
from abc import ABC, abstractmethod
from typing import List, Optional




class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     List[str]   ):
												# test for the above condition
												self.test()

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					0
												lowerCamelCase_							  =					False
												while not completed:
																			if counter == 1:
																										self.reset()
																			lowerCamelCase_							  =					self.advance()
																			if not self.does_advance(__UpperCamelCase   ):
																										raise Exception(
																										    """Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true."""   )

																			lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					self.update(__UpperCamelCase   )
																			counter += 1

																			if counter > 1_0_0_0_0:
																										raise Exception("""update() does not fulfill the constraint."""   )

												if self.remaining() != 0:
																			raise Exception("""Custom Constraint is not defined correctly."""   )

					@abstractmethod
					def    lowercase__      (	self					:     str   ):
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''   )

					@abstractmethod
					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     int   ):
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''   )

					@abstractmethod
					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ):
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''   )

					@abstractmethod
					def    lowercase__      (	self					:     List[str]   ):
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''   )

					@abstractmethod
					def    lowercase__      (	self					:     List[str]   ):
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''   )



					@abstractmethod
					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     List[str]=False   ):
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''   )




class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     Tuple   ,					__UpperCamelCase					:     List[int]   ):
												super(__UpperCamelCase   ,					self   ).__init__()

												if not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) or len(__UpperCamelCase   ) == 0:
																			raise ValueError(F'''`token_ids` has to be a non-empty list, but is {token_ids}.'''   )
												if any((not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) or token_id < 0) for token_id in token_ids   ):
																			raise ValueError(F'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.'''   )

												lowerCamelCase_							  =					token_ids

												lowerCamelCase_							  =					len(self.token_ids   )
												lowerCamelCase_							  =					-1  # the index of the currently fulfilled step
												lowerCamelCase_							  =					False

					def    lowercase__      (	self					:     Optional[int]   ):
												if self.completed:
																			return None
												return self.token_ids[self.fulfilled_idx + 1]

					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     int   ):
												if not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																			raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(__UpperCamelCase   )}'''   )

												if self.completed:
																			return False

												return token_id == self.token_ids[self.fulfilled_idx + 1]

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     int   ):
												if not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																			raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(__UpperCamelCase   )}'''   )

												lowerCamelCase_							  =					False
												lowerCamelCase_							  =					False
												lowerCamelCase_							  =					False

												if self.does_advance(__UpperCamelCase   ):
																			self.fulfilled_idx += 1
																			lowerCamelCase_							  =					True
																			if self.fulfilled_idx == (self.seqlen - 1):
																										lowerCamelCase_							  =					True
																			lowerCamelCase_							  =					completed
												else:
																			# failed to make progress.
																			lowerCamelCase_							  =					True
																			self.reset()
												return stepped, completed, reset

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					False
												lowerCamelCase_							  =					0

					def    lowercase__      (	self					:     Optional[Any]   ):
												return self.seqlen - (self.fulfilled_idx + 1)



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Union[str, Any]=False   ):
												lowerCamelCase_							  =					PhrasalConstraint(self.token_ids   )

												if stateful:
																			lowerCamelCase_							  =					self.seqlen
																			lowerCamelCase_							  =					self.fulfilled_idx
																			lowerCamelCase_							  =					self.completed

												return new_constraint




class       __A:

					def __init__(	self					:     int   ,					__UpperCamelCase					:     List[List[int]]   ,					__UpperCamelCase					:     str=True   ):
												lowerCamelCase_							  =					max([len(__UpperCamelCase   ) for one in nested_token_ids]   )

												lowerCamelCase_							  =					{}
												for token_ids in nested_token_ids:
																			lowerCamelCase_							  =					root
																			for tidx, token_id in enumerate(__UpperCamelCase   ):
																										if token_id not in level:
																																	lowerCamelCase_							  =					{}

