code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import json
  import os
  from functools import lru_cache
  from typing import List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  _lowerCAmelCase       :      List[str]							=							logging.get_logger(__name__)
  
  
  _lowerCAmelCase       :      Tuple							=							{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
  
  # See all MVP models at https://huggingface.co/models?filter=mvp
  _lowerCAmelCase       :      Optional[Any]							=							{
      '''vocab_file''': {
          '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
      },
      '''added_tokens.json''': {
          '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
      },
      '''merges_file''': {
          '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
      },
  }
  
  _lowerCAmelCase       :      Any							=							{
      '''RUCAIBox/mvp''': 10_24,
  }
  
  @lru_cache()
  def 		__snake_case		(   )						->     List[str]:
  
  
  
  
  
  					'''simple docstring'''
  
  					_UpperCAmelCase    :			List[str]              =					(
  					    list(range(ord("!"       )		,						ord("~"       ) + 1       )       ) + list(range(ord("¡"       )		,						ord("¬"       ) + 1       )       ) + list(range(ord("®"       )		,						ord("ÿ"       ) + 1       )       )
  					)
  					_UpperCAmelCase    :			List[Any]              =					bs[:]
  					_UpperCAmelCase    :			Tuple              =					0
  					for b in range(2**8       ):
  										if b not in bs:
  															bs.append(SCREAMING_SNAKE_CASE__       )
  															cs.append(2**8 + n       )
  															n += 1
  					_UpperCAmelCase    :			Dict              =					[chr(SCREAMING_SNAKE_CASE__       ) for n in cs]
  					return dict(zip(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]       )						->     Union[str, Any]:
  
  
  
  
  
  					'''simple docstring'''
  
  					_UpperCAmelCase    :			int              =					set()
  					_UpperCAmelCase    :			Optional[int]              =					word[0]
  					for char in word[1:]:
  										pairs.add((prev_char, char)       )
  										_UpperCAmelCase    :			int              =					char
  					return pairs
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       lowercase__		):
  						__SCREAMING_SNAKE_CASE		:     str				  =				VOCAB_FILES_NAMES
  						__SCREAMING_SNAKE_CASE		:     str				  =				PRETRAINED_VOCAB_FILES_MAP
  						__SCREAMING_SNAKE_CASE		:     List[Any]				  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  						__SCREAMING_SNAKE_CASE		:     int				  =				["""input_ids""", """attention_mask"""]
  
  
  
  						def __init__(  self  :			Optional[int]  ,			A  :			Optional[int]  ,			A  :			Union[str, Any]  ,			A  :			Union[str, Any]="replace"  ,			A  :			Tuple="<s>"  ,			A  :			str="</s>"  ,			A  :			List[Any]="</s>"  ,			A  :			List[str]="<s>"  ,			A  :			Dict="<unk>"  ,			A  :			List[Any]="<pad>"  ,			A  :			List[str]="<mask>"  ,			A  :			Optional[Any]=False  ,			**A  :			Union[str, Any]  ,			):
  											_UpperCAmelCase    :			List[Any]              =					AddedToken(_A  ,			lstrip=_A  ,			rstrip=_A		) if isinstance(_A  ,			_A		) else bos_token
  											_UpperCAmelCase    :			Tuple              =					AddedToken(_A  ,			lstrip=_A  ,			rstrip=_A		) if isinstance(_A  ,			_A		) else eos_token
  											_UpperCAmelCase    :			List[Any]              =					AddedToken(_A  ,			lstrip=_A  ,			rstrip=_A		) if isinstance(_A  ,			_A		) else sep_token
  											_UpperCAmelCase    :			Optional[Any]              =					AddedToken(_A  ,			lstrip=_A  ,			rstrip=_A		) if isinstance(_A  ,			_A		) else cls_token
  											_UpperCAmelCase    :			Optional[int]              =					AddedToken(_A  ,			lstrip=_A  ,			rstrip=_A		) if isinstance(_A  ,			_A		) else unk_token
  											_UpperCAmelCase    :			str              =					AddedToken(_A  ,			lstrip=_A  ,			rstrip=_A		) if isinstance(_A  ,			_A		) else pad_token
  
