




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__UpperCamelCase   :			Union[str, Any]          = logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")

@dataclass
class 						__lowerCAmelCase      :
							UpperCamelCase__   	=    field(
							    default='''cifar10''' ,			metadata={'''help''': '''Name of a dataset from the datasets package'''}					)
							UpperCamelCase__   	=    field(
							    default=_a ,			metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}					)
							UpperCamelCase__   	=    field(
							    default=_a ,			metadata={'''help''': '''The column name of the images in the files.'''}					)
							UpperCamelCase__   	=    field(default=_a ,			metadata={'''help''': '''A folder containing the training data.'''}					)
							UpperCamelCase__   	=    field(default=_a ,			metadata={'''help''': '''A folder containing the validation data.'''}					)
							UpperCamelCase__   	=    field(
							    default=0.1_5 ,			metadata={'''help''': '''Percent to split off of train for validation.'''}					)
							UpperCamelCase__   	=    field(
							    default=_a ,			metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
							            '''value if set.'''
							        )
							    } ,			)
							UpperCamelCase__   	=    field(
							    default=_a ,			metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
							            '''value if set.'''
							        )
							    } ,			)
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a									=				{}
								if self.train_dir is not None:
									a									=				self.train_dir
								if self.validation_dir is not None:
									a									=				self.validation_dir
								a									=				data_files if data_files else None







@dataclass
class 						__lowerCAmelCase      :
							UpperCamelCase__   	=    field(
							    default=_a ,			metadata={
							        '''help''': (
							            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
							        )
							    } ,			)
							UpperCamelCase__   	=    field(
							    default=_a ,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}					)
							UpperCamelCase__   	=    field(
							    default=_a ,			metadata={
							        '''help''': (
							            '''Override some existing default config settings when a model is trained from scratch. Example: '''
							            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
							        )
							    } ,			)
							UpperCamelCase__   	=    field(
							    default=_a ,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}					)
							UpperCamelCase__   	=    field(
							    default='''main''' ,			metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} ,			)
							UpperCamelCase__   	=    field(default=_a ,			metadata={'''help''': '''Name or path of preprocessor config.'''}					)
							UpperCamelCase__   	=    field(
							    default=_a ,			metadata={
							        '''help''': (
							            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
							            '''with private models).'''
							        )
							    } ,			)
							UpperCamelCase__   	=    field(
							    default=0.7_5 ,			metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}					)
							UpperCamelCase__   	=    field(
							    default=_a ,			metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}					)







@dataclass
class 						__lowerCAmelCase      (      _a					):
							UpperCamelCase__   	=    field(
							    default=1e-3 ,			metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}					)







def 						__A						(      __lowerCamelCase       ) ->			Any:
	a									=				torch.stack([example["""pixel_values"""] for example in examples]       )
	return {"pixel_values": pixel_values}





def 						__A						(      ) ->			List[str]:
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	a									=				HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)       )
	if len(sys.argv       ) == 2 and sys.argv[1].endswith(""".json"""       ):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		a							,					a							,					a									=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
	else:
		a							,					a							,					a									=				parser.parse_args_into_dataclasses()

	# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
	# information sent is the one passed as arguments along with your Python/PyTorch versions.
	send_example_telemetry("""run_mae"""				,     _snake_case				,     _snake_case       )

	# Setup logging
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,     datefmt="""%m/%d/%Y %H:%M:%S"""				,     handlers=[logging.StreamHandler(sys.stdout       )]				,     )

	if training_args.should_log:
		# The default of training_args.log_level is passive, so we set log level at info here to have that default.
		transformers.utils.logging.set_verbosity_info()

	a									=				training_args.get_process_log_level()
	logger.setLevel(_snake_case       )
	transformers.utils.logging.set_verbosity(_snake_case       )
	transformers.utils.logging.enable_default_handler()
	transformers.utils.logging.enable_explicit_format()

	# Log on each process the small summary:
	logger.warning(
	    f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
	    + f'distributed training: {bool(training_args.local_rank != -1       )}, 16-bits training: {training_args.fpaa}'       )
	logger.info(f'Training/evaluation parameters {training_args}'       )

	# Detecting last checkpoint.
	a									=				None
	if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
		a									=				get_last_checkpoint(training_args.output_dir       )
		if last_checkpoint is None and len(os.listdir(training_args.output_dir       )       ) > 0:
			raise ValueError(
			    f'Output directory ({training_args.output_dir}) already exists and is not empty. '
			    """Use --overwrite_output_dir to overcome."""       )
		elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
			logger.info(
			    f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
			    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""       )

    # Initialize our dataset.
	a									=				load_dataset(
	    data_args.dataset_name				,     data_args.dataset_config_name				,     data_files=data_args.data_files				,     cache_dir=model_args.cache_dir				,     use_auth_token=True if model_args.use_auth_token else None				,     )

