'''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	(      lowerCAmelCase		,lowerCAmelCase   ):
      '''simple docstring'''


      @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					,)			->			Optional[Any]:



         '''simple docstring'''



         super().__init__()

         UpperCAmelCase__							:				int   				=  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__							:				Optional[Any]   				=  False

         UpperCAmelCase__							:				Optional[int]   				=  nn.Linear(lowerCamelCase_					,lowerCamelCase_					,bias=lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  nn.Dropout(p=lowerCamelCase_							)

         UpperCAmelCase__							:				Union[str, Any]   				=  nn.ModuleList()
         for lyr_num in range(lowerCamelCase_							):
            # FiLM conditional T5 decoder
            UpperCAmelCase__							:				Optional[int]   				=  DecoderLayer(d_model=lowerCamelCase_					,d_kv=lowerCamelCase_					,num_heads=lowerCamelCase_					,d_ff=lowerCamelCase_					,dropout_rate=lowerCamelCase_							)
            self.decoders.append(lowerCamelCase_							)

         UpperCAmelCase__							:				int   				=  TaLayerNorm(lowerCamelCase_							)

         UpperCAmelCase__							:				List[str]   				=  nn.Dropout(p=lowerCamelCase_							)
         UpperCAmelCase__							:				Tuple   				=  nn.Linear(lowerCamelCase_					,lowerCamelCase_					,bias=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			List[Any]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				Dict   				=  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__							:				Any   				=  self.conditioning_emb(lowerCamelCase_							).unsqueeze(1							)

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

         UpperCAmelCase__							:				int   				=  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__							:				Tuple   				=  torch.broadcast_to(
             torch.arange(lowerCamelCase_					,device=decoder_input_tokens.device							)					,(batch, seq_length)					,)

         UpperCAmelCase__							:				Dict   				=  self.position_encoding(lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  self.continuous_inputs_projection(lowerCamelCase_							)
         inputs += position_encodings
         UpperCAmelCase__							:				Any   				=  self.dropout(lowerCamelCase_							)

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

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

         # cross attend style: concat encodings
         UpperCAmelCase__							:				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(
                lowerCamelCase_					,conditioning_emb=lowerCamelCase_					,encoder_hidden_states=lowerCamelCase_					,encoder_attention_mask=lowerCamelCase_					,)[0]

         UpperCAmelCase__							:				int   				=  self.decoder_norm(lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  self.post_dropout(lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  self.spec_out(lowerCamelCase_							)
         return spec_out




class 				_lowercase	(      nn.Module   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_=1e-6							)			->			str:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__							:				Dict   				=  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 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  self.layer[0](
             lowerCamelCase_					,conditioning_emb=lowerCamelCase_					,attention_mask=lowerCamelCase_					,)

         if encoder_hidden_states is not None:
            UpperCAmelCase__							:				List[str]   				=  torch.where(encoder_attention_mask > 0					,0					,-1e10							).to(
                encoder_hidden_states.dtype							)

            UpperCAmelCase__							:				Dict   				=  self.layer[1](
                lowerCamelCase_					,key_value_states=lowerCamelCase_					,attention_mask=lowerCamelCase_					,)

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

         return (hidden_states,)




class 				_lowercase	(      nn.Module   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			int:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=None					,lowerCamelCase_=None					,)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.layer_norm(lowerCamelCase_							)

         if conditioning_emb is not None:
            UpperCAmelCase__							:				str   				=  self.FiLMLayer(lowerCamelCase_					,lowerCamelCase_							)

         # Self-attention block
         UpperCAmelCase__							:				int   				=  self.attention(lowerCamelCase_							)

         UpperCAmelCase__							:				List[str]   				=  hidden_states + self.dropout(lowerCamelCase_							)

         return hidden_states




class 				_lowercase	(      nn.Module   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__							:				Optional[Any]   				=  Attention(query_dim=lowerCamelCase_					,heads=lowerCamelCase_					,dim_head=lowerCamelCase_					,out_bias=lowerCamelCase_					,scale_qk=lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  TaLayerNorm(lowerCamelCase_					,eps=lowerCamelCase_							)
         UpperCAmelCase__							:				Tuple   				=  nn.Dropout(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=None					,lowerCamelCase_=None					,)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  self.layer_norm(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  self.attention(
             lowerCamelCase_					,encoder_hidden_states=lowerCamelCase_					,attention_mask=attention_mask.squeeze(1							)					,)
         UpperCAmelCase__							:				List[str]   				=  hidden_states + self.dropout(lowerCamelCase_							)
         return layer_output




class 				_lowercase	(      nn.Module   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__							:				str   				=  TaDenseGatedActDense(d_model=lowerCamelCase_					,d_ff=lowerCamelCase_					,dropout_rate=lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  TaFiLMLayer(in_features=d_model * 4					,out_features=lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  TaLayerNorm(lowerCamelCase_					,eps=lowerCamelCase_							)
         UpperCAmelCase__							:				List[str]   				=  nn.Dropout(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=None							)			->			List[str]:



         '''simple docstring'''



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

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




class 				_lowercase	(      nn.Module   ):
      '''simple docstring'''


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



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__							:				List[Any]   				=  nn.Linear(lowerCamelCase_					,lowerCamelCase_					,bias=lowerCamelCase_							)
         UpperCAmelCase__							:				Dict   				=  nn.Linear(lowerCamelCase_					,lowerCamelCase_					,bias=lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  nn.Linear(lowerCamelCase_					,lowerCamelCase_					,bias=lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  nn.Dropout(lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  NewGELUActivation()




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  self.act(self.wi_a(lowerCamelCase_							)							)
         UpperCAmelCase__							:				List[Any]   				=  self.wi_a(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  hidden_gelu * hidden_linear
         UpperCAmelCase__							:				List[Any]   				=  self.dropout(lowerCamelCase_							)

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




class 				_lowercase	(      nn.Module   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_=1e-6							)			->			int:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__							:				List[Any]   				=  nn.Parameter(torch.ones(lowerCamelCase_							)							)
         UpperCAmelCase__							:				str   				=  eps




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  hidden_states.to(torch.floataa							).pow(2							).mean(-1					,keepdim=lowerCamelCase_							)
         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__							:				str   				=  hidden_states.to(self.weight.dtype							)

         return self.weight * hidden_states




class 				_lowercase	(      nn.Module   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			torch.Tensor:



         '''simple docstring'''



         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi							) * (input + 0.04_4715 * torch.pow(lowerCamelCase_					,3.0							))							))




class 				_lowercase	(      nn.Module   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__							:				List[Any]   				=  nn.Linear(lowerCamelCase_					,out_features * 2					,bias=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



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





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__     :	List[Any]				=			{'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__     :	int				=			['YolosFeatureExtractor']
  UpperCamelCase__     :	int				=			['YolosImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__     :	int				=			[
      'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'YolosForObjectDetection',
      'YolosModel',
      'YolosPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_yolos import YolosFeatureExtractor
    from .image_processing_yolos import YolosImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_yolos import (
        YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
        YolosForObjectDetection,
        YolosModel,
        YolosPreTrainedModel,
    )


else:
  import sys

  UpperCamelCase__     :	int				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




