

"""simple docstring"""






import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


_lowerCAmelCase						=		logging.getLogger(__name__)

def 		lowerCamelCase__		(					_lowerCamelCase=2 ,   _lowerCamelCase=3 ,   _lowerCamelCase=16 ,   _lowerCamelCase = 10 ,   _lowerCamelCase = 2   ):

    '''simple docstring'''



    def get_dataset(_lowerCamelCase   ):
        _lowerCAmelCase       :   Optional[Any]  =    torch.randn(batch_size * n_batches ,   1   )
        return TensorDataset(_lowerCamelCase ,   a * x + b + 0.1 * torch.randn(batch_size * n_batches ,   1   )   )

    _lowerCAmelCase       :   Any  =    get_dataset(_lowerCamelCase   )
    _lowerCAmelCase       :   str  =    get_dataset(_lowerCamelCase   )
    _lowerCAmelCase       :   Optional[Any]  =    DataLoader(_lowerCamelCase ,   shuffle=_lowerCamelCase ,   batch_size=_lowerCamelCase ,   num_workers=4   )
    _lowerCAmelCase       :   int  =    DataLoader(_lowerCamelCase ,   shuffle=_lowerCamelCase ,   batch_size=_lowerCamelCase ,   num_workers=4   )
    return (train_dataloader, valid_dataloader)

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    []
    for epoch in range(_lowerCamelCase   ):
        # Train quickly
        model.train()
        for batch in dataloader:
            _lowerCAmelCase,					_lowerCAmelCase       :   Union[str, Any]  =    batch
            _lowerCAmelCase       :   Union[str, Any]  =    model(_lowerCamelCase   )
            _lowerCAmelCase       :   int  =    torch.nn.functional.mse_loss(_lowerCamelCase ,   _lowerCamelCase   )
            accelerator.backward(_lowerCamelCase   )
            optimizer.step()
            optimizer.zero_grad()
        rands.append(random.random()   )  # Introduce some randomness
        if scheduler is not None:
            scheduler.step()
    return rands







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   int  =    nn.Parameter(torch.randn(1      )      )
      _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.randn(1      )      )







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return x * self.a + self.b







class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42      )
          _lowerCAmelCase       :   Union[str, Any]  =    DummyModel()
          _lowerCAmelCase       :   List[Any]  =    torch.optim.Adam(params=model.parameters()    ,lr=1E-3      )
          _lowerCAmelCase,					_lowerCAmelCase       :   Optional[Any]  =    dummy_dataloaders()
          _lowerCAmelCase       :   str  =    ProjectConfiguration(total_limit=1    ,project_dir=_A    ,automatic_checkpoint_naming=_A      )
          # Train baseline
          _lowerCAmelCase       :   Any  =    Accelerator(project_config=_A      )
          _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   str  =    accelerator.prepare(
              _A    ,_A    ,_A    ,_A      )
          # Save initial
          accelerator.save_state()

          # Save second state
          accelerator.save_state()
          self.assertEqual(len(os.listdir(accelerator.project_dir      )      )    ,1      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42      )
          _lowerCAmelCase       :   int  =    DummyModel()
          _lowerCAmelCase       :   Any  =    torch.optim.Adam(params=model.parameters()    ,lr=1E-3      )
          _lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    dummy_dataloaders()
          # Train baseline
          _lowerCAmelCase       :   Any  =    Accelerator()
          _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   List[str]  =    accelerator.prepare(
              _A    ,_A    ,_A    ,_A      )
          # Save initial
          _lowerCAmelCase       :   Any  =    os.path.join(_A    ,'initial'      )
          accelerator.save_state(_A      )
          ((_lowerCAmelCase),					(_lowerCAmelCase))       :   List[Any]  =    model.a.item(), model.b.item()
          _lowerCAmelCase       :   Any  =    optimizer.state_dict()
          _lowerCAmelCase       :   Any  =    train(3    ,_A    ,_A    ,_A    ,_A      )
          ((_lowerCAmelCase),					(_lowerCAmelCase))       :   List[Any]  =    model.a.item(), model.b.item()
          _lowerCAmelCase       :   List[str]  =    optimizer.state_dict()

          # Train partially
          set_seed(42      )
          _lowerCAmelCase       :   Dict  =    DummyModel()
          _lowerCAmelCase       :   Any  =    torch.optim.Adam(params=model.parameters()    ,lr=1E-3      )
          _lowerCAmelCase,					_lowerCAmelCase       :   int  =    dummy_dataloaders()
          _lowerCAmelCase       :   str  =    Accelerator()
          _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   int  =    accelerator.prepare(
              _A    ,_A    ,_A    ,_A      )
          accelerator.load_state(_A      )
          ((_lowerCAmelCase),					(_lowerCAmelCase))       :   List[str]  =    model.a.item(), model.b.item()
          _lowerCAmelCase       :   Any  =    optimizer.state_dict()
          self.assertEqual(_A    ,_A      )
          self.assertEqual(_A    ,_A      )
          self.assertEqual(_A    ,_A      )