	# If we don't have a validation split, split off a percentage of train as validation.
	a									=				None if """validation""" in ds.keys() else data_args.train_val_split
	if isinstance(data_args.train_val_split				,     _snake_case       ) and data_args.train_val_split > 0.0:
		a									=				ds["""train"""].train_test_split(data_args.train_val_split       )
		a									=				split["""train"""]
		a									=				split["""test"""]

	# Load pretrained model and image processor
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	a									=				{
	    """cache_dir""": model_args.cache_dir,
	    """revision""": model_args.model_revision,
	    """use_auth_token""": True if model_args.use_auth_token else None,
	}
	if model_args.config_name:
		a									=				ViTMAEConfig.from_pretrained(model_args.config_name				,     **_snake_case       )
	elif model_args.model_name_or_path:
		a									=				ViTMAEConfig.from_pretrained(model_args.model_name_or_path				,     **_snake_case       )
	else:
		a									=				ViTMAEConfig()
		logger.warning("""You are instantiating a new config instance from scratch."""       )
		if model_args.config_overrides is not None:
			logger.info(f'Overriding config: {model_args.config_overrides}'       )
			config.update_from_string(model_args.config_overrides       )
			logger.info(f'New config: {config}'       )

    # adapt config
	config.update(
	    {
	        """mask_ratio""": model_args.mask_ratio,
	        """norm_pix_loss""": model_args.norm_pix_loss,
	    }       )

	# create image processor
	if model_args.image_processor_name:
		a									=				ViTImageProcessor.from_pretrained(model_args.image_processor_name				,     **_snake_case       )
	elif model_args.model_name_or_path:
		a									=				ViTImageProcessor.from_pretrained(model_args.model_name_or_path				,     **_snake_case       )
	else:
		a									=				ViTImageProcessor()

	# create model
	if model_args.model_name_or_path:
		a									=				ViTMAEForPreTraining.from_pretrained(
		    model_args.model_name_or_path				,     from_tf=bool(""".ckpt""" in model_args.model_name_or_path       )				,     config=_snake_case				,     cache_dir=model_args.cache_dir				,     revision=model_args.model_revision				,     use_auth_token=True if model_args.use_auth_token else None				,     )
	else:
		logger.info("""Training new model from scratch"""       )
		a									=				ViTMAEForPreTraining(_snake_case       )

	if training_args.do_train:
		a									=				ds["""train"""].column_names
	else:
		a									=				ds["""validation"""].column_names

	if data_args.image_column_name is not None:
		a									=				data_args.image_column_name
	elif "image" in column_names:
		a									=				"""image"""
	elif "img" in column_names:
		a									=				"""img"""
	else:
		a									=				column_names[0]

	# transformations as done in original MAE paper
	# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
	if "shortest_edge" in image_processor.size:
		a									=				image_processor.size["""shortest_edge"""]
	else:
		a									=				(image_processor.size["""height"""], image_processor.size["""width"""])
	a									=				Compose(
	    [
	        Lambda(lambda __lowerCamelCase       : img.convert("""RGB"""       ) if img.mode != "RGB" else img       ),
	        RandomResizedCrop(_snake_case				,     scale=(0.2, 1.0)				,     interpolation=InterpolationMode.BICUBIC       ),
	        RandomHorizontalFlip(),
	        ToTensor(),
	        Normalize(mean=image_processor.image_mean				,     std=image_processor.image_std       ),
	    ]       )

	def preprocess_images(__lowerCamelCase       ):
		a									=				[transforms(_snake_case       ) for image in examples[image_column_name]]
		return examples

	if training_args.do_train:
		if "train" not in ds:
			raise ValueError("""--do_train requires a train dataset"""       )
		if data_args.max_train_samples is not None:
			a									=				ds["""train"""].shuffle(seed=training_args.seed       ).select(range(data_args.max_train_samples       )       )
		# Set the training transforms
		ds["train"].set_transform(_snake_case       )

	if training_args.do_eval:
		if "validation" not in ds:
			raise ValueError("""--do_eval requires a validation dataset"""       )
		if data_args.max_eval_samples is not None:
			a									=				(
			    ds["""validation"""].shuffle(seed=training_args.seed       ).select(range(data_args.max_eval_samples       )       )
			)
		# Set the validation transforms
		ds["validation"].set_transform(_snake_case       )

	# Compute absolute learning rate
	a									=				(
	    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
	)
	if training_args.base_learning_rate is not None:
		a									=				training_args.base_learning_rate * total_train_batch_size / 256

	# Initialize our trainer
	a									=				Trainer(
	    model=_snake_case				,     args=_snake_case				,     train_dataset=ds["""train"""] if training_args.do_train else None				,     eval_dataset=ds["""validation"""] if training_args.do_eval else None				,     tokenizer=_snake_case				,     data_collator=_snake_case				,     )