																										lowerCamelCase_							  =					level[token_id]

												if no_subsets and self.has_subsets(__UpperCamelCase   ,					__UpperCamelCase   ):
																			raise ValueError(
																			    """Each list in `nested_token_ids` can't be a complete subset of another list, but is"""
																			    F''' {nested_token_ids}.'''   )

												lowerCamelCase_							  =					root

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.trie

												for current_token in current_seq:
																			lowerCamelCase_							  =					start[current_token]

												lowerCamelCase_							  =					list(start.keys()   )

												return next_tokens

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					self.next_tokens(__UpperCamelCase   )

												return len(__UpperCamelCase   ) == 0

					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					list(root.values()   )
												if len(__UpperCamelCase   ) == 0:
																			return 1
												else:
																			return sum([self.count_leaves(__UpperCamelCase   ) for nn in next_nodes]   )



					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Any   ):
												lowerCamelCase_							  =					self.count_leaves(__UpperCamelCase   )
												return len(__UpperCamelCase   ) != leaf_count




class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     Optional[Any]   ,					__UpperCamelCase					:     List[List[int]]   ):
												super(__UpperCamelCase   ,					self   ).__init__()

												if not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) or len(__UpperCamelCase   ) == 0:
																			raise ValueError(F'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'''   )
												if any(not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) for token_ids in nested_token_ids   ):
																			raise ValueError(F'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.'''   )
												if any(
												    any((not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) or token_id < 0) for token_id in token_ids   )
												    for token_ids in nested_token_ids   ):
																			raise ValueError(
																			    F'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.'''   )

												lowerCamelCase_							  =					DisjunctiveTrie(__UpperCamelCase   )
												lowerCamelCase_							  =					nested_token_ids

												lowerCamelCase_							  =					self.trie.max_height
												lowerCamelCase_							  =					[]
												lowerCamelCase_							  =					False

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					self.trie.next_tokens(self.current_seq   )

												if len(__UpperCamelCase   ) == 0:
																			return None
												else:
																			return token_list

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     int   ):
												if not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																			raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(__UpperCamelCase   )}'''   )

												lowerCamelCase_							  =					self.trie.next_tokens(self.current_seq   )

												return token_id in next_tokens

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     int   ):
												if not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																			raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(__UpperCamelCase   )}'''   )

												lowerCamelCase_							  =					False
												lowerCamelCase_							  =					False
												lowerCamelCase_							  =					False

												if self.does_advance(__UpperCamelCase   ):
																			self.current_seq.append(__UpperCamelCase   )
																			lowerCamelCase_							  =					True
												else:
																			lowerCamelCase_							  =					True
																			self.reset()

												lowerCamelCase_							  =					self.trie.reached_leaf(self.current_seq   )
												lowerCamelCase_							  =					completed

												return stepped, completed, reset

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					False
												lowerCamelCase_							  =					[]

					def    lowercase__      (	self					:     str   ):
												if self.completed:
																			# since this can be completed without reaching max height
																			return 0
												else:
																			return self.seqlen - len(self.current_seq   )



					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     int=False   ):
												lowerCamelCase_							  =					DisjunctiveConstraint(self.token_ids   )

												if stateful:
																			lowerCamelCase_							  =					self.seqlen
																			lowerCamelCase_							  =					self.current_seq
																			lowerCamelCase_							  =					self.completed

												return new_constraint




class       __A:

					def __init__(	self					:     Tuple   ,					__UpperCamelCase					:     List[Constraint]   ):
												lowerCamelCase_							  =					constraints

												# max # of steps required to fulfill a given constraint
												lowerCamelCase_							  =					max([c.seqlen for c in constraints]   )
												lowerCamelCase_							  =					len(__UpperCamelCase   )
												lowerCamelCase_							  =					False

												self.init_state()

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					[]
												lowerCamelCase_							  =					None
												lowerCamelCase_							  =					[constraint.copy(stateful=__UpperCamelCase   ) for constraint in self.constraints]

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					0
												if self.inprogress_constraint:
																			# extra points for having a constraint mid-fulfilled
																			add += self.max_seqlen - self.inprogress_constraint.remaining()

												return (len(self.complete_constraints   ) * self.max_seqlen) + add

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					[]
												if self.inprogress_constraint is None:
																			for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
																										lowerCamelCase_							  =					constraint.advance()
																										if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																																	token_list.append(__UpperCamelCase   )
																										elif isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																																	token_list.extend(__UpperCamelCase   )
												else:
																			lowerCamelCase_							  =					self.inprogress_constraint.advance()
																			if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																										token_list.append(__UpperCamelCase   )
																			elif isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																										token_list.extend(__UpperCamelCase   )

												if len(__UpperCamelCase   ) == 0:
																			return None
												else:
																			return token_list

