




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowercase_		=							logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

lowercase_		=							{
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowercase_		=							{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowercase_		=							sorted(arg_to_scheduler.keys())
lowercase_		=							'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''





class 		A__    (       pl.LightningModule			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase=None    ,					lowerCamelCase="base"    ,					lowerCamelCase=None    ,					lowerCamelCase=None    ,					lowerCamelCase=None    ,					**lowerCamelCase    ,					)				->       int:




						"""simple docstring"""






						super().__init__()
						# TODO: move to self.save_hyperparameters()
						# self.save_hyperparameters()
						# can also expand arguments into trainer signature for easier reading

						self.save_hyperparameters(lowerCamelCase						)
						__magic_name__ :							Optional[Any]		       =							0
						__magic_name__ :							int		       =							Path(self.hparams.output_dir						)
						__magic_name__ :							Union[str, Any]		       =							self.hparams.cache_dir if self.hparams.cache_dir else None
						if config is None:
											__magic_name__ :							Dict		       =							AutoConfig.from_pretrained(
											    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path    ,					**({'''num_labels''': num_labels} if num_labels is not None else {})    ,					cache_dir=lowerCamelCase    ,					**lowerCamelCase    ,					)
						else:
											__magic_name__ :							int		       =							config

						__magic_name__ :							Tuple		       =							('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
						for p in extra_model_params:
											if getattr(self.hparams    ,					lowerCamelCase    ,					lowerCamelCase						):
																assert hasattr(self.config    ,					lowerCamelCase						), F'''model config doesn\'t have a `{p}` attribute'''
																setattr(self.config    ,					lowerCamelCase    ,					getattr(self.hparams    ,					lowerCamelCase						)						)

						if tokenizer is None:
											__magic_name__ :							List[Any]		       =							AutoTokenizer.from_pretrained(
											    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,					cache_dir=lowerCamelCase    ,					)
						else:
											__magic_name__ :							Optional[Any]		       =							tokenizer
						__magic_name__ :							Dict		       =							MODEL_MODES[mode]
						if model is None:
											__magic_name__ :							Union[str, Any]		       =							self.model_type.from_pretrained(
											    self.hparams.model_name_or_path    ,					from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path						)    ,					config=self.config    ,					cache_dir=lowerCamelCase    ,					)
						else:
											__magic_name__ :							str		       =							model






	def 			lowercase				(     self    ,					*lowerCamelCase    ,					**lowerCamelCase						)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							self.model_type.from_pretrained(*lowerCamelCase    ,					**lowerCamelCase						)






	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							arg_to_scheduler[self.hparams.lr_scheduler]
						__magic_name__ :							Any		       =							get_schedule_func(
						    self.opt    ,					num_warmup_steps=self.hparams.warmup_steps    ,					num_training_steps=self.total_steps()						)
						__magic_name__ :							Optional[Any]		       =							{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
						return scheduler






	def 			lowercase				(     self						)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							self.model
						__magic_name__ :							List[str]		       =							['''bias''', '''LayerNorm.weight''']
						__magic_name__ :							Optional[int]		       =							[
						    {
						        '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)],
						        '''weight_decay''': self.hparams.weight_decay,
						    },
						    {
						        '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay						)],
						        '''weight_decay''': 0.0,
						    },
						]
						if self.hparams.adafactor:
											__magic_name__ :							int		       =							Adafactor(
											    lowerCamelCase    ,					lr=self.hparams.learning_rate    ,					scale_parameter=lowerCamelCase    ,					relative_step=lowerCamelCase						)

						else:
											__magic_name__ :							str		       =							AdamW(
											    lowerCamelCase    ,					lr=self.hparams.learning_rate    ,					eps=self.hparams.adam_epsilon						)
						__magic_name__ :							Optional[Any]		       =							optimizer

						__magic_name__ :							Dict		       =							self.get_lr_scheduler()

						return [optimizer], [scheduler]






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Tuple:




						"""simple docstring"""






						return self.validation_step(lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Any:




						"""simple docstring"""






						return self.validation_end(lowerCamelCase						)






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Any		       =							max(1    ,					self.hparams.gpus						)  # TODO: consider num_tpu_cores
						__magic_name__ :							Union[str, Any]		       =							self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
						return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       int:




						"""simple docstring"""






						if stage == "test":
											__magic_name__ :							int		       =							len(self.test_dataloader().dataset						)
						else:
											__magic_name__ :							Tuple		       =							self.get_dataloader('''train'''    ,					self.hparams.train_batch_size    ,					shuffle=lowerCamelCase						)
											__magic_name__ :							Optional[Any]		       =							len(self.train_dataloader().dataset						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = False						)				->       Optional[int]:




						"""simple docstring"""






						raise NotImplementedError('''You must implement this for your task'''						)






	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						return self.train_loader






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




						"""simple docstring"""






						return self.get_dataloader('''dev'''    ,					self.hparams.eval_batch_size    ,					shuffle=lowerCamelCase						)






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						return self.get_dataloader('''test'''    ,					self.hparams.eval_batch_size    ,					shuffle=lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Union[str, Any]:




						"""simple docstring"""






						return os.path.join(
						    self.hparams.data_dir    ,					'''cached_{}_{}_{}'''.format(
						        lowerCamelCase    ,					list(filter(lowerCamelCase    ,					self.hparams.model_name_or_path.split('''/'''						)						)						).pop()    ,					str(self.hparams.max_seq_length						)    ,					)    ,					)






	@pl.utilities.rank_zero_only
	def 			lowercase				(     self    ,					lowerCamelCase						)				->       None:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.output_dir.joinpath('''best_tfmr'''						)
						__magic_name__ :							Optional[Any]		       =							self.step_count
						self.model.save_pretrained(lowerCamelCase						)
						self.tokenizer.save_pretrained(lowerCamelCase						)






	@staticmethod
	def 			lowercase				(     lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						parser.add_argument(
						    '''--model_name_or_path'''    ,					default=lowerCamelCase    ,					type=lowerCamelCase    ,					required=lowerCamelCase    ,					help='''Path to pretrained model or model identifier from huggingface.co/models'''    ,					)
						parser.add_argument(
						    '''--config_name'''    ,					default=''''''    ,					type=lowerCamelCase    ,					help='''Pretrained config name or path if not the same as model_name'''						)
						parser.add_argument(
						    '''--tokenizer_name'''    ,					default=lowerCamelCase    ,					type=lowerCamelCase    ,					help='''Pretrained tokenizer name or path if not the same as model_name'''    ,					)
						parser.add_argument(
						    '''--cache_dir'''    ,					default=''''''    ,					type=lowerCamelCase    ,					help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''    ,					)
						parser.add_argument(
						    '''--encoder_layerdrop'''    ,					type=lowerCamelCase    ,					help='''Encoder layer dropout probability (Optional). Goes into model.config'''    ,					)
						parser.add_argument(
						    '''--decoder_layerdrop'''    ,					type=lowerCamelCase    ,					help='''Decoder layer dropout probability (Optional). Goes into model.config'''    ,					)
						parser.add_argument(
						    '''--dropout'''    ,					type=lowerCamelCase    ,					help='''Dropout probability (Optional). Goes into model.config'''    ,					)
						parser.add_argument(
						    '''--attention_dropout'''    ,					type=lowerCamelCase    ,					help='''Attention dropout probability (Optional). Goes into model.config'''    ,					)
						parser.add_argument('''--learning_rate'''    ,					default=5e-5    ,					type=lowerCamelCase    ,					help='''The initial learning rate for Adam.'''						)
						parser.add_argument(
						    '''--lr_scheduler'''    ,					default='''linear'''    ,					choices=lowerCamelCase    ,					metavar=lowerCamelCase    ,					type=lowerCamelCase    ,					help='''Learning rate scheduler'''    ,					)
						parser.add_argument('''--weight_decay'''    ,					default=0.0    ,					type=lowerCamelCase    ,					help='''Weight decay if we apply some.'''						)
						parser.add_argument('''--adam_epsilon'''    ,					default=1e-8    ,					type=lowerCamelCase    ,					help='''Epsilon for Adam optimizer.'''						)
						parser.add_argument('''--warmup_steps'''    ,					default=0    ,					type=lowerCamelCase    ,					help='''Linear warmup over warmup_steps.'''						)
						parser.add_argument('''--num_workers'''    ,					default=4    ,					type=lowerCamelCase    ,					help='''kwarg passed to DataLoader'''						)
						parser.add_argument('''--num_train_epochs'''    ,					dest='''max_epochs'''    ,					default=3    ,					type=lowerCamelCase						)
						parser.add_argument('''--train_batch_size'''    ,					default=32    ,					type=lowerCamelCase						)
						parser.add_argument('''--eval_batch_size'''    ,					default=32    ,					type=lowerCamelCase						)
						parser.add_argument('''--adafactor'''    ,					action='''store_true'''						)





class 		A__    (       pl.Callback			):






