





"""simple docstring"""


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, ChunkPipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		import torch

		from transformers.modeling_outputs import BaseModelOutput

		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__magic_name__       :					List[str]						=			logging.get_logger(__name__)






@add_end_docstrings(__SCREAMING_SNAKE_CASE	)
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   **_A    ):






									'''simple docstring'''







									super().__init__(**_A    )

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

									requires_backends(self						,   """vision"""    )
									self.check_model_type(_A    )







			def __call__(      self						,   _A						,   _A = None						,   **_A						,   ):






									'''simple docstring'''







									if "text_queries" in kwargs:
															UpperCamelCase				:		str				   =			kwargs.pop("""text_queries"""    )

									if isinstance(_A						,   (str, Image.Image)    ):
															UpperCamelCase				:		Optional[Any]				   =			{"image": image, "candidate_labels": candidate_labels}
									else:
															UpperCamelCase				:		Any				   =			image
									UpperCamelCase				:		Optional[Any]				   =			super().__call__(_A						,   **_A    )
									return results







			def   _a      (      self						,   **_A    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			{}
									if "threshold" in kwargs:
															UpperCamelCase				:		Dict				   =			kwargs["threshold"]
									if "top_k" in kwargs:
															UpperCamelCase				:		int				   =			kwargs["top_k"]
									return {}, {}, postprocess_params







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			load_image(inputs["""image"""]    )
									UpperCamelCase				:		Tuple				   =			inputs["candidate_labels"]
									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		int				   =			candidate_labels.split(""","""    )

									UpperCamelCase				:		List[Any]				   =			torch.tensor([[image.height, image.width]]						,   dtype=torch.intaa    )
									for i, candidate_label in enumerate(_A    ):
															UpperCamelCase				:		int				   =			self.tokenizer(_A						,   return_tensors=self.framework    )
															UpperCamelCase				:		Optional[Any]				   =			self.image_processor(_A						,   return_tensors=self.framework    )
															yield {
															    "is_last": i == len(_A    ) - 1,
															    "target_size": target_size,
															    "candidate_label": candidate_label,
															    **text_inputs,
															    **image_features,
															}







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			model_inputs.pop("""target_size"""    )
									UpperCamelCase				:		List[str]				   =			model_inputs.pop("""candidate_label"""    )
									UpperCamelCase				:		Tuple				   =			model_inputs.pop("""is_last"""    )

									UpperCamelCase				:		List[Any]				   =			self.model(**_A    )

									UpperCamelCase				:		List[str]				   =			{"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
									return model_outputs







			def   _a      (      self						,   _A						,   _A=0.1						,   _A=None    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			[]
									for model_output in model_outputs:
															UpperCamelCase				:		str				   =			model_output["candidate_label"]
															UpperCamelCase				:		Optional[Any]				   =			BaseModelOutput(_A    )
															UpperCamelCase				:		List[str]				   =			self.image_processor.post_process_object_detection(
															    outputs=_A						,   threshold=_A						,   target_sizes=model_output["""target_size"""]    )[0]

															for index in outputs["scores"].nonzero():
																					UpperCamelCase				:		Any				   =			outputs["scores"][index].item()
																					UpperCamelCase				:		Union[str, Any]				   =			self._get_bounding_box(outputs["""boxes"""][index][0]    )

																					UpperCamelCase				:		Optional[Any]				   =			{"score": score, "label": label, "box": box}
																					results.append(_A    )

									UpperCamelCase				:		int				   =			sorted(_A						,   key=lambda _A    : x["score"]						,   reverse=_A    )
									if top_k:
															UpperCamelCase				:		Optional[Any]				   =			results[:top_k]

									return results







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									if self.framework != "pt":
															raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch."""    )
									UpperCamelCase				:		List[str]				   =			box.int().tolist()
									UpperCamelCase				:		Tuple				   =			{
									    "xmin": xmin,
									    "ymin": ymin,
									    "xmax": xmax,
									    "ymax": ymax,
									}
									return bbox






"""simple docstring"""

import argparse
import gc
import json
import os

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		     =    1_6
_UpperCamelCase		     =    3_2



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						str:
 return int(x / 2**2_0   )






class 			__a					:





       """simple docstring"""


       def __enter__(   self   ):





        """simple docstring"""

        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        lowerCAmelCase__   :				str 			=				torch.cuda.memory_allocated()
        return self






       def __exit__(   self			,					*snake_case   ):





        """simple docstring"""

        gc.collect()
        torch.cuda.empty_cache()
        lowerCAmelCase__   :				List[str] 			=				torch.cuda.memory_allocated()
        lowerCAmelCase__   :				Optional[int] 			=				torch.cuda.max_memory_allocated()
        lowerCAmelCase__   :				List[str] 			=				bamb(self.end - self.begin   )
        lowerCAmelCase__   :				Union[str, Any] 			=				bamb(self.peak - self.begin   )
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__ = 1_6					,					lowercase__ = "bert-base-cased"					,					lowercase__ = 3_2_0					,					lowercase__ = 1_6_0					,					)			->						str:
 lowerCAmelCase__   :				Union[str, Any] 			=				AutoTokenizer.from_pretrained(lowercase__   )
 lowerCAmelCase__   :				Optional[Any] 			=				load_dataset(
     "glue"					,					"mrpc"					,					split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}   )

