





import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self					)						->							Dict:
										super().__init__()
										_snake_case													=			nn.Linear(3			,  4					)
										_snake_case													=			nn.BatchNormad(4					)
										_snake_case													=			nn.Linear(4			,  5					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tuple:
										return self.lineara(self.batchnorm(self.lineara(lowerCAmelCase_					)					)					)





class      UpperCamelCase_						(					_lowerCamelCase   ):




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							Dict:
										return (args[0] + 1,) + args[1:], kwargs





class      UpperCamelCase_						(					_lowerCamelCase   ):




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							int:
										return output + 1





class      UpperCamelCase_						(					unittest.TestCase   ):




					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										_snake_case													=			ModelForTest()
										_snake_case													=			ModelHook()

										add_hook_to_module(lowerCAmelCase_			,  lowerCAmelCase_					)
										self.assertEqual(test_model._hf_hook			,  lowerCAmelCase_					)
										self.assertTrue(hasattr(lowerCAmelCase_			,  '_old_forward'					)					)

										# Check adding the hook did not change the name or the signature
										self.assertEqual(test_model.forward.__name__			,  'forward'					)
										self.assertListEqual(list(inspect.signature(test_model.forward					).parameters					)			,  ['x']					)

										remove_hook_from_module(lowerCAmelCase_					)
										self.assertFalse(hasattr(lowerCAmelCase_			,  '_hf_hook'					)					)
										self.assertFalse(hasattr(lowerCAmelCase_			,  '_old_forward'					)					)




					def 		lowerCAmelCase     (						self					)						->							Dict:
										_snake_case													=			ModelForTest()
										_snake_case													=			ModelHook()

										add_hook_to_module(lowerCAmelCase_			,  lowerCAmelCase_					)
										add_hook_to_module(lowerCAmelCase_			,  lowerCAmelCase_			,  append=lowerCAmelCase_					)

										self.assertEqual(isinstance(test_model._hf_hook			,  lowerCAmelCase_					)			,  lowerCAmelCase_					)
										self.assertEqual(len(test_model._hf_hook.hooks					)			,  2					)
										self.assertTrue(hasattr(lowerCAmelCase_			,  '_old_forward'					)					)

										# Check adding the hook did not change the name or the signature
										self.assertEqual(test_model.forward.__name__			,  'forward'					)
										self.assertListEqual(list(inspect.signature(test_model.forward					).parameters					)			,  ['x']					)

										remove_hook_from_module(lowerCAmelCase_					)
										self.assertFalse(hasattr(lowerCAmelCase_			,  '_hf_hook'					)					)
										self.assertFalse(hasattr(lowerCAmelCase_			,  '_old_forward'					)					)




					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			ModelForTest()
										_snake_case													=			torch.randn(2			,  3					)
										_snake_case													=			test_model(x + 1					)
										_snake_case													=			test_model(x + 2					)

										_snake_case													=			PreForwardHook()
										add_hook_to_module(lowerCAmelCase_			,  lowerCAmelCase_					)
										_snake_case													=			test_model(lowerCAmelCase_					)
										self.assertTrue(torch.allclose(lowerCAmelCase_			,  lowerCAmelCase_			,  atol=1E-5					)					)

										# Attaching a hook to a model when it already has one replaces, does not chain
										_snake_case													=			PreForwardHook()
										add_hook_to_module(lowerCAmelCase_			,  lowerCAmelCase_					)
										_snake_case													=			test_model(lowerCAmelCase_					)
										self.assertTrue(torch.allclose(lowerCAmelCase_			,  lowerCAmelCase_			,  atol=1E-5					)					)

										# You need to use the sequential hook to chain two or more hooks
										_snake_case													=			SequentialHook(PreForwardHook()			,  PreForwardHook()					)
										add_hook_to_module(lowerCAmelCase_			,  lowerCAmelCase_					)

										_snake_case													=			test_model(lowerCAmelCase_					)
										assert torch.allclose(lowerCAmelCase_			,  lowerCAmelCase_			,  atol=1E-5					)




					def 		lowerCAmelCase     (						self					)						->							Any:
										_snake_case													=			ModelForTest()
										_snake_case													=			torch.randn(2			,  3					)
										_snake_case													=			test_model(lowerCAmelCase_					)

										_snake_case													=			PostForwardHook()
										add_hook_to_module(lowerCAmelCase_			,  lowerCAmelCase_					)
										_snake_case													=			test_model(lowerCAmelCase_					)
										self.assertTrue(torch.allclose(lowerCAmelCase_			,  output + 1			,  atol=1E-5					)					)

										# Attaching a hook to a model when it already has one replaces, does not chain
										_snake_case													=			PostForwardHook()
										add_hook_to_module(lowerCAmelCase_			,  lowerCAmelCase_					)
										_snake_case													=			test_model(lowerCAmelCase_					)
										self.assertTrue(torch.allclose(lowerCAmelCase_			,  output + 1			,  atol=1E-5					)					)

										# You need to use the sequential hook to chain two or more hooks
										_snake_case													=			SequentialHook(PostForwardHook()			,  PostForwardHook()					)
										add_hook_to_module(lowerCAmelCase_			,  lowerCAmelCase_					)

										_snake_case													=			test_model(lowerCAmelCase_					)
										assert torch.allclose(lowerCAmelCase_			,  output + 2			,  atol=1E-5					)




					def 		lowerCAmelCase     (						self					)						->							int:
										_snake_case													=			ModelForTest()
										_snake_case													=			torch.randn(2			,  3					)
										_snake_case													=			test_model(lowerCAmelCase_					)

										_snake_case													=			PostForwardHook()
										add_hook_to_module(lowerCAmelCase_			,  lowerCAmelCase_					)
										_snake_case													=			test_model(lowerCAmelCase_					)
										self.assertTrue(torch.allclose(lowerCAmelCase_			,  output + 1					)					)
										self.assertTrue(outputa.requires_grad					)

										_snake_case													=			True
										_snake_case													=			test_model(lowerCAmelCase_					)
										self.assertFalse(outputa.requires_grad					)




					@require_multi_gpu
					def 		lowerCAmelCase     (						self					)						->							List[str]:
										_snake_case													=			ModelForTest()
										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)

										# This will move each submodule on different devices
										add_hook_to_module(model.lineara			,  AlignDevicesHook(execution_device=0					)					)
										add_hook_to_module(model.batchnorm			,  AlignDevicesHook(execution_device=0					)					)
										add_hook_to_module(model.lineara			,  AlignDevicesHook(execution_device=1					)					)

										self.assertEqual(model.lineara.weight.device			,  torch.device(0					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device(0					)					)
										self.assertEqual(model.batchnorm.running_mean.device			,  torch.device(0					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device(1					)					)

										# We can still make a forward pass. The input does not need to be on any particular device
										_snake_case													=			torch.randn(2			,  3					)
										_snake_case													=			model(lowerCAmelCase_					)
										self.assertEqual(output.device			,  torch.device(1					)					)

										# We can add a general hook to put back output on same device as input.
										add_hook_to_module(lowerCAmelCase_			,  AlignDevicesHook(io_same_device=lowerCAmelCase_					)					)
										_snake_case													=			torch.randn(2			,  3					).to(0					)
										_snake_case													=			model(lowerCAmelCase_					)
										self.assertEqual(output.device			,  torch.device(0					)					)




					def 		lowerCAmelCase     (						self					)						->							Any:
										_snake_case													=			ModelForTest()

										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)

										# This will move each submodule on different devices
										_snake_case													=			{'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}

										add_hook_to_module(model.lineara			,  AlignDevicesHook(**lowerCAmelCase_					)					)
										add_hook_to_module(model.batchnorm			,  AlignDevicesHook(**lowerCAmelCase_					)					)
										add_hook_to_module(model.lineara			,  AlignDevicesHook(**lowerCAmelCase_					)					)

