


"""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 		snake_case_(       a__							,					a__			):






     @register_to_config
     def __init__(	self  :					str					,				UpperCamelCase_  :					int = 1_2_8					,				UpperCamelCase_  :					int = 2_5_6					,				UpperCamelCase_  :					float = 2_000.0					,				UpperCamelCase_  :					int = 7_6_8					,				UpperCamelCase_  :					int = 1_2					,				UpperCamelCase_  :					int = 1_2					,				UpperCamelCase_  :					int = 6_4					,				UpperCamelCase_  :					int = 2_0_4_8					,				UpperCamelCase_  :					float = 0.1					,				):
           super().__init__()

           lowerCAmelCase					:	Optional[int]       =   nn.Sequential(
               nn.Linear(UpperCamelCase_					,				d_model * 4					,				bias=UpperCamelCase_				)					,				nn.SiLU()					,				nn.Linear(d_model * 4					,				d_model * 4					,				bias=UpperCamelCase_				)					,				nn.SiLU()					,				)

           lowerCAmelCase					:	Dict       =   nn.Embedding(UpperCamelCase_					,				UpperCamelCase_				)
           lowerCAmelCase					:	str       =   False

           lowerCAmelCase					:	Optional[Any]       =   nn.Linear(UpperCamelCase_					,				UpperCamelCase_					,				bias=UpperCamelCase_				)

           lowerCAmelCase					:	Union[str, Any]       =   nn.Dropout(p=UpperCamelCase_				)

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

           lowerCAmelCase					:	Union[str, Any]       =   TaLayerNorm(UpperCamelCase_				)

           lowerCAmelCase					:	Optional[int]       =   nn.Dropout(p=UpperCamelCase_				)
           lowerCAmelCase					:	int       =   nn.Linear(UpperCamelCase_					,				UpperCamelCase_					,				bias=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					List[str]				):
           lowerCAmelCase					:	Union[str, Any]       =   torch.mul(query_input.unsqueeze(-1				)					,				key_input.unsqueeze(-2				)				)
           return mask.unsqueeze(-3				)






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					str				):
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	Tuple       =   decoder_input_tokens.shape
           assert decoder_noise_time.shape == (batch,)

           # decoder_noise_time is in [0, 1), so rescale to expected timing range.
           lowerCAmelCase					:	Union[str, Any]       =   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					:	str       =   self.conditioning_emb(UpperCamelCase_				).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					:	int       =   torch.broadcast_to(
               torch.arange(UpperCamelCase_					,				device=decoder_input_tokens.device				)					,				(batch, seq_length)					,				)

           lowerCAmelCase					:	Any       =   self.position_encoding(UpperCamelCase_				)

           lowerCAmelCase					:	Tuple       =   self.continuous_inputs_projection(UpperCamelCase_				)
           inputs += position_encodings
           lowerCAmelCase					:	Tuple       =   self.dropout(UpperCamelCase_				)

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

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

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

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

           lowerCAmelCase					:	List[str]       =   self.decoder_norm(UpperCamelCase_				)
           lowerCAmelCase					:	Any       =   self.post_dropout(UpperCamelCase_				)

           lowerCAmelCase					:	int       =   self.spec_out(UpperCamelCase_				)
           return spec_out



class 		snake_case_(       nn.Module			):






     def __init__(	self  :					Optional[int]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					Any=1E-6				):
           super().__init__()
           lowerCAmelCase					:	Union[str, Any]       =   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  :					Dict					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Optional[int]=None					,				UpperCamelCase_  :					Tuple=None					,				UpperCamelCase_  :					Optional[int]=None					,				UpperCamelCase_  :					List[Any]=None					,				UpperCamelCase_  :					Any=None					,				):
           lowerCAmelCase					:	Optional[int]       =   self.layer[0](
               UpperCamelCase_					,				conditioning_emb=UpperCamelCase_					,				attention_mask=UpperCamelCase_					,				)

           if encoder_hidden_states is not None:
                 lowerCAmelCase					:	List[str]       =   torch.where(encoder_attention_mask > 0					,				0					,				-1E10				).to(
                     encoder_hidden_states.dtype				)

                 lowerCAmelCase					:	Optional[int]       =   self.layer[1](
                     UpperCamelCase_					,				key_value_states=UpperCamelCase_					,				attention_mask=UpperCamelCase_					,				)

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

           return (hidden_states,)



class 		snake_case_(       nn.Module			):






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






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					List[Any]=None					,				UpperCamelCase_  :					Optional[Any]=None					,				):
           # pre_self_attention_layer_norm
           lowerCAmelCase					:	List[str]       =   self.layer_norm(UpperCamelCase_				)

           if conditioning_emb is not None:
                 lowerCAmelCase					:	Optional[Any]       =   self.FiLMLayer(UpperCamelCase_					,				UpperCamelCase_				)

           # Self-attention block
           lowerCAmelCase					:	int       =   self.attention(UpperCamelCase_				)

           lowerCAmelCase					:	str       =   hidden_states + self.dropout(UpperCamelCase_				)

           return hidden_states



class 		snake_case_(       nn.Module			):






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






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					Tuple=None					,				UpperCamelCase_  :					List[str]=None					,				):
           lowerCAmelCase					:	Optional[Any]       =   self.layer_norm(UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   self.attention(
               UpperCamelCase_					,				encoder_hidden_states=UpperCamelCase_					,				attention_mask=attention_mask.squeeze(1				)					,				)
           lowerCAmelCase					:	Tuple       =   hidden_states + self.dropout(UpperCamelCase_				)
           return layer_output



class 		snake_case_(       nn.Module			):






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






     def      lowerCamelCase__     (	self  :					str					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					List[Any]=None				):
           lowerCAmelCase					:	str       =   self.layer_norm(UpperCamelCase_				)
           if conditioning_emb is not None:
                 lowerCAmelCase					:	Tuple       =   self.film(UpperCamelCase_					,				UpperCamelCase_				)

           lowerCAmelCase					:	Tuple       =   self.DenseReluDense(UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   hidden_states + self.dropout(UpperCamelCase_				)
           return hidden_states



class 		snake_case_(       nn.Module			):






     def __init__(	self  :					Any					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					List[Any]				):
           super().__init__()
           lowerCAmelCase					:	Union[str, Any]       =   nn.Linear(UpperCamelCase_					,				UpperCamelCase_					,				bias=UpperCamelCase_				)
           lowerCAmelCase					:	Any       =   nn.Linear(UpperCamelCase_					,				UpperCamelCase_					,				bias=UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   nn.Linear(UpperCamelCase_					,				UpperCamelCase_					,				bias=UpperCamelCase_				)
           lowerCAmelCase					:	Dict       =   nn.Dropout(UpperCamelCase_				)
           lowerCAmelCase					:	Any       =   NewGELUActivation()






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					Optional[Any]				):
           lowerCAmelCase					:	str       =   self.act(self.wi_a(UpperCamelCase_				)				)
           lowerCAmelCase					:	Tuple       =   self.wi_a(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[int]       =   hidden_gelu * hidden_linear
           lowerCAmelCase					:	Union[str, Any]       =   self.dropout(UpperCamelCase_				)

           lowerCAmelCase					:	Dict       =   self.wo(UpperCamelCase_				)
           return hidden_states



class 		snake_case_(       nn.Module			):






     def __init__(	self  :					int					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Optional[Any]=1E-6				):
           super().__init__()
           lowerCAmelCase					:	Optional[int]       =   nn.Parameter(torch.ones(UpperCamelCase_				)				)
           lowerCAmelCase					:	Optional[Any]       =   eps






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Optional[int]				):
           # 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					:	Optional[Any]       =   hidden_states.to(torch.floataa				).pow(2				).mean(-1					,				keepdim=UpperCamelCase_				)
           lowerCAmelCase					:	str       =   hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

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

           return self.weight * hidden_states



class 		snake_case_(       nn.Module			):






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					torch.Tensor				):
           return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.044_715 * torch.pow(UpperCamelCase_					,				3.0				))				))



class 		snake_case_(       nn.Module			):






     def __init__(	self  :					int					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					List[str]				):
           super().__init__()
           lowerCAmelCase					:	Optional[Any]       =   nn.Linear(UpperCamelCase_					,				out_features * 2					,				bias=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Optional[int]				):
           lowerCAmelCase					:	Tuple       =   self.scale_bias(UpperCamelCase_				)
           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   torch.chunk(UpperCamelCase_					,				2					,				-1				)
           lowerCAmelCase					:	List[str]       =   x * (1 + scale) + shift
           return x






"""simple docstring"""





def      _snake_case						(						_snake_case				:			int ,   _snake_case				:			int			):
      return base * power(_snake_case ,   (exponent - 1)			) if exponent else 1


if __name__ == "__main__":
 print('''Raise base to the power of exponent using recursion...''')
 snake_case__		:     Union[str, Any]    				=				int(input('''Enter the base: ''').strip())
 snake_case__		:     Optional[Any]    				=				int(input('''Enter the exponent: ''').strip())
 snake_case__		:     Any    				=				power(base, abs(exponent))
 if exponent < 0:  # power() does not properly deal w/ negative exponents
  snake_case__		:     Dict    				=				1 / result
 print(f"""{base} to the power of {exponent} is {result}""")


