


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


snake_case_  :	List[Any]							  =							torch.cuda.is_available()
snake_case_  :	List[Any]							  =							[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
snake_case_  :	Union[str, Any]							  =							['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
snake_case_  :	Tuple							  =							{
    '''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   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     ='''\n'''.join(a__)
				Path(a__).open('''w''').writelines(a__)







def   lowerCamelCase(      a__):
				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   A__							(						UpperCamelCase__     ):




						@classmethod
						def 							__UpperCamelCase (				cls    :					Tuple     )							->   int:


										"""simple docstring"""





										return cls




						@require_torch_multi_gpu
						def 							__UpperCamelCase (				self    :					Dict     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     ={
										    '''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 							__UpperCamelCase (				self    :					Tuple ,			_a    :					List[Any] ,			_a    :					Any=True     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     ={
										    '''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     )
										_SCREAMING_SNAKE_CASE	     =CHEAP_ARGS.copy()
										_SCREAMING_SNAKE_CASE	     =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
										_SCREAMING_SNAKE_CASE	     =self.get_auto_remove_tmp_dir()
										args_d.update(data_dir=_a ,			output_dir=_a ,			**_a     )

										def convert(_a    :					int ,			_a    :					Dict     ):
														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}"

										_SCREAMING_SNAKE_CASE	     =[x for x in (convert(_a ,			_a     ) for k, v in args_d.items()) if len(_a     )]
										_SCREAMING_SNAKE_CASE	     =[sys.executable, f"{self.test_file_dir}/distillation.py"] + cli_args
										execute_subprocess_async(_a ,			env=self.get_env()     )

										_SCREAMING_SNAKE_CASE	     =os.listdir(_a     )
										_SCREAMING_SNAKE_CASE	     ={os.path.basename(_a     ) for p in contents}
										_SCREAMING_SNAKE_CASE	     =[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)
										_SCREAMING_SNAKE_CASE	     =os.path.join(_a ,			'''metrics.json'''     )
										_SCREAMING_SNAKE_CASE	     ='''rouge2'''

										_SCREAMING_SNAKE_CASE	     =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     )
										_SCREAMING_SNAKE_CASE	     =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     )
										_SCREAMING_SNAKE_CASE	     =int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1     )
										self.assertEqual(len(metrics['''val''']     ) ,			_a     )



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case_  :	Tuple							  =							logging.get_logger(__name__)

snake_case_  :	int							  =							{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_  :	Dict							  =							{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json''',
    },
}

class   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Optional[Any] ,			_a    :					Tuple=None ,			_a    :					Union[str, Any]=None ,			_a    :					Dict=False ,			_a    :					int=True ,			_a    :					str=False ,			_a    :					int="<s>" ,			_a    :					int="</s>" ,			_a    :					Optional[int]="<unk>" ,			_a    :					Dict="<sep>" ,			_a    :					Optional[int]="<pad>" ,			_a    :					str="<cls>" ,			_a    :					List[Any]="<mask>" ,			_a    :					Optional[Any]=["<eop>", "<eod>"] ,			**_a    :					Any ,			)							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =AddedToken(_a ,			lstrip=_a ,			rstrip=_a     ) if isinstance(_a ,			_a     ) else mask_token

										super().__init__(
										    vocab_file=_a ,			tokenizer_file=_a ,			do_lower_case=_a ,			remove_space=_a ,			keep_accents=_a ,			bos_token=_a ,			eos_token=_a ,			unk_token=_a ,			sep_token=_a ,			pad_token=_a ,			cls_token=_a ,			mask_token=_a ,			additional_special_tokens=_a ,			**_a ,			)

										_SCREAMING_SNAKE_CASE	     =3
										_SCREAMING_SNAKE_CASE	     =do_lower_case
										_SCREAMING_SNAKE_CASE	     =remove_space
										_SCREAMING_SNAKE_CASE	     =keep_accents
										_SCREAMING_SNAKE_CASE	     =vocab_file
										_SCREAMING_SNAKE_CASE	     =False if not self.vocab_file else True

										try:
														import jieba
										except ModuleNotFoundError as error:
														raise error.__class__(
														    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
														    '''See https://pypi.org/project/jieba/ for installation.'''     )
										_SCREAMING_SNAKE_CASE	     =jieba
										_SCREAMING_SNAKE_CASE	     =str.maketrans(''' \n''' ,			'''\u2582\u2583'''     )




						def 							__UpperCamelCase (				self    :					Any ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[self.cls_token_id]
										if token_ids_a is None:
														return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[2]

										if token_ids_a is None:
														return len(token_ids_a + sep     ) * [0] + cls_segment_id
										return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id




						def 							__UpperCamelCase (				self    :					str ,			_a    :					str ,			_a    :					Optional[str] = None     )							->   Tuple[str]:


										"""simple docstring"""





										if not self.can_save_slow_tokenizer:
														raise ValueError(
														    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
														    '''tokenizer.'''     )

										if not os.path.isdir(_a     ):
														logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
														return
										_SCREAMING_SNAKE_CASE	     =os.path.join(
										    _a ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

										if os.path.abspath(self.vocab_file     ) != os.path.abspath(_a     ):
														copyfile(self.vocab_file ,			_a     )

										return (out_vocab_file,)




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					str ,			*_a    :					Optional[int] ,			**_a    :					Any     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[
										    ''' '''.join([x.translate(self.translator     ) for x in self.jieba.cut(_a ,			cut_all=_a     )]     )
										    for text in batch_text_or_text_pairs
										]
										return super()._batch_encode_plus(_a ,			*_a ,			**_a     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			*_a    :					Union[str, Any] ,			**_a    :					Any     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =super()._decode(*_a ,			**_a     )
										_SCREAMING_SNAKE_CASE	     =text.replace(''' ''' ,			''''''     ).replace('''\u2582''' ,			''' '''     ).replace('''\u2583''' ,			'''\n'''     )
										return text