





'''simple docstring'''






import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

__UpperCamelCase										=  logging.getLogger()
__UpperCamelCase										=  torch.cuda.is_available()
__UpperCamelCase										=  {
    "max_tokens_per_batch": None,
    "supervise_forward": True,
    "normalize_hidden": True,
    "label_smoothing": 0.2,
    "eval_max_gen_length": None,
    "eval_beams": 1,
    "val_metric": "loss",
    "save_top_k": 1,
    "adafactor": True,
    "early_stopping_patience": 2,
    "logger_name": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
    "num_workers": 2,
    "alpha_hid": 0,
    "freeze_embeds": True,
    "enc_only": False,
    "tgt_suffix": "",
    "resume_from_checkpoint": None,
    "sortish_sampler": True,
    "student_decoder_layers": 1,
    "val_check_interval": 1.0,
    "output_dir": "",
    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
    "n_tpu_cores": 0,
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
    "accumulate_grad_batches": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 42,
    "model_name_or_path": "sshleifer/bart-tiny-random",
    "config_name": "",
    "tokenizer_name": "facebook/bart-large",
    "do_lower_case": False,
    "learning_rate": 0.3,
    "lr_scheduler": "linear",
    "weight_decay": 0.0,
    "adam_epsilon": 1E-08,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 12,
    "max_target_length": 12,
    "val_max_target_length": 12,
    "test_max_target_length": 12,
    "fast_dev_run": False,
    "no_cache": False,
    "n_train": -1,
    "n_val": -1,
    "n_test": -1,
    "student_encoder_layers": 1,
    "freeze_encoder": False,
    "auto_scale_batch_size": False,
    "overwrite_output_dir": False,
    "student": None,
}







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase   )   ->							Optional[Any]:



			"""simple docstring"""


			__snake_case				:			List[str]       			=    """\n""".join(_lowerCamelCase   )
			Path(_lowerCamelCase   ).open("""w"""   ).writelines(_lowerCamelCase   )


__UpperCamelCase										=  [" Sam ate lunch today.", "Sams lunch ingredients."]
__UpperCamelCase										=  ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__UpperCamelCase										=  "patrickvonplaten/t5-tiny-random"
__UpperCamelCase										=  "sshleifer/t5-tinier-random"
__UpperCamelCase										=  "sshleifer/bart-tiny-random"
__UpperCamelCase										=  "sshleifer/tiny-mbart"
__UpperCamelCase										=  "sshleifer/tiny-marian-en-de"
__UpperCamelCase										=  "stas/tiny-wmt19-en-de"


__UpperCamelCase										=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







def 				_a	(					_lowerCamelCase   )   ->							List[Any]:



			"""simple docstring"""


			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(_lowerCamelCase						,  F'''{split}.source'''   )						,  _lowerCamelCase   )
						_dump_articles(os.path.join(_lowerCamelCase						,  F'''{split}.target'''   )						,  _lowerCamelCase   )
			return tmp_dir




class    _A   (				__lowercase		):

		@classmethod
		def   lowercase__    (      cls				:      Optional[Any] )				->						List[Any]:


					"""simple docstring"""

					logging.disable(logging.CRITICAL )  # remove noisy download output from tracebacks
					return cls

		@slow
		@require_torch_gpu
		def   lowercase__    (      self				:      Union[str, Any] )				->						Tuple:


					"""simple docstring"""

					__snake_case				:			Tuple       			=    list_models()
					__snake_case				:			str       			=    """sshleifer"""
					__snake_case				:			List[Any]       			=    [x.modelId for x in model_list if x.modelId.startswith(__magic_name__ )]
					__snake_case				:			Tuple       			=    ["""sshleifer/blenderbot-3B""", """sshleifer/blenderbot-90M"""]
					__snake_case				:			Any       			=    []
					for m in model_ids:
								if m in allowed_to_be_broken:
											continue
								try:
											AutoConfig.from_pretrained(__magic_name__ )
								except Exception:
											failures.append(__magic_name__ )
					assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''

		def   lowercase__    (      self				:      int )				->						Optional[int]:


					"""simple docstring"""

					__snake_case				:			str       			=    {"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
					self._test_distiller_cli(__magic_name__ )

		def   lowercase__    (      self				:      List[str] )				->						List[str]:


					"""simple docstring"""

