





'''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    lowerCAmelCase_		(		__magic_name__						,__magic_name__		):




 @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			,     )    ->							str:
     super().__init__()

     _lowerCAmelCase				=      nn.Sequential(
         nn.Linear(_lowerCAmelCase			,     d_model * 4			,     bias=_lowerCAmelCase		)			,     nn.SiLU()			,     nn.Linear(d_model * 4			,     d_model * 4			,     bias=_lowerCAmelCase		)			,     nn.SiLU()			,     )

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

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

     _lowerCAmelCase				=      nn.Dropout(p=_lowerCAmelCase		)

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

     _lowerCAmelCase				=      TaLayerNorm(_lowerCAmelCase		)

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




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							List[Any]:
     _lowerCAmelCase				=      torch.mul(query_input.unsqueeze(-1		)			,     key_input.unsqueeze(-2		)		)
     return mask.unsqueeze(-3		)





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Union[str, 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(_lowerCAmelCase		).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(_lowerCAmelCase			,     device=decoder_input_tokens.device		)			,     (batch, seq_length)			,     )

     _lowerCAmelCase				=      self.position_encoding(_lowerCAmelCase		)

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

     # 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(_lowerCAmelCase			,     _lowerCAmelCase		)) 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(
             _lowerCAmelCase			,     conditioning_emb=_lowerCAmelCase			,     encoder_hidden_states=_lowerCAmelCase			,     encoder_attention_mask=_lowerCAmelCase			,     )[0]

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

     _lowerCAmelCase				=      self.spec_out(_lowerCAmelCase		)
     return spec_out







class    lowerCAmelCase_		(		nn.Module		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=1E-6		)    ->							Dict:
     super().__init__()
     _lowerCAmelCase				=      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			,     )    ->							int:
     _lowerCAmelCase				=      self.layer[0](
         _lowerCAmelCase			,     conditioning_emb=_lowerCAmelCase			,     attention_mask=_lowerCAmelCase			,     )

     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](
             _lowerCAmelCase			,     key_value_states=_lowerCAmelCase			,     attention_mask=_lowerCAmelCase			,     )

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

     return (hidden_states,)







class    lowerCAmelCase_		(		nn.Module		):




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





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     )    ->							List[str]:
     # pre_self_attention_layer_norm
     _lowerCAmelCase				=      self.layer_norm(_lowerCAmelCase		)

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

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

     _lowerCAmelCase				=      hidden_states + self.dropout(_lowerCAmelCase		)

     return hidden_states







class    lowerCAmelCase_		(		nn.Module		):




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





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







class    lowerCAmelCase_		(		nn.Module		):




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





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase=None		)    ->							Optional[int]:
     _lowerCAmelCase				=      self.layer_norm(_lowerCAmelCase		)
     if conditioning_emb is not None:
         _lowerCAmelCase				=      self.film(_lowerCAmelCase			,     _lowerCAmelCase		)

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







class    lowerCAmelCase_		(		nn.Module		):




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





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

     _lowerCAmelCase				=      self.wo(_lowerCAmelCase		)
     return hidden_states







class    lowerCAmelCase_		(		nn.Module		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase=1E-6		)    ->							Union[str, Any]:
     super().__init__()
     _lowerCAmelCase				=      nn.Parameter(torch.ones(_lowerCAmelCase		)		)
     _lowerCAmelCase				=      eps





 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							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=_lowerCAmelCase		)
     _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    lowerCAmelCase_		(		nn.Module		):




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							torch.Tensor:
     return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.044715 * torch.pow(_lowerCAmelCase			,     3.0		))		))







class    lowerCAmelCase_		(		nn.Module		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Dict:
     super().__init__()
     _lowerCAmelCase				=      nn.Linear(_lowerCAmelCase			,     out_features * 2			,     bias=_lowerCAmelCase		)





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









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					bool = False    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected string as input, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )
    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      input_str.split("_"    )

    _lowerCAmelCase				=      0 if use_pascal else 1

    _lowerCAmelCase				=      words[start_index:]

    _lowerCAmelCase				=      [word[0].upper() + word[1:] for word in words_to_capitalize]

    _lowerCAmelCase				=      "" if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
       from doctest import testmod

       testmod()


