


import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


snake_case							:       Tuple										=	logging.getLogger(__name__)







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'summarization'
 SCREAMING_SNAKE_CASE__ =			['loss']
 SCREAMING_SNAKE_CASE__ =			ROUGE_KEYS
 SCREAMING_SNAKE_CASE__ =			'rouge2'




 def __init__(						self   ,    _lowerCamelCase   ,    **_lowerCamelCase       ):
       if hparams.sortish_sampler and hparams.gpus > 1:
             a		:Optional[int]									=			False
       elif hparams.max_tokens_per_batch is not None:
             if hparams.gpus > 1:
                   raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training'''       )
             if hparams.sortish_sampler:
                   raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'''       )

       super().__init__(_lowerCamelCase   ,    num_labels=_lowerCamelCase   ,    mode=self.mode   ,    **_lowerCamelCase       )
       use_task_specific_params(self.model   ,    '''summarization'''       )
       save_git_info(self.hparams.output_dir       )
       a		:int									=			Path(self.output_dir       ) / '''metrics.json'''
       a		:str									=			Path(self.output_dir       ) / '''hparams.pkl'''
       pickle_save(self.hparams   ,    self.hparams_save_path       )
       a		:Dict									=			0
       a		:List[Any]									=			defaultdict(_lowerCamelCase       )
       a		:Dict									=			self.config.model_type
       a		:int									=			self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

       a		:dict									=			{
           "data_dir": self.hparams.data_dir,
           "max_source_length": self.hparams.max_source_length,
           "prefix": self.model.config.prefix or "",
       }
       a		:Optional[int]									=			{
           '''train''': self.hparams.n_train,
           '''val''': self.hparams.n_val,
           '''test''': self.hparams.n_test,
       }
       a		:Optional[Any]									=			{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

       a		:int									=			{
           '''train''': self.hparams.max_target_length,
           '''val''': self.hparams.val_max_target_length,
           '''test''': self.hparams.test_max_target_length,
       }
       assert self.target_lens["train"] <= self.target_lens["val"], F'''target_lens: {self.target_lens}'''
       assert self.target_lens["train"] <= self.target_lens["test"], F'''target_lens: {self.target_lens}'''
       if self.hparams.freeze_embeds:
             freeze_embeds(self.model       )
       if self.hparams.freeze_encoder:
             freeze_params(self.model.get_encoder()       )
             assert_all_frozen(self.model.get_encoder()       )

       a		:List[Any]									=			get_git_info()['''repo_sha''']
       a		:Optional[int]									=			hparams.num_workers
       a		:Dict									=			None  # default to config
       if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer   ,    _lowerCamelCase       ):
             a		:Optional[Any]									=			self.tokenizer.lang_code_to_id[hparams.tgt_lang]
             a		:List[str]									=			self.decoder_start_token_id
       a		:Optional[Any]									=			(
           SeqaSeqDataset if hasattr(self.tokenizer   ,    '''prepare_seq2seq_batch'''       ) else LegacySeqaSeqDataset
       )
       a		:int									=			False
       a		:List[Any]									=			self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
       if self.hparams.eval_max_gen_length is not None:
             a		:Dict									=			self.hparams.eval_max_gen_length
       else:
             a		:Optional[int]									=			self.model.config.max_length
       a		:str									=			self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:int									=			{
           k: self.tokenizer.batch_decode(v.tolist()       ) if '''mask''' not in k else v.shape for k, v in batch.items()
       }
       save_json(_lowerCamelCase   ,    Path(self.output_dir       ) / '''text_batch.json'''       )
       save_json({k: v.tolist() for k, v in batch.items()}   ,    Path(self.output_dir       ) / '''tok_batch.json'''       )

       a		:str									=			True
       return readable_batch




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    **_lowerCamelCase       ):
       return self.model(_lowerCamelCase   ,    **_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:Optional[int]									=			self.tokenizer.batch_decode(
           _lowerCamelCase   ,    skip_special_tokens=_lowerCamelCase   ,    clean_up_tokenization_spaces=_lowerCamelCase       )
       return lmap(str.strip   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:Optional[Any]									=			self.tokenizer.pad_token_id
       a				,							a		:Tuple									=			batch['''input_ids'''], batch['''attention_mask''']
       a		:List[str]									=			batch['''labels''']
       if isinstance(self.model   ,    _lowerCamelCase       ):
             a		:Union[str, Any]									=			self.model._shift_right(_lowerCamelCase       )
       else:
             a		:int									=			shift_tokens_right(_lowerCamelCase   ,    _lowerCamelCase       )
       if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
             a		:Any									=			decoder_input_ids
             self.save_readable_batch(_lowerCamelCase       )

