




"""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   =       16
A   =       32

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase = 16				,       UpperCamelCase = "bert-base-cased"							)			->			Optional[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: int		=					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE							)
			__UpperCAmelCase	: List[str]		=					load_dataset("glue"				,       "mrpc"							)

			def tokenize_function(UpperCamelCase							):
						# max_length=None => use the model max length (it's actually the default)
						__UpperCAmelCase	: Union[str, Any]		=					tokenizer(examples["sentence1"]				,       examples["sentence2"]				,       truncation=_SCREAMING_SNAKE_CASE				,       max_length=_SCREAMING_SNAKE_CASE							)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			__UpperCAmelCase	: int		=					datasets.map(
			    _SCREAMING_SNAKE_CASE				,       batched=_SCREAMING_SNAKE_CASE				,       remove_columns=["idx", "sentence1", "sentence2"]				,       load_from_cache_file=_SCREAMING_SNAKE_CASE							)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			__UpperCAmelCase	: str		=					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(_SCREAMING_SNAKE_CASE				,       padding="max_length"				,       max_length=128				,       return_tensors="pt"							)
						return tokenizer.pad(_SCREAMING_SNAKE_CASE				,       padding="longest"				,       return_tensors="pt"							)

			# Instantiate dataloaders.
			__UpperCAmelCase	: Optional[int]		=					DataLoader(
			    tokenized_datasets["train"]				,       shuffle=_SCREAMING_SNAKE_CASE				,       collate_fn=_SCREAMING_SNAKE_CASE				,       batch_size=_SCREAMING_SNAKE_CASE							)
			__UpperCAmelCase	: Union[str, Any]		=					DataLoader(
			    tokenized_datasets["validation"]				,       shuffle=_SCREAMING_SNAKE_CASE				,       collate_fn=_SCREAMING_SNAKE_CASE				,       batch_size=_SCREAMING_SNAKE_CASE							)

			return train_dataloader, eval_dataloader

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			model.eval()
			__UpperCAmelCase	: List[str]		=					0
			for step, batch in enumerate(_SCREAMING_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	: str		=					model(**_SCREAMING_SNAKE_CASE							)
						__UpperCAmelCase	: str		=					outputs.logits.argmax(dim=-1							)
						# It is slightly faster to call this once, than multiple times
						__UpperCAmelCase	,     __UpperCAmelCase	: List[str]		=					accelerator.gather(
						    (predictions, batch["labels"])							)  # If we are in a multiprocess environment, the last batch has duplicates
						if accelerator.use_distributed:
									if step == len(_SCREAMING_SNAKE_CASE							) - 1:
												__UpperCAmelCase	: List[str]		=					predictions[: len(eval_dataloader.dataset							) - samples_seen]
												__UpperCAmelCase	: Optional[int]		=					references[: len(eval_dataloader.dataset							) - samples_seen]
									else:
												samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=_SCREAMING_SNAKE_CASE				,       references=_SCREAMING_SNAKE_CASE				,       )

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

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			Union[str, Any]:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					Accelerator()

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

			set_seed(_SCREAMING_SNAKE_CASE							)
			__UpperCAmelCase	,     __UpperCAmelCase	: Tuple		=					get_dataloaders(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE							)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__UpperCAmelCase	: Optional[int]		=					AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE				,       return_dict=_SCREAMING_SNAKE_CASE							)

			# Instantiate optimizer
			__UpperCAmelCase	: List[str]		=					(
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			__UpperCAmelCase	: List[str]		=					optimizer_cls(params=model.parameters()				,       lr=_SCREAMING_SNAKE_CASE							)

			if accelerator.state.deepspeed_plugin is not None:
						__UpperCAmelCase	: Any		=					accelerator.state.deepspeed_plugin.deepspeed_config[
						    "gradient_accumulation_steps"
						]
			else:
						__UpperCAmelCase	: Any		=					1
			__UpperCAmelCase	: Any		=					(len(_SCREAMING_SNAKE_CASE							) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						__UpperCAmelCase	: int		=					get_linear_schedule_with_warmup(
						    optimizer=_SCREAMING_SNAKE_CASE				,       num_warmup_steps=0				,       num_training_steps=_SCREAMING_SNAKE_CASE				,       )
			else:
						__UpperCAmelCase	: Dict		=					DummyScheduler(_SCREAMING_SNAKE_CASE				,       total_num_steps=_SCREAMING_SNAKE_CASE				,       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.
			__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: int		=					accelerator.prepare(
			    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE							)

