
"""simple docstring"""
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE_			      =					16
SCREAMING_SNAKE_CASE_			      =					32

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						# Initialize accelerator
						if args.with_tracking:
												__lowerCAmelCase        =       Accelerator(
												    cpu=args.cpu				,  mixed_precision=args.mixed_precision				,  log_with="""all"""				,  project_dir=args.project_dir		)
						else:
												__lowerCAmelCase        =       Accelerator(cpu=args.cpu				,  mixed_precision=args.mixed_precision		)

						if hasattr(args.checkpointing_steps				,  """isdigit"""		):
												if args.checkpointing_steps == "epoch":
																		__lowerCAmelCase        =       args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		__lowerCAmelCase        =       int(args.checkpointing_steps		)
												else:
																		raise ValueError(
																		    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""		)
						else:
												__lowerCAmelCase        =       None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__lowerCAmelCase        =       config["""lr"""]
						__lowerCAmelCase        =       int(config["""num_epochs"""]		)
						__lowerCAmelCase        =       int(config["""seed"""]		)
						__lowerCAmelCase        =       int(config["""batch_size"""]		)

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												__lowerCAmelCase        =       os.path.split(_lowerCAmelCase		)[-1].split("""."""		)[0]
												accelerator.init_trackers(_lowerCAmelCase				,  _lowerCAmelCase		)

						__lowerCAmelCase        =       AutoTokenizer.from_pretrained("""bert-base-cased"""		)
						__lowerCAmelCase        =       load_dataset("""glue"""				,  """mrpc"""		)
						__lowerCAmelCase        =       evaluate.load("""glue"""				,  """mrpc"""		)

						def tokenize_function(_lowerCAmelCase		):
												# max_length=None => use the model max length (it's actually the default)
												__lowerCAmelCase        =       tokenizer(examples["""sentence1"""]				,  examples["""sentence2"""]				,  truncation=_lowerCAmelCase				,  max_length=_lowerCAmelCase		)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												__lowerCAmelCase        =       datasets.map(
												    _lowerCAmelCase				,  batched=_lowerCAmelCase				,  remove_columns=["""idx""", """sentence1""", """sentence2"""]				,  )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__lowerCAmelCase        =       tokenized_datasets.rename_column("""label"""				,  """labels"""		)

						# If the batch size is too big we use gradient accumulation
						__lowerCAmelCase        =       1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__lowerCAmelCase        =       batch_size // MAX_GPU_BATCH_SIZE
												__lowerCAmelCase        =       MAX_GPU_BATCH_SIZE

						def collate_fn(_lowerCAmelCase		):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__lowerCAmelCase        =       128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__lowerCAmelCase        =       16
												elif accelerator.mixed_precision != "no":
																		__lowerCAmelCase        =       8
												else:
																		__lowerCAmelCase        =       None

												return tokenizer.pad(
												    _lowerCAmelCase				,  padding="""longest"""				,  max_length=_lowerCAmelCase				,  pad_to_multiple_of=_lowerCAmelCase				,  return_tensors="""pt"""				,  )

						# Instantiate dataloaders.
						__lowerCAmelCase        =       DataLoader(
						    tokenized_datasets["""train"""]				,  shuffle=_lowerCAmelCase				,  collate_fn=_lowerCAmelCase				,  batch_size=_lowerCAmelCase		)
						__lowerCAmelCase        =       DataLoader(
						    tokenized_datasets["""validation"""]				,  shuffle=_lowerCAmelCase				,  collate_fn=_lowerCAmelCase				,  batch_size=_lowerCAmelCase		)

						set_seed(_lowerCAmelCase		)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__lowerCAmelCase        =       AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,  return_dict=_lowerCAmelCase		)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						__lowerCAmelCase        =       model.to(accelerator.device		)

						# Instantiate optimizer
						__lowerCAmelCase        =       AdamW(params=model.parameters()				,  lr=_lowerCAmelCase		)

						# Instantiate scheduler
						__lowerCAmelCase        =       get_linear_schedule_with_warmup(
						    optimizer=_lowerCAmelCase				,  num_warmup_steps=100				,  num_training_steps=(len(_lowerCAmelCase		) * num_epochs) // gradient_accumulation_steps				,  )

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						__lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase        =       accelerator.prepare(
						    _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		)

						# We need to keep track of how many total steps we have iterated over
						__lowerCAmelCase        =       0
						# We also need to keep track of the stating epoch so files are named properly
						__lowerCAmelCase        =       0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""		)
																		accelerator.load_state(args.resume_from_checkpoint		)
																		__lowerCAmelCase        =       os.path.basename(args.resume_from_checkpoint		)
												else:
																		# Get the most recent checkpoint
																		__lowerCAmelCase        =       [f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
																		dirs.sort(key=os.path.getctime		)
																		__lowerCAmelCase        =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												__lowerCAmelCase        =       os.path.splitext(_lowerCAmelCase		)[0]

												if "epoch" in training_difference:
																		__lowerCAmelCase        =       int(training_difference.replace("""epoch_"""				,  """"""		)		) + 1
																		__lowerCAmelCase        =       None
												else:
																		__lowerCAmelCase        =       int(training_difference.replace("""step_"""				,  """"""		)		)
																		__lowerCAmelCase        =       resume_step // len(_lowerCAmelCase		)
																		resume_step -= starting_epoch * len(_lowerCAmelCase		)

    # Now we train the model
						for epoch in range(_lowerCAmelCase				,  _lowerCAmelCase		):
												model.train()
												if args.with_tracking:
																		__lowerCAmelCase        =       0
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		__lowerCAmelCase        =       accelerator.skip_first_batches(_lowerCAmelCase				,  _lowerCAmelCase		)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		__lowerCAmelCase        =       train_dataloader
												for step, batch in enumerate(_lowerCAmelCase		):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device		)
																		__lowerCAmelCase        =       model(**_lowerCAmelCase		)
																		__lowerCAmelCase        =       outputs.loss
																		__lowerCAmelCase        =       loss / gradient_accumulation_steps
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(_lowerCAmelCase		)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

																		if isinstance(_lowerCAmelCase				,  _lowerCAmelCase		):
																								__lowerCAmelCase        =       f"""step_{overall_step}"""
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				__lowerCAmelCase        =       os.path.join(args.output_dir				,  _lowerCAmelCase		)
																														accelerator.save_state(_lowerCAmelCase		)

												model.eval()
												for step, batch in enumerate(_lowerCAmelCase		):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device		)
																		with torch.no_grad():
																								__lowerCAmelCase        =       model(**_lowerCAmelCase		)
																		__lowerCAmelCase        =       outputs.logits.argmax(dim=-1		)
																		__lowerCAmelCase ,      __lowerCAmelCase        =       accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
																		metric.add_batch(
																		    predictions=_lowerCAmelCase				,  references=_lowerCAmelCase				,  )

												__lowerCAmelCase        =       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"""epoch {epoch}:"""				,  _lowerCAmelCase		)
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        """accuracy""": eval_metric["""accuracy"""],
																		        """f1""": eval_metric["""f1"""],
																		        """train_loss""": total_loss.item() / len(_lowerCAmelCase		),
																		        """epoch""": epoch,
																		    }				,  step=_lowerCAmelCase				,  )

												if checkpointing_steps == "epoch":
																		__lowerCAmelCase        =       f"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								__lowerCAmelCase        =       os.path.join(args.output_dir				,  _lowerCAmelCase		)
																		accelerator.save_state(_lowerCAmelCase		)

						if args.with_tracking:
												accelerator.end_training()

def 				lowercase      ():
						__lowerCAmelCase        =       argparse.ArgumentParser(description="""Simple example of training script."""		)
						parser.add_argument(
						    """--mixed_precision"""				,  type=_lowerCAmelCase				,  default=_lowerCAmelCase				,  choices=["""no""", """fp16""", """bf16""", """fp8"""]				,  help="""Whether to use mixed precision. Choose"""
						    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
						    """and an Nvidia Ampere GPU."""				,  )
						parser.add_argument("""--cpu"""				,  action="""store_true"""				,  help="""If passed, will train on the CPU."""		)
						parser.add_argument(
						    """--checkpointing_steps"""				,  type=_lowerCAmelCase				,  default=_lowerCAmelCase				,  help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""				,  )
						parser.add_argument(
						    """--resume_from_checkpoint"""				,  type=_lowerCAmelCase				,  default=_lowerCAmelCase				,  help="""If the training should continue from a checkpoint folder."""				,  )
						parser.add_argument(
						    """--with_tracking"""				,  action="""store_true"""				,  help="""Whether to load in all available experiment trackers from the environment and use them for logging."""				,  )
						parser.add_argument(
						    """--output_dir"""				,  type=_lowerCAmelCase				,  default="""."""				,  help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				,  )
						parser.add_argument(
						    """--project_dir"""				,  type=_lowerCAmelCase				,  default="""logs"""				,  help="""Location on where to store experiment tracking logs` and relevent project information"""				,  )
						__lowerCAmelCase        =       parser.parse_args()
						__lowerCAmelCase        =       {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(_lowerCAmelCase				,  _lowerCAmelCase		)


if __name__ == "__main__":
			main()


