
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 experiment tracking 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							=       16
__lowerCamelCase							=       32


def 			UpperCamelCase					(    __lowerCamelCase     : Accelerator				,					__lowerCamelCase     : int = 16    ):
       snake_case       :   Any							=      AutoTokenizer.from_pretrained("bert-base-cased"    )
       snake_case       :   Tuple							=      load_dataset("glue"				,					"mrpc"    )

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

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

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

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

       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							=       mocked_dataloaders  # noqa: F811


def 			UpperCamelCase					(    __lowerCamelCase     : int				,					__lowerCamelCase     : Optional[int]    ):





       if os.environ.get("TESTING_MOCKED_DATALOADERS"				,					_lowerCamelCase    ) == "1":
              snake_case       :   List[str]							=      2
       # Initialize Accelerator

       # New Code #
       # We pass in "all" to `log_with` to grab all available trackers in the environment
       # Note: If using a custom `Tracker` class, should be passed in here such as:
       # >>> log_with = ["all", MyCustomTrackerClassInstance()]
       if args.with_tracking:
              snake_case       :   Any							=      Accelerator(
                  cpu=args.cpu				,					mixed_precision=args.mixed_precision				,					log_with="all"				,					project_dir=args.project_dir    )
       else:
              snake_case       :   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
       snake_case       :   Optional[Any]							=      config["lr"]
       snake_case       :   Optional[int]							=      int(config["num_epochs"]    )
       snake_case       :   List[Any]							=      int(config["seed"]    )
       snake_case       :   Any							=      int(config["batch_size"]    )
       set_seed(_lowerCamelCase    )

       snake_case       :   int							=      get_dataloaders(_lowerCamelCase				,					_lowerCamelCase    )
       snake_case       :   Union[str, Any]							=      evaluate.load("glue"				,					"mrpc"    )

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

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       snake_case       :   List[Any]							=      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"				,					return_dict=_lowerCamelCase    )

       # 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       :   Tuple							=      model.to(accelerator.device    )

       # Instantiate optimizer
       snake_case       :   Dict							=      AdamW(params=model.parameters()				,					lr=_lowerCamelCase    )

       # Instantiate scheduler
       snake_case       :   Optional[int]							=      get_linear_schedule_with_warmup(
           optimizer=_lowerCamelCase				,					num_warmup_steps=100				,					num_training_steps=(len(_lowerCamelCase    ) * 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       :   List[str]							=      accelerator.prepare(
           _lowerCamelCase				,					_lowerCamelCase				,					_lowerCamelCase				,					_lowerCamelCase				,					_lowerCamelCase    )

       # New Code #
       # We need to initialize the trackers we use. Overall configurations can also be stored
       if args.with_tracking:
              snake_case       :   List[Any]							=      os.path.split(_lowerCamelCase    )[-1].split("."    )[0]
              accelerator.init_trackers(_lowerCamelCase				,					_lowerCamelCase    )

       # Now we train the model
       for epoch in range(_lowerCamelCase    ):
              model.train()
              # New Code #
              # For our tracking example, we will log the total loss of each epoch
              if args.with_tracking:
                     snake_case       :   Tuple							=      0
              for step, batch in enumerate(_lowerCamelCase    ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device    )
                     snake_case       :   List[Any]							=      model(**_lowerCamelCase    )
                     snake_case       :   Optional[int]							=      outputs.loss
                     # New Code #
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     snake_case       :   int							=      loss / gradient_accumulation_steps
                     accelerator.backward(_lowerCamelCase    )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

              snake_case       :   Tuple							=      metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"""epoch {epoch}:"""				,					_lowerCamelCase    )

