


'''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
__A	=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')

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


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




     def 				snake_case__						(      self):
        UpperCAmelCase__  :					List[Any]        =     {}
        if self.train_dir is not None:
           UpperCAmelCase__  :					List[Any]        =     self.train_dir
        if self.validation_dir is not None:
           UpperCAmelCase__  :					Tuple        =     self.validation_dir
        UpperCAmelCase__  :					Optional[Any]        =     data_files if data_files else None


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


class 		_snake_case					:




     def __init__(      self							,							_lowerCamelCase=192							,							_lowerCamelCase=32							,							_lowerCamelCase=4							,							_lowerCamelCase=0.6):
        UpperCAmelCase__  :					Tuple        =     input_size
        UpperCAmelCase__  :					Any        =     mask_patch_size
        UpperCAmelCase__  :					Optional[int]        =     model_patch_size
        UpperCAmelCase__  :					Tuple        =     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""")

        UpperCAmelCase__  :					Tuple        =     self.input_size // self.mask_patch_size
        UpperCAmelCase__  :					List[str]        =     self.mask_patch_size // self.model_patch_size

        UpperCAmelCase__  :					str        =     self.rand_size**2
        UpperCAmelCase__  :					Union[str, Any]        =     int(np.ceil(self.token_count * self.mask_ratio))




     def __call__(      self):
        UpperCAmelCase__  :					Any        =     np.random.permutation(self.token_count)[: self.mask_count]
        UpperCAmelCase__  :					Dict        =     np.zeros(self.token_count							,							dtype=_lowerCamelCase)
        UpperCAmelCase__  :					Dict        =     1

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

        return torch.tensor(mask.flatten())







def     _UpperCamelCase      (		UpperCamelCase__					):
   UpperCAmelCase__  :					List[Any]        =     torch.stack([example["""pixel_values"""] for example in examples]					)
   UpperCAmelCase__  :					Tuple        =     torch.stack([example["""mask"""] for example in examples]					)
   return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def     _UpperCamelCase      (		):
   # 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.

   UpperCAmelCase__  :					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.
      UpperCAmelCase__  :					Optional[int]        =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
   else:
      UpperCAmelCase__  :					List[Any]        =     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""" ,      UpperCamelCase__ ,      UpperCamelCase__					)

   # 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()

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

   # Create config
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   UpperCAmelCase__  :					int        =     {
       """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:
      UpperCAmelCase__  :					Union[str, Any]        =     AutoConfig.from_pretrained(model_args.config_name_or_path ,      **UpperCamelCase__					)
   elif model_args.model_name_or_path:
      UpperCAmelCase__  :					str        =     AutoConfig.from_pretrained(model_args.model_name_or_path ,      **UpperCamelCase__					)
   else:
      UpperCAmelCase__  :					Dict        =     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(UpperCamelCase__ ,      """decoder_type"""					):
      UpperCAmelCase__  :					str        =     """simmim"""

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

   # create model
   if model_args.model_name_or_path:
      UpperCAmelCase__  :					Tuple        =     AutoModelForMaskedImageModeling.from_pretrained(
          model_args.model_name_or_path ,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path					) ,      config=UpperCamelCase__ ,      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"""					)
      UpperCAmelCase__  :					Tuple        =     AutoModelForMaskedImageModeling.from_config(UpperCamelCase__					)

   if training_args.do_train:
      UpperCAmelCase__  :					Optional[Any]        =     ds["""train"""].column_names
   else:
      UpperCAmelCase__  :					Union[str, Any]        =     ds["""validation"""].column_names

   if data_args.image_column_name is not None:
      UpperCAmelCase__  :					Optional[Any]        =     data_args.image_column_name
   elif "image" in column_names:
      UpperCAmelCase__  :					Any        =     """image"""
   elif "img" in column_names:
      UpperCAmelCase__  :					Dict        =     """img"""
   else:
      UpperCAmelCase__  :					Tuple        =     column_names[0]

