






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


__UpperCAmelCase								=						logging.getLogger(__name__)



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    List[Any]       						=  '''summarization'''
		_snake_case    :    Optional[int]       						=  ['''loss''']
		_snake_case    :    str       						=  ROUGE_KEYS
		_snake_case    :    Tuple       						=  '''rouge2'''



		def __init__(							self				,		_UpperCamelCase				,		**_UpperCamelCase   )	->							List[str]:
							if hparams.sortish_sampler and hparams.gpus > 1:
												UpperCAmelCase_    :      Union[str, Any]            =						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__(_UpperCamelCase				,		num_labels=_UpperCamelCase				,		mode=self.mode				,		**_UpperCamelCase   )
							use_task_specific_params(self.model				,		'summarization'   )
							save_git_info(self.hparams.output_dir   )
							UpperCAmelCase_    :      Optional[int]            =						Path(self.output_dir   ) / 'metrics.json'
							UpperCAmelCase_    :      str            =						Path(self.output_dir   ) / 'hparams.pkl'
							pickle_save(self.hparams				,		self.hparams_save_path   )
							UpperCAmelCase_    :      int            =						0
							UpperCAmelCase_    :      Optional[int]            =						defaultdict(_UpperCamelCase   )
							UpperCAmelCase_    :      Union[str, Any]            =						self.config.model_type
							UpperCAmelCase_    :      Any            =						self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

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

							UpperCAmelCase_    :      str            =						{
							    '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()   )

							UpperCAmelCase_    :      Any            =						get_git_info()['repo_sha']
							UpperCAmelCase_    :      int            =						hparams.num_workers
							UpperCAmelCase_    :      List[Any]            =						None  # default to config
							if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Dict            =						self.tokenizer.lang_code_to_id[hparams.tgt_lang]
												UpperCAmelCase_    :      Dict            =						self.decoder_start_token_id
							UpperCAmelCase_    :      List[Any]            =						(
							    SeqaSeqDataset if hasattr(self.tokenizer				,		'prepare_seq2seq_batch'   ) else LegacySeqaSeqDataset
							)
							UpperCAmelCase_    :      str            =						False
							UpperCAmelCase_    :      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:
												UpperCAmelCase_    :      Optional[int]            =						self.hparams.eval_max_gen_length
							else:
												UpperCAmelCase_    :      Optional[Any]            =						self.model.config.max_length
							UpperCAmelCase_    :      List[str]            =						self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Dict[str, List[str]]:
							UpperCAmelCase_    :      Optional[Any]            =						{
							    k: self.tokenizer.batch_decode(v.tolist()   ) if 'mask' not in k else v.shape for k, v in batch.items()
							}
							save_json(_UpperCamelCase				,		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'   )

							UpperCAmelCase_    :      Union[str, Any]            =						True
							return readable_batch



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		**_UpperCamelCase   )	->							List[str]:
							return self.model(_UpperCamelCase				,		**_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_    :      Dict            =						self.tokenizer.batch_decode(
							    _UpperCamelCase				,		skip_special_tokens=_UpperCamelCase				,		clean_up_tokenization_spaces=_UpperCamelCase   )
							return lmap(str.strip				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Tuple:
							UpperCAmelCase_    :      int            =						self.tokenizer.pad_token_id
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Dict            =						batch['input_ids'], batch['attention_mask']
							UpperCAmelCase_    :      int            =						batch['labels']
							if isinstance(self.model				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      str            =						self.model._shift_right(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      str            =						shift_tokens_right(_UpperCamelCase				,		_UpperCamelCase   )
							if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
												UpperCAmelCase_    :      Any            =						decoder_input_ids
												self.save_readable_batch(_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						self(_UpperCamelCase				,		attention_mask=_UpperCamelCase				,		decoder_input_ids=_UpperCamelCase				,		use_cache=_UpperCamelCase   )
							UpperCAmelCase_    :      Tuple            =						outputs['logits']
							if self.hparams.label_smoothing == 0:
												# Same behavior as modeling_bart.py, besides ignoring pad_token_id
												UpperCAmelCase_    :      Any            =						nn.CrossEntropyLoss(ignore_index=_UpperCamelCase   )

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



		@property
		def 					__UpperCAmelCase			(							self   )	->							int:
							return self.tokenizer.pad_token_id



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							UpperCAmelCase_    :      List[Any]            =						self._step(_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						dict(zip(self.loss_names				,		_UpperCamelCase   )   )
							# tokens per batch
							UpperCAmelCase_    :      Tuple            =						batch['input_ids'].ne(self.pad   ).sum() + batch['labels'].ne(self.pad   ).sum()
							UpperCAmelCase_    :      Dict            =						batch['input_ids'].shape[0]
							UpperCAmelCase_    :      Optional[int]            =						batch['input_ids'].eq(self.pad   ).sum()
							UpperCAmelCase_    :      List[str]            =						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 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							return self._generative_step(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase="val"   )	->							Dict:
							self.step_count += 1
							UpperCAmelCase_    :      int            =						{k: torch.stack([x[k] for x in outputs]   ).mean() for k in self.loss_names}
							UpperCAmelCase_    :      Optional[Any]            =						losses['loss']
							UpperCAmelCase_    :      Tuple            =						{
							    k: np.array([x[k] for x in outputs]   ).mean() for k in self.metric_names + ['gen_time', 'gen_len']
							}
							UpperCAmelCase_    :      Tuple            =						(
							    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
							)
							UpperCAmelCase_    :      torch.FloatTensor            =						torch.tensor(_UpperCamelCase   ).type_as(_UpperCamelCase   )
							generative_metrics.update({k: v.item() for k, v in losses.items()}   )
							losses.update(_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						{f"{prefix}_avg_{k}": x for k, x in losses.items()}
							UpperCAmelCase_    :      Optional[int]            =						self.step_count
							self.metrics[prefix].append(_UpperCamelCase   )  # callback writes this to self.metrics_save_path
							UpperCAmelCase_    :      int            =						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 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							return calculate_rouge(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							dict:
							UpperCAmelCase_    :      Dict            =						time.time()

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							int:
							return self._generative_step(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Dict:
							return self.validation_epoch_end(_UpperCamelCase				,		prefix='test'   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							SeqaSeqDataset:
							UpperCAmelCase_    :      int            =						self.n_obs[type_path]
							UpperCAmelCase_    :      List[str]            =						self.target_lens[type_path]
							UpperCAmelCase_    :      List[str]            =						self.dataset_class(
							    self.tokenizer				,		type_path=_UpperCamelCase				,		n_obs=_UpperCamelCase				,		max_target_length=_UpperCamelCase				,		**self.dataset_kwargs				,		)
							return dataset



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = False   )	->							DataLoader:
							UpperCAmelCase_    :      List[str]            =						self.get_dataset(_UpperCamelCase   )

							if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
												UpperCAmelCase_    :      Optional[Any]            =						dataset.make_sortish_sampler(_UpperCamelCase				,		distributed=self.hparams.gpus > 1   )
												return DataLoader(
												    _UpperCamelCase				,		batch_size=_UpperCamelCase				,		collate_fn=dataset.collate_fn				,		shuffle=_UpperCamelCase				,		num_workers=self.num_workers				,		sampler=_UpperCamelCase				,		)

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



		def 					__UpperCAmelCase			(							self   )	->							DataLoader:
							UpperCAmelCase_    :      List[Any]            =						self.get_dataloader('train'				,		batch_size=self.hparams.train_batch_size				,		shuffle=_UpperCamelCase   )
							return dataloader



		def 					__UpperCAmelCase			(							self   )	->							DataLoader:
							return self.get_dataloader('val'				,		batch_size=self.hparams.eval_batch_size   )



		def 					__UpperCAmelCase			(							self   )	->							DataLoader:
							return self.get_dataloader('test'				,		batch_size=self.hparams.eval_batch_size   )



