
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_snake_case      = getLogger(__name__)


_snake_case      = '''cuda''' if torch.cuda.is_available() else '''cpu'''

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase = 8						,     _lowercase = DEFAULT_DEVICE						,     _lowercase=False						,     _lowercase="summarization"						,     _lowercase=None						,     **_lowercase						,     )       ->      Dict:
     UpperCamelCase						    =					Path(_lowercase			).open('w'						,     encoding='utf-8'			)
     UpperCamelCase						    =					str(_lowercase			)
     UpperCamelCase						    =					AutoModelForSeqaSeqLM.from_pretrained(_lowercase			).to(_lowercase			)
     if fpaa:
          UpperCamelCase						    =					model.half()

     UpperCamelCase						    =					AutoTokenizer.from_pretrained(_lowercase			)
     logger.info(F'Inferred tokenizer type: {tokenizer.__class__}'			)  # if this is wrong, check config.model_type.

     UpperCamelCase						    =					time.time()
     # update config with task specific params
     use_task_specific_params(_lowercase						,     _lowercase			)
     if prefix is None:
          UpperCamelCase						    =					prefix or getattr(model.config						,     'prefix'						,     ''			) or ''
     for examples_chunk in tqdm(list(chunks(_lowercase						,     _lowercase			)			)			):
          UpperCamelCase						    =					[prefix + text for text in examples_chunk]
          UpperCamelCase						    =					tokenizer(_lowercase						,     return_tensors='pt'						,     truncation=_lowercase						,     padding='longest'			).to(_lowercase			)
          UpperCamelCase						    =					model.generate(
              input_ids=batch.input_ids						,     attention_mask=batch.attention_mask						,     **_lowercase						,     )
          UpperCamelCase						    =					tokenizer.batch_decode(_lowercase						,     skip_special_tokens=_lowercase						,     clean_up_tokenization_spaces=_lowercase			)
          for hypothesis in dec:
               fout.write(hypothesis + '\n'			)
               fout.flush()
     fout.close()
     UpperCamelCase						    =					int(time.time() - start_time			)  # seconds
     UpperCamelCase						    =					len(_lowercase			)
     return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs						,     4			)}

def  __lowerCamelCase					( )       ->      List[str]:
     return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'			)

def  __lowerCamelCase					( _lowercase=True			)       ->      Any:
     UpperCamelCase						    =					argparse.ArgumentParser()
     parser.add_argument('model_name'						,     type=_lowercase						,     help='like facebook/bart-large-cnn,t5-base, etc.'			)
     parser.add_argument('input_path'						,     type=_lowercase						,     help='like cnn_dm/test.source'			)
     parser.add_argument('save_path'						,     type=_lowercase						,     help='where to save summaries'			)
     parser.add_argument('--reference_path'						,     type=_lowercase						,     required=_lowercase						,     help='like cnn_dm/test.target'			)
     parser.add_argument('--score_path'						,     type=_lowercase						,     required=_lowercase						,     default='metrics.json'						,     help='where to save metrics'			)
     parser.add_argument('--device'						,     type=_lowercase						,     required=_lowercase						,     default=_lowercase						,     help='cuda, cuda:1, cpu etc.'			)
     parser.add_argument(
         '--prefix'						,     type=_lowercase						,     required=_lowercase						,     default=_lowercase						,     help='will be added to the begininng of src examples'			)
     parser.add_argument('--task'						,     type=_lowercase						,     default='summarization'						,     help='used for task_specific_params + metrics'			)
     parser.add_argument('--bs'						,     type=_lowercase						,     default=8						,     required=_lowercase						,     help='batch size'			)
     parser.add_argument(
         '--n_obs'						,     type=_lowercase						,     default=-1						,     required=_lowercase						,     help='How many observations. Defaults to all.'			)
     parser.add_argument('--fp16'						,     action='store_true'			)
     parser.add_argument('--dump-args'						,     action='store_true'						,     help='print the custom hparams with the results'			)
     parser.add_argument(
         '--info'						,     nargs='?'						,     type=_lowercase						,     const=datetime_now()						,     help=(
             'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
             ' lang=en-ru. If no value is passed, the current datetime string will be used.'
         )						,     )
     # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
     UpperCamelCase    ,		UpperCamelCase						    =					parser.parse_known_args()
     UpperCamelCase						    =					parse_numeric_n_bool_cl_kwargs(_lowercase			)
     if parsed_args and verbose:
          print(F'parsed the following generate kwargs: {parsed_args}'			)
     UpperCamelCase						    =					[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path			).readlines()]
     if args.n_obs > 0:
          UpperCamelCase						    =					examples[: args.n_obs]
     Path(args.save_path			).parent.mkdir(exist_ok=_lowercase			)

