


'''simple docstring'''
import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_reformer import ReformerTokenizer
else:
							__SCREAMING_SNAKE_CASE				:							Optional[Any]		       =		None


__SCREAMING_SNAKE_CASE				:							List[Any]		       =		logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE				:							Optional[Any]		       =		'''▁'''

__SCREAMING_SNAKE_CASE				:							str		       =		{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__SCREAMING_SNAKE_CASE				:							List[Any]		       =		{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

__SCREAMING_SNAKE_CASE				:							Optional[Any]		       =		{
    '''google/reformer-crime-and-punishment''': 524288,
}






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						_UpperCAmelCase		:					Any      	=VOCAB_FILES_NAMES
						_UpperCAmelCase		:					int      	=PRETRAINED_VOCAB_FILES_MAP
						_UpperCAmelCase		:					List[Any]      	=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCAmelCase		:					Optional[int]      	=["input_ids", "attention_mask"]
						_UpperCAmelCase		:					Optional[Any]      	=ReformerTokenizer






						def __init__(     self       :			Optional[int]   ,     lowerCAmelCase       :			str=None   ,     lowerCAmelCase       :			Union[str, Any]=None   ,     lowerCAmelCase       :			Optional[int]="</s>"   ,     lowerCAmelCase       :			Tuple="<unk>"   ,     lowerCAmelCase       :			Dict=[]   ,     **lowerCAmelCase       :			int   ,     ):
													super().__init__(
													    lowerCAmelCase   ,     tokenizer_file=lowerCAmelCase   ,     eos_token=lowerCAmelCase   ,     unk_token=lowerCAmelCase   ,     additional_special_tokens=lowerCAmelCase   ,     **lowerCAmelCase   ,     )

													A_ 						=		vocab_file
													A_ 						=		False if not self.vocab_file else True






						def 							_UpperCAmelCase					(     self       :			Any   ,     lowerCAmelCase       :			str   ,     lowerCAmelCase       :			Optional[str] = None  ):
													if not self.can_save_slow_tokenizer:
																				raise ValueError(
																				    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																				    "tokenizer."  )

													if not os.path.isdir(lowerCAmelCase  ):
																				logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
																				return
													A_ 						=		os.path.join(
													    lowerCAmelCase   ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

													if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowerCAmelCase  ):
																				copyfile(self.vocab_file   ,     lowerCAmelCase  )

													return (out_vocab_file,)









'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE				:							str		       =		logging.getLogger()







def 			a_							(  ):
							A_ 						=		argparse.ArgumentParser()
							parser.add_argument("-f"   )
							A_ 						=		parser.parse_args()
							return args.f







class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		logging.StreamHandler(sys.stdout  )
													logger.addHandler(lowerCAmelCase  )

													A_ 						=		self.get_auto_remove_tmp_dir()
													A_ 						=		F"\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

													with patch.object(lowerCAmelCase   ,     "argv"   ,     lowerCAmelCase  ):
																				A_ 						=		run_glue_with_pabee.main()
																				for value in result.values():
																											self.assertGreaterEqual(lowerCAmelCase   ,     0.7_5  )