		@staticmethod
		def 					__UpperCAmelCase			(							_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							BaseTransformer.add_model_specific_args(_UpperCamelCase				,		_UpperCamelCase   )
							add_generic_args(_UpperCamelCase				,		_UpperCamelCase   )
							parser.add_argument(
							    '--max_source_length'				,		default=1_0_2_4				,		type=_UpperCamelCase				,		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=5_6				,		type=_UpperCamelCase				,		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=1_4_2				,		type=_UpperCamelCase				,		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=1_4_2				,		type=_UpperCamelCase				,		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=_UpperCamelCase   )
							parser.add_argument('--overwrite_output_dir'				,		action='store_true'				,		default=_UpperCamelCase   )
							parser.add_argument('--max_tokens_per_batch'				,		type=_UpperCamelCase				,		default=_UpperCamelCase   )
							parser.add_argument('--logger_name'				,		type=_UpperCamelCase				,		choices=['default', 'wandb', 'wandb_shared']				,		default='default'   )
							parser.add_argument('--n_train'				,		type=_UpperCamelCase				,		default=-1				,		required=_UpperCamelCase				,		help='# examples. -1 means use all.'   )
							parser.add_argument('--n_val'				,		type=_UpperCamelCase				,		default=5_0_0				,		required=_UpperCamelCase				,		help='# examples. -1 means use all.'   )
							parser.add_argument('--n_test'				,		type=_UpperCamelCase				,		default=-1				,		required=_UpperCamelCase				,		help='# examples. -1 means use all.'   )
							parser.add_argument(
							    '--task'				,		type=_UpperCamelCase				,		default='summarization'				,		required=_UpperCamelCase				,		help='# examples. -1 means use all.'   )
							parser.add_argument('--label_smoothing'				,		type=_UpperCamelCase				,		default=0.0				,		required=_UpperCamelCase   )
							parser.add_argument('--src_lang'				,		type=_UpperCamelCase				,		default=''				,		required=_UpperCamelCase   )
							parser.add_argument('--tgt_lang'				,		type=_UpperCamelCase				,		default=''				,		required=_UpperCamelCase   )
							parser.add_argument('--eval_beams'				,		type=_UpperCamelCase				,		default=_UpperCamelCase				,		required=_UpperCamelCase   )
							parser.add_argument(
							    '--val_metric'				,		type=_UpperCamelCase				,		default=_UpperCamelCase				,		required=_UpperCamelCase				,		choices=['bleu', 'rouge2', 'loss', None]   )
							parser.add_argument('--eval_max_gen_length'				,		type=_UpperCamelCase				,		default=_UpperCamelCase				,		help='never generate more than n tokens'   )
							parser.add_argument('--save_top_k'				,		type=_UpperCamelCase				,		default=1				,		required=_UpperCamelCase				,		help='How many checkpoints to save'   )
							parser.add_argument(
							    '--early_stopping_patience'				,		type=_UpperCamelCase				,		default=-1				,		required=_UpperCamelCase				,		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   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    int       						=  '''translation'''
		_snake_case    :    Dict       						=  ['''loss''']
		_snake_case    :    str       						=  ['''bleu''']
		_snake_case    :    str       						=  '''bleu'''



		def __init__(							self				,		_UpperCamelCase				,		**_UpperCamelCase   )	->							List[str]:
							super().__init__(_UpperCamelCase				,		**_UpperCamelCase   )
							UpperCAmelCase_    :      List[str]            =						hparams.src_lang
							UpperCAmelCase_    :      List[str]            =						hparams.tgt_lang



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							dict:
							return calculate_bleu(_UpperCamelCase				,		_UpperCamelCase   )





def      lowercase__      (     __snake_case    :							Dict     ,       __snake_case    :							List[Any]=None  ):


					'''simple docstring'''


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

					if model is None:
										if "summarization" in args.task:
															UpperCAmelCase_    :      SummarizationModule            =						SummarizationModule(__snake_case  )
										else:
															UpperCAmelCase_    :      SummarizationModule            =						TranslationModule(__snake_case  )
					UpperCAmelCase_    :      List[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'  )
					):
										UpperCAmelCase_    :      Dict            =						True  # don't pollute wandb logs unnecessarily
					elif args.logger_name == "wandb":
										from pytorch_lightning.loggers import WandbLogger

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

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

										UpperCAmelCase_    :      str            =						WandbLogger(name=model.output_dir.name     ,       project=F"hf_{dataset}"  )

					if args.early_stopping_patience >= 0:
										UpperCAmelCase_    :      Any            =						get_early_stopping_callback(model.val_metric     ,       args.early_stopping_patience  )
					else:
										UpperCAmelCase_    :      Dict            =						False

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

					UpperCAmelCase_    :      Tuple            =						''
					UpperCAmelCase_    :      List[Any]            =						sorted(glob.glob(os.path.join(args.output_dir     ,       '*.ckpt'  )     ,       recursive=__snake_case  )  )
					if checkpoints:
										UpperCAmelCase_    :      int            =						checkpoints[-1]
										UpperCAmelCase_    :      Optional[Any]            =						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__":
		__UpperCAmelCase								=						argparse.ArgumentParser()
		__UpperCAmelCase								=						pl.Trainer.add_argparse_args(parser)
		__UpperCAmelCase								=						SummarizationModule.add_model_specific_args(parser, os.getcwd())

		__UpperCAmelCase								=						parser.parse_args()

		main(args)














import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)




def      lowercase__      (     __snake_case    :							List[Any]     ,       __snake_case    :							List[str]=False  ):


					'''simple docstring'''


					try:
										UpperCAmelCase_    :      int            =						os.environ[key]
					except KeyError:
										# KEY isn't set, default to `default`.
										UpperCAmelCase_    :      Optional[int]            =						default
					else:
										# KEY is set, convert it to True or False.
										try:
															UpperCAmelCase_    :      List[Any]            =						strtobool(__snake_case  )
										except ValueError:
															# More values are supported, but let's keep the message simple.
															raise ValueError(F"If set, {key} must be yes or no."  )
					return _value


