


"""simple docstring"""





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
snake_case__		:     Optional[int]    				=				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''')

snake_case__		:     Union[str, Any]    				=				list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
snake_case__		:     Optional[Any]    				=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class 		snake_case_:
     __UpperCamelCase  =					field(
         default='''cifar10'''							,					metadata={'''help''': '''Name of a dataset from the datasets package'''}			)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}							,					)
     __UpperCamelCase  =					field(default=a__							,					metadata={'''help''': '''A folder containing the training data.'''}			)
     __UpperCamelCase  =					field(default=a__							,					metadata={'''help''': '''A folder containing the validation data.'''}			)
     __UpperCamelCase  =					field(
         default=0.15							,					metadata={'''help''': '''Percent to split off of train for validation.'''}			)
     __UpperCamelCase  =					field(default=32							,					metadata={'''help''': '''The size of the square patches to use for masking.'''}			)
     __UpperCamelCase  =					field(
         default=0.6							,					metadata={'''help''': '''Percentage of patches to mask.'''}							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                 '''value if set.'''
             )
         }							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                 '''value if set.'''
             )
         }							,					)






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Tuple       =   {}
           if self.train_dir is not None:
                 lowerCAmelCase					:	List[Any]       =   self.train_dir
           if self.validation_dir is not None:
                 lowerCAmelCase					:	Tuple       =   self.validation_dir
           lowerCAmelCase					:	Dict       =   data_files if data_files else None



@dataclass
class 		snake_case_:
     __UpperCamelCase  =					field(
         default=a__							,					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.'''
             )
         }							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(a__			)}							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
     __UpperCamelCase  =					field(
         default=a__							,					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'''
             )
         }							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}							,					)
     __UpperCamelCase  =					field(
         default='''main'''							,					metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}							,					)
     __UpperCamelCase  =					field(default=a__							,					metadata={'''help''': '''Name or path of preprocessor config.'''}			)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={
             '''help''': (
                 '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                 '''with private models).'''
             )
         }							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={
             '''help''': (
                 '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
             )
         }							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={
             '''help''': (
                 '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
             )
         }							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''Stride to use for the encoder.'''}							,					)



class 		snake_case_:






     def __init__(	self  :					Dict					,				UpperCamelCase_  :					List[Any]=1_9_2					,				UpperCamelCase_  :					Union[str, Any]=3_2					,				UpperCamelCase_  :					str=4					,				UpperCamelCase_  :					Optional[int]=0.6				):
           lowerCAmelCase					:	Optional[int]       =   input_size
           lowerCAmelCase					:	List[Any]       =   mask_patch_size
           lowerCAmelCase					:	Any       =   model_patch_size
           lowerCAmelCase					:	Any       =   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					:	int       =   self.input_size // self.mask_patch_size
           lowerCAmelCase					:	List[Any]       =   self.mask_patch_size // self.model_patch_size

           lowerCAmelCase					:	Tuple       =   self.rand_size**2
           lowerCAmelCase					:	Optional[Any]       =   int(np.ceil(self.token_count * self.mask_ratio				)				)






     def __call__(	self  :					str				):
           lowerCAmelCase					:	Any       =   np.random.permutation(self.token_count				)[: self.mask_count]
           lowerCAmelCase					:	Optional[Any]       =   np.zeros(self.token_count					,				dtype=UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   1

           lowerCAmelCase					:	Optional[int]       =   mask.reshape((self.rand_size, self.rand_size)				)
           lowerCAmelCase					:	List[str]       =   mask.repeat(self.scale					,				axis=0				).repeat(self.scale					,				axis=1				)

           return torch.tensor(mask.flatten()				)



def      _snake_case						(						_snake_case				:			str			):
      lowerCAmelCase					:	int       =   torch.stack([example['''pixel_values'''] for example in examples]			)
      lowerCAmelCase					:	Any       =   torch.stack([example['''mask'''] for example in examples]			)
      return {"pixel_values": pixel_values, "bool_masked_pos": mask}


def      _snake_case						(						):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      lowerCAmelCase					:	int       =   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					:	Union[str, Any]       =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
      else:
            lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	int       =   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''' ,   _snake_case ,   _snake_case			)

