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 the checkpointing capability,
# 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
#
########################################################################

_lowerCamelCase    :      int	 =						16
_lowerCamelCase    :      str	 =						32





def 		__lowerCamelCase							(UpperCAmelCase__   :							Accelerator					,       UpperCAmelCase__   :							int = 1_6     ):
					SCREAMING_SNAKE_CASE				=				AutoTokenizer.from_pretrained("bert-base-cased"     )
					SCREAMING_SNAKE_CASE				=				load_dataset("glue"					,       "mrpc"     )

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

					def collate_fn(UpperCAmelCase__   :							Union[str, Any]     ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										SCREAMING_SNAKE_CASE				=				1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															SCREAMING_SNAKE_CASE				=				1_6
										elif accelerator.mixed_precision != "no":
															SCREAMING_SNAKE_CASE				=				8
										else:
															SCREAMING_SNAKE_CASE				=				None

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

					# Instantiate dataloaders.
					SCREAMING_SNAKE_CASE				=				DataLoader(
					    tokenized_datasets["train"]					,       shuffle=UpperCAmelCase__					,       collate_fn=UpperCAmelCase__					,       batch_size=UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				DataLoader(
					    tokenized_datasets["validation"]					,       shuffle=UpperCAmelCase__					,       collate_fn=UpperCAmelCase__					,       batch_size=UpperCAmelCase__     )

					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

							_lowerCamelCase    :      Dict	 =						mocked_dataloaders  # noqa: F811





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							Optional[Any]     ):
					# For testing only
					if os.environ.get("TESTING_MOCKED_DATALOADERS"					,       UpperCAmelCase__     ) == "1":
										SCREAMING_SNAKE_CASE				=				2
					# Initialize accelerator
					SCREAMING_SNAKE_CASE				=				Accelerator(cpu=args.cpu					,       mixed_precision=args.mixed_precision     )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					SCREAMING_SNAKE_CASE				=				config["lr"]
					SCREAMING_SNAKE_CASE				=				int(config["num_epochs"]     )
					SCREAMING_SNAKE_CASE				=				int(config["seed"]     )
					SCREAMING_SNAKE_CASE				=				int(config["batch_size"]     )

					# New Code #
					# Parse out whether we are saving every epoch or after a certain number of batches
					if hasattr(args.checkpointing_steps					,       "isdigit"     ):
										if args.checkpointing_steps == "epoch":
															SCREAMING_SNAKE_CASE				=				args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															SCREAMING_SNAKE_CASE				=				int(args.checkpointing_steps     )
										else:
															raise ValueError(
															    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."     )
					else:
										SCREAMING_SNAKE_CASE				=				None

					set_seed(UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				get_dataloaders(UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				evaluate.load("glue"					,       "mrpc"     )

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

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

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

					# Instantiate optimizer
					SCREAMING_SNAKE_CASE				=				AdamW(params=model.parameters()					,       lr=UpperCAmelCase__     )

					# Instantiate scheduler
					SCREAMING_SNAKE_CASE				=				get_linear_schedule_with_warmup(
					    optimizer=UpperCAmelCase__					,       num_warmup_steps=1_0_0					,       num_training_steps=(len(UpperCAmelCase__     ) * 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.
					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				accelerator.prepare(
					    UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )

					# New Code #
					# We need to keep track of how many total steps we have iterated over
					SCREAMING_SNAKE_CASE				=				0
					# We also need to keep track of the stating epoch so files are named properly
					SCREAMING_SNAKE_CASE				=				0

