



import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      __lowerCAmelCase     (		UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		List[Any]			       =    RobertaTokenizer
					A__				:		Optional[int]			       =    RobertaTokenizerFast
					A__				:		Union[str, Any]			       =    True
					A__				:		Union[str, Any]			       =    {"cls_token": "<s>"}



					def     _a							(     self					:       str					):






								"""simple docstring"""


								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								A__       =    [
								    'l',
								    'o',
								    'w',
								    'e',
								    'r',
								    's',
								    't',
								    'i',
								    'd',
								    'n',
								    '\u0120',
								    '\u0120l',
								    '\u0120n',
								    '\u0120lo',
								    '\u0120low',
								    'er',
								    '\u0120lowest',
								    '\u0120newer',
								    '\u0120wider',
								    '<unk>',
								]
								A__       =    dict(zip(_snake_case				,					range(len(_snake_case					)					)					)					)
								A__       =    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
								A__       =    {'unk_token': '<unk>'}

								A__       =    os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES['vocab_file']					)
								A__       =    os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES['merges_file']					)
								with open(self.vocab_file				,					'w'				,					encoding='utf-8'					) as fp:
											fp.write(json.dumps(_snake_case					) + '\n'					)
								with open(self.merges_file				,					'w'				,					encoding='utf-8'					) as fp:
											fp.write('\n'.join(_snake_case					)					)



					def     _a							(     self					:       Optional[Any]				,					**_snake_case					:       str					):






								"""simple docstring"""


								kwargs.update(self.special_tokens_map					)
								return self.tokenizer_class.from_pretrained(self.tmpdirname				,					**_snake_case					)



					def     _a							(     self					:       Union[str, Any]				,					**_snake_case					:       Dict					):






								"""simple docstring"""


								kwargs.update(self.special_tokens_map					)
								return RobertaTokenizerFast.from_pretrained(self.tmpdirname				,					**_snake_case					)



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								A__       =    'lower newer'
								A__       =    'lower newer'
								return input_text, output_text



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    self.tokenizer_class(self.vocab_file				,					self.merges_file				,					**self.special_tokens_map					)
								A__       =    'lower newer'
								A__       =    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
								A__       =    tokenizer.tokenize(_snake_case					)  # , add_prefix_space=True)
								self.assertListEqual(_snake_case				,					_snake_case					)

								A__       =    tokens + [tokenizer.unk_token]
								A__       =    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case					)				,					_snake_case					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    self.get_tokenizer()

								self.assertListEqual(tokenizer.encode('Hello world!'				,					add_special_tokens=_snake_case					)				,					[0, 3_14_14, 2_32, 3_28, 2]					)
								self.assertListEqual(
								    tokenizer.encode('Hello world! cécé herlolip 418'				,					add_special_tokens=_snake_case					)				,					[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]				,					)



					@slow
					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.tokenizer_class.from_pretrained('roberta-base'					)

								A__       =    tokenizer.encode('sequence builders'				,					add_special_tokens=_snake_case					)
								A__       =    tokenizer.encode('multi-sequence build'				,					add_special_tokens=_snake_case					)

								A__       =    tokenizer.encode(
								    'sequence builders'				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)
								A__       =    tokenizer.encode(
								    'sequence builders'				,					'multi-sequence build'				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)

								A__       =    tokenizer.build_inputs_with_special_tokens(_snake_case					)
								A__       =    tokenizer.build_inputs_with_special_tokens(_snake_case				,					_snake_case					)

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    self.get_tokenizer()

								A__       =    'Encode this sequence.'
								A__       =    tokenizer.byte_encoder[' '.encode('utf-8'					)[0]]

								# Testing encoder arguments
								A__       =    tokenizer.encode(_snake_case				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
								self.assertNotEqual(_snake_case				,					_snake_case					)

								A__       =    tokenizer.encode(_snake_case				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
								self.assertEqual(_snake_case				,					_snake_case					)

								tokenizer.add_special_tokens({'bos_token': '<s>'}					)
								A__       =    tokenizer.encode(_snake_case				,					add_special_tokens=_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
								self.assertNotEqual(_snake_case				,					_snake_case					)

								# Testing spaces after special tokens
								A__       =    '<mask>'
								tokenizer.add_special_tokens(
								    {'mask_token': AddedToken(_snake_case				,					lstrip=_snake_case				,					rstrip=_snake_case					)}					)  # mask token has a left space
								A__       =    tokenizer.convert_tokens_to_ids(_snake_case					)

								A__       =    'Encode <mask> sequence'
								A__       =    'Encode <mask>sequence'

								A__       =    tokenizer.encode(_snake_case					)
								A__       =    encoded.index(_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
								self.assertEqual(_snake_case				,					_snake_case					)

								A__       =    tokenizer.encode(_snake_case					)
								A__       =    encoded.index(_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
								self.assertNotEqual(_snake_case				,					_snake_case					)



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       int					):






								"""simple docstring"""


								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
														A__       =    self.rust_tokenizer_class.from_pretrained(_snake_case				,					**_snake_case					)
														A__       =    self.tokenizer_class.from_pretrained(_snake_case				,					**_snake_case					)
														A__       =    'A, <mask> AllenNLP sentence.'
														A__       =    tokenizer_r.encode_plus(_snake_case				,					add_special_tokens=_snake_case				,					return_token_type_ids=_snake_case					)
														A__       =    tokenizer_p.encode_plus(_snake_case				,					add_special_tokens=_snake_case				,					return_token_type_ids=_snake_case					)

														# token_type_ids should put 0 everywhere
														self.assertEqual(sum(tokens_r['token_type_ids']					)				,					sum(tokens_p['token_type_ids']					)					)

														# attention_mask should put 1 everywhere, so sum over length should be 1
														self.assertEqual(
														    sum(tokens_r['attention_mask']					) / len(tokens_r['attention_mask']					)				,					sum(tokens_p['attention_mask']					) / len(tokens_p['attention_mask']					)				,					)

														A__       =    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
														A__       =    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']					)

														# Rust correctly handles the space before the mask while python doesnt
														self.assertSequenceEqual(tokens_p['input_ids']				,					[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)
														self.assertSequenceEqual(tokens_r['input_ids']				,					[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)

														self.assertSequenceEqual(
														    _snake_case				,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
														self.assertSequenceEqual(
														    _snake_case				,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								for trim_offsets, add_prefix_space in itertools.product([True, False]				,					repeat=2					):
											A__       =    self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)

											A__       =    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
											A__       =    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

											self.assertEqual(pre_tokenizer_state['add_prefix_space']				,					_snake_case					)

											self.assertEqual(post_processor_state['add_prefix_space']				,					_snake_case					)
											self.assertEqual(post_processor_state['trim_offsets']				,					_snake_case					)



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
														A__       =    'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
														A__       =    F'''{text_of_1_token} {text_of_1_token}'''

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					) + 1, len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					) + 1, len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					), len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					), len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    F''' {text}'''

														# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
														#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
														# )
														# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
														# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
														# self.assertEqual(
														#     encoding.offset_mapping[1],
														#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
														# )

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(1, 1 + len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(1 + len(_snake_case					) + 1, 1 + len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, 1 + len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(1 + len(_snake_case					), 1 + len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, 1 + len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(1 + len(_snake_case					), 1 + len(_snake_case					) + 1 + len(_snake_case					))				,					)









import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


SCREAMING_SNAKE_CASE__       =      logging.getLogger(__name__)


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      List[Any]:
			return (preds == labels).mean()


@dataclass
class      __lowerCAmelCase     :
					"""simple docstring"""






					A__				:		str			       =    field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
					A__				:		Optional[str]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
					A__				:		Optional[str]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}      )
					A__				:		Optional[str]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,     )







@dataclass
class      __lowerCAmelCase     :
					"""simple docstring"""






					A__				:		str			       =    field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()      )}      )
					A__				:		str			       =    field(metadata={"help": "Should contain the data files for the task."}      )
					A__				:		int			       =    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."
					        )
					    }							,     )
					A__				:		bool			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Overwrite the cached training and evaluation sets"}      )







def A						(       )						->      Any:
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			A__       =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
			A__     , A__     , A__       =    parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir					)
			    and os.listdir(training_args.output_dir					)
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
						    ' --overwrite_output_dir to overcome.'					)

			# Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' ,	datefmt='%m/%d/%Y %H:%M:%S' ,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,	)
			logger.warning(
			    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s' ,	training_args.local_rank ,	training_args.device ,	training_args.n_gpu ,	bool(training_args.local_rank != -1					) ,	training_args.fpaa ,	)
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank					):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info('Training/evaluation parameters %s' ,	__UpperCamelCase					)

			# Set seed
			set_seed(training_args.seed					)

			try:
						A__       =    processors[data_args.task_name]()
						A__       =    processor.get_labels()
						A__       =    len(__UpperCamelCase					)
			except KeyError:
						raise ValueError('Task not found: %s' % (data_args.task_name)					)

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			A__       =    AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,	num_labels=__UpperCamelCase ,	finetuning_task=data_args.task_name ,	cache_dir=model_args.cache_dir ,	)
			A__       =    AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,	cache_dir=model_args.cache_dir ,	)
			A__       =    AutoModelForMultipleChoice.from_pretrained(
			    model_args.model_name_or_path ,	from_tf=bool('.ckpt' in model_args.model_name_or_path					) ,	config=__UpperCamelCase ,	cache_dir=model_args.cache_dir ,	)

			# Get datasets
			A__       =    (
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir ,	tokenizer=__UpperCamelCase ,	task=data_args.task_name ,	max_seq_length=data_args.max_seq_length ,	overwrite_cache=data_args.overwrite_cache ,	mode=Split.train ,	)
			    if training_args.do_train
			    else None
			)
			A__       =    (
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir ,	tokenizer=__UpperCamelCase ,	task=data_args.task_name ,	max_seq_length=data_args.max_seq_length ,	overwrite_cache=data_args.overwrite_cache ,	mode=Split.dev ,	)
			    if training_args.do_eval
			    else None
			)

			def compute_metrics(__UpperCamelCase					) -> Dict:
						A__       =    np.argmax(p.predictions ,	axis=1					)
						return {"acc": simple_accuracy(__UpperCamelCase ,	p.label_ids					)}

			# Data collator
			A__       =    DataCollatorWithPadding(__UpperCamelCase ,	pad_to_multiple_of=8					) if training_args.fpaa else None

			# Initialize our Trainer
			A__       =    Trainer(
			    model=__UpperCamelCase ,	args=__UpperCamelCase ,	train_dataset=__UpperCamelCase ,	eval_dataset=__UpperCamelCase ,	compute_metrics=__UpperCamelCase ,	data_collator=__UpperCamelCase ,	)

			# Training
			if training_args.do_train:
						trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
						trainer.save_model()
						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						if trainer.is_world_master():
									tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
			A__       =    {}
			if training_args.do_eval:
						logger.info('*** Evaluate ***'					)

						A__       =    trainer.evaluate()

						A__       =    os.path.join(training_args.output_dir ,	'eval_results.txt'					)
						if trainer.is_world_master():
									with open(__UpperCamelCase ,	'w'					) as writer:
												logger.info('***** Eval results *****'					)
												for key, value in result.items():
															logger.info('  %s = %s' ,	__UpperCamelCase ,	__UpperCamelCase					)
															writer.write('%s = %s\n' % (key, value)					)

												results.update(__UpperCamelCase					)

			return results


def A						(       __UpperCamelCase					)						->      List[Any]:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()




