






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a__       =     '''▁'''

a__       =     {'''vocab_file''': '''spiece.model'''}

a__       =     {
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

a__       =     {
    '''google/pegasus-xsum''': 5_1_2,
}


a__       =     logging.get_logger(__name__)


class    _lowerCAmelCase			(		__a      ):



			"""simple docstring"""



			_lowercase				:   List[str]      =  VOCAB_FILES_NAMES

			_lowercase				:   Tuple      =  VOCAB_FILES_NAMES
			_lowercase				:   Any      =  PRETRAINED_VOCAB_FILES_MAP
			_lowercase				:   List[str]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_lowercase				:   Dict      =  ['''input_ids''', '''attention_mask''']



			def __init__(      self    :    str			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    str="<pad>"			,		UpperCamelCase__    :    str="</s>"			,		UpperCamelCase__    :    Dict="<unk>"			,		UpperCamelCase__    :    int="<mask_2>"			,		UpperCamelCase__    :    Union[str, Any]="<mask_1>"			,		UpperCamelCase__    :    str=None			,		UpperCamelCase__    :    List[str]=1_0_3			,		UpperCamelCase__    :    Optional[Dict[str, Any]] = None			,		**UpperCamelCase__    :    List[str]			,		):






										'''simple docstring'''







										snake_case__	  =       offset
										if additional_special_tokens is not None:
																	if not isinstance(snake_case__			,		snake_case__):
																								raise TypeError(
																								    F'''additional_special_tokens should be of type {type(snake_case__)}, but is'''
																								    F''' {type(snake_case__)}''')

																	snake_case__	  =       (
																	    ([mask_token_sent] + additional_special_tokens)
																	    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																	    else additional_special_tokens
																	)
																	# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																	additional_special_tokens_extended += [
																	    F'''<unk_{i}>''' for i in range(len(snake_case__)			,		self.offset - 1)
																	]

																	if len(set(snake_case__)) != len(snake_case__):
																								raise ValueError(
																								    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
																								    F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''')
																	snake_case__	  =       additional_special_tokens_extended
										else:
																	snake_case__	  =       [mask_token_sent] if mask_token_sent is not None else []
																	additional_special_tokens += [F'''<unk_{i}>''' for i in range(2			,		self.offset)]

										snake_case__	  =       {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=snake_case__			,		unk_token=snake_case__			,		mask_token=snake_case__			,		pad_token=snake_case__			,		mask_token_sent=snake_case__			,		offset=snake_case__			,		additional_special_tokens=snake_case__			,		sp_model_kwargs=self.sp_model_kwargs			,		**snake_case__			,		)
										snake_case__	  =       mask_token_sent
										snake_case__	  =       vocab_file
										snake_case__	  =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
										self.sp_model.Load(snake_case__)

										# add special tokens to encoder dict
										snake_case__	  =       {
										    0: self.pad_token,
										    1: self.eos_token,
										}

										if self.mask_token_sent is not None:
																	self.encoder.update(
																	    {
																	        2: self.mask_token_sent,
																	        3: self.mask_token,
																	    })

										if self.offset > 0:
																	# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																	# mask_token_sent is already added to list -> so start at 1
																	self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,		self.offset - 1)})

										snake_case__	  =       {v: k for k, v in self.encoder.items()}



			@property
			def 					__magic_name__							(      self    :    Union[str, Any]):






										'''simple docstring'''







										return len(self.sp_model) + self.offset



			def 					__magic_name__							(      self    :    Optional[int]):






										'''simple docstring'''







										snake_case__	  =       {self.convert_ids_to_tokens(snake_case__): i for i in range(self.vocab_size)}
										vocab.update(self.added_tokens_encoder)
										return vocab



			def __getstate__(      self    :    Optional[int]):






										'''simple docstring'''







										snake_case__	  =       self.__dict__.copy()
										snake_case__	  =       None
										return state



			def __setstate__(      self    :    List[Any]			,		UpperCamelCase__    :    Dict):






