code: 
  
  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_       :				Optional[int]		   =      logging.getLogger()
  UpperCAmelCase_       :				Optional[int]		   =      torch.cuda.is_available()
  UpperCAmelCase_       :				Optional[int]		   =      {
      '''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        __SCREAMING_SNAKE_CASE    (      a__						:      Path ,a__						:      list					)				->							Dict:
  	__A   : Tuple								= """\n""".join(a__					)
  	Path(a__					).open("""w"""					).writelines(a__					)
  
  
  UpperCAmelCase_       :				Union[str, Any]		   =      [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
  UpperCAmelCase_       :				Optional[Any]		   =      ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
  UpperCAmelCase_       :				str		   =      '''patrickvonplaten/t5-tiny-random'''
  UpperCAmelCase_       :				Tuple		   =      '''sshleifer/t5-tinier-random'''
  UpperCAmelCase_       :				str		   =      '''sshleifer/bart-tiny-random'''
  UpperCAmelCase_       :				str		   =      '''sshleifer/tiny-mbart'''
  UpperCAmelCase_       :				Tuple		   =      '''sshleifer/tiny-marian-en-de'''
  UpperCAmelCase_       :				Union[str, Any]		   =      '''stas/tiny-wmt19-en-de'''
  
  
  UpperCAmelCase_       :				Tuple		   =      logging.StreamHandler(sys.stdout)
  logger.addHandler(stream_handler)
  logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Dict					)				->							Optional[int]:
  	for split in ["train", "val", "test"]:
  		_dump_articles(os.path.join(a__ ,f"""{split}.source"""					) ,a__					)
  		_dump_articles(os.path.join(a__ ,f"""{split}.target"""					) ,a__					)
  	return tmp_dir
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    str       ):
  		logging.disable(logging.CRITICAL       )  # remove noisy download output from tracebacks
  		return cls
  
  
  
  
  	@slow
  	@require_torch_gpu
  	def 					lowerCAmelCase_					(    self	:    Optional[Any]       ):
  		__A   : Tuple								= list_models()
  		__A   : Dict								= """sshleifer"""
  		__A   : Tuple								= [x.modelId for x in model_list if x.modelId.startswith(__A       )]
  		__A   : List[str]								= ["""sshleifer/blenderbot-3B""", """sshleifer/blenderbot-90M"""]
  		__A   : List[Any]								= []
  		for m in model_ids:
  			if m in allowed_to_be_broken:
  				continue
  			try:
  				AutoConfig.from_pretrained(__A       )
  			except Exception:
  				failures.append(__A       )
  		assert not failures, F"""The following models could not be loaded through AutoConfig: {failures}"""
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
  		__A   : Optional[int]								= {"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
  		self._test_distiller_cli(__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[Any]       ):
  		__A   : List[Any]								= {
  		    """student_encoder_layers""": 2,
  		    """student_decoder_layers""": 1,
  		    """max_epochs""": 4,
  		    """val_check_interval""": 0.2_5,
  		    """alpha_hid""": 2.0,
  		    """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
  		}
  		__A   : Any								= self._test_distiller_cli(__A				,			check_contents=__A       )
  
  		__A   : str								= list(Path(model.output_dir       ).glob("""*.ckpt"""       )       )
  		self.assertEqual(1				,			len(__A       )       )
  		__A   : Optional[int]								= list(Path(model.output_dir       ).glob("""**/*.bin"""       )       )
  		self.assertEqual(len(__A       )				,			2       )
  		__A   : Any								= lmap(str.strip				,			Path(model.hparams.data_dir       ).joinpath("""test.source"""       ).open().readlines()       )
  		__A   : Optional[Any]								= tempfile.mktemp()  # XXX: not being cleaned up
  		generate_summaries_or_translations(__A				,			__A				,			str(model.output_dir / """best_tfmr"""       )       )
  		self.assertTrue(Path(__A       ).exists()       )
  
  		__A   : str								= self.get_auto_remove_tmp_dir()
  		convert_pl_to_hf(ckpts[0]				,			transformer_ckpts[0].parent				,			__A       )
  		assert os.path.exists(os.path.join(__A				,			"""pytorch_model.bin"""       )       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    str       ):
  		__A   : Tuple								= AutoModelForSeqaSeqLM.from_pretrained(__A       )
  		__A						,							__A   : str								= model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
  		__A   : Optional[Any]								= torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]				,			dtype=torch.long				,			device=model.device       )
  		__A   : str								= target_ids[:, :-1].contiguous()  # Why this line?
  		__A   : Any								= target_ids[:, 1:].clone()  # why clone?
  		__A   : Optional[int]								= model(
  		    __A				,			attention_mask=__A				,			decoder_input_ids=__A				,			labels=__A				,			use_cache=__A       ).loss
  