"""simple docstring"""
import os
import unittest

from transformers import ElectraTokenizerFast
from transformers.models.electra.tokenization_electra import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    ElectraTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class      lowerCAmelCase_						(							A__							,							unittest.TestCase				):



							'''simple docstring'''





							_snake_case		     =						ElectraTokenizer
							_snake_case		     =						ElectraTokenizerFast
							_snake_case		     =						True
							_snake_case		     =						True
							_snake_case		     =						filter_non_english




							def  A__		(				self							)   ->       Union[str, Any]:
													super().setUp()

													__lowerCAmelCase        =       [
													    """[UNK]""",
													    """[CLS]""",
													    """[SEP]""",
													    """[PAD]""",
													    """[MASK]""",
													    """want""",
													    """##want""",
													    """##ed""",
													    """wa""",
													    """un""",
													    """runn""",
													    """##ing""",
													    """,""",
													    """low""",
													    """lowest""",
													]
													__lowerCAmelCase        =       os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES["""vocab_file"""]							)
													with open(self.vocab_file  ,							"""w"""  ,							encoding="""utf-8"""							) as vocab_writer:
																			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)




							def  A__		(				self  ,							snake_case_							)   ->       str:
													__lowerCAmelCase        =       """UNwant\u00E9d,running"""
													__lowerCAmelCase        =       """unwanted, running"""
													return input_text, output_text




							def  A__		(				self							)   ->       List[Any]:
													__lowerCAmelCase        =       self.tokenizer_class(self.vocab_file							)

													__lowerCAmelCase        =       tokenizer.tokenize("""UNwant\u00E9d,running"""							)
													self.assertListEqual(snake_case_  ,							["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]							)
													self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_							)  ,							[9, 6, 7, 12, 10, 11]							)




							def  A__		(				self							)   ->       Any:
													if not self.test_rust_tokenizer:
																			return

													__lowerCAmelCase        =       self.get_tokenizer()
													__lowerCAmelCase        =       self.get_rust_tokenizer()

													__lowerCAmelCase        =       """UNwant\u00E9d,running"""

													__lowerCAmelCase        =       tokenizer.tokenize(snake_case_							)
													__lowerCAmelCase        =       rust_tokenizer.tokenize(snake_case_							)
													self.assertListEqual(snake_case_  ,							snake_case_							)

													__lowerCAmelCase        =       tokenizer.encode(snake_case_  ,							add_special_tokens=snake_case_							)
													__lowerCAmelCase        =       rust_tokenizer.encode(snake_case_  ,							add_special_tokens=snake_case_							)
													self.assertListEqual(snake_case_  ,							snake_case_							)

													__lowerCAmelCase        =       self.get_rust_tokenizer()
													__lowerCAmelCase        =       tokenizer.encode(snake_case_							)
													__lowerCAmelCase        =       rust_tokenizer.encode(snake_case_							)
													self.assertListEqual(snake_case_  ,							snake_case_							)

													# With lower casing
													__lowerCAmelCase        =       self.get_tokenizer(do_lower_case=snake_case_							)
													__lowerCAmelCase        =       self.get_rust_tokenizer(do_lower_case=snake_case_							)

													__lowerCAmelCase        =       """UNwant\u00E9d,running"""

													__lowerCAmelCase        =       tokenizer.tokenize(snake_case_							)
													__lowerCAmelCase        =       rust_tokenizer.tokenize(snake_case_							)
													self.assertListEqual(snake_case_  ,							snake_case_							)

													__lowerCAmelCase        =       tokenizer.encode(snake_case_  ,							add_special_tokens=snake_case_							)
													__lowerCAmelCase        =       rust_tokenizer.encode(snake_case_  ,							add_special_tokens=snake_case_							)
													self.assertListEqual(snake_case_  ,							snake_case_							)

													__lowerCAmelCase        =       self.get_rust_tokenizer()
													__lowerCAmelCase        =       tokenizer.encode(snake_case_							)
													__lowerCAmelCase        =       rust_tokenizer.encode(snake_case_							)
													self.assertListEqual(snake_case_  ,							snake_case_							)




							def  A__		(				self							)   ->       Union[str, Any]:
													__lowerCAmelCase        =       BasicTokenizer()

													self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""							)  ,							["""ah""", """\u535A""", """\u63A8""", """zz"""]							)




							def  A__		(				self							)   ->       str:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """							)  ,							["""hello""", """!""", """how""", """are""", """you""", """?"""]							)
													self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""							)  ,							["""hello"""]							)




							def  A__		(				self							)   ->       str:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_  ,							strip_accents=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """							)  ,							["""hällo""", """!""", """how""", """are""", """you""", """?"""]							)
													self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""							)  ,							["""h\u00E9llo"""]							)




