import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


lowerCamelCase_    :		Optional[int]							=	logging.get_logger(__name__)


@dataclass
class 						a__							:
			A__    :       str        =      field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()		)}		)
			A__    :       str        =      field(
			    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}		)
			A__    :       int        =      field(
			    default=1_28				,						metadata={
			        'help': (
			            'The maximum total input sequence length after tokenization. Sequences longer '
			            'than this will be truncated, sequences shorter will be padded.'
			        )
			    }				,						)
			A__    :       bool        =      field(
			    default=__lowercase				,						metadata={'help': 'Overwrite the cached training and evaluation sets'}		)



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       self.task_name.lower()






class 						a__							(       __lowercase		):
			A__    :       str        =      "train"
			A__    :       Optional[Any]        =      "dev"
			A__    :       int        =      "test"






class 						a__							(       __lowercase		):
			A__    :       GlueDataTrainingArguments
			A__    :       str
			A__    :       List[InputFeatures]



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = Split.train				,						UpperCAmelCase = None				,						)			->   int:
								warnings.warn(
								    'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
								    'library. You can have a look at this example script for pointers: '
								    'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'				,						_a				,						)
								__a		 =       args
								__a		 =       glue_processors[args.task_name]()
								__a		 =       glue_output_modes[args.task_name]
								if isinstance(_a				,						_a   ):
													try:
																		__a		 =       Split[mode]
													except KeyError:
																		raise KeyError('mode is not a valid split name'   )
        # Load data features from cache or dataset file
								__a		 =       os.path.join(
								    cache_dir if cache_dir is not None else args.data_dir				,						f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''				,						)
								__a		 =       self.processor.get_labels()
								if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
								    "RobertaTokenizer",
								    "RobertaTokenizerFast",
								    "XLMRobertaTokenizer",
								    "BartTokenizer",
								    "BartTokenizerFast",
								):
													# HACK(label indices are swapped in RoBERTa pretrained model)
													__a		 =       label_list[2], label_list[1]
								__a		 =       label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								__a		 =       cached_features_file + '''.lock'''
								with FileLock(_a   ):
													if os.path.exists(_a   ) and not args.overwrite_cache:
																		__a		 =       time.time()
																		__a		 =       torch.load(_a   )
																		logger.info(
																		    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''				,						time.time() - start   )
													else:
																		logger.info(f'''Creating features from dataset file at {args.data_dir}'''   )

																		if mode == Split.dev:
																							__a		 =       self.processor.get_dev_examples(args.data_dir   )
																		elif mode == Split.test:
																							__a		 =       self.processor.get_test_examples(args.data_dir   )
																		else:
																							__a		 =       self.processor.get_train_examples(args.data_dir   )
																		if limit_length is not None:
																							__a		 =       examples[:limit_length]
																		__a		 =       glue_convert_examples_to_features(
																		    _a				,						_a				,						max_length=args.max_seq_length				,						label_list=_a				,						output_mode=self.output_mode				,						)
																		__a		 =       time.time()
																		torch.save(self.features				,						_a   )
																		# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																		logger.info(
																		    f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''   )



			def __len__(						self   )			->   int:
								return len(self.features   )



			def __getitem__(						self				,						UpperCAmelCase   )			->   InputFeatures:
								return self.features[i]







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								return self.label_list







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_    :		Dict							=	{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		int							=	[
	    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """SwinForImageClassification""",
	    """SwinForMaskedImageModeling""",
	    """SwinModel""",
	    """SwinPreTrainedModel""",
	    """SwinBackbone""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFSwinForImageClassification""",
	    """TFSwinForMaskedImageModeling""",
	    """TFSwinModel""",
	    """TFSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swin import (
		    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwinBackbone,
		    SwinForImageClassification,
		    SwinForMaskedImageModeling,
		    SwinModel,
		    SwinPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		    TFSwinPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		List[Any]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




