





'''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   _lowercase						(							a		,					a				):


   @register_to_config
   def __init__( self    ,   _UpperCAmelCase = 128    ,   _UpperCAmelCase = 256    ,   _UpperCAmelCase = 2000.0    ,   _UpperCAmelCase = 768    ,   _UpperCAmelCase = 12    ,   _UpperCAmelCase = 12    ,   _UpperCAmelCase = 64    ,   _UpperCAmelCase = 2_048    ,   _UpperCAmelCase = 0.1    ,   ):
       super().__init__()

       A       :       str							=						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       :       Any							=						nn.Embedding(_UpperCAmelCase    ,   _UpperCAmelCase					)
       A       :       Any							=						False

       A       :       List[str]							=						nn.Linear(_UpperCAmelCase    ,   _UpperCAmelCase    ,   bias=_UpperCAmelCase					)

       A       :       Any							=						nn.Dropout(p=_UpperCAmelCase					)

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

       A       :       int							=						TaLayerNorm(_UpperCAmelCase					)

       A       :       Optional[int]							=						nn.Dropout(p=_UpperCAmelCase					)
       A       :       Any							=						nn.Linear(_UpperCAmelCase    ,   _UpperCAmelCase    ,   bias=_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       List[Any]							=						torch.mul(query_input.unsqueeze(-1					)    ,   key_input.unsqueeze(-2					)					)
       return mask.unsqueeze(-3					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A,	A,	A       :       str							=						decoder_input_tokens.shape
       assert decoder_noise_time.shape == (batch,)

       # decoder_noise_time is in [0, 1), so rescale to expected timing range.
       A       :       List[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					)

       A       :       Union[str, Any]							=						self.conditioning_emb(_UpperCAmelCase					).unsqueeze(1					)

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

       A       :       Dict							=						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       :       List[str]							=						torch.broadcast_to(
           torch.arange(_UpperCAmelCase    ,   device=decoder_input_tokens.device					)    ,   (batch, seq_length)    ,   )

       A       :       Optional[int]							=						self.position_encoding(_UpperCAmelCase					)

       A       :       Any							=						self.continuous_inputs_projection(_UpperCAmelCase					)
       inputs += position_encodings
       A       :       Optional[int]							=						self.dropout(_UpperCAmelCase					)

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

       # Translate encoding masks to encoder-decoder masks.
       A       :       List[str]							=						[(x, self.encoder_decoder_mask(_UpperCAmelCase    ,   _UpperCAmelCase					)) for x, y in encodings_and_masks]

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

       for lyr in self.decoders:
           A       :       Tuple							=						lyr(
               _UpperCAmelCase    ,   conditioning_emb=_UpperCAmelCase    ,   encoder_hidden_states=_UpperCAmelCase    ,   encoder_attention_mask=_UpperCAmelCase    ,   )[0]

       A       :       str							=						self.decoder_norm(_UpperCAmelCase					)
       A       :       List[Any]							=						self.post_dropout(_UpperCAmelCase					)

       A       :       int							=						self.spec_out(_UpperCAmelCase					)
       return spec_out







class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase=1E-6					):
       super().__init__()
       A       :       Dict							=						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 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   ):
       A       :       Any							=						self.layer[0](
           _UpperCAmelCase    ,   conditioning_emb=_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   )

       if encoder_hidden_states is not None:
           A       :       Optional[Any]							=						torch.where(encoder_attention_mask > 0    ,   0    ,   -1E1_0					).to(
               encoder_hidden_states.dtype					)

           A       :       Any							=						self.layer[1](
               _UpperCAmelCase    ,   key_value_states=_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   )

       # Apply Film Conditional Feed Forward layer
       A       :       int							=						self.layer[-1](_UpperCAmelCase    ,   _UpperCAmelCase					)

       return (hidden_states,)







class   _lowercase						(							nn.Module				):


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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   ):
       # pre_self_attention_layer_norm
       A       :       Any							=						self.layer_norm(_UpperCAmelCase					)

       if conditioning_emb is not None:
           A       :       Dict							=						self.FiLMLayer(_UpperCAmelCase    ,   _UpperCAmelCase					)

       # Self-attention block
       A       :       Dict							=						self.attention(_UpperCAmelCase					)

       A       :       List[str]							=						hidden_states + self.dropout(_UpperCAmelCase					)

       return hidden_states







class   _lowercase						(							nn.Module				):


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


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







class   _lowercase						(							nn.Module				):


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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None					):
       A       :       int							=						self.layer_norm(_UpperCAmelCase					)
       if conditioning_emb is not None:
           A       :       Dict							=						self.film(_UpperCAmelCase    ,   _UpperCAmelCase					)

       A       :       List[str]							=						self.DenseReluDense(_UpperCAmelCase					)
       A       :       str							=						hidden_states + self.dropout(_UpperCAmelCase					)
       return hidden_states







class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       super().__init__()
       A       :       List[str]							=						nn.Linear(_UpperCAmelCase    ,   _UpperCAmelCase    ,   bias=_UpperCAmelCase					)
       A       :       str							=						nn.Linear(_UpperCAmelCase    ,   _UpperCAmelCase    ,   bias=_UpperCAmelCase					)
       A       :       Optional[int]							=						nn.Linear(_UpperCAmelCase    ,   _UpperCAmelCase    ,   bias=_UpperCAmelCase					)
       A       :       List[str]							=						nn.Dropout(_UpperCAmelCase					)
       A       :       Optional[Any]							=						NewGELUActivation()


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       Tuple							=						self.act(self.wi_a(_UpperCAmelCase					)					)
       A       :       Optional[int]							=						self.wi_a(_UpperCAmelCase					)
       A       :       Union[str, Any]							=						hidden_gelu * hidden_linear
       A       :       Optional[Any]							=						self.dropout(_UpperCAmelCase					)

       A       :       Optional[Any]							=						self.wo(_UpperCAmelCase					)
       return hidden_states







class   _lowercase						(							nn.Module				):


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


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       # 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

       A       :       str							=						hidden_states.to(torch.floataa					).pow(2					).mean(-1    ,   keepdim=_UpperCAmelCase					)
       A       :       Any							=						hidden_states * torch.rsqrt(variance + self.variance_epsilon					)

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

       return self.weight * hidden_states







class   _lowercase						(							nn.Module				):


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       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				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       super().__init__()
       A       :       Any							=						nn.Linear(_UpperCAmelCase    ,   out_features * 2    ,   bias=_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Tuple							=						self.scale_bias(_UpperCAmelCase					)
       A,	A       :       List[str]							=						torch.chunk(_UpperCAmelCase    ,   2    ,   -1					)
       A       :       int							=						x * (1 + scale) + shift
       return x







'''simple docstring'''






from __future__ import annotations


class   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase					):
       A       :       str							=						data
       A       :       Node | None							=						None
       A       :       Node | None							=						None







def 							_lowerCamelCase(  UpperCamelCase__    : Node | None		)		->  None:  # In Order traversal of the tree
    if tree:
        display(tree.left		)
        print(tree.data		)
        display(tree.right		)
def 							_lowerCamelCase(  UpperCamelCase__    : Node | None		)		->  int:
    return 1 + max(depth_of_tree(tree.left		)    ,       depth_of_tree(tree.right		)		) if tree else 0
def 							_lowerCamelCase(  UpperCamelCase__    : Node		)		->  bool:
    if not tree:
        return True
    if tree.left and tree.right:
        return is_full_binary_tree(tree.left		) and is_full_binary_tree(tree.right		)
    else:
        return not tree.left and not tree.right
def 							_lowerCamelCase(  )		->  None:  # Main function for testing.
    A       :       Optional[int]							=						Node(1		)
    A       :       Tuple							=						Node(2		)
    A       :       Dict							=						Node(3		)
    A       :       List[str]							=						Node(4		)
    A       :       Union[str, Any]							=						Node(5		)
    A       :       str							=						Node(6		)
    A       :       Any							=						Node(7		)
    A       :       str							=						Node(8		)
    A       :       Optional[int]							=						Node(9		)

    print(is_full_binary_tree(UpperCamelCase__		)		)
    print(depth_of_tree(UpperCamelCase__		)		)
    print('''Tree is: '''		)
    display(UpperCamelCase__		)


if __name__ == "__main__":
     main()
