

"""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


__A	:       List[Any]        =						logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

__A	:       str        =						{
    '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
__A	:       Union[str, Any]        =						{
    '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
}
__A	:       str        =						sorted(arg_to_scheduler.keys())
__A	:       Optional[Any]        =						'{' + ', '.join(arg_to_scheduler_choices) + '}'





class      __UpperCamelCase					(  pl.LightningModule		):


		def __init__(self					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						argparse.Namespace	,	__SCREAMING_SNAKE_CASE					:						Any=None	,	__SCREAMING_SNAKE_CASE					:						Any="base"	,	__SCREAMING_SNAKE_CASE					:						int=None	,	__SCREAMING_SNAKE_CASE					:						str=None	,	__SCREAMING_SNAKE_CASE					:						Dict=None	,	**__SCREAMING_SNAKE_CASE					:						Optional[Any]	,	):
							super().__init__()
							# TODO: move to self.save_hyperparameters()
							# self.save_hyperparameters()
							# can also expand arguments into trainer signature for easier reading

							self.save_hyperparameters(_A)
							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=_A	,	**_A	,	)
							else:
												A     =   config

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

							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=_A	,	)
							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=_A	,	)
							else:
												A     =   model


		def    SCREAMING_SNAKE_CASE__  (self					:						Any	,	*__SCREAMING_SNAKE_CASE					:						Dict	,	**__SCREAMING_SNAKE_CASE					:						Optional[Any]):
							A     =   self.model_type.from_pretrained(*_A	,	**_A)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							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    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							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)],
							        '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(
												    _A	,	lr=self.hparams.learning_rate	,	scale_parameter=_A	,	relative_step=_A)

							else:
												A     =   AdamW(
												    _A	,	lr=self.hparams.learning_rate	,	eps=self.hparams.adam_epsilon)
							A     =   optimizer

							A     =   self.get_lr_scheduler()

							return [optimizer], [scheduler]


		def    SCREAMING_SNAKE_CASE__  (self					:						str	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]):
							return self.validation_step(_A	,	_A)


		def    SCREAMING_SNAKE_CASE__  (self					:						str	,	__SCREAMING_SNAKE_CASE					:						int):
							return self.validation_end(_A)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							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    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						List[Any]):


							if mode == "test":
												A     =   len(self.test_dataloader().dataset)
							else:
												A     =   self.get_dataloader("train"	,	self.hparams.train_batch_size	,	shuffle=_A)
												A     =   len(self.train_dataloader().dataset)


		def    SCREAMING_SNAKE_CASE__  (self					:						str	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						bool = False):
							raise NotImplementedError("You must implement this for your task")


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							return self.train_loader


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							return self.get_dataloader("dev"	,	self.hparams.eval_batch_size	,	shuffle=_A)


		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							return self.get_dataloader("test"	,	self.hparams.eval_batch_size	,	shuffle=_A)


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


		@pl.utilities.rank_zero_only
		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						Dict[str, Any]):
							A     =   self.output_dir.joinpath("best_tfmr")
							A     =   self.step_count
							self.model.save_pretrained(_A)
							self.tokenizer.save_pretrained(_A)


		@staticmethod
		def    SCREAMING_SNAKE_CASE__  (__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Optional[int]):
							parser.add_argument(
							    "--model_name_or_path"	,	default=_A	,	type=_A	,	required=_A	,	help="Path to pretrained model or model identifier from huggingface.co/models"	,	)
							parser.add_argument(
							    "--config_name"	,	default=""	,	type=_A	,	help="Pretrained config name or path if not the same as model_name")
							parser.add_argument(
							    "--tokenizer_name"	,	default=_A	,	type=_A	,	help="Pretrained tokenizer name or path if not the same as model_name"	,	)
							parser.add_argument(
							    "--cache_dir"	,	default=""	,	type=_A	,	help="Where do you want to store the pre-trained models downloaded from huggingface.co"	,	)
							parser.add_argument(
							    "--encoder_layerdrop"	,	type=_A	,	help="Encoder layer dropout probability (Optional). Goes into model.config"	,	)
							parser.add_argument(
							    "--decoder_layerdrop"	,	type=_A	,	help="Decoder layer dropout probability (Optional). Goes into model.config"	,	)
							parser.add_argument(
							    "--dropout"	,	type=_A	,	help="Dropout probability (Optional). Goes into model.config"	,	)
							parser.add_argument(
							    "--attention_dropout"	,	type=_A	,	help="Attention dropout probability (Optional). Goes into model.config"	,	)
							parser.add_argument("--learning_rate"	,	default=5E-5	,	type=_A	,	help="The initial learning rate for Adam.")
							parser.add_argument(
							    "--lr_scheduler"	,	default="linear"	,	choices=_A	,	metavar=_A	,	type=_A	,	help="Learning rate scheduler"	,	)
							parser.add_argument("--weight_decay"	,	default=0.0	,	type=_A	,	help="Weight decay if we apply some.")
							parser.add_argument("--adam_epsilon"	,	default=1E-8	,	type=_A	,	help="Epsilon for Adam optimizer.")
							parser.add_argument("--warmup_steps"	,	default=0	,	type=_A	,	help="Linear warmup over warmup_steps.")
							parser.add_argument("--num_workers"	,	default=4	,	type=_A	,	help="kwarg passed to DataLoader")
							parser.add_argument("--num_train_epochs"	,	dest="max_epochs"	,	default=3	,	type=_A)
							parser.add_argument("--train_batch_size"	,	default=3_2	,	type=_A)
							parser.add_argument("--eval_batch_size"	,	default=3_2	,	type=_A)
							parser.add_argument("--adafactor"	,	action="store_true")





