


'''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							,							_lowerCamelCase = 128							,							_lowerCamelCase = 256							,							_lowerCamelCase = 2000.0							,							_lowerCamelCase = 768							,							_lowerCamelCase = 12							,							_lowerCamelCase = 12							,							_lowerCamelCase = 64							,							_lowerCamelCase = 2048							,							_lowerCamelCase = 0.1							,							):
								super().__init__()

								UpperCAmelCase__					:   List[str]       				=       nn.Sequential(
								    nn.Linear(_lowerCamelCase							,							d_model * 4							,							bias=_lowerCamelCase)							,							nn.SiLU()							,							nn.Linear(d_model * 4							,							d_model * 4							,							bias=_lowerCamelCase)							,							nn.SiLU()							,							)

								UpperCAmelCase__					:   Union[str, Any]       				=       nn.Embedding(_lowerCamelCase							,							_lowerCamelCase)
								UpperCAmelCase__					:   int       				=       False

								UpperCAmelCase__					:   Dict       				=       nn.Linear(_lowerCamelCase							,							_lowerCamelCase							,							bias=_lowerCamelCase)

								UpperCAmelCase__					:   Optional[int]       				=       nn.Dropout(p=_lowerCamelCase)

								UpperCAmelCase__					:   Dict       				=       nn.ModuleList()
								for lyr_num in range(_lowerCamelCase):
											# FiLM conditional T5 decoder
											UpperCAmelCase__					:   List[str]       				=       DecoderLayer(d_model=_lowerCamelCase							,							d_kv=_lowerCamelCase							,							num_heads=_lowerCamelCase							,							d_ff=_lowerCamelCase							,							dropout_rate=_lowerCamelCase)
											self.decoders.append(_lowerCamelCase)

								UpperCAmelCase__					:   Any       				=       TaLayerNorm(_lowerCamelCase)

								UpperCAmelCase__					:   Dict       				=       nn.Dropout(p=_lowerCamelCase)
								UpperCAmelCase__					:   int       				=       nn.Linear(_lowerCamelCase							,							_lowerCamelCase							,							bias=_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Union[str, Any]       				=       torch.mul(query_input.unsqueeze(-1)							,							key_input.unsqueeze(-2))
								return mask.unsqueeze(-3)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   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.
								UpperCAmelCase__					:   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)

								UpperCAmelCase__					:   Optional[int]       				=       self.conditioning_emb(_lowerCamelCase).unsqueeze(1)

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

								UpperCAmelCase__					:   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.
								UpperCAmelCase__					:   str       				=       torch.broadcast_to(
								    torch.arange(_lowerCamelCase							,							device=decoder_input_tokens.device)							,							(batch, seq_length)							,							)

								UpperCAmelCase__					:   Tuple       				=       self.position_encoding(_lowerCamelCase)

								UpperCAmelCase__					:   Optional[Any]       				=       self.continuous_inputs_projection(_lowerCamelCase)
								inputs += position_encodings
								UpperCAmelCase__					:   Dict       				=       self.dropout(_lowerCamelCase)

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

								# Translate encoding masks to encoder-decoder masks.
								UpperCAmelCase__					:   Tuple       				=       [(x, self.encoder_decoder_mask(_lowerCamelCase							,							_lowerCamelCase)) for x, y in encodings_and_masks]

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

								for lyr in self.decoders:
											UpperCAmelCase__					:   Optional[Any]       				=       lyr(
											    _lowerCamelCase							,							conditioning_emb=_lowerCamelCase							,							encoder_hidden_states=_lowerCamelCase							,							encoder_attention_mask=_lowerCamelCase							,							)[0]

								UpperCAmelCase__					:   Dict       				=       self.decoder_norm(_lowerCamelCase)
								UpperCAmelCase__					:   List[Any]       				=       self.post_dropout(_lowerCamelCase)

								UpperCAmelCase__					:   Any       				=       self.spec_out(_lowerCamelCase)
								return spec_out


class 						_snake_case   (       nn.Module		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase=1e-6):
								super().__init__()
								UpperCAmelCase__					:   Tuple       				=       nn.ModuleList()

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

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

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




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							):
								UpperCAmelCase__					:   Tuple       				=       self.layer[0](
								    _lowerCamelCase							,							conditioning_emb=_lowerCamelCase							,							attention_mask=_lowerCamelCase							,							)

								if encoder_hidden_states is not None:
											UpperCAmelCase__					:   List[Any]       				=       torch.where(encoder_attention_mask > 0							,							0							,							-1e1_0).to(
											    encoder_hidden_states.dtype)

											UpperCAmelCase__					:   int       				=       self.layer[1](
											    _lowerCamelCase							,							key_value_states=_lowerCamelCase							,							attention_mask=_lowerCamelCase							,							)