							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_  ,							strip_accents=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """							)  ,							["""hallo""", """!""", """how""", """are""", """you""", """?"""]							)
													self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""							)  ,							["""hello"""]							)




							def  A__		(				self							)   ->       Dict:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """							)  ,							["""hallo""", """!""", """how""", """are""", """you""", """?"""]							)
													self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""							)  ,							["""hello"""]							)




							def  A__		(				self							)   ->       List[str]:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """							)  ,							["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]							)




							def  A__		(				self							)   ->       Any:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_  ,							strip_accents=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """							)  ,							["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]							)




							def  A__		(				self							)   ->       Tuple:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_  ,							strip_accents=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """							)  ,							["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]							)




							def  A__		(				self							)   ->       Dict:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_  ,							never_split=["""[UNK]"""]							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""							)  ,							["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]							)




							def  A__		(				self							)   ->       List[str]:
													__lowerCAmelCase        =       ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

													__lowerCAmelCase        =       {}
													for i, token in enumerate(snake_case_							):
																			__lowerCAmelCase        =       i
													__lowerCAmelCase        =       WordpieceTokenizer(vocab=snake_case_  ,							unk_token="""[UNK]"""							)

													self.assertListEqual(tokenizer.tokenize(""""""							)  ,							[]							)

													self.assertListEqual(tokenizer.tokenize("""unwanted running"""							)  ,							["""un""", """##want""", """##ed""", """runn""", """##ing"""]							)

													self.assertListEqual(tokenizer.tokenize("""unwantedX running"""							)  ,							["""[UNK]""", """runn""", """##ing"""]							)




							def  A__		(				self							)   ->       str:
													self.assertTrue(_is_whitespace(""" """							)							)
													self.assertTrue(_is_whitespace("""\t"""							)							)
													self.assertTrue(_is_whitespace("""\r"""							)							)
													self.assertTrue(_is_whitespace("""\n"""							)							)
													self.assertTrue(_is_whitespace("""\u00A0"""							)							)

													self.assertFalse(_is_whitespace("""A"""							)							)
													self.assertFalse(_is_whitespace("""-"""							)							)




							def  A__		(				self							)   ->       Optional[int]:
													self.assertTrue(_is_control("""\u0005"""							)							)

													self.assertFalse(_is_control("""A"""							)							)
													self.assertFalse(_is_control(""" """							)							)
													self.assertFalse(_is_control("""\t"""							)							)
													self.assertFalse(_is_control("""\r"""							)							)




							def  A__		(				self							)   ->       int:
													self.assertTrue(_is_punctuation("""-"""							)							)
													self.assertTrue(_is_punctuation("""$"""							)							)
													self.assertTrue(_is_punctuation("""`"""							)							)
													self.assertTrue(_is_punctuation("""."""							)							)

													self.assertFalse(_is_punctuation("""A"""							)							)
													self.assertFalse(_is_punctuation(""" """							)							)




							def  A__		(				self							)   ->       str:
													__lowerCAmelCase        =       self.get_tokenizer()
													__lowerCAmelCase        =       self.get_rust_tokenizer()

													# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
													self.assertListEqual([tokenizer.tokenize(snake_case_							) for t in ["""Test""", """\xad""", """test"""]]  ,							[["""[UNK]"""], [], ["""[UNK]"""]]							)

													self.assertListEqual(
													    [rust_tokenizer.tokenize(snake_case_							) for t in ["""Test""", """\xad""", """test"""]]  ,							[["""[UNK]"""], [], ["""[UNK]"""]]							)




							@slow
							def  A__		(				self							)   ->       List[str]:
													__lowerCAmelCase        =       self.tokenizer_class.from_pretrained("""google/electra-base-discriminator"""							)

													__lowerCAmelCase        =       tokenizer.encode("""sequence builders"""  ,							add_special_tokens=snake_case_							)
													__lowerCAmelCase        =       tokenizer.encode("""multi-sequence build"""  ,							add_special_tokens=snake_case_							)

