from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Dict:



			"""simple docstring"""







			for param, grad_param in zip(model_a.parameters(),		model_b.parameters()       ):
						if not param.requires_grad:
									continue
						if not did_step:
									# Grads should not be in sync
									assert (
									    torch.allclose(param.grad,		grad_param.grad       ) is False
									), f'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
						else:
									# Grads should be in sync
									assert (
									    torch.allclose(param.grad,		grad_param.grad       ) is True
									), f'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=True       )->       int:



			"""simple docstring"""







			model.train()
			lowercase							=		model(UpperCAmelCase       )
			lowercase							=		F.mse_loss(UpperCAmelCase,		target.to(output.device       )       )
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(UpperCAmelCase       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase=False       )->       str:



			"""simple docstring"""







			set_seed(42       )
			lowercase							=		RegressionModel()
			lowercase							=		deepcopy(UpperCAmelCase       )
			lowercase							=		RegressionDataset(length=80       )
			lowercase							=		DataLoader(UpperCAmelCase,		batch_size=16       )
			model.to(accelerator.device       )
			if sched:
						lowercase							=		AdamW(params=model.parameters(),		lr=1e-3       )
						lowercase							=		AdamW(params=ddp_model.parameters(),		lr=1e-3       )
						lowercase							=		LambdaLR(UpperCAmelCase,		lr_lambda=lambda UpperCAmelCase       : epoch**0.65       )
						lowercase							=		LambdaLR(UpperCAmelCase,		lr_lambda=lambda UpperCAmelCase       : epoch**0.65       )
			# Make a copy of `model`
			if sched:
						lowercase		,lowercase		,lowercase		,lowercase							=		accelerator.prepare(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
			else:
						lowercase		,lowercase							=		accelerator.prepare(UpperCAmelCase,		UpperCAmelCase       )
			if sched:
						return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
			return model, ddp_model, dataloader






def        __UpperCAmelCase    (       UpperCAmelCase       )->       int:



			"""simple docstring"""







			lowercase		,lowercase		,lowercase							=		get_training_setup(UpperCAmelCase       )
			# Use a single batch
			lowercase		,lowercase							=		next(iter(UpperCAmelCase       )       ).values()
			for iteration in range(3       ):
						# Gather the distributed inputs and targs for the base model
						lowercase		,lowercase							=		accelerator.gather((ddp_input, ddp_target)       )
						lowercase		,lowercase							=		input.to(accelerator.device       ), target.to(accelerator.device       )
						# Perform our initial ground truth step in non "DDP"
						step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(UpperCAmelCase       ):
												step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						else:
									# Sync grads
									step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )

						# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
						check_model_parameters(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						for param, ddp_param in zip(model.parameters(),		ddp_model.parameters()       ):
									if not param.requires_grad:
												continue
									assert torch.allclose(
									    param.grad,		ddp_param.grad       ), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

						# Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration       )
						lowercase							=		ddp_input[torch.randperm(len(UpperCAmelCase       )       )]






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







			lowercase		,lowercase		,lowercase							=		get_training_setup(UpperCAmelCase       )
			# Use a single batch
			lowercase		,lowercase							=		next(iter(UpperCAmelCase       )       ).values()
			for iteration in range(3       ):
						# Gather the distributed inputs and targs for the base model
						lowercase		,lowercase							=		accelerator.gather((ddp_input, ddp_target)       )
						lowercase		,lowercase							=		input.to(accelerator.device       ), target.to(accelerator.device       )
						# Perform our initial ground truth step in non "DDP"
						step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(UpperCAmelCase       ):
												step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						else:
									# Sync grads
									step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )

						# DDP model and model should only be in sync when not (iteration % 2 == 0)
						for param, ddp_param in zip(model.parameters(),		ddp_model.parameters()       ):
									if not param.requires_grad:
												continue
									if iteration % 2 == 0:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad,		ddp_param.grad       ) is False
												), f'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
									else:
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad,		ddp_param.grad       ) is True
												), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration       )
						lowercase							=		ddp_input[torch.randperm(len(UpperCAmelCase       )       )]






def        __UpperCAmelCase    (       UpperCAmelCase=False,		UpperCAmelCase=False       )->       Union[str, Any]:



			"""simple docstring"""







			lowercase							=		Accelerator(
			    split_batches=UpperCAmelCase,		dispatch_batches=UpperCAmelCase,		gradient_accumulation_steps=2       )
			# Test that context manager behaves properly
			lowercase		,lowercase		,lowercase							=		get_training_setup(UpperCAmelCase       )
			for iteration, batch in enumerate(UpperCAmelCase       ):
						lowercase		,lowercase							=		batch.values()
						# Gather the distributed inputs and targs for the base model
						lowercase		,lowercase							=		accelerator.gather((ddp_input, ddp_target)       )
						lowercase		,lowercase							=		input.to(accelerator.device       ), target.to(accelerator.device       )
						# Perform our initial ground truth step in non "DDP"
						step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						# Do "gradient accumulation" (noop)
						with accelerator.accumulate(UpperCAmelCase       ):
									step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )

						# DDP model and model should only be in sync when not (iteration % 2 == 0)
						for param, ddp_param in zip(model.parameters(),		ddp_model.parameters()       ):
									if not param.requires_grad:
												continue
									if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCAmelCase       ) - 1):
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad,		ddp_param.grad       ) is True
												), f'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
									else:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad,		ddp_param.grad       ) is False
												), f'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration       )
						lowercase							=		ddp_input[torch.randperm(len(UpperCAmelCase       )       )]
			GradientState._reset_state()






def        __UpperCAmelCase    (       UpperCAmelCase=False,		UpperCAmelCase=False       )->       int:



			"""simple docstring"""







			lowercase							=		Accelerator(
			    split_batches=UpperCAmelCase,		dispatch_batches=UpperCAmelCase,		gradient_accumulation_steps=2       )
			# Test that context manager behaves properly
			lowercase		,lowercase		,lowercase		,lowercase		,lowercase		,lowercase		,lowercase							=		get_training_setup(UpperCAmelCase,		UpperCAmelCase       )
			for iteration, batch in enumerate(UpperCAmelCase       ):
						lowercase		,lowercase							=		batch.values()
						# Gather the distributed inputs and targs for the base model
						lowercase		,lowercase							=		accelerator.gather((ddp_input, ddp_target)       )
						lowercase		,lowercase							=		input.to(accelerator.device       ), target.to(accelerator.device       )
						# Perform our initial ground truth step in non "DDP"
						model.train()
						ddp_model.train()
						step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						opt.step()

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCAmelCase       )):
									if split_batches:
												sched.step()
									else:
												for _ in range(accelerator.num_processes       ):
															sched.step()
						opt.zero_grad()
						# Perform gradient accumulation under wrapper
						with accelerator.accumulate(UpperCAmelCase       ):
									step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
									ddp_opt.step()
									ddp_sched.step()
									ddp_opt.zero_grad()

						# Learning rates should be the same
						assert (
						    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
						), f'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
						lowercase							=		(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCAmelCase       ))
						if accelerator.num_processes > 1:
									check_model_parameters(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						# Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration       )
			GradientState._reset_state()






def        __UpperCAmelCase    (       )->       Any:



			"""simple docstring"""







			lowercase							=		Accelerator()

			lowercase							=		RegressionDataset(length=80       )
			lowercase							=		DataLoader(UpperCAmelCase,		batch_size=16       )
			lowercase							=		RegressionDataset(length=96       )
			lowercase							=		DataLoader(UpperCAmelCase,		batch_size=16       )
			lowercase		,lowercase							=		accelerator.prepare(UpperCAmelCase,		UpperCAmelCase       )
			assert accelerator.gradient_state.active_dataloader is None
			for iteration, _ in enumerate(UpperCAmelCase       ):
						assert id(accelerator.gradient_state.active_dataloader       ) == id(UpperCAmelCase       )
						if iteration < len(UpperCAmelCase       ) - 1:
									assert not accelerator.gradient_state.end_of_dataloader
									if iteration == 1:
												for batch_num, _ in enumerate(UpperCAmelCase       ):
															assert id(accelerator.gradient_state.active_dataloader       ) == id(UpperCAmelCase       )
															if batch_num < len(UpperCAmelCase       ) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
															else:
																		assert accelerator.gradient_state.end_of_dataloader
						else:
									assert accelerator.gradient_state.end_of_dataloader
			assert accelerator.gradient_state.active_dataloader is None






def        __UpperCAmelCase    (       )->       Any:



			"""simple docstring"""







			lowercase							=		Accelerator()
			lowercase							=		accelerator.state
			if state.local_process_index == 0:
						print('''**Test `accumulate` gradient accumulation with dataloader break**'''       )
			test_dataloader_break()
			if state.distributed_type == DistributedType.NO:
						if state.local_process_index == 0:
									print('''**Test NOOP `no_sync` context manager**'''       )
						test_noop_sync(UpperCAmelCase       )
			if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
						if state.local_process_index == 0:
									print('''**Test Distributed `no_sync` context manager**'''       )
						test_distributed_sync(UpperCAmelCase       )
			if state.distributed_type == DistributedType.MULTI_GPU:
						for split_batch in [True, False]:
									for dispatch_batches in [True, False]:
												if state.local_process_index == 0:
															print(
															    '''**Test `accumulate` gradient accumulation, ''',		f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**',		)
												test_gradient_accumulation(UpperCAmelCase,		UpperCAmelCase       )

    # Currently will break on torch 2.0 +, need to investigate why
			if is_torch_version('''<''',		'''2.0'''       ) or state.distributed_type == DistributedType.NO:
						if state.local_process_index == 0:
									print(
									    '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''',		'''`split_batches=False`, `dispatch_batches=False`**''',		)
						test_gradient_accumulation_with_opt_and_scheduler()
						if state.distributed_type == DistributedType.MULTI_GPU:
									for split_batch in [True, False]:
												for dispatch_batches in [True, False]:
															if not split_batch and not dispatch_batches:
																		continue
															if state.local_process_index == 0:
																		print(
																		    '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''',		f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**',		)
															test_gradient_accumulation_with_opt_and_scheduler(UpperCAmelCase,		UpperCAmelCase       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       str:



			"""simple docstring"""







			main()


if __name__ == "__main__":
	main()






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					(    _A       ,     _A    ):

						@register_to_config
						def __init__(	self				:      List[Any]							,							__lowerCamelCase				:      int = 1_28							,							__lowerCamelCase				:      int = 2_56							,							__lowerCamelCase				:      float = 2000.0							,							__lowerCamelCase				:      int = 7_68							,							__lowerCamelCase				:      int = 12							,							__lowerCamelCase				:      int = 12							,							__lowerCamelCase				:      int = 64							,							__lowerCamelCase				:      int = 20_48							,							__lowerCamelCase				:      float = 0.1							,							)							->		Optional[int]:


									'''simple docstring'''

									super().__init__()

									lowercase							=		nn.Sequential(
									    nn.Linear(__lowerCamelCase							,							d_model * 4							,							bias=__lowerCamelCase   )							,							nn.SiLU()							,							nn.Linear(d_model * 4							,							d_model * 4							,							bias=__lowerCamelCase   )							,							nn.SiLU()							,							)

									lowercase							=		nn.Embedding(__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		False

									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )

									lowercase							=		nn.Dropout(p=__lowerCamelCase   )

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

									lowercase							=		TaLayerNorm(__lowerCamelCase   )

									lowercase							=		nn.Dropout(p=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )

						def        __a		(	self				:      Any							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

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



						def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Any   )							->		List[Any]:


									'''simple docstring'''

									lowercase		,lowercase		,lowercase							=		decoder_input_tokens.shape
									assert decoder_noise_time.shape == (batch,)

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

									lowercase							=		self.conditioning_emb(__lowerCamelCase   ).unsqueeze(1   )

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

									lowercase							=		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.
									lowercase							=		torch.broadcast_to(
									    torch.arange(__lowerCamelCase							,							device=decoder_input_tokens.device   )							,							(batch, seq_length)							,							)

									lowercase							=		self.position_encoding(__lowerCamelCase   )

									lowercase							=		self.continuous_inputs_projection(__lowerCamelCase   )
									inputs += position_encodings
									lowercase							=		self.dropout(__lowerCamelCase   )

