




import argparse
import os

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, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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_   		=     16
a_   		=     32




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_ = 16      )       ->	Any:

    """simple docstring"""


    __UpperCamelCase  		=						AutoTokenizer.from_pretrained('''bert-base-cased'''      )
    __UpperCamelCase  		=						load_dataset('''glue'''					,     '''mrpc'''      )

    def tokenize_function(lowercase_      ):
        # max_length=None => use the model max length (it's actually the default)
        __UpperCamelCase  		=						tokenizer(examples['''sentence1''']					,     examples['''sentence2''']					,     truncation=lowercase_					,     max_length=lowercase_      )
        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():
        __UpperCamelCase  		=						datasets.map(
            lowercase_					,     batched=lowercase_					,     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
    __UpperCamelCase  		=						tokenized_datasets.rename_column('''label'''					,     '''labels'''      )

    def collate_fn(lowercase_      ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __UpperCamelCase  		=						1_28 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            __UpperCamelCase  		=						16
        elif accelerator.mixed_precision != "no":
            __UpperCamelCase  		=						8
        else:
            __UpperCamelCase  		=						None

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

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

    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_   		=     mocked_dataloaders  # noqa: F811




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Any:

    """simple docstring"""


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

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

    # If the batch size is too big we use gradient accumulation
    __UpperCamelCase  		=						1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        __UpperCamelCase  		=						batch_size // MAX_GPU_BATCH_SIZE
        __UpperCamelCase  		=						MAX_GPU_BATCH_SIZE

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

    # 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).
    __UpperCamelCase  		=						model.to(accelerator.device      )

    # Instantiate optimizer
    __UpperCamelCase  		=						AdamW(params=model.parameters()					,     lr=lowercase_      )

    # Instantiate scheduler
    __UpperCamelCase  		=						get_linear_schedule_with_warmup(
        optimizer=lowercase_					,     num_warmup_steps=1_00					,     num_training_steps=(len(lowercase_      ) * num_epochs) // gradient_accumulation_steps					,     )

    # 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.
    __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						accelerator.prepare(
        lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )

    # Now we train the model
    for epoch in range(lowercase_      ):
        model.train()
        for step, batch in enumerate(lowercase_      ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device      )
            __UpperCamelCase  		=						model(**lowercase_      )
            __UpperCamelCase  		=						outputs.loss
            __UpperCamelCase  		=						loss / gradient_accumulation_steps
            accelerator.backward(lowercase_      )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        __UpperCamelCase  		=						0
        for step, batch in enumerate(lowercase_      ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device      )
            with torch.no_grad():
                __UpperCamelCase  		=						model(**lowercase_      )
            __UpperCamelCase  		=						outputs.logits.argmax(dim=-1      )
            __UpperCamelCase		,      __UpperCamelCase  		=						accelerator.gather((predictions, batch['''labels'''])      )
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(lowercase_      ) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    __UpperCamelCase  		=						predictions[: len(eval_dataloader.dataset      ) - samples_seen]
                    __UpperCamelCase  		=						references[: len(eval_dataloader.dataset      ) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=lowercase_					,     references=lowercase_					,     )

        __UpperCamelCase  		=						metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"epoch {epoch}:"					,     lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  )       ->	List[str]:

    """simple docstring"""


    __UpperCamelCase  		=						argparse.ArgumentParser(description='''Simple example of training script.'''      )
    parser.add_argument(
        '''--mixed_precision'''					,     type=lowercase_					,     default=lowercase_					,     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.'''      )
    __UpperCamelCase  		=						parser.parse_args()
    __UpperCamelCase  		=						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(lowercase_					,     lowercase_      )


if __name__ == "__main__":
 main()












import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
a_   		=     logging.get_logger(__name__)




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )
    if "model" in sd.keys():
        __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )['''model''']

    # pop unnecessary weights
    __UpperCamelCase  		=						[
        '''decoder.version''',
        '''decoder.output_projection.weight''',
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(lowercase_      )

    __UpperCamelCase  		=						{
        '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
        '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
        '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
        '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
    }
    for old_key, new_key in keys_to_rename.items():
        if old_key in sd:
            __UpperCamelCase  		=						sd.pop(lowercase_      )

    __UpperCamelCase  		=						list(sd.keys()      )
    for key in keys:
        if ".qkv_proj." in key:
            __UpperCamelCase  		=						sd[key]
            # We split QKV in separate Q,K,V

            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.q_proj.'''      )
            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.k_proj.'''      )
            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.v_proj.'''      )

            __UpperCamelCase  		=						value.shape[0]
            assert depth % 3 == 0
            # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
            # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
            __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						torch.split(lowercase_					,     depth // 3					,     dim=0      )

            __UpperCamelCase  		=						q
            __UpperCamelCase  		=						k
            __UpperCamelCase  		=						v
            del sd[key]

    return sd




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						load_checkpoint(lowercase_      )

    if config is not None:
        __UpperCamelCase  		=						OPTConfig.from_pretrained(lowercase_      )
    else:
        __UpperCamelCase  		=						OPTConfig()

    __UpperCamelCase  		=						OPTModel(lowercase_      ).half().eval()
    model.load_state_dict(lowercase_      )

    # Check results
    Path(lowercase_      ).mkdir(exist_ok=lowercase_      )
    model.save_pretrained(lowercase_      )


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--fairseq_path",
     type=str,
     help=(
         "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
         " https://huggingface.co/models?other=opt_metasq"
     ),
 )
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
 a_   		=     parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)