      # 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					:	List[str]       =   training_args.get_process_log_level()
      logger.setLevel(_snake_case			)
      transformers.utils.logging.set_verbosity(_snake_case			)
      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					:	str       =   None
      if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
            lowerCAmelCase					:	List[Any]       =   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					:	Optional[Any]       =   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					:	Optional[int]       =   None if '''validation''' in ds.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split ,   _snake_case			) and data_args.train_val_split > 0.0:
            lowerCAmelCase					:	List[str]       =   ds['''train'''].train_test_split(data_args.train_val_split			)
            lowerCAmelCase					:	List[str]       =   split['''train''']
            lowerCAmelCase					:	Dict       =   split['''test''']

      # Create config
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      lowerCAmelCase					:	List[Any]       =   {
          '''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					:	Optional[Any]       =   AutoConfig.from_pretrained(model_args.config_name_or_path ,   **_snake_case			)
      elif model_args.model_name_or_path:
            lowerCAmelCase					:	Union[str, Any]       =   AutoConfig.from_pretrained(model_args.model_name_or_path ,   **_snake_case			)
      else:
            lowerCAmelCase					:	Union[str, Any]       =   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(_snake_case ,   '''decoder_type'''			):
            lowerCAmelCase					:	Tuple       =   '''simmim'''

      # adapt config
      lowerCAmelCase					:	List[str]       =   model_args.image_size if model_args.image_size is not None else config.image_size
      lowerCAmelCase					:	int       =   model_args.patch_size if model_args.patch_size is not None else config.patch_size
      lowerCAmelCase					:	str       =   (
          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					:	Dict       =   AutoImageProcessor.from_pretrained(model_args.image_processor_name ,   **_snake_case			)
      elif model_args.model_name_or_path:
            lowerCAmelCase					:	Optional[int]       =   AutoImageProcessor.from_pretrained(model_args.model_name_or_path ,   **_snake_case			)
      else:
            lowerCAmelCase					:	List[str]       =   {
                conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
            }
            lowerCAmelCase					:	List[Any]       =   IMAGE_PROCESSOR_TYPES[model_args.model_type]()

      # create model
      if model_args.model_name_or_path:
            lowerCAmelCase					:	Any       =   AutoModelForMaskedImageModeling.from_pretrained(
                model_args.model_name_or_path ,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path			) ,   config=_snake_case ,   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					:	int       =   AutoModelForMaskedImageModeling.from_config(_snake_case			)

      if training_args.do_train:
            lowerCAmelCase					:	Union[str, Any]       =   ds['''train'''].column_names
      else:
            lowerCAmelCase					:	str       =   ds['''validation'''].column_names

      if data_args.image_column_name is not None:
            lowerCAmelCase					:	Dict       =   data_args.image_column_name
      elif "image" in column_names:
            lowerCAmelCase					:	Union[str, Any]       =   '''image'''
      elif "img" in column_names:
            lowerCAmelCase					:	Optional[int]       =   '''img'''
      else:
            lowerCAmelCase					:	str       =   column_names[0]

      # transformations as done in original SimMIM paper
      # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
      lowerCAmelCase					:	str       =   Compose(
          [
              Lambda(lambda _snake_case			: 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					:	Union[str, Any]       =   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(_snake_case				:			Union[str, Any]			):
            lowerCAmelCase					:	Union[str, Any]       =   [transforms(_snake_case			) for image in examples[image_column_name]]
            lowerCAmelCase					:	Optional[int]       =   [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					:	Optional[int]       =   ds['''train'''].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
            # Set the training transforms
            ds["train"].set_transform(_snake_case			)

      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					:	Dict       =   (
                      ds['''validation'''].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
                  )
            # Set the validation transforms
            ds["validation"].set_transform(_snake_case			)

      # Initialize our trainer
      lowerCAmelCase					:	Any       =   Trainer(
          model=_snake_case ,   args=_snake_case ,   train_dataset=ds['''train'''] if training_args.do_train else None ,   eval_dataset=ds['''validation'''] if training_args.do_eval else None ,   tokenizer=_snake_case ,   data_collator=_snake_case ,   )

      # Training
      if training_args.do_train:
            lowerCAmelCase					:	List[Any]       =   None
            if training_args.resume_from_checkpoint is not None:
                  lowerCAmelCase					:	Any       =   training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  lowerCAmelCase					:	Dict       =   last_checkpoint
            lowerCAmelCase					:	Union[str, Any]       =   trainer.train(resume_from_checkpoint=_snake_case			)
            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					:	Optional[int]       =   trainer.evaluate()
            trainer.log_metrics('''eval''' ,   _snake_case			)
            trainer.save_metrics('''eval''' ,   _snake_case			)

      # Write model card and (optionally) push to hub
      lowerCAmelCase					:	List[Any]       =   {
          '''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(**_snake_case			)
      else:
            trainer.create_model_card(**_snake_case			)


if __name__ == "__main__":
 main()






"""simple docstring"""





import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     Adafactor,
     AdamW,
     get_constant_schedule,
     get_constant_schedule_with_warmup,
     get_cosine_schedule_with_warmup,
     get_cosine_with_hard_restarts_schedule_with_warmup,
     get_inverse_sqrt_schedule,
     get_linear_schedule_with_warmup,
     get_polynomial_decay_schedule_with_warmup,
 )


def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			Union[str, Any]=10			):
      lowerCAmelCase					:	Dict       =   []
      for _ in range(_snake_case			):
            lrs.append(scheduler.get_lr()[0]			)
            scheduler.step()
      return lrs


def      _snake_case						(						_snake_case				:			Optional[int] ,   _snake_case				:			int=10			):
      lowerCAmelCase					:	Optional[int]       =   []
      for step in range(_snake_case			):
            lrs.append(scheduler.get_lr()[0]			)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        lowerCAmelCase					:	List[str]       =   os.path.join(_snake_case ,   '''schedule.bin'''			)
                        torch.save(scheduler.state_dict() ,   _snake_case			)

                        lowerCAmelCase					:	List[Any]       =   torch.load(_snake_case			)
                        scheduler.load_state_dict(_snake_case			)
      return lrs



@require_torch
class 		snake_case_(       unittest.TestCase			):






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Any				):
           self.assertEqual(len(UpperCamelCase_				)					,				len(UpperCamelCase_				)				)
           for a, b in zip(UpperCamelCase_					,				UpperCamelCase_				):
                 self.assertAlmostEqual(UpperCamelCase_					,				UpperCamelCase_					,				delta=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase					:	Any       =   torch.tensor([0.1, -0.2, -0.1]					,				requires_grad=UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   torch.tensor([0.4, 0.2, -0.5]				)
           lowerCAmelCase					:	List[Any]       =   nn.MSELoss()
           # No warmup, constant schedule, no gradient clipping
           lowerCAmelCase					:	Union[str, Any]       =   AdamW(params=[w]					,				lr=2E-1					,				weight_decay=0.0				)
           for _ in range(1_0_0				):
                 lowerCAmelCase					:	Union[str, Any]       =   criterion(UpperCamelCase_					,				UpperCamelCase_				)
                 loss.backward()
                 optimizer.step()
                 w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
                 w.grad.zero_()
           self.assertListAlmostEqual(w.tolist()					,				[0.4, 0.2, -0.5]					,				tol=1E-2				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Tuple       =   torch.tensor([0.1, -0.2, -0.1]					,				requires_grad=UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   torch.tensor([0.4, 0.2, -0.5]				)
           lowerCAmelCase					:	Optional[int]       =   nn.MSELoss()
           # No warmup, constant schedule, no gradient clipping
           lowerCAmelCase					:	Any       =   Adafactor(
               params=[w]					,				lr=1E-2					,				eps=(1E-30, 1E-3)					,				clip_threshold=1.0					,				decay_rate=-0.8					,				betaa=UpperCamelCase_					,				weight_decay=0.0					,				relative_step=UpperCamelCase_					,				scale_parameter=UpperCamelCase_					,				warmup_init=UpperCamelCase_					,				)
           for _ in range(1_0_0_0				):
                 lowerCAmelCase					:	List[Any]       =   criterion(UpperCamelCase_					,				UpperCamelCase_				)
                 loss.backward()
                 optimizer.step()
                 w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
                 w.grad.zero_()
           self.assertListAlmostEqual(w.tolist()					,				[0.4, 0.2, -0.5]					,				tol=1E-2				)