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						if (
						    trainer.is_global_zero and trainer.global_rank == 0
						):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
											pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.





class 		A__    (       pl.Callback			):






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							int		       =							trainer.lr_schedulers[0]['''scheduler''']
						__magic_name__ :							str		       =							{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
						pl_module.logger.log_metrics(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Tuple:




						"""simple docstring"""






						rank_zero_info('''***** Validation results *****'''						)
						__magic_name__ :							Optional[int]		       =							trainer.callback_metrics
						# Log results
						for key in sorted(lowerCamelCase						):
											if key not in ["log", "progress_bar"]:
																rank_zero_info('''{} = {}\n'''.format(lowerCamelCase    ,					str(metrics[key]						)						)						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Dict:




						"""simple docstring"""






						rank_zero_info('''***** Test results *****'''						)
						__magic_name__ :							List[str]		       =							trainer.callback_metrics
						# Log and save results to file
						__magic_name__ :							Any		       =							os.path.join(pl_module.hparams.output_dir    ,					'''test_results.txt'''						)
						with open(lowerCamelCase    ,					'''w'''						) as writer:
											for key in sorted(lowerCamelCase						):
																if key not in ["log", "progress_bar"]:
																					rank_zero_info('''{} = {}\n'''.format(lowerCamelCase    ,					str(metrics[key]						)						)						)
																					writer.write('''{} = {}\n'''.format(lowerCamelCase    ,					str(metrics[key]						)						)						)







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->Dict:






					"""simple docstring"""






					parser.add_argument(
					    '''--output_dir''',     default=__A,     type=__A,     required=__A,     help='''The output directory where the model predictions and checkpoints will be written.''',     )
					parser.add_argument(
					    '''--fp16''',     action='''store_true''',     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''',     )

					parser.add_argument(
					    '''--fp16_opt_level''',     type=__A,     default='''O2''',     help=(
					        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
					        '''See details at https://nvidia.github.io/apex/amp.html'''
					    ),     )
					parser.add_argument('''--n_tpu_cores''',     dest='''tpu_cores''',     type=__A	)
					parser.add_argument('''--max_grad_norm''',     dest='''gradient_clip_val''',     default=1.0,     type=__A,     help='''Max gradient norm'''	)
					parser.add_argument('''--do_train''',     action='''store_true''',     help='''Whether to run training.'''	)
					parser.add_argument('''--do_predict''',     action='''store_true''',     help='''Whether to run predictions on the test set.'''	)
					parser.add_argument(
					    '''--gradient_accumulation_steps''',     dest='''accumulate_grad_batches''',     type=__A,     default=1,     help='''Number of updates steps to accumulate before performing a backward/update pass.''',     )
					parser.add_argument('''--seed''',     type=__A,     default=42,     help='''random seed for initialization'''	)
					parser.add_argument(
					    '''--data_dir''',     default=__A,     type=__A,     required=__A,     help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''',     )







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase=None,     UpperCAmelCase=True,     UpperCAmelCase=None,     UpperCAmelCase=[],     UpperCAmelCase=None,     UpperCAmelCase=None,     **UpperCAmelCase,     )  ->Optional[Any]:






					"""simple docstring"""






					pl.seed_everything(args.seed	)

					# init model
					__magic_name__ :							Optional[int]		       =							Path(model.hparams.output_dir	)
					odir.mkdir(exist_ok=__A	)

					# add custom checkpoints
					if checkpoint_callback is None:
										__magic_name__ :							Tuple		       =							pl.callbacks.ModelCheckpoint(
										    filepath=args.output_dir,     prefix='''checkpoint''',     monitor='''val_loss''',     mode='''min''',     save_top_k=1	)
					if early_stopping_callback:
										extra_callbacks.append(__A	)
					if logging_callback is None:
										__magic_name__ :							str		       =							LoggingCallback()

					__magic_name__ :							Dict		       =							{}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										__magic_name__ :							int		       =							16
										# train_params["amp_level"] = args.fp16_opt_level

					if args.gpus > 1:
										__magic_name__ :							List[Any]		       =							'''auto'''  # "ddp"
										__magic_name__ :							Tuple		       =							'''ddp'''

