
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCamelCase_						:     str	       =							16
UpperCamelCase_						:     Any	       =							32



def 		UpperCamelCase     (       _UpperCAmelCase  :   Accelerator ,			_UpperCAmelCase  :   int = 16 ,			_UpperCAmelCase  :   str = "bert-base-cased"  )   ->			Any:






						'''simple docstring'''

						_lowercase       :	str           =   AutoTokenizer.from_pretrained(_UpperCAmelCase  )
						_lowercase       :	List[str]           =   load_dataset("glue" ,			"mrpc"  )

						def tokenize_function(_UpperCAmelCase  :   Any  ):
												# max_length=None => use the model max length (it's actually the default)
												_lowercase       :	List[Any]           =   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
						_lowercase       :	Optional[Any]           =   datasets.map(
						    _UpperCAmelCase ,			batched=_UpperCAmelCase ,			remove_columns=["idx", "sentence1", "sentence2"] ,			load_from_cache_file=_UpperCAmelCase  )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						_lowercase       :	List[Any]           =   tokenized_datasets.rename_column("label" ,			"labels"  )

						def collate_fn(_UpperCAmelCase  :   List[str]  ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(_UpperCAmelCase ,			padding="max_length" ,			max_length=128 ,			return_tensors="pt"  )
												return tokenizer.pad(_UpperCAmelCase ,			padding="longest" ,			return_tensors="pt"  )

						# Instantiate dataloaders.
						_lowercase       :	str           =   DataLoader(
						    tokenized_datasets["train"] ,			shuffle=_UpperCAmelCase ,			collate_fn=_UpperCAmelCase ,			batch_size=_UpperCAmelCase  )
						_lowercase       :	Dict           =   DataLoader(
						    tokenized_datasets["validation"] ,			shuffle=_UpperCAmelCase ,			collate_fn=_UpperCAmelCase ,			batch_size=_UpperCAmelCase  )

						return train_dataloader, eval_dataloader



def 		UpperCamelCase     (       _UpperCAmelCase  :   int ,			_UpperCAmelCase  :   Union[str, Any] ,			_UpperCAmelCase  :   int ,			_UpperCAmelCase  :   str  )   ->			List[Any]:






						'''simple docstring'''

						model.eval()
						_lowercase       :	Dict           =   0
						for step, batch in enumerate(_UpperCAmelCase  ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device  )
												with torch.no_grad():
																		_lowercase       :	Optional[Any]           =   model(**_UpperCAmelCase  )
												_lowercase       :	List[str]           =   outputs.logits.argmax(dim=-1  )
												# It is slightly faster to call this once, than multiple times
												_lowercase      ,    _lowercase       :	List[Any]           =   accelerator.gather(
												    (predictions, batch["labels"])  )  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																		if step == len(_UpperCAmelCase  ) - 1:
																								_lowercase       :	List[str]           =   predictions[: len(eval_dataloader.dataset  ) - samples_seen]
																								_lowercase       :	Tuple           =   references[: len(eval_dataloader.dataset  ) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=_UpperCAmelCase ,			references=_UpperCAmelCase ,			)

						_lowercase       :	Optional[Any]           =   metric.compute()
						return eval_metric["accuracy"]



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[Any] ,			_UpperCAmelCase  :   Tuple  )   ->			Optional[int]:






						'''simple docstring'''

						_lowercase       :	Any           =   Accelerator()

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_lowercase       :	Optional[Any]           =   config["lr"]
						_lowercase       :	Union[str, Any]           =   int(config["num_epochs"]  )
						_lowercase       :	Optional[int]           =   int(config["seed"]  )
						_lowercase       :	Any           =   int(config["batch_size"]  )
						_lowercase       :	int           =   args.model_name_or_path

						set_seed(_UpperCAmelCase  )
						_lowercase      ,    _lowercase       :	List[str]           =   get_dataloaders(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_lowercase       :	Tuple           =   AutoModelForSequenceClassification.from_pretrained(_UpperCAmelCase ,			return_dict=_UpperCAmelCase  )

						# Instantiate optimizer
						_lowercase       :	Dict           =   (
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						_lowercase       :	Optional[int]           =   optimizer_cls(params=model.parameters() ,			lr=_UpperCAmelCase  )

						if accelerator.state.deepspeed_plugin is not None:
												_lowercase       :	int           =   accelerator.state.deepspeed_plugin.deepspeed_config[
												    "gradient_accumulation_steps"
												]
						else:
												_lowercase       :	str           =   1
						_lowercase       :	Optional[int]           =   (len(_UpperCAmelCase  ) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												_lowercase       :	Union[str, Any]           =   get_linear_schedule_with_warmup(
												    optimizer=_UpperCAmelCase ,			num_warmup_steps=0 ,			num_training_steps=_UpperCAmelCase ,			)
						else:
												_lowercase       :	Union[str, Any]           =   DummyScheduler(_UpperCAmelCase ,			total_num_steps=_UpperCAmelCase ,			warmup_num_steps=0  )

