


"""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     _UpperCamelCase    (  lowerCAmelCase__			,lowerCAmelCase__						):
							'''simple docstring'''







							@register_to_config
							def __init__(	self       ,  __a = 1_28       ,  __a = 2_56       ,  __a = 2_0_0_0.0       ,  __a = 7_68       ,  __a = 12       ,  __a = 12       ,  __a = 64       ,  __a = 20_48       ,  __a = 0.1       ,  ):
										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      snake_case							(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     torch.mul(query_input.unsqueeze(-1      )       ,  key_input.unsqueeze(-2      )      )
										return mask.unsqueeze(-3      )



							def      snake_case							(	self       ,  __a       ,  __a       ,  __a      ):
										__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     _UpperCamelCase    (  nn.Module						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a=1e-6      ):
										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      snake_case							(	self       ,  __a       ,  __a=None       ,  __a=None       ,  __a=None       ,  __a=None       ,  __a=None       ,  ):
										__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       ,  -1e1_0      ).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     _UpperCamelCase    (  nn.Module						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a       ,  __a       ,  __a      ):
										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      snake_case							(	self       ,  __a       ,  __a=None       ,  __a=None       ,  ):
										# 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     _UpperCamelCase    (  nn.Module						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a      ):
										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      snake_case							(	self       ,  __a       ,  __a=None       ,  __a=None       ,  ):
										__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     _UpperCamelCase    (  nn.Module						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a       ,  __a       ,  __a      ):
										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      snake_case							(	self       ,  __a       ,  __a=None      ):
										__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     _UpperCamelCase    (  nn.Module						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a       ,  __a      ):
										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      snake_case							(	self       ,  __a      ):
										__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     _UpperCamelCase    (  nn.Module						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a=1e-6      ):
										super().__init__()
										__lowerCAmelCase							       =     nn.Parameter(torch.ones(__a      )      )
										__lowerCAmelCase							       =     eps



							def      snake_case							(	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							       =     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     _UpperCamelCase    (  nn.Module						):
							'''simple docstring'''







							def      snake_case							(	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     _UpperCamelCase    (  nn.Module						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										super().__init__()
										__lowerCAmelCase							       =     nn.Linear(__a       ,  out_features * 2       ,  bias=__a      )



							def      snake_case							(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     self.scale_bias(__a      )
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     torch.chunk(__a       ,  2       ,  -1      )
										__lowerCAmelCase							       =     x * (1 + scale) + shift
										return x








"""simple docstring"""


import baseaa


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			return baseaa.baaencode(string.encode("utf-8" ) )


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			return baseaa.baadecode(_UpperCamelCase ).decode("utf-8" )


if __name__ == "__main__":
	A	:  List[str]            =     "Hello World!"
	A	:  Any            =     baseaa_encode(test)
	print(encoded)

	A	:  List[Any]            =     baseaa_decode(encoded)
	print(decoded)




