





"""simple docstring"""




import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[str]         =   []
						if isinstance(_lowercase	,						_lowercase       ):
												for v in tree.values():
																		shapes.extend(_fetch_dims(_lowercase       )       )
						elif isinstance(_lowercase	,						(list, tuple)       ):
												for t in tree:
																		shapes.extend(_fetch_dims(_lowercase       )       )
						elif isinstance(_lowercase	,						torch.Tensor       ):
												shapes.append(tree.shape       )
						else:
												raise ValueError('''Not supported'''       )

						return shapes





@torch.jit.ignore
def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[Any]         =   []
						for d in reversed(_lowercase       ):
												idx.append(flat_idx % d       )
												_UpperCamelCase      : Dict         =   flat_idx // d

						return tuple(reversed(_lowercase       )       )





@torch.jit.ignore
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase = None	,						_lowercase = None	,						):



						def reduce_edge_list(_lowercase       ) -> None:
												_UpperCamelCase      : int         =   True
												for i in range(len(_lowercase       )       ):
																		_UpperCamelCase      : List[Any]         =   -1 * (i + 1)
																		l[reversed_idx] &= tally
																		_UpperCamelCase      : List[str]         =   l[reversed_idx]

						if start_edges is None:
												_UpperCamelCase      : Dict         =   [s == 0 for s in start]
												reduce_edge_list(_lowercase       )
						if end_edges is None:
												_UpperCamelCase      : Optional[int]         =   [e == (d - 1) for e, d in zip(_lowercase	,						_lowercase       )]
												reduce_edge_list(_lowercase       )

						# Base cases. Either start/end are empty and we're done, or the final,
						# one-dimensional tensor can be simply sliced
						if len(_lowercase       ) == 0:
												return [()]
						elif len(_lowercase       ) == 1:
												return [(slice(start[0]	,						end[0] + 1       ),)]

						_UpperCamelCase      : List[Tuple[slice, ...]]         =   []
						_UpperCamelCase      : List[slice]         =   []

						# Dimensions common to start and end can be selected directly
						for s, e in zip(_lowercase	,						_lowercase       ):
												if s == e:
																		path_list.append(slice(_lowercase	,						s + 1       )       )
												else:
																		break

						_UpperCamelCase      : Tuple[slice, ...]         =   tuple(_lowercase       )
						_UpperCamelCase      : int         =   len(_lowercase       )

						# start == end, and we're done
						if divergence_idx == len(_lowercase       ):
												return [path]

						def upper() -> Tuple[Tuple[slice, ...], ...]:
												assert start_edges is not None
												assert end_edges is not None

												_UpperCamelCase      : Union[str, Any]         =   start[divergence_idx]
												return tuple(
												    path + (slice(_lowercase	,						sdi + 1       ),) + s
												    for s in _get_minimal_slice_set(
												        start[divergence_idx + 1 :]	,						[d - 1 for d in dims[divergence_idx + 1 :]]	,						dims[divergence_idx + 1 :]	,						start_edges=start_edges[divergence_idx + 1 :]	,						end_edges=[True for _ in end_edges[divergence_idx + 1 :]]	,						)       )

						def lower() -> Tuple[Tuple[slice, ...], ...]:
												assert start_edges is not None
												assert end_edges is not None

												_UpperCamelCase      : Union[str, Any]         =   end[divergence_idx]
												return tuple(
												    path + (slice(_lowercase	,						edi + 1       ),) + s
												    for s in _get_minimal_slice_set(
												        [0 for _ in start[divergence_idx + 1 :]]	,						end[divergence_idx + 1 :]	,						dims[divergence_idx + 1 :]	,						start_edges=[True for _ in start_edges[divergence_idx + 1 :]]	,						end_edges=end_edges[divergence_idx + 1 :]	,						)       )

						# If both start and end are at the edges of the subtree rooted at
						# divergence_idx, we can just select the whole subtree at once
						if start_edges[divergence_idx] and end_edges[divergence_idx]:
												slices.append(path + (slice(start[divergence_idx]	,						end[divergence_idx] + 1       ),)       )
						# If just start is at the edge, we can grab almost all of the subtree,
						# treating only the ragged bottom edge as an edge case
						elif start_edges[divergence_idx]:
												slices.append(path + (slice(start[divergence_idx]	,						end[divergence_idx]       ),)       )
												slices.extend(lower()       )
						# Analogous to the previous case, but the top is ragged this time
						elif end_edges[divergence_idx]:
												slices.extend(upper()       )
												slices.append(path + (slice(start[divergence_idx] + 1	,						end[divergence_idx] + 1       ),)       )
						# If both sides of the range are ragged, we need to handle both sides
						# separately. If there's contiguous meat in between them, we can index it
						# in one big chunk
						else:
												slices.extend(upper()       )
												_UpperCamelCase      : List[Any]         =   end[divergence_idx] - start[divergence_idx]
												if middle_ground > 1:
																		slices.append(path + (slice(start[divergence_idx] + 1	,						end[divergence_idx]       ),)       )
												slices.extend(lower()       )

