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
#
########################################################################


SCREAMING_SNAKE_CASE		:     Dict         =	16
SCREAMING_SNAKE_CASE		:     Optional[int]         =	32






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Accelerator		, _SCREAMING_SNAKE_CASE     :				DatasetDict		, _SCREAMING_SNAKE_CASE     :				List[int]		, _SCREAMING_SNAKE_CASE     :				List[int]		, _SCREAMING_SNAKE_CASE     :				int = 16			):
						UpperCamelCase_		:							str  =		AutoTokenizer.from_pretrained("""bert-base-cased"""			)
						UpperCamelCase_		:							int  =		DatasetDict(
						    {
						        """train""": dataset["""train"""].select(_SCREAMING_SNAKE_CASE			),
						        """validation""": dataset["""train"""].select(_SCREAMING_SNAKE_CASE			),
						        """test""": dataset["""validation"""],
						    }			)

						def tokenize_function(_SCREAMING_SNAKE_CASE     :				List[Any]			):
												# max_length=None => use the model max length (it's actually the default)
												UpperCamelCase_		:							int  =		tokenizer(examples["""sentence1"""]		, examples["""sentence2"""]		, truncation=_SCREAMING_SNAKE_CASE		, max_length=_SCREAMING_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():
												UpperCamelCase_		:							Optional[int]  =		datasets.map(
												    _SCREAMING_SNAKE_CASE		, batched=_SCREAMING_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
						UpperCamelCase_		:							str  =		tokenized_datasets.rename_column("""label"""		, """labels"""			)

						def collate_fn(_SCREAMING_SNAKE_CASE     :				str			):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												UpperCamelCase_		:							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":
																		UpperCamelCase_		:							Any  =		16
												elif accelerator.mixed_precision != "no":
																		UpperCamelCase_		:							int  =		8
												else:
																		UpperCamelCase_		:							List[Any]  =		None

												return tokenizer.pad(
												    _SCREAMING_SNAKE_CASE		, padding="""longest"""		, max_length=_SCREAMING_SNAKE_CASE		, pad_to_multiple_of=_SCREAMING_SNAKE_CASE		, return_tensors="""pt"""		, )

						# Instantiate dataloaders.
						UpperCamelCase_		:							List[str]  =		DataLoader(
						    tokenized_datasets["""train"""]		, shuffle=_SCREAMING_SNAKE_CASE		, collate_fn=_SCREAMING_SNAKE_CASE		, batch_size=_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Union[str, Any]  =		DataLoader(
						    tokenized_datasets["""validation"""]		, shuffle=_SCREAMING_SNAKE_CASE		, collate_fn=_SCREAMING_SNAKE_CASE		, batch_size=_SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							Optional[int]  =		DataLoader(
						    tokenized_datasets["""test"""]		, shuffle=_SCREAMING_SNAKE_CASE		, collate_fn=_SCREAMING_SNAKE_CASE		, batch_size=_SCREAMING_SNAKE_CASE			)

						return train_dataloader, eval_dataloader, test_dataloader






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

						UpperCamelCase_		:							int  =		evaluate.load("""glue"""		, """mrpc"""			)

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

						set_seed(_SCREAMING_SNAKE_CASE			)

						# New Code #
						# Create our folds:
						UpperCamelCase_		:							List[str]  =		kfold.split(np.zeros(datasets["""train"""].num_rows			)		, datasets["""train"""]["""label"""]			)
						UpperCamelCase_		:							Dict  =		[]
						# Iterate over them
						for i, (train_idxs, valid_idxs) in enumerate(_SCREAMING_SNAKE_CASE			):
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Any  =		get_fold_dataloaders(
												    _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, )
												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												UpperCamelCase_		:							List[Any]  =		AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""		, return_dict=_SCREAMING_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).
												UpperCamelCase_		:							Any  =		model.to(accelerator.device			)

												# Instantiate optimizer
												UpperCamelCase_		:							str  =		AdamW(params=model.parameters()		, lr=_SCREAMING_SNAKE_CASE			)

												# Instantiate scheduler
												UpperCamelCase_		:							List[str]  =		get_linear_schedule_with_warmup(
												    optimizer=_SCREAMING_SNAKE_CASE		, num_warmup_steps=100		, num_training_steps=(len(_SCREAMING_SNAKE_CASE			) * 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_		:							Any  =		accelerator.prepare(
												    _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

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

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

																		UpperCamelCase_		:							int  =		metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(f'''epoch {epoch}:'''		, _SCREAMING_SNAKE_CASE			)

												# New Code #
												# We also run predictions on the test set at the very end
												UpperCamelCase_		:							Dict  =		[]
												for step, batch in enumerate(_SCREAMING_SNAKE_CASE			):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device			)
																		with torch.no_grad():
																								UpperCamelCase_		:							int  =		model(**_SCREAMING_SNAKE_CASE			)
																		UpperCamelCase_		:							Any  =		outputs.logits
																		UpperCamelCase_,UpperCamelCase_		:							Union[str, 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(_SCREAMING_SNAKE_CASE		, 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:
						UpperCamelCase_		:							Tuple  =		torch.cat(_SCREAMING_SNAKE_CASE		, dim=0			)
						UpperCamelCase_		:							List[Any]  =		torch.stack(_SCREAMING_SNAKE_CASE		, dim=0			).sum(dim=0			).div(int(args.num_folds			)			).argmax(dim=-1			)
						UpperCamelCase_		:							str  =		metric.compute(predictions=_SCREAMING_SNAKE_CASE		, references=_SCREAMING_SNAKE_CASE			)
						accelerator.print("""Average test metrics from all folds:"""		, _SCREAMING_SNAKE_CASE			)