       a		:Dict									=			self(_lowerCamelCase   ,    attention_mask=_lowerCamelCase   ,    decoder_input_ids=_lowerCamelCase   ,    use_cache=_lowerCamelCase       )
       a		:Optional[int]									=			outputs['''logits''']
       if self.hparams.label_smoothing == 0:
             # Same behavior as modeling_bart.py, besides ignoring pad_token_id
             a		:List[str]									=			nn.CrossEntropyLoss(ignore_index=_lowerCamelCase       )

             assert lm_logits.shape[-1] == self.vocab_size
             a		:Tuple									=			ce_loss_fct(lm_logits.view(-1   ,    lm_logits.shape[-1]       )   ,    tgt_ids.view(-1       )       )
       else:
             a		:Optional[int]									=			nn.functional.log_softmax(_lowerCamelCase   ,    dim=-1       )
             a				,							a		:Any									=			label_smoothed_nll_loss(
                 _lowerCamelCase   ,    _lowerCamelCase   ,    self.hparams.label_smoothing   ,    ignore_index=_lowerCamelCase       )
       return (loss,)




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return self.tokenizer.pad_token_id




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       a		:List[str]									=			self._step(_lowerCamelCase       )

       a		:Dict									=			dict(zip(self.loss_names   ,    _lowerCamelCase       )       )
       # tokens per batch
       a		:str									=			batch['''input_ids'''].ne(self.pad       ).sum() + batch['''labels'''].ne(self.pad       ).sum()
       a		:Optional[Any]									=			batch['''input_ids'''].shape[0]
       a		:Union[str, Any]									=			batch['''input_ids'''].eq(self.pad       ).sum()
       a		:int									=			batch['''input_ids'''].eq(self.pad       ).float().mean()
       # TODO(SS): make a wandb summary metric for this
       return {"loss": loss_tensors[0], "log": logs}




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       return self._generative_step(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase="val"       ):
       self.step_count += 1
       a		:Dict									=			{k: torch.stack([x[k] for x in outputs]       ).mean() for k in self.loss_names}
       a		:Any									=			losses['''loss''']
       a		:Dict									=			{
           k: np.array([x[k] for x in outputs]       ).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
       }
       a		:Tuple									=			(
           generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
       )
       a		:torch.FloatTensor									=			torch.tensor(_lowerCamelCase       ).type_as(_lowerCamelCase       )
       generative_metrics.update({k: v.item() for k, v in losses.items()}       )
       losses.update(_lowerCamelCase       )
       a		:Optional[int]									=			{F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
       a		:Optional[int]									=			self.step_count
       self.metrics[prefix].append(_lowerCamelCase       )  # callback writes this to self.metrics_save_path
       a		:str									=			flatten_list([x['''preds'''] for x in outputs]       )
       return {
           "log": all_metrics,
           "preds": preds,
           F'''{prefix}_loss''': loss,
           F'''{prefix}_{self.val_metric}''': metric_tensor,
       }




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       return calculate_rouge(_lowerCamelCase   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:Dict									=			time.time()

       # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
       a		:Any									=			self.model.generate(
           batch['''input_ids''']   ,    attention_mask=batch['''attention_mask''']   ,    use_cache=_lowerCamelCase   ,    decoder_start_token_id=self.decoder_start_token_id   ,    num_beams=self.eval_beams   ,    max_length=self.eval_max_length   ,    )
       a		:int									=			(time.time() - ta) / batch['''input_ids'''].shape[0]
       a		:List[str]									=			self.ids_to_clean_text(_lowerCamelCase       )
       a		:List[str]									=			self.ids_to_clean_text(batch['''labels''']       )
       a		:Union[str, Any]									=			self._step(_lowerCamelCase       )
       a		:Any									=			dict(zip(self.loss_names   ,    _lowerCamelCase       )       )
       a		:Dict									=			self.calc_generative_metrics(_lowerCamelCase   ,    _lowerCamelCase       )
       a		:Union[str, Any]									=			np.mean(lmap(_lowerCamelCase   ,    _lowerCamelCase       )       )
       base_metrics.update(gen_time=_lowerCamelCase   ,    gen_len=_lowerCamelCase   ,    preds=_lowerCamelCase   ,    target=_lowerCamelCase   ,    **_lowerCamelCase       )
       return base_metrics