						# 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.
						_lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase       :	int           =   accelerator.prepare(
						    _UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

						# We need to keep track of how many total steps we have iterated over
						_lowercase       :	List[Any]           =   0
						# We also need to keep track of the stating epoch so files are named properly
						_lowercase       :	str           =   0
						_lowercase       :	List[Any]           =   evaluate.load("glue" ,			"mrpc"  )
						_lowercase       :	str           =   num_epochs

						if args.partial_train_epoch is not None:
												_lowercase       :	Dict           =   args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint  )
												_lowercase       :	Tuple           =   args.resume_from_checkpoint.split("epoch_"  )[1]
												_lowercase       :	Union[str, Any]           =   ""
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												_lowercase       :	Dict           =   int(_UpperCAmelCase  ) + 1
												_lowercase       :	str           =   evaluation_loop(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )
												accelerator.print("resumed checkpoint performance:" ,			_UpperCAmelCase  )
												accelerator.print("resumed checkpoint's scheduler's lr:" ,			lr_scheduler.get_lr()[0]  )
												accelerator.print("resumed optimizers's lr:" ,			optimizer.param_groups[0]["lr"]  )
												with open(os.path.join(args.output_dir ,			f"""state_{starting_epoch-1}.json"""  ) ,			"r"  ) as f:
																		_lowercase       :	Tuple           =   json.load(_UpperCAmelCase  )
																		assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["lr"] == lr_scheduler.get_lr()[0]
																		), "Scheduler learning rate mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
																		), "Optimizer learning rate mismatch, loading from checkpoint failed"
																		assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
																		return

    # Now we train the model
						_lowercase       :	int           =   {}
						for epoch in range(_UpperCAmelCase ,			_UpperCAmelCase  ):
												model.train()
												for step, batch in enumerate(_UpperCAmelCase  ):
																		_lowercase       :	List[str]           =   model(**_UpperCAmelCase  )
																		_lowercase       :	Dict           =   outputs.loss
																		_lowercase       :	int           =   loss / gradient_accumulation_steps
																		accelerator.backward(_UpperCAmelCase  )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1
												_lowercase       :	Tuple           =   f"""epoch_{epoch}"""
												_lowercase       :	Tuple           =   os.path.join(args.output_dir ,			_UpperCAmelCase  )
												accelerator.save_state(_UpperCAmelCase  )
												_lowercase       :	str           =   evaluation_loop(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )
												_lowercase       :	List[str]           =   accuracy
												_lowercase       :	Union[str, Any]           =   lr_scheduler.get_lr()[0]
												_lowercase       :	Optional[Any]           =   optimizer.param_groups[0]["lr"]
												_lowercase       :	Any           =   epoch
												_lowercase       :	Dict           =   overall_step
												accelerator.print(f"""epoch {epoch}:""" ,			_UpperCAmelCase  )

												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		with open(os.path.join(args.output_dir ,			f"""state_{epoch}.json"""  ) ,			"w"  ) as f:
																								json.dump(_UpperCAmelCase ,			_UpperCAmelCase  )



def 		UpperCamelCase     (       )   ->			List[str]:






						'''simple docstring'''

						_lowercase       :	Any           =   argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."  )
						parser.add_argument(
						    "--model_name_or_path" ,			type=_UpperCAmelCase ,			default="bert-base-cased" ,			help="Path to pretrained model or model identifier from huggingface.co/models." ,			required=_UpperCAmelCase ,			)
						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." ,			)
						parser.add_argument(
						    "--partial_train_epoch" ,			type=_UpperCAmelCase ,			default=_UpperCAmelCase ,			help="If passed, the training will stop after this number of epochs." ,			)
						parser.add_argument(
						    "--num_epochs" ,			type=_UpperCAmelCase ,			default=2 ,			help="Number of train epochs." ,			)
						_lowercase       :	Optional[int]           =   parser.parse_args()
						_lowercase       :	Dict           =   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

						training_function(_UpperCAmelCase ,			_UpperCAmelCase  )


if __name__ == "__main__":
							main()






from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from ..image_utils import load_image


if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

UpperCamelCase_						:     int	       =							logging.get_logger(__name__)


UpperCamelCase_						:     List[Any]	       =							Dict[str, Any]
UpperCamelCase_						:     Optional[int]	       =							List[Prediction]