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

			if args.partial_train_epoch is not None:
						__UpperCAmelCase	: int		=					args.partial_train_epoch

			if args.resume_from_checkpoint:
						accelerator.load_state(args.resume_from_checkpoint							)
						__UpperCAmelCase	: List[str]		=					args.resume_from_checkpoint.split("epoch_"							)[1]
						__UpperCAmelCase	: List[Any]		=					""
						for char in epoch_string:
									if char.isdigit():
												state_epoch_num += char
									else:
												break
						__UpperCAmelCase	: Tuple		=					int(_SCREAMING_SNAKE_CASE							) + 1
						__UpperCAmelCase	: str		=					evaluation_loop(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE							)
						accelerator.print("resumed checkpoint performance:"				,       _SCREAMING_SNAKE_CASE							)
						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:
									__UpperCAmelCase	: Dict		=					json.load(_SCREAMING_SNAKE_CASE							)
									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
			__UpperCAmelCase	: Any		=					{}
			for epoch in range(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE							):
						model.train()
						for step, batch in enumerate(_SCREAMING_SNAKE_CASE							):
									__UpperCAmelCase	: str		=					model(**_SCREAMING_SNAKE_CASE							)
									__UpperCAmelCase	: Any		=					outputs.loss
									__UpperCAmelCase	: Dict		=					loss / gradient_accumulation_steps
									accelerator.backward(_SCREAMING_SNAKE_CASE							)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

						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(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE							)

def 					_UpperCamelCase    (			)			->			Union[str, Any]:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."							)
			parser.add_argument(
			    "--model_name_or_path"				,       type=_SCREAMING_SNAKE_CASE				,       default="bert-base-cased"				,       help="Path to pretrained model or model identifier from huggingface.co/models."				,       required=_SCREAMING_SNAKE_CASE				,       )
			parser.add_argument(
			    "--output_dir"				,       type=_SCREAMING_SNAKE_CASE				,       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=_SCREAMING_SNAKE_CASE				,       default=_SCREAMING_SNAKE_CASE				,       help="If the training should continue from a checkpoint folder."				,       )
			parser.add_argument(
			    "--partial_train_epoch"				,       type=_SCREAMING_SNAKE_CASE				,       default=_SCREAMING_SNAKE_CASE				,       help="If passed, the training will stop after this number of epochs."				,       )
			parser.add_argument(
			    "--num_epochs"				,       type=_SCREAMING_SNAKE_CASE				,       default=2				,       help="Number of train epochs."				,       )
			__UpperCAmelCase	: str		=					parser.parse_args()
			__UpperCAmelCase	: Tuple		=					{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

			training_function(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					main()








"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A   =       logging.get_logger(__name__)

A   =       {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}



class      a__				(	__magic_name__   ):
			lowercase_    =	"table-transformer"
			lowercase_    =	["past_key_values"]
			lowercase_    =	{
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}


			def __init__(      self      :    List[Any]      ,  UpperCamelCase_      :    int=True      ,  UpperCamelCase_      :    Tuple=None      ,  UpperCamelCase_      :    Optional[int]=3      ,  UpperCamelCase_      :    Any=100      ,  UpperCamelCase_      :    Dict=6      ,  UpperCamelCase_      :    List[str]=2048      ,  UpperCamelCase_      :    str=8      ,  UpperCamelCase_      :    Any=6      ,  UpperCamelCase_      :    Dict=2048      ,  UpperCamelCase_      :    str=8      ,  UpperCamelCase_      :    Any=0.0      ,  UpperCamelCase_      :    str=0.0      ,  UpperCamelCase_      :    Union[str, Any]=True      ,  UpperCamelCase_      :    Optional[int]="relu"      ,  UpperCamelCase_      :    str=256      ,  UpperCamelCase_      :    str=0.1      ,  UpperCamelCase_      :    Tuple=0.0      ,  UpperCamelCase_      :    Dict=0.0      ,  UpperCamelCase_      :    Dict=0.02      ,  UpperCamelCase_      :    Optional[Any]=1.0      ,  UpperCamelCase_      :    Optional[int]=False      ,  UpperCamelCase_      :    int="sine"      ,  UpperCamelCase_      :    Optional[Any]="resnet50"      ,  UpperCamelCase_      :    List[str]=True      ,  UpperCamelCase_      :    int=False      ,  UpperCamelCase_      :    Tuple=1      ,  UpperCamelCase_      :    Any=5      ,  UpperCamelCase_      :    Optional[Any]=2      ,  UpperCamelCase_      :    int=1      ,  UpperCamelCase_      :    List[str]=1      ,  UpperCamelCase_      :    List[str]=5      ,  UpperCamelCase_      :    Union[str, Any]=2      ,  UpperCamelCase_      :    Optional[int]=0.1      ,  **UpperCamelCase_      :    int      ,  ):