  		__A   : Optional[Any]								= model(__A				,			attention_mask=__A				,			decoder_input_ids=__A				,			use_cache=__A       ).logits
  
  		__A   : Dict								= nn.functional.log_softmax(__A				,			dim=-1       )
  		__A						,							__A   : Tuple								= label_smoothed_nll_loss(
  		    __A				,			__A				,			0.1				,			ignore_index=model.config.pad_token_id       )
  		with self.assertRaises(__A       ):
  			# TODO: understand why this breaks
  			self.assertEqual(__A				,			__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
  		__A   : Tuple								= {
  		    """student_encoder_layers""": 2,
  		    """student_decoder_layers""": 1,
  		    """num_train_epochs""": 4,
  		    """val_check_interval""": 0.2_5,
  		    """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""",
  		}
  		__A   : Dict								= self._test_distiller_cli(__A				,			check_contents=__A       )
  		assert model.model.config.model_type == "mbart"
  
  		__A   : Tuple								= list(Path(model.output_dir       ).glob("""*.ckpt"""       )       )
  		self.assertEqual(1				,			len(__A       )       )
  		__A   : Union[str, Any]								= list(Path(model.output_dir       ).glob("""**/*.bin"""       )       )
  		__A   : List[Any]								= list(Path(model.output_dir       ).glob("""best_tfmr/*"""       )       )
  		assert len(__A       ) > 2
  		self.assertEqual(len(__A       )				,			2       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Dict       ):
  		__A   : Optional[Any]								= {
  		    """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(__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Dict       ):
  		__A   : str								= {
  		    """teacher""": T5_TINY,
  		    """student""": T5_TINIER,
  		    """model_name_or_path""": T5_TINIER,
  		    """tokenizer_name""": T5_TINIER,
  		}
  		self._test_distiller_cli(__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    List[Any]				,			__A	:    List[Any]=True       ):
  		__A   : str								= {
  		    """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(__A       )
  		__A   : dict								= CHEAP_ARGS.copy()
  		__A   : List[Any]								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
  		__A   : Tuple								= self.get_auto_remove_tmp_dir()
  
  		args_d.update(data_dir=__A				,			output_dir=__A				,			**__A       )
  		__A   : Tuple								= distill_main(argparse.Namespace(**__A       )       )
  		if not check_contents:
  			return model
  		__A   : int								= os.listdir(__A       )
  		__A   : List[str]								= {os.path.basename(__A       ) for p in contents}
  		__A   : Any								= [p for p in contents if p.endswith("""ckpt"""       )]
  		assert len(__A       ) > 0
  
  		self.assertIn("""test_generations.txt"""				,			__A       )
  		self.assertIn("""test_results.txt"""				,			__A       )
  
  		__A   : List[Any]								= load_json(model.metrics_save_path       )
  		__A   : List[str]								= metrics["""val"""][-1]
  		self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]				,			0.0_1       )
  		self.assertGreaterEqual(1.0				,			last_step_stats["""val_avg_gen_time"""]       )
  		self.assertIsInstance(last_step_stats[F"""val_avg_{model.val_metric}"""]				,			__A       )
  		__A   : List[Any]								= int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1       )
  		self.assertEqual(len(metrics["""val"""]       )				,			__A       )
  		self.assertEqual(len(metrics["""test"""]       )				,			1       )
  		return model
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  
  
  
  
  	@parameterized.expand(
  	    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]				,			)
  	def 					lowerCAmelCase_					(    self	:    str				,			__A	:    str       ):
  		__A   : dict								= CHEAP_ARGS.copy()
  		__A   : List[Any]								= """translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
  		__A   : Dict								= 0.1 if task == """translation""" else 0
  
  		__A   : Tuple								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
  		__A   : Optional[int]								= self.get_auto_remove_tmp_dir()
  		args_d.update(
  		    data_dir=__A				,			model_name_or_path=__A				,			tokenizer_name=__A				,			train_batch_size=2				,			eval_batch_size=2				,			output_dir=__A				,			do_predict=__A				,			task=__A				,			src_lang="""en_XX"""				,			tgt_lang="""ro_RO"""				,			freeze_encoder=__A				,			freeze_embeds=__A				,			)
  		assert "n_train" in args_d
  		__A   : Optional[Any]								= argparse.Namespace(**__A       )
  		__A   : Union[str, Any]								= main(__A       )
  