     if args.reference_path is None and Path(args.score_path			).exists():
          warnings.warn(F'score_path {args.score_path} will be overwritten unless you type ctrl-c.'			)

     if args.device == "cpu" and args.fpaa:
          # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
          raise ValueError('Can\'t mix --fp16 and --device cpu'			)

     UpperCamelCase						    =					generate_summaries_or_translations(
         _lowercase						,     args.save_path						,     args.model_name						,     batch_size=args.bs						,     device=args.device						,     fpaa=args.fpaa						,     task=args.task						,     prefix=args.prefix						,     **_lowercase						,     )

     if args.reference_path is None:
          return {}

     # Compute scores
     UpperCamelCase						    =					calculate_bleu if 'translation' in args.task else calculate_rouge
     UpperCamelCase						    =					[x.rstrip() for x in open(args.save_path			).readlines()]
     UpperCamelCase						    =					[x.rstrip() for x in open(args.reference_path			).readlines()][: len(_lowercase			)]
     UpperCamelCase						    =					score_fn(_lowercase						,     _lowercase			)
     scores.update(_lowercase			)

     if args.dump_args:
          scores.update(_lowercase			)
     if args.info:
          UpperCamelCase						    =					args.info

     if verbose:
          print(_lowercase			)

     if args.score_path is not None:
          json.dump(_lowercase						,     open(args.score_path						,     'w'			)			)

     return scores


if __name__ == "__main__":
 # Usage for MT:
 # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
 run_generate(verbose=True)




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
_snake_case      = 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  :

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default="cifar10"			,	metadata={"help": "Name of a dataset from the datasets package"}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default=__magic_name__			,	metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default=__magic_name__			,	metadata={"help": "The column name of the images in the files."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(default=__magic_name__			,	metadata={"help": "A folder containing the training data."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(default=__magic_name__			,	metadata={"help": "A folder containing the validation data."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[float]       						=field(
     default=0.15			,	metadata={"help": "Percent to split off of train for validation."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=field(
     default=__magic_name__			,	metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of training examples to this "
             "value if set."
         )
     }			,	)
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=field(
     default=__magic_name__			,	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"""



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




@dataclass
class       _lowerCAmelCase  :

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					str       						=field(
     default=__magic_name__			,	metadata={
         "help": (
             "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
         )
     }			,	)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default=__magic_name__			,	metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default=__magic_name__			,	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"
         )
     }			,	)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default=__magic_name__			,	metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}							)
 SCREAMING_SNAKE_CASE_				:					str       						=field(
     default="main"			,	metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}			,	)
 SCREAMING_SNAKE_CASE_				:					str       						=field(default=__magic_name__			,	metadata={"help": "Name or path of preprocessor config."}							)
 SCREAMING_SNAKE_CASE_				:					bool       						=field(
     default=__magic_name__			,	metadata={
         "help": (
             "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
             "with private models)."
         )
     }			,	)
 SCREAMING_SNAKE_CASE_				:					float       						=field(
     default=0.75			,	metadata={"help": "The ratio of the number of masked tokens in the input sequence."}							)
 SCREAMING_SNAKE_CASE_				:					bool       						=field(
     default=__magic_name__			,	metadata={"help": "Whether or not to train with normalized pixel values as target."}							)




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					float       						=field(
     default=1E-3			,	metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}							)




def  __lowerCamelCase					( _lowercase			)       ->      Optional[int]:
     UpperCamelCase						    =					torch.stack([example['pixel_values'] for example in examples]			)
     return {"pixel_values": pixel_values}

def  __lowerCamelCase					( )       ->      List[Any]:
     # 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.