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

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

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

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

									lowercase							=		self.decoder_norm(__lowerCamelCase   )
									lowercase							=		self.post_dropout(__lowerCamelCase   )

									lowercase							=		self.spec_out(__lowerCamelCase   )
									return spec_out






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      List[str]=1E-6   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		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        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      List[Any]=None							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      int=None							,							)							->		Any:


									'''simple docstring'''

									lowercase							=		self.layer[0](
									    __lowerCamelCase							,							conditioning_emb=__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							)

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

												lowercase							=		self.layer[1](
												    __lowerCamelCase							,							key_value_states=__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							)

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

									return (hidden_states,)






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Any   )							->		Dict:


									'''simple docstring'''

									super().__init__()
									lowercase							=		TaLayerNorm(__lowerCamelCase   )
									lowercase							=		TaFiLMLayer(in_features=d_model * 4							,							out_features=__lowerCamelCase   )
									lowercase							=		Attention(query_dim=__lowerCamelCase							,							heads=__lowerCamelCase							,							dim_head=__lowerCamelCase							,							out_bias=__lowerCamelCase							,							scale_qk=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Optional[int]=None							,							__lowerCamelCase				:      List[str]=None							,							)							->		Tuple:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )

									if conditioning_emb is not None:
												lowercase							=		self.FiLMLayer(__lowerCamelCase							,							__lowerCamelCase   )

									# Self-attention block
									lowercase							=		self.attention(__lowerCamelCase   )

									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )

									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      int							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any]   )							->		Any:


									'''simple docstring'''

									super().__init__()
									lowercase							=		Attention(query_dim=__lowerCamelCase							,							heads=__lowerCamelCase							,							dim_head=__lowerCamelCase							,							out_bias=__lowerCamelCase							,							scale_qk=__lowerCamelCase   )
									lowercase							=		TaLayerNorm(__lowerCamelCase							,							eps=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      List[str]=None							,							)							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )
									lowercase							=		self.attention(
									    __lowerCamelCase							,							encoder_hidden_states=__lowerCamelCase							,							attention_mask=attention_mask.squeeze(1   )							,							)
									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )
									return layer_output






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Tuple   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		TaDenseGatedActDense(d_model=__lowerCamelCase							,							d_ff=__lowerCamelCase							,							dropout_rate=__lowerCamelCase   )
									lowercase							=		TaFiLMLayer(in_features=d_model * 4							,							out_features=__lowerCamelCase   )
									lowercase							=		TaLayerNorm(__lowerCamelCase							,							eps=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      str							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      str=None   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )
									if conditioning_emb is not None:
												lowercase							=		self.film(__lowerCamelCase							,							__lowerCamelCase   )

									lowercase							=		self.DenseReluDense(__lowerCamelCase   )
									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )
									return hidden_states






class      __lowercase					(    nn.Module    ):

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


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )
									lowercase							=		NewGELUActivation()



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      Optional[int]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.act(self.wi_a(__lowerCamelCase   )   )
									lowercase							=		self.wi_a(__lowerCamelCase   )
									lowercase							=		hidden_gelu * hidden_linear
									lowercase							=		self.dropout(__lowerCamelCase   )

									lowercase							=		self.wo(__lowerCamelCase   )
									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Dict=1E-6   )							->		Optional[Any]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Parameter(torch.ones(__lowerCamelCase   )   )
									lowercase							=		eps



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      str   )							->		Optional[Any]:


									'''simple docstring'''

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

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

									return self.weight * hidden_states






class      __lowercase					(    nn.Module    ):

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      torch.Tensor   )							->		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    ):

						def __init__(	self				:      List[Any]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Tuple   )							->		Optional[int]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Linear(__lowerCamelCase							,							out_features * 2							,							bias=__lowerCamelCase   )



						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		self.scale_bias(__lowerCamelCase   )
									lowercase		,lowercase							=		torch.chunk(__lowerCamelCase							,							2							,							-1   )
									lowercase							=		x * (1 + scale) + shift
									return x