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       return self._generative_step(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       return self.validation_epoch_end(_lowerCamelCase   ,    prefix='''test'''       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:Any									=			self.n_obs[type_path]
       a		:Union[str, Any]									=			self.target_lens[type_path]
       a		:Optional[int]									=			self.dataset_class(
           self.tokenizer   ,    type_path=_lowerCamelCase   ,    n_obs=_lowerCamelCase   ,    max_target_length=_lowerCamelCase   ,    **self.dataset_kwargs   ,    )
       return dataset




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase = False       ):
       a		:List[str]									=			self.get_dataset(_lowerCamelCase       )

       if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
             a		:Dict									=			dataset.make_sortish_sampler(_lowerCamelCase   ,    distributed=self.hparams.gpus > 1       )
             return DataLoader(
                 _lowerCamelCase   ,    batch_size=_lowerCamelCase   ,    collate_fn=dataset.collate_fn   ,    shuffle=_lowerCamelCase   ,    num_workers=self.num_workers   ,    sampler=_lowerCamelCase   ,    )

       elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
             a		:Any									=			dataset.make_dynamic_sampler(
                 self.hparams.max_tokens_per_batch   ,    distributed=self.hparams.gpus > 1       )
             return DataLoader(
                 _lowerCamelCase   ,    batch_sampler=_lowerCamelCase   ,    collate_fn=dataset.collate_fn   ,    num_workers=self.num_workers   ,    )
       else:
             return DataLoader(
                 _lowerCamelCase   ,    batch_size=_lowerCamelCase   ,    collate_fn=dataset.collate_fn   ,    shuffle=_lowerCamelCase   ,    num_workers=self.num_workers   ,    sampler=_lowerCamelCase   ,    )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			self.get_dataloader('''train'''   ,    batch_size=self.hparams.train_batch_size   ,    shuffle=_lowerCamelCase       )
       return dataloader




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return self.get_dataloader('''val'''   ,    batch_size=self.hparams.eval_batch_size       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return self.get_dataloader('''test'''   ,    batch_size=self.hparams.eval_batch_size       )




 @staticmethod
 def 			SCREAMING_SNAKE_CASE__							(						_lowerCamelCase   ,    _lowerCamelCase       ):
       BaseTransformer.add_model_specific_args(_lowerCamelCase   ,    _lowerCamelCase       )
       add_generic_args(_lowerCamelCase   ,    _lowerCamelCase       )
       parser.add_argument(
           '''--max_source_length'''   ,    default=1024   ,    type=_lowerCamelCase   ,    help=(
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )   ,    )
       parser.add_argument(
           '''--max_target_length'''   ,    default=56   ,    type=_lowerCamelCase   ,    help=(
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )   ,    )
       parser.add_argument(
           '''--val_max_target_length'''   ,    default=142   ,    type=_lowerCamelCase   ,    help=(
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )   ,    )
       parser.add_argument(
           '''--test_max_target_length'''   ,    default=142   ,    type=_lowerCamelCase   ,    help=(
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )   ,    )
       parser.add_argument('''--freeze_encoder'''   ,    action='''store_true'''       )
       parser.add_argument('''--freeze_embeds'''   ,    action='''store_true'''       )
       parser.add_argument('''--sortish_sampler'''   ,    action='''store_true'''   ,    default=_lowerCamelCase       )
       parser.add_argument('''--overwrite_output_dir'''   ,    action='''store_true'''   ,    default=_lowerCamelCase       )
       parser.add_argument('''--max_tokens_per_batch'''   ,    type=_lowerCamelCase   ,    default=_lowerCamelCase       )
       parser.add_argument('''--logger_name'''   ,    type=_lowerCamelCase   ,    choices=['''default''', '''wandb''', '''wandb_shared''']   ,    default='''default'''       )
       parser.add_argument('''--n_train'''   ,    type=_lowerCamelCase   ,    default=-1   ,    required=_lowerCamelCase   ,    help='''# examples. -1 means use all.'''       )
       parser.add_argument('''--n_val'''   ,    type=_lowerCamelCase   ,    default=500   ,    required=_lowerCamelCase   ,    help='''# examples. -1 means use all.'''       )
       parser.add_argument('''--n_test'''   ,    type=_lowerCamelCase   ,    default=-1   ,    required=_lowerCamelCase   ,    help='''# examples. -1 means use all.'''       )
       parser.add_argument(
           '''--task'''   ,    type=_lowerCamelCase   ,    default='''summarization'''   ,    required=_lowerCamelCase   ,    help='''# examples. -1 means use all.'''       )
       parser.add_argument('''--label_smoothing'''   ,    type=_lowerCamelCase   ,    default=0.0   ,    required=_lowerCamelCase       )
       parser.add_argument('''--src_lang'''   ,    type=_lowerCamelCase   ,    default=''''''   ,    required=_lowerCamelCase       )
       parser.add_argument('''--tgt_lang'''   ,    type=_lowerCamelCase   ,    default=''''''   ,    required=_lowerCamelCase       )
       parser.add_argument('''--eval_beams'''   ,    type=_lowerCamelCase   ,    default=_lowerCamelCase   ,    required=_lowerCamelCase       )
       parser.add_argument(
           '''--val_metric'''   ,    type=_lowerCamelCase   ,    default=_lowerCamelCase   ,    required=_lowerCamelCase   ,    choices=['''bleu''', '''rouge2''', '''loss''', None]       )
       parser.add_argument('''--eval_max_gen_length'''   ,    type=_lowerCamelCase   ,    default=_lowerCamelCase   ,    help='''never generate more than n tokens'''       )
       parser.add_argument('''--save_top_k'''   ,    type=_lowerCamelCase   ,    default=1   ,    required=_lowerCamelCase   ,    help='''How many checkpoints to save'''       )
       parser.add_argument(
           '''--early_stopping_patience'''   ,    type=_lowerCamelCase   ,    default=-1   ,    required=_lowerCamelCase   ,    help=(
               '''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
               ''' val_check_interval will effect it.'''
           )   ,    )
       return parser







