



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__	      =       logging.get_logger(__name__)





@dataclass
class 					__lowerCAmelCase :
				__lowerCamelCase =				field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()       )}       )
				__lowerCamelCase =				field(
				    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}       )
				__lowerCamelCase =				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.'''
				        )
				    }       ,						)
				__lowerCamelCase =				field(
				    default=lowerCamelCase__       ,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.task_name.lower()





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				'''train'''
				__lowerCamelCase =				'''dev'''
				__lowerCamelCase =				'''test'''





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				42
				__lowerCamelCase =				42
				__lowerCamelCase =				42




				def __init__(							self						,				_snake_case						,				_snake_case						,				_snake_case = None						,				_snake_case = Split.train						,				_snake_case = 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"""						,				_snake_case						,				)
									_lowerCAmelCase						=    args
									_lowerCAmelCase						=    glue_processors[args.task_name]()
									_lowerCAmelCase						=    glue_output_modes[args.task_name]
									if isinstance(_snake_case						,				_snake_case		):
														try:
																			_lowerCAmelCase						=    Split[mode]
														except KeyError:
																			raise KeyError("""mode is not a valid split name"""		)
        # Load data features from cache or dataset file
									_lowerCAmelCase						=    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}'						,				)
									_lowerCAmelCase						=    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)
														_lowerCAmelCase	,	_lowerCAmelCase						=    label_list[2], label_list[1]
									_lowerCAmelCase						=    label_list

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



									"""simple docstring"""





									return self.features[i]




				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.label_list








"""simple docstring"""

import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def __lowercase			(							_a		,			_a    ):
    # Load checkpoint
    snake_case_ :     Optional[Any]				=	torch.load(_a		,			map_location='''cpu'''    )

    snake_case_ :     Union[str, Any]				=	chkpt['''model''']

    # We have the base model one level deeper than the original XLM repository
    snake_case_ :     Dict				=	{}
    for k, v in state_dict.items():
        if "pred_layer" in k:
            snake_case_ :     Union[str, Any]				=	v
        else:
            snake_case_ :     Dict				=	v

    snake_case_ :     Union[str, Any]				=	chkpt['''params''']
    snake_case_ :     int				=	{n: v for n, v in config.items() if not isinstance(_a		,			(torch.FloatTensor, numpy.ndarray)    )}

    snake_case_ :     int				=	chkpt['''dico_word2id''']
    snake_case_ :     str				=	{s + '''</w>''' if s.find('''@@'''    ) == -1 and i > 13 else s.replace('''@@'''		,			''''''    ): i for s, i in vocab.items()}

    # Save pytorch-model
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
    snake_case_ :     Any				=	pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

    print(f"Save PyTorch model to {pytorch_weights_dump_path}"    )
    torch.save(_a		,			_a    )

    print(f"Save configuration file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )

    print(f"Save vocab file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )


if __name__ == "__main__":
     lowercase__    :  Optional[Any]							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     lowercase__    :  List[str]							    =	parser.parse_args()
     convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





