




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


a__		    =    logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

a__		    =    {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
a__		    =    {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
a__		    =    sorted(arg_to_scheduler.keys())
a__		    =    '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''


class       SCREAMING_SNAKE_CASE_							(					pl.LightningModule      ):



	"""simple docstring"""


	def __init__(				self		:	List[Any]				,    lowerCAmelCase		:	argparse.Namespace				,    lowerCAmelCase		:	Tuple=None				,    lowerCAmelCase		:	Optional[Any]="base"				,    lowerCAmelCase		:	Any=None				,    lowerCAmelCase		:	Union[str, Any]=None				,    lowerCAmelCase		:	Dict=None				,    **lowerCAmelCase		:	Union[str, Any]				,    )  ->					Tuple:







						"""simple docstring"""







						super().__init__()
						# TODO: move to self.save_hyperparameters()
						# self.save_hyperparameters()
						# can also expand arguments into trainer signature for easier reading

						self.save_hyperparameters(lowerCAmelCase  )
						__UpperCamelCase				:  Optional[Any]								=			0
						__UpperCamelCase				:  Optional[Any]								=			Path(self.hparams.output_dir  )
						__UpperCamelCase				:  Any								=			self.hparams.cache_dir if self.hparams.cache_dir else None
						if config is None:
											__UpperCamelCase				:  Any								=			AutoConfig.from_pretrained(
											    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path				,    **({"""num_labels""": num_labels} if num_labels is not None else {})				,    cache_dir=lowerCAmelCase				,    **lowerCAmelCase				,    )
						else:
											__UpperCamelCase				:  PretrainedConfig								=			config

						__UpperCamelCase				:  Tuple								=			("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
						for p in extra_model_params:
											if getattr(self.hparams				,    lowerCAmelCase				,    lowerCAmelCase  ):
																assert hasattr(self.config				,    lowerCAmelCase  ), F'''model config doesn\'t have a `{p}` attribute'''
																setattr(self.config				,    lowerCAmelCase				,    getattr(self.hparams				,    lowerCAmelCase  )  )

						if tokenizer is None:
											__UpperCamelCase				:  Dict								=			AutoTokenizer.from_pretrained(
											    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,    cache_dir=lowerCAmelCase				,    )
						else:
											__UpperCamelCase				:  PreTrainedTokenizer								=			tokenizer
						__UpperCamelCase				:  str								=			MODEL_MODES[mode]
						if model is None:
											__UpperCamelCase				:  Tuple								=			self.model_type.from_pretrained(
											    self.hparams.model_name_or_path				,    from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path  )				,    config=self.config				,    cache_dir=lowerCAmelCase				,    )
						else:
											__UpperCamelCase				:  Union[str, Any]								=			model






	def 				lowerCamelCase__    (				self		:	List[Any]				,    *lowerCAmelCase		:	Any				,    **lowerCAmelCase		:	Any  )  ->					Dict:







						"""simple docstring"""







						__UpperCamelCase				:  Optional[int]								=			self.model_type.from_pretrained(*lowerCAmelCase				,    **lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	int  )  ->					int:







						"""simple docstring"""







						__UpperCamelCase				:  Union[str, Any]								=			arg_to_scheduler[self.hparams.lr_scheduler]
						__UpperCamelCase				:  Dict								=			get_schedule_func(
						    self.opt				,    num_warmup_steps=self.hparams.warmup_steps				,    num_training_steps=self.total_steps()  )
						__UpperCamelCase				:  Union[str, Any]								=			{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
						return scheduler






	def 				lowerCamelCase__    (				self		:	Any  )  ->					Optional[int]:







						"""simple docstring"""







						__UpperCamelCase				:  Union[str, Any]								=			self.model
						__UpperCamelCase				:  Any								=			["""bias""", """LayerNorm.weight"""]
						__UpperCamelCase				:  Tuple								=			[
						    {
						        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay  )],
						        """weight_decay""": self.hparams.weight_decay,
						    },
						    {
						        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay  )],
						        """weight_decay""": 0.0,
						    },
						]
						if self.hparams.adafactor:
											__UpperCamelCase				:  Dict								=			Adafactor(
											    lowerCAmelCase				,    lr=self.hparams.learning_rate				,    scale_parameter=lowerCAmelCase				,    relative_step=lowerCAmelCase  )

						else:
											__UpperCamelCase				:  Any								=			AdamW(
											    lowerCAmelCase				,    lr=self.hparams.learning_rate				,    eps=self.hparams.adam_epsilon  )
						__UpperCamelCase				:  Optional[int]								=			optimizer

