

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
_lowerCamelCase      :					Tuple					    =      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'''







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

       def     __UpperCamelCase     (							self     ):







             '''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__  :



       '''simple docstring'''







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





@dataclass
class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    field(
           default=1e-3				,							metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}			)





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





def      A__					(			)					->Dict:
      # 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'''     ,				__A     ,				__A			)

      # 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(__A			)
      transformers.utils.logging.set_verbosity(__A			)
      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     ,				__A			) 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     ,				**__A			)
      elif model_args.model_name_or_path:
            __A    =ViTMAEConfig.from_pretrained(model_args.model_name_or_path     ,				**__A			)
      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     ,				**__A			)
      elif model_args.model_name_or_path:
            __A    =ViTImageProcessor.from_pretrained(model_args.model_name_or_path     ,				**__A			)
      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=__A     ,				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(__A			)

      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 __A			: img.convert('''RGB'''			) if img.mode != "RGB" else img			),
              RandomResizedCrop(__A     ,				scale=(0.2, 1.0)     ,				interpolation=InterpolationMode.BICUBIC			),
              RandomHorizontalFlip(),
              ToTensor(),
              Normalize(mean=image_processor.image_mean     ,				std=image_processor.image_std			),
          ]			)

      def preprocess_images(__A  :						Optional[Any]			):
            __A    =[transforms(__A			) 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(__A			)

      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(__A			)

      # 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 / 2_56

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

      # 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=__A			)
            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'''     ,				__A			)
            trainer.save_metrics('''eval'''     ,				__A			)

      # 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(**__A			)
      else:
            trainer.create_model_card(**__A			)





def      A__					(			__A  :						Optional[int]			)					->str:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
   main()





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration





def      A__					(			__A  :						Any     ,				__A  :						str     ,				__A  :						str     ,				__A  :						Path     ,				__A  :						str = None     ,				__A  :						str = None     ,				__A  :						str = None     ,				)					->Optional[Any]:
      if config_name_or_path is None:
            __A    ='''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

      if generator_tokenizer_name_or_path is None:
            __A    =generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            __A    =question_encoder_name_or_path

      __A    =RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

      # Save model.
      __A    =RagConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)

      __A    =gen_config
      __A    =question_encoder_config

      __A    =model_class.from_pretrained_question_encoder_generator(
          __A     ,				__A     ,				config=__A			)
      rag_model.save_pretrained(__A			)

      # Sanity check.
      model_class.from_pretrained(__A			)

      # Save tokenizers.
      __A    =AutoTokenizer.from_pretrained(__A			)
      gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''			)
      __A    =AutoTokenizer.from_pretrained(__A			)
      question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      argparse.ArgumentParser()
   parser.add_argument(
       '''--model_type''',
       choices=['''rag_sequence''', '''rag_token'''],
       required=True,
       type=str,
       help='''RAG model type: rag_sequence, rag_token''',
   )
   parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
   parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
   parser.add_argument(
       '''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
   )

   parser.add_argument(
       '''--generator_tokenizer_name_or_path''',
       type=str,
       help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
   )
   parser.add_argument(
       '''--question_encoder_tokenizer_name_or_path''',
       type=str,
       help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
   )
   parser.add_argument(
       '''--config_name_or_path''',
       type=str,
       help=(
           '''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
           ''' ``model_type``'''
       ),
   )

   _lowerCamelCase      :					str					    =      parser.parse_args()

   _lowerCamelCase      :					Optional[int]					    =      Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )


