




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCamelCase								=					logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')

UpperCamelCase								=					list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCamelCase								=					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 			__lowerCamelCase	:




    """simple docstring"""

    snake_case__ =				field(
        default="cifar10"     ,    metadata={"help": "Name of a dataset from the datasets package"}   )
    snake_case__ =				field(
        default=UpperCamelCase__     ,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
    snake_case__ =				field(
        default=UpperCamelCase__     ,    metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}     ,    )
    snake_case__ =				field(default=UpperCamelCase__     ,    metadata={"help": "A folder containing the training data."}   )
    snake_case__ =				field(default=UpperCamelCase__     ,    metadata={"help": "A folder containing the validation data."}   )
    snake_case__ =				field(
        default=0.1_5     ,    metadata={"help": "Percent to split off of train for validation."}   )
    snake_case__ =				field(default=3_2     ,    metadata={"help": "The size of the square patches to use for masking."}   )
    snake_case__ =				field(
        default=0.6     ,    metadata={"help": "Percentage of patches to mask."}     ,    )
    snake_case__ =				field(
        default=UpperCamelCase__     ,    metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        }     ,    )
    snake_case__ =				field(
        default=UpperCamelCase__     ,    metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        }     ,    )


    def        a				(	self					:					List[Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			{}
         if self.train_dir is not None:
              lowerCAmelCase__          =			self.train_dir
         if self.validation_dir is not None:
              lowerCAmelCase__          =			self.validation_dir
         lowerCAmelCase__          =			data_files if data_files else None






@dataclass
class 			__lowerCamelCase	:




    """simple docstring"""

    snake_case__ =				field(
        default=UpperCamelCase__     ,    metadata={
            "help": (
                "The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
                "checkpoint identifier on the hub. "
                "Don't set if you want to train a model from scratch."
            )
        }     ,    )
    snake_case__ =				field(
        default=UpperCamelCase__     ,    metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(UpperCamelCase__   )}     ,    )
    snake_case__ =				field(
        default=UpperCamelCase__     ,    metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
    snake_case__ =				field(
        default=UpperCamelCase__     ,    metadata={
            "help": (
                "Override some existing default config settings when a model is trained from scratch. Example: "
                "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
            )
        }     ,    )
    snake_case__ =				field(
        default=UpperCamelCase__     ,    metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"}     ,    )
    snake_case__ =				field(
        default="main"     ,    metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}     ,    )
    snake_case__ =				field(default=UpperCamelCase__     ,    metadata={"help": "Name or path of preprocessor config."}   )
    snake_case__ =				field(
        default=UpperCamelCase__     ,    metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        }     ,    )
    snake_case__ =				field(
        default=UpperCamelCase__     ,    metadata={
            "help": (
                "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
            )
        }     ,    )
    snake_case__ =				field(
        default=UpperCamelCase__     ,    metadata={
            "help": (
                "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
            )
        }     ,    )
    snake_case__ =				field(
        default=UpperCamelCase__     ,    metadata={"help": "Stride to use for the encoder."}     ,    )






class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=192       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=32       ,     SCREAMING_SNAKE_CASE__					:					str=4       ,     SCREAMING_SNAKE_CASE__					:					Dict=0.6       )				->	int:
         lowerCAmelCase__          =			input_size
         lowerCAmelCase__          =			mask_patch_size
         lowerCAmelCase__          =			model_patch_size
         lowerCAmelCase__          =			mask_ratio

         if self.input_size % self.mask_patch_size != 0:
              raise ValueError("Input size must be divisible by mask patch size"       )
         if self.mask_patch_size % self.model_patch_size != 0:
              raise ValueError("Mask patch size must be divisible by model patch size"       )

         lowerCAmelCase__          =			self.input_size // self.mask_patch_size
         lowerCAmelCase__          =			self.mask_patch_size // self.model_patch_size

         lowerCAmelCase__          =			self.rand_size**2
         lowerCAmelCase__          =			int(np.ceil(self.token_count * self.mask_ratio       )       )


    def __call__(	self					:					Any       )				->	Union[str, Any]:
         lowerCAmelCase__          =			np.random.permutation(self.token_count       )[: self.mask_count]
         lowerCAmelCase__          =			np.zeros(self.token_count       ,     dtype=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			1

         lowerCAmelCase__          =			mask.reshape((self.rand_size, self.rand_size)       )
         lowerCAmelCase__          =			mask.repeat(self.scale       ,     axis=0       ).repeat(self.scale       ,     axis=1       )

         return torch.tensor(mask.flatten()       )






def 							_A       (						lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     lowerCAmelCase__          =			torch.stack([example["pixel_values"] for example in examples]      )
     lowerCAmelCase__          =			torch.stack([example["mask"] for example in examples]      )
     return {"pixel_values": pixel_values, "bool_masked_pos": mask}



def 							_A       (						):
     """simple docstring"""

     lowerCAmelCase__          =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
     if len(sys.argv      ) == 2 and sys.argv[1].endswith(".json"      ):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
     else:
          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			parser.parse_args_into_dataclasses()

     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
     send_example_telemetry("run_mim"       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )

     # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"       ,    datefmt="%m/%d/%Y %H:%M:%S"       ,    handlers=[logging.StreamHandler(sys.stdout      )]       ,    )

     if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()

     lowerCAmelCase__          =			training_args.get_process_log_level()
     logger.setLevel(lowerCAmelCase_      )
     transformers.utils.logging.set_verbosity(lowerCAmelCase_      )
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()

     # Log on each process the small summary:
     logger.warning(
         F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
         + F'distributed training: {bool(training_args.local_rank != -1      )}, 16-bits training: {training_args.fpaa}'      )
     logger.info(F'Training/evaluation parameters {training_args}'      )

     # Detecting last checkpoint.
     lowerCAmelCase__          =			None
     if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
          lowerCAmelCase__          =			get_last_checkpoint(training_args.output_dir      )
          if last_checkpoint is None and len(os.listdir(training_args.output_dir      )      ) > 0:
               raise ValueError(
                   F'Output directory ({training_args.output_dir}) already exists and is not empty. '
                   "Use --overwrite_output_dir to overcome."      )
          elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
               logger.info(
                   F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                   "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."      )

    # Initialize our dataset.
     lowerCAmelCase__          =			load_dataset(
         data_args.dataset_name       ,    data_args.dataset_config_name       ,    data_files=data_args.data_files       ,    cache_dir=model_args.cache_dir       ,    use_auth_token=True if model_args.use_auth_token else None       ,    )

     # If we don't have a validation split, split off a percentage of train as validation.
     lowerCAmelCase__          =			None if "validation" in ds.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split       ,    lowerCAmelCase_      ) and data_args.train_val_split > 0.0:
          lowerCAmelCase__          =			ds["train"].train_test_split(data_args.train_val_split      )
          lowerCAmelCase__          =			split["train"]
          lowerCAmelCase__          =			split["test"]

     # Create config
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     lowerCAmelCase__          =			{
         "cache_dir": model_args.cache_dir,
         "revision": model_args.model_revision,
         "use_auth_token": True if model_args.use_auth_token else None,
     }
     if model_args.config_name_or_path:
          lowerCAmelCase__          =			AutoConfig.from_pretrained(model_args.config_name_or_path       ,    **lowerCAmelCase_      )
     elif model_args.model_name_or_path:
          lowerCAmelCase__          =			AutoConfig.from_pretrained(model_args.model_name_or_path       ,    **lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			CONFIG_MAPPING[model_args.model_type]()
          logger.warning("You are instantiating a new config instance from scratch."      )
          if model_args.config_overrides is not None:
               logger.info(F'Overriding config: {model_args.config_overrides}'      )
               config.update_from_string(model_args.config_overrides      )
               logger.info(F'New config: {config}'      )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
     if hasattr(lowerCAmelCase_       ,    "decoder_type"      ):
          lowerCAmelCase__          =			"simmim"

     # adapt config
     lowerCAmelCase__          =			model_args.image_size if model_args.image_size is not None else config.image_size
     lowerCAmelCase__          =			model_args.patch_size if model_args.patch_size is not None else config.patch_size
     lowerCAmelCase__          =			(
         model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
     )

     config.update(
         {
             "image_size": model_args.image_size,
             "patch_size": model_args.patch_size,
             "encoder_stride": model_args.encoder_stride,
         }      )

     # create image processor
     if model_args.image_processor_name:
          lowerCAmelCase__          =			AutoImageProcessor.from_pretrained(model_args.image_processor_name       ,    **lowerCAmelCase_      )
     elif model_args.model_name_or_path:
          lowerCAmelCase__          =			AutoImageProcessor.from_pretrained(model_args.model_name_or_path       ,    **lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			{
              conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
          }
          lowerCAmelCase__          =			IMAGE_PROCESSOR_TYPES[model_args.model_type]()

     # create model
     if model_args.model_name_or_path:
          lowerCAmelCase__          =			AutoModelForMaskedImageModeling.from_pretrained(
              model_args.model_name_or_path       ,    from_tf=bool(".ckpt" in model_args.model_name_or_path      )       ,    config=lowerCAmelCase_       ,    cache_dir=model_args.cache_dir       ,    revision=model_args.model_revision       ,    use_auth_token=True if model_args.use_auth_token else None       ,    )
     else:
          logger.info("Training new model from scratch"      )
          lowerCAmelCase__          =			AutoModelForMaskedImageModeling.from_config(lowerCAmelCase_      )

     if training_args.do_train:
          lowerCAmelCase__          =			ds["train"].column_names
     else:
          lowerCAmelCase__          =			ds["validation"].column_names

     if data_args.image_column_name is not None:
          lowerCAmelCase__          =			data_args.image_column_name
     elif "image" in column_names:
          lowerCAmelCase__          =			"image"
     elif "img" in column_names:
          lowerCAmelCase__          =			"img"
     else:
          lowerCAmelCase__          =			column_names[0]