					# We need to load the checkpoint back in before training here with `load_state`
					# The total number of epochs is adjusted based on where the state is being loaded from,
					# as we assume continuation of the same training script
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"     )
															accelerator.load_state(args.resume_from_checkpoint     )
															SCREAMING_SNAKE_CASE				=				os.path.basename(args.resume_from_checkpoint     )
										else:
															# Get the most recent checkpoint
															SCREAMING_SNAKE_CASE				=				[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
															dirs.sort(key=os.path.getctime     )
															SCREAMING_SNAKE_CASE				=				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										SCREAMING_SNAKE_CASE				=				os.path.splitext(UpperCAmelCase__     )[0]

										if "epoch" in training_difference:
															SCREAMING_SNAKE_CASE				=				int(training_difference.replace("epoch_"					,       ""     )     ) + 1
															SCREAMING_SNAKE_CASE				=				None
										else:
															SCREAMING_SNAKE_CASE				=				int(training_difference.replace("step_"					,       ""     )     )
															SCREAMING_SNAKE_CASE				=				resume_step // len(UpperCAmelCase__     )
															resume_step -= starting_epoch * len(UpperCAmelCase__     )

    # Now we train the model
					for epoch in range(UpperCAmelCase__					,       UpperCAmelCase__     ):
										model.train()
										# New Code #
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															SCREAMING_SNAKE_CASE				=				accelerator.skip_first_batches(UpperCAmelCase__					,       UpperCAmelCase__     )
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															SCREAMING_SNAKE_CASE				=				train_dataloader
										for step, batch in enumerate(UpperCAmelCase__     ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device     )
															SCREAMING_SNAKE_CASE				=				model(**UpperCAmelCase__     )
															SCREAMING_SNAKE_CASE				=				outputs.loss
															SCREAMING_SNAKE_CASE				=				loss / gradient_accumulation_steps
															accelerator.backward(UpperCAmelCase__     )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
															# New Code #
															overall_step += 1

															# New Code #
															# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
															# These are saved to folders named `step_{overall_step}`
															# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
															# If mixed precision was used, will also save a "scalar.bin" file
															if isinstance(UpperCAmelCase__					,       UpperCAmelCase__     ):
																				SCREAMING_SNAKE_CASE				=				F"step_{overall_step}"
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														SCREAMING_SNAKE_CASE				=				os.path.join(args.output_dir					,       UpperCAmelCase__     )
																									accelerator.save_state(UpperCAmelCase__     )

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

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

										# New Code #
										# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
										# These are saved to folders named `epoch_{epoch}`
										# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
										# If mixed precision was used, will also save a "scalar.bin" file
										if checkpointing_steps == "epoch":
															SCREAMING_SNAKE_CASE				=				F"epoch_{epoch}"
															if args.output_dir is not None:
																				SCREAMING_SNAKE_CASE				=				os.path.join(args.output_dir					,       UpperCAmelCase__     )
															accelerator.save_state(UpperCAmelCase__     )





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				argparse.ArgumentParser(description="Simple example of training script."     )
					parser.add_argument(
					    "--mixed_precision"					,       type=UpperCAmelCase__					,       default=UpperCAmelCase__					,       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."     )
					parser.add_argument(
					    "--checkpointing_steps"					,       type=UpperCAmelCase__					,       default=UpperCAmelCase__					,       help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."					,       )
					parser.add_argument(
					    "--output_dir"					,       type=UpperCAmelCase__					,       default="."					,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."					,       )
					parser.add_argument(
					    "--resume_from_checkpoint"					,       type=UpperCAmelCase__					,       default=UpperCAmelCase__					,       help="If the training should continue from a checkpoint folder."					,       )
					SCREAMING_SNAKE_CASE				=				parser.parse_args()
					SCREAMING_SNAKE_CASE				=				{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
					training_function(UpperCAmelCase__					,       UpperCAmelCase__     )


if __name__ == "__main__":
							main()





from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
							import pyximport

							pyximport.install(setup_args={'''include_dirs''': np.get_include()})
							from . import algos_graphormer  # noqa E402





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							int = 5_1_2     ):
					SCREAMING_SNAKE_CASE				=				x.shape[1] if len(x.shape     ) > 1 else 1
					SCREAMING_SNAKE_CASE				=				1 + np.arange(0					,       feature_num * offset					,       UpperCAmelCase__					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				x + feature_offset
					return x