						return slices





@torch.jit.ignore
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   t.shape[:no_batch_dims]
						_UpperCamelCase      : Dict         =   list(_flat_idx_to_idx(_lowercase	,						_lowercase       )       )
						# _get_minimal_slice_set is inclusive
						_UpperCamelCase      : Union[str, Any]         =   list(_flat_idx_to_idx(flat_end - 1	,						_lowercase       )       )

						# Get an ordered list of slices to perform
						_UpperCamelCase      : int         =   _get_minimal_slice_set(
						    _lowercase	,						_lowercase	,						_lowercase	,						)

						_UpperCamelCase      : Any         =   [t[s] for s in slices]

						return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]       ) for s in sliced_tensors]       )





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase = False	,						_lowercase = None	,						_lowercase = False	,						):



						if not (len(_lowercase       ) > 0):
												raise ValueError('''Must provide at least one input'''       )

						_UpperCamelCase      : str         =   [shape[:no_batch_dims] for shape in _fetch_dims(_lowercase       )]
						_UpperCamelCase      : Optional[int]         =   tuple([max(_lowercase       ) for s in zip(*_lowercase       )]       )

						def _prep_inputs(_lowercase       ) -> torch.Tensor:
												if not low_mem:
																		if not sum(t.shape[:no_batch_dims]       ) == no_batch_dims:
																								_UpperCamelCase      : List[Any]         =   t.expand(orig_batch_dims + t.shape[no_batch_dims:]       )
																		_UpperCamelCase      : Any         =   t.reshape(-1	,						*t.shape[no_batch_dims:]       )
												else:
																		_UpperCamelCase      : Any         =   t.expand(orig_batch_dims + t.shape[no_batch_dims:]       )
												return t

						_UpperCamelCase      : Dict[str, Any]         =   tensor_tree_map(_prep_inputs	,						_lowercase       )
						_UpperCamelCase      : Optional[int]         =   None
						if _out is not None:
												_UpperCamelCase      : Dict         =   tensor_tree_map(lambda _lowercase       : t.view([-1] + list(t.shape[no_batch_dims:]       )       )	,						_out       )

						_UpperCamelCase      : Dict         =   1
						for d in orig_batch_dims:
												flat_batch_dim *= d

						_UpperCamelCase      : Union[str, Any]         =   flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

						def _select_chunk(_lowercase       ) -> torch.Tensor:
												return t[i : i + chunk_size] if t.shape[0] != 1 else t

						_UpperCamelCase      : Dict         =   0
						_UpperCamelCase      : Any         =   prepped_outputs
						for _ in range(_lowercase       ):
												# Chunk the input
												if not low_mem:
																		_UpperCamelCase      : List[str]         =   _select_chunk
												else:
																		_UpperCamelCase      : int         =   partial(
																		    _chunk_slice	,						flat_start=_lowercase	,						flat_end=min(_lowercase	,						i + chunk_size       )	,						no_batch_dims=len(_lowercase       )	,						)

												_UpperCamelCase      : Dict[str, Any]         =   tensor_tree_map(_lowercase	,						_lowercase       )

												# Run the layer on the chunk
												_UpperCamelCase      : List[Any]         =   layer(**_lowercase       )

												# Allocate space for the output
												if out is None:
																		_UpperCamelCase      : Union[str, Any]         =   tensor_tree_map(lambda _lowercase       : t.new_zeros((flat_batch_dim,) + t.shape[1:]       )	,						_lowercase       )