								# Apply Film Conditional Feed Forward layer
								UpperCAmelCase__					:   Union[str, Any]       				=       self.layer[-1](_lowerCamelCase							,							_lowerCamelCase)

								return (hidden_states,)


class 						_snake_case   (       nn.Module		):




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




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							):
								# pre_self_attention_layer_norm
								UpperCAmelCase__					:   int       				=       self.layer_norm(_lowerCamelCase)

								if conditioning_emb is not None:
											UpperCAmelCase__					:   int       				=       self.FiLMLayer(_lowerCamelCase							,							_lowerCamelCase)

								# Self-attention block
								UpperCAmelCase__					:   Dict       				=       self.attention(_lowerCamelCase)

								UpperCAmelCase__					:   List[Any]       				=       hidden_states + self.dropout(_lowerCamelCase)

								return hidden_states


class 						_snake_case   (       nn.Module		):




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




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							):
								UpperCAmelCase__					:   int       				=       self.layer_norm(_lowerCamelCase)
								UpperCAmelCase__					:   Union[str, Any]       				=       self.attention(
								    _lowerCamelCase							,							encoder_hidden_states=_lowerCamelCase							,							attention_mask=attention_mask.squeeze(1)							,							)
								UpperCAmelCase__					:   Union[str, Any]       				=       hidden_states + self.dropout(_lowerCamelCase)
								return layer_output


class 						_snake_case   (       nn.Module		):




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




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase=None):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.layer_norm(_lowerCamelCase)
								if conditioning_emb is not None:
											UpperCAmelCase__					:   Dict       				=       self.film(_lowerCamelCase							,							_lowerCamelCase)

								UpperCAmelCase__					:   Union[str, Any]       				=       self.DenseReluDense(_lowerCamelCase)
								UpperCAmelCase__					:   List[Any]       				=       hidden_states + self.dropout(_lowerCamelCase)
								return hidden_states


class 						_snake_case   (       nn.Module		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								super().__init__()
								UpperCAmelCase__					:   Any       				=       nn.Linear(_lowerCamelCase							,							_lowerCamelCase							,							bias=_lowerCamelCase)
								UpperCAmelCase__					:   List[Any]       				=       nn.Linear(_lowerCamelCase							,							_lowerCamelCase							,							bias=_lowerCamelCase)
								UpperCAmelCase__					:   Union[str, Any]       				=       nn.Linear(_lowerCamelCase							,							_lowerCamelCase							,							bias=_lowerCamelCase)
								UpperCAmelCase__					:   int       				=       nn.Dropout(_lowerCamelCase)
								UpperCAmelCase__					:   List[Any]       				=       NewGELUActivation()




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   Optional[int]       				=       self.act(self.wi_a(_lowerCamelCase))
								UpperCAmelCase__					:   Dict       				=       self.wi_a(_lowerCamelCase)
								UpperCAmelCase__					:   Union[str, Any]       				=       hidden_gelu * hidden_linear
								UpperCAmelCase__					:   Any       				=       self.dropout(_lowerCamelCase)

								UpperCAmelCase__					:   str       				=       self.wo(_lowerCamelCase)
								return hidden_states


class 						_snake_case   (       nn.Module		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=1e-6):
								super().__init__()
								UpperCAmelCase__					:   Optional[int]       				=       nn.Parameter(torch.ones(_lowerCamelCase))
								UpperCAmelCase__					:   Dict       				=       eps




					def 			snake_case__  ( self							,							_lowerCamelCase):
								# 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

								UpperCAmelCase__					:   Any       				=       hidden_states.to(torch.floataa).pow(2).mean(-1							,							keepdim=_lowerCamelCase)
								UpperCAmelCase__					:   Union[str, Any]       				=       hidden_states * torch.rsqrt(variance + self.variance_epsilon)

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

								return self.weight * hidden_states


class 						_snake_case   (       nn.Module		):




					def 			snake_case__  ( self							,							_lowerCamelCase):
								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(_lowerCamelCase							,							3.0))))


class 						_snake_case   (       nn.Module		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								super().__init__()
								UpperCAmelCase__					:   Tuple       				=       nn.Linear(_lowerCamelCase							,							out_features * 2							,							bias=_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Any       				=       self.scale_bias(_lowerCamelCase)
								UpperCAmelCase__				,    UpperCAmelCase__					:   Optional[Any]       				=       torch.chunk(_lowerCamelCase							,							2							,							-1)
								UpperCAmelCase__					:   List[Any]       				=       x * (1 + scale) + shift
								return x



'''simple docstring'''



def       _UpperCamelCase (						UpperCamelCase__ = 1_0	, UpperCamelCase__ = 2_2   ):
			UpperCAmelCase__					:   List[str]       				=       range(1	, UpperCamelCase__   )
			UpperCAmelCase__					:   int       				=       range(1	, UpperCamelCase__   )
			return sum(
			    1 for power in powers for base in bases if len(str(base**power   )   ) == power   )


if __name__ == "__main__":
		print(f"""{solution(10, 22) = }""")