



'''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 				SCREAMING_SNAKE_CASE(							A__							,		A__			):







	"""simple docstring"""






	@register_to_config
	def __init__(						self			:     int		,			__snake_case			:     int = 128		,			__snake_case			:     int = 256		,			__snake_case			:     float = 20_00.0		,			__snake_case			:     int = 768		,			__snake_case			:     int = 12		,			__snake_case			:     int = 12		,			__snake_case			:     int = 64		,			__snake_case			:     int = 2048		,			__snake_case			:     float = 0.1		,			)			->		Any:
								super().__init__()

								UpperCAmelCase :       List[Any] 							=		nn.Sequential(
								    nn.Linear(__snake_case		,			d_model * 4		,			bias=__snake_case		)		,			nn.SiLU()		,			nn.Linear(d_model * 4		,			d_model * 4		,			bias=__snake_case		)		,			nn.SiLU()		,			)

								UpperCAmelCase :       Union[str, Any] 							=		nn.Embedding(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Any 							=		False

								UpperCAmelCase :       Union[str, Any] 							=		nn.Linear(__snake_case		,			__snake_case		,			bias=__snake_case		)

								UpperCAmelCase :       Tuple 							=		nn.Dropout(p=__snake_case		)

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

								UpperCAmelCase :       Optional[int] 							=		TaLayerNorm(__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		nn.Dropout(p=__snake_case		)
								UpperCAmelCase :       Tuple 							=		nn.Linear(__snake_case		,			__snake_case		,			bias=__snake_case		)







	def      A	(						self			:     Any		,			__snake_case			:     List[Any]		,			__snake_case			:     Optional[int]		)			->		Dict:
								UpperCAmelCase :       Dict 							=		torch.mul(query_input.unsqueeze(-1		)		,			key_input.unsqueeze(-2		)		)
								return mask.unsqueeze(-3		)







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     Dict		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		)			->		Any:
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		decoder_input_tokens.shape
								assert decoder_noise_time.shape == (batch,)

								# decoder_noise_time is in [0, 1), so rescale to expected timing range.
								UpperCAmelCase :       Union[str, 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[Any] 							=		self.conditioning_emb(__snake_case		).unsqueeze(1		)

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

								UpperCAmelCase :       Optional[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.
								UpperCAmelCase :       int 							=		torch.broadcast_to(
								    torch.arange(__snake_case		,			device=decoder_input_tokens.device		)		,			(batch, seq_length)		,			)

								UpperCAmelCase :       Optional[Any] 							=		self.position_encoding(__snake_case		)

								UpperCAmelCase :       Tuple 							=		self.continuous_inputs_projection(__snake_case		)
								inputs += position_encodings
								UpperCAmelCase :       Optional[Any] 							=		self.dropout(__snake_case		)

								# decoder: No padding present.
								UpperCAmelCase :       List[str] 							=		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(__snake_case		,			__snake_case		)) for x, y in encodings_and_masks]

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

								for lyr in self.decoders:
															UpperCAmelCase :       Any 							=		lyr(
															    __snake_case		,			conditioning_emb=__snake_case		,			encoder_hidden_states=__snake_case		,			encoder_attention_mask=__snake_case		,			)[0]

								UpperCAmelCase :       List[Any] 							=		self.decoder_norm(__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		self.post_dropout(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		self.spec_out(__snake_case		)
								return spec_out

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def __init__(						self			:     Optional[Any]		,			__snake_case			:     Dict		,			__snake_case			:     Optional[int]		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[int]		,			__snake_case			:     Dict		,			__snake_case			:     Union[str, Any]=1E-6		)			->		Optional[Any]:
								super().__init__()
								UpperCAmelCase :       Union[str, Any] 							=		nn.ModuleList()

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

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

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







	def      A	(						self			:     Optional[int]		,			__snake_case			:     List[Any]		,			__snake_case			:     int=None		,			__snake_case			:     List[str]=None		,			__snake_case			:     Optional[int]=None		,			__snake_case			:     Optional[Any]=None		,			__snake_case			:     Any=None		,			)			->		List[str]:
								UpperCAmelCase :       Optional[int] 							=		self.layer[0](
								    __snake_case		,			conditioning_emb=__snake_case		,			attention_mask=__snake_case		,			)

								if encoder_hidden_states is not None:
															UpperCAmelCase :       Any 							=		torch.where(encoder_attention_mask > 0		,			0		,			-1E10		).to(
															    encoder_hidden_states.dtype		)

															UpperCAmelCase :       str 							=		self.layer[1](
															    __snake_case		,			key_value_states=__snake_case		,			attention_mask=__snake_case		,			)

								# Apply Film Conditional Feed Forward layer
								UpperCAmelCase :       Tuple 							=		self.layer[-1](__snake_case		,			__snake_case		)

								return (hidden_states,)

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






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







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Optional[int]=None		,			__snake_case			:     Union[str, Any]=None		,			)			->		Optional[Any]:
								# pre_self_attention_layer_norm
								UpperCAmelCase :       Union[str, Any] 							=		self.layer_norm(__snake_case		)

								if conditioning_emb is not None:
															UpperCAmelCase :       Optional[int] 							=		self.FiLMLayer(__snake_case		,			__snake_case		)