					__snake_case				:			Optional[int]       			=    {
					    """student_encoder_layers""": 2,
					    """student_decoder_layers""": 1,
					    """max_epochs""": 4,
					    """val_check_interval""": 0.25,
					    """alpha_hid""": 2.0,
					    """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
					}
					__snake_case				:			Any       			=    self._test_distiller_cli(__magic_name__     ,			check_contents=__magic_name__ )

					__snake_case				:			Dict       			=    list(Path(model.output_dir ).glob("""*.ckpt""" ) )
					self.assertEqual(1     ,			len(__magic_name__ ) )
					__snake_case				:			int       			=    list(Path(model.output_dir ).glob("""**/*.bin""" ) )
					self.assertEqual(len(__magic_name__ )     ,			2 )
					__snake_case				:			str       			=    lmap(str.strip     ,			Path(model.hparams.data_dir ).joinpath("""test.source""" ).open().readlines() )
					__snake_case				:			Any       			=    tempfile.mktemp()  # XXX: not being cleaned up
					generate_summaries_or_translations(__magic_name__     ,			__magic_name__     ,			str(model.output_dir / """best_tfmr""" ) )
					self.assertTrue(Path(__magic_name__ ).exists() )

					__snake_case				:			str       			=    self.get_auto_remove_tmp_dir()
					convert_pl_to_hf(ckpts[0]     ,			transformer_ckpts[0].parent     ,			__magic_name__ )
					assert os.path.exists(os.path.join(__magic_name__     ,			"""pytorch_model.bin""" ) )

		def   lowercase__    (      self				:      Optional[int] )				->						Tuple:


					"""simple docstring"""

					__snake_case				:			Optional[Any]       			=    AutoModelForSeqaSeqLM.from_pretrained(__magic_name__ )
					__snake_case			,    __snake_case				:			Optional[Any]       			=    model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
					__snake_case				:			Optional[Any]       			=    torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]     ,			dtype=torch.long     ,			device=model.device )
					__snake_case				:			Any       			=    target_ids[:, :-1].contiguous()  # Why this line?
					__snake_case				:			Optional[int]       			=    target_ids[:, 1:].clone()  # why clone?
					__snake_case				:			Union[str, Any]       			=    model(
					    __magic_name__     ,			attention_mask=__magic_name__     ,			decoder_input_ids=__magic_name__     ,			labels=__magic_name__     ,			use_cache=__magic_name__ ).loss

					__snake_case				:			List[str]       			=    model(__magic_name__     ,			attention_mask=__magic_name__     ,			decoder_input_ids=__magic_name__     ,			use_cache=__magic_name__ ).logits

					__snake_case				:			Optional[int]       			=    nn.functional.log_softmax(__magic_name__     ,			dim=-1 )
					__snake_case			,    __snake_case				:			Union[str, Any]       			=    label_smoothed_nll_loss(
					    __magic_name__     ,			__magic_name__     ,			0.1     ,			ignore_index=model.config.pad_token_id )
					with self.assertRaises(__magic_name__ ):
								# TODO: understand why this breaks
								self.assertEqual(__magic_name__     ,			__magic_name__ )

		def   lowercase__    (      self				:      Union[str, Any] )				->						Tuple:


					"""simple docstring"""

					__snake_case				:			int       			=    {
					    """student_encoder_layers""": 2,
					    """student_decoder_layers""": 1,
					    """num_train_epochs""": 4,
					    """val_check_interval""": 0.25,
					    """alpha_hid""": 2.0,
					    """task""": """translation""",
					    """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
					    """tokenizer_name""": MBART_TINY,
					    """teacher""": MBART_TINY,
					    """src_lang""": """en_XX""",
					    """tgt_lang""": """ro_RO""",
					}
					__snake_case				:			Union[str, Any]       			=    self._test_distiller_cli(__magic_name__     ,			check_contents=__magic_name__ )
					assert model.model.config.model_type == "mbart"

					__snake_case				:			str       			=    list(Path(model.output_dir ).glob("""*.ckpt""" ) )
					self.assertEqual(1     ,			len(__magic_name__ ) )
					__snake_case				:			int       			=    list(Path(model.output_dir ).glob("""**/*.bin""" ) )
					__snake_case				:			Union[str, Any]       			=    list(Path(model.output_dir ).glob("""best_tfmr/*""" ) )
					assert len(__magic_name__ ) > 2
					self.assertEqual(len(__magic_name__ )     ,			2 )

		def   lowercase__    (      self				:      str )				->						Any:


					"""simple docstring"""

					__snake_case				:			str       			=    {
					    """student_encoder_layers""": 1,
					    """student_decoder_layers""": 1,
					    """alpha_hid""": 2.0,
					    """teacher""": T5_TINY,
					    """model_name_or_path""": T5_TINY,
					    """tokenizer_name""": T5_TINY,
					}
					self._test_distiller_cli(__magic_name__ )

		def   lowercase__    (      self				:      int )				->						Union[str, Any]:


					"""simple docstring"""

					__snake_case				:			Dict       			=    {
					    """teacher""": T5_TINY,
					    """student""": T5_TINIER,
					    """model_name_or_path""": T5_TINIER,
					    """tokenizer_name""": T5_TINIER,
					}
					self._test_distiller_cli(__magic_name__ )

		def   lowercase__    (      self				:      Any     ,			__magic_name__				:      Optional[int]     ,			__magic_name__				:      Tuple=True )				->						List[Any]:


					"""simple docstring"""

					__snake_case				:			Tuple       			=    {
					    """label_smoothing""": 0.0,
					    """early_stopping_patience""": -1,
					    """train_batch_size""": 1,
					    """eval_batch_size""": 2,
					    """max_epochs""": 2,
					    """alpha_mlm""": 0.2,
					    """alpha_ce""": 0.8,
					    """do_predict""": True,
					    """model_name_or_path""": """sshleifer/tinier_bart""",
					    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
					    """val_check_interval""": 0.5,
					}
					default_updates.update(__magic_name__ )
					__snake_case				:			dict       			=    CHEAP_ARGS.copy()
					__snake_case				:			Optional[Any]       			=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
					__snake_case				:			Optional[Any]       			=    self.get_auto_remove_tmp_dir()

					args_d.update(data_dir=__magic_name__     ,			output_dir=__magic_name__     ,			**__magic_name__ )
					__snake_case				:			Tuple       			=    distill_main(argparse.Namespace(**__magic_name__ ) )
					if not check_contents:
								return model
					__snake_case				:			List[Any]       			=    os.listdir(__magic_name__ )
					__snake_case				:			Tuple       			=    {os.path.basename(__magic_name__ ) for p in contents}
					__snake_case				:			List[str]       			=    [p for p in contents if p.endswith("""ckpt""" )]
					assert len(__magic_name__ ) > 0

					self.assertIn("""test_generations.txt"""     ,			__magic_name__ )
					self.assertIn("""test_results.txt"""     ,			__magic_name__ )

					__snake_case				:			Optional[Any]       			=    load_json(model.metrics_save_path )
					__snake_case				:			Dict       			=    metrics["""val"""][-1]
					self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]     ,			0.01 )
					self.assertGreaterEqual(1.0     ,			last_step_stats["""val_avg_gen_time"""] )
					self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']     ,			__magic_name__ )
					__snake_case				:			Tuple       			=    int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1 )
					self.assertEqual(len(metrics["""val"""] )     ,			__magic_name__ )
					self.assertEqual(len(metrics["""test"""] )     ,			1 )
					return model





class    _A   (				__lowercase		):

		@parameterized.expand(
		    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]     ,			)
		def   lowercase__    (      self				:      str     ,			__magic_name__				:      str )				->						Dict:


					"""simple docstring"""

					__snake_case				:			dict       			=    CHEAP_ARGS.copy()
					__snake_case				:			Dict       			=    """translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
					__snake_case				:			int       			=    0.1 if task == """translation""" else 0

					__snake_case				:			Tuple       			=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
					__snake_case				:			List[str]       			=    self.get_auto_remove_tmp_dir()
					args_d.update(
					    data_dir=__magic_name__     ,			model_name_or_path=__magic_name__     ,			tokenizer_name=__magic_name__     ,			train_batch_size=2     ,			eval_batch_size=2     ,			output_dir=__magic_name__     ,			do_predict=__magic_name__     ,			task=__magic_name__     ,			src_lang="""en_XX"""     ,			tgt_lang="""ro_RO"""     ,			freeze_encoder=__magic_name__     ,			freeze_embeds=__magic_name__     ,			)
					assert "n_train" in args_d
					__snake_case				:			int       			=    argparse.Namespace(**__magic_name__ )
					__snake_case				:			Optional[int]       			=    main(__magic_name__ )

					__snake_case				:			Optional[int]       			=    module.model.get_input_embeddings()
					assert not input_embeds.weight.requires_grad
					if model == T5_TINY:
								__snake_case				:			Tuple       			=    module.model.lm_head
								assert not lm_head.weight.requires_grad
								assert (lm_head.weight == input_embeds.weight).all().item()
					elif model == FSMT_TINY:
								__snake_case				:			Union[str, Any]       			=    module.model.model
								__snake_case				:			Dict       			=    fsmt.decoder.embed_positions
								assert not embed_pos.weight.requires_grad
								assert not fsmt.decoder.embed_tokens.weight.requires_grad
								# check that embeds are not the same
								assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
					else:
								__snake_case				:			Optional[int]       			=    module.model.model
								__snake_case				:			str       			=    bart.decoder.embed_positions
								assert not embed_pos.weight.requires_grad
								assert not bart.shared.weight.requires_grad
								# check that embeds are the same
								assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
								assert bart.decoder.embed_tokens == bart.shared

					__snake_case				:			Optional[Any]       			=    load_json(module.output_dir / """text_batch.json""" )
					assert isinstance(__magic_name__     ,			__magic_name__ )
					assert len(__magic_name__ ) >= 4

		def   lowercase__    (      self				:      int )				->						Dict:


					"""simple docstring"""

					__snake_case				:			dict       			=    CHEAP_ARGS.copy()

					__snake_case				:			Optional[int]       			=    """summarization"""
					__snake_case				:			List[str]       			=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )

					args_d.update(
					    data_dir=__magic_name__     ,			tokenizer_name=__magic_name__     ,			train_batch_size=2     ,			eval_batch_size=2     ,			do_predict=__magic_name__     ,			task=__magic_name__     ,			src_lang="""en_XX"""     ,			tgt_lang="""ro_RO"""     ,			freeze_encoder=__magic_name__     ,			freeze_embeds=__magic_name__     ,			)

					# test models whose config includes the extra_model_args
					__snake_case				:			Optional[Any]       			=    BART_TINY
					__snake_case				:			int       			=    self.get_auto_remove_tmp_dir()
					__snake_case				:			int       			=    args_d.copy()
					args_da.update(
					    model_name_or_path=__magic_name__     ,			output_dir=__magic_name__     ,			)
					__snake_case				:			int       			=    ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
					for p in extra_model_params:
								__snake_case				:			Tuple       			=    0.5
					__snake_case				:			int       			=    argparse.Namespace(**__magic_name__ )
					__snake_case				:			List[Any]       			=    main(__magic_name__ )
					for p in extra_model_params:
								assert getattr(model.config     ,			__magic_name__ ) == 0.5, f'''failed to override the model config for param {p}'''

					# test models whose config doesn't include the extra_model_args
					__snake_case				:			int       			=    T5_TINY
					__snake_case				:			List[Any]       			=    self.get_auto_remove_tmp_dir()
					__snake_case				:			Tuple       			=    args_d.copy()
					args_da.update(
					    model_name_or_path=__magic_name__     ,			output_dir=__magic_name__     ,			)
					__snake_case				:			List[str]       			=    """encoder_layerdrop"""
					__snake_case				:			Optional[int]       			=    0.5
					__snake_case				:			Dict       			=    argparse.Namespace(**__magic_name__ )
					with pytest.raises(__magic_name__ ) as excinfo:
								__snake_case				:			List[Any]       			=    main(__magic_name__ )
					assert str(excinfo.value ) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''

		def   lowercase__    (      self				:      Union[str, Any] )				->						Any:


					"""simple docstring"""

					__snake_case				:			dict       			=    CHEAP_ARGS.copy()

					__snake_case				:			str       			=    """summarization"""
					__snake_case				:			Dict       			=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )

					__snake_case				:			int       			=    BART_TINY
					__snake_case				:			Tuple       			=    self.get_auto_remove_tmp_dir()

					args_d.update(
					    data_dir=__magic_name__     ,			model_name_or_path=__magic_name__     ,			output_dir=__magic_name__     ,			tokenizer_name=__magic_name__     ,			train_batch_size=2     ,			eval_batch_size=2     ,			do_predict=__magic_name__     ,			task=__magic_name__     ,			src_lang="""en_XX"""     ,			tgt_lang="""ro_RO"""     ,			freeze_encoder=__magic_name__     ,			freeze_embeds=__magic_name__     ,			)