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							Optional[Any]=True     ):
					requires_backends(UpperCAmelCase__					,       ["cython"]     )

					if keep_features and "edge_attr" in item.keys():  # edge_attr
										SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_attr"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				np.ones((len(item["edge_index"][0]     ), 1)					,       dtype=np.intaa     )  # same embedding for all

					if keep_features and "node_feat" in item.keys():  # input_nodes
										SCREAMING_SNAKE_CASE				=				np.asarray(item["node_feat"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				np.ones((item["num_nodes"], 1)					,       dtype=np.intaa     )  # same embedding for all

					SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_index"]					,       dtype=np.intaa     )

					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1
					SCREAMING_SNAKE_CASE				=				item["num_nodes"]

					if len(edge_attr.shape     ) == 1:
										SCREAMING_SNAKE_CASE				=				edge_attr[:, None]
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1

					# node adj matrix [num_nodes, num_nodes] bool
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes]					,       dtype=UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				True

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				algos_graphormer.floyd_warshall(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.amax(UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				algos_graphormer.gen_edge_input(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes + 1, num_nodes + 1]					,       dtype=np.single     )  # with graph token

					# combine
					SCREAMING_SNAKE_CASE				=				input_nodes + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				attn_bias
					SCREAMING_SNAKE_CASE				=				attn_edge_type
					SCREAMING_SNAKE_CASE				=				shortest_path_result.astype(np.intaa     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				np.sum(UpperCAmelCase__					,       axis=1     ).reshape(-1     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				item["in_degree"]  # for undirected graph
					SCREAMING_SNAKE_CASE				=				input_edges + 1  # we shift all indices by one for padding
					if "labels" not in item:
										SCREAMING_SNAKE_CASE				=				item["y"]

					return item


class   lowercase			:




							def __init__(  self      :    int		,							_UpperCamelCase      :    Any=20		,							_UpperCamelCase      :    List[str]=False					)    -> Union[str, Any]:



												'''simple docstring'''

												if not is_cython_available():
																	raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

												SCREAMING_SNAKE_CASE				=				spatial_pos_max
												SCREAMING_SNAKE_CASE				=				on_the_fly_processing





							def __call__(  self      :    Union[str, Any]		,							_UpperCamelCase      :    List[dict]					)    -> Dict[str, Any]:



												'''simple docstring'''

												if self.on_the_fly_processing:
																	SCREAMING_SNAKE_CASE				=				[preprocess_item(_UpperCamelCase					) for i in features]

												if not isinstance(features[0]		,							_UpperCamelCase					):
																	SCREAMING_SNAKE_CASE				=				[vars(_UpperCamelCase					) for f in features]
												SCREAMING_SNAKE_CASE				=				{}

												SCREAMING_SNAKE_CASE				=				max(len(i["input_nodes"]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_nodes"][0]					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["attn_edge_type"][0][0]					)
												SCREAMING_SNAKE_CASE				=				max(len(i["input_edges"][0][0]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_edges"][0][0][0]					)
												SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							max_node_num + 1		,							max_node_num + 1		,							dtype=torch.float					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(
												    _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)

												for ix, f in enumerate(_UpperCamelCase					):
																	for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																						SCREAMING_SNAKE_CASE				=				torch.tensor(f[k]					)

																	if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]					) > 0:
																						SCREAMING_SNAKE_CASE				=				float("-inf"					)

																	SCREAMING_SNAKE_CASE				=				f["attn_bias"]
																	SCREAMING_SNAKE_CASE				=				f[
																	    "attn_edge_type"
																	]
																	SCREAMING_SNAKE_CASE				=				f["spatial_pos"]
																	SCREAMING_SNAKE_CASE				=				f["in_degree"]
																	SCREAMING_SNAKE_CASE				=				f["input_nodes"]
																	SCREAMING_SNAKE_CASE				=				f["input_edges"]

												SCREAMING_SNAKE_CASE				=				batch["in_degree"]

												SCREAMING_SNAKE_CASE				=				features[0]["labels"]
												if len(_UpperCamelCase					) == 1:  # one task
																	if isinstance(sample[0]		,							_UpperCamelCase					):  # regression
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
																	else:  # binary classification
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
												else:  # multi task classification, left to float to keep the NaNs
																	SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.stack([i["labels"] for i in features]		,							axis=0					)					)

												return batch




