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


_snake_case								=  16
_snake_case								=  32




def      _UpperCamelCase      (	snake_case__,			snake_case__,			snake_case__,			snake_case__,			snake_case__ = 16							)  ->      Tuple:
							__UpperCAmelCase  :	Union[str, Any]   			=      AutoTokenizer.from_pretrained("bert-base-cased"							)
							__UpperCAmelCase  :	Union[str, Any]   			=      DatasetDict(
							    {
							        "train": dataset["train"].select(snake_case__							),
							        "validation": dataset["train"].select(snake_case__							),
							        "test": dataset["validation"],
							    }							)

							def tokenize_function(snake_case__							):
														# max_length=None => use the model max length (it's actually the default)
														__UpperCAmelCase  :	Dict   			=      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():
														__UpperCAmelCase  :	Dict   			=      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
							__UpperCAmelCase  :	List[str]   			=      tokenized_datasets.rename_column("label",			"labels"							)

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

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

							# Instantiate dataloaders.
							__UpperCAmelCase  :	Tuple   			=      DataLoader(
							    tokenized_datasets["train"],			shuffle=snake_case__,			collate_fn=snake_case__,			batch_size=snake_case__							)
							__UpperCAmelCase  :	int   			=      DataLoader(
							    tokenized_datasets["validation"],			shuffle=snake_case__,			collate_fn=snake_case__,			batch_size=snake_case__							)

							__UpperCAmelCase  :	Dict   			=      DataLoader(
							    tokenized_datasets["test"],			shuffle=snake_case__,			collate_fn=snake_case__,			batch_size=snake_case__							)

							return train_dataloader, eval_dataloader, test_dataloader




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

							__UpperCAmelCase  :	Dict   			=      evaluate.load("glue",			"mrpc"							)

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

							set_seed(snake_case__							)

							# New Code #
							# Create our folds:
							__UpperCAmelCase  :	int   			=      kfold.split(np.zeros(datasets["train"].num_rows							),			datasets["train"]["label"]							)
							__UpperCAmelCase  :	Optional[Any]   			=      []
							# Iterate over them
							for i, (train_idxs, valid_idxs) in enumerate(snake_case__							):
														__UpperCAmelCase			,		__UpperCAmelCase			,		__UpperCAmelCase  :	List[str]   			=      get_fold_dataloaders(
														    snake_case__,			snake_case__,			snake_case__,			snake_case__,			)
														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														__UpperCAmelCase  :	Union[str, Any]   			=      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).
														__UpperCAmelCase  :	Any   			=      model.to(accelerator.device							)

														# Instantiate optimizer
														__UpperCAmelCase  :	str   			=      AdamW(params=model.parameters(),			lr=snake_case__							)

														# Instantiate scheduler
														__UpperCAmelCase  :	List[str]   			=      get_linear_schedule_with_warmup(
														    optimizer=snake_case__,			num_warmup_steps=100,			num_training_steps=(len(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  :	Optional[int]   			=      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__							):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device							)
																												__UpperCAmelCase  :	Union[str, Any]   			=      model(**snake_case__							)
																												__UpperCAmelCase  :	Union[str, Any]   			=      outputs.loss
																												__UpperCAmelCase  :	int   			=      loss / gradient_accumulation_steps
																												accelerator.backward(snake_case__							)
																												if step % gradient_accumulation_steps == 0:
																																			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():
																																			__UpperCAmelCase  :	List[str]   			=      model(**snake_case__							)
																												__UpperCAmelCase  :	List[str]   			=      outputs.logits.argmax(dim=-1							)
																												__UpperCAmelCase			,		__UpperCAmelCase  :	int   			=      accelerator.gather_for_metrics((predictions, batch["labels"])							)
																												metric.add_batch(
																												    predictions=snake_case__,			references=snake_case__,			)

																					__UpperCAmelCase  :	Tuple   			=      metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(f'''epoch {epoch}:''',			snake_case__							)