class      __UpperCamelCase					(  pl.Callback		):


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						int):
							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(_A)


		def    SCREAMING_SNAKE_CASE__  (self					:						str	,	__SCREAMING_SNAKE_CASE					:						pl.Trainer	,	__SCREAMING_SNAKE_CASE					:						pl.LightningModule):
							rank_zero_info("***** Validation results *****")
							A     =   trainer.callback_metrics
							# Log results
							for key in sorted(_A):
												if key not in ["log", "progress_bar"]:
																	rank_zero_info("{} = {}\n".format(_A	,	str(metrics[key])))


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						pl.Trainer	,	__SCREAMING_SNAKE_CASE					:						pl.LightningModule):
							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(_A	,	"w") as writer:
												for key in sorted(_A):
																	if key not in ["log", "progress_bar"]:
																						rank_zero_info("{} = {}\n".format(_A	,	str(metrics[key])))
																						writer.write("{} = {}\n".format(_A	,	str(metrics[key])))

def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					#  To allow all pl args uncomment the following line
					#  parser = pl.Trainer.add_argparse_args(parser)
					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   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__=None     ,				lowercase__=True     ,				lowercase__=[]     ,				lowercase__=None     ,				lowercase__=None     ,				**lowercase__     ,				):
					"""simple docstring"""
					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     =   {}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										A     =   16
										A     =   args.fpaa_opt_level

					if args.gpus > 1:
										A     =   'ddp'

					A     =   args.accumulate_grad_batches
					A     =   extra_train_kwargs.get("accelerator"     ,				__a )
					A     =   extra_train_kwargs.get("profiler"     ,				__a )

					A     =   pl.Trainer.from_argparse_args(
					    __a     ,				weights_summary=__a     ,				callbacks=[logging_callback] + extra_callbacks     ,				logger=__a     ,				checkpoint_callback=__a     ,				**__a     ,				)

					if args.do_train:
										trainer.fit(__a )

					return trainer












import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils
class SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						@patch('diffusers.utils.hub_utils.get_full_repo_name'						)
						def UpperCAmelCase_					(   self   :   Optional[int]		, _A   :   Mock						)				->     None:







							"""simple docstring"""


							snake_case_		:      Optional[Any]           =						'full_repo_name'
							with TemporaryDirectory() as tmpdir:
								# Dummy args values
								snake_case_		:      Optional[int]           =						Mock()
								snake_case_		:      Union[str, Any]           =						tmpdir
								snake_case_		:      Optional[Any]           =						0
								snake_case_		:      Union[str, Any]           =						'hub_token'
								snake_case_		:      Any           =						'dataset_name'
								snake_case_		:      Tuple           =						0.0_1
								snake_case_		:      Dict           =						100000
								snake_case_		:      Any           =						10000
								snake_case_		:      str           =						0.0_1
								snake_case_		:      Any           =						0.0_2
								snake_case_		:      Optional[Any]           =						0.0_3
								snake_case_		:      Tuple           =						0.0_0_0_5
								snake_case_		:      Tuple           =						0.0_0_0_0_0_1
								snake_case_		:      str           =						1
								snake_case_		:      Optional[int]           =						10
								snake_case_		:      List[str]           =						0.0_0_1
								snake_case_		:      Any           =						0.1
								snake_case_		:      List[str]           =						0.2
								snake_case_		:      Dict           =						True

								# Model card mush be rendered and saved
								diffusers.utils.hub_utils.create_model_card(_A		, model_name='model_name'						)
								self.assertTrue((Path(_A						) / 'README.md').is_file()						)

