






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


SCREAMING_SNAKE_CASE						:						Tuple					   =       logging.get_logger(__name__)





@dataclass
class       snake_case_       :



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							str   			=					field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()       )}       )
	SCREAMING_SNAKE_CASE_:							List[str]   			=					field(
	    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}       )
	SCREAMING_SNAKE_CASE_:							Tuple   			=					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."""
	        )
	    }    ,							)
	SCREAMING_SNAKE_CASE_:							Any   			=					field(
	    default=_UpperCAmelCase    ,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}       )


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.task_name.lower()





class       snake_case_       (    _UpperCAmelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							List[Any]   			=					"""train"""
	SCREAMING_SNAKE_CASE_:							Tuple   			=					"""dev"""
	SCREAMING_SNAKE_CASE_:							int   			=					"""test"""





class       snake_case_       (    _UpperCAmelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Any   			=					42
	SCREAMING_SNAKE_CASE_:							str   			=					42
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					42


	def __init__(      self  ,					__a  ,					__a  ,					__a = None  ,					__a = Split.train  ,					__a = None  ,					):


					"""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'  ,					lowercase_  ,					)
					A__							 =       args
					A__							 =       glue_processors[args.task_name]()
					A__							 =       glue_output_modes[args.task_name]
					if isinstance(lowercase_  ,					lowercase_						):
									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(lowercase_						):
									if os.path.exists(lowercase_						) and not args.overwrite_cache:
													A__							 =       time.time()
													A__							 =       torch.load(lowercase_						)
													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(
													    lowercase_  ,					lowercase_  ,					max_length=args.max_seq_length  ,					label_list=lowercase_  ,					output_mode=self.output_mode  ,					)
													A__							 =       time.time()
													torch.save(self.features  ,					lowercase_						)
													# ^ 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						):


					"""simple docstring"""

					return len(self.features						)


	def __getitem__(      self  ,					__a						):


					"""simple docstring"""

					return self.features[i]






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return self.label_list










"""simple docstring"""






# Copyright 2021 The HuggingFace 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 pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE						:						Dict					   =       '''Create a default config file for Accelerate with only a few flags set.'''



def  __lowerCamelCase					(					lowerCAmelCase__="no"       ,lowerCAmelCase__ = default_json_config_file       ,lowerCAmelCase__ = False				):
				A__							 =       Path(lowerCAmelCase__				)
				path.parent.mkdir(parents=lowerCAmelCase__       ,exist_ok=lowerCAmelCase__				)
				if path.exists():
								print(
								    f'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''				)
								return False
				A__							 =       mixed_precision.lower()
				if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
								raise ValueError(
								    f'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''				)
				A__							 =       {
				    'compute_environment': 'LOCAL_MACHINE',
				    'mixed_precision': mixed_precision,
				}
				if torch.cuda.is_available():
								A__							 =       torch.cuda.device_count()
								A__							 =       num_gpus
								A__							 =       False
								if num_gpus > 1:
												A__							 =       'MULTI_GPU'
								else:
												A__							 =       'NO'
				elif is_xpu_available() and use_xpu:
								A__							 =       torch.xpu.device_count()
								A__							 =       num_xpus
								A__							 =       False
								if num_xpus > 1:
												A__							 =       'MULTI_XPU'
								else:
												A__							 =       'NO'
				elif is_npu_available():
								A__							 =       torch.npu.device_count()
								A__							 =       num_npus
								A__							 =       False
								if num_npus > 1:
												A__							 =       'MULTI_NPU'
								else:
												A__							 =       'NO'
				else:
								A__							 =       0
								A__							 =       True
								A__							 =       1
								A__							 =       'NO'
				A__							 =       ClusterConfig(**lowerCAmelCase__				)
				config.to_json_file(lowerCAmelCase__				)
				return path



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       parser.add_parser('default'       ,parents=lowerCAmelCase__       ,help=lowerCAmelCase__       ,formatter_class=lowerCAmelCase__				)
				parser.add_argument(
				    '--config_file'       ,default=lowerCAmelCase__       ,help=(
				        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
				        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
				        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
				        'with \'huggingface\'.'
				    )       ,dest='save_location'       ,)

				parser.add_argument(
				    '--mixed_precision'       ,choices=['no', 'fp16', 'bf16']       ,type=lowerCAmelCase__       ,help='Whether or not to use mixed precision training. '
				    'Choose between FP16 and BF16 (bfloat16) training. '
				    'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'       ,default='no'       ,)
				parser.set_defaults(func=lowerCAmelCase__				)
				return parser







def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       write_basic_config(args.mixed_precision       ,args.save_location				)
				if config_file:
								print(f'''accelerate configuration saved at {config_file}'''				)