								# Self-attention block
								UpperCAmelCase :       Tuple 							=		self.attention(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		hidden_states + self.dropout(__snake_case		)

								return hidden_states

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     Tuple		,			__snake_case			:     int		,			__snake_case			:     List[Any]		,			__snake_case			:     Any		)			->		Union[str, Any]:
								super().__init__()
								UpperCAmelCase :       Optional[int] 							=		Attention(query_dim=__snake_case		,			heads=__snake_case		,			dim_head=__snake_case		,			out_bias=__snake_case		,			scale_qk=__snake_case		)
								UpperCAmelCase :       List[str] 							=		TaLayerNorm(__snake_case		,			eps=__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		nn.Dropout(__snake_case		)







	def      A	(						self			:     Any		,			__snake_case			:     int		,			__snake_case			:     int=None		,			__snake_case			:     int=None		,			)			->		List[Any]:
								UpperCAmelCase :       Tuple 							=		self.layer_norm(__snake_case		)
								UpperCAmelCase :       Any 							=		self.attention(
								    __snake_case		,			encoder_hidden_states=__snake_case		,			attention_mask=attention_mask.squeeze(1		)		,			)
								UpperCAmelCase :       Optional[Any] 							=		hidden_states + self.dropout(__snake_case		)
								return layer_output

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def __init__(						self			:     int		,			__snake_case			:     Tuple		,			__snake_case			:     str		,			__snake_case			:     Tuple		,			__snake_case			:     int		)			->		int:
								super().__init__()
								UpperCAmelCase :       List[str] 							=		TaDenseGatedActDense(d_model=__snake_case		,			d_ff=__snake_case		,			dropout_rate=__snake_case		)
								UpperCAmelCase :       int 							=		TaFiLMLayer(in_features=d_model * 4		,			out_features=__snake_case		)
								UpperCAmelCase :       Any 							=		TaLayerNorm(__snake_case		,			eps=__snake_case		)
								UpperCAmelCase :       Dict 							=		nn.Dropout(__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     Dict=None		)			->		str:
								UpperCAmelCase :       List[Any] 							=		self.layer_norm(__snake_case		)
								if conditioning_emb is not None:
															UpperCAmelCase :       Tuple 							=		self.film(__snake_case		,			__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		self.DenseReluDense(__snake_case		)
								UpperCAmelCase :       Tuple 							=		hidden_states + self.dropout(__snake_case		)
								return hidden_states

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def __init__(						self			:     Optional[Any]		,			__snake_case			:     int		,			__snake_case			:     Any		,			__snake_case			:     Union[str, Any]		)			->		str:
								super().__init__()
								UpperCAmelCase :       Tuple 							=		nn.Linear(__snake_case		,			__snake_case		,			bias=__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		nn.Linear(__snake_case		,			__snake_case		,			bias=__snake_case		)
								UpperCAmelCase :       Tuple 							=		nn.Linear(__snake_case		,			__snake_case		,			bias=__snake_case		)
								UpperCAmelCase :       int 							=		nn.Dropout(__snake_case		)
								UpperCAmelCase :       Dict 							=		NewGELUActivation()







	def      A	(						self			:     Optional[int]		,			__snake_case			:     List[Any]		)			->		Tuple:
								UpperCAmelCase :       List[str] 							=		self.act(self.wi_a(__snake_case		)		)
								UpperCAmelCase :       str 							=		self.wi_a(__snake_case		)
								UpperCAmelCase :       Any 							=		hidden_gelu * hidden_linear
								UpperCAmelCase :       Any 							=		self.dropout(__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		self.wo(__snake_case		)
								return hidden_states

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






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







	def      A	(						self			:     List[Any]		,			__snake_case			:     Dict		)			->		List[str]:
								# 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 :       Tuple 							=		hidden_states.to(torch.floataa		).pow(2		).mean(-1		,			keepdim=__snake_case		)
								UpperCAmelCase :       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]:
															UpperCAmelCase :       Optional[int] 							=		hidden_states.to(self.weight.dtype		)

								return self.weight * hidden_states

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def      A	(						self			:     Tuple		,			__snake_case			:     torch.Tensor		)			->		torch.Tensor:
								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.04_47_15 * torch.pow(__snake_case		,			3.0		))		))

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def __init__(						self			:     Optional[int]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		)			->		List[str]:
								super().__init__()
								UpperCAmelCase :       List[str] 							=		nn.Linear(__snake_case		,			out_features * 2		,			bias=__snake_case		)







	def      A	(						self			:     Dict		,			__snake_case			:     List[str]		,			__snake_case			:     str		)			->		int:
								UpperCAmelCase :       Dict 							=		self.scale_bias(__snake_case		)
								UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		torch.chunk(__snake_case		,			2		,			-1		)
								UpperCAmelCase :       Optional[int] 							=		x * (1 + scale) + shift
								return x









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A		       =						{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTMAEForPreTraining',
       'ViTMAELayer',
       'ViTMAEModel',
       'ViTMAEPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TFViTMAEForPreTraining',
       'TFViTMAEModel',
       'TFViTMAEPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_mae import (
          VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMAEForPreTraining,
          ViTMAELayer,
          ViTMAEModel,
          ViTMAEPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