												# Put the chunk in its pre-allocated space
												if isinstance(_lowercase	,						_lowercase       ):

																		def assign(_lowercase	,						_lowercase       ) -> None:
																								for k, v in da.items():
																														if isinstance(_lowercase	,						_lowercase       ):
																																				assign(_lowercase	,						da[k]       )
																														else:
																																				if _add_into_out:
																																										v[i : i + chunk_size] += da[k]
																																				else:
																																										_UpperCamelCase      : Tuple         =   da[k]

																		assign(_lowercase	,						_lowercase       )
												elif isinstance(_lowercase	,						_lowercase       ):
																		for xa, xa in zip(_lowercase	,						_lowercase       ):
																								if _add_into_out:
																														xa[i : i + chunk_size] += xa
																								else:
																														_UpperCamelCase      : int         =   xa
												elif isinstance(_lowercase	,						torch.Tensor       ):
																		if _add_into_out:
																								out[i : i + chunk_size] += output_chunk
																		else:
																								_UpperCamelCase      : Union[str, Any]         =   output_chunk
												else:
																		raise ValueError('''Not supported'''       )

												i += chunk_size

						_UpperCamelCase      : List[Any]         =   tensor_tree_map(lambda _lowercase       : t.view(orig_batch_dims + t.shape[1:]       )	,						_lowercase       )

						return out






class 			_a						:






				def __init__(			self		:   str,			lowerCAmelCase__		:   int = 5_1_2,			)     ->				List[str]:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   max_chunk_size
										_UpperCamelCase      : Optional[int]         =   None
										_UpperCamelCase      : Optional[tuple]         =   None






				def     snake_case     (			self		:   Any,			lowerCAmelCase__		:   Callable,			lowerCAmelCase__		:   tuple,			lowerCAmelCase__		:   int							)     ->				Dict:




										'''simple docstring'''



										logging.info('''Tuning chunk size...'''							)

										if min_chunk_size >= self.max_chunk_size:
																return min_chunk_size

										_UpperCamelCase      : List[int]         =   [2**l for l in range(int(math.log(self.max_chunk_size,			2							)							) + 1							)]
										_UpperCamelCase      : List[str]         =   [c for c in candidates if c > min_chunk_size]
										_UpperCamelCase      : int         =   [min_chunk_size] + candidates
										candidates[-1] += 4

										def test_chunk_size(lowerCAmelCase__		:   int							) -> bool:
																try:
																						with torch.no_grad():
																												fn(*__lowerCamelCase,			chunk_size=__lowerCamelCase							)
																						return True
																except RuntimeError:
																						return False

										_UpperCamelCase      : Dict         =   0
										_UpperCamelCase      : Dict         =   len(__lowerCamelCase							) - 1
										while i > min_viable_chunk_size_index:
																_UpperCamelCase      : str         =   test_chunk_size(candidates[i]							)
																if not viable:
																						_UpperCamelCase      : List[str]         =   (min_viable_chunk_size_index + i) // 2
																else:
																						_UpperCamelCase      : List[Any]         =   i
																						_UpperCamelCase      : Dict         =   (i + len(__lowerCamelCase							) - 1) // 2

										return candidates[min_viable_chunk_size_index]






				def     snake_case     (			self		:   Any,			lowerCAmelCase__		:   Iterable,			lowerCAmelCase__		:   Iterable							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   True
										for aa, aa in zip(__lowerCamelCase,			__lowerCamelCase							):
																assert type(__lowerCamelCase							) == type(__lowerCamelCase							)
																if isinstance(__lowerCamelCase,			(list, tuple)							):
																						consistent &= self._compare_arg_caches(__lowerCamelCase,			__lowerCamelCase							)
																elif isinstance(__lowerCamelCase,			__lowerCamelCase							):
																						_UpperCamelCase      : Optional[int]         =   [v for _, v in sorted(aa.items(),			key=lambda lowerCAmelCase__							: x[0]							)]
																						_UpperCamelCase      : int         =   [v for _, v in sorted(aa.items(),			key=lambda lowerCAmelCase__							: x[0]							)]
																						consistent &= self._compare_arg_caches(__lowerCamelCase,			__lowerCamelCase							)
																else:
																						consistent &= aa == aa

										return consistent






				def     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   Callable,			lowerCAmelCase__		:   tuple,			lowerCAmelCase__		:   int,			)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   True
										_UpperCamelCase      : tuple         =   tree_map(lambda lowerCAmelCase__							: a.shape if isinstance(__lowerCamelCase,			torch.Tensor							) else a,			__lowerCamelCase,			__lowerCamelCase							)
										if self.cached_arg_data is not None:
																# If args have changed shape/value, we need to re-tune
																assert len(self.cached_arg_data							) == len(__lowerCamelCase							)
																_UpperCamelCase      : Union[str, Any]         =   self._compare_arg_caches(self.cached_arg_data,			__lowerCamelCase							)
										else:
																# Otherwise, we can reuse the precomputed value
																_UpperCamelCase      : Any         =   False

										if not consistent:
																_UpperCamelCase      : List[str]         =   self._determine_favorable_chunk_size(
																    __lowerCamelCase,			__lowerCamelCase,			__lowerCamelCase,			)
																_UpperCamelCase      : Dict         =   arg_data

										assert self.cached_chunk_size is not None

										return self.cached_chunk_size










"""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						(				_lowerCAmelCase     ,    _lowerCAmelCase    ):






				@register_to_config
				def __init__(			self		:   Any,			lowerCAmelCase__		:   int = 1_2_8,			lowerCAmelCase__		:   int = 2_5_6,			lowerCAmelCase__		:   float = 2_000.0,			lowerCAmelCase__		:   int = 7_6_8,			lowerCAmelCase__		:   int = 1_2,			lowerCAmelCase__		:   int = 1_2,			lowerCAmelCase__		:   int = 6_4,			lowerCAmelCase__		:   int = 2_0_4_8,			lowerCAmelCase__		:   float = 0.1,			)     ->				Any:




										'''simple docstring'''



										super().__init__()

										_UpperCamelCase      : Any         =   nn.Sequential(
										    nn.Linear(lowerCAmelCase__,			d_model * 4,			bias=lowerCAmelCase__							),			nn.SiLU(),			nn.Linear(d_model * 4,			d_model * 4,			bias=lowerCAmelCase__							),			nn.SiLU(),			)

										_UpperCamelCase      : List[Any]         =   nn.Embedding(lowerCAmelCase__,			lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   False

										_UpperCamelCase      : Optional[Any]         =   nn.Linear(lowerCAmelCase__,			lowerCAmelCase__,			bias=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   nn.Dropout(p=lowerCAmelCase__							)

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

										_UpperCamelCase      : Optional[int]         =   TaLayerNorm(lowerCAmelCase__							)

										_UpperCamelCase      : Dict         =   nn.Dropout(p=lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   nn.Linear(lowerCAmelCase__,			lowerCAmelCase__,			bias=lowerCAmelCase__							)






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   Union[str, Any],			lowerCAmelCase__		:   Optional[int]							)     ->				List[str]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   Optional[Any]							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase      : Union[str, Any]         =   decoder_input_tokens.shape
										assert decoder_noise_time.shape == (batch,)

										# decoder_noise_time is in [0, 1), so rescale to expected timing range.
										_UpperCamelCase      : Any         =   get_timestep_embedding(
										    decoder_noise_time * self.config.max_decoder_noise_time,			embedding_dim=self.config.d_model,			max_period=self.config.max_decoder_noise_time,			).to(dtype=self.dtype							)

										_UpperCamelCase      : Union[str, Any]         =   self.conditioning_emb(lowerCAmelCase__							).unsqueeze(1							)

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

										_UpperCamelCase      : int         =   decoder_input_tokens.shape[1]

										# If we want to use relative positions for audio context, we can just offset
										# this sequence by the length of encodings_and_masks.
										_UpperCamelCase      : int         =   torch.broadcast_to(
										    torch.arange(lowerCAmelCase__,			device=decoder_input_tokens.device							),			(batch, seq_length),			)

										_UpperCamelCase      : Dict         =   self.position_encoding(lowerCAmelCase__							)

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

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

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

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

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

										_UpperCamelCase      : Any         =   self.decoder_norm(lowerCAmelCase__							)
										_UpperCamelCase      : Tuple         =   self.post_dropout(lowerCAmelCase__							)

										_UpperCamelCase      : Union[str, Any]         =   self.spec_out(lowerCAmelCase__							)
										return spec_out




class 			_a						(				nn.Module    ):






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




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : Optional[int]         =   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     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[Any]=None,			lowerCAmelCase__		:   List[str]=None,			lowerCAmelCase__		:   List[Any]=None,			lowerCAmelCase__		:   Optional[Any]=None,			lowerCAmelCase__		:   Any=None,			)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.layer[0](
										    lowerCAmelCase__,			conditioning_emb=lowerCAmelCase__,			attention_mask=lowerCAmelCase__,			)

										if encoder_hidden_states is not None:
																_UpperCamelCase      : Any         =   torch.where(encoder_attention_mask > 0,			0,			-1e1_0							).to(
																    encoder_hidden_states.dtype							)

																_UpperCamelCase      : int         =   self.layer[1](
																    lowerCAmelCase__,			key_value_states=lowerCAmelCase__,			attention_mask=lowerCAmelCase__,			)

