






import argparse

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
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCAmelCase__      			=						16
UpperCAmelCase__      			=						32







def     a_   (__A				,       __A = 16      )				->						Optional[int]:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					AutoTokenizer.from_pretrained("bert-base-cased"      )
					__a					:						Optional[Any]      =					load_dataset("glue"				,       "mrpc"      )

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

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

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

					# Instantiate dataloaders.
					__a					:						Optional[int]      =					DataLoader(
					    tokenized_datasets["train"]				,       shuffle=__A				,       collate_fn=__A				,       batch_size=__A				,       drop_last=__A      )
					__a					:						Optional[Any]      =					DataLoader(
					    tokenized_datasets["validation"]				,       shuffle=__A				,       collate_fn=__A				,       batch_size=__A				,       drop_last=(accelerator.mixed_precision == "fp8")				,       )

					return train_dataloader, eval_dataloader







def     a_   (__A				,       __A      )				->						Optional[Any]:



					"""simple docstring"""
					# Initialize accelerator
					__a					:						Union[str, Any]      =					Accelerator(cpu=args.cpu				,       mixed_precision=args.mixed_precision      )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__a					:						List[str]      =					config["lr"]
					__a					:						int      =					int(config["num_epochs"]      )
					__a					:						Optional[Any]      =					int(config["seed"]      )
					__a					:						List[Any]      =					int(config["batch_size"]      )

					__a					:						str      =					evaluate.load("glue"				,       "mrpc"      )

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

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

					# 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).
					__a					:						Tuple      =					model.to(accelerator.device      )
					# Instantiate optimizer
					__a					:						Union[str, Any]      =					AdamW(params=model.parameters()				,       lr=__A      )

					# Instantiate scheduler
					__a					:						Optional[Any]      =					get_linear_schedule_with_warmup(
					    optimizer=__A				,       num_warmup_steps=100				,       num_training_steps=(len(__A      ) * 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.

					__a      ,			__a      ,			__a      ,			__a      ,			__a					:						List[str]      =					accelerator.prepare(
					    __A				,       __A				,       __A				,       __A				,       __A      )

					# Now we train the model
					for epoch in range(__A      ):
										model.train()
										for step, batch in enumerate(__A      ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device      )
															__a					:						Any      =					model(**__A      )
															__a					:						List[Any]      =					outputs.loss
															__a					:						List[str]      =					loss / gradient_accumulation_steps
															accelerator.backward(__A      )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

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

										__a					:						List[str]      =					metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'epoch {epoch}:'				,       __A      )







def     a_   ()				->						List[Any]:



					"""simple docstring"""
					__a					:						Any      =					argparse.ArgumentParser(description="Simple example of training script."      )
					parser.add_argument(
					    "--mixed_precision"				,       type=__A				,       default=__A				,       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."      )
					__a					:						str      =					parser.parse_args()
					__a					:						Tuple      =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(__A				,       __A      )


if __name__ == "__main__":
			main()













from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

UpperCAmelCase__      			=						logging.get_logger(__name__)




@add_end_docstrings(__UpperCamelCase      )
class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						def __init__(self:					int ,					**__UpperCAmelCase:					Optional[Any]   )    ->     str:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase   )
											requires_backends(self ,					"vision"   )
											requires_backends(self ,					"torch"   )

											if self.framework != "pt":
																raise ValueError(f'The {self.__class__} is only available in PyTorch.'   )

											self.check_model_type(__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Optional[int] ,					**__UpperCAmelCase:					Optional[int]   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						Any      =					{}
											__a					:						Union[str, Any]      =					{}
											__a					:						List[str]      =					{}
											# preprocess args
											if "points_per_batch" in kwargs:
																__a					:						Dict      =					kwargs["points_per_batch"]
											if "points_per_crop" in kwargs:
																__a					:						Tuple      =					kwargs["points_per_crop"]
											if "crops_n_layers" in kwargs:
																__a					:						Dict      =					kwargs["crops_n_layers"]
											if "crop_overlap_ratio" in kwargs:
																__a					:						int      =					kwargs["crop_overlap_ratio"]
											if "crop_n_points_downscale_factor" in kwargs:
																__a					:						Optional[int]      =					kwargs["crop_n_points_downscale_factor"]
											# postprocess args
											if "pred_iou_thresh" in kwargs:
																__a					:						int      =					kwargs["pred_iou_thresh"]
											if "stability_score_offset" in kwargs:
																__a					:						Any      =					kwargs["stability_score_offset"]
											if "mask_threshold" in kwargs:
																__a					:						int      =					kwargs["mask_threshold"]
											if "stability_score_thresh" in kwargs:
																__a					:						Any      =					kwargs["stability_score_thresh"]
											if "crops_nms_thresh" in kwargs:
																__a					:						int      =					kwargs["crops_nms_thresh"]
											if "output_rle_mask" in kwargs:
																__a					:						Optional[Any]      =					kwargs["output_rle_mask"]
											if "output_bboxes_mask" in kwargs:
																__a					:						Optional[Any]      =					kwargs["output_bboxes_mask"]
											return preprocess_kwargs, forward_params, postprocess_kwargs



						def __call__(self:					Union[str, Any] ,					__UpperCAmelCase:					str ,					*__UpperCAmelCase:					Optional[Any] ,					__UpperCAmelCase:					str=None ,					__UpperCAmelCase:					Union[str, Any]=None ,					**__UpperCAmelCase:					Optional[int]   )    ->     Tuple:




											'''simple docstring'''



											return super().__call__(__UpperCAmelCase ,					*__UpperCAmelCase ,					num_workers=__UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Optional[Any] ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					int=64 ,					__UpperCAmelCase:					int = 0 ,					__UpperCAmelCase:					float = 512 / 1500 ,					__UpperCAmelCase:					Optional[int] = 32 ,					__UpperCAmelCase:					Optional[int] = 1 ,					)    ->     str:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					load_image(__UpperCAmelCase   )
											__a					:						List[str]      =					self.image_processor.size["longest_edge"]
											__a      ,			__a      ,			__a      ,			__a					:						int      =					self.image_processor.generate_crop_boxes(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					self.image_processor(images=__UpperCAmelCase ,					return_tensors="pt"   )

											with self.device_placement():
																if self.framework == "pt":
																					__a					:						str      =					self.get_inference_context()
																					with inference_context():
																										__a					:						int      =					self._ensure_tensor_on_device(__UpperCAmelCase ,					device=self.device   )
																										__a					:						Union[str, Any]      =					self.model.get_image_embeddings(model_inputs.pop("pixel_values"   )   )
																										__a					:						Optional[Any]      =					image_embeddings

											__a					:						Dict      =					grid_points.shape[1]
											__a					:						Any      =					points_per_batch if points_per_batch is not None else n_points

											if points_per_batch <= 0:
																raise ValueError(
																    "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
																    "To return all points at once, set points_per_batch to None"   )

											for i in range(0 ,					__UpperCAmelCase ,					__UpperCAmelCase   ):
																__a					:						List[str]      =					grid_points[:, i : i + points_per_batch, :, :]
																__a					:						str      =					input_labels[:, i : i + points_per_batch]
																__a					:						str      =					i == n_points - points_per_batch
																yield {
																    "input_points": batched_points,
																    "input_labels": labels,
																    "input_boxes": crop_boxes,
																    "is_last": is_last,
																    **model_inputs,
																}



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					Optional[Any] ,					__UpperCAmelCase:					Any=0.88 ,					__UpperCAmelCase:					Optional[int]=0.95 ,					__UpperCAmelCase:					Optional[int]=0 ,					__UpperCAmelCase:					int=1 ,					)    ->     List[str]:




											'''simple docstring'''



											__a					:						List[Any]      =					model_inputs.pop("input_boxes"   )
											__a					:						Optional[Any]      =					model_inputs.pop("is_last"   )
											__a					:						Dict      =					model_inputs.pop("original_sizes"   ).tolist()
											__a					:						int      =					model_inputs.pop("reshaped_input_sizes"   ).tolist()

											__a					:						Optional[Any]      =					self.model(**__UpperCAmelCase   )

											# post processing happens here in order to avoid CPU GPU copies of ALL the masks
											__a					:						Union[str, Any]      =					model_outputs["pred_masks"]
											__a					:						Optional[Any]      =					self.image_processor.post_process_masks(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					binarize=__UpperCAmelCase   )
											__a					:						Optional[Any]      =					model_outputs["iou_scores"]
											__a      ,			__a      ,			__a					:						Dict      =					self.image_processor.filter_masks(
											    masks[0] ,					iou_scores[0] ,					original_sizes[0] ,					input_boxes[0] ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					)
											return {
											    "masks": masks,
											    "is_last": is_last,
											    "boxes": boxes,
											    "iou_scores": iou_scores,
											}



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					List[str]=False ,					__UpperCAmelCase:					int=False ,					__UpperCAmelCase:					List[str]=0.7 ,					)    ->     Dict:




											'''simple docstring'''



											__a					:						Dict      =					[]
											__a					:						Optional[Any]      =					[]
											__a					:						Optional[Any]      =					[]
											for model_output in model_outputs:
																all_scores.append(model_output.pop("iou_scores"   )   )
																all_masks.extend(model_output.pop("masks"   )   )
																all_boxes.append(model_output.pop("boxes"   )   )

											__a					:						Any      =					torch.cat(__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					torch.cat(__UpperCAmelCase   )
											__a      ,			__a      ,			__a      ,			__a					:						Tuple      =					self.image_processor.post_process_for_mask_generation(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )

											__a					:						Optional[int]      =					defaultdict(__UpperCAmelCase   )
											for output in model_outputs:
																for k, v in output.items():
																					extra[k].append(__UpperCAmelCase   )

											__a					:						int      =					{}
											if output_rle_mask:
																__a					:						Optional[int]      =					rle_mask

											if output_bboxes_mask:
																__a					:						List[str]      =					bounding_boxes

											return {"masks": output_masks, "scores": iou_scores, **optional, **extra}





