






'''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_       (     __snake_case					,							__snake_case					):




      """simple docstring"""




      @register_to_config
      def __init__(			self      :		int  ,   UpperCAmelCase      :		Optional[Any] = 128  ,   UpperCAmelCase      :		Union[str, Any] = 256  ,   UpperCAmelCase      :		Dict = 2_0_0_0.0  ,   UpperCAmelCase      :		str = 768  ,   UpperCAmelCase      :		List[str] = 12  ,   UpperCAmelCase      :		Optional[Any] = 12  ,   UpperCAmelCase      :		List[str] = 64  ,   UpperCAmelCase      :		Any = 2048  ,   UpperCAmelCase      :		Any = 0.1  ,   )	->	List[Any]:


       '''simple docstring'''

       super().__init__()

       lowercase       : Tuple						=nn.Sequential(
           nn.Linear(__UpperCamelCase  ,   d_model * 4  ,   bias=__UpperCamelCase		)  ,   nn.SiLU()  ,   nn.Linear(d_model * 4  ,   d_model * 4  ,   bias=__UpperCamelCase		)  ,   nn.SiLU()  ,   )

       lowercase       : Any						=nn.Embedding(__UpperCamelCase  ,   __UpperCamelCase		)
       lowercase       : int						=False

       lowercase       : Tuple						=nn.Linear(__UpperCamelCase  ,   __UpperCamelCase  ,   bias=__UpperCamelCase		)

       lowercase       : Dict						=nn.Dropout(p=__UpperCamelCase		)

       lowercase       : Tuple						=nn.ModuleList()
       for lyr_num in range(__UpperCamelCase		):
        # FiLM conditional T5 decoder
        lowercase       : Optional[Any]						=DecoderLayer(d_model=__UpperCamelCase  ,   d_kv=__UpperCamelCase  ,   num_heads=__UpperCamelCase  ,   d_ff=__UpperCamelCase  ,   dropout_rate=__UpperCamelCase		)
        self.decoders.append(__UpperCamelCase		)

       lowercase       : str						=TaLayerNorm(__UpperCamelCase		)

       lowercase       : Optional[Any]						=nn.Dropout(p=__UpperCamelCase		)
       lowercase       : str						=nn.Linear(__UpperCamelCase  ,   __UpperCamelCase  ,   bias=__UpperCamelCase		)






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		List[Any]  ,   UpperCAmelCase      :		str		)	->	Dict:


       '''simple docstring'''

       lowercase       : Optional[Any]						=torch.mul(query_input.unsqueeze(-1		)  ,   key_input.unsqueeze(-2		)		)
       return mask.unsqueeze(-3		)






      def 					A__   (			self      :		Dict  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		List[Any]  ,   UpperCAmelCase      :		Dict		)	->	Optional[int]:


       '''simple docstring'''

       lowercase	,		lowercase	,		lowercase       : 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.
       lowercase       : Dict						=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		)

       lowercase       : List[str]						=self.conditioning_emb(__UpperCamelCase		).unsqueeze(1		)

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

       lowercase       : List[Any]						=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.
       lowercase       : str						=torch.broadcast_to(
           torch.arange(__UpperCamelCase  ,   device=decoder_input_tokens.device		)  ,   (batch, seq_length)  ,   )

       lowercase       : Union[str, Any]						=self.position_encoding(__UpperCamelCase		)

       lowercase       : Union[str, Any]						=self.continuous_inputs_projection(__UpperCamelCase		)
       inputs += position_encodings
       lowercase       : List[Any]						=self.dropout(__UpperCamelCase		)

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

       # Translate encoding masks to encoder-decoder masks.
       lowercase       : Any						=[(x, self.encoder_decoder_mask(__UpperCamelCase  ,   __UpperCamelCase		)) for x, y in encodings_and_masks]

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

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

       lowercase       : Optional[int]						=self.decoder_norm(__UpperCamelCase		)
       lowercase       : List[Any]						=self.post_dropout(__UpperCamelCase		)

       lowercase       : Union[str, Any]						=self.spec_out(__UpperCamelCase		)
       return spec_out





class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def __init__(			self      :		Union[str, Any]  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		List[str]  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		Dict=1e-6		)	->	Tuple:


       '''simple docstring'''

       super().__init__()
       lowercase       : Optional[Any]						=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 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   UpperCAmelCase      :		Tuple=None  ,   UpperCAmelCase      :		Any=None  ,   UpperCAmelCase      :		Any=None  ,   UpperCAmelCase      :		List[str]=None  ,   )	->	Any:


       '''simple docstring'''

       lowercase       : Tuple						=self.layer[0](
           __UpperCamelCase  ,   conditioning_emb=__UpperCamelCase  ,   attention_mask=__UpperCamelCase  ,   )

       if encoder_hidden_states is not None:
        lowercase       : Optional[Any]						=torch.where(encoder_attention_mask > 0  ,   0  ,   -1e10		).to(
            encoder_hidden_states.dtype		)

        lowercase       : Tuple						=self.layer[1](
            __UpperCamelCase  ,   key_value_states=__UpperCamelCase  ,   attention_mask=__UpperCamelCase  ,   )

       # Apply Film Conditional Feed Forward layer
       lowercase       : Any						=self.layer[-1](__UpperCamelCase  ,   __UpperCamelCase		)

       return (hidden_states,)





class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def __init__(			self      :		Union[str, Any]  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		Union[str, Any]		)	->	List[Any]:


       '''simple docstring'''

       super().__init__()
       lowercase       : int						=TaLayerNorm(__UpperCamelCase		)
       lowercase       : List[Any]						=TaFiLMLayer(in_features=d_model * 4  ,   out_features=__UpperCamelCase		)
       lowercase       : Dict						=Attention(query_dim=__UpperCamelCase  ,   heads=__UpperCamelCase  ,   dim_head=__UpperCamelCase  ,   out_bias=__UpperCamelCase  ,   scale_qk=__UpperCamelCase		)
       lowercase       : Optional[int]						=nn.Dropout(__UpperCamelCase		)






      def 					A__   (			self      :		Optional[int]  ,   UpperCAmelCase      :		List[str]  ,   UpperCAmelCase      :		str=None  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   )	->	str:


       '''simple docstring'''

       lowercase       : Any						=self.layer_norm(__UpperCamelCase		)

       if conditioning_emb is not None:
        lowercase       : Dict						=self.FiLMLayer(__UpperCamelCase  ,   __UpperCamelCase		)

       # Self-attention block
       lowercase       : List[str]						=self.attention(__UpperCamelCase		)

       lowercase       : Optional[Any]						=hidden_states + self.dropout(__UpperCamelCase		)

       return hidden_states





class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def __init__(			self      :		List[str]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Union[str, Any]		)	->	Optional[int]:


       '''simple docstring'''

       super().__init__()
       lowercase       : str						=Attention(query_dim=__UpperCamelCase  ,   heads=__UpperCamelCase  ,   dim_head=__UpperCamelCase  ,   out_bias=__UpperCamelCase  ,   scale_qk=__UpperCamelCase		)
       lowercase       : str						=TaLayerNorm(__UpperCamelCase  ,   eps=__UpperCamelCase		)
       lowercase       : Union[str, Any]						=nn.Dropout(__UpperCamelCase		)






      def 					A__   (			self      :		Optional[int]  ,   UpperCAmelCase      :		Optional[int]  ,   UpperCAmelCase      :		str=None  ,   UpperCAmelCase      :		str=None  ,   )	->	Tuple:


       '''simple docstring'''

       lowercase       : List[str]						=self.layer_norm(__UpperCamelCase		)
       lowercase       : int						=self.attention(
           __UpperCamelCase  ,   encoder_hidden_states=__UpperCamelCase  ,   attention_mask=attention_mask.squeeze(1		)  ,   )
       lowercase       : Tuple						=hidden_states + self.dropout(__UpperCamelCase		)
       return layer_output





class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def __init__(			self      :		Any  ,   UpperCAmelCase      :		List[str]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		List[Any]		)	->	List[Any]:


       '''simple docstring'''

       super().__init__()
       lowercase       : Optional[Any]						=TaDenseGatedActDense(d_model=__UpperCamelCase  ,   d_ff=__UpperCamelCase  ,   dropout_rate=__UpperCamelCase		)
       lowercase       : str						=TaFiLMLayer(in_features=d_model * 4  ,   out_features=__UpperCamelCase		)
       lowercase       : Optional[Any]						=TaLayerNorm(__UpperCamelCase  ,   eps=__UpperCamelCase		)
       lowercase       : Optional[int]						=nn.Dropout(__UpperCamelCase		)