	# Training
	if training_args.do_train:
		a									=				None
		if training_args.resume_from_checkpoint is not None:
			a									=				training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			a									=				last_checkpoint
		a									=				trainer.train(resume_from_checkpoint=_snake_case       )
		trainer.save_model()
		trainer.log_metrics("""train"""				,     train_result.metrics       )
		trainer.save_metrics("""train"""				,     train_result.metrics       )
		trainer.save_state()

	# Evaluation
	if training_args.do_eval:
		a									=				trainer.evaluate()
		trainer.log_metrics("""eval"""				,     _snake_case       )
		trainer.save_metrics("""eval"""				,     _snake_case       )

	# Write model card and (optionally) push to hub
	a									=				{
	    """tasks""": """masked-auto-encoding""",
	    """dataset""": data_args.dataset_name,
	    """tags""": ["""masked-auto-encoding"""],
	}
	if training_args.push_to_hub:
		trainer.push_to_hub(**_snake_case       )
	else:
		trainer.create_model_card(**_snake_case       )





def 						__A						(      __lowerCamelCase       ) ->			Optional[Any]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
		main()









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


_SCREAMING_SNAKE_CASE   =  logging.getLogger(__name__)






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		int					='''summarization'''
       __lowerCAmelCase    :		str					=['''loss''']
       __lowerCAmelCase    :		Dict					=ROUGE_KEYS
       __lowerCAmelCase    :		str					='''rouge2'''





       def __init__(				self	:Dict,      snake_case	:List[Any],      **snake_case	:Tuple):



          """simple docstring"""

          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__(snake_case,      num_labels=snake_case,      mode=self.mode,      **snake_case)
          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(snake_case)
          _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,      snake_case):
             _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 				UpperCamelCase__						(				self	:str,      snake_case	:Dict[str, torch.Tensor]):



          """simple docstring"""

          _lowercase              ={
              k: self.tokenizer.batch_decode(v.tolist()) if 'mask' not in k else v.shape for k, v in batch.items()
          }
          save_json(snake_case,      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 				UpperCamelCase__						(				self	:Dict,      snake_case	:List[str],      **snake_case	:List[Any]):



          """simple docstring"""

          return self.model(snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:List[int]):



          """simple docstring"""

          _lowercase              =self.tokenizer.batch_decode(
              snake_case,      skip_special_tokens=snake_case,      clean_up_tokenization_spaces=snake_case)
          return lmap(str.strip,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:dict):



          """simple docstring"""

          _lowercase              =self.tokenizer.pad_token_id
          _lowercase       ,       _lowercase              =batch['input_ids'], batch['attention_mask']
          _lowercase              =batch['labels']
          if isinstance(self.model,      snake_case):
             _lowercase              =self.model._shift_right(snake_case)
          else:
             _lowercase              =shift_tokens_right(snake_case,      snake_case)
          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(snake_case)

          _lowercase              =self(snake_case,      attention_mask=snake_case,      decoder_input_ids=snake_case,      use_cache=snake_case)
          _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=snake_case)

             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(snake_case,      dim=-1)
             _lowercase       ,       _lowercase              =label_smoothed_nll_loss(
                 snake_case,      snake_case,      self.hparams.label_smoothing,      ignore_index=snake_case)
          return (loss,)





       @property
       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return self.tokenizer.pad_token_id





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:Dict,      snake_case	:str):



          """simple docstring"""

          _lowercase              =self._step(snake_case)

          _lowercase              =dict(zip(self.loss_names,      snake_case))
          # 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 				UpperCamelCase__						(				self	:List[Any],      snake_case	:Dict,      snake_case	:List[Any]):



          """simple docstring"""

          return self._generative_step(snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:List[str],      snake_case	:str="val"):



          """simple docstring"""

          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(snake_case).type_as(snake_case)
          generative_metrics.update({k: v.item() for k, v in losses.items()})
          losses.update(snake_case)
          _lowercase              ={f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
          _lowercase              =self.step_count
          self.metrics[prefix].append(snake_case)  # 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 				UpperCamelCase__						(				self	:Any,      snake_case	:str,      snake_case	:List[str]):



          """simple docstring"""

          return calculate_rouge(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:str,      snake_case	:dict):



          """simple docstring"""

          _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=snake_case,      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(snake_case)
          _lowercase              =self.ids_to_clean_text(batch['labels'])
          _lowercase              =self._step(snake_case)
          _lowercase              =dict(zip(self.loss_names,      snake_case))
          _lowercase              =self.calc_generative_metrics(snake_case,      snake_case)
          _lowercase              =np.mean(lmap(snake_case,      snake_case))
          base_metrics.update(gen_time=snake_case,      gen_len=snake_case,      preds=snake_case,      target=snake_case,      **snake_case)
          return base_metrics





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:str,      snake_case	:Optional[int]):



          """simple docstring"""

          return self._generative_step(snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:List[str]):



          """simple docstring"""

          return self.validation_epoch_end(snake_case,      prefix='test')





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:str):



          """simple docstring"""

          _lowercase              =self.n_obs[type_path]
          _lowercase              =self.target_lens[type_path]
          _lowercase              =self.dataset_class(
              self.tokenizer,      type_path=snake_case,      n_obs=snake_case,      max_target_length=snake_case,      **self.dataset_kwargs,      )
          return dataset





       def 				UpperCamelCase__						(				self	:str,      snake_case	:str,      snake_case	:int,      snake_case	:bool = False):



          """simple docstring"""

          _lowercase              =self.get_dataset(snake_case)

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

          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(
                 snake_case,      batch_sampler=snake_case,      collate_fn=dataset.collate_fn,      num_workers=self.num_workers,      )
          else:
             return DataLoader(
                 snake_case,      batch_size=snake_case,      collate_fn=dataset.collate_fn,      shuffle=snake_case,      num_workers=self.num_workers,      sampler=snake_case,      )





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          _lowercase              =self.get_dataloader('train',      batch_size=self.hparams.train_batch_size,      shuffle=snake_case)
          return dataloader





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

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





       @staticmethod
       def 				UpperCamelCase__						(				snake_case	:Dict,      snake_case	:List[str]):



          """simple docstring"""

          BaseTransformer.add_model_specific_args(snake_case,      snake_case)
          add_generic_args(snake_case,      snake_case)
          parser.add_argument(
              '--max_source_length',      default=1024,      type=snake_case,      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=snake_case,      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=snake_case,      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=snake_case,      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=snake_case)
          parser.add_argument('--overwrite_output_dir',      action='store_true',      default=snake_case)
          parser.add_argument('--max_tokens_per_batch',      type=snake_case,      default=snake_case)
          parser.add_argument('--logger_name',      type=snake_case,      choices=['default', 'wandb', 'wandb_shared'],      default='default')
          parser.add_argument('--n_train',      type=snake_case,      default=-1,      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument('--n_val',      type=snake_case,      default=500,      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument('--n_test',      type=snake_case,      default=-1,      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument(
              '--task',      type=snake_case,      default='summarization',      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument('--label_smoothing',      type=snake_case,      default=0.0,      required=snake_case)
          parser.add_argument('--src_lang',      type=snake_case,      default='',      required=snake_case)
          parser.add_argument('--tgt_lang',      type=snake_case,      default='',      required=snake_case)
          parser.add_argument('--eval_beams',      type=snake_case,      default=snake_case,      required=snake_case)
          parser.add_argument(
              '--val_metric',      type=snake_case,      default=snake_case,      required=snake_case,      choices=['bleu', 'rouge2', 'loss', None])
          parser.add_argument('--eval_max_gen_length',      type=snake_case,      default=snake_case,      help='never generate more than n tokens')
          parser.add_argument('--save_top_k',      type=snake_case,      default=1,      required=snake_case,      help='How many checkpoints to save')
          parser.add_argument(
              '--early_stopping_patience',      type=snake_case,      default=-1,      required=snake_case,      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 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Union[str, Any]					='''translation'''
       __lowerCAmelCase    :		Any					=['''loss''']
       __lowerCAmelCase    :		Optional[int]					=['''bleu''']
       __lowerCAmelCase    :		Any					='''bleu'''





       def __init__(				self	:str,      snake_case	:Union[str, Any],      **snake_case	:Any):



          """simple docstring"""

          super().__init__(snake_case,      **snake_case)
          _lowercase              =hparams.src_lang
          _lowercase              =hparams.tgt_lang





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:Union[str, Any],      snake_case	:Any):



          """simple docstring"""

          return calculate_bleu(snake_case,      snake_case)





def        _snake_case   (_snake_case				:       Dict      ,    _snake_case				:       int=None)					->	SummarizationModule:
   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:
         _lowercase              =SummarizationModule(_snake_case)
      else:
         _lowercase              =TranslationModule(_snake_case)
   _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'      ,    _snake_case)
      _lowercase              =WandbLogger(name=model.output_dir.name      ,    project=_snake_case)

   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(
       _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

   _lowercase              =''
   _lowercase              =sorted(glob.glob(os.path.join(args.output_dir      ,    '*.ckpt')      ,    recursive=_snake_case))
   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__":
    _SCREAMING_SNAKE_CASE   =  argparse.ArgumentParser()
    _SCREAMING_SNAKE_CASE   =  pl.Trainer.add_argparse_args(parser)
    _SCREAMING_SNAKE_CASE   =  SummarizationModule.add_model_specific_args(parser, os.getcwd())

    _SCREAMING_SNAKE_CASE   =  parser.parse_args()

    main(args)