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

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

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

 def collate_fn(lowercase__   ):
  # 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(lowercase__					,					padding="max_length"					,					max_length=1_2_8					,					return_tensors="pt"   )
  return tokenizer.pad(lowercase__					,					padding="longest"					,					return_tensors="pt"   )

 # Instantiate dataloaders.
 lowerCAmelCase__   :				Tuple 			=				DataLoader(
     tokenized_datasets["train"]					,					shuffle=lowercase__					,					collate_fn=lowercase__					,					batch_size=lowercase__   )
 lowerCAmelCase__   :				Optional[Any] 			=				DataLoader(
     tokenized_datasets["validation"]					,					shuffle=lowercase__					,					collate_fn=lowercase__					,					batch_size=lowercase__   )

 return train_dataloader, eval_dataloader



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Dict:
 # Initialize accelerator
 lowerCAmelCase__   :				Optional[Any] 			=				Accelerator()

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

 set_seed(lowercase__   )
 lowerCAmelCase__			,						lowerCAmelCase__   :				Optional[Any] 			=				get_dataloaders(lowercase__					,					lowercase__					,					lowercase__					,					args.n_train					,					args.n_val   )

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

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

 if accelerator.state.deepspeed_plugin is not None:
  lowerCAmelCase__   :				List[str] 			=				accelerator.state.deepspeed_plugin.deepspeed_config[
      "gradient_accumulation_steps"
  ]
 else:
  lowerCAmelCase__   :				Dict 			=				1
 lowerCAmelCase__   :				List[str] 			=				(len(lowercase__   ) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  lowerCAmelCase__   :				Any 			=				get_linear_schedule_with_warmup(
      optimizer=lowercase__					,					num_warmup_steps=0					,					num_training_steps=lowercase__					,					)
 else:
  lowerCAmelCase__   :				Optional[int] 			=				DummyScheduler(lowercase__					,					total_num_steps=lowercase__					,					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.
 lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Dict 			=				accelerator.prepare(
     lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )

 # We need to keep track of how many total steps we have iterated over
 lowerCAmelCase__   :				str 			=				0
 # We also need to keep track of the stating epoch so files are named properly
 lowerCAmelCase__   :				Tuple 			=				0

 # Now we train the model
 lowerCAmelCase__   :				List[Any] 			=				{}
 for epoch in range(lowercase__					,					lowercase__   ):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(lowercase__   ):
    lowerCAmelCase__   :				Optional[Any] 			=				model(**lowercase__   )
    lowerCAmelCase__   :				Optional[int] 			=				outputs.loss
    lowerCAmelCase__   :				Any 			=				loss / gradient_accumulation_steps
    accelerator.backward(lowercase__   )
    if step % gradient_accumulation_steps == 0:
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()

    overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin   )   )   )
  accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used   )   )
  accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked   )   )
  accelerator.print(
      "Total Peak Memory consumed during the train (max): {}".format(
          tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
  lowerCAmelCase__   :				Optional[int] 			=				tracemalloc.peaked + bamb(tracemalloc.begin   )
  if args.peak_memory_upper_bound is not None:
   assert (
       train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
   ), "Peak memory usage exceeded the upper bound"

 accelerator.wait_for_everyone()
 if accelerator.is_main_process:
  with open(os.path.join(args.output_dir					,					"peak_memory_utilization.json"   )					,					"w"   ) as f:
   json.dump(lowercase__					,					lowercase__   )



def       SCREAMING_SNAKE_CASE							( )			->						Optional[Any]:
 lowerCAmelCase__   :				int 			=				argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."   )
 parser.add_argument(
     "--model_name_or_path"					,					type=lowercase__					,					default="bert-base-cased"					,					help="Path to pretrained model or model identifier from huggingface.co/models."					,					required=lowercase__					,					)
 parser.add_argument(
     "--output_dir"					,					type=lowercase__					,					default="."					,					help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."					,					)
 parser.add_argument(
     "--peak_memory_upper_bound"					,					type=lowercase__					,					default=lowercase__					,					help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."					,					)
 parser.add_argument(
     "--n_train"					,					type=lowercase__					,					default=3_2_0					,					help="Number of training examples to use."					,					)
 parser.add_argument(
     "--n_val"					,					type=lowercase__					,					default=1_6_0					,					help="Number of validation examples to use."					,					)
 parser.add_argument(
     "--num_epochs"					,					type=lowercase__					,					default=1					,					help="Number of train epochs."					,					)
 lowerCAmelCase__   :				Optional[int] 			=				parser.parse_args()
 lowerCAmelCase__   :				Dict 			=				{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
 training_function(lowercase__					,					lowercase__   )


if __name__ == "__main__":
    main()