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Optional[List[int]]   ):
												self.init_state()

												if token_ids is not None:
																			for token in token_ids:
																										# completes or steps **one** constraint
																										lowerCamelCase_  ,   lowerCamelCase_							  =					self.add(__UpperCamelCase   )

																										# the entire list of constraints are fulfilled
																										if self.completed:
																																	break

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ):
												if not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																			raise ValueError(F'''`token_id` should be an `int`, but is `{token_id}`.'''   )

												lowerCamelCase_  ,   lowerCamelCase_							  =					False, False

												if self.completed:
																			lowerCamelCase_							  =					True
																			lowerCamelCase_							  =					False
																			return complete, stepped

												if self.inprogress_constraint is not None:
																			# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
																			# job, simply update the state

																			lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					self.inprogress_constraint.update(__UpperCamelCase   )
																			if reset:
																										# 1. If the next token breaks the progress, then we must restart.
																										#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

																										#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
																										#     constraint, not the full list of constraints.

																										self.pending_constraints.append(self.inprogress_constraint.copy(stateful=__UpperCamelCase   )   )
																										lowerCamelCase_							  =					None

																			if complete:
																										# 2. If the next token completes the constraint, move it to completed list, set
																										#     inprogress to None. If there are no pending constraints either, then this full list of constraints
																										#     is complete.

																										self.complete_constraints.append(self.inprogress_constraint   )
																										lowerCamelCase_							  =					None

																										if len(self.pending_constraints   ) == 0:
																																	# we're done!
																																	lowerCamelCase_							  =					True

												else:
																			# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
																			# of constraints?

																			for cidx, pending_constraint in enumerate(self.pending_constraints   ):
																										if pending_constraint.does_advance(__UpperCamelCase   ):
																																	lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					pending_constraint.update(__UpperCamelCase   )

																																	if not stepped:
																																								raise Exception(
																																								    """`constraint.update(token_id)` is not yielding incremental progress, """
																																								    """even though `constraint.does_advance(token_id)` is true."""   )

																																	if complete:
																																								self.complete_constraints.append(__UpperCamelCase   )
																																								lowerCamelCase_							  =					None

																																	if not complete and stepped:
																																								lowerCamelCase_							  =					pending_constraint

																																	if complete or stepped:
																																								# If we made any progress at all, then it's at least not a "pending constraint".

																																								lowerCamelCase_							  =					(
																																								    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
																																								)

																																								if len(self.pending_constraints   ) == 0 and self.inprogress_constraint is None:
																																															# If there's no longer any pending after this and no inprogress either, then we must be
																																															# complete.

																																															lowerCamelCase_							  =					True

																																								break  # prevent accidentally stepping through multiple constraints with just one token.

												return complete, stepped



					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     List[Any]=True   ):
												lowerCamelCase_							  =					ConstraintListState(self.constraints   )  # we actually never though self.constraints objects
												# throughout this process. So it's at initialization state.

												if stateful:
																			lowerCamelCase_							  =					[
																			    constraint.copy(stateful=__UpperCamelCase   ) for constraint in self.complete_constraints
																			]
																			if self.inprogress_constraint is not None:
																										lowerCamelCase_							  =					self.inprogress_constraint.copy(stateful=__UpperCamelCase   )
																			lowerCamelCase_							  =					[constraint.copy() for constraint in self.pending_constraints]

												return new_state







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(				UpperCAmelCase     ,		UpperCAmelCase       ):

					@register_to_config
					def __init__(	self					:     int   ,					__UpperCamelCase					:     int = 1_2_8   ,					__UpperCamelCase					:     int = 2_5_6   ,					__UpperCamelCase					:     float = 2000.0   ,					__UpperCamelCase					:     int = 7_6_8   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 6_4   ,					__UpperCamelCase					:     int = 2_0_4_8   ,					__UpperCamelCase					:     float = 0.1   ,					):
												super().__init__()

												lowerCamelCase_							  =					nn.Sequential(
												    nn.Linear(__UpperCamelCase   ,					d_model * 4   ,					bias=__UpperCamelCase   )   ,					nn.SiLU()   ,					nn.Linear(d_model * 4   ,					d_model * 4   ,					bias=__UpperCamelCase   )   ,					nn.SiLU()   ,					)

												lowerCamelCase_							  =					nn.Embedding(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					False