										# Apply Film Conditional Feed Forward layer
										_UpperCamelCase      : Optional[int]         =   self.layer[-1](lowerCAmelCase__,			lowerCAmelCase__							)

										return (hidden_states,)




class 			_a						(				nn.Module    ):






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




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : int         =   TaLayerNorm(lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   TaFiLMLayer(in_features=d_model * 4,			out_features=lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   Attention(query_dim=lowerCAmelCase__,			heads=lowerCAmelCase__,			dim_head=lowerCAmelCase__,			out_bias=lowerCAmelCase__,			scale_qk=lowerCAmelCase__							)
										_UpperCamelCase      : List[str]         =   nn.Dropout(lowerCAmelCase__							)






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




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.layer_norm(lowerCAmelCase__							)

										if conditioning_emb is not None:
																_UpperCamelCase      : str         =   self.FiLMLayer(lowerCAmelCase__,			lowerCAmelCase__							)

										# Self-attention block
										_UpperCamelCase      : Tuple         =   self.attention(lowerCAmelCase__							)

										_UpperCamelCase      : Dict         =   hidden_states + self.dropout(lowerCAmelCase__							)

										return hidden_states




class 			_a						(				nn.Module    ):






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




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : Any         =   Attention(query_dim=lowerCAmelCase__,			heads=lowerCAmelCase__,			dim_head=lowerCAmelCase__,			out_bias=lowerCAmelCase__,			scale_qk=lowerCAmelCase__							)
										_UpperCamelCase      : Tuple         =   TaLayerNorm(lowerCAmelCase__,			eps=lowerCAmelCase__							)
										_UpperCamelCase      : int         =   nn.Dropout(lowerCAmelCase__							)






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Union[str, Any]=None,			)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   self.layer_norm(lowerCAmelCase__							)
										_UpperCamelCase      : Tuple         =   self.attention(
										    lowerCAmelCase__,			encoder_hidden_states=lowerCAmelCase__,			attention_mask=attention_mask.squeeze(1							),			)
										_UpperCamelCase      : str         =   hidden_states + self.dropout(lowerCAmelCase__							)
										return layer_output




class 			_a						(				nn.Module    ):






				def __init__(			self		:   Tuple,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   str							)     ->				Tuple:




										'''simple docstring'''



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






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




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.layer_norm(lowerCAmelCase__							)
										if conditioning_emb is not None:
																_UpperCamelCase      : Dict         =   self.film(lowerCAmelCase__,			lowerCAmelCase__							)

										_UpperCamelCase      : str         =   self.DenseReluDense(lowerCAmelCase__							)
										_UpperCamelCase      : str         =   hidden_states + self.dropout(lowerCAmelCase__							)
										return hidden_states




class 			_a						(				nn.Module    ):






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




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : List[str]         =   nn.Linear(lowerCAmelCase__,			lowerCAmelCase__,			bias=lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   nn.Linear(lowerCAmelCase__,			lowerCAmelCase__,			bias=lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   nn.Linear(lowerCAmelCase__,			lowerCAmelCase__,			bias=lowerCAmelCase__							)
										_UpperCamelCase      : List[str]         =   nn.Dropout(lowerCAmelCase__							)
										_UpperCamelCase      : Union[str, Any]         =   NewGELUActivation()






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   Tuple							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   self.act(self.wi_a(lowerCAmelCase__							)							)
										_UpperCamelCase      : Dict         =   self.wi_a(lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   hidden_gelu * hidden_linear
										_UpperCamelCase      : Any         =   self.dropout(lowerCAmelCase__							)

										_UpperCamelCase      : Optional[int]         =   self.wo(lowerCAmelCase__							)
										return hidden_states




class 			_a						(				nn.Module    ):






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




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : Optional[Any]         =   nn.Parameter(torch.ones(lowerCAmelCase__							)							)
										_UpperCamelCase      : Tuple         =   eps






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   Tuple							)     ->				Any:




										'''simple docstring'''



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

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

										return self.weight * hidden_states




class 			_a						(				nn.Module    ):






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   torch.Tensor							)     ->				torch.Tensor:




										'''simple docstring'''



										return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi							) * (input + 0.044_715 * torch.pow(lowerCAmelCase__,			3.0							))							))




class 			_a						(				nn.Module    ):






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




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : Tuple         =   nn.Linear(lowerCAmelCase__,			out_features * 2,			bias=lowerCAmelCase__							)






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Any							)     ->				int:




										'''simple docstring'''



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



