

"""simple docstring"""






import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin






class     _UpperCAmelCