
'''simple docstring'''





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


UpperCAmelCase    					=   16
UpperCAmelCase    					=   32

def      A (       A_							:					Accelerator    ,							A_							:					DatasetDict    ,							A_							:					List[int]    ,							A_							:					List[int]    ,							A_							:					int = 16       ):
						snake_case       :     int =    AutoTokenizer.from_pretrained('''bert-base-cased'''       )
						snake_case       :     Any =    DatasetDict(
						    {
						        '''train''': dataset['''train'''].select(A_       ),
						        '''validation''': dataset['''train'''].select(A_       ),
						        '''test''': dataset['''validation'''],
						    }       )

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

						def collate_fn(A_							:					Union[str, Any]       ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												snake_case       :     Optional[Any] =    128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		snake_case       :     int =    16
												elif accelerator.mixed_precision != "no":
																		snake_case       :     Tuple =    8
												else:
																		snake_case       :     Any =    None

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

						# Instantiate dataloaders.
						snake_case       :     Optional[int] =    DataLoader(
						    tokenized_datasets['''train''']    ,							shuffle=A_    ,							collate_fn=A_    ,							batch_size=A_       )
						snake_case       :     Any =    DataLoader(
						    tokenized_datasets['''validation''']    ,							shuffle=A_    ,							collate_fn=A_    ,							batch_size=A_       )

						snake_case       :     Optional[int] =    DataLoader(
						    tokenized_datasets['''test''']    ,							shuffle=A_    ,							collate_fn=A_    ,							batch_size=A_       )

						return train_dataloader, eval_dataloader, test_dataloader

def      A (       A_							:					Optional[Any]    ,							A_							:					str       ):
						# New Code #
						snake_case       :     int =    []
						# Download the dataset
						snake_case       :     List[str] =    load_dataset('''glue'''    ,							'''mrpc'''       )
						# Create our splits
						snake_case       :     Dict =    StratifiedKFold(n_splits=int(args.num_folds       )       )
						# Initialize accelerator
						snake_case       :     Optional[int] =    Accelerator(cpu=args.cpu    ,							mixed_precision=args.mixed_precision       )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case       :     List[Any] =    config['''lr''']
						snake_case       :     str =    int(config['''num_epochs''']       )
						snake_case       :     List[Any] =    int(config['''seed''']       )
						snake_case       :     Dict =    int(config['''batch_size''']       )

						snake_case       :     List[str] =    evaluate.load('''glue'''    ,							'''mrpc'''       )

						# If the batch size is too big we use gradient accumulation
						snake_case       :     str =    1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												snake_case       :     Optional[int] =    batch_size // MAX_GPU_BATCH_SIZE
												snake_case       :     Any =    MAX_GPU_BATCH_SIZE

						set_seed(A_       )

						# New Code #
						# Create our folds:
						snake_case       :     Tuple =    kfold.split(np.zeros(datasets['''train'''].num_rows       )    ,							datasets['''train''']['''label''']       )
						snake_case       :     int =    []
						# Iterate over them
						for i, (train_idxs, valid_idxs) in enumerate(A_       ):
												snake_case,					snake_case,					snake_case       :     Optional[Any] =    get_fold_dataloaders(
												    A_    ,							A_    ,							A_    ,							A_    ,							)
												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												snake_case       :     Union[str, Any] =    AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''    ,							return_dict=A_       )

												# 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).
												snake_case       :     List[Any] =    model.to(accelerator.device       )

												# Instantiate optimizer
												snake_case       :     Tuple =    AdamW(params=model.parameters()    ,							lr=A_       )

												# Instantiate scheduler
												snake_case       :     Optional[Any] =    get_linear_schedule_with_warmup(
												    optimizer=A_    ,							num_warmup_steps=100    ,							num_training_steps=(len(A_       ) * 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.
												snake_case,					snake_case,					snake_case,					snake_case,					snake_case       :     Union[str, Any] =    accelerator.prepare(
												    A_    ,							A_    ,							A_    ,							A_    ,							A_       )

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

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

																		snake_case       :     Union[str, Any] =    metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(F"""epoch {epoch}:"""    ,							A_       )

												# New Code #
												# We also run predictions on the test set at the very end
												snake_case       :     Optional[Any] =    []
												for step, batch in enumerate(A_       ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device       )
																		with torch.no_grad():
																								snake_case       :     Any =    model(**A_       )
																		snake_case       :     int =    outputs.logits
																		snake_case,					snake_case       :     Optional[Any] =    accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
																		fold_predictions.append(predictions.cpu()       )
																		if i == 0:
																								# We need all of the test predictions
																								test_references.append(references.cpu()       )
        # Use accelerator.print to print only on the main process.
												test_predictions.append(torch.cat(A_    ,							dim=0       )       )
												# We now need to release all our memory and get rid of the current model, optimizer, etc
												accelerator.free_memory()
						# New Code #
						# Finally we check the accuracy of our folded results:
						snake_case       :     Dict =    torch.cat(A_    ,							dim=0       )
						snake_case       :     Optional[int] =    torch.stack(A_    ,							dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
						snake_case       :     Optional[int] =    metric.compute(predictions=A_    ,							references=A_       )
						accelerator.print('''Average test metrics from all folds:'''    ,							A_       )

def      A (       ):
						snake_case       :     Optional[Any] =    argparse.ArgumentParser(description='''Simple example of training script.'''       )
						parser.add_argument(
						    '''--mixed_precision'''    ,							type=A_    ,							default=A_    ,							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.'''       )
						# New Code #
						parser.add_argument('''--num_folds'''    ,							type=A_    ,							default=3    ,							help='''The number of splits to perform across the dataset'''       )
						snake_case       :     List[Any] =    parser.parse_args()
						snake_case       :     str =    {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(A_    ,							A_       )


if __name__ == "__main__":
						main()



'''simple docstring'''





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


UpperCAmelCase    					=   logging.get_logger(__name__)

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

UpperCAmelCase    					=   {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase    					=   {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = GPTaTokenizer




				def __init__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=None,    SCREAMING_SNAKE_CASE_	:				Any=None,    SCREAMING_SNAKE_CASE_	:				List[str]=None,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Optional[int]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Dict="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=False,    **SCREAMING_SNAKE_CASE_	:				Tuple,    ):
										super().__init__(
										    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    bos_token=SCREAMING_SNAKE_CASE_,    eos_token=SCREAMING_SNAKE_CASE_,    add_prefix_space=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										snake_case       :     Optional[Any] =    kwargs.pop('''add_bos_token''',    SCREAMING_SNAKE_CASE_      )

										snake_case       :     Dict =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get('''add_prefix_space''',    SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
																snake_case       :     List[Any] =    getattr(SCREAMING_SNAKE_CASE_,    pre_tok_state.pop('''type'''      )      )
																snake_case       :     Dict =    add_prefix_space
																snake_case       :     List[Any] =    pre_tok_class(**SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[Any] =    add_prefix_space




				def 	__snake_case							(							self	:				Optional[Any],    *SCREAMING_SNAKE_CASE_	:				Dict,    **SCREAMING_SNAKE_CASE_	:				List[str]      ):
										snake_case       :     str =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )
										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     int =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )

										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										snake_case       :     List[Any] =    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_      )
										return tuple(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				"Conversation"      ):
										snake_case       :     int =    []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,    add_special_tokens=SCREAMING_SNAKE_CASE_      ) + [self.eos_token_id]      )

										if len(SCREAMING_SNAKE_CASE_      ) > self.model_max_length:
																snake_case       :     str =    input_ids[-self.model_max_length :]
										return input_ids

