
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


lowercase__	:Optional[Any]										=					logging.getLogger(__name__)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : List[str]       ="""summarization"""
							lowercase_ : str       =["""loss"""]
							lowercase_ : str       =ROUGE_KEYS
							lowercase_ : List[Any]       ="""rouge2"""




							def __init__(       self     ,A__     ,**A__):

												if hparams.sortish_sampler and hparams.gpus > 1:
																	lowercase					=		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__(A__     ,num_labels=A__     ,mode=self.mode     ,**A__)
												use_task_specific_params(self.model     ,'''summarization''')
												save_git_info(self.hparams.output_dir)
												lowercase					=		Path(self.output_dir) / '''metrics.json'''
												lowercase					=		Path(self.output_dir) / '''hparams.pkl'''
												pickle_save(self.hparams     ,self.hparams_save_path)
												lowercase					=		0
												lowercase					=		defaultdict(A__)
												lowercase					=		self.config.model_type
												lowercase					=		self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

												lowercase					=		{
												    '''data_dir''': self.hparams.data_dir,
												    '''max_source_length''': self.hparams.max_source_length,
												    '''prefix''': self.model.config.prefix or '''''',
												}
												lowercase					=		{
												    '''train''': self.hparams.n_train,
												    '''val''': self.hparams.n_val,
												    '''test''': self.hparams.n_test,
												}
												lowercase					=		{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

												lowercase					=		{
												    '''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())

												lowercase					=		get_git_info()['''repo_sha''']
												lowercase					=		hparams.num_workers
												lowercase					=		None  # default to config
												if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer     ,A__):
																	lowercase					=		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
																	lowercase					=		self.decoder_start_token_id
												lowercase					=		(
												    SeqaSeqDataset if hasattr(self.tokenizer     ,'''prepare_seq2seq_batch''') else LegacySeqaSeqDataset
												)
												lowercase					=		False
												lowercase					=		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:
																	lowercase					=		self.hparams.eval_max_gen_length
												else:
																	lowercase					=		self.model.config.max_length
												lowercase					=		self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric




							def 				A__   (       self     ,A__):
												lowercase					=		{
												    k: self.tokenizer.batch_decode(v.tolist()) if '''mask''' not in k else v.shape for k, v in batch.items()
												}
												save_json(A__     ,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''')

												lowercase					=		True
												return readable_batch




							def 				A__   (       self     ,A__     ,**A__):
												return self.model(A__     ,**A__)




							def 				A__   (       self     ,A__):
												lowercase					=		self.tokenizer.batch_decode(
												    A__     ,skip_special_tokens=A__     ,clean_up_tokenization_spaces=A__)
												return lmap(str.strip     ,A__)




							def 				A__   (       self     ,A__):
												lowercase					=		self.tokenizer.pad_token_id
												lowercase			,		lowercase					=		batch['''input_ids'''], batch['''attention_mask''']
												lowercase					=		batch['''labels''']
												if isinstance(self.model     ,A__):
																	lowercase					=		self.model._shift_right(A__)
												else:
																	lowercase					=		shift_tokens_right(A__     ,A__)
												if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
																	lowercase					=		decoder_input_ids
																	self.save_readable_batch(A__)

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

																	assert lm_logits.shape[-1] == self.vocab_size
																	lowercase					=		ce_loss_fct(lm_logits.view(-1     ,lm_logits.shape[-1])     ,tgt_ids.view(-1))
												else:
																	lowercase					=		nn.functional.log_softmax(A__     ,dim=-1)
																	lowercase			,		lowercase					=		label_smoothed_nll_loss(
																	    A__     ,A__     ,self.hparams.label_smoothing     ,ignore_index=A__)
												return (loss,)




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




							def 				A__   (       self     ,A__     ,A__):
												lowercase					=		self._step(A__)

												lowercase					=		dict(zip(self.loss_names     ,A__))
												# tokens per batch
												lowercase					=		batch['''input_ids'''].ne(self.pad).sum() + batch['''labels'''].ne(self.pad).sum()
												lowercase					=		batch['''input_ids'''].shape[0]
												lowercase					=		batch['''input_ids'''].eq(self.pad).sum()
												lowercase					=		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 				A__   (       self     ,A__     ,A__):
												return self._generative_step(A__)




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




							def 				A__   (       self     ,A__):
												lowercase					=		time.time()

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




							def 				A__   (       self     ,A__     ,A__):
												return self._generative_step(A__)




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




							def 				A__   (       self     ,A__):
												lowercase					=		self.n_obs[type_path]
												lowercase					=		self.target_lens[type_path]
												lowercase					=		self.dataset_class(
												    self.tokenizer     ,type_path=A__     ,n_obs=A__     ,max_target_length=A__     ,**self.dataset_kwargs     ,)
												return dataset




							def 				A__   (       self     ,A__     ,A__     ,A__ = False):
												lowercase					=		self.get_dataset(A__)

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

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




							def 				A__   (       self):
												lowercase					=		self.get_dataloader('''train'''     ,batch_size=self.hparams.train_batch_size     ,shuffle=A__)
												return dataloader




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




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





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




							def __init__(       self     ,A__     ,**A__):
												super().__init__(A__     ,**A__)
												lowercase					=		hparams.src_lang
												lowercase					=		hparams.tgt_lang





							def 				A__   (       self     ,A__     ,A__):
												return calculate_bleu(A__     ,A__)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=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:
															lowercase					=		SummarizationModule(_UpperCamelCase	)
										else:
															lowercase					=		TranslationModule(_UpperCamelCase	)
					lowercase					=		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'''	)
					):
										lowercase					=		True  # don't pollute wandb logs unnecessarily
					elif args.logger_name == "wandb":
										from pytorch_lightning.loggers import WandbLogger

										lowercase					=		os.environ.get('''WANDB_PROJECT'''				,    _UpperCamelCase	)
										lowercase					=		WandbLogger(name=model.output_dir.name				,    project=_UpperCamelCase	)