						__UpperCamelCase				:  Union[str, Any]								=			self.get_lr_scheduler()

						return [optimizer], [scheduler]






	def 				lowerCamelCase__    (				self		:	List[str]				,    lowerCAmelCase		:	int				,    lowerCAmelCase		:	Optional[Any]  )  ->					Union[str, Any]:







						"""simple docstring"""







						return self.validation_step(lowerCAmelCase				,    lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	List[str]				,    lowerCAmelCase		:	List[str]  )  ->					str:







						"""simple docstring"""







						return self.validation_end(lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	str  )  ->					int:







						"""simple docstring"""







						__UpperCamelCase				:  Dict								=			max(1				,    self.hparams.gpus  )  # TODO: consider num_tpu_cores
						__UpperCamelCase				:  Any								=			self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
						return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






	def 				lowerCamelCase__    (				self		:	int				,    lowerCAmelCase		:	List[str]  )  ->					List[str]:







						"""simple docstring"""







						if stage == "test":
											__UpperCamelCase				:  str								=			len(self.test_dataloader().dataset  )
						else:
											__UpperCamelCase				:  Optional[int]								=			self.get_dataloader("""train"""				,    self.hparams.train_batch_size				,    shuffle=lowerCAmelCase  )
											__UpperCamelCase				:  Any								=			len(self.train_dataloader().dataset  )






	def 				lowerCamelCase__    (				self		:	List[str]				,    lowerCAmelCase		:	str				,    lowerCAmelCase		:	int				,    lowerCAmelCase		:	bool = False  )  ->					Any:







						"""simple docstring"""







						raise NotImplementedError("""You must implement this for your task"""  )






	def 				lowerCamelCase__    (				self		:	Optional[Any]  )  ->					List[str]:







						"""simple docstring"""







						return self.train_loader






	def 				lowerCamelCase__    (				self		:	int  )  ->					List[str]:







						"""simple docstring"""







						return self.get_dataloader("""dev"""				,    self.hparams.eval_batch_size				,    shuffle=lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					List[Any]:







						"""simple docstring"""







						return self.get_dataloader("""test"""				,    self.hparams.eval_batch_size				,    shuffle=lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	List[Any]				,    lowerCAmelCase		:	List[Any]  )  ->					Tuple:







						"""simple docstring"""







						return os.path.join(
						    self.hparams.data_dir				,    """cached_{}_{}_{}""".format(
						        lowerCAmelCase				,    list(filter(lowerCAmelCase				,    self.hparams.model_name_or_path.split("""/"""  )  )  ).pop()				,    str(self.hparams.max_seq_length  )				,    )				,    )






	@pl.utilities.rank_zero_only
	def 				lowerCamelCase__    (				self		:	Optional[int]				,    lowerCAmelCase		:	Dict[str, Any]  )  ->					None:







						"""simple docstring"""







						__UpperCamelCase				:  Optional[Any]								=			self.output_dir.joinpath("""best_tfmr"""  )
						__UpperCamelCase				:  List[Any]								=			self.step_count
						self.model.save_pretrained(lowerCAmelCase  )
						self.tokenizer.save_pretrained(lowerCAmelCase  )






	@staticmethod
	def 				lowerCamelCase__    (				lowerCAmelCase		:	Dict				,    lowerCAmelCase		:	str  )  ->					Union[str, Any]:







						"""simple docstring"""







						parser.add_argument(
						    """--model_name_or_path"""				,    default=lowerCAmelCase				,    type=lowerCAmelCase				,    required=lowerCAmelCase				,    help="""Path to pretrained model or model identifier from huggingface.co/models"""				,    )
						parser.add_argument(
						    """--config_name"""				,    default=""""""				,    type=lowerCAmelCase				,    help="""Pretrained config name or path if not the same as model_name"""  )
						parser.add_argument(
						    """--tokenizer_name"""				,    default=lowerCAmelCase				,    type=lowerCAmelCase				,    help="""Pretrained tokenizer name or path if not the same as model_name"""				,    )
						parser.add_argument(
						    """--cache_dir"""				,    default=""""""				,    type=lowerCAmelCase				,    help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""				,    )
						parser.add_argument(
						    """--encoder_layerdrop"""				,    type=lowerCAmelCase				,    help="""Encoder layer dropout probability (Optional). Goes into model.config"""				,    )
						parser.add_argument(
						    """--decoder_layerdrop"""				,    type=lowerCAmelCase				,    help="""Decoder layer dropout probability (Optional). Goes into model.config"""				,    )
						parser.add_argument(
						    """--dropout"""				,    type=lowerCAmelCase				,    help="""Dropout probability (Optional). Goes into model.config"""				,    )
						parser.add_argument(
						    """--attention_dropout"""				,    type=lowerCAmelCase				,    help="""Attention dropout probability (Optional). Goes into model.config"""				,    )
						parser.add_argument("""--learning_rate"""				,    default=5E-5				,    type=lowerCAmelCase				,    help="""The initial learning rate for Adam."""  )
						parser.add_argument(
						    """--lr_scheduler"""				,    default="""linear"""				,    choices=lowerCAmelCase				,    metavar=lowerCAmelCase				,    type=lowerCAmelCase				,    help="""Learning rate scheduler"""				,    )
						parser.add_argument("""--weight_decay"""				,    default=0.0				,    type=lowerCAmelCase				,    help="""Weight decay if we apply some."""  )
						parser.add_argument("""--adam_epsilon"""				,    default=1E-8				,    type=lowerCAmelCase				,    help="""Epsilon for Adam optimizer."""  )
						parser.add_argument("""--warmup_steps"""				,    default=0				,    type=lowerCAmelCase				,    help="""Linear warmup over warmup_steps."""  )
						parser.add_argument("""--num_workers"""				,    default=4				,    type=lowerCAmelCase				,    help="""kwarg passed to DataLoader"""  )
						parser.add_argument("""--num_train_epochs"""				,    dest="""max_epochs"""				,    default=3				,    type=lowerCAmelCase  )
						parser.add_argument("""--train_batch_size"""				,    default=32				,    type=lowerCAmelCase  )
						parser.add_argument("""--eval_batch_size"""				,    default=32				,    type=lowerCAmelCase  )
						parser.add_argument("""--adafactor"""				,    action="""store_true"""  )






class       SCREAMING_SNAKE_CASE_							(					pl.Callback      ):



	"""simple docstring"""


	def 				lowerCamelCase__    (				self		:	int				,    lowerCAmelCase		:	Optional[Any]				,    lowerCAmelCase		:	int  )  ->					Dict:







						"""simple docstring"""







						if (
						    trainer.is_global_zero and trainer.global_rank == 0
						):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
											pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class       SCREAMING_SNAKE_CASE_							(					pl.Callback      ):



	"""simple docstring"""


	def 				lowerCamelCase__    (				self		:	Any				,    lowerCAmelCase		:	Dict				,    lowerCAmelCase		:	Tuple  )  ->					Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase				:  List[str]								=			trainer.lr_schedulers[0]["""scheduler"""]
						__UpperCamelCase				:  List[str]								=			{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
						pl_module.logger.log_metrics(lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	List[str]				,    lowerCAmelCase		:	pl.Trainer				,    lowerCAmelCase		:	pl.LightningModule  )  ->					List[str]:







						"""simple docstring"""







						rank_zero_info("""***** Validation results *****"""  )
						__UpperCamelCase				:  Optional[Any]								=			trainer.callback_metrics
						# Log results
						for key in sorted(lowerCAmelCase  ):
											if key not in ["log", "progress_bar"]:
																rank_zero_info("""{} = {}\n""".format(lowerCAmelCase				,    str(metrics[key]  )  )  )






	def 				lowerCamelCase__    (				self		:	Union[str, Any]				,    lowerCAmelCase		:	pl.Trainer				,    lowerCAmelCase		:	pl.LightningModule  )  ->					Tuple:







