
'''simple docstring'''

from typing import Any


class 	a__     :


	def __init__(self : List[Any],					__UpperCAmelCase : Any      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= data
					SCREAMING_SNAKE_CASE  :						Any      					= None






	def __repr__(self : str      )     ->			str:

					"""simple docstring"""

					return F'''Node({self.data})'''



class 	a__     :


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= None


	def __iter__(self : int      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.head
					while node:
									yield node.data
									SCREAMING_SNAKE_CASE  :						Tuple      					= node.next


	def __len__(self : str      )     ->			int:

					"""simple docstring"""

					return sum(1 for _ in self      )


	def __repr__(self : Optional[Any]      )     ->			str:

					"""simple docstring"""

					return "->".join([str(__UpperCAmelCase      ) for item in self]      )


	def __getitem__(self : Any,					__UpperCAmelCase : int      )     ->			Any:

					"""simple docstring"""

					if not 0 <= index < len(self      ):
									raise ValueError('''list index out of range.'''      )
					for i, node in enumerate(self      ):
									if i == index:
													return node
					return None


	def __setitem__(self : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : Any      )     ->			None:

					"""simple docstring"""

					if not 0 <= index < len(self      ):
									raise ValueError('''list index out of range.'''      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.head
					for _ in range(__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						str      					= current.next
					SCREAMING_SNAKE_CASE  :						str      					= data


	def 				lowercase__  (self : int,					__UpperCAmelCase : Any      )     ->			None:

					"""simple docstring"""

					self.insert_nth(len(self      ),					__UpperCAmelCase      )


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Any      )     ->			None:

					"""simple docstring"""

					self.insert_nth(0,					__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : int,					__UpperCAmelCase : Any      )     ->			None:

					"""simple docstring"""

					if not 0 <= index <= len(self      ):
									raise IndexError('''list index out of range'''      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= Node(__UpperCAmelCase      )
					if self.head is None:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= new_node
					elif index == 0:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.head  # link new_node to head
									SCREAMING_SNAKE_CASE  :						List[str]      					= new_node
					else:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.head
									for _ in range(index - 1      ):
													SCREAMING_SNAKE_CASE  :						Any      					= temp.next
									SCREAMING_SNAKE_CASE  :						Any      					= temp.next
									SCREAMING_SNAKE_CASE  :						List[Any]      					= new_node


	def 				lowercase__  (self : Any      )     ->			None:  # print every node data

					"""simple docstring"""

					print(self      )


	def 				lowercase__  (self : List[Any]      )     ->			Any:

					"""simple docstring"""

					return self.delete_nth(0      )


	def 				lowercase__  (self : Optional[Any]      )     ->			Any:  # delete from tail

					"""simple docstring"""

					return self.delete_nth(len(self      ) - 1      )


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : int = 0      )     ->			Any:

					"""simple docstring"""

					if not 0 <= index <= len(self      ) - 1:  # test if index is valid
									raise IndexError('''List index out of range.'''      )
					SCREAMING_SNAKE_CASE  :						str      					= self.head  # default first node
					if index == 0:
									SCREAMING_SNAKE_CASE  :						List[Any]      					= self.head.next
					else:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.head
									for _ in range(index - 1      ):
													SCREAMING_SNAKE_CASE  :						List[str]      					= temp.next
									SCREAMING_SNAKE_CASE  :						Dict      					= temp.next
									SCREAMING_SNAKE_CASE  :						Dict      					= temp.next.next
					return delete_node.data


	def 				lowercase__  (self : Any      )     ->			bool:

					"""simple docstring"""

					return self.head is None






	def 				lowercase__  (self : List[str]      )     ->			None:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= None
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.head

					while current:
									# Store the current node's next node.
									SCREAMING_SNAKE_CASE  :						int      					= current.next
									# Make the current node's next point backwards
									SCREAMING_SNAKE_CASE  :						int      					= prev
									# Make the previous node be the current node
									SCREAMING_SNAKE_CASE  :						str      					= current
									# Make the current node the next node (to progress iteration)
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= next_node
					# Return prev in order to put the head at the end
					SCREAMING_SNAKE_CASE  :						str      					= prev






