_SNAKE_CASE    (			__lowercase :      int     )    ->   list[int]:





    """simple docstring"""

    if length <= 0 or not isinstance(__lowercase      ,					__lowercase     ):
        raise ValueError("""Length must be a positive integer."""     )
    return [n * (2 * n - 1) for n in range(__lowercase     )]


if __name__ == "__main__":
  print(hexagonal_numbers(length=5))
  print(hexagonal_numbers(length=10))