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		Optional[int]  ,   UpperCAmelCase      :		int=None		)	->	str:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=self.layer_norm(__UpperCamelCase		)
       if conditioning_emb is not None:
        lowercase       : List[Any]						=self.film(__UpperCamelCase  ,   __UpperCamelCase		)

       lowercase       : List[str]						=self.DenseReluDense(__UpperCamelCase		)
       lowercase       : Optional[Any]						=hidden_states + self.dropout(__UpperCamelCase		)
       return hidden_states





class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def __init__(			self      :		Tuple  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		Dict		)	->	int:


       '''simple docstring'''

       super().__init__()
       lowercase       : Any						=nn.Linear(__UpperCamelCase  ,   __UpperCamelCase  ,   bias=__UpperCamelCase		)
       lowercase       : str						=nn.Linear(__UpperCamelCase  ,   __UpperCamelCase  ,   bias=__UpperCamelCase		)
       lowercase       : Union[str, Any]						=nn.Linear(__UpperCamelCase  ,   __UpperCamelCase  ,   bias=__UpperCamelCase		)
       lowercase       : Any						=nn.Dropout(__UpperCamelCase		)
       lowercase       : Union[str, Any]						=NewGELUActivation()






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		str		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=self.act(self.wi_a(__UpperCamelCase		)		)
       lowercase       : Dict						=self.wi_a(__UpperCamelCase		)
       lowercase       : Optional[int]						=hidden_gelu * hidden_linear
       lowercase       : str						=self.dropout(__UpperCamelCase		)

       lowercase       : Tuple						=self.wo(__UpperCamelCase		)
       return hidden_states





class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def __init__(			self      :		str  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		Optional[int]=1e-6		)	->	Any:


       '''simple docstring'''

       super().__init__()
       lowercase       : Tuple						=nn.Parameter(torch.ones(__UpperCamelCase		)		)
       lowercase       : Tuple						=eps






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Optional[int]		)	->	Any:


       '''simple docstring'''

       lowercase       : Optional[int]						=hidden_states.to(torch.floataa		).pow(2		).mean(-1  ,   keepdim=__UpperCamelCase		)
       lowercase       : Optional[Any]						=hidden_states * torch.rsqrt(variance + self.variance_epsilon		)

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

       return self.weight * hidden_states





class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		Optional[int]		)	->	Any:


       '''simple docstring'''

       return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.0_4_4_7_1_5 * torch.pow(__UpperCamelCase  ,   3.0		))		))





class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def __init__(			self      :		Any  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		Union[str, Any]		)	->	List[Any]:


       '''simple docstring'''

       super().__init__()
       lowercase       : Union[str, Any]						=nn.Linear(__UpperCamelCase  ,   out_features * 2  ,   bias=__UpperCamelCase		)






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		List[str]		)	->	Dict:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=self.scale_bias(__UpperCamelCase		)
       lowercase	,		lowercase       : Tuple						=torch.chunk(__UpperCamelCase  ,   2  ,   -1		)
       lowercase       : Optional[int]						=x * (1 + scale) + shift
       return x






from ..utils import DummyObject, requires_backends


class SCREAMING_SNAKE_CASE	(		metaclass=__snake_case				):






			"""simple docstring"""




			__A    	=	["""flax""", """transformers"""]


			def __init__(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(self			,					['flax', 'transformers']				)


			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







class SCREAMING_SNAKE_CASE	(		metaclass=__snake_case				):






			"""simple docstring"""




			__A    	=	["""flax""", """transformers"""]


			def __init__(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(self			,					['flax', 'transformers']				)


			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







class SCREAMING_SNAKE_CASE	(		metaclass=__snake_case				):






			"""simple docstring"""




			__A    	=	["""flax""", """transformers"""]


			def __init__(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(self			,					['flax', 'transformers']				)


			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







class SCREAMING_SNAKE_CASE	(		metaclass=__snake_case				):






			"""simple docstring"""




			__A    	=	["""flax""", """transformers"""]


			def __init__(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(self			,					['flax', 'transformers']				)


			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)