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						int      					= LinkedList()
				assert linked_list.is_empty() is True
				assert str(_SCREAMING_SNAKE_CASE       ) == ""

				try:
								linked_list.delete_head()
								raise AssertionError  # This should not happen.
				except IndexError:
								assert True  # This should happen.

				try:
								linked_list.delete_tail()
								raise AssertionError  # This should not happen.
				except IndexError:
								assert True  # This should happen.

				for i in range(10       ):
								assert len(_SCREAMING_SNAKE_CASE       ) == i
								linked_list.insert_nth(_SCREAMING_SNAKE_CASE    ,	i + 1       )
				assert str(_SCREAMING_SNAKE_CASE       ) == "->".join(str(_SCREAMING_SNAKE_CASE       ) for i in range(1    ,	11       )       )

				linked_list.insert_head(0       )
				linked_list.insert_tail(11       )
				assert str(_SCREAMING_SNAKE_CASE       ) == "->".join(str(_SCREAMING_SNAKE_CASE       ) for i in range(0    ,	12       )       )

				assert linked_list.delete_head() == 0
				assert linked_list.delete_nth(9       ) == 10
				assert linked_list.delete_tail() == 11
				assert len(_SCREAMING_SNAKE_CASE       ) == 9
				assert str(_SCREAMING_SNAKE_CASE       ) == "->".join(str(_SCREAMING_SNAKE_CASE       ) for i in range(1    ,	10       )       )

				assert all(linked_list[i] == i + 1 for i in range(0    ,	9       )       ) is True

				for i in range(0    ,	9       ):
								SCREAMING_SNAKE_CASE  :						List[str]      					= -i
				assert all(linked_list[i] == -i for i in range(0    ,	9       )       ) is True

				linked_list.reverse()
				assert str(_SCREAMING_SNAKE_CASE       ) == "->".join(str(_SCREAMING_SNAKE_CASE       ) for i in range(-8    ,	1       )       )






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						str      					= [
				    -9,
				    1_00,
				    Node(77_34_51_12       ),
				    '''dlrow olleH''',
				    7,
				    55_55,
				    0,
				    -192.5_5555,
				    '''Hello, world!''',
				    77.9,
				    Node(10       ),
				    None,
				    None,
				    12.20,
				]
				SCREAMING_SNAKE_CASE  :						int      					= LinkedList()

				for i in test_input:
								linked_list.insert_tail(_SCREAMING_SNAKE_CASE       )

				# Check if it's empty or not
				assert linked_list.is_empty() is False
				assert (
				    str(_SCREAMING_SNAKE_CASE       ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
				    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
				)

				# Delete the head
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= linked_list.delete_head()
				assert result == -9
				assert (
				    str(_SCREAMING_SNAKE_CASE       ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
				    "Hello, world!->77.9->Node(10)->None->None->12.2"
				)

				# Delete the tail
				SCREAMING_SNAKE_CASE  :						int      					= linked_list.delete_tail()
				assert result == 12.2
				assert (
				    str(_SCREAMING_SNAKE_CASE       ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
				    "Hello, world!->77.9->Node(10)->None->None"
				)

				# Delete a node in specific location in linked list
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= linked_list.delete_nth(10       )
				assert result is None
				assert (
				    str(_SCREAMING_SNAKE_CASE       ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
				    "Hello, world!->77.9->Node(10)->None"
				)

				# Add a Node instance to its head
				linked_list.insert_head(Node('''Hello again, world!'''       )       )
				assert (
				    str(_SCREAMING_SNAKE_CASE       )
				    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
				    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
				)

				# Add None to its tail
				linked_list.insert_tail(_SCREAMING_SNAKE_CASE       )
				assert (
				    str(_SCREAMING_SNAKE_CASE       )
				    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
				    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
				)

				# Reverse the linked list
				linked_list.reverse()
				assert (
				    str(_SCREAMING_SNAKE_CASE       )
				    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
				    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
				)






def       __lowercase  ():
				from doctest import testmod

				testmod()

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= LinkedList()
				linked_list.insert_head(input('''Inserting 1st at head '''       ).strip()       )
				linked_list.insert_head(input('''Inserting 2nd at head '''       ).strip()       )
				print('''\nPrint list:'''       )
				linked_list.print_list()
				linked_list.insert_tail(input('''\nInserting 1st at tail '''       ).strip()       )
				linked_list.insert_tail(input('''Inserting 2nd at tail '''       ).strip()       )
				print('''\nPrint list:'''       )
				linked_list.print_list()
				print('''\nDelete head'''       )
				linked_list.delete_head()
				print('''Delete tail'''       )
				linked_list.delete_tail()
				print('''\nPrint list:'''       )
				linked_list.print_list()
				print('''\nReverse linked list'''       )
				linked_list.reverse()
				print('''\nPrint list:'''       )
				linked_list.print_list()
				print('''\nString representation of linked list:'''       )
				print(_SCREAMING_SNAKE_CASE       )
				print('''\nReading/changing Node data using indexing:'''       )
				print(F'''Element at Position 1: {linked_list[1]}'''       )
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= input('''Enter New Value: '''       ).strip()
				print('''New list:'''       )
				print(_SCREAMING_SNAKE_CASE       )
				print(F'''length of linked_list is : {len(_SCREAMING_SNAKE_CASE       )}'''       )


if __name__ == "__main__":
	main()









'''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 	a__     (				_lowercase,			_lowercase	):


	@register_to_config
	def __init__(self : List[str],					__UpperCAmelCase : int = 128,					__UpperCAmelCase : int = 256,					__UpperCAmelCase : float = 2000.0,					__UpperCAmelCase : int = 768,					__UpperCAmelCase : int = 12,					__UpperCAmelCase : int = 12,					__UpperCAmelCase : int = 64,					__UpperCAmelCase : int = 2048,					__UpperCAmelCase : float = 0.1,					)     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()

					SCREAMING_SNAKE_CASE  :						int      					= nn.Sequential(
					    nn.Linear(__UpperCAmelCase,					d_model * 4,					bias=__UpperCAmelCase      ),					nn.SiLU(),					nn.Linear(d_model * 4,					d_model * 4,					bias=__UpperCAmelCase      ),					nn.SiLU(),					)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Embedding(__UpperCAmelCase,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= False

					SCREAMING_SNAKE_CASE  :						str      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Dropout(p=__UpperCAmelCase      )

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

					SCREAMING_SNAKE_CASE  :						str      					= TaLayerNorm(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Dropout(p=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[str]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.mul(query_input.unsqueeze(-1      ),					key_input.unsqueeze(-2      )      )
					return mask.unsqueeze(-3      )






	def 				lowercase__  (self : str,					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= decoder_input_tokens.shape
					assert decoder_noise_time.shape == (batch,)

					# decoder_noise_time is in [0, 1), so rescale to expected timing range.
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.conditioning_emb(__UpperCAmelCase      ).unsqueeze(1      )

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

					SCREAMING_SNAKE_CASE  :						Tuple      					= 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.
					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.broadcast_to(
					    torch.arange(__UpperCAmelCase,					device=decoder_input_tokens.device      ),					(batch, seq_length),					)

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.position_encoding(__UpperCAmelCase      )

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

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

					# Translate encoding masks to encoder-decoder masks.
					SCREAMING_SNAKE_CASE  :						Tuple      					= [(x, self.encoder_decoder_mask(__UpperCAmelCase,					__UpperCAmelCase      )) for x, y in encodings_and_masks]

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

					for lyr in self.decoders:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= lyr(
									    __UpperCAmelCase,					conditioning_emb=__UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					encoder_attention_mask=__UpperCAmelCase,					)[0]