										# Parameters have been offloaded, so on the meta device
										self.assertEqual(model.lineara.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('meta'					)					)
										# Buffers are not included in the offload by default, so are on the execution device
										_snake_case													=			torch.device(hook_kwargs['execution_device']					)
										self.assertEqual(model.batchnorm.running_mean.device			,  lowerCAmelCase_					)

										_snake_case													=			torch.randn(2			,  3					)
										_snake_case													=			model(lowerCAmelCase_					)
										self.assertEqual(output.device			,  lowerCAmelCase_					)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_module(model.lineara					)
										remove_hook_from_module(model.batchnorm					)
										remove_hook_from_module(model.lineara					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)

										# Now test with buffers included in the offload
										_snake_case													=			{
										    'execution_device': 0 if torch.cuda.is_available() else 'cpu',
										    'offload': True,
										    'offload_buffers': True,
										}

										add_hook_to_module(model.lineara			,  AlignDevicesHook(**lowerCAmelCase_					)					)
										add_hook_to_module(model.batchnorm			,  AlignDevicesHook(**lowerCAmelCase_					)					)
										add_hook_to_module(model.lineara			,  AlignDevicesHook(**lowerCAmelCase_					)					)

										# Parameters have been offloaded, so on the meta device, buffers included
										self.assertEqual(model.lineara.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.batchnorm.running_mean.device			,  torch.device('meta'					)					)

										_snake_case													=			torch.randn(2			,  3					)
										_snake_case													=			model(lowerCAmelCase_					)
										self.assertEqual(output.device			,  lowerCAmelCase_					)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_module(model.lineara					)
										remove_hook_from_module(model.batchnorm					)
										remove_hook_from_module(model.lineara					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)




					def 		lowerCAmelCase     (						self					)						->							Any:
										_snake_case													=			ModelForTest()

										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)

										# This will move each submodule on different devices
										_snake_case													=			0 if torch.cuda.is_available() else 'cpu'
										attach_align_device_hook(lowerCAmelCase_			,  execution_device=lowerCAmelCase_			,  offload=lowerCAmelCase_					)

										# Parameters have been offloaded, so on the meta device
										self.assertEqual(model.lineara.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('meta'					)					)
										# Buffers are not included in the offload by default, so are on the execution device
										_snake_case													=			torch.device(lowerCAmelCase_					)
										self.assertEqual(model.batchnorm.running_mean.device			,  lowerCAmelCase_					)

										_snake_case													=			torch.randn(2			,  3					)
										_snake_case													=			model(lowerCAmelCase_					)
										self.assertEqual(output.device			,  lowerCAmelCase_					)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(lowerCAmelCase_					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)

										# Now test with buffers included in the offload
										attach_align_device_hook(lowerCAmelCase_			,  execution_device=lowerCAmelCase_			,  offload=lowerCAmelCase_			,  offload_buffers=lowerCAmelCase_					)

										# Parameters have been offloaded, so on the meta device, buffers included
										self.assertEqual(model.lineara.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.batchnorm.running_mean.device			,  torch.device('meta'					)					)

										_snake_case													=			torch.randn(2			,  3					)
										_snake_case													=			model(lowerCAmelCase_					)
										self.assertEqual(output.device			,  lowerCAmelCase_					)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(lowerCAmelCase_					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)






					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										_snake_case													=			ModelForTest()

										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)

										# This will move each submodule on different devices
										_snake_case													=			0 if torch.cuda.is_available() else 'cpu'
										attach_align_device_hook(
										    lowerCAmelCase_			,  execution_device=lowerCAmelCase_			,  offload=lowerCAmelCase_			,  weights_map=model.state_dict()					)

										# Parameters have been offloaded, so on the meta device
										self.assertEqual(model.lineara.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('meta'					)					)
										# Buffers are not included in the offload by default, so are on the execution device
										_snake_case													=			torch.device(lowerCAmelCase_					)
										self.assertEqual(model.batchnorm.running_mean.device			,  lowerCAmelCase_					)