          _lowerCAmelCase       :   List[str]  =    train(2    ,_A    ,_A    ,_A    ,_A      )
          # Save everything
          _lowerCAmelCase       :   List[str]  =    os.path.join(_A    ,'checkpoint'      )
          accelerator.save_state(_A      )

          # Load everything back in and make sure all states work
          accelerator.load_state(_A      )
          test_rands += train(1    ,_A    ,_A    ,_A    ,_A      )
          ((_lowerCAmelCase),					(_lowerCAmelCase))       :   Tuple  =    model.a.item(), model.b.item()
          _lowerCAmelCase       :   str  =    optimizer.state_dict()
          self.assertEqual(_A    ,_A      )
          self.assertEqual(_A    ,_A      )
          self.assertEqual(_A    ,_A      )
          self.assertEqual(_A    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42      )
          _lowerCAmelCase       :   int  =    DummyModel()
          _lowerCAmelCase       :   List[Any]  =    torch.optim.Adam(params=model.parameters()    ,lr=1E-3      )
          _lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    dummy_dataloaders()
          _lowerCAmelCase       :   Union[str, Any]  =    ProjectConfiguration(automatic_checkpoint_naming=_A      )

          # Train baseline
          _lowerCAmelCase       :   List[Any]  =    Accelerator(project_dir=_A    ,project_config=_A      )
          _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   str  =    accelerator.prepare(
              _A    ,_A    ,_A    ,_A      )
          # Save initial
          accelerator.save_state()
          ((_lowerCAmelCase),					(_lowerCAmelCase))       :   Union[str, Any]  =    model.a.item(), model.b.item()
          _lowerCAmelCase       :   str  =    optimizer.state_dict()
          _lowerCAmelCase       :   Optional[Any]  =    train(3    ,_A    ,_A    ,_A    ,_A      )
          ((_lowerCAmelCase),					(_lowerCAmelCase))       :   int  =    model.a.item(), model.b.item()
          _lowerCAmelCase       :   Union[str, Any]  =    optimizer.state_dict()

          # Train partially
          set_seed(42      )
          _lowerCAmelCase       :   List[Any]  =    DummyModel()
          _lowerCAmelCase       :   Tuple  =    torch.optim.Adam(params=model.parameters()    ,lr=1E-3      )
          _lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    dummy_dataloaders()
          _lowerCAmelCase       :   Dict  =    ProjectConfiguration(iteration=1    ,automatic_checkpoint_naming=_A      )
          _lowerCAmelCase       :   List[str]  =    Accelerator(project_dir=_A    ,project_config=_A      )
          _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    accelerator.prepare(
              _A    ,_A    ,_A    ,_A      )
          accelerator.load_state(os.path.join(_A    ,'checkpoints'    ,'checkpoint_0'      )      )
          ((_lowerCAmelCase),					(_lowerCAmelCase))       :   List[str]  =    model.a.item(), model.b.item()
          _lowerCAmelCase       :   List[str]  =    optimizer.state_dict()
          self.assertEqual(_A    ,_A      )
          self.assertEqual(_A    ,_A      )
          self.assertEqual(_A    ,_A      )

          _lowerCAmelCase       :   Dict  =    train(2    ,_A    ,_A    ,_A    ,_A      )
          # Save everything
          accelerator.save_state()