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'translation'
 SCREAMING_SNAKE_CASE__ =			['loss']
 SCREAMING_SNAKE_CASE__ =			['bleu']
 SCREAMING_SNAKE_CASE__ =			'bleu'




 def __init__(						self   ,    _lowerCamelCase   ,    **_lowerCamelCase       ):
       super().__init__(_lowerCamelCase   ,    **_lowerCamelCase       )
       a		:str									=			hparams.src_lang
       a		:Optional[Any]									=			hparams.tgt_lang




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       return calculate_bleu(_lowerCamelCase   ,    _lowerCamelCase       )







def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[Any]					,    UpperCAmelCase_		:						List[str]=None		):




      """simple docstring"""





      Path(args.output_dir		).mkdir(exist_ok=UpperCAmelCase_		)
      check_output_dir(UpperCAmelCase_					,    expected_items=3		)

      if model is None:
            if "summarization" in args.task:
                  a		:SummarizationModule									=			SummarizationModule(UpperCAmelCase_		)
            else:
                  a		:SummarizationModule									=			TranslationModule(UpperCAmelCase_		)
      a		:str									=			Path(args.data_dir		).name
      if (
          args.logger_name == "default"
          or args.fast_dev_run
          or str(args.output_dir		).startswith('''/tmp'''		)
          or str(args.output_dir		).startswith('''/var'''		)
      ):
            a		:Tuple									=			True  # don't pollute wandb logs unnecessarily
      elif args.logger_name == "wandb":
            from pytorch_lightning.loggers import WandbLogger

            a		:List[Any]									=			os.environ.get('''WANDB_PROJECT'''					,    UpperCAmelCase_		)
            a		:int									=			WandbLogger(name=model.output_dir.name					,    project=UpperCAmelCase_		)

      elif args.logger_name == "wandb_shared":
            from pytorch_lightning.loggers import WandbLogger

            a		:Optional[Any]									=			WandbLogger(name=model.output_dir.name					,    project=F'''hf_{dataset}'''		)

      if args.early_stopping_patience >= 0:
            a		:Optional[int]									=			get_early_stopping_callback(model.val_metric					,    args.early_stopping_patience		)
      else:
            a		:List[Any]									=			False

      a		:Tuple									=			args.val_metric == '''loss'''
      a		:pl.Trainer									=			generic_train(
          UpperCAmelCase_					,    UpperCAmelCase_					,    logging_callback=SeqaSeqLoggingCallback()					,    checkpoint_callback=get_checkpoint_callback(
              args.output_dir					,    model.val_metric					,    args.save_top_k					,    UpperCAmelCase_		)					,    early_stopping_callback=UpperCAmelCase_					,    logger=UpperCAmelCase_					,    )
      pickle_save(model.hparams					,    model.output_dir / '''hparams.pkl'''		)
      if not args.do_predict:
            return model

      a		:str									=			''''''
      a		:Any									=			sorted(glob.glob(os.path.join(args.output_dir					,    '''*.ckpt'''		)					,    recursive=UpperCAmelCase_		)		)
      if checkpoints:
            a		:List[Any]									=			checkpoints[-1]
            a		:str									=			checkpoints[-1]
      trainer.logger.log_hyperparams(model.hparams		)