  		__A   : Optional[int]								= module.model.get_input_embeddings()
  		assert not input_embeds.weight.requires_grad
  		if model == T5_TINY:
  			__A   : Optional[int]								= module.model.lm_head
  			assert not lm_head.weight.requires_grad
  			assert (lm_head.weight == input_embeds.weight).all().item()
  		elif model == FSMT_TINY:
  			__A   : int								= module.model.model
  			__A   : Optional[int]								= 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:
  			__A   : Dict								= module.model.model
  			__A   : int								= 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
  
  		__A   : str								= load_json(module.output_dir / """text_batch.json"""       )
  		assert isinstance(__A				,			__A       )
  		assert len(__A       ) >= 4
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Any       ):
  		__A   : dict								= CHEAP_ARGS.copy()
  
  		__A   : Tuple								= """summarization"""
  		__A   : List[Any]								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
  
  		args_d.update(
  		    data_dir=__A				,			tokenizer_name=__A				,			train_batch_size=2				,			eval_batch_size=2				,			do_predict=__A				,			task=__A				,			src_lang="""en_XX"""				,			tgt_lang="""ro_RO"""				,			freeze_encoder=__A				,			freeze_embeds=__A				,			)
  
  		# test models whose config includes the extra_model_args
  		__A   : str								= BART_TINY
  		__A   : Optional[Any]								= self.get_auto_remove_tmp_dir()
  		__A   : int								= args_d.copy()
  		args_da.update(
  		    model_name_or_path=__A				,			output_dir=__A				,			)
  		__A   : List[Any]								= ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
  		for p in extra_model_params:
  			__A   : Optional[Any]								= 0.5
  		__A   : List[str]								= argparse.Namespace(**__A       )
  		__A   : Union[str, Any]								= main(__A       )
  		for p in extra_model_params:
  			assert getattr(model.config				,			__A       ) == 0.5, F"""failed to override the model config for param {p}"""
  
  		# test models whose config doesn't include the extra_model_args
  		__A   : Optional[Any]								= T5_TINY
  		__A   : Union[str, Any]								= self.get_auto_remove_tmp_dir()
  		__A   : Optional[int]								= args_d.copy()
  		args_da.update(
  		    model_name_or_path=__A				,			output_dir=__A				,			)
  		__A   : Any								= """encoder_layerdrop"""
  		__A   : Tuple								= 0.5
  		__A   : Union[str, Any]								= argparse.Namespace(**__A       )
  		with pytest.raises(__A       ) as excinfo:
  			__A   : Tuple								= main(__A       )
  		assert str(excinfo.value       ) == F"""model config doesn't have a `{unsupported_param}` attribute"""
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[str]       ):
  		__A   : dict								= CHEAP_ARGS.copy()
  
  		__A   : Optional[Any]								= """summarization"""
  		__A   : Optional[Any]								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
  
  		__A   : Tuple								= BART_TINY
  		__A   : Any								= self.get_auto_remove_tmp_dir()
  
  		args_d.update(
  		    data_dir=__A				,			model_name_or_path=__A				,			output_dir=__A				,			tokenizer_name=__A				,			train_batch_size=2				,			eval_batch_size=2				,			do_predict=__A				,			task=__A				,			src_lang="""en_XX"""				,			tgt_lang="""ro_RO"""				,			freeze_encoder=__A				,			freeze_embeds=__A				,			)
  
  		# emulate finetune.py
  		__A   : Optional[int]								= argparse.ArgumentParser()
  		__A   : Any								= pl.Trainer.add_argparse_args(__A       )
  		__A   : List[str]								= SummarizationModule.add_model_specific_args(__A				,			os.getcwd()       )
  		__A   : Any								= {"""--help""": True}
  
  		# --help test
  		with pytest.raises(__A       ) as excinfo:
  			with CaptureStdout() as cs:
  				__A   : Optional[int]								= parser.parse_args(__A       )
  			assert False, "--help is expected to sys.exit"
  		assert excinfo.type == SystemExit
  		__A   : 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
  		__A   : Optional[int]								= """non_existing_scheduler"""
  		__A   : Dict								= {F"""--lr_scheduler={unsupported_param}"""}
  		with pytest.raises(__A       ) as excinfo:
  			with CaptureStderr() as cs:
  				__A   : Optional[int]								= parser.parse_args(__A       )
  			assert False, "invalid argument is expected to sys.exit"
  		assert excinfo.type == SystemExit
  		__A   : str								= 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
  		__A   : List[str]								= """cosine"""
  		__A   : Tuple								= args_d.copy()
  		__A   : Any								= supported_param
  		__A   : int								= argparse.Namespace(**__A       )
  		__A   : Dict								= main(__A       )
  		assert (
  		    getattr(model.hparams				,			"""lr_scheduler"""       ) == supported_param
  		), F"""lr_scheduler={supported_param} shouldn't fail"""
  
