
'''simple docstring'''
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					,   UpperCAmelCase					,   UpperCAmelCase=None					,   UpperCAmelCase="base"					,   UpperCAmelCase=None					,   UpperCAmelCase=None					,   UpperCAmelCase=None					,   **UpperCAmelCase					,   ):
				super().__init__()
				# TODO: move to self.save_hyperparameters()
				# self.save_hyperparameters()
				# can also expand arguments into trainer signature for easier reading

				self.save_hyperparameters(UpperCAmelCase	)
				a_      				=						0
				a_      				=						Path(self.hparams.output_dir	)
				a_      				=						self.hparams.cache_dir if self.hparams.cache_dir else None
				if config is None:
					a_      				=						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=UpperCAmelCase					,   **UpperCAmelCase					,   )
				else:
					a_      				=						config

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

				if tokenizer is None:
					a_      				=						AutoTokenizer.from_pretrained(
					    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,   cache_dir=UpperCAmelCase					,   )
				else:
					a_      				=						tokenizer
				a_      				=						MODEL_MODES[mode]
				if model is None:
					a_      				=						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=UpperCAmelCase					,   )
				else:
					a_      				=						model




			def 					lowerCAmelCase__					(       self					,   *UpperCAmelCase					,   **UpperCAmelCase	):
				a_      				=						self.model_type.from_pretrained(*UpperCAmelCase					,   **UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						arg_to_scheduler[self.hparams.lr_scheduler]
				a_      				=						get_schedule_func(
				    self.opt					,   num_warmup_steps=self.hparams.warmup_steps					,   num_training_steps=self.total_steps()	)
				a_      				=						{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
				return scheduler




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model
				a_      				=						["""bias""", """LayerNorm.weight"""]
				a_      				=						[
				    {
				        """params""": [
				            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)
				        ],  # check this named paramters
				        """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:
					a_      				=						Adafactor(
					    UpperCAmelCase					,   lr=self.hparams.learning_rate					,   scale_parameter=UpperCAmelCase					,   relative_step=UpperCAmelCase	)

				else:
					a_      				=						AdamW(
					    UpperCAmelCase					,   lr=self.hparams.learning_rate					,   eps=self.hparams.adam_epsilon	)
				a_      				=						optimizer

				a_      				=						self.get_lr_scheduler()

				return [optimizer], [scheduler]




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase	):
				return self.validation_step(UpperCAmelCase					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				return self.validation_end(UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						max(1					,   self.hparams.gpus	)  # TODO: consider num_tpu_cores
				a_      				=						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
				return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				if stage == "test":
					a_      				=						len(self.test_dataloader().dataset	)
				else:
					a_      				=						self.get_dataloader("""train"""					,   self.hparams.train_batch_size					,   shuffle=UpperCAmelCase	)
					a_      				=						len(self.train_dataloader().dataset	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = False	):
				raise NotImplementedError("""You must implement this for your task"""	)




			def 					lowerCAmelCase__					(       self	):
				return self.train_loader




			def 					lowerCAmelCase__					(       self	):
				return self.get_dataloader("""dev"""					,   self.hparams.eval_batch_size					,   shuffle=UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				return self.get_dataloader("""test"""					,   self.hparams.eval_batch_size					,   shuffle=UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				return os.path.join(
				    self.hparams.data_dir					,   """cached_{}_{}_{}""".format(
				        UpperCAmelCase					,   list(filter(UpperCAmelCase					,   self.hparams.model_name_or_path.split("""/"""	)	)	).pop()					,   str(self.hparams.max_seq_length	)					,   )					,   )




			@pl.utilities.rank_zero_only
			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						self.output_dir.joinpath("""best_tfmr"""	)
				a_      				=						self.step_count
				self.model.save_pretrained(UpperCAmelCase	)
				self.tokenizer.save_pretrained(UpperCAmelCase	)






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







class   a_ (      pl.Callback					):




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase	):
				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 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase	):
				# print(pl_module.model.rag)
				for name, param in pl_module.model.rag.named_parameters():
					if param.grad is None:
						print(UpperCAmelCase	)







class   a_ (      pl.Callback					):




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						trainer.lr_schedulers[0]["""scheduler"""]
				a_      				=						{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
				pl_module.logger.log_metrics(UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase	):
				rank_zero_info("""***** Validation results *****"""	)
				a_      				=						trainer.callback_metrics
				# Log results
				for key in sorted(UpperCAmelCase	):
					if key not in ["log", "progress_bar"]:
						rank_zero_info("""{} = {}\n""".format(UpperCAmelCase					,   str(metrics[key]	)	)	)






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







def      UpperCamelCase_				(      A__  ,      A__						):
	#  To allow all pl args uncomment the following line
	#  parser = pl.Trainer.add_argparse_args(parser)
	parser.add_argument(
	    """--output_dir"""  ,      default=str(Path(A__						).parent / """test_run""" / """model_checkpoints"""						)  ,      type=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=str(Path(A__						).parent / """test_run""" / """dummy-train-data"""						)  ,      type=A__  ,      help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""  ,      )






def      UpperCamelCase_				(      A__  ,      A__  ,      A__=None  ,      A__=True  ,      A__=[]  ,      A__=None  ,      A__=None  ,      **A__  ,      ):
	pl.seed_everything(args.seed						)

	# init model
	a_      				=						Path(model.hparams.output_dir						)
	odir.mkdir(exist_ok=A__						)

	# add custom checkpoints
	if checkpoint_callback is None:
		a_      				=						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:
		a_      				=						LoggingCallback()

	a_      				=						{}

	if args.fpaa:
		a_      				=						16

	if args.gpus > 1:
		a_      				=						"""auto"""
		a_      				=						"""ddp"""

	a_      				=						args.accumulate_grad_batches
	a_      				=						None
	a_      				=						"""auto"""

	a_      				=						pl.Trainer.from_argparse_args(
	    A__  ,      weights_summary=A__  ,      callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]  ,      logger=A__  ,      val_check_interval=1  ,      num_sanity_val_steps=2  ,      **A__  ,      )

	if args.do_train:
		trainer.fit(A__						)

	else:
		print("""RAG modeling tests with new set functions successfuly executed!"""						)
	return trainer


'''simple docstring'''
import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowercase__											=False







class   a_ (      unittest.TestCase					):




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=32	):
				set_seed(0	)
				a_      				=						UNetaDModel(sample_size=UpperCAmelCase					,   in_channels=3					,   out_channels=3	)
				a_      				=						torch.optim.SGD(model.parameters()					,   lr=0.00_01	)
				return model, optimizer






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				a_      				=						DDPMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )
				a_      				=						DDIMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				a_      				=						[torch.randn((4, 3, 32, 32)	).clip(-1					,   1	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randn((4, 3, 32, 32)	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randint(0					,   10_00					,   (4,)	).long().to(UpperCAmelCase	) for _ in range(4	)]

				# train with a DDPM scheduler
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddpm_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddim_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
