





"""simple docstring"""




import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   42
				UpperCamelCase								=   jnp.floataa
				UpperCamelCase								=   True






				def     snake_case     (			self		:   Dict							)     ->				Tuple:




										'''simple docstring'''



										super().setup()
										_UpperCamelCase      : Any         =   nn.Dense(5,			dtype=self.dtype							)






				def __call__(			self		:   List[str],			*lowerCAmelCase__		:   Optional[int],			**lowerCAmelCase__		:   int							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   super().__call__(*lowerCAmelCase__,			**lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   self.cls(outputs[2]							)
										return outputs[:2] + (cls_out,)




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   FlaxBigBirdForNaturalQuestionsModule





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						def cross_entropy(_lowercase	,						_lowercase	,						_lowercase=None       ):
												_UpperCamelCase      : List[str]         =   logits.shape[-1]
												_UpperCamelCase      : Optional[Any]         =   (labels[..., None] == jnp.arange(_lowercase       )[None]).astype('''f4'''       )
												_UpperCamelCase      : Union[str, Any]         =   jax.nn.log_softmax(_lowercase	,						axis=-1       )
												_UpperCamelCase      : List[Any]         =   -jnp.sum(labels * logits	,						axis=-1       )
												if reduction is not None:
																		_UpperCamelCase      : int         =   reduction(_lowercase       )
												return loss

						_UpperCamelCase      : Any         =   partial(_lowercase	,						reduction=jnp.mean       )
						_UpperCamelCase      : List[str]         =   cross_entropy(_lowercase	,						_lowercase       )
						_UpperCamelCase      : Optional[Any]         =   cross_entropy(_lowercase	,						_lowercase       )
						_UpperCamelCase      : List[str]         =   cross_entropy(_lowercase	,						_lowercase       )
						return (start_loss + end_loss + pooled_loss) / 3




@dataclass
class 			_a						:
				UpperCamelCase								=   "google/bigbird-roberta-base"
				UpperCamelCase								=   3000
				UpperCamelCase								=   1_0500

				UpperCamelCase								=   128
				UpperCamelCase								=   3

				UpperCamelCase								=   1
				UpperCamelCase								=   5

				# tx_args
				UpperCamelCase								=   3e-5
				UpperCamelCase								=   0.0
				UpperCamelCase								=   2_0000
				UpperCamelCase								=   0.0095

				UpperCamelCase								=   "bigbird-roberta-natural-questions"
				UpperCamelCase								=   "training-expt"
				UpperCamelCase								=   "data/nq-training.jsonl"
				UpperCamelCase								=   "data/nq-validation.jsonl"






				def     snake_case     (			self		:   List[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										os.makedirs(self.base_dir,			exist_ok=lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   os.path.join(self.base_dir,			self.save_dir							)
										_UpperCamelCase      : Optional[Any]         =   self.batch_size_per_device * jax.device_count()




@dataclass
class 			_a						:
				UpperCamelCase								=   42
				UpperCamelCase								=   4096  # no dynamic padding on TPUs






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




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   self.collate_fn(lowerCAmelCase__							)
										_UpperCamelCase      : Optional[Any]         =   jax.tree_util.tree_map(lowerCAmelCase__,			lowerCAmelCase__							)
										return batch






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




										'''simple docstring'''



										_UpperCamelCase       ,							_UpperCamelCase      : Optional[int]         =   self.fetch_inputs(features['''input_ids''']							)
										_UpperCamelCase      : Optional[Any]         =   {
										    '''input_ids''': jnp.array(lowerCAmelCase__,			dtype=jnp.intaa							),
										    '''attention_mask''': jnp.array(lowerCAmelCase__,			dtype=jnp.intaa							),
										    '''start_labels''': jnp.array(features['''start_token'''],			dtype=jnp.intaa							),
										    '''end_labels''': jnp.array(features['''end_token'''],			dtype=jnp.intaa							),
										    '''pooled_labels''': jnp.array(features['''category'''],			dtype=jnp.intaa							),
										}
										return batch






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   list							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   [self._fetch_inputs(lowerCAmelCase__							) for ids in input_ids]
										return zip(*lowerCAmelCase__							)






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




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   [1 for _ in range(len(lowerCAmelCase__							)							)]
										while len(lowerCAmelCase__							) < self.max_length:
																input_ids.append(self.pad_id							)
																attention_mask.append(0							)
										return input_ids, attention_mask





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase=None       ):
						if seed is not None:
												_UpperCamelCase      : int         =   dataset.shuffle(seed=_lowercase       )
						for i in range(len(_lowercase       ) // batch_size       ):
												_UpperCamelCase      : Tuple         =   dataset[i * batch_size : (i + 1) * batch_size]
												yield dict(_lowercase       )





@partial(jax.pmap	,						axis_name='''batch'''       )
def 	a_    (							_lowercase	,						_lowercase	,						**_lowercase       ):
						def loss_fn(_lowercase       ):
												_UpperCamelCase      : List[str]         =   model_inputs.pop('''start_labels'''       )
												_UpperCamelCase      : Union[str, Any]         =   model_inputs.pop('''end_labels'''       )
												_UpperCamelCase      : Optional[int]         =   model_inputs.pop('''pooled_labels'''       )