  
  
  
  
  
style_context: 
  
  import os
  from typing import List, Union
  
  import tensorflow as tf
  from tensorflow_text import BertTokenizer as BertTokenizerLayer
  from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs
  
  from .tokenization_bert import BertTokenizer
  
  
  
  
  
  
  class     lowerCamelCase_			(  tf.keras.layers.Layer  ):
  
  
  
  
  	def __init__(    self	:    int				,			__A	:    List				,			__A	:    bool				,			__A	:    int = None				,			__A	:    int = None				,			__A	:    int = None				,			__A	:    str = "longest"				,			__A	:    bool = True				,			__A	:    int = 512				,			__A	:    int = None				,			__A	:    bool = True				,			__A	:    bool = True				,			__A	:    bool = True				,			**__A	:    Any				,			):
  		super().__init__()
  		if use_fast_bert_tokenizer:
  			__A   : Optional[int]								= FastBertTokenizer(
  			    __A				,			token_out_type=tf.intaa				,			lower_case_nfd_strip_accents=__A				,			**__A       )
  		else:
  			__A   : Optional[Any]								= tf.lookup.StaticVocabularyTable(
  			    tf.lookup.KeyValueTensorInitializer(
  			        keys=__A				,			key_dtype=tf.string				,			values=tf.range(tf.size(__A				,			out_type=tf.intaa       )				,			dtype=tf.intaa       )				,			value_dtype=tf.intaa				,			)				,			num_oov_buckets=1				,			)
  			__A   : Any								= BertTokenizerLayer(
  			    __A				,			token_out_type=tf.intaa				,			lower_case=__A				,			**__A       )
  
  		__A   : Union[str, Any]								= vocab_list
  		__A   : List[str]								= do_lower_case
  		__A   : List[Any]								= cls_token_id or vocab_list.index("""[CLS]"""       )
  		__A   : Tuple								= sep_token_id or vocab_list.index("""[SEP]"""       )
  		__A   : str								= pad_token_id or vocab_list.index("""[PAD]"""       )
  		__A   : Optional[int]								= ShrinkLongestTrimmer(max_length - 3				,			axis=1       )  # Allow room for special tokens
  		__A   : Union[str, Any]								= max_length
  		__A   : List[Any]								= padding
  		__A   : Optional[int]								= truncation
  		__A   : Tuple								= pad_to_multiple_of
  		__A   : Optional[Any]								= return_token_type_ids
  		__A   : int								= return_attention_mask
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    List[Any]				,			__A	:    "PreTrainedTokenizerBase"				,			**__A	:    List[str]       ):  # noqa: F821
  		__A   : Any								= kwargs.pop("""do_lower_case"""				,			__A       )
  		__A   : Dict								= tokenizer.do_lower_case if do_lower_case is None else do_lower_case
  		__A   : List[str]								= kwargs.pop("""cls_token_id"""				,			__A       )
  		__A   : str								= tokenizer.cls_token_id if cls_token_id is None else cls_token_id
  		__A   : str								= kwargs.pop("""sep_token_id"""				,			__A       )
  		__A   : Dict								= tokenizer.sep_token_id if sep_token_id is None else sep_token_id
  		__A   : Tuple								= kwargs.pop("""pad_token_id"""				,			__A       )
  		__A   : List[Any]								= tokenizer.pad_token_id if pad_token_id is None else pad_token_id
  
  		__A   : Union[str, Any]								= tokenizer.get_vocab()
  		__A   : Any								= sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]				,			key=lambda __A       : x[1]       )
  		__A   : int								= [entry[0] for entry in vocab]
  		return cls(
  		    vocab_list=__A				,			do_lower_case=__A				,			cls_token_id=__A				,			sep_token_id=__A				,			pad_token_id=__A				,			**__A				,			)
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    List[str]				,			__A	:    Union[str, os.PathLike]				,			*__A	:    Tuple				,			**__A	:    Optional[int]       ):
  		try:
  			__A   : int								= BertTokenizer.from_pretrained(__A				,			*__A				,			**__A       )
  		except:  # noqa: E722
  			from .tokenization_bert_fast import BertTokenizerFast
  