     # transformations as done in original SimMIM paper
     # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
     lowerCAmelCase__          =			Compose(
         [
             Lambda(lambda lowerCAmelCase_      : img.convert("RGB"      ) if img.mode != "RGB" else img      ),
             RandomResizedCrop(model_args.image_size       ,    scale=(0.67, 1.0)       ,    ratio=(3.0 / 4.0, 4.0 / 3.0)      ),
             RandomHorizontalFlip(),
             ToTensor(),
             Normalize(mean=image_processor.image_mean       ,    std=image_processor.image_std      ),
         ]      )

     # create mask generator
     lowerCAmelCase__          =			MaskGenerator(
         input_size=model_args.image_size       ,    mask_patch_size=data_args.mask_patch_size       ,    model_patch_size=model_args.patch_size       ,    mask_ratio=data_args.mask_ratio       ,    )

     def preprocess_images(lowerCAmelCase_					: Optional[Any]      ):
          lowerCAmelCase__          =			[transforms(lowerCAmelCase_      ) for image in examples[image_column_name]]
          lowerCAmelCase__          =			[mask_generator() for i in range(len(examples[image_column_name]      )      )]

          return examples

     if training_args.do_train:
          if "train" not in ds:
               raise ValueError("--do_train requires a train dataset"      )
          if data_args.max_train_samples is not None:
               lowerCAmelCase__          =			ds["train"].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
          # Set the training transforms
          ds["train"].set_transform(lowerCAmelCase_      )

     if training_args.do_eval:
          if "validation" not in ds:
               raise ValueError("--do_eval requires a validation dataset"      )
          if data_args.max_eval_samples is not None:
               lowerCAmelCase__          =			(
                   ds["validation"].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
               )
          # Set the validation transforms
          ds["validation"].set_transform(lowerCAmelCase_      )

     # Initialize our trainer
     lowerCAmelCase__          =			Trainer(
         model=lowerCAmelCase_       ,    args=lowerCAmelCase_       ,    train_dataset=ds["train"] if training_args.do_train else None       ,    eval_dataset=ds["validation"] if training_args.do_eval else None       ,    tokenizer=lowerCAmelCase_       ,    data_collator=lowerCAmelCase_       ,    )

     # Training
     if training_args.do_train:
          lowerCAmelCase__          =			None
          if training_args.resume_from_checkpoint is not None:
               lowerCAmelCase__          =			training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               lowerCAmelCase__          =			last_checkpoint
          lowerCAmelCase__          =			trainer.train(resume_from_checkpoint=lowerCAmelCase_      )
          trainer.save_model()
          trainer.log_metrics("train"       ,    train_result.metrics      )
          trainer.save_metrics("train"       ,    train_result.metrics      )
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          lowerCAmelCase__          =			trainer.evaluate()
          trainer.log_metrics("eval"       ,    lowerCAmelCase_      )
          trainer.save_metrics("eval"       ,    lowerCAmelCase_      )

     # Write model card and (optionally) push to hub
     lowerCAmelCase__          =			{
         "finetuned_from": model_args.model_name_or_path,
         "tasks": "masked-image-modeling",
         "dataset": data_args.dataset_name,
         "tags": ["masked-image-modeling"],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**lowerCAmelCase_      )
     else:
          trainer.create_model_card(**lowerCAmelCase_      )


if __name__ == "__main__":
     main()






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
     from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments





@require_torch
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	int:
         for model_result in results.values():
              for batch_size, sequence_length in zip(model_result["bs"]       ,     model_result["ss"]       ):
                   lowerCAmelCase__          =			model_result["result"][batch_size][sequence_length]
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					int       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sgugger/tiny-distilbert-classification"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     only_pretrain_model=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Optional[Any]       )				->	int:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     torchscript=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    @unittest.skipIf(torch_device == "cpu"       ,     "Cant do half precision"       )
    def        a				(	self					:					Dict       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Union[str, Any]       )				->	Tuple:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         # set architectures equal to `None`
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Any       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    @unittest.skipIf(torch_device == "cpu"       ,     "Can't do half precision"       )
    def        a				(	self					:					int       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					Optional[int]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Optional[Any]       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					List[str]       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					Optional[int]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					List[Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         with tempfile.TemporaryDirectory() as tmp_dir:
              lowerCAmelCase__          =			PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     save_to_csv=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     inference_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_time.csv"       )       ,     train_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_mem.csv"       )       ,     inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_mem.csv"       )       ,     train_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_time.csv"       )       ,     env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
              benchmark.run()
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_time.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_time.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_mem.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_mem.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ).exists()       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"

         def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE__					:					List[Any]       ):
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "sequential"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "cumulative"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "current"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "total"       )       )

         with tempfile.TemporaryDirectory() as tmp_dir:
              lowerCAmelCase__          =			PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     log_filename=os.path.join(SCREAMING_SNAKE_CASE__       ,     "log.txt"       )       ,     log_print=SCREAMING_SNAKE_CASE__       ,     trace_memory_line_by_line=SCREAMING_SNAKE_CASE__       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			benchmark.run()
              _check_summary_is_not_empty(result.inference_summary       )
              _check_summary_is_not_empty(result.train_summary       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "log.txt"       )       ).exists()       )