          # Load everything back in and make sure all states work
          accelerator.load_state(os.path.join(_A    ,'checkpoints'    ,'checkpoint_1'      )      )
          test_rands += train(1    ,_A    ,_A    ,_A    ,_A      )
          ((_lowerCAmelCase),					(_lowerCAmelCase))       :   str  =    model.a.item(), model.b.item()
          _lowerCAmelCase       :   Dict  =    optimizer.state_dict()
          self.assertEqual(_A    ,_A      )
          self.assertEqual(_A    ,_A      )
          self.assertEqual(_A    ,_A      )
          self.assertEqual(_A    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    torch.tensor([1, 2, 3]      )
      _lowerCAmelCase       :   Union[str, Any]  =    torch.tensor([2, 3, 4]      )
      _lowerCAmelCase       :   str  =    DummyModel()
      _lowerCAmelCase       :   Union[str, Any]  =    torch.optim.Adam(net.parameters()      )
      _lowerCAmelCase       :   Any  =    Accelerator()
      with self.assertRaises(_A      ) as ve:
          accelerator.register_for_checkpointing(_A    ,_A    ,_A    ,_A      )
      _lowerCAmelCase       :   int  =    str(ve.exception      )
      self.assertTrue('Item at index 0' in message      )
      self.assertTrue('Item at index 1' in message      )
      self.assertFalse('Item at index 2' in message      )
      self.assertFalse('Item at index 3' in message      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42      )
          _lowerCAmelCase       :   str  =    DummyModel()
          _lowerCAmelCase       :   Optional[Any]  =    torch.optim.Adam(params=model.parameters()    ,lr=1E-3      )
          _lowerCAmelCase       :   Any  =    torch.optim.lr_scheduler.StepLR(_A    ,step_size=1    ,gamma=0.9_9      )
          _lowerCAmelCase,					_lowerCAmelCase       :   Optional[Any]  =    dummy_dataloaders()
          _lowerCAmelCase       :   Optional[Any]  =    ProjectConfiguration(automatic_checkpoint_naming=_A      )
          # Train baseline
          _lowerCAmelCase       :   Optional[Any]  =    Accelerator(project_dir=_A    ,project_config=_A      )
          _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    accelerator.prepare(
              _A    ,_A    ,_A    ,_A    ,_A      )
          # Save initial
          accelerator.save_state()
          _lowerCAmelCase       :   str  =    scheduler.state_dict()
          train(3    ,_A    ,_A    ,_A    ,_A    ,_A      )
          self.assertNotEqual(_A    ,scheduler.state_dict()      )

          # Load everything back in and make sure all states work
          accelerator.load_state(os.path.join(_A    ,'checkpoints'    ,'checkpoint_0'      )      )
          self.assertEqual(_A    ,scheduler.state_dict()      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42      )
          _lowerCAmelCase       :   Any  =    DummyModel()
          _lowerCAmelCase       :   Tuple  =    ProjectConfiguration(automatic_checkpoint_naming=_A    ,total_limit=2      )
          # Train baseline
          _lowerCAmelCase       :   int  =    Accelerator(project_dir=_A    ,project_config=_A      )
          _lowerCAmelCase       :   Dict  =    accelerator.prepare(_A      )
          # Save 3 states:
          for _ in range(11      ):
              accelerator.save_state()
          self.assertTrue(not os.path.exists(os.path.join(_A    ,'checkpoints'    ,'checkpoint_0'      )      )      )
          self.assertTrue(os.path.exists(os.path.join(_A    ,'checkpoints'    ,'checkpoint_9'      )      )      )
          self.assertTrue(os.path.exists(os.path.join(_A    ,'checkpoints'    ,'checkpoint_10'      )      )      )







  @require_cuda
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__      )]
      execute_subprocess_async(_A    ,env=os.environ.copy()      )


if __name__ == "__main__":
 _lowerCAmelCase						=		"""/tmp/accelerate/state_checkpointing"""
 _lowerCAmelCase						=		DummyModel()
 _lowerCAmelCase						=		torch.optim.Adam(params=model.parameters(), lr=1E-3)
 _lowerCAmelCase						=		torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
 _lowerCAmelCase	,   _lowerCAmelCase						=		dummy_dataloaders()
 _lowerCAmelCase						=		ProjectConfiguration(automatic_checkpoint_naming=True)
 # Train baseline
 _lowerCAmelCase						=		Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="""no""")
 if accelerator.process_index == 0:
  if os.path.exists(savedir):
   shutil.rmtree(savedir)
  os.makedirs(savedir)
 _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase						=		accelerator.prepare(
     model, optimizer, train_dataloader, valid_dataloader, scheduler
 )
 _lowerCAmelCase	,   _lowerCAmelCase						=		accelerator.prepare(model, optimizer)
 train(3, model, train_dataloader, optimizer, accelerator, scheduler)
 # Check that the intial optimizer is loaded on the GPU
 for group in optimizer.param_groups:
  _lowerCAmelCase						=		group["""params"""][0].device
  break
 assert param_device.type == accelerator.device.type
 _lowerCAmelCase						=		model.cpu()
 accelerator.wait_for_everyone()
 accelerator.save_state()
 accelerator.wait_for_everyone()

 # Check CPU state
 accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""cpu""")
 for group in optimizer.param_groups:
  _lowerCAmelCase						=		group["""params"""][0].device
  break
 assert (
     param_device.type == torch.device("""cpu""").type
 ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

 # Check device state
 model.to(accelerator.device)
 accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""on_device""")
 for group in optimizer.param_groups:
  _lowerCAmelCase						=		group["""params"""][0].device
  break
 assert (
     param_device.type == accelerator.device.type
 ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

 # Check error
 with pytest.raises(TypeError, match="""Unsupported optimizer map location passed"""):
  accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""invalid""")
 accelerator.wait_for_everyone()
 if accelerator.process_index == 0:
  shutil.rmtree(savedir)
 accelerator.wait_for_everyone()








