



"""simple docstring"""
import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     int		=		PriorTransformer
   __lowerCAmelCase						:     Dict		=		'hidden_states'

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			4
    UpperCAmelCase			:      Optional[Any]			=			8
    UpperCAmelCase			:      Union[str, Any]			=			7

    UpperCAmelCase			:      Any			=			floats_tensor((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[Any]			=			floats_tensor((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			floats_tensor((batch_size, num_embeddings, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    return {
        "hidden_states": hidden_states,
        "timestep": 2,
        "proj_embedding": proj_embedding,
        "encoder_hidden_states": encoder_hidden_states,
    }

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE=0		)						->   Any:





    '''simple docstring'''




    torch.manual_seed(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			4
    UpperCAmelCase			:      Any			=			8
    UpperCAmelCase			:      List[Any]			=			7

    UpperCAmelCase			:      Any			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[Any]			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			torch.randn((batch_size, num_embeddings, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    return {
        "hidden_states": hidden_states,
        "timestep": 2,
        "proj_embedding": proj_embedding,
        "encoder_hidden_states": encoder_hidden_states,
    }

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    return (4, 8)

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    return (4, 8)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			{
        """num_attention_heads""": 2,
        """attention_head_dim""": 4,
        """num_layers""": 2,
        """embedding_dim""": 8,
        """num_embeddings""": 7,
        """additional_embeddings""": 4,
    }
    UpperCAmelCase			:      Tuple			=			self.dummy_input
    return init_dict, inputs_dict

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Optional[Any]			=			PriorTransformer.from_pretrained(
        """hf-internal-testing/prior-dummy"""   ,  output_loading_info=_SCREAMING_SNAKE_CASE		)
    self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
    self.assertEqual(len(loading_info["""missing_keys"""]		)   ,  0		)

    model.to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			model(**self.dummy_input		)[0]

    assert hidden_states is not None, "Make sure output is not None"

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Tuple			=			self.prepare_init_args_and_inputs_for_common()

    UpperCAmelCase			:      str			=			self.model_class(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			inspect.signature(model.forward		)
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    UpperCAmelCase			:      Union[str, Any]			=			[*signature.parameters.keys()]

    UpperCAmelCase			:      int			=			["""hidden_states""", """timestep"""]
    self.assertListEqual(arg_names[:2]   ,  _SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""		)
    UpperCAmelCase			:      Dict			=			model.to(_SCREAMING_SNAKE_CASE		)

    if hasattr(_SCREAMING_SNAKE_CASE   ,  """set_default_attn_processor"""		):
     model.set_default_attn_processor()

    UpperCAmelCase			:      str			=			self.get_dummy_seed_input()

    with torch.no_grad():
     UpperCAmelCase			:      str			=			model(**_SCREAMING_SNAKE_CASE		)[0]

    UpperCAmelCase			:      Dict			=			output[0, :5].flatten().cpu()
    print(_SCREAMING_SNAKE_CASE		)

    # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
    # the expected output slices are not the same for CPU and GPU.
    UpperCAmelCase			:      Dict			=			torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]		)
    self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  rtol=1E-2		)		)



@slow
class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE=1   ,  _SCREAMING_SNAKE_CASE=768   ,  _SCREAMING_SNAKE_CASE=77   ,  _SCREAMING_SNAKE_CASE=0		)						->   int:





    '''simple docstring'''




    torch.manual_seed(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			batch_size
    UpperCAmelCase			:      int			=			embedding_dim
    UpperCAmelCase			:      Tuple			=			num_embeddings

    UpperCAmelCase			:      Optional[Any]			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			torch.randn((batch_size, num_embeddings, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    return {
        "hidden_states": hidden_states,
        "timestep": 2,
        "proj_embedding": proj_embedding,
        "encoder_hidden_states": encoder_hidden_states,
    }

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
           [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
           # fmt: on
       ]		)
   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""   ,  subfolder="""prior"""		)
    model.to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			self.get_dummy_seed_input(seed=_SCREAMING_SNAKE_CASE		)

    with torch.no_grad():
     UpperCAmelCase			:      Any			=			model(**_SCREAMING_SNAKE_CASE		)[0]

    assert list(sample.shape		) == [1, 768]

    UpperCAmelCase			:      int			=			sample[0, :8].flatten().cpu()
    print(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[int]			=			torch.tensor(_SCREAMING_SNAKE_CASE		)

    assert torch_all_close(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  atol=1E-3		)





"""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__						(      UpperCAmelCase__   ,    UpperCAmelCase__				):

   @register_to_config
   def __init__(     self   ,  _SCREAMING_SNAKE_CASE = 128   ,  _SCREAMING_SNAKE_CASE = 256   ,  _SCREAMING_SNAKE_CASE = 2000.0   ,  _SCREAMING_SNAKE_CASE = 768   ,  _SCREAMING_SNAKE_CASE = 12   ,  _SCREAMING_SNAKE_CASE = 12   ,  _SCREAMING_SNAKE_CASE = 64   ,  _SCREAMING_SNAKE_CASE = 2048   ,  _SCREAMING_SNAKE_CASE = 0.1   ,  )						->   int:





    '''simple docstring'''




    super().__init__()

    UpperCAmelCase			:      Tuple			=			nn.Sequential(
        nn.Linear(_SCREAMING_SNAKE_CASE   ,  d_model * 4   ,  bias=_SCREAMING_SNAKE_CASE		)   ,  nn.SiLU()   ,  nn.Linear(d_model * 4   ,  d_model * 4   ,  bias=_SCREAMING_SNAKE_CASE		)   ,  nn.SiLU()   ,  )

    UpperCAmelCase			:      Tuple			=			nn.Embedding(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			False

    UpperCAmelCase			:      Optional[Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			nn.Dropout(p=_SCREAMING_SNAKE_CASE		)

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

    UpperCAmelCase			:      str			=			TaLayerNorm(_SCREAMING_SNAKE_CASE		)

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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			torch.mul(query_input.unsqueeze(-1		)   ,  key_input.unsqueeze(-2		)		)
    return mask.unsqueeze(-3		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      str			=			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			:      Any			=			self.conditioning_emb(_SCREAMING_SNAKE_CASE		).unsqueeze(1		)

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

    UpperCAmelCase			:      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.
    UpperCAmelCase			:      int			=			torch.broadcast_to(
        torch.arange(_SCREAMING_SNAKE_CASE   ,  device=decoder_input_tokens.device		)   ,  (batch, seq_length)   ,  )

    UpperCAmelCase			:      List[str]			=			self.position_encoding(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Dict			=			self.continuous_inputs_projection(_SCREAMING_SNAKE_CASE		)
    inputs += position_encodings
    UpperCAmelCase			:      List[str]			=			self.dropout(_SCREAMING_SNAKE_CASE		)

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

    # Translate encoding masks to encoder-decoder masks.
    UpperCAmelCase			:      Optional[int]			=			[(x, self.encoder_decoder_mask(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_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			:      Union[str, Any]			=			torch.cat([x[1] for x in encodings_and_encdec_masks]   ,  dim=-1		)

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

    UpperCAmelCase			:      List[Any]			=			self.decoder_norm(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			self.post_dropout(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			self.spec_out(_SCREAMING_SNAKE_CASE		)
    return spec_out



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=1E-6		)						->   List[str]:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      List[Any]			=			nn.ModuleList()

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

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

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




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			self.layer[0](
        _SCREAMING_SNAKE_CASE   ,  conditioning_emb=_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  )

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

     UpperCAmelCase			:      List[Any]			=			self.layer[1](
         _SCREAMING_SNAKE_CASE   ,  key_value_states=_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  )

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

    return (hidden_states,)



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

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





    '''simple docstring'''




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




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.layer_norm(_SCREAMING_SNAKE_CASE		)

    if conditioning_emb is not None:
     UpperCAmelCase			:      str			=			self.FiLMLayer(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    # Self-attention block
    UpperCAmelCase			:      List[Any]			=			self.attention(_SCREAMING_SNAKE_CASE		)

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

    return hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

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





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      List[str]			=			Attention(query_dim=_SCREAMING_SNAKE_CASE   ,  heads=_SCREAMING_SNAKE_CASE   ,  dim_head=_SCREAMING_SNAKE_CASE   ,  out_bias=_SCREAMING_SNAKE_CASE   ,  scale_qk=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			TaLayerNorm(_SCREAMING_SNAKE_CASE   ,  eps=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			self.layer_norm(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			self.attention(
        _SCREAMING_SNAKE_CASE   ,  encoder_hidden_states=_SCREAMING_SNAKE_CASE   ,  attention_mask=attention_mask.squeeze(1		)   ,  )
    UpperCAmelCase			:      Dict			=			hidden_states + self.dropout(_SCREAMING_SNAKE_CASE		)
    return layer_output



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




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




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None		)						->   Any:





    '''simple docstring'''




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

    UpperCAmelCase			:      Tuple			=			self.DenseReluDense(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			hidden_states + self.dropout(_SCREAMING_SNAKE_CASE		)
    return hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      Any			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			NewGELUActivation()




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




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

    UpperCAmelCase			:      Optional[int]			=			self.wo(_SCREAMING_SNAKE_CASE		)
    return hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=1E-6		)						->   List[str]:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      List[str]			=			nn.Parameter(torch.ones(_SCREAMING_SNAKE_CASE		)		)
    UpperCAmelCase			:      Optional[Any]			=			eps




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			hidden_states.to(torch.floataa		).pow(2		).mean(-1   ,  keepdim=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			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    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   torch.Tensor:





    '''simple docstring'''




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



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

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





    '''simple docstring'''




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




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.scale_bias(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			,	UpperCAmelCase			:      Optional[int]			=			torch.chunk(_SCREAMING_SNAKE_CASE   ,  2   ,  -1		)
    UpperCAmelCase			:      Optional[int]			=			x * (1 + scale) + shift
    return x