					SCREAMING_SNAKE_CASE  :						str      					= self.decoder_norm(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= self.post_dropout(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.spec_out(__UpperCAmelCase      )
					return spec_out



class 	a__     (				nn.Module	):


	def __init__(self : Union[str, Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Any=1e-6      )     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.ModuleList()

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

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

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






	def 				lowercase__  (self : int,					__UpperCAmelCase : int,					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : Dict=None,					__UpperCAmelCase : List[Any]=None,					__UpperCAmelCase : Optional[Any]=None,					__UpperCAmelCase : Dict=None,					)     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.layer[0](
					    __UpperCAmelCase,					conditioning_emb=__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					)

					if encoder_hidden_states is not None:
									SCREAMING_SNAKE_CASE  :						Any      					= torch.where(encoder_attention_mask > 0,					0,					-1e10      ).to(
									    encoder_hidden_states.dtype      )

									SCREAMING_SNAKE_CASE  :						Any      					= self.layer[1](
									    __UpperCAmelCase,					key_value_states=__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					)

					# Apply Film Conditional Feed Forward layer
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.layer[-1](__UpperCAmelCase,					__UpperCAmelCase      )

					return (hidden_states,)



class 	a__     (				nn.Module	):


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

					"""simple docstring"""

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






	def 				lowercase__  (self : int,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : List[str]=None,					)     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self.layer_norm(__UpperCAmelCase      )

					if conditioning_emb is not None:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.FiLMLayer(__UpperCAmelCase,					__UpperCAmelCase      )

					# Self-attention block
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.attention(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= hidden_states + self.dropout(__UpperCAmelCase      )

					return hidden_states



class 	a__     (				nn.Module	):


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

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Attention(query_dim=__UpperCAmelCase,					heads=__UpperCAmelCase,					dim_head=__UpperCAmelCase,					out_bias=__UpperCAmelCase,					scale_qk=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= TaLayerNorm(__UpperCAmelCase,					eps=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Dropout(__UpperCAmelCase      )






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Any=None,					__UpperCAmelCase : List[str]=None,					)     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.layer_norm(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= self.attention(
					    __UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					attention_mask=attention_mask.squeeze(1      ),					)
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= hidden_states + self.dropout(__UpperCAmelCase      )
					return layer_output



class 	a__     (				nn.Module	):


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

					"""simple docstring"""

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






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Any,					__UpperCAmelCase : List[Any]=None      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= self.layer_norm(__UpperCAmelCase      )
					if conditioning_emb is not None:
									SCREAMING_SNAKE_CASE  :						Tuple      					= self.film(__UpperCAmelCase,					__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Dict      					= self.DenseReluDense(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= hidden_states + self.dropout(__UpperCAmelCase      )
					return hidden_states



class 	a__     (				nn.Module	):


	def __init__(self : Optional[Any],					__UpperCAmelCase : Any,					__UpperCAmelCase : str,					__UpperCAmelCase : str      )     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Dropout(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= NewGELUActivation()






	def 				lowercase__  (self : Any,					__UpperCAmelCase : List[str]      )     ->			List[str]:

					"""simple docstring"""

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

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.wo(__UpperCAmelCase      )
					return hidden_states



class 	a__     (				nn.Module	):


	def __init__(self : Dict,					__UpperCAmelCase : Dict,					__UpperCAmelCase : Any=1e-6      )     ->			Union[str, Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Parameter(torch.ones(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= eps






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Any      )     ->			int:

					"""simple docstring"""

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

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

					return self.weight * hidden_states



class 	a__     (				nn.Module	):


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : 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(__UpperCAmelCase,					3.0      ))      ))



class 	a__     (				nn.Module	):


	def __init__(self : str,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : List[str]      )     ->			List[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Linear(__UpperCAmelCase,					out_features * 2,					bias=__UpperCAmelCase      )






	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Dict,					__UpperCAmelCase : int      )     ->			List[str]:

					"""simple docstring"""

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