													__lowerCAmelCase        =       tokenizer.build_inputs_with_special_tokens(snake_case_							)
													__lowerCAmelCase        =       tokenizer.build_inputs_with_special_tokens(snake_case_  ,							snake_case_							)

													assert encoded_sentence == [101] + text + [102]
													assert encoded_pair == [101] + text + [102] + text_a + [102]




							def  A__		(				self							)   ->       int:
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																									__lowerCAmelCase        =       self.rust_tokenizer_class.from_pretrained(snake_case_  ,							**snake_case_							)

																									__lowerCAmelCase        =       f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
																									__lowerCAmelCase        =       tokenizer_r.encode_plus(
																									    snake_case_  ,							return_attention_mask=snake_case_  ,							return_token_type_ids=snake_case_  ,							return_offsets_mapping=snake_case_  ,							add_special_tokens=snake_case_  ,							)

																									__lowerCAmelCase        =       tokenizer_r.do_lower_case if hasattr(snake_case_  ,							"""do_lower_case"""							) else False
																									__lowerCAmelCase        =       (
																									    [
																									        ((0, 0), tokenizer_r.cls_token),
																									        ((0, 1), """A"""),
																									        ((1, 2), ""","""),
																									        ((3, 5), """na"""),
																									        ((5, 6), """##ï"""),
																									        ((6, 8), """##ve"""),
																									        ((9, 15), tokenizer_r.mask_token),
																									        ((16, 21), """Allen"""),
																									        ((21, 23), """##NL"""),
																									        ((23, 24), """##P"""),
																									        ((25, 33), """sentence"""),
																									        ((33, 34), """."""),
																									        ((0, 0), tokenizer_r.sep_token),
																									    ]
																									    if not do_lower_case
																									    else [
																									        ((0, 0), tokenizer_r.cls_token),
																									        ((0, 1), """a"""),
																									        ((1, 2), ""","""),
																									        ((3, 8), """naive"""),
																									        ((9, 15), tokenizer_r.mask_token),
																									        ((16, 21), """allen"""),
																									        ((21, 23), """##nl"""),
																									        ((23, 24), """##p"""),
																									        ((25, 33), """sentence"""),
																									        ((33, 34), """."""),
																									        ((0, 0), tokenizer_r.sep_token),
																									    ]
																									)

																									self.assertEqual(
																									    [e[1] for e in expected_results]  ,							tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]							)							)
																									self.assertEqual([e[0] for e in expected_results]  ,							tokens["""offset_mapping"""]							)




							def  A__		(				self							)   ->       List[Any]:
													__lowerCAmelCase        =       ["""的""", """人""", """有"""]
													__lowerCAmelCase        =       """""".join(snake_case_							)
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																									__lowerCAmelCase        =       True
																									__lowerCAmelCase        =       self.tokenizer_class.from_pretrained(snake_case_  ,							**snake_case_							)
																									__lowerCAmelCase        =       self.rust_tokenizer_class.from_pretrained(snake_case_  ,							**snake_case_							)

																									__lowerCAmelCase        =       tokenizer_p.encode(snake_case_  ,							add_special_tokens=snake_case_							)
																									__lowerCAmelCase        =       tokenizer_r.encode(snake_case_  ,							add_special_tokens=snake_case_							)

																									__lowerCAmelCase        =       tokenizer_r.convert_ids_to_tokens(snake_case_							)
																									__lowerCAmelCase        =       tokenizer_p.convert_ids_to_tokens(snake_case_							)

																									# it is expected that each Chinese character is not preceded by "##"
																									self.assertListEqual(snake_case_  ,							snake_case_							)
																									self.assertListEqual(snake_case_  ,							snake_case_							)

																									__lowerCAmelCase        =       False
																									__lowerCAmelCase        =       self.rust_tokenizer_class.from_pretrained(snake_case_  ,							**snake_case_							)
																									__lowerCAmelCase        =       self.tokenizer_class.from_pretrained(snake_case_  ,							**snake_case_							)

																									__lowerCAmelCase        =       tokenizer_r.encode(snake_case_  ,							add_special_tokens=snake_case_							)
																									__lowerCAmelCase        =       tokenizer_p.encode(snake_case_  ,							add_special_tokens=snake_case_							)

																									__lowerCAmelCase        =       tokenizer_r.convert_ids_to_tokens(snake_case_							)
																									__lowerCAmelCase        =       tokenizer_p.convert_ids_to_tokens(snake_case_							)

																									# it is expected that only the first Chinese character is not preceded by "##".
																									__lowerCAmelCase        =       [
																									    f"""##{token}""" if idx != 0 else token for idx, token in enumerate(snake_case_							)
																									]
																									self.assertListEqual(snake_case_  ,							snake_case_							)
																									self.assertListEqual(snake_case_  ,							snake_case_							)
