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


__a  :     int       =  logging.get_logger(__name__)




@dataclass
class    _UpperCamelCase      :


			"""simple docstring"""






			__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=128						,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=_UpperCAmelCase						,metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							self.task_name.lower()




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Dict			 =						'''train'''
			__a  : Tuple			 =						'''dev'''
			__a  : Optional[int]			 =						'''test'''




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : GlueDataTrainingArguments
			__a  : str
			__a  : List[InputFeatures]





			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = Split.train      ,   lowerCAmelCase__ = None      ,   )     ->					Union[str, Any]:

							'''simple docstring'''


							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'''      ,   lowerCAmelCase__      ,   )
							__lowercase 			=							args
							__lowercase 			=							glue_processors[args.task_name]()
							__lowercase 			=							glue_output_modes[args.task_name]
							if isinstance(lowerCAmelCase__      ,   lowerCAmelCase__						):
											try:
															__lowercase 			=							Split[mode]
											except KeyError:
															raise KeyError('''mode is not a valid split name'''						)
        # Load data features from cache or dataset file
							__lowercase 			=							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}"      ,   )
							__lowercase 			=							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)
											__lowercase			,     __lowercase 			=							label_list[2], label_list[1]
							__lowercase 			=							label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							__lowercase 			=							cached_features_file + '''.lock'''
							with FileLock(lowerCAmelCase__						):
											if os.path.exists(lowerCAmelCase__						) and not args.overwrite_cache:
															__lowercase 			=							time.time()
															__lowercase 			=							torch.load(lowerCAmelCase__						)
															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:
																			__lowercase 			=							self.processor.get_dev_examples(args.data_dir						)
															elif mode == Split.test:
																			__lowercase 			=							self.processor.get_test_examples(args.data_dir						)
															else:
																			__lowercase 			=							self.processor.get_train_examples(args.data_dir						)
															if limit_length is not None:
																			__lowercase 			=							examples[:limit_length]
															__lowercase 			=							glue_convert_examples_to_features(
															    lowerCAmelCase__      ,   lowerCAmelCase__      ,   max_length=args.max_seq_length      ,   label_list=lowerCAmelCase__      ,   output_mode=self.output_mode      ,   )
															__lowercase 			=							time.time()
															torch.save(self.features      ,   lowerCAmelCase__						)
															# ^ 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						)     ->					Tuple:

							'''simple docstring'''


							return len(self.features						)





			def __getitem__(   self      ,   lowerCAmelCase__						)     ->					InputFeatures:

							'''simple docstring'''


							return self.features[i]





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[int]:

							'''simple docstring'''


							return self.label_list
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a  :     Any       =  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     List[Any]       =  [
		    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """CpmAntForCausalLM""",
		    """CpmAntModel""",
		    """CpmAntPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
		from .tokenization_cpmant import CpmAntTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_cpmant import (
				    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CpmAntForCausalLM,
				    CpmAntModel,
				    CpmAntPreTrainedModel,
				)


else:
		import sys

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