import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
    print('#################### AVAILABLE DEVICES ####################')
    print(jax.devices())
    print('###########################################################')

    # setup for wandb sweep
    lowerCAmelCase__							= Args()
    lowerCAmelCase__							= wandb.init(project='bigbird-natural-questions', config=args.__dict__)
    lowerCAmelCase__							= dict(logger.config)
    del wandb_args["batch_size"]
    lowerCAmelCase__							= replace(args, **wandb_args)
    lowerCAmelCase__							= args.base_dir + '-' + wandb.run.id
    lowerCAmelCase__							= replace(args, base_dir=base_dir)
    print(args)

    lowerCAmelCase__							= load_dataset('json', data_files=args.tr_data_path)['train']
    lowerCAmelCase__							= load_dataset('json', data_files=args.val_data_path)['train']

    # drop extra batch for now
    lowerCAmelCase__							= range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
    lowerCAmelCase__							= tr_dataset.shuffle().select(indices)
    lowerCAmelCase__							= range(len(val_dataset) - len(val_dataset) % args.batch_size)
    lowerCAmelCase__							= val_dataset.shuffle().select(indices)

    if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
        lowerCAmelCase__							= tr_dataset.shuffle().select(range(80000))
        lowerCAmelCase__							= val_dataset.shuffle().select(range(8000))

    print(tr_dataset)
    print(val_dataset)

    lowerCAmelCase__							= FlaxBigBirdForNaturalQuestions.from_pretrained(
        args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
    )
    lowerCAmelCase__							= BigBirdTokenizerFast.from_pretrained(args.model_id)
    lowerCAmelCase__							= DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

    lowerCAmelCase__							= {
        'lr': args.lr,
        'init_lr': args.init_lr,
        'warmup_steps': args.warmup_steps,
        'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
        'weight_decay': args.weight_decay,
    }
    lowerCAmelCase__						,		lowerCAmelCase__							= build_tx(**tx_args)

    lowerCAmelCase__							= Trainer(
        args=args,
        data_collator=data_collator,
        model_save_fn=model.save_pretrained,
        train_step_fn=train_step,
        val_step_fn=val_step,
        logger=logger,
        scheduler_fn=lr,
    )

    lowerCAmelCase__							= None
    lowerCAmelCase__							= trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
    try:
        trainer.train(state, tr_dataset, val_dataset)
    except KeyboardInterrupt:
        print('Oooops; TRAINING STOPPED UNFORTUNATELY')

    print('SAVING WEIGHTS IN `final-weights`')
    lowerCAmelCase__							= jax_utils.unreplicate(state.params)
    model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)

from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


lowerCAmelCase__							= logging.get_logger(__name__)

lowerCAmelCase__							= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__							= {
    'vocab_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json',
    },
    'merges_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'openai/clip-vit-base-patch32': (
            'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase__							= {
    'openai/clip-vit-base-patch32': 77,
}
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					VOCAB_FILES_NAMES
 UpperCAmelCase_	 =					PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_	 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_	 =					['input_ids', 'attention_mask']
 UpperCAmelCase_	 =					CLIPTokenizer




 def __init__(			self					:  Optional[int]			,    lowercase__					:  Optional[int]=None			,    lowercase__					:  Tuple=None			,    lowercase__					:  Tuple=None			,    lowercase__					:  str="<|endoftext|>"			,    lowercase__					:  List[str]="<|startoftext|>"			,    lowercase__					:  Union[str, Any]="<|endoftext|>"			,    lowercase__					:  Any="<|endoftext|>"			,    **lowercase__					:  Any			,    ):







        '''simple docstring'''



        super().__init__(
            lowercase__			,    lowercase__			,    tokenizer_file=lowercase__			,    unk_token=lowercase__			,    bos_token=lowercase__			,    eos_token=lowercase__			,    pad_token=lowercase__			,    **lowercase__			,    )

        if not isinstance(self.backend_tokenizer.pre_tokenizer			,    pre_tokenizers.Sequence):
               raise ValueError(
                   'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
                   ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
                   ' to be compatible with this version.The easiest way to do so is'
                   ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
                   ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
                   ' transformers.')

        self._wrap_decode_method_backend_tokenizer()




 def 		__snake_case						(			self					:  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.backend_tokenizer.decode

        def new_decode_method(*lowercase__					:  str			,    **lowercase__					:  int):
               lowerCAmelCase__     	=					orig_decode_method(*lowercase__			,    **lowercase__)
               lowerCAmelCase__     	=					text.replace(self.backend_tokenizer.model.end_of_word_suffix			,    ' ').strip()
               return text

        lowerCAmelCase__     	=					new_decode_method




 def 		__snake_case						(			self					:  Tuple			,    lowercase__					:  List[int]			,    lowercase__					:  Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.bos_token_id]
        lowerCAmelCase__     	=					[self.eos_token_id]

        if token_ids_a is None:
               return bos_token + token_ids_a + eos_token
        return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token




 def 		__snake_case						(			self					:  Any			,    lowercase__					:  List[int]			,    lowercase__					:  Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.bos_token_id]
        lowerCAmelCase__     	=					[self.eos_token_id]

        if token_ids_a is None:
               return len(bos_token + token_ids_a + eos_token) * [0]
        return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token) * [0]




 def 		__snake_case						(			self					:  Dict			,    lowercase__					:  str			,    lowercase__					:  Optional[str] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					self._tokenizer.model.save(lowercase__			,    name=lowercase__)
        return tuple(lowercase__)
