'''simple docstring'''

def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						if a < 0 or b < 0:
												raise ValueError('the value of both inputs must be positive'      )

						_lowerCAmelCase										=str(bin(_snake_case      )      )[2:]  # remove the leading "0b"
						_lowerCAmelCase										=str(bin(_snake_case      )      )[2:]  # remove the leading "0b"

						_lowerCAmelCase										=max(len(_snake_case      )			,      len(_snake_case      )      )

						return "0b" + "".join(
						    str(int(char_a == '1' and char_b == '1'      )      )
						    for char_a, char_b in zip(a_binary.zfill(_snake_case      )			,      b_binary.zfill(_snake_case      )      )      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()






'''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   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase):







		"""simple docstring"""






		@register_to_config
		def __init__(   self		,						__A = 128		,						__A = 256		,						__A = 2_000.0		,						__A = 768		,						__A = 12		,						__A = 12		,						__A = 64		,						__A = 2048		,						__A = 0.1		,						) ->     str:
								super().__init__()

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

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

								_lowerCAmelCase										=nn.Dropout(p=__A				)

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

								_lowerCAmelCase										=TaLayerNorm(__A				)

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






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






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=decoder_input_tokens.shape
								assert decoder_noise_time.shape == (batch,)

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

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

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

								_lowerCAmelCase										=self.position_encoding(__A				)

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

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

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

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

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

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

								_lowerCAmelCase										=self.spec_out(__A				)
								return spec_out

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A=1E-6				) ->     Union[str, Any]:
								super().__init__()
								_lowerCAmelCase										=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 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						) ->     Any:
								_lowerCAmelCase										=self.layer[0](
								    __A		,						conditioning_emb=__A		,						attention_mask=__A		,						)

								if encoder_hidden_states is not None:
														_lowerCAmelCase										=torch.where(encoder_attention_mask > 0		,						0		,						-1E10				).to(
														    encoder_hidden_states.dtype				)

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

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

								return (hidden_states,)

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






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






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						) ->     List[Any]:
								# pre_self_attention_layer_norm
								_lowerCAmelCase										=self.layer_norm(__A				)

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

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

								_lowerCAmelCase										=hidden_states + self.dropout(__A				)

								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     Optional[int]:
								super().__init__()
								_lowerCAmelCase										=Attention(query_dim=__A		,						heads=__A		,						dim_head=__A		,						out_bias=__A		,						scale_qk=__A				)
								_lowerCAmelCase										=TaLayerNorm(__A		,						eps=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						) ->     Tuple:
								_lowerCAmelCase										=self.layer_norm(__A				)
								_lowerCAmelCase										=self.attention(
								    __A		,						encoder_hidden_states=__A		,						attention_mask=attention_mask.squeeze(1				)		,						)
								_lowerCAmelCase										=hidden_states + self.dropout(__A				)
								return layer_output

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=TaDenseGatedActDense(d_model=__A		,						d_ff=__A		,						dropout_rate=__A				)
								_lowerCAmelCase										=TaFiLMLayer(in_features=d_model * 4		,						out_features=__A				)
								_lowerCAmelCase										=TaLayerNorm(__A		,						eps=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     List[Any]:
								_lowerCAmelCase										=self.layer_norm(__A				)
								if conditioning_emb is not None:
														_lowerCAmelCase										=self.film(__A		,						__A				)

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

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A				) ->     Union[str, Any]:
								super().__init__()
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)
								_lowerCAmelCase										=NewGELUActivation()






		def 		UpperCamelCase__       (   self		,						__A				) ->     List[Any]:
								_lowerCAmelCase										=self.act(self.wi_a(__A				)				)
								_lowerCAmelCase										=self.wi_a(__A				)
								_lowerCAmelCase										=hidden_gelu * hidden_linear
								_lowerCAmelCase										=self.dropout(__A				)

								_lowerCAmelCase										=self.wo(__A				)
								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A=1E-6				) ->     int:
								super().__init__()
								_lowerCAmelCase										=nn.Parameter(torch.ones(__A				)				)
								_lowerCAmelCase										=eps






		def 		UpperCamelCase__       (   self		,						__A				) ->     Dict:
								# 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										=hidden_states.to(torch.floataa				).pow(2				).mean(-1		,						keepdim=__A				)
								_lowerCAmelCase										=hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

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

								return self.weight * hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self		,						__A				) ->     torch.Tensor:
								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.044_715 * torch.pow(__A		,						3.0				))				))

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=nn.Linear(__A		,						out_features * 2		,						bias=__A				)






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