					__magic_name__ :							int		       =							args.accumulate_grad_batches
					__magic_name__ :							List[Any]		       =							None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
					__magic_name__ :							Tuple		       =							'''auto'''

					__magic_name__ :							Union[str, Any]		       =							pl.Trainer.from_argparse_args(
					    __A,     weights_summary=__A,     callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()],     logger=__A,     **__A,     )

					if args.do_train:
										trainer.fit(__A	)

					return trainer









import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase = False,     UpperCAmelCase = 1,     UpperCAmelCase = 1,     UpperCAmelCase = 1_0000,     )  ->List[Any]:






					"""simple docstring"""






					assert len(timesteps.shape	) == 1, "Timesteps should be a 1d-array"

					__magic_name__ :							List[str]		       =							embedding_dim // 2
					__magic_name__ :							Dict		       =							-math.log(UpperCAmelCase	) * torch.arange(
					    start=0,     end=UpperCAmelCase,     dtype=torch.floataa,     device=timesteps.device	)
					__magic_name__ :							Any		       =							exponent / (half_dim - downscale_freq_shift)

					__magic_name__ :							Tuple		       =							torch.exp(UpperCAmelCase	)
					__magic_name__ :							Tuple		       =							timesteps[:, None].float() * emb[None, :]

					# scale embeddings
					__magic_name__ :							Optional[Any]		       =							scale * emb

					# concat sine and cosine embeddings
					__magic_name__ :							Optional[int]		       =							torch.cat([torch.sin(UpperCAmelCase	), torch.cos(UpperCAmelCase	)],     dim=-1	)

					# flip sine and cosine embeddings
					if flip_sin_to_cos:
										__magic_name__ :							Tuple		       =							torch.cat([emb[:, half_dim:], emb[:, :half_dim]],     dim=-1	)

					# zero pad
					if embedding_dim % 2 == 1:
										__magic_name__ :							List[str]		       =							torch.nn.functional.pad(UpperCAmelCase,     (0, 1, 0, 0)	)
					return emb







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase=False,     UpperCAmelCase=0	)  ->Dict:






					"""simple docstring"""






					__magic_name__ :							Any		       =							np.arange(UpperCAmelCase,     dtype=np.floataa	)
					__magic_name__ :							List[str]		       =							np.arange(UpperCAmelCase,     dtype=np.floataa	)
					__magic_name__ :							List[str]		       =							np.meshgrid(UpperCAmelCase,     UpperCAmelCase	)  # here w goes first
					__magic_name__ :							Tuple		       =							np.stack(UpperCAmelCase,     axis=0	)

					__magic_name__ :							Any		       =							grid.reshape([2, 1, grid_size, grid_size]	)
					__magic_name__ :							Dict		       =							get_ad_sincos_pos_embed_from_grid(UpperCAmelCase,     UpperCAmelCase	)
					if cls_token and extra_tokens > 0:
										__magic_name__ :							List[Any]		       =							np.concatenate([np.zeros([extra_tokens, embed_dim]	), pos_embed],     axis=0	)
					return pos_embed







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->List[Any]:






					"""simple docstring"""






					if embed_dim % 2 != 0:
										raise ValueError('''embed_dim must be divisible by 2'''	)

					# use half of dimensions to encode grid_h
					__magic_name__ :							Union[str, Any]		       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2,     grid[0]	)  # (H*W, D/2)
					__magic_name__ :							Dict		       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2,     grid[1]	)  # (H*W, D/2)

					__magic_name__ :							Tuple		       =							np.concatenate([emb_h, emb_w],     axis=1	)  # (H*W, D)
					return emb







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->List[Any]:






					"""simple docstring"""






					if embed_dim % 2 != 0:
										raise ValueError('''embed_dim must be divisible by 2'''	)

					__magic_name__ :							str		       =							np.arange(embed_dim // 2,     dtype=np.floataa	)
					omega /= embed_dim / 2.0
					__magic_name__ :							str		       =							1.0 / 1_0000**omega  # (D/2,)

					__magic_name__ :							List[str]		       =							pos.reshape(-1	)  # (M,)
					__magic_name__ :							Dict		       =							np.einsum('''m,d->md''',     UpperCAmelCase,     UpperCAmelCase	)  # (M, D/2), outer product

					__magic_name__ :							Union[str, Any]		       =							np.sin(UpperCAmelCase	)  # (M, D/2)
					__magic_name__ :							str		       =							np.cos(UpperCAmelCase	)  # (M, D/2)