__UpperCAmelCase								=						parse_flag_from_env('RUN_SLOW', default=False)




def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					return unittest.skip('Test was skipped'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


					return unittest.skipUnless(_run_slow_tests     ,       'test is slow'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							List[str]  ):


					'''simple docstring'''


					return unittest.skipUnless(not torch.cuda.is_available()     ,       'test requires only a CPU'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


					return unittest.skipUnless(torch.cuda.is_available()     ,       'test requires a GPU'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							List[str]  ):


					'''simple docstring'''


					return unittest.skipUnless(is_xpu_available()     ,       'test requires a XPU'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					return unittest.skipUnless(is_mps_available()     ,       'test requires a `mps` backend support in `torch`'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


					return unittest.skipUnless(
					    is_transformers_available() and is_datasets_available()     ,       'test requires the Hugging Face suite'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					return unittest.skipUnless(is_bnb_available()     ,       'test requires the bitsandbytes library'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Dict  ):


					'''simple docstring'''


					return unittest.skipUnless(is_tpu_available()     ,       'test requires TPU'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


					return unittest.skipUnless(torch.cuda.device_count() == 1     ,       'test requires a GPU'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Dict  ):


					'''simple docstring'''


					return unittest.skipUnless(torch.xpu.device_count() == 1     ,       'test requires a XPU'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Optional[int]  ):


					'''simple docstring'''


					return unittest.skipUnless(torch.cuda.device_count() > 1     ,       'test requires multiple GPUs'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					return unittest.skipUnless(torch.xpu.device_count() > 1     ,       'test requires multiple XPUs'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Dict  ):


					'''simple docstring'''


					return unittest.skipUnless(is_safetensors_available()     ,       'test requires safetensors'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


					return unittest.skipUnless(is_deepspeed_available()     ,       'test requires DeepSpeed'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							List[Any]  ):


					'''simple docstring'''


					return unittest.skipUnless(is_torch_version('>='     ,       '1.12.0'  )     ,       'test requires torch version >= 1.12.0'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							Dict=None     ,       __snake_case    :							Dict=None  ):


					'''simple docstring'''


					if test_case is None:
										return partial(__snake_case     ,       version=__snake_case  )
					return unittest.skipUnless(is_torch_version('>='     ,       __snake_case  )     ,       F"test requires torch version >= {version}"  )(__snake_case  )




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					return unittest.skipUnless(is_tensorboard_available()     ,       'test requires Tensorboard'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							List[str]  ):


					'''simple docstring'''


					return unittest.skipUnless(is_wandb_available()     ,       'test requires wandb'  )(__snake_case  )




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					return unittest.skipUnless(is_comet_ml_available()     ,       'test requires comet_ml'  )(__snake_case  )


__UpperCAmelCase								=						(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)




def      lowercase__      (     __snake_case    :							List[Any]  ):


					'''simple docstring'''


					return unittest.skipUnless(
					    _atleast_one_tracker_available     ,       'test requires at least one tracker to be available and for `comet_ml` to not be installed'     ,       )(__snake_case  )



class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    Union[str, Any]       						=  True



		@classmethod
		def 					__UpperCAmelCase			(							cls   )	->							Union[str, Any]:
							UpperCAmelCase_    :      List[Any]            =						tempfile.mkdtemp()



		@classmethod
		def 					__UpperCAmelCase			(							cls   )	->							List[str]:
							if os.path.exists(cls.tmpdir   ):
												shutil.rmtree(cls.tmpdir   )



		def 					__UpperCAmelCase			(							self   )	->							str:
							if self.clear_on_setup:
												for path in Path(self.tmpdir   ).glob('**/*'   ):
																	if path.is_file():
																						path.unlink()
																	elif path.is_dir():
																						shutil.rmtree(_UpperCamelCase   )





class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							super().tearDown()
							# Reset the state of the AcceleratorState singleton.
							AcceleratorState._reset_state()
							PartialState._reset_state()





class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Any:
							UpperCAmelCase_    :      List[Any]            =						mocks if isinstance(_UpperCamelCase				,		(tuple, list)   ) else [mocks]
							for m in self.mocks:
												m.start()
												self.addCleanup(m.stop   )





def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      int            =						AcceleratorState()
					UpperCAmelCase_    :      str            =						tensor[None].clone().to(state.device  )
					UpperCAmelCase_    :      List[str]            =						gather(__snake_case  ).cpu()
					UpperCAmelCase_    :      List[Any]            =						tensor[0].cpu()
					for i in range(tensors.shape[0]  ):
										if not torch.equal(tensors[i]     ,       __snake_case  ):
															return False
					return True



class   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Any:
							UpperCAmelCase_    :      str            =						returncode
							UpperCAmelCase_    :      Optional[Any]            =						stdout
							UpperCAmelCase_    :      Optional[Any]            =						stderr





async def      lowercase__      (     __snake_case    :							Optional[Any]     ,       __snake_case    :							Optional[int]  ):