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

   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:
         UpperCAmelCase__  :					List[Any]        =     (
             ds["""validation"""].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
         )
      # Set the validation transforms
      ds["validation"].set_transform(UpperCamelCase__					)

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

   # Training
   if training_args.do_train:
      UpperCAmelCase__  :					str        =     None
      if training_args.resume_from_checkpoint is not None:
         UpperCAmelCase__  :					List[Any]        =     training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         UpperCAmelCase__  :					Dict        =     last_checkpoint
      UpperCAmelCase__  :					str        =     trainer.train(resume_from_checkpoint=UpperCamelCase__					)
      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:
      UpperCAmelCase__  :					int        =     trainer.evaluate()
      trainer.log_metrics("""eval""" ,      UpperCamelCase__					)
      trainer.save_metrics("""eval""" ,      UpperCamelCase__					)

   # Write model card and (optionally) push to hub
   UpperCAmelCase__  :					Optional[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(**UpperCamelCase__					)
   else:
      trainer.create_model_card(**UpperCamelCase__					)


if __name__ == "__main__":
  main()



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A	={
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['BertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BertForMaskedLM',
		    'BertForMultipleChoice',
		    'BertForNextSentencePrediction',
		    'BertForPreTraining',
		    'BertForQuestionAnswering',
		    'BertForSequenceClassification',
		    'BertForTokenClassification',
		    'BertLayer',
		    'BertLMHeadModel',
		    'BertModel',
		    'BertPreTrainedModel',
		    'load_tf_weights_in_bert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBertEmbeddings',
		    'TFBertForMaskedLM',
		    'TFBertForMultipleChoice',
		    'TFBertForNextSentencePrediction',
		    'TFBertForPreTraining',
		    'TFBertForQuestionAnswering',
		    'TFBertForSequenceClassification',
		    'TFBertForTokenClassification',
		    'TFBertLMHeadModel',
		    'TFBertMainLayer',
		    'TFBertModel',
		    'TFBertPreTrainedModel',
		]
try:
		if not is_tensorflow_text_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['TFBertTokenizer']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'FlaxBertForCausalLM',
		    'FlaxBertForMaskedLM',
		    'FlaxBertForMultipleChoice',
		    'FlaxBertForNextSentencePrediction',
		    'FlaxBertForPreTraining',
		    'FlaxBertForQuestionAnswering',
		    'FlaxBertForSequenceClassification',
		    'FlaxBertForTokenClassification',
		    'FlaxBertModel',
		    'FlaxBertPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
		from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_fast import BertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bert import (
				    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BertForMaskedLM,
				    BertForMultipleChoice,
				    BertForNextSentencePrediction,
				    BertForPreTraining,
				    BertForQuestionAnswering,
				    BertForSequenceClassification,
				    BertForTokenClassification,
				    BertLayer,
				    BertLMHeadModel,
				    BertModel,
				    BertPreTrainedModel,
				    load_tf_weights_in_bert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_bert import (
				    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBertEmbeddings,
				    TFBertForMaskedLM,
				    TFBertForMultipleChoice,
				    TFBertForNextSentencePrediction,
				    TFBertForPreTraining,
				    TFBertForQuestionAnswering,
				    TFBertForSequenceClassification,
				    TFBertForTokenClassification,
				    TFBertLMHeadModel,
				    TFBertMainLayer,
				    TFBertModel,
				    TFBertPreTrainedModel,
				)

		try:
				if not is_tensorflow_text_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_tf import TFBertTokenizer

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_bert import (
				    FlaxBertForCausalLM,
				    FlaxBertForMaskedLM,
				    FlaxBertForMultipleChoice,
				    FlaxBertForNextSentencePrediction,
				    FlaxBertForPreTraining,
				    FlaxBertForQuestionAnswering,
				    FlaxBertForSequenceClassification,
				    FlaxBertForTokenClassification,
				    FlaxBertModel,
				    FlaxBertPreTrainedModel,
				)

else:
		import sys

		__A	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)