					# emulate finetune.py
					__snake_case				:			List[Any]       			=    argparse.ArgumentParser()
					__snake_case				:			int       			=    pl.Trainer.add_argparse_args(__magic_name__ )
					__snake_case				:			Optional[int]       			=    SummarizationModule.add_model_specific_args(__magic_name__     ,			os.getcwd() )
					__snake_case				:			Optional[Any]       			=    {"""--help""": True}

					# --help test
					with pytest.raises(__magic_name__ ) as excinfo:
								with CaptureStdout() as cs:
											__snake_case				:			Union[str, Any]       			=    parser.parse_args(__magic_name__ )
								assert False, "--help is expected to sys.exit"
					assert excinfo.type == SystemExit
					__snake_case				:			List[Any]       			=    lightning_base.arg_to_scheduler_metavar
					assert expected in cs.out, "--help is expected to list the supported schedulers"

					# --lr_scheduler=non_existing_scheduler test
					__snake_case				:			str       			=    """non_existing_scheduler"""
					__snake_case				:			List[str]       			=    {f'''--lr_scheduler={unsupported_param}'''}
					with pytest.raises(__magic_name__ ) as excinfo:
								with CaptureStderr() as cs:
											__snake_case				:			Optional[int]       			=    parser.parse_args(__magic_name__ )
								assert False, "invalid argument is expected to sys.exit"
					assert excinfo.type == SystemExit
					__snake_case				:			Tuple       			=    f'''invalid choice: \'{unsupported_param}\''''
					assert expected in cs.err, f'''should have bailed on invalid choice of scheduler {unsupported_param}'''

					# --lr_scheduler=existing_scheduler test
					__snake_case				:			str       			=    """cosine"""
					__snake_case				:			Tuple       			=    args_d.copy()
					__snake_case				:			Optional[Any]       			=    supported_param
					__snake_case				:			Tuple       			=    argparse.Namespace(**__magic_name__ )
					__snake_case				:			Optional[int]       			=    main(__magic_name__ )
					assert (
					    getattr(model.hparams     ,			"""lr_scheduler""" ) == supported_param
					), f'''lr_scheduler={supported_param} shouldn\'t fail'''









'''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    _A   (				__lowercase	,				unittest.TestCase		):
		lowercase__:      str							    =      ElectraTokenizer
		lowercase__:      Dict							    =      ElectraTokenizerFast
		lowercase__:      Union[str, Any]							    =      True
		lowercase__:      Tuple							    =      True
		lowercase__:      int							    =      filter_non_english

		def   lowercase__    (      self				:      Tuple )				->						Tuple:


					"""simple docstring"""

					super().setUp()

					__snake_case				:			Union[str, Any]       			=    [
					    """[UNK]""",
					    """[CLS]""",
					    """[SEP]""",
					    """[PAD]""",
					    """[MASK]""",
					    """want""",
					    """##want""",
					    """##ed""",
					    """wa""",
					    """un""",
					    """runn""",
					    """##ing""",
					    """,""",
					    """low""",
					    """lowest""",
					]
					__snake_case				:			List[Any]       			=    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   lowercase__    (      self				:      List[Any]     ,			__magic_name__				:      str )				->						str:


					"""simple docstring"""

					__snake_case				:			List[str]       			=    """UNwant\u00E9d,running"""
					__snake_case				:			Any       			=    """unwanted, running"""
					return input_text, output_text

		def   lowercase__    (      self				:      Union[str, Any] )				->						List[str]:


					"""simple docstring"""

					__snake_case				:			Tuple       			=    self.tokenizer_class(self.vocab_file )

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

		def   lowercase__    (      self				:      Dict )				->						int:


					"""simple docstring"""

					if not self.test_rust_tokenizer:
								return

					__snake_case				:			str       			=    self.get_tokenizer()
					__snake_case				:			Union[str, Any]       			=    self.get_rust_tokenizer()

					__snake_case				:			Tuple       			=    """UNwant\u00E9d,running"""

					__snake_case				:			List[str]       			=    tokenizer.tokenize(__magic_name__ )
					__snake_case				:			Dict       			=    rust_tokenizer.tokenize(__magic_name__ )
					self.assertListEqual(__magic_name__     ,			__magic_name__ )

