




'''simple docstring'''




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


a_   :	Tuple							   = logging.get_logger(__name__)

a_   :	str							   = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_   :	Tuple							   = {
    """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"""
        ),
    },
}

a_   :	str							   = {
    """openai/clip-vit-base-patch32""": 77,
}


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Union[str, Any]   		=VOCAB_FILES_NAMES
       lowercase			:					Optional[int]   		=PRETRAINED_VOCAB_FILES_MAP
       lowercase			:					Any   		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase			:					str   		=['input_ids', 'attention_mask']
       lowercase			:					List[Any]   		=CLIPTokenizer






       def __init__(							self,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase="<|endoftext|>",	lowerCAmelCase="<|startoftext|>",	lowerCAmelCase="<|endoftext|>",	lowerCAmelCase="<|endoftext|>",	**lowerCAmelCase,	):



              """simple docstring"""







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

              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   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.backend_tokenizer.decode

              def new_decode_method(*lowerCAmelCase,	**lowerCAmelCase						):
                     lowerCamelCase_							=orig_decode_method(*lowerCAmelCase,	**lowerCAmelCase						)
                     lowerCamelCase_							=text.replace(self.backend_tokenizer.model.end_of_word_suffix,	''' '''						).strip()
                     return text

              lowerCamelCase_							=new_decode_method






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = 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   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = 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   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              lowerCamelCase_							=self._tokenizer.model.save(lowerCAmelCase,	name=lowerCAmelCase						)
              return tuple(lowerCAmelCase						)










'''simple docstring'''




import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

a_   :	Any							   = 32





def   a_							(   __snake_case							:    Accelerator     , __snake_case							:    int = 16     )							->		List[str]:



       """simple docstring"""
       lowerCamelCase_							=AutoTokenizer.from_pretrained('''bert-base-cased'''     )
       lowerCamelCase_							=load_dataset('''glue'''     , '''mrpc'''     )

       def tokenize_function(__snake_case							:    str     ):
              # max_length=None => use the model max length (it's actually the default)
              lowerCamelCase_							=tokenizer(examples['''sentence1''']     , examples['''sentence2''']     , truncation=__snake_case     , max_length=__snake_case     )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              lowerCamelCase_							=datasets.map(
                  __snake_case     , batched=__snake_case     , remove_columns=['''idx''', '''sentence1''', '''sentence2''']     , )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       lowerCamelCase_							=tokenized_datasets.rename_column('''label'''     , '''labels'''     )

       def collate_fn(__snake_case							:    Union[str, Any]     ):
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     lowerCamelCase_							=16
              elif accelerator.mixed_precision != "no":
                     lowerCamelCase_							=8
              else:
                     lowerCamelCase_							=None

              return tokenizer.pad(
                  __snake_case     , padding='''longest'''     , pad_to_multiple_of=__snake_case     , return_tensors='''pt'''     , )

       # Instantiate dataloaders.
       lowerCamelCase_							=DataLoader(
           tokenized_datasets['''train''']     , shuffle=__snake_case     , collate_fn=__snake_case     , batch_size=__snake_case     )
       lowerCamelCase_							=DataLoader(
           tokenized_datasets['''validation''']     , shuffle=__snake_case     , collate_fn=__snake_case     , batch_size=__snake_case     )

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     a_   :	List[Any]							   = mocked_dataloaders  # noqa: F811





def   a_							(   __snake_case							:    Optional[int]     , __snake_case							:    List[str]     )							->		Tuple:



       """simple docstring"""
       # For testing only
       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     , __snake_case     ) == "1":
              lowerCamelCase_							=2
       # Initialize accelerator
       lowerCamelCase_							=Accelerator(cpu=args.cpu     , mixed_precision=args.mixed_precision     )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowerCamelCase_							=config['''lr''']
       lowerCamelCase_							=int(config['''num_epochs''']     )
       lowerCamelCase_							=int(config['''seed''']     )
       lowerCamelCase_							=int(config['''batch_size''']     )

       lowerCamelCase_							=evaluate.load('''glue'''     , '''mrpc'''     )

       # New Code #
       # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
       # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
       # half each time. From this, we can calculate the number of gradient accumulation steps needed
       # and modify the Accelerator object as a result
       @find_executable_batch_size(starting_batch_size=int(__snake_case     )     )
       def inner_training_loop(__snake_case							:    Tuple     ):
              # Since we need to modify the outside accelerator object, we need to bring it
              # to the local scope
              nonlocal accelerator

              # We can calculate the number of gradient accumulation steps based on the current
              # batch size vs the starting batch size
              lowerCamelCase_							=observed_batch_size // batch_size

              # And then set it in the Accelerator directly:
              lowerCamelCase_							=num_gradient_accumulation_steps

              # Next we need to free all of the stored model references in the Accelerator each time
              accelerator.free_memory()

              # And set the seed so our results are reproducable each reset
              set_seed(__snake_case     )

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              lowerCamelCase_							=AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     , return_dict=__snake_case     )

              # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
              # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
              # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
              lowerCamelCase_							=model.to(accelerator.device     )

              # Instantiate optimizer
              lowerCamelCase_							=AdamW(params=model.parameters()     , lr=__snake_case     )
              lowerCamelCase_,   lowerCamelCase_							=get_dataloaders(__snake_case     , __snake_case     )

              # Instantiate scheduler
              lowerCamelCase_							=get_linear_schedule_with_warmup(
                  optimizer=__snake_case     , num_warmup_steps=100     , num_training_steps=(len(__snake_case     ) * num_epochs)     , )

              # Prepare everything
              # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
              # prepare method.
              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=accelerator.prepare(
                  __snake_case     , __snake_case     , __snake_case     , __snake_case     , __snake_case     )

              # Now we train the model
              for epoch in range(__snake_case     ):
                     model.train()
                     for step, batch in enumerate(__snake_case     ):
                            # And perform gradient accumulation
                            with accelerator.accumulate(__snake_case     ):
                                   # We could avoid this line since we set the accelerator with `device_placement=True`.
                                   batch.to(accelerator.device     )
                                   lowerCamelCase_							=model(**__snake_case     )
                                   lowerCamelCase_							=outputs.loss
                                   accelerator.backward(__snake_case     )
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                     model.eval()
                     for step, batch in enumerate(__snake_case     ):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device     )
                            with torch.no_grad():
                                   lowerCamelCase_							=model(**__snake_case     )
                            lowerCamelCase_							=outputs.logits.argmax(dim=-1     )
                            lowerCamelCase_,   lowerCamelCase_							=accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
                            metric.add_batch(
                                predictions=__snake_case     , references=__snake_case     , )

                     lowerCamelCase_							=metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(F'''epoch {epoch}:'''     , __snake_case     )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
       inner_training_loop()





def   a_							(   )							->		Any:



       """simple docstring"""
       lowerCamelCase_							=argparse.ArgumentParser(description='''Simple example of training script.'''     )
       parser.add_argument(
           '''--mixed_precision'''     , type=__snake_case     , default=__snake_case     , choices=['''no''', '''fp16''', '''bf16''', '''fp8''']     , help='''Whether to use mixed precision. Choose'''
           '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
           '''and an Nvidia Ampere GPU.'''     , )
       parser.add_argument('''--cpu'''     , action='''store_true'''     , help='''If passed, will train on the CPU.'''     )
       lowerCamelCase_							=parser.parse_args()
       # New Code #
       # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
       lowerCamelCase_							={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
       training_function(__snake_case     , __snake_case     )


if __name__ == "__main__":
     main()




