



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)

UpperCAmelCase														=					logging.getLogger()

def 			A_      (	):

				"""simple docstring"""
				a__				      =		argparse.ArgumentParser()
				parser.add_argument("""-f"""		)
				a__				      =		parser.parse_args()
				return args.f




class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def _a      (							self		):
											a__				      =		logging.StreamHandler(sys.stdout		)
											logger.addHandler(a_		)


							def _a      (							self  ,  a_		):
											a__				      =		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(a_  ,  """argv"""  ,  a_		):
																			a__				      =		run_glue_deebert.main()
																			for value in result.values():
																							self.assertGreaterEqual(a_  ,  0.666		)







							@slow
							@require_torch_non_multi_gpu
							def _a      (							self		):
											a__				      =		"""
            --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(a_		)

											a__				      =		"""
            --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(a_		)

											a__				      =		"""
            --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(a_		)






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


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{"""vocab_file""": """spiece.model"""}

UpperCAmelCase							 =					{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

UpperCAmelCase							 =					{"""bert_for_seq_generation""": 512}

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Optional[Any]         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Any         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      List[str]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      List[int]         =					[]
							UpperCamelCase__				:      Optional[int]         =					["""input_ids""", """attention_mask"""]


							def __init__(							self  ,  a_  ,  a_="<s>"  ,  a_="</s>"  ,  a_="<unk>"  ,  a_="<pad>"  ,  a_="<::::>"  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		{} if sp_model_kwargs is None else sp_model_kwargs

											# Add extra_ids to the special token list
											super().__init__(
											    bos_token=a_  ,  eos_token=a_  ,  unk_token=a_  ,  pad_token=a_  ,  sep_token=a_  ,  sp_model_kwargs=self.sp_model_kwargs  ,  **a_  ,  )

											a__				      =		vocab_file

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(a_		)


							@property
							def _a      (							self		):
											return self.sp_model.get_piece_size()


							def _a      (							self		):
											a__				      =		{self.convert_ids_to_tokens(a_		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab


							def __getstate__(							self		):
											a__				      =		self.__dict__.copy()
											a__				      =		None
											return state


							def __setstate__(							self  ,  a_		):
											a__				      =		d

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

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


							def _a      (							self  ,  a_		):
											return self.sp_model.encode(a_  ,  out_type=a_		)


							def _a      (							self  ,  a_		):
											return self.sp_model.piece_to_id(a_		)


							def _a      (							self  ,  a_		):
											a__				      =		self.sp_model.IdToPiece(a_		)
											return token


							def _a      (							self  ,  a_		):
											a__				      =		[]
											a__				      =		""""""
											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(a_		) + token
																			a__				      =		[]
															else:
																			current_sub_tokens.append(a_		)
											out_string += self.sp_model.decode(a_		)
											return out_string.strip()







							def _a      (							self  ,  a_  ,  a_ = None		):
											if not os.path.isdir(a_		):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
															return
											a__				      =		os.path.join(
											    a_  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

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

											return (out_vocab_file,)