												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )

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

												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					torch.mul(query_input.unsqueeze(-1   )   ,					key_input.unsqueeze(-2   )   )
												return mask.unsqueeze(-3   )



					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     List[Any]   ):
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					decoder_input_tokens.shape
												assert decoder_noise_time.shape == (batch,)

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

												lowerCamelCase_							  =					self.conditioning_emb(__UpperCamelCase   ).unsqueeze(1   )

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

												lowerCamelCase_							  =					decoder_input_tokens.shape[1]

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

												lowerCamelCase_							  =					self.position_encoding(__UpperCamelCase   )

												lowerCamelCase_							  =					self.continuous_inputs_projection(__UpperCamelCase   )
												inputs += position_encodings
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

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

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

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

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

												lowerCamelCase_							  =					self.decoder_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.post_dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.spec_out(__UpperCamelCase   )
												return spec_out





class       __A(				nn.Module       ):

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     int=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					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					:     Optional[int]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     List[str]=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Optional[Any]=None   ,					__UpperCamelCase					:     Tuple=None   ,					):
												lowerCamelCase_							  =					self.layer[0](
												    __UpperCamelCase   ,					conditioning_emb=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

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

																			lowerCamelCase_							  =					self.layer[1](
																			    __UpperCamelCase   ,					key_value_states=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

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

												return (hidden_states,)





class       __A(				nn.Module       ):

					def __init__(	self					:     Optional[int]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict   ):
												super().__init__()
												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   )
												lowerCamelCase_							  =					TaFiLMLayer(in_features=d_model * 4   ,					out_features=__UpperCamelCase   )
												lowerCamelCase_							  =					Attention(query_dim=__UpperCamelCase   ,					heads=__UpperCamelCase   ,					dim_head=__UpperCamelCase   ,					out_bias=__UpperCamelCase   ,					scale_qk=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )



					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												# pre_self_attention_layer_norm
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )

												if conditioning_emb is not None:
																			lowerCamelCase_							  =					self.FiLMLayer(__UpperCamelCase   ,					__UpperCamelCase   )

												# Self-attention block
												lowerCamelCase_							  =					self.attention(__UpperCamelCase   )

												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )

												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     List[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Optional[int]   ):
												super().__init__()
												lowerCamelCase_							  =					Attention(query_dim=__UpperCamelCase   ,					heads=__UpperCamelCase   ,					dim_head=__UpperCamelCase   ,					out_bias=__UpperCamelCase   ,					scale_qk=__UpperCamelCase   )
												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   ,					eps=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.attention(
												    __UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ,					attention_mask=attention_mask.squeeze(1   )   ,					)
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return layer_output





class       __A(				nn.Module       ):

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



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict=None   ):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												if conditioning_emb is not None:
																			lowerCamelCase_							  =					self.film(__UpperCamelCase   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					self.DenseReluDense(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     Dict   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )
												lowerCamelCase_							  =					NewGELUActivation()



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.act(self.wi_a(__UpperCamelCase   )   )
												lowerCamelCase_							  =					self.wi_a(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_gelu * hidden_linear
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.wo(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     str   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Any=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Parameter(torch.ones(__UpperCamelCase   )   )
												lowerCamelCase_							  =					eps



					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Any   ):
												# 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

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

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

												return self.weight * hidden_states





class       __A(				nn.Module       ):

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     torch.Tensor   ):
												return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.044715 * torch.pow(__UpperCamelCase   ,					3.0   ))   ))





class       __A(				nn.Module       ):

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					out_features * 2   ,					bias=__UpperCamelCase   )



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     str   ):
												lowerCamelCase_							  =					self.scale_bias(__UpperCamelCase   )
												lowerCamelCase_  ,   lowerCamelCase_							  =					torch.chunk(__UpperCamelCase   ,					2   ,					-1   )
												lowerCamelCase_							  =					x * (1 + scale) + shift
												return x