										_snake_case													=			torch.randn(2			,  3					)
										_snake_case													=			model(lowerCAmelCase_					)
										self.assertEqual(output.device			,  lowerCAmelCase_					)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(lowerCAmelCase_					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)

										# Now test with buffers included in the offload
										attach_align_device_hook(
										    lowerCAmelCase_			,  execution_device=lowerCAmelCase_			,  offload=lowerCAmelCase_			,  weights_map=model.state_dict()			,  offload_buffers=lowerCAmelCase_			,  )

										# Parameters have been offloaded, so on the meta device, buffers included
										self.assertEqual(model.lineara.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('meta'					)					)
										self.assertEqual(model.batchnorm.running_mean.device			,  torch.device('meta'					)					)

										_snake_case													=			torch.randn(2			,  3					)
										_snake_case													=			model(lowerCAmelCase_					)
										self.assertEqual(output.device			,  lowerCAmelCase_					)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(lowerCAmelCase_					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.batchnorm.weight.device			,  torch.device('cpu'					)					)
										self.assertEqual(model.lineara.weight.device			,  torch.device('cpu'					)					)










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_						(					_lowerCamelCase							,					_lowerCamelCase   ):




					@register_to_config
					def __init__(						self			,  lowerCAmelCase_ = 128			,  lowerCAmelCase_ = 256			,  lowerCAmelCase_ = 20_00.0			,  lowerCAmelCase_ = 768			,  lowerCAmelCase_ = 12			,  lowerCAmelCase_ = 12			,  lowerCAmelCase_ = 64			,  lowerCAmelCase_ = 2048			,  lowerCAmelCase_ = 0.1			,  )						->							Union[str, Any]:
										super().__init__()

										_snake_case													=			nn.Sequential(
										    nn.Linear(lowerCAmelCase_			,  d_model * 4			,  bias=lowerCAmelCase_					)			,  nn.SiLU()			,  nn.Linear(d_model * 4			,  d_model * 4			,  bias=lowerCAmelCase_					)			,  nn.SiLU()			,  )

										_snake_case													=			nn.Embedding(lowerCAmelCase_			,  lowerCAmelCase_					)
										_snake_case													=			False

										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)

										_snake_case													=			nn.Dropout(p=lowerCAmelCase_					)

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

										_snake_case													=			TaLayerNorm(lowerCAmelCase_					)

										_snake_case													=			nn.Dropout(p=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Tuple:
										_snake_case													=			torch.mul(query_input.unsqueeze(-1					)			,  key_input.unsqueeze(-2					)					)
										return mask.unsqueeze(-3					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case							,       _snake_case							,       _snake_case													=			decoder_input_tokens.shape
										assert decoder_noise_time.shape == (batch,)

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

										_snake_case													=			self.conditioning_emb(lowerCAmelCase_					).unsqueeze(1					)

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

										_snake_case													=			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.
										_snake_case													=			torch.broadcast_to(
										    torch.arange(lowerCAmelCase_			,  device=decoder_input_tokens.device					)			,  (batch, seq_length)			,  )

										_snake_case													=			self.position_encoding(lowerCAmelCase_					)

										_snake_case													=			self.continuous_inputs_projection(lowerCAmelCase_					)
										inputs += position_encodings
										_snake_case													=			self.dropout(lowerCAmelCase_					)

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

										# Translate encoding masks to encoder-decoder masks.
										_snake_case													=			[(x, self.encoder_decoder_mask(lowerCAmelCase_			,  lowerCAmelCase_					)) for x, y in encodings_and_masks]

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

										for lyr in self.decoders:
															_snake_case													=			lyr(
															    lowerCAmelCase_			,  conditioning_emb=lowerCAmelCase_			,  encoder_hidden_states=lowerCAmelCase_			,  encoder_attention_mask=lowerCAmelCase_			,  )[0]

										_snake_case													=			self.decoder_norm(lowerCAmelCase_					)
										_snake_case													=			self.post_dropout(lowerCAmelCase_					)