@require_torch
class 		snake_case_(       unittest.TestCase			):
     __UpperCamelCase  =					nn.Linear(50							,					50			) if is_torch_available() else None
     __UpperCamelCase  =					AdamW(m.parameters()							,					lr=10.0			) if is_torch_available() else None
     __UpperCamelCase  =					10






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Union[str, Any]=None				):
           self.assertEqual(len(UpperCamelCase_				)					,				len(UpperCamelCase_				)				)
           for a, b in zip(UpperCamelCase_					,				UpperCamelCase_				):
                 self.assertAlmostEqual(UpperCamelCase_					,				UpperCamelCase_					,				delta=UpperCamelCase_					,				msg=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Tuple       =   {'''num_warmup_steps''': 2, '''num_training_steps''': 1_0}
           # schedulers doct format
           # function: (sched_args_dict, expected_learning_rates)
           lowerCAmelCase					:	Optional[Any]       =   {
               get_constant_schedule: ({}, [10.0] * self.num_steps),
               get_constant_schedule_with_warmup: (
                   {'''num_warmup_steps''': 4},
                   [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
               ),
               get_linear_schedule_with_warmup: (
                   {**common_kwargs},
                   [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
               ),
               get_cosine_schedule_with_warmup: (
                   {**common_kwargs},
                   [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
               ),
               get_cosine_with_hard_restarts_schedule_with_warmup: (
                   {**common_kwargs, '''num_cycles''': 2},
                   [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
               ),
               get_polynomial_decay_schedule_with_warmup: (
                   {**common_kwargs, '''power''': 2.0, '''lr_end''': 1E-7},
                   [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
               ),
               get_inverse_sqrt_schedule: (
                   {'''num_warmup_steps''': 2},
                   [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
               ),
           }

           for scheduler_func, data in scheds.items():
                 lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   data

                 lowerCAmelCase					:	List[Any]       =   scheduler_func(self.optimizer					,				**UpperCamelCase_				)
                 self.assertEqual(len([scheduler.get_lr()[0]]				)					,				1				)
                 lowerCAmelCase					:	str       =   unwrap_schedule(UpperCamelCase_					,				self.num_steps				)
                 self.assertListAlmostEqual(
                     UpperCamelCase_					,				UpperCamelCase_					,				tol=1E-2					,				msg=F'''failed for {scheduler_func} in normal scheduler'''					,				)

                 lowerCAmelCase					:	Optional[int]       =   scheduler_func(self.optimizer					,				**UpperCamelCase_				)
                 if scheduler_func.__name__ != "get_constant_schedule":
                       LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase_				)  # wrap to test picklability of the schedule
                 lowerCAmelCase					:	List[Any]       =   unwrap_and_save_reload_schedule(UpperCamelCase_					,				self.num_steps				)
                 self.assertListEqual(UpperCamelCase_					,				UpperCamelCase_					,				msg=F'''failed for {scheduler_func} in save and reload'''				)



class 		snake_case_:






     def __init__(	self  :					List[Any]					,				UpperCamelCase_  :					Any				):
           lowerCAmelCase					:	Tuple       =   fn






     def __call__(	self  :					Union[str, Any]					,				*UpperCamelCase_  :					Optional[Any]					,				**UpperCamelCase_  :					List[Any]				):
           return self.fn(*UpperCamelCase_					,				**UpperCamelCase_				)






     @classmethod
     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Optional[int]				):
           lowerCAmelCase					:	Union[str, Any]       =   list(map(self					,				scheduler.lr_lambdas				)				)