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

										lowercase					=		WandbLogger(name=model.output_dir.name				,    project=f'hf_{dataset}'	)

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

					lowercase					=		args.val_metric == '''loss'''
					lowercase					=		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

					lowercase					=		''''''
					lowercase					=		sorted(glob.glob(os.path.join(args.output_dir				,    '''*.ckpt'''	)				,    recursive=_UpperCamelCase	)	)
					if checkpoints:
										lowercase					=		checkpoints[-1]
										lowercase					=		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__":
							lowercase__	:str										=					argparse.ArgumentParser()
							lowercase__	:Dict										=					pl.Trainer.add_argparse_args(parser)
							lowercase__	:Optional[Any]										=					SummarizationModule.add_model_specific_args(parser, os.getcwd())

							lowercase__	:int										=					parser.parse_args()

							main(args)






# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowercase__	:Any										=					"Run commands across TPU VMs for initial setup before running `accelerate launch`."



def  UpperCamelCase					(							lowerCAmelCase__=None	):
					'''simple docstring'''
					if subparsers is not None:
										lowercase					=		subparsers.add_parser('''tpu-config'''				,    description=_description	)
					else:
										lowercase					=		argparse.ArgumentParser('''Accelerate tpu-config command'''				,    description=_description	)
					# Core arguments
					lowercase					=		parser.add_argument_group(
					    '''Config Arguments'''				,    '''Arguments that can be configured through `accelerate config`.'''	)
					config_args.add_argument(
					    '''--config_file'''				,    type=lowerCAmelCase__				,    default=lowerCAmelCase__				,    help='''Path to the config file to use for accelerate.'''				,    )
					config_args.add_argument(
					    '''--tpu_name'''				,    default=lowerCAmelCase__				,    help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''				,    )
					config_args.add_argument(
					    '''--tpu_zone'''				,    default=lowerCAmelCase__				,    help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''				,    )
					lowercase					=		parser.add_argument_group('''TPU Arguments'''				,    '''Arguments for options ran inside the TPU.'''	)
					pod_args.add_argument(
					    '''--use_alpha'''				,    action='''store_true'''				,    help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''				,    )
					pod_args.add_argument(
					    '''--command_file'''				,    default=lowerCAmelCase__				,    help='''The path to the file containing the commands to run on the pod on startup.'''				,    )
					pod_args.add_argument(
					    '''--command'''				,    action='''append'''				,    nargs='''+'''				,    help='''A command to run on the pod. Can be passed multiple times.'''				,    )
					pod_args.add_argument(
					    '''--install_accelerate'''				,    action='''store_true'''				,    help='''Whether to install accelerate on the pod. Defaults to False.'''				,    )
					pod_args.add_argument(
					    '''--accelerate_version'''				,    default='''latest'''				,    help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''				,    )
					pod_args.add_argument(
					    '''--debug'''				,    action='''store_true'''				,    help='''If set, will print the command that would be run instead of running it.'''	)

					if subparsers is not None:
										parser.set_defaults(func=lowerCAmelCase__	)
					return parser



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		None

					# Get the default from the config file if it exists.
					if args.config_file is not None or os.path.isfile(lowerCAmelCase__	):
										lowercase					=		load_config_from_file(args.config_file	)
										if not args.command_file and defaults.command_file is not None and not args.command:
															lowercase					=		defaults.command_file
										if not args.command and defaults.commands is not None:
															lowercase					=		defaults.commands
										if not args.tpu_name:
															lowercase					=		defaults.tpu_name
										if not args.tpu_zone:
															lowercase					=		defaults.tpu_zone
					if args.accelerate_version == "dev":
										lowercase					=		'''git+https://github.com/huggingface/accelerate.git'''
					elif args.accelerate_version == "latest":
										lowercase					=		'''accelerate -U'''
					elif isinstance(parse(args.accelerate_version	)				,    lowerCAmelCase__	):
										lowercase					=		f'accelerate=={args.accelerate_version}'

					if not args.command_file and not args.command:
										raise ValueError('''You must specify either a command file or a command to run on the pod.'''	)

					if args.command_file:
										with open(args.command_file				,    '''r'''	) as f:
															lowercase					=		[f.read().splitlines()]

    # To turn list of lists into list of strings
					if isinstance(args.command[0]				,    lowerCAmelCase__	):
										lowercase					=		[line for cmd in args.command for line in cmd]
					# Default to the shared folder and install accelerate
					lowercase					=		['''cd /usr/share''']
					if args.install_accelerate:
										new_cmd += [f'pip install {args.accelerate_version}']
					new_cmd += args.command
					lowercase					=		'''; '''.join(lowerCAmelCase__	)

					# Then send it to gcloud
					# Eventually try to use google-api-core to do this instead of subprocess
					lowercase					=		['''gcloud''']
					if args.use_alpha:
										cmd += ["alpha"]
					cmd += [
					    "compute",
					    "tpus",
					    "tpu-vm",
					    "ssh",
					    args.tpu_name,
					    "--zone",
					    args.tpu_zone,
					    "--command",
					    args.command,
					    "--worker",
					    "all",
					]
					if args.debug:
										print(f'Running {" ".join(lowerCAmelCase__	)}'	)
										return
					subprocess.run(lowerCAmelCase__	)
					print('''Successfully setup pod.'''	)



def  UpperCamelCase					(							):
					'''simple docstring'''
					lowercase					=		tpu_command_parser()
					lowercase					=		parser.parse_args()

					tpu_command_launcher(lowerCAmelCase__	)