  			__A   : Optional[int]								= BertTokenizerFast.from_pretrained(__A				,			*__A				,			**__A       )
  		return cls.from_tokenizer(__A				,			**__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    Optional[Any]       ):
  		if self.do_lower_case:
  			__A   : Tuple								= case_fold_utfa(__A       )
  		__A   : Optional[int]								= self.tf_tokenizer.tokenize(__A       )
  		return tokens.merge_dims(1				,			-1       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    Optional[Any]				,			__A	:    Dict=None				,			__A	:    List[Any]=None				,			__A	:    str=None				,			__A	:    Union[str, Any]=None				,			__A	:    Union[str, Any]=None				,			__A	:    int=None				,			__A	:    Any=None				,			):
  		if padding is None:
  			__A   : List[Any]								= self.padding
  		if padding not in ("longest", "max_length"):
  			raise ValueError("""Padding must be either 'longest' or 'max_length'!"""       )
  		if max_length is not None and text_pair is not None:
  			# Because we have to instantiate a Trimmer to do it properly
  			raise ValueError("""max_length cannot be overridden at call time when truncating paired texts!"""       )
  		if max_length is None:
  			__A   : Any								= self.max_length
  		if truncation is None:
  			__A   : int								= self.truncation
  		if pad_to_multiple_of is None:
  			__A   : List[str]								= self.pad_to_multiple_of
  		if return_token_type_ids is None:
  			__A   : int								= self.return_token_type_ids
  		if return_attention_mask is None:
  			__A   : Any								= self.return_attention_mask
  		if not isinstance(__A				,			tf.Tensor       ):
  			__A   : str								= tf.convert_to_tensor(__A       )
  		if text_pair is not None and not isinstance(__A				,			tf.Tensor       ):
  			__A   : Optional[Any]								= tf.convert_to_tensor(__A       )
  		if text_pair is not None:
  			if text.shape.rank > 1:
  				raise ValueError("""text argument should not be multidimensional when a text pair is supplied!"""       )
  			if text_pair.shape.rank > 1:
  				raise ValueError("""text_pair should not be multidimensional!"""       )
  		if text.shape.rank == 2:
  			__A						,							__A   : Optional[int]								= text[:, 0], text[:, 1]
  		__A   : List[Any]								= self.unpaired_tokenize(__A       )
  		if text_pair is None:  # Unpaired text
  			if truncation:
  				__A   : Union[str, Any]								= text[:, : max_length - 2]  # Allow room for special tokens
  			__A						,							__A   : List[Any]								= combine_segments(
  			    (text,)				,			start_of_sequence_id=self.cls_token_id				,			end_of_segment_id=self.sep_token_id       )
  		else:  # Paired text
  			__A   : Tuple								= self.unpaired_tokenize(__A       )
  			if truncation:
  				__A						,							__A   : Optional[int]								= self.paired_trimmer.trim([text, text_pair]       )
  			__A						,							__A   : int								= combine_segments(
  			    (text, text_pair)				,			start_of_sequence_id=self.cls_token_id				,			end_of_segment_id=self.sep_token_id       )
  		if padding == "longest":
  			__A   : str								= input_ids.bounding_shape(axis=1       )
  			if pad_to_multiple_of is not None:
  				# No ceiling division in tensorflow, so we negate floordiv instead
  				__A   : Optional[Any]								= pad_to_multiple_of * (-tf.math.floordiv(-pad_length				,			__A       ))
  		else:
  			__A   : Dict								= max_length
  
  		__A						,							__A   : str								= pad_model_inputs(__A				,			max_seq_length=__A				,			pad_value=self.pad_token_id       )
  		__A   : List[str]								= {"""input_ids""": input_ids}
  		if return_attention_mask:
  			__A   : Any								= attention_mask
  		if return_token_type_ids:
  			__A						,							__A   : Tuple								= pad_model_inputs(
  			    __A				,			max_seq_length=__A				,			pad_value=self.pad_token_id       )
  			__A   : List[str]								= token_type_ids
  		return output
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		return {
  		    "vocab_list": self.vocab_list,
  		    "do_lower_case": self.do_lower_case,
  		    "cls_token_id": self.cls_token_id,
  		    "sep_token_id": self.sep_token_id,
  		    "pad_token_id": self.pad_token_id,
  		}
  
  
  
  
  
  