@add_end_docstrings(__snake_case       )
class   __lowercase						(						__snake_case       ):






	def __init__(self				: Dict      ,	*snake_case				: List[Any]      ,	**snake_case				: Tuple    ) ->			Any:
							super().__init__(*snake_case      ,	**snake_case    )

							if self.framework == "tf":
													raise ValueError(F"""The {self.__class__} is only available in PyTorch."""    )

							requires_backends(self      ,	"vision"    )
							self.check_model_type(
							    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()    )    )






	def       _a(self				: Optional[int]      ,	**snake_case				: Union[str, Any]    ) ->			Any:
							_lowercase       :	List[Any]           =   {}
							if "threshold" in kwargs:
													_lowercase       :	Optional[Any]           =   kwargs["threshold"]
							return {}, {}, postprocess_kwargs






	def __call__(self				: str      ,	*snake_case				: Dict      ,	**snake_case				: Optional[int]    ) ->			Union[Predictions, List[Prediction]]:
							return super().__call__(*snake_case      ,	**snake_case    )






	def       _a(self				: Tuple      ,	snake_case				: str    ) ->			Dict:
							_lowercase       :	List[str]           =   load_image(snake_case    )
							_lowercase       :	str           =   torch.IntTensor([[image.height, image.width]]    )
							_lowercase       :	int           =   self.image_processor(images=[image]      ,	return_tensors="pt"    )
							if self.tokenizer is not None:
													_lowercase       :	List[Any]           =   self.tokenizer(text=inputs["words"]      ,	boxes=inputs["boxes"]      ,	return_tensors="pt"    )
							_lowercase       :	str           =   target_size
							return inputs






	def       _a(self				: List[Any]      ,	snake_case				: Any    ) ->			str:
							_lowercase       :	Dict           =   model_inputs.pop("target_size"    )
							_lowercase       :	str           =   self.model(**snake_case    )
							_lowercase       :	Union[str, Any]           =   outputs.__class__({"target_size": target_size, **outputs}    )
							if self.tokenizer is not None:
													_lowercase       :	str           =   model_inputs["bbox"]
							return model_outputs






	def       _a(self				: str      ,	snake_case				: Tuple      ,	snake_case				: Dict=0.9    ) ->			List[str]:
							_lowercase       :	Optional[Any]           =   model_outputs["target_size"]
							if self.tokenizer is not None:
													# This is a LayoutLMForTokenClassification variant.
													# The OCR got the boxes and the model classified the words.
													_lowercase      ,    _lowercase       :	str           =   target_size[0].tolist()

													def unnormalize(snake_case				: str    ):
																			return self._get_bounding_box(
																			    torch.Tensor(
																			        [
																			            (width * bbox[0] / 1000),
																			            (height * bbox[1] / 1000),
																			            (width * bbox[2] / 1000),
																			            (height * bbox[3] / 1000),
																			        ]    )    )

													_lowercase      ,    _lowercase       :	Any           =   model_outputs["logits"].squeeze(0    ).softmax(dim=-1    ).max(dim=-1    )
													_lowercase       :	Optional[Any]           =   [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
													_lowercase       :	Dict           =   [unnormalize(snake_case    ) for bbox in model_outputs["bbox"].squeeze(0    )]
													_lowercase       :	Tuple           =   ["score", "label", "box"]
													_lowercase       :	int           =   [dict(zip(snake_case      ,	snake_case    )    ) for vals in zip(scores.tolist()      ,	snake_case      ,	snake_case    ) if vals[0] > threshold]
							else:
													# This is a regular ForObjectDetectionModel
													_lowercase       :	str           =   self.image_processor.post_process_object_detection(snake_case      ,	snake_case      ,	snake_case    )
													_lowercase       :	List[Any]           =   raw_annotations[0]
													_lowercase       :	str           =   raw_annotation["scores"]
													_lowercase       :	Optional[int]           =   raw_annotation["labels"]
													_lowercase       :	Dict           =   raw_annotation["boxes"]

													_lowercase       :	int           =   scores.tolist()
													_lowercase       :	List[str]           =   [self.model.config.idalabel[label.item()] for label in labels]
													_lowercase       :	List[Any]           =   [self._get_bounding_box(snake_case    ) for box in boxes]

													# {"scores": [...], ...} --> [{"score":x, ...}, ...]
													_lowercase       :	Union[str, Any]           =   ["score", "label", "box"]
													_lowercase       :	Tuple           =   [
													    dict(zip(snake_case      ,	snake_case    )    )
													    for vals in zip(raw_annotation["scores"]      ,	raw_annotation["labels"]      ,	raw_annotation["boxes"]    )
													]

							return annotation






	def       _a(self				: int      ,	snake_case				: "torch.Tensor"    ) ->			Dict[str, int]:
							if self.framework != "pt":
													raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."    )
							_lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase       :	Optional[int]           =   box.int().tolist()
							_lowercase       :	Tuple           =   {
							    "xmin": xmin,
							    "ymin": ymin,
							    "xmax": xmax,
							    "ymax": ymax,
							}
							return bbox