              # New Code #
              # To actually log, we call `Accelerator.log`
              # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
              if args.with_tracking:
                     accelerator.log(
                         {
                             "accuracy": eval_metric["accuracy"],
                             "f1": eval_metric["f1"],
                             "train_loss": total_loss.item() / len(_lowerCamelCase    ),
                             "epoch": epoch,
                         }				,					step=_lowerCamelCase				,					)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
       if args.with_tracking:
              accelerator.end_training()


def 			UpperCamelCase					(    ):
       snake_case       :   List[str]							=      argparse.ArgumentParser(description="Simple example of training script."    )
       parser.add_argument(
           "--mixed_precision"				,					type=_lowerCamelCase				,					default=_lowerCamelCase				,					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(
           "--with_tracking"				,					action="store_true"				,					help="Whether to load in all available experiment trackers from the environment and use them for logging."				,					)
       parser.add_argument(
           "--project_dir"				,					type=_lowerCamelCase				,					default="logs"				,					help="Location on where to store experiment tracking logs` and relevent project information"				,					)
       snake_case       :   Any							=      parser.parse_args()
       snake_case       :   Union[str, Any]							=      {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(_lowerCamelCase				,					_lowerCamelCase    )


if __name__ == "__main__":
   main()







class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:Union[str, Any]						)		-> None:
								__UpperCamelCase	:   dict[str, TrieNode]			   =					{}  # Mapping from char to TrieNode
								__UpperCamelCase	:   List[str]			   =					False







	def    _lowerCamelCase				(					self		:Any     ,   a		:list[str]						)		-> None:
								for word in words:
															self.insert(a						)







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str						)		-> None:
								__UpperCamelCase	:   Dict			   =					self
								for char in word:
															if char not in curr.nodes:
																						__UpperCamelCase	:   List[Any]			   =					TrieNode()
															__UpperCamelCase	:   List[Any]			   =					curr.nodes[char]
								__UpperCamelCase	:   Union[str, Any]			   =					True







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str						)		-> bool:
								__UpperCamelCase	:   Union[str, Any]			   =					self
								for char in word:
															if char not in curr.nodes:
																						return False
															__UpperCamelCase	:   Union[str, Any]			   =					curr.nodes[char]
								return curr.is_leaf







	def    _lowerCamelCase				(					self		:Any     ,   a		:str						)		-> None:

								def _delete(a		:TrieNode     ,   a		:str     ,   a		:int						) -> bool:
															if index == len(a						):
																						# If word does not exist
																						if not curr.is_leaf:
																													return False
																						__UpperCamelCase	:   str			   =					False
																						return len(curr.nodes						) == 0
															__UpperCamelCase	:   List[Any]			   =					word[index]
															__UpperCamelCase	:   Optional[int]			   =					curr.nodes.get(a						)
															# If char not in current trie node
															if not char_node:
																						return False
															# Flag to check if node can be deleted
															__UpperCamelCase	:   int			   =					_delete(a     ,   a     ,   index + 1						)
															if delete_curr:
																						del curr.nodes[char]
																						return len(curr.nodes						) == 0
															return delete_curr

								_delete(self     ,   a     ,   0						)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			TrieNode						,  _lowerCamelCase :			str)					->    None:






							'''simple docstring'''





							if node.is_leaf:
														print(_lowerCamelCase						,  end=" ")

							for key, value in node.nodes.items():
														print_words(_lowerCamelCase						,  word + key)




def 	_SCREAMING_SNAKE_CASE					(		)					->    bool:






							'''simple docstring'''





							__UpperCamelCase	:   int			   =					"banana bananas bandana band apple all beast".split()
							__UpperCamelCase	:   Union[str, Any]			   =					TrieNode()
							root.insert_many(_lowerCamelCase)
							# print_words(root, "")
							assert all(root.find(_lowerCamelCase) for word in words)
							assert root.find("banana")
							assert not root.find("bandanas")
							assert not root.find("apps")
							assert root.find("apple")
							assert root.find("all")
							root.delete("all")
							assert not root.find("all")
							root.delete("banana")
							assert not root.find("banana")
							assert root.find("bananas")
							return True




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			bool)					->    None:






							'''simple docstring'''





							print(str(_lowerCamelCase)						,  "works!" if passes else "doesn't work :(")




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							assert test_trie()




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							print_results("Testing trie functionality"						,  test_trie())


if __name__ == "__main__":
		main()