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							Union[str, Any]  =		argparse.ArgumentParser(description="""Simple example of training script."""			)
						parser.add_argument(
						    """--mixed_precision"""		, type=_SCREAMING_SNAKE_CASE		, default=_SCREAMING_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."""			)
						# New Code #
						parser.add_argument("""--num_folds"""		, type=_SCREAMING_SNAKE_CASE		, default=3		, help="""The number of splits to perform across the dataset"""			)
						UpperCamelCase_		:							Dict  =		parser.parse_args()
						UpperCamelCase_		:							List[Any]  =		{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
	main()







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     Dict         =	{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE		:     Dict         =	[
    "small",
    "small-base",
    "medium",
    "medium-base",
    "intermediate",
    "intermediate-base",
    "large",
    "large-base",
    "xlarge",
    "xlarge-base",
]

SCREAMING_SNAKE_CASE		:     int         =	{
    "vocab_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
        "funnel-transformer/small-base": (
            "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
        "funnel-transformer/large-base": (
            "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
        ),
    },
}
SCREAMING_SNAKE_CASE		:     Any         =	{F'''funnel-transformer/{name}''': 512 for name in _model_names}
SCREAMING_SNAKE_CASE		:     Tuple         =	{F'''funnel-transformer/{name}''': {"do_lower_case": True} for name in _model_names}

class        UpperCamelCase						(				__a				):
						a__		:Dict      =			VOCAB_FILES_NAMES
						a__		:Dict      =			PRETRAINED_VOCAB_FILES_MAP
						a__		:Dict      =			PRETRAINED_INIT_CONFIGURATION
						a__		:str      =			FunnelTokenizer
						a__		:List[str]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__		:int      =			2





						def __init__(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=True			,     __UpperCamelCase="<unk>"			,     __UpperCamelCase="<sep>"			,     __UpperCamelCase="<pad>"			,     __UpperCamelCase="<cls>"			,     __UpperCamelCase="<mask>"			,     __UpperCamelCase="<s>"			,     __UpperCamelCase="</s>"			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=None			,     __UpperCamelCase="##"			,     **__UpperCamelCase			,     )				->				List[Any]:
												super().__init__(
												    __UpperCamelCase			,     tokenizer_file=__UpperCamelCase			,     do_lower_case=__UpperCamelCase			,     unk_token=__UpperCamelCase			,     sep_token=__UpperCamelCase			,     pad_token=__UpperCamelCase			,     cls_token=__UpperCamelCase			,     mask_token=__UpperCamelCase			,     bos_token=__UpperCamelCase			,     eos_token=__UpperCamelCase			,     clean_text=__UpperCamelCase			,     tokenize_chinese_chars=__UpperCamelCase			,     strip_accents=__UpperCamelCase			,     wordpieces_prefix=__UpperCamelCase			,     **__UpperCamelCase			,     )

												UpperCamelCase_		:							List[str]  =		json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
												if (
												    normalizer_state.get("""lowercase"""			,     __UpperCamelCase						) != do_lower_case
												    or normalizer_state.get("""strip_accents"""			,     __UpperCamelCase						) != strip_accents
												    or normalizer_state.get("""handle_chinese_chars"""			,     __UpperCamelCase						) != tokenize_chinese_chars
												):
																		UpperCamelCase_		:							List[Any]  =		getattr(__UpperCamelCase			,     normalizer_state.pop("""type"""						)						)
																		UpperCamelCase_		:							Union[str, Any]  =		do_lower_case
																		UpperCamelCase_		:							Tuple  =		strip_accents
																		UpperCamelCase_		:							Dict  =		tokenize_chinese_chars
																		UpperCamelCase_		:							Union[str, Any]  =		normalizer_class(**__UpperCamelCase						)

												UpperCamelCase_		:							Dict  =		do_lower_case





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase=None						)				->				Dict:
												UpperCamelCase_		:							Any  =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																		output += token_ids_a + [self.sep_token_id]

												return output





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				List[int]:
												UpperCamelCase_		:							Optional[Any]  =		[self.sep_token_id]
												UpperCamelCase_		:							Any  =		[self.cls_token_id]
												if token_ids_a is None:
																		return len(cls						) * [self.cls_token_type_id] + len(token_ids_a + sep						) * [0]
												return len(cls						) * [self.cls_token_type_id] + len(token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Tuple[str]:
												UpperCamelCase_		:							int  =		self._tokenizer.model.save(__UpperCamelCase			,     name=__UpperCamelCase						)
												return tuple(__UpperCamelCase						)