														# New Code #
														# We also run predictions on the test set at the very end
														__UpperCAmelCase  :	List[str]   			=      []
														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():
																												__UpperCAmelCase  :	Union[str, Any]   			=      model(**snake_case__							)
																					__UpperCAmelCase  :	str   			=      outputs.logits
																					__UpperCAmelCase			,		__UpperCAmelCase  :	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(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  :	Union[str, Any]   			=      torch.cat(snake_case__,			dim=0							)
							__UpperCAmelCase  :	Tuple   			=      torch.stack(snake_case__,			dim=0							).sum(dim=0							).div(int(args.num_folds							)							).argmax(dim=-1							)
							__UpperCAmelCase  :	Any   			=      metric.compute(predictions=snake_case__,			references=snake_case__							)
							accelerator.print("Average test metrics from all folds:",			snake_case__							)




def      _UpperCamelCase      (	)  ->      Dict:
							__UpperCAmelCase  :	Tuple   			=      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."							)
							# New Code #
							parser.add_argument("--num_folds",			type=snake_case__,			default=3,			help="The number of splits to perform across the dataset"							)
							__UpperCAmelCase  :	str   			=      parser.parse_args()
							__UpperCAmelCase  :	List[Any]   			=      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
							training_function(snake_case__,			snake_case__							)


if __name__ == "__main__":
	main()











'''simple docstring'''







import argparse
import os
import re


lowerCAmelCase_					:    Any      	=			'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase_					:    List[str]      	=			re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
lowerCAmelCase_					:    Tuple      	=			re.compile(R'\s*\(\s*"(\S[^"]+)"')






def  _lowerCamelCase				( lowercase						:      Any    ,							lowercase						:      bool = False						)	->	Optional[Any]:
				with open(lowercase    ,							"r"    ,							encoding="utf-8"						) as f:
								_a								= f.read()

				_a								= content.split("\n"						)
				_a								= []
				_a								= 0
				while line_idx < len(lowercase						):
								if _re_intro_mapping.search(lines[line_idx]						) is not None:
												_a								= len(re.search(r"^(\s*)\S"    ,							lines[line_idx]						).groups()[0]						) + 8
												# Start of a new mapping!
												while not lines[line_idx].startswith(" " * indent + "("						):
																new_lines.append(lines[line_idx]						)
																line_idx += 1

												_a								= []
												while lines[line_idx].strip() != "]":
																# Blocks either fit in one line or not
																if lines[line_idx].strip() == "(":
																				_a								= line_idx
																				while not lines[line_idx].startswith(" " * indent + ")"						):
																								line_idx += 1
																				blocks.append("\n".join(lines[start_idx : line_idx + 1]						)						)
																else:
																				blocks.append(lines[line_idx]						)
																line_idx += 1

												# Sort blocks by their identifiers
												_a								= sorted(lowercase    ,							key=lambda lowercase						: _re_identifier.search(lowercase						).groups()[0]						)
												new_lines += blocks
								else:
												new_lines.append(lines[line_idx]						)
												line_idx += 1

				if overwrite:
								with open(lowercase    ,							"w"    ,							encoding="utf-8"						) as f:
												f.write("\n".join(lowercase						)						)
				elif "\n".join(lowercase						) != content:
								return True






def  _lowerCamelCase				( lowercase						:      bool = False						)	->	List[str]:
				_a								= [os.path.join(lowercase    ,							lowercase						) for f in os.listdir(lowercase						) if f.endswith(".py"						)]
				_a								= [sort_auto_mapping(lowercase    ,							overwrite=lowercase						) for fname in fnames]

				if not overwrite and any(lowercase						):
								_a								= [f for f, d in zip(lowercase    ,							lowercase						) if d]
								raise ValueError(
								    F'The following files have auto mappings that need sorting: {", ".join(lowercase						)}. Run `make style` to fix'
								    " this."						)


if __name__ == "__main__":
					lowerCAmelCase_					:    Any      	=			argparse.ArgumentParser()
					parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
					lowerCAmelCase_					:    Optional[int]      	=			parser.parse_args()

					sort_all_auto_mappings(not args.check_only)