												_UpperCamelCase      : str         =   state.apply_fn(**_lowercase	,						params=_lowercase	,						dropout_rng=_lowercase	,						train=_lowercase       )
												_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase      : Dict         =   outputs

												return state.loss_fn(
												    _lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						)

						_UpperCamelCase       ,							_UpperCamelCase      : List[Any]         =   jax.random.split(_lowercase       )
						_UpperCamelCase      : Any         =   jax.value_and_grad(_lowercase       )
						_UpperCamelCase       ,							_UpperCamelCase      : Tuple         =   grad_fn(state.params       )
						_UpperCamelCase      : Optional[int]         =   jax.lax.pmean({'''loss''': loss}	,						axis_name='''batch'''       )
						_UpperCamelCase      : Any         =   jax.lax.pmean(_lowercase	,						'''batch'''       )

						_UpperCamelCase      : int         =   state.apply_gradients(grads=_lowercase       )
						return state, metrics, new_drp_rng





@partial(jax.pmap	,						axis_name='''batch'''       )
def 	a_    (							_lowercase	,						**_lowercase       ):
						_UpperCamelCase      : Tuple         =   model_inputs.pop('''start_labels'''       )
						_UpperCamelCase      : Dict         =   model_inputs.pop('''end_labels'''       )
						_UpperCamelCase      : List[str]         =   model_inputs.pop('''pooled_labels'''       )

						_UpperCamelCase      : Union[str, Any]         =   state.apply_fn(**_lowercase	,						params=state.params	,						train=_lowercase       )
						_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase      : Optional[int]         =   outputs

						_UpperCamelCase      : Union[str, Any]         =   state.loss_fn(_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       )
						_UpperCamelCase      : Optional[Any]         =   jax.lax.pmean({'''loss''': loss}	,						axis_name='''batch'''       )
						return metrics




class 			_a						(				train_state.TrainState    ):
				UpperCamelCase								=   struct.field(pytree_node=_lowerCAmelCase    )




@dataclass
class 			_a						:
				UpperCamelCase								=   42
				UpperCamelCase								=   42
				UpperCamelCase								=   42
				UpperCamelCase								=   42
				UpperCamelCase								=   42
				UpperCamelCase								=   42
				UpperCamelCase								=   None






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




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   model.params
										_UpperCamelCase      : Dict         =   TrainState.create(
										    apply_fn=model.__call__,			params=lowerCAmelCase__,			tx=lowerCAmelCase__,			loss_fn=lowerCAmelCase__,			)
										if ckpt_dir is not None:
																_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase      : Any         =   restore_checkpoint(lowerCAmelCase__,			lowerCAmelCase__							)
																_UpperCamelCase      : List[str]         =   {
																    '''lr''': args.lr,
																    '''init_lr''': args.init_lr,
																    '''warmup_steps''': args.warmup_steps,
																    '''num_train_steps''': num_train_steps,
																    '''weight_decay''': args.weight_decay,
																}
																_UpperCamelCase       ,							_UpperCamelCase      : Optional[int]         =   build_tx(**lowerCAmelCase__							)
																_UpperCamelCase      : List[Any]         =   train_state.TrainState(
																    step=lowerCAmelCase__,			apply_fn=model.__call__,			params=lowerCAmelCase__,			tx=lowerCAmelCase__,			opt_state=lowerCAmelCase__,			)
																_UpperCamelCase      : Optional[Any]         =   args
																_UpperCamelCase      : List[Any]         =   data_collator
																_UpperCamelCase      : int         =   lr
																_UpperCamelCase      : int         =   params
										_UpperCamelCase      : str         =   jax_utils.replicate(lowerCAmelCase__							)
										return state






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




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.args
										_UpperCamelCase      : str         =   len(lowerCAmelCase__							) // args.batch_size

										_UpperCamelCase      : Optional[Any]         =   jax.random.PRNGKey(0							)
										_UpperCamelCase      : str         =   jax.random.split(lowerCAmelCase__,			jax.device_count()							)
										for epoch in range(args.max_epochs							):
																_UpperCamelCase      : Dict         =   jnp.array(0,			dtype=jnp.floataa							)
																_UpperCamelCase      : int         =   get_batched_dataset(lowerCAmelCase__,			args.batch_size,			seed=lowerCAmelCase__							)
																_UpperCamelCase      : int         =   0
																for batch in tqdm(lowerCAmelCase__,			total=lowerCAmelCase__,			desc=f"""Running EPOCH-{epoch}"""							):
																						_UpperCamelCase      : Any         =   self.data_collator(lowerCAmelCase__							)
																						_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase      : Optional[Any]         =   self.train_step_fn(lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							)
																						running_loss += jax_utils.unreplicate(metrics['''loss''']							)
																						i += 1
																						if i % args.logging_steps == 0:
																												_UpperCamelCase      : str         =   jax_utils.unreplicate(state.step							)
																												_UpperCamelCase      : Optional[int]         =   running_loss.item() / i
																												_UpperCamelCase      : str         =   self.scheduler_fn(state_step - 1							)