										_snake_case													=			self.spec_out(lowerCAmelCase_					)
										return spec_out





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=1E-6					)						->							Tuple:
										super().__init__()
										_snake_case													=			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			,  )						->							Tuple:
										_snake_case													=			self.layer[0](
										    lowerCAmelCase_			,  conditioning_emb=lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  )

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

															_snake_case													=			self.layer[1](
															    lowerCAmelCase_			,  key_value_states=lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  )

										# Apply Film Conditional Feed Forward layer
										_snake_case													=			self.layer[-1](lowerCAmelCase_			,  lowerCAmelCase_					)

										return (hidden_states,)





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Tuple:
										super().__init__()
										_snake_case													=			TaLayerNorm(lowerCAmelCase_					)
										_snake_case													=			TaFiLMLayer(in_features=d_model * 4			,  out_features=lowerCAmelCase_					)
										_snake_case													=			Attention(query_dim=lowerCAmelCase_			,  heads=lowerCAmelCase_			,  dim_head=lowerCAmelCase_			,  out_bias=lowerCAmelCase_			,  scale_qk=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							str:
										# pre_self_attention_layer_norm
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)

										if conditioning_emb is not None:
															_snake_case													=			self.FiLMLayer(lowerCAmelCase_			,  lowerCAmelCase_					)

										# Self-attention block
										_snake_case													=			self.attention(lowerCAmelCase_					)

										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)

										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							str:
										super().__init__()
										_snake_case													=			Attention(query_dim=lowerCAmelCase_			,  heads=lowerCAmelCase_			,  dim_head=lowerCAmelCase_			,  out_bias=lowerCAmelCase_			,  scale_qk=lowerCAmelCase_					)
										_snake_case													=			TaLayerNorm(lowerCAmelCase_			,  eps=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							Dict:
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)
										_snake_case													=			self.attention(
										    lowerCAmelCase_			,  encoder_hidden_states=lowerCAmelCase_			,  attention_mask=attention_mask.squeeze(1					)			,  )
										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)
										return layer_output





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							List[str]:
										super().__init__()
										_snake_case													=			TaDenseGatedActDense(d_model=lowerCAmelCase_			,  d_ff=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_					)
										_snake_case													=			TaFiLMLayer(in_features=d_model * 4			,  out_features=lowerCAmelCase_					)
										_snake_case													=			TaLayerNorm(lowerCAmelCase_			,  eps=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None					)						->							Union[str, Any]:
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)
										if conditioning_emb is not None:
															_snake_case													=			self.film(lowerCAmelCase_			,  lowerCAmelCase_					)

										_snake_case													=			self.DenseReluDense(lowerCAmelCase_					)
										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)
										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Union[str, Any]:
										super().__init__()
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)
										_snake_case													=			NewGELUActivation()






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Any:
										_snake_case													=			self.act(self.wi_a(lowerCAmelCase_					)					)
										_snake_case													=			self.wi_a(lowerCAmelCase_					)
										_snake_case													=			hidden_gelu * hidden_linear
										_snake_case													=			self.dropout(lowerCAmelCase_					)

										_snake_case													=			self.wo(lowerCAmelCase_					)
										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_=1E-6					)						->							str:
										super().__init__()
										_snake_case													=			nn.Parameter(torch.ones(lowerCAmelCase_					)					)
										_snake_case													=			eps






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							int:
										# 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

										_snake_case													=			hidden_states.to(torch.floataa					).pow(2					).mean(-1			,  keepdim=lowerCAmelCase_					)
										_snake_case													=			hidden_states * torch.rsqrt(variance + self.variance_epsilon					)

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

										return self.weight * hidden_states





class      UpperCamelCase_						(					nn.Module   ):




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





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Any:
										super().__init__()
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  out_features * 2			,  bias=lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case													=			self.scale_bias(lowerCAmelCase_					)
										_snake_case							,       _snake_case													=			torch.chunk(lowerCAmelCase_			,  2			,  -1					)
										_snake_case													=			x * (1 + scale) + shift
										return x