										'''simple docstring'''







										snake_case__	  =       d

										# for backward compatibility
										if not hasattr(self			,		"""sp_model_kwargs"""):
																	snake_case__	  =       {}

										snake_case__	  =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
										self.sp_model.Load(self.vocab_file)



			def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    str):






										'''simple docstring'''







										return self.sp_model.encode(snake_case__			,		out_type=snake_case__)



			def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    str):






										'''simple docstring'''







										if token in self.decoder:
																	return self.decoder[token]
										elif token in self.added_tokens_decoder:
																	return self.added_tokens_decoder[token]
										snake_case__	  =       self.sp_model.piece_to_id(snake_case__)
										return sp_id + self.offset



			def 					__magic_name__							(      self    :    Dict			,		UpperCamelCase__    :    int):






										'''simple docstring'''







										if index in self.encoder:
																	return self.encoder[index]
										elif index in self.added_tokens_encoder:
																	return self.added_tokens_encoder[index]
										else:
																	snake_case__	  =       self.sp_model.IdToPiece(index - self.offset)
										return token



			def 					__magic_name__							(      self    :    Optional[int]			,		UpperCamelCase__    :    Optional[int]):






										'''simple docstring'''







										snake_case__	  =       []
										snake_case__	  =       """"""
										for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																								out_string += self.sp_model.decode(snake_case__) + token
																								snake_case__	  =       []
																	else:
																								current_sub_tokens.append(snake_case__)
										out_string += self.sp_model.decode(snake_case__)
										return out_string.strip()



			def 					__magic_name__							(      self    :    int			,		UpperCamelCase__    :    str=False):






										'''simple docstring'''







										return 1



			def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    Optional[int]):






										'''simple docstring'''







										snake_case__	  =       set(self.all_special_ids)  # call it once instead of inside list comp
										all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

										return [1 if x in all_special_ids else 0 for x in seq]



			def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    List			,		UpperCamelCase__    :    Optional[List] = None			,		UpperCamelCase__    :    bool = False):






										'''simple docstring'''







										if already_has_special_tokens:
																	return self._special_token_mask(snake_case__)
										elif token_ids_a is None:
																	return self._special_token_mask(snake_case__) + [1]
										else:
																	return self._special_token_mask(token_ids_a + token_ids_a) + [1]



			def 					__magic_name__							(      self    :    Tuple			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    str=None):






										'''simple docstring'''







										if token_ids_a is None:
																	return token_ids_a + [self.eos_token_id]
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return token_ids_a + token_ids_a + [self.eos_token_id]



			def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Optional[str] = None):






										'''simple docstring'''







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

										if os.path.abspath(self.vocab_file) != os.path.abspath(snake_case__) and os.path.isfile(self.vocab_file):
																	copyfile(self.vocab_file			,		snake_case__)
										elif not os.path.isfile(self.vocab_file):
																	with open(snake_case__			,		"""wb""") as fi:
																								snake_case__	  =       self.sp_model.serialized_model_proto()
																								fi.write(snake_case__)

										return (out_vocab_file,)








import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

a__       =     logging.getLogger()

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



class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       logging.StreamHandler(sys.stdout)
											logger.addHandler(UpperCamelCase__)



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    List[str]):






											'''simple docstring'''







											snake_case__	  =       get_gpu_count()

											if n_gpu > 1:
																		pass
																		# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
																		# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
																		# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
																		# cmd = [sys.executable] + distributed_args + args
																		# execute_subprocess_async(cmd, env=self.get_env())
																		# XXX: test the results - need to save them first into .json file
											else:
																		args.insert(0			,		"""run_glue_deebert.py""")
																		with patch.object(UpperCamelCase__			,		"""argv"""			,		UpperCamelCase__):
																									snake_case__	  =       run_glue_deebert.main()
																									for value in result.values():
																																self.assertGreaterEqual(UpperCamelCase__			,		0.6_66)



				@slow
				@require_torch_non_multi_gpu
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__	  =       """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
											self.run_and_check(UpperCamelCase__)

											snake_case__	  =       """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
											self.run_and_check(UpperCamelCase__)

											snake_case__	  =       """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
											self.run_and_check(UpperCamelCase__)