					__magic_name__ :							int		       =							np.concatenate([emb_sin, emb_cos],     axis=1	)  # (M, D)
					return emb





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase=224    ,					lowerCamelCase=224    ,					lowerCamelCase=16    ,					lowerCamelCase=3    ,					lowerCamelCase=768    ,					lowerCamelCase=False    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					)				->       Any:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Dict		       =							(height // patch_size) * (width // patch_size)
						__magic_name__ :							Tuple		       =							flatten
						__magic_name__ :							List[Any]		       =							layer_norm

						__magic_name__ :							Optional[Any]		       =							nn.Convad(
						    lowerCamelCase    ,					lowerCamelCase    ,					kernel_size=(patch_size, patch_size)    ,					stride=lowerCamelCase    ,					bias=lowerCamelCase						)
						if layer_norm:
											__magic_name__ :							Optional[int]		       =							nn.LayerNorm(lowerCamelCase    ,					elementwise_affine=lowerCamelCase    ,					eps=1e-6						)
						else:
											__magic_name__ :							int		       =							None

						__magic_name__ :							List[Any]		       =							get_ad_sincos_pos_embed(lowerCamelCase    ,					int(num_patches**0.5						)						)
						self.register_buffer('''pos_embed'''    ,					torch.from_numpy(lowerCamelCase						).float().unsqueeze(0						)    ,					persistent=lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.proj(lowerCamelCase						)
						if self.flatten:
											__magic_name__ :							Dict		       =							latent.flatten(2						).transpose(1    ,					2						)  # BCHW -> BNC
						if self.layer_norm:
											__magic_name__ :							Tuple		       =							self.norm(lowerCamelCase						)
						return latent + self.pos_embed





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = "silu"    ,					lowerCamelCase = None    ,					lowerCamelCase = None    ,					lowerCamelCase=None    ,					)				->       Tuple:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Union[str, Any]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)

						if cond_proj_dim is not None:
											__magic_name__ :							Any		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					bias=lowerCamelCase						)
						else:
											__magic_name__ :							Union[str, Any]		       =							None

						__magic_name__ :							Union[str, Any]		       =							get_activation(lowerCamelCase						)

						if out_dim is not None:
											__magic_name__ :							Tuple		       =							out_dim
						else:
											__magic_name__ :							List[str]		       =							time_embed_dim
						__magic_name__ :							List[str]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)

						if post_act_fn is None:
											__magic_name__ :							List[str]		       =							None
						else:
											__magic_name__ :							Dict		       =							get_activation(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Tuple:




						"""simple docstring"""






						if condition is not None:
											__magic_name__ :							List[Any]		       =							sample + self.cond_proj(lowerCamelCase						)
						__magic_name__ :							List[str]		       =							self.linear_a(lowerCamelCase						)

						if self.act is not None:
											__magic_name__ :							str		       =							self.act(lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							self.linear_a(lowerCamelCase						)

						if self.post_act is not None:
											__magic_name__ :							List[str]		       =							self.post_act(lowerCamelCase						)
						return sample





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Any		       =							num_channels
						__magic_name__ :							Tuple		       =							flip_sin_to_cos
						__magic_name__ :							List[Any]		       =							downscale_freq_shift






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							get_timestep_embedding(
						    lowerCamelCase    ,					self.num_channels    ,					flip_sin_to_cos=self.flip_sin_to_cos    ,					downscale_freq_shift=self.downscale_freq_shift    ,					)
						return t_emb





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 256    ,					lowerCamelCase = 1.0    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase=False						)				->       int:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Optional[int]		       =							nn.Parameter(torch.randn(lowerCamelCase						) * scale    ,					requires_grad=lowerCamelCase						)
						__magic_name__ :							List[str]		       =							log
						__magic_name__ :							Optional[int]		       =							flip_sin_to_cos

						if set_W_to_weight:
											# to delete later
											__magic_name__ :							Tuple		       =							nn.Parameter(torch.randn(lowerCamelCase						) * scale    ,					requires_grad=lowerCamelCase						)

											__magic_name__ :							List[str]		       =							self.W






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[str]:




						"""simple docstring"""






						if self.log:
											__magic_name__ :							Tuple		       =							torch.log(lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							x[:, None] * self.weight[None, :] * 2 * np.pi

						if self.flip_sin_to_cos:
											__magic_name__ :							Any		       =							torch.cat([torch.cos(lowerCamelCase						), torch.sin(lowerCamelCase						)]    ,					dim=-1						)
						else:
											__magic_name__ :							Dict		       =							torch.cat([torch.sin(lowerCamelCase						), torch.cos(lowerCamelCase						)]    ,					dim=-1						)
						return out





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       int:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Tuple		       =							height
						__magic_name__ :							Dict		       =							width
						__magic_name__ :							Any		       =							num_embed
						__magic_name__ :							List[str]		       =							embed_dim

						__magic_name__ :							Tuple		       =							nn.Embedding(self.num_embed    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Embedding(self.height    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Embedding(self.width    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							self.emb(lowerCamelCase						)

						__magic_name__ :							Dict		       =							self.height_emb(torch.arange(self.height    ,					device=index.device						).view(1    ,					self.height						)						)

						# 1 x H x D -> 1 x H x 1 x D
						__magic_name__ :							Any		       =							height_emb.unsqueeze(2						)

						__magic_name__ :							Optional[int]		       =							self.width_emb(torch.arange(self.width    ,					device=index.device						).view(1    ,					self.width						)						)

						# 1 x W x D -> 1 x 1 x W x D
						__magic_name__ :							str		       =							width_emb.unsqueeze(1						)

						__magic_name__ :							Dict		       =							height_emb + width_emb

						# 1 x H x W x D -> 1 x L xD
						__magic_name__ :							Any		       =							pos_emb.view(1    ,					self.height * self.width    ,					-1						)

						__magic_name__ :							Optional[Any]		       =							emb + pos_emb[:, : emb.shape[1], :]

						return emb





class 		A__    (       nn.Module			):






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




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							List[Any]		       =							dropout_prob > 0
						__magic_name__ :							Optional[int]		       =							nn.Embedding(num_classes + use_cfg_embedding    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							num_classes
						__magic_name__ :							Union[str, Any]		       =							dropout_prob






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Tuple:




						"""simple docstring"""






						if force_drop_ids is None:
											__magic_name__ :							Dict		       =							torch.rand(labels.shape[0]    ,					device=labels.device						) < self.dropout_prob
						else:
											__magic_name__ :							List[str]		       =							torch.tensor(force_drop_ids == 1						)
						__magic_name__ :							Optional[int]		       =							torch.where(lowerCamelCase    ,					self.num_classes    ,					lowerCamelCase						)
						return labels






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							self.dropout_prob > 0
						if (self.training and use_dropout) or (force_drop_ids is not None):
											__magic_name__ :							Optional[Any]		       =							self.token_drop(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							int		       =							self.embedding_table(lowerCamelCase						)
						return embeddings





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 1024    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 10    ,					)				->       str:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							int		       =							num_image_text_embeds
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					self.num_image_text_embeds * cross_attention_dim						)
						__magic_name__ :							List[str]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							text_embeds.shape[0]

						# image
						__magic_name__ :							Optional[Any]		       =							self.image_embeds(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							image_text_embeds.reshape(lowerCamelCase    ,					self.num_image_text_embeds    ,					-1						)

						# text
						__magic_name__ :							Dict		       =							self.text_proj(lowerCamelCase						)

						return torch.cat([image_text_embeds, text_embeds]    ,					dim=1						)





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 32    ,					)				->       Union[str, Any]:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							List[Any]		       =							num_image_text_embeds
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					self.num_image_text_embeds * cross_attention_dim						)
						__magic_name__ :							List[Any]		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							image_embeds.shape[0]

						# image
						__magic_name__ :							Any		       =							self.image_embeds(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							image_embeds.reshape(lowerCamelCase    ,					self.num_image_text_embeds    ,					-1						)
						__magic_name__ :							Union[str, Any]		       =							self.norm(lowerCamelCase						)
						return image_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=0.1						)				->       List[str]:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Dict		       =							Timesteps(num_channels=256    ,					flip_sin_to_cos=lowerCamelCase    ,					downscale_freq_shift=1						)
						__magic_name__ :							int		       =							TimestepEmbedding(in_channels=256    ,					time_embed_dim=lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							LabelEmbedding(lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							str		       =							self.time_proj(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.timestep_embedder(timesteps_proj.to(dtype=lowerCamelCase						)						)  # (N, D)