  											# Mask token behave like a normal word, i.e. include the space before it
  											_UpperCAmelCase    :			List[str]              =					AddedToken(_A  ,			lstrip=_A  ,			rstrip=_A		) if isinstance(_A  ,			_A		) else mask_token
  
  											super().__init__(
  											    errors=_A  ,			bos_token=_A  ,			eos_token=_A  ,			unk_token=_A  ,			sep_token=_A  ,			cls_token=_A  ,			pad_token=_A  ,			mask_token=_A  ,			add_prefix_space=_A  ,			**_A  ,			)
  											with open(_A  ,			encoding="utf-8"		) as vocab_handle:
  																_UpperCAmelCase    :			int              =					json.load(_A		)
  											_UpperCAmelCase    :			str              =					{v: k for k, v in self.encoder.items()}
  											_UpperCAmelCase    :			Optional[Any]              =					errors  # how to handle errors in decoding
  											_UpperCAmelCase    :			int              =					bytes_to_unicode()
  											_UpperCAmelCase    :			Union[str, Any]              =					{v: k for k, v in self.byte_encoder.items()}
  											with open(_A  ,			encoding="utf-8"		) as merges_handle:
  																_UpperCAmelCase    :			Dict              =					merges_handle.read().split("\n"		)[1:-1]
  											_UpperCAmelCase    :			Any              =					[tuple(merge.split()		) for merge in bpe_merges]
  											_UpperCAmelCase    :			List[Any]              =					dict(zip(_A  ,			range(len(_A		)		)		)		)
  											_UpperCAmelCase    :			Optional[int]              =					{}
  											_UpperCAmelCase    :			Optional[int]              =					add_prefix_space
  
  											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
  											_UpperCAmelCase    :			Dict              =					re.compile(R"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"		)
  
  
  
  						@property
  						def 		snake_case_      (  self  :			Any		):
  											return len(self.encoder		)
  
  
  
  						def 		snake_case_      (  self  :			List[Any]		):
  											return dict(self.encoder  ,			**self.added_tokens_encoder		)
  
  
  
  						def 		snake_case_      (  self  :			str  ,			A  :			Optional[Any]		):
  											if token in self.cache:
  																return self.cache[token]
  											_UpperCAmelCase    :			Any              =					tuple(_A		)
  											_UpperCAmelCase    :			List[str]              =					get_pairs(_A		)
  
  											if not pairs:
  																return token
  
  											while True:
  																_UpperCAmelCase    :			List[str]              =					min(_A  ,			key=lambda A		: self.bpe_ranks.get(_A  ,			float("inf"		)		)		)
  																if bigram not in self.bpe_ranks:
  																					break
  																_UpperCAmelCase    :			List[str]              =					bigram
  																_UpperCAmelCase    :			List[Any]              =					[]
  																_UpperCAmelCase    :			str              =					0
  																while i < len(_A		):
  																					try:
  																										_UpperCAmelCase    :			Optional[int]              =					word.index(_A  ,			_A		)
  																					except ValueError:
  																										new_word.extend(word[i:]		)
  																										break
  																					else:
  																										new_word.extend(word[i:j]		)
  																										_UpperCAmelCase    :			str              =					j
  
  																					if word[i] == first and i < len(_A		) - 1 and word[i + 1] == second:
  																										new_word.append(first + second		)
  																										i += 2
  																					else:
  																										new_word.append(word[i]		)
  																										i += 1
  																_UpperCAmelCase    :			Union[str, Any]              =					tuple(_A		)
  																_UpperCAmelCase    :			List[Any]              =					new_word
  																if len(_A		) == 1:
  																					break
  																else:
  																					_UpperCAmelCase    :			Tuple              =					get_pairs(_A		)
  											_UpperCAmelCase    :			Union[str, Any]              =					' '.join(_A		)
  											_UpperCAmelCase    :			str              =					word
  											return word
  