      # test() without a model tests using the best checkpoint automatically
      trainer.test()
      return model


if __name__ == "__main__":
     snake_case							:       Optional[int]										=	argparse.ArgumentParser()
     snake_case							:       Tuple										=	pl.Trainer.add_argparse_args(parser)
     snake_case							:       List[Any]										=	SummarizationModule.add_model_specific_args(parser, os.getcwd())

     snake_case							:       Union[str, Any]										=	parser.parse_args()

     main(args)









import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         Adafactor,
         AdamW,
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_inverse_sqrt_schedule,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
     )
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Union[str, Any]=10		):




      """simple docstring"""





      a		:Tuple									=			[]
      for _ in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
      return lrs
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Tuple=10		):




      """simple docstring"""





      a		:List[Any]									=			[]
      for step in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        a		:Union[str, Any]									=			os.path.join(UpperCAmelCase_					,    '''schedule.bin'''		)
                        torch.save(scheduler.state_dict()					,    UpperCAmelCase_		)

                        a		:int									=			torch.load(UpperCAmelCase_		)
                        scheduler.load_state_dict(UpperCAmelCase_		)
      return lrs







@require_torch
class 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:int									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:int									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Tuple									=			AdamW(params=[w]   ,    lr=2e-1   ,    weight_decay=0.0       )
       for _ in range(100       ):
             a		:int									=			criterion(_lowerCamelCase   ,    _lowerCamelCase       )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()   ,    [0.4, 0.2, -0.5]   ,    tol=1e-2       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:Union[str, Any]									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:List[Any]									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Any									=			Adafactor(
           params=[w]   ,    lr=1e-2   ,    eps=(1e-30, 1e-3)   ,    clip_threshold=1.0   ,    decay_rate=-0.8   ,    betaa=_lowerCamelCase   ,    weight_decay=0.0   ,    relative_step=_lowerCamelCase   ,    scale_parameter=_lowerCamelCase   ,    warmup_init=_lowerCamelCase   ,    )
       for _ in range(1000       ):
             a		:str									=			criterion(_lowerCamelCase   ,    _lowerCamelCase       )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()   ,    [0.4, 0.2, -0.5]   ,    tol=1e-2       )







@require_torch
class 						_snake_case    ( unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			nn.Linear(50		,       50				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			AdamW(m.parameters()		,       lr=10.0				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			10




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase   ,    msg=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			{'''num_warmup_steps''': 2, '''num_training_steps''': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       a		:Any									=			{
           get_constant_schedule: ({}, [10.0] * self.num_steps),
           get_constant_schedule_with_warmup: (
               {'''num_warmup_steps''': 4},
               [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
           ),
           get_linear_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
           ),
           get_cosine_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
           ),
           get_cosine_with_hard_restarts_schedule_with_warmup: (
               {**common_kwargs, '''num_cycles''': 2},
               [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
           ),
           get_polynomial_decay_schedule_with_warmup: (
               {**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
               [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
           ),
           get_inverse_sqrt_schedule: (
               {'''num_warmup_steps''': 2},
               [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
           ),
       }

       for scheduler_func, data in scheds.items():
             a				,							a		:List[str]									=			data

             a		:str									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             self.assertEqual(len([scheduler.get_lr()[0]]       )   ,    1       )
             a		:Optional[Any]									=			unwrap_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListAlmostEqual(
                 _lowerCamelCase   ,    _lowerCamelCase   ,    tol=1e-2   ,    msg=F'''failed for {scheduler_func} in normal scheduler'''   ,    )

             a		:List[Any]									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             if scheduler_func.__name__ != "get_constant_schedule":
                   LambdaScheduleWrapper.wrap_scheduler(_lowerCamelCase       )  # wrap to test picklability of the schedule
             a		:Dict									=			unwrap_and_save_reload_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    msg=F'''failed for {scheduler_func} in save and reload'''       )







class 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase       ):
       a		:Dict									=			fn




 def __call__(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       return self.fn(*_lowerCamelCase   ,    **_lowerCamelCase       )




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:str									=			list(map(self   ,    scheduler.lr_lambdas       )       )