						"""simple docstring"""




						if backbone_config is not None and use_timm_backbone:
									raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")

						if not use_timm_backbone:
									if backbone_config is None:
												logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
												__UpperCAmelCase	: int		=					CONFIG_MAPPING["resnet"](out_features=["stage4"])
									elif isinstance(UpperCamelCase_      ,  UpperCamelCase_):
												__UpperCAmelCase	: List[Any]		=					backbone_config.get("model_type")
												__UpperCAmelCase	: List[Any]		=					CONFIG_MAPPING[backbone_model_type]
												__UpperCAmelCase	: Optional[int]		=					config_class.from_dict(UpperCamelCase_)
									# set timm attributes to None
									__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: List[Any]		=					None, None, None

						__UpperCAmelCase	: str		=					use_timm_backbone
						__UpperCAmelCase	: int		=					backbone_config
						__UpperCAmelCase	: Dict		=					num_channels
						__UpperCAmelCase	: Optional[Any]		=					num_queries
						__UpperCAmelCase	: List[Any]		=					d_model
						__UpperCAmelCase	: Union[str, Any]		=					encoder_ffn_dim
						__UpperCAmelCase	: int		=					encoder_layers
						__UpperCAmelCase	: Union[str, Any]		=					encoder_attention_heads
						__UpperCAmelCase	: str		=					decoder_ffn_dim
						__UpperCAmelCase	: Any		=					decoder_layers
						__UpperCAmelCase	: Dict		=					decoder_attention_heads
						__UpperCAmelCase	: List[Any]		=					dropout
						__UpperCAmelCase	: int		=					attention_dropout
						__UpperCAmelCase	: str		=					activation_dropout
						__UpperCAmelCase	: Union[str, Any]		=					activation_function
						__UpperCAmelCase	: Optional[int]		=					init_std
						__UpperCAmelCase	: Dict		=					init_xavier_std
						__UpperCAmelCase	: Tuple		=					encoder_layerdrop
						__UpperCAmelCase	: Any		=					decoder_layerdrop
						__UpperCAmelCase	: Tuple		=					encoder_layers
						__UpperCAmelCase	: Optional[int]		=					auxiliary_loss
						__UpperCAmelCase	: List[str]		=					position_embedding_type
						__UpperCAmelCase	: List[str]		=					backbone
						__UpperCAmelCase	: Optional[int]		=					use_pretrained_backbone
						__UpperCAmelCase	: Union[str, Any]		=					dilation
						# Hungarian matcher
						__UpperCAmelCase	: List[Any]		=					class_cost
						__UpperCAmelCase	: Tuple		=					bbox_cost
						__UpperCAmelCase	: int		=					giou_cost
						# Loss coefficients
						__UpperCAmelCase	: Any		=					mask_loss_coefficient
						__UpperCAmelCase	: Optional[Any]		=					dice_loss_coefficient
						__UpperCAmelCase	: Any		=					bbox_loss_coefficient
						__UpperCAmelCase	: int		=					giou_loss_coefficient
						__UpperCAmelCase	: List[Any]		=					eos_coefficient
						super().__init__(is_encoder_decoder=UpperCamelCase_      ,  **UpperCamelCase_)


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return self.encoder_attention_heads


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return self.d_model






class      a__				(	__magic_name__   ):
			lowercase_    =	version.parse("1.11"   )


			@property
			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						        ("pixel_mask", {0: "batch"}),
						    ])


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return 1e-5


			@property
			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						return 12