"""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    (    a__							,							a__						):

  @register_to_config
  def __init__(		self    ,_A = 128    ,_A = 256    ,_A = 2_0_0_0.0    ,_A = 768    ,_A = 12    ,_A = 12    ,_A = 64    ,_A = 2048    ,_A = 0.1    ,):
      '''simple docstring'''

      super().__init__()

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

      _lowerCAmelCase       :   Any  =    nn.Embedding(_A    ,_A      )
      _lowerCAmelCase       :   Tuple  =    False

      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )

      _lowerCAmelCase       :   int  =    nn.Dropout(p=_A      )

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

      _lowerCAmelCase       :   Optional[Any]  =    TaLayerNorm(_A      )

      _lowerCAmelCase       :   List[str]  =    nn.Dropout(p=_A      )
      _lowerCAmelCase       :   Optional[Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

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







  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    decoder_input_tokens.shape
      assert decoder_noise_time.shape == (batch,)

      # decoder_noise_time is in [0, 1), so rescale to expected timing range.
      _lowerCAmelCase       :   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      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.conditioning_emb(_A      ).unsqueeze(1      )

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

      _lowerCAmelCase       :   str  =    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       :   Union[str, Any]  =    torch.broadcast_to(
          torch.arange(_A    ,device=decoder_input_tokens.device      )    ,(batch, seq_length)    ,)

      _lowerCAmelCase       :   Any  =    self.position_encoding(_A      )

      _lowerCAmelCase       :   str  =    self.continuous_inputs_projection(_A      )
      inputs += position_encodings
      _lowerCAmelCase       :   int  =    self.dropout(_A      )

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

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

      # cross attend style: concat encodings
      _lowerCAmelCase       :   Dict  =    torch.cat([x[0] for x in encodings_and_encdec_masks]    ,dim=1      )
      _lowerCAmelCase       :   Tuple  =    torch.cat([x[1] for x in encodings_and_encdec_masks]    ,dim=-1      )

      for lyr in self.decoders:
          _lowerCAmelCase       :   Tuple  =    lyr(
              _A    ,conditioning_emb=_A    ,encoder_hidden_states=_A    ,encoder_attention_mask=_A    ,)[0]

      _lowerCAmelCase       :   Any  =    self.decoder_norm(_A      )
      _lowerCAmelCase       :   List[Any]  =    self.post_dropout(_A      )

      _lowerCAmelCase       :   int  =    self.spec_out(_A      )
      return spec_out







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A=1E-6      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Optional[Any]  =    nn.ModuleList()

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

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

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







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,_A=None    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self.layer[0](
          _A    ,conditioning_emb=_A    ,attention_mask=_A    ,)

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

          _lowerCAmelCase       :   str  =    self.layer[1](
              _A    ,key_value_states=_A    ,attention_mask=_A    ,)

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

      return (hidden_states,)







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

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







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.layer_norm(_A      )

      if conditioning_emb is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    self.FiLMLayer(_A    ,_A      )

      # Self-attention block
      _lowerCAmelCase       :   Union[str, Any]  =    self.attention(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    hidden_states + self.dropout(_A      )

      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   List[str]  =    Attention(query_dim=_A    ,heads=_A    ,dim_head=_A    ,out_bias=_A    ,scale_qk=_A      )
      _lowerCAmelCase       :   Optional[int]  =    TaLayerNorm(_A    ,eps=_A      )
      _lowerCAmelCase       :   Tuple  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.layer_norm(_A      )
      _lowerCAmelCase       :   str  =    self.attention(
          _A    ,encoder_hidden_states=_A    ,attention_mask=attention_mask.squeeze(1      )    ,)
      _lowerCAmelCase       :   Any  =    hidden_states + self.dropout(_A      )
      return layer_output







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

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







  def __lowerCamelCase		(		self    ,_A    ,_A=None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.layer_norm(_A      )
      if conditioning_emb is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    self.film(_A    ,_A      )

      _lowerCAmelCase       :   str  =    self.DenseReluDense(_A      )
      _lowerCAmelCase       :   Tuple  =    hidden_states + self.dropout(_A      )
      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Any  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Dropout(_A      )
      _lowerCAmelCase       :   int  =    NewGELUActivation()







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

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

      _lowerCAmelCase       :   Dict  =    self.wo(_A      )
      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A=1E-6      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Parameter(torch.ones(_A      )      )
      _lowerCAmelCase       :   Optional[int]  =    eps







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

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

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

      return self.weight * hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __lowerCamelCase		(		self    ,_A      ):
      '''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(_A    ,3.0      ))      ))







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   List[str]  =    nn.Linear(_A    ,out_features * 2    ,bias=_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

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