						"""simple docstring"""







						rank_zero_info("""***** Test results *****"""  )
						__UpperCamelCase				:  int								=			trainer.callback_metrics
						# Log and save results to file
						__UpperCamelCase				:  Tuple								=			os.path.join(pl_module.hparams.output_dir				,    """test_results.txt"""  )
						with open(lowerCAmelCase				,    """w"""  ) as writer:
											for key in sorted(lowerCAmelCase  ):
																if key not in ["log", "progress_bar"]:
																					rank_zero_info("""{} = {}\n""".format(lowerCAmelCase				,    str(metrics[key]  )  )  )
																					writer.write("""{} = {}\n""".format(lowerCAmelCase				,    str(metrics[key]  )  )  )






def 							A__   (snake_case      :		Union[str, Any]		,  snake_case      :		Tuple							)    ->	None:
					#  To allow all pl args uncomment the following line
					#  parser = pl.Trainer.add_argparse_args(parser)
					parser.add_argument(
					    """--output_dir"""		,  default=snake_case		,  type=snake_case		,  required=snake_case		,  help="""The output directory where the model predictions and checkpoints will be written."""		,  )
					parser.add_argument(
					    """--fp16"""		,  action="""store_true"""		,  help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""		,  )

					parser.add_argument(
					    """--fp16_opt_level"""		,  type=snake_case		,  default="""O2"""		,  help=(
					        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
					        """See details at https://nvidia.github.io/apex/amp.html"""
					    )		,  )
					parser.add_argument("""--n_tpu_cores"""		,  dest="""tpu_cores"""		,  type=snake_case							)
					parser.add_argument("""--max_grad_norm"""		,  dest="""gradient_clip_val"""		,  default=1.0		,  type=snake_case		,  help="""Max gradient norm"""							)
					parser.add_argument("""--do_train"""		,  action="""store_true"""		,  help="""Whether to run training."""							)
					parser.add_argument("""--do_predict"""		,  action="""store_true"""		,  help="""Whether to run predictions on the test set."""							)
					parser.add_argument(
					    """--gradient_accumulation_steps"""		,  dest="""accumulate_grad_batches"""		,  type=snake_case		,  default=1		,  help="""Number of updates steps to accumulate before performing a backward/update pass."""		,  )
					parser.add_argument("""--seed"""		,  type=snake_case		,  default=42		,  help="""random seed for initialization"""							)
					parser.add_argument(
					    """--data_dir"""		,  default=snake_case		,  type=snake_case		,  required=snake_case		,  help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""		,  )







def 							A__   (snake_case      :		BaseTransformer		,  snake_case      :		argparse.Namespace		,  snake_case      :		str=None		,  snake_case      :		int=True		,  snake_case      :		Optional[Any]=None		,  snake_case      :		Union[str, Any]=[]		,  snake_case      :		int=None		,  snake_case      :		List[str]=None		,  **snake_case      :		Tuple		,  )    ->	Tuple:
					pl.seed_everything(args.seed							)

					# init model
					__UpperCamelCase				:  List[Any]								=			Path(model.hparams.output_dir							)
					odir.mkdir(exist_ok=snake_case							)

					# add custom checkpoints
					if checkpoint_callback is None:
										__UpperCamelCase				:  List[str]								=			pl.callbacks.ModelCheckpoint(
										    filepath=args.output_dir		,  prefix="""checkpoint"""		,  monitor="""val_loss"""		,  mode="""min"""		,  save_top_k=1							)
					if early_stopping_callback:
										extra_callbacks.append(snake_case							)
					if logging_callback is None:
										__UpperCamelCase				:  Union[str, Any]								=			LoggingCallback()

					__UpperCamelCase				:  List[str]								=			{}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										__UpperCamelCase				:  Optional[int]								=			16
										# train_params["amp_level"] = args.fp16_opt_level

					if args.gpus > 1:
										__UpperCamelCase				:  str								=			"""auto"""  # "ddp"
										__UpperCamelCase				:  Any								=			"""ddp"""

					__UpperCamelCase				:  int								=			args.accumulate_grad_batches
					__UpperCamelCase				:  Dict								=			None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
					__UpperCamelCase				:  List[str]								=			"""auto"""