					__snake_case				:			int       			=    tokenizer.encode(__magic_name__     ,			add_special_tokens=__magic_name__ )
					__snake_case				:			str       			=    rust_tokenizer.encode(__magic_name__     ,			add_special_tokens=__magic_name__ )
					self.assertListEqual(__magic_name__     ,			__magic_name__ )

					__snake_case				:			Dict       			=    self.get_rust_tokenizer()
					__snake_case				:			Optional[int]       			=    tokenizer.encode(__magic_name__ )
					__snake_case				:			int       			=    rust_tokenizer.encode(__magic_name__ )
					self.assertListEqual(__magic_name__     ,			__magic_name__ )

					# With lower casing
					__snake_case				:			str       			=    self.get_tokenizer(do_lower_case=__magic_name__ )
					__snake_case				:			str       			=    self.get_rust_tokenizer(do_lower_case=__magic_name__ )

					__snake_case				:			int       			=    """UNwant\u00E9d,running"""

					__snake_case				:			int       			=    tokenizer.tokenize(__magic_name__ )
					__snake_case				:			List[str]       			=    rust_tokenizer.tokenize(__magic_name__ )
					self.assertListEqual(__magic_name__     ,			__magic_name__ )

					__snake_case				:			Any       			=    tokenizer.encode(__magic_name__     ,			add_special_tokens=__magic_name__ )
					__snake_case				:			Tuple       			=    rust_tokenizer.encode(__magic_name__     ,			add_special_tokens=__magic_name__ )
					self.assertListEqual(__magic_name__     ,			__magic_name__ )

					__snake_case				:			List[Any]       			=    self.get_rust_tokenizer()
					__snake_case				:			Tuple       			=    tokenizer.encode(__magic_name__ )
					__snake_case				:			Optional[int]       			=    rust_tokenizer.encode(__magic_name__ )
					self.assertListEqual(__magic_name__     ,			__magic_name__ )

		def   lowercase__    (      self				:      Union[str, Any] )				->						str:


					"""simple docstring"""

					__snake_case				:			Union[str, Any]       			=    BasicTokenizer()

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

		def   lowercase__    (      self				:      Tuple )				->						Tuple:


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    BasicTokenizer(do_lower_case=__magic_name__ )

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

		def   lowercase__    (      self				:      Optional[Any] )				->						Tuple:


					"""simple docstring"""

					__snake_case				:			Tuple       			=    BasicTokenizer(do_lower_case=__magic_name__     ,			strip_accents=__magic_name__ )

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

		def   lowercase__    (      self				:      Optional[int] )				->						Any:


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    BasicTokenizer(do_lower_case=__magic_name__     ,			strip_accents=__magic_name__ )

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

		def   lowercase__    (      self				:      Any )				->						Optional[Any]:


					"""simple docstring"""

					__snake_case				:			Any       			=    BasicTokenizer(do_lower_case=__magic_name__ )

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

		def   lowercase__    (      self				:      Dict )				->						int:


					"""simple docstring"""

					__snake_case				:			Any       			=    BasicTokenizer(do_lower_case=__magic_name__ )

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

		def   lowercase__    (      self				:      Union[str, Any] )				->						Tuple:


					"""simple docstring"""

					__snake_case				:			str       			=    BasicTokenizer(do_lower_case=__magic_name__     ,			strip_accents=__magic_name__ )

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

		def   lowercase__    (      self				:      str )				->						int:


					"""simple docstring"""

					__snake_case				:			str       			=    BasicTokenizer(do_lower_case=__magic_name__     ,			strip_accents=__magic_name__ )

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

		def   lowercase__    (      self				:      Optional[Any] )				->						Optional[Any]:


					"""simple docstring"""

					__snake_case				:			Tuple       			=    BasicTokenizer(do_lower_case=__magic_name__     ,			never_split=["""[UNK]"""] )

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

		def   lowercase__    (      self				:      List[str] )				->						Tuple:


					"""simple docstring"""

					__snake_case				:			str       			=    ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

					__snake_case				:			Union[str, Any]       			=    {}
					for i, token in enumerate(__magic_name__ ):
								__snake_case				:			List[str]       			=    i
					__snake_case				:			Union[str, Any]       			=    WordpieceTokenizer(vocab=__magic_name__     ,			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   lowercase__    (      self				:      str )				->						Dict:


					"""simple docstring"""

					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   lowercase__    (      self				:      Optional[int] )				->						Optional[int]:


					"""simple docstring"""

					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   lowercase__    (      self				:      Tuple )				->						Dict:


					"""simple docstring"""

					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   lowercase__    (      self				:      Union[str, Any] )				->						Optional[Any]:


					"""simple docstring"""

					__snake_case				:			Union[str, Any]       			=    self.get_tokenizer()
					__snake_case				:			Union[str, Any]       			=    self.get_rust_tokenizer()

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

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

		@slow
		def   lowercase__    (      self				:      int )				->						Dict:


					"""simple docstring"""

					__snake_case				:			Tuple       			=    self.tokenizer_class.from_pretrained("""google/electra-base-discriminator""" )

					__snake_case				:			Optional[int]       			=    tokenizer.encode("""sequence builders"""     ,			add_special_tokens=__magic_name__ )
					__snake_case				:			Optional[Any]       			=    tokenizer.encode("""multi-sequence build"""     ,			add_special_tokens=__magic_name__ )

					__snake_case				:			List[str]       			=    tokenizer.build_inputs_with_special_tokens(__magic_name__ )
					__snake_case				:			Any       			=    tokenizer.build_inputs_with_special_tokens(__magic_name__     ,			__magic_name__ )

					assert encoded_sentence == [1_01] + text + [1_02]
					assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]

		def   lowercase__    (      self				:      List[str] )				->						Any:


					"""simple docstring"""

					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
											__snake_case				:			Tuple       			=    self.rust_tokenizer_class.from_pretrained(__magic_name__     ,			**__magic_name__ )

											__snake_case				:			int       			=    f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
											__snake_case				:			Optional[Any]       			=    tokenizer_r.encode_plus(
											    __magic_name__     ,			return_attention_mask=__magic_name__     ,			return_token_type_ids=__magic_name__     ,			return_offsets_mapping=__magic_name__     ,			add_special_tokens=__magic_name__     ,			)

											__snake_case				:			Optional[int]       			=    tokenizer_r.do_lower_case if hasattr(__magic_name__     ,			"""do_lower_case""" ) else False
											__snake_case				:			List[str]       			=    (
											    [
											        ((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   lowercase__    (      self				:      Optional[int] )				->						int:


					"""simple docstring"""

					__snake_case				:			Any       			=    ["""的""", """人""", """有"""]
					__snake_case				:			Union[str, Any]       			=    """""".join(__magic_name__ )
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
											__snake_case				:			Tuple       			=    True
											__snake_case				:			List[str]       			=    self.tokenizer_class.from_pretrained(__magic_name__     ,			**__magic_name__ )
											__snake_case				:			Tuple       			=    self.rust_tokenizer_class.from_pretrained(__magic_name__     ,			**__magic_name__ )

											__snake_case				:			Tuple       			=    tokenizer_p.encode(__magic_name__     ,			add_special_tokens=__magic_name__ )
											__snake_case				:			Optional[int]       			=    tokenizer_r.encode(__magic_name__     ,			add_special_tokens=__magic_name__ )

											__snake_case				:			Union[str, Any]       			=    tokenizer_r.convert_ids_to_tokens(__magic_name__ )
											__snake_case				:			Tuple       			=    tokenizer_p.convert_ids_to_tokens(__magic_name__ )

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

											__snake_case				:			Dict       			=    False
											__snake_case				:			str       			=    self.rust_tokenizer_class.from_pretrained(__magic_name__     ,			**__magic_name__ )
											__snake_case				:			Tuple       			=    self.tokenizer_class.from_pretrained(__magic_name__     ,			**__magic_name__ )

											__snake_case				:			List[Any]       			=    tokenizer_r.encode(__magic_name__     ,			add_special_tokens=__magic_name__ )
											__snake_case				:			str       			=    tokenizer_p.encode(__magic_name__     ,			add_special_tokens=__magic_name__ )

											__snake_case				:			Optional[Any]       			=    tokenizer_r.convert_ids_to_tokens(__magic_name__ )
											__snake_case				:			List[Any]       			=    tokenizer_p.convert_ids_to_tokens(__magic_name__ )

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


