"""simple docstring"""






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


_A        =     1_6
_A        =     3_2




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase = 16	,       __UpperCAmelCase = "bert-base-cased"							)				-> Any:
				SCREAMING_SNAKE_CASE__			=					AutoTokenizer.from_pretrained(_UpperCAmelCase							)
				SCREAMING_SNAKE_CASE__			=					load_dataset("glue"	,       "mrpc"							)

				def tokenize_function(__UpperCAmelCase							):
								# 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
				SCREAMING_SNAKE_CASE__			=					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
				SCREAMING_SNAKE_CASE__			=					tokenized_datasets.rename_column("label"	,       "labels"							)

				def collate_fn(__UpperCAmelCase							):
								# 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.
				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




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase							)				-> List[Any]:
				model.eval()
				SCREAMING_SNAKE_CASE__			=					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():
												SCREAMING_SNAKE_CASE__			=					model(**_UpperCAmelCase							)
								SCREAMING_SNAKE_CASE__			=					outputs.logits.argmax(dim=-1							)
								# It is slightly faster to call this once, than multiple times
								SCREAMING_SNAKE_CASE__			=					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:
																SCREAMING_SNAKE_CASE__			=					predictions[: len(eval_dataloader.dataset							) - samples_seen]
																SCREAMING_SNAKE_CASE__			=					references[: len(eval_dataloader.dataset							) - samples_seen]
												else:
																samples_seen += references.shape[0]
								metric.add_batch(
								    predictions=_UpperCAmelCase	,       references=_UpperCAmelCase	,       )

				SCREAMING_SNAKE_CASE__			=					metric.compute()
				return eval_metric["accuracy"]




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase							)				-> Optional[int]:
				SCREAMING_SNAKE_CASE__			=					Accelerator()

				# 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"]							)
				SCREAMING_SNAKE_CASE__			=					args.model_name_or_path

				set_seed(_UpperCAmelCase							)
				SCREAMING_SNAKE_CASE__			=					get_dataloaders(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase							)

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

				# Instantiate optimizer
				SCREAMING_SNAKE_CASE__			=					(
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				SCREAMING_SNAKE_CASE__			=					optimizer_cls(params=model.parameters()	,       lr=_UpperCAmelCase							)

				if accelerator.state.deepspeed_plugin is not None:
								SCREAMING_SNAKE_CASE__			=					accelerator.state.deepspeed_plugin.deepspeed_config[
								    "gradient_accumulation_steps"
								]
				else:
								SCREAMING_SNAKE_CASE__			=					1
				SCREAMING_SNAKE_CASE__			=					(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
				):
								SCREAMING_SNAKE_CASE__			=					get_linear_schedule_with_warmup(
								    optimizer=_UpperCAmelCase	,       num_warmup_steps=0	,       num_training_steps=_UpperCAmelCase	,       )
				else:
								SCREAMING_SNAKE_CASE__			=					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.
				SCREAMING_SNAKE_CASE__			=					accelerator.prepare(
				    _UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase							)

				# 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
				SCREAMING_SNAKE_CASE__			=					evaluate.load("glue"	,       "mrpc"							)
				SCREAMING_SNAKE_CASE__			=					num_epochs

				if args.partial_train_epoch is not None:
								SCREAMING_SNAKE_CASE__			=					args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint							)
								SCREAMING_SNAKE_CASE__			=					args.resume_from_checkpoint.split("epoch_"							)[1]
								SCREAMING_SNAKE_CASE__			=					""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								SCREAMING_SNAKE_CASE__			=					int(_UpperCAmelCase							) + 1
								SCREAMING_SNAKE_CASE__			=					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:
												SCREAMING_SNAKE_CASE__			=					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
				SCREAMING_SNAKE_CASE__			=					{}
				for epoch in range(_UpperCAmelCase	,       _UpperCAmelCase							):
								model.train()
								for step, batch in enumerate(_UpperCAmelCase							):
												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()

												overall_step += 1
								SCREAMING_SNAKE_CASE__			=					F"""epoch_{epoch}"""
								SCREAMING_SNAKE_CASE__			=					os.path.join(args.output_dir	,       _UpperCAmelCase							)
								accelerator.save_state(_UpperCAmelCase							)
								SCREAMING_SNAKE_CASE__			=					evaluation_loop(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase							)
								SCREAMING_SNAKE_CASE__			=					accuracy
								SCREAMING_SNAKE_CASE__			=					lr_scheduler.get_lr()[0]
								SCREAMING_SNAKE_CASE__			=					optimizer.param_groups[0]["lr"]
								SCREAMING_SNAKE_CASE__			=					epoch
								SCREAMING_SNAKE_CASE__			=					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     SCREAMING_SNAKE_CASE       (	)				-> List[str]:
				SCREAMING_SNAKE_CASE__			=					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."	,       )
				SCREAMING_SNAKE_CASE__			=					parser.parse_args()
				SCREAMING_SNAKE_CASE__			=					{"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