					__UpperCamelCase				:  Any								=			pl.Trainer.from_argparse_args(
					    snake_case		,  weights_summary=snake_case		,  callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]		,  logger=snake_case		,  **snake_case		,  )

					if args.do_train:
										trainer.fit(snake_case							)

					return trainer












from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__		    =    logging.get_logger(__name__)

a__		    =    {
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}


class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	__magic_name__      :					Any			  =		'mobilevit'






	def __init__(				self		:	int				,    lowerCAmelCase		:	Dict=3				,    lowerCAmelCase		:	Dict=256				,    lowerCAmelCase		:	Any=2				,    lowerCAmelCase		:	Any=[144, 192, 240]				,    lowerCAmelCase		:	int=[16, 32, 64, 96, 128, 160, 640]				,    lowerCAmelCase		:	List[Any]=4				,    lowerCAmelCase		:	Tuple=2.0				,    lowerCAmelCase		:	Tuple=4.0				,    lowerCAmelCase		:	List[str]="silu"				,    lowerCAmelCase		:	Tuple=3				,    lowerCAmelCase		:	Tuple=32				,    lowerCAmelCase		:	Any=0.1				,    lowerCAmelCase		:	List[str]=0.0				,    lowerCAmelCase		:	Union[str, Any]=0.1				,    lowerCAmelCase		:	List[str]=0.02				,    lowerCAmelCase		:	Optional[Any]=1E-5				,    lowerCAmelCase		:	Union[str, Any]=True				,    lowerCAmelCase		:	List[str]=256				,    lowerCAmelCase		:	List[Any]=[6, 12, 18]				,    lowerCAmelCase		:	Tuple=0.1				,    lowerCAmelCase		:	List[str]=255				,    **lowerCAmelCase		:	Union[str, Any]				,    )  ->					Optional[Any]:







						"""simple docstring"""







						super().__init__(**lowerCAmelCase  )

						__UpperCamelCase				:  Tuple								=			num_channels
						__UpperCamelCase				:  Union[str, Any]								=			image_size
						__UpperCamelCase				:  List[Any]								=			patch_size
						__UpperCamelCase				:  Optional[int]								=			hidden_sizes
						__UpperCamelCase				:  Union[str, Any]								=			neck_hidden_sizes
						__UpperCamelCase				:  List[str]								=			num_attention_heads
						__UpperCamelCase				:  Union[str, Any]								=			mlp_ratio
						__UpperCamelCase				:  Tuple								=			expand_ratio
						__UpperCamelCase				:  Optional[Any]								=			hidden_act
						__UpperCamelCase				:  int								=			conv_kernel_size
						__UpperCamelCase				:  Any								=			output_stride
						__UpperCamelCase				:  int								=			hidden_dropout_prob
						__UpperCamelCase				:  Union[str, Any]								=			attention_probs_dropout_prob
						__UpperCamelCase				:  Dict								=			classifier_dropout_prob
						__UpperCamelCase				:  Any								=			initializer_range
						__UpperCamelCase				:  List[str]								=			layer_norm_eps
						__UpperCamelCase				:  Union[str, Any]								=			qkv_bias

						# decode head attributes for semantic segmentation
						__UpperCamelCase				:  Any								=			aspp_out_channels
						__UpperCamelCase				:  Union[str, Any]								=			atrous_rates
						__UpperCamelCase				:  Union[str, Any]								=			aspp_dropout_prob
						__UpperCamelCase				:  List[str]								=			semantic_loss_ignore_index






class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	__magic_name__      :					Optional[Any]			  =		version.parse('1.11'      )






	@property
	def 				lowerCamelCase__    (				self		:	Any  )  ->					Mapping[str, Mapping[int, str]]:







						"""simple docstring"""







						return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]  )






	@property
	def 				lowerCamelCase__    (				self		:	List[Any]  )  ->					Mapping[str, Mapping[int, str]]:







						"""simple docstring"""







						if self.task == "image-classification":
											return OrderedDict([("""logits""", {0: """batch"""})]  )
						else:
											return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]  )






	@property
	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					float:







						"""simple docstring"""







						return 1E-4