						__magic_name__ :							Optional[Any]		       =							self.class_embedder(lowerCamelCase						)  # (N, D)

						__magic_name__ :							Dict		       =							timesteps_emb + class_labels  # (N, D)

						return conditioning





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = 64						)				->       str:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							List[Any]		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							AttentionPooling(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Dict		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							self.norma(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							self.pool(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.proj(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.norma(lowerCamelCase						)
						return hidden_states





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       Tuple:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Any		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							int		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.text_proj(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							self.text_norm(lowerCamelCase						)

						# image
						__magic_name__ :							Tuple		       =							self.image_proj(lowerCamelCase						)

						return time_image_embeds + time_text_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       Optional[int]:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							int		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							str		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							self.image_proj(lowerCamelCase						)
						__magic_name__ :							int		       =							self.image_norm(lowerCamelCase						)
						return time_image_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       List[str]:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Optional[Any]		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Any		       =							nn.Sequential(
						    nn.Convad(3    ,					16    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(16    ,					16    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(16    ,					32    ,					3    ,					padding=1    ,					stride=2						)    ,					nn.SiLU()    ,					nn.Convad(32    ,					32    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(32    ,					96    ,					3    ,					padding=1    ,					stride=2						)    ,					nn.SiLU()    ,					nn.Convad(96    ,					96    ,					3    ,					padding=1						)    ,					nn.SiLU()    ,					nn.Convad(96    ,					256    ,					3    ,					padding=1    ,					stride=2						)    ,					nn.SiLU()    ,					nn.Convad(256    ,					4    ,					3    ,					padding=1						)    ,					)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Tuple:




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							self.image_proj(lowerCamelCase						)
						__magic_name__ :							str		       =							self.image_norm(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							self.input_hint_block(lowerCamelCase						)
						return time_image_embeds, hint





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       int:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							str		       =							dtype
						__magic_name__ :							str		       =							nn.Parameter(torch.randn(1    ,					lowerCamelCase						) / embed_dim**0.5						)
						__magic_name__ :							Tuple		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							str		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							Dict		       =							num_heads
						__magic_name__ :							List[Any]		       =							embed_dim // self.num_heads






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__       ,			__magic_name__       ,			__magic_name__ :							str		       =							x.size()

						def shape(lowerCamelCase						):
											# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
											__magic_name__ :							Union[str, Any]		       =							x.view(lowerCamelCase    ,					-1    ,					self.num_heads    ,					self.dim_per_head						)
											# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
											__magic_name__ :							str		       =							x.transpose(1    ,					2						)
											# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
											__magic_name__ :							List[str]		       =							x.reshape(bs * self.num_heads    ,					-1    ,					self.dim_per_head						)
											# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
											__magic_name__ :							int		       =							x.transpose(1    ,					2						)
											return x

						__magic_name__ :							List[Any]		       =							x.mean(dim=1    ,					keepdim=lowerCamelCase						) + self.positional_embedding.to(x.dtype						)
						__magic_name__ :							int		       =							torch.cat([class_token, x]    ,					dim=1						)  # (bs, length+1, width)

						# (bs*n_heads, class_token_length, dim_per_head)
						__magic_name__ :							int		       =							shape(self.q_proj(lowerCamelCase						)						)
						# (bs*n_heads, length+class_token_length, dim_per_head)
						__magic_name__ :							str		       =							shape(self.k_proj(lowerCamelCase						)						)
						__magic_name__ :							Any		       =							shape(self.v_proj(lowerCamelCase						)						)

						# (bs*n_heads, class_token_length, length+class_token_length):
						__magic_name__ :							Optional[int]		       =							1 / math.sqrt(math.sqrt(self.dim_per_head						)						)
						__magic_name__ :							Union[str, Any]		       =							torch.einsum('''bct,bcs->bts'''    ,					q * scale    ,					k * scale						)  # More stable with f16 than dividing afterwards
						__magic_name__ :							Union[str, Any]		       =							torch.softmax(weight.float()    ,					dim=-1						).type(weight.dtype						)

						# (bs*n_heads, dim_per_head, class_token_length)
						__magic_name__ :							Dict		       =							torch.einsum('''bts,bcs->bct'''    ,					lowerCamelCase    ,					lowerCamelCase						)

						# (bs, length+1, width)
						__magic_name__ :							Optional[int]		       =							a.reshape(lowerCamelCase    ,					-1    ,					1						).transpose(1    ,					2						)

						return a[:, 0, :]  # cls_token