					'''simple docstring'''


					while True:
										UpperCAmelCase_    :      Dict            =						await stream.readline()
										if line:
															callback(__snake_case  )
										else:
															break




async def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							Dict=None     ,       __snake_case    :							str=None     ,       __snake_case    :							Dict=None     ,       __snake_case    :							List[str]=False     ,       __snake_case    :							Optional[int]=False  ):


					'''simple docstring'''


					if echo:
										print('\nRunning: '     ,       ' '.join(__snake_case  )  )

					UpperCAmelCase_    :      Optional[Any]            =						await asyncio.create_subprocess_exec(
					    cmd[0]     ,       *cmd[1:]     ,       stdin=__snake_case     ,       stdout=asyncio.subprocess.PIPE     ,       stderr=asyncio.subprocess.PIPE     ,       env=__snake_case     ,       )

					# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
					# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
					#
					# If it starts hanging, will need to switch to the following code. The problem is that no data
					# will be seen until it's done and if it hangs for example there will be no debug info.
					# out, err = await p.communicate()
					# return _RunOutput(p.returncode, out, err)

					UpperCAmelCase_    :      Any            =						[]
					UpperCAmelCase_    :      str            =						[]

					def tee(__snake_case    :							Dict     ,       __snake_case    :							Union[str, Any]     ,       __snake_case    :							Tuple     ,       __snake_case    :							Optional[int]=""  ):
										UpperCAmelCase_    :      List[str]            =						line.decode('utf-8'  ).rstrip()
										sink.append(__snake_case  )
										if not quiet:
															print(__snake_case     ,       __snake_case     ,       file=__snake_case  )

    # XXX: the timeout doesn't seem to make any difference here
					await asyncio.wait(
					    [
					        asyncio.create_task(_read_stream(p.stdout     ,       lambda __snake_case  : tee(__snake_case     ,       __snake_case     ,       sys.stdout     ,       label='stdout:'  )  )  ),
					        asyncio.create_task(_read_stream(p.stderr     ,       lambda __snake_case  : tee(__snake_case     ,       __snake_case     ,       sys.stderr     ,       label='stderr:'  )  )  ),
					    ]     ,       timeout=__snake_case     ,       )
					return _RunOutput(await p.wait()     ,       __snake_case     ,       __snake_case  )




def      lowercase__      (     __snake_case    :							Optional[Any]     ,       __snake_case    :							List[Any]=None     ,       __snake_case    :							str=None     ,       __snake_case    :							Tuple=180     ,       __snake_case    :							Dict=False     ,       __snake_case    :							Optional[Any]=True  ):


					'''simple docstring'''


					UpperCAmelCase_    :      str            =						asyncio.get_event_loop()
					UpperCAmelCase_    :      int            =						loop.run_until_complete(
					    _stream_subprocess(__snake_case     ,       env=__snake_case     ,       stdin=__snake_case     ,       timeout=__snake_case     ,       quiet=__snake_case     ,       echo=__snake_case  )  )

					UpperCAmelCase_    :      int            =						' '.join(__snake_case  )
					if result.returncode > 0:
										UpperCAmelCase_    :      int            =						'\n'.join(result.stderr  )
										raise RuntimeError(
										    F"'{cmd_str}' failed with returncode {result.returncode}\n\n"
										    F"The combined stderr from workers follows:\n{stderr}"  )

					return result



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		pass





def      lowercase__      (     __snake_case    :							List[str]     ,       __snake_case    :							List[Any]=False  ):


					'''simple docstring'''


					try:
										UpperCAmelCase_    :      List[Any]            =						subprocess.check_output(__snake_case     ,       stderr=subprocess.STDOUT  )
										if return_stdout:
															if hasattr(__snake_case     ,       'decode'  ):
																				UpperCAmelCase_    :      str            =						output.decode('utf-8'  )
															return output
					except subprocess.CalledProcessError as e:
										raise SubprocessCallException(
										    F"Command `{' '.join(__snake_case  )}` failed with the following error:\n\n{e.output.decode()}"  ) from e