  
  
  						def 		snake_case_      (  self  :			Dict  ,			A  :			List[Any]		):
  											_UpperCAmelCase    :			int              =					[]
  											for token in re.findall(self.pat  ,			_A		):
  																_UpperCAmelCase    :			Union[str, Any]              =					''.join(
  																    self.byte_encoder[b] for b in token.encode("utf-8"		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
  																bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A		).split(" "		)		)
  											return bpe_tokens
  
  
  
  						def 		snake_case_      (  self  :			Optional[Any]  ,			A  :			Any		):
  											return self.encoder.get(_A  ,			self.encoder.get(self.unk_token		)		)
  
  
  
  						def 		snake_case_      (  self  :			Optional[int]  ,			A  :			List[str]		):
  											return self.decoder.get(_A		)
  
  
  
  						def 		snake_case_      (  self  :			Tuple  ,			A  :			Any		):
  											_UpperCAmelCase    :			Tuple              =					''.join(_A		)
  											_UpperCAmelCase    :			Tuple              =					bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8"  ,			errors=self.errors		)
  											return text
  
  
  
  						def 		snake_case_      (  self  :			Optional[int]  ,			A  :			str  ,			A  :			Optional[str] = None		):
  											if not os.path.isdir(_A		):
  																logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
  																return
  											_UpperCAmelCase    :			Optional[int]              =					os.path.join(
  											    _A  ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
  											_UpperCAmelCase    :			Dict              =					os.path.join(
  											    _A  ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)
  
  											with open(_A  ,			"w"  ,			encoding="utf-8"		) as f:
  																f.write(json.dumps(self.encoder  ,			indent=2  ,			sort_keys=_A  ,			ensure_ascii=_A		) + "\n"		)
  
  											_UpperCAmelCase    :			Dict              =					0
  											with open(_A  ,			"w"  ,			encoding="utf-8"		) as writer:
  																writer.write("#version: 0.2\n"		)
  																for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,			key=lambda A		: kv[1]		):
  																					if index != token_index:
  																										logger.warning(
  																										    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
  																										    " Please check that the tokenizer is not corrupted!"		)
  																										_UpperCAmelCase    :			Any              =					token_index
  																					writer.write(" ".join(_A		) + "\n"		)
  																					index += 1
  
  											return vocab_file, merge_file
  
  
  
  						def 		snake_case_      (  self  :			Union[str, Any]  ,			A  :			List[int]  ,			A  :			Optional[List[int]] = None		):
  											if token_ids_a is None:
  																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  											_UpperCAmelCase    :			Union[str, Any]              =					[self.cls_token_id]
  											_UpperCAmelCase    :			Tuple              =					[self.sep_token_id]
  											return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  						def 		snake_case_      (  self  :			Dict  ,			A  :			List[int]  ,			A  :			Optional[List[int]] = None  ,			A  :			bool = False		):
  											if already_has_special_tokens:
  																return super().get_special_tokens_mask(
  																    token_ids_a=_A  ,			token_ids_a=_A  ,			already_has_special_tokens=_A		)
  
  											if token_ids_a is None:
  																return [1] + ([0] * len(_A		)) + [1]
  											return [1] + ([0] * len(_A		)) + [1, 1] + ([0] * len(_A		)) + [1]
  
  
  
  						def 		snake_case_      (  self  :			str  ,			A  :			List[int]  ,			A  :			Optional[List[int]] = None		):
  											_UpperCAmelCase    :			Optional[Any]              =					[self.sep_token_id]
  											_UpperCAmelCase    :			int              =					[self.cls_token_id]
  
  											if token_ids_a is None:
  																return len(cls + token_ids_a + sep		) * [0]
  											return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]
  