     UpperCamelCase						    =					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.
          UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
     else:
          UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					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'						,     _lowercase						,     _lowercase			)

     # 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()

     UpperCamelCase						    =					training_args.get_process_log_level()
     logger.setLevel(_lowercase			)
     transformers.utils.logging.set_verbosity(_lowercase			)
     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.
     UpperCamelCase						    =					None
     if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
          UpperCamelCase						    =					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.
     UpperCamelCase						    =					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.
     UpperCamelCase						    =					None if 'validation' in ds.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split						,     _lowercase			) and data_args.train_val_split > 0.0:
          UpperCamelCase						    =					ds['train'].train_test_split(data_args.train_val_split			)
          UpperCamelCase						    =					split['train']
          UpperCamelCase						    =					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.
     UpperCamelCase						    =					{
         '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:
          UpperCamelCase						    =					ViTMAEConfig.from_pretrained(model_args.config_name						,     **_lowercase			)
     elif model_args.model_name_or_path:
          UpperCamelCase						    =					ViTMAEConfig.from_pretrained(model_args.model_name_or_path						,     **_lowercase			)
     else:
          UpperCamelCase						    =					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:
          UpperCamelCase						    =					ViTImageProcessor.from_pretrained(model_args.image_processor_name						,     **_lowercase			)
     elif model_args.model_name_or_path:
          UpperCamelCase						    =					ViTImageProcessor.from_pretrained(model_args.model_name_or_path						,     **_lowercase			)
     else:
          UpperCamelCase						    =					ViTImageProcessor()

     # create model
     if model_args.model_name_or_path:
          UpperCamelCase						    =					ViTMAEForPreTraining.from_pretrained(
              model_args.model_name_or_path						,     from_tf=bool('.ckpt' in model_args.model_name_or_path			)						,     config=_lowercase						,     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'			)
          UpperCamelCase						    =					ViTMAEForPreTraining(_lowercase			)

     if training_args.do_train:
          UpperCamelCase						    =					ds['train'].column_names
     else:
          UpperCamelCase						    =					ds['validation'].column_names

     if data_args.image_column_name is not None:
          UpperCamelCase						    =					data_args.image_column_name
     elif "image" in column_names:
          UpperCamelCase						    =					'image'
     elif "img" in column_names:
          UpperCamelCase						    =					'img'
     else:
          UpperCamelCase						    =					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:
          UpperCamelCase						    =					image_processor.size['shortest_edge']
     else:
          UpperCamelCase						    =					(image_processor.size['height'], image_processor.size['width'])
     UpperCamelCase						    =					Compose(
         [
             Lambda(lambda _lowercase			: img.convert('RGB'			) if img.mode != "RGB" else img			),
             RandomResizedCrop(_lowercase						,     scale=(0.2, 1.0)						,     interpolation=InterpolationMode.BICUBIC			),
             RandomHorizontalFlip(),
             ToTensor(),
             Normalize(mean=image_processor.image_mean						,     std=image_processor.image_std			),
         ]			)

     def preprocess_images(_lowercase			):
          UpperCamelCase						    =					[transforms(_lowercase			) 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:
               UpperCamelCase						    =					ds['train'].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
          # Set the training transforms
          ds["train"].set_transform(_lowercase			)

     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:
               UpperCamelCase						    =					(
                   ds['validation'].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
               )
          # Set the validation transforms
          ds["validation"].set_transform(_lowercase			)

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

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

     # Training
     if training_args.do_train:
          UpperCamelCase						    =					None
          if training_args.resume_from_checkpoint is not None:
               UpperCamelCase						    =					training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               UpperCamelCase						    =					last_checkpoint
          UpperCamelCase						    =					trainer.train(resume_from_checkpoint=_lowercase			)
          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:
          UpperCamelCase						    =					trainer.evaluate()
          trainer.log_metrics('eval'						,     _lowercase			)
          trainer.save_metrics('eval'						,     _lowercase			)

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

def  __lowerCamelCase					( _lowercase			)       ->      int:
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()