																												_UpperCamelCase      : int         =   self.evaluate(lowerCAmelCase__,			lowerCAmelCase__							)
																												_UpperCamelCase      : Tuple         =   {
																												    '''step''': state_step.item(),
																												    '''eval_loss''': eval_loss.item(),
																												    '''tr_loss''': tr_loss,
																												    '''lr''': lr.item(),
																												}
																												tqdm.write(str(lowerCAmelCase__							)							)
																												self.logger.log(lowerCAmelCase__,			commit=lowerCAmelCase__							)

																						if i % args.save_steps == 0:
																												self.save_checkpoint(args.save_dir + f"""-e{epoch}-s{i}""",			state=lowerCAmelCase__							)






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




										'''simple docstring'''



										_UpperCamelCase      : str         =   get_batched_dataset(lowerCAmelCase__,			self.args.batch_size							)
										_UpperCamelCase      : List[str]         =   len(lowerCAmelCase__							) // self.args.batch_size
										_UpperCamelCase      : Optional[int]         =   jnp.array(0,			dtype=jnp.floataa							)
										_UpperCamelCase      : Union[str, Any]         =   0
										for batch in tqdm(lowerCAmelCase__,			total=lowerCAmelCase__,			desc='''Evaluating ... '''							):
																_UpperCamelCase      : Union[str, Any]         =   self.data_collator(lowerCAmelCase__							)
																_UpperCamelCase      : Union[str, Any]         =   self.val_step_fn(lowerCAmelCase__,			**lowerCAmelCase__							)
																running_loss += jax_utils.unreplicate(metrics['''loss''']							)
																i += 1
										return running_loss / i






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




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   jax_utils.unreplicate(lowerCAmelCase__							)
										print(f"""SAVING CHECKPOINT IN {save_dir}""",			end=''' ... '''							)
										self.model_save_fn(lowerCAmelCase__,			params=state.params							)
										with open(os.path.join(lowerCAmelCase__,			'''opt_state.msgpack'''							),			'''wb'''							) as f:
																f.write(to_bytes(state.opt_state							)							)
										joblib.dump(self.args,			os.path.join(lowerCAmelCase__,			'''args.joblib'''							)							)
										joblib.dump(self.data_collator,			os.path.join(lowerCAmelCase__,			'''data_collator.joblib'''							)							)
										with open(os.path.join(lowerCAmelCase__,			'''training_state.json'''							),			'''w'''							) as f:
																json.dump({'''step''': state.step.item()},			lowerCAmelCase__							)
										print('''DONE'''							)





def 	a_    (							_lowercase	,						_lowercase       ):
						print(F"""RESTORING CHECKPOINT FROM {save_dir}"""	,						end=''' ... '''       )
						with open(os.path.join(_lowercase	,						'''flax_model.msgpack'''       )	,						'''rb'''       ) as f:
												_UpperCamelCase      : Optional[Any]         =   from_bytes(state.params	,						f.read()       )

						with open(os.path.join(_lowercase	,						'''opt_state.msgpack'''       )	,						'''rb'''       ) as f:
												_UpperCamelCase      : Optional[int]         =   from_bytes(state.opt_state	,						f.read()       )

						_UpperCamelCase      : str         =   joblib.load(os.path.join(_lowercase	,						'''args.joblib'''       )       )
						_UpperCamelCase      : List[Any]         =   joblib.load(os.path.join(_lowercase	,						'''data_collator.joblib'''       )       )

						with open(os.path.join(_lowercase	,						'''training_state.json'''       )	,						'''r'''       ) as f:
												_UpperCamelCase      : Optional[int]         =   json.load(_lowercase       )
						_UpperCamelCase      : Dict         =   training_state['''step''']

						print('''DONE'''       )
						return params, opt_state, step, args, data_collator





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Any         =   num_train_steps - warmup_steps
						_UpperCamelCase      : Tuple         =   optax.linear_schedule(init_value=_lowercase	,						end_value=_lowercase	,						transition_steps=_lowercase       )
						_UpperCamelCase      : Optional[int]         =   optax.linear_schedule(init_value=_lowercase	,						end_value=1E-7	,						transition_steps=_lowercase       )
						_UpperCamelCase      : Dict         =   optax.join_schedules(schedules=[warmup_fn, decay_fn]	,						boundaries=[warmup_steps]       )
						return lr






def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						def weight_decay_mask(_lowercase       ):
												_UpperCamelCase      : Union[str, Any]         =   traverse_util.flatten_dict(_lowercase       )
												_UpperCamelCase      : Any         =   {k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
												return traverse_util.unflatten_dict(_lowercase       )

						_UpperCamelCase      : List[str]         =   scheduler_fn(_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       )

						_UpperCamelCase      : str         =   optax.adamw(learning_rate=_lowercase	,						weight_decay=_lowercase	,						mask=_lowercase       )
						return tx, lr










"""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