  
  
  						def 		snake_case_      (  self  :			str  ,			A  :			List[Any]  ,			A  :			str=False  ,			**A  :			List[Any]		):
  											_UpperCAmelCase    :			List[Any]              =					kwargs.pop("add_prefix_space"  ,			self.add_prefix_space		)
  											if (is_split_into_words or add_prefix_space) and (len(_A		) > 0 and not text[0].isspace()):
  																_UpperCAmelCase    :			Tuple              =					' ' + text
  											return (text, kwargs)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import os
  import sys
  from pathlib import Path
  
  import torch
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
  from utils import load_json
  
  
  _lowerCAmelCase       :      Optional[Any]							=							torch.cuda.is_available()
  _lowerCAmelCase       :      str							=							[" Sam ate lunch today.", "Sams lunch ingredients."]
  _lowerCAmelCase       :      Dict							=							["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
  _lowerCAmelCase       :      Union[str, Any]							=							{
      "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 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Path		,						SCREAMING_SNAKE_CASE__			:		list       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Tuple              =					"\n".join(SCREAMING_SNAKE_CASE__       )
       Path(SCREAMING_SNAKE_CASE__       ).open("w"       ).writelines(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for split in ["train", "val", "test"]:
            _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.source'       )		,						SCREAMING_SNAKE_CASE__       )
            _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.target'       )		,						SCREAMING_SNAKE_CASE__       )
       return tmp_dir
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
  
  
  
        @classmethod
        def 		snake_case_      (  cls  :			Optional[int]		):
             return cls
  
  
  
        @require_torch_multi_gpu
        def 		snake_case_      (  self  :			Union[str, Any]		):
             _UpperCAmelCase    :			Dict              =					{
                 "no_teacher": True,
                 "freeze_encoder": True,
                 "gpus": 2,
                 "overwrite_output_dir": True,
                 "sortish_sampler": True,
             }
             self._test_distiller_cli_fork(A  ,			check_contents=A		)
  
  
  
        def 		snake_case_      (  self  :			List[str]  ,			A  :			List[Any]  ,			A  :			Optional[Any]=True		):
             _UpperCAmelCase    :			int              =					{
                 "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		)
             _UpperCAmelCase    :			dict              =					CHEAP_ARGS.copy()
             _UpperCAmelCase    :			Union[str, Any]              =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
             _UpperCAmelCase    :			List[Any]              =					self.get_auto_remove_tmp_dir()
             args_d.update(data_dir=A  ,			output_dir=A  ,			**A		)
  
             def convert(A  :			List[Any]  ,			A  :			Optional[int]		):
                  if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                       return ""
                  if v is False or v is None:
                       return ""
                  if v is True:  # or len(str(v))==0:
                       return f'--{k}'
                  return f'--{k}={v}'
  
             _UpperCAmelCase    :			Tuple              =					[x for x in (convert(A  ,			A		) for k, v in args_d.items()) if len(A		)]
             _UpperCAmelCase    :			Union[str, Any]              =					[sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
             execute_subprocess_async(A  ,			env=self.get_env()		)
  
             _UpperCAmelCase    :			int              =					os.listdir(A		)
             _UpperCAmelCase    :			List[Any]              =					{os.path.basename(A		) for p in contents}
             _UpperCAmelCase    :			List[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		)
  
             # get the following from the module, (we don't have access to `model` here)
             _UpperCAmelCase    :			List[Any]              =					os.path.join(A  ,			"metrics.json"		)
             _UpperCAmelCase    :			str              =					"rouge2"
  
             _UpperCAmelCase    :			List[Any]              =					load_json(A		)
             # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
             print(A		)
             _UpperCAmelCase    :			List[str]              =					metrics["val"][-1]
             self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]  ,			0.01		)
             self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}']  ,			A		)
             self.assertEqual(len(metrics["test"]		)  ,			1		)
             _UpperCAmelCase    :			Dict              =					int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1		)
             self.assertEqual(len(metrics["val"]		)  ,			A		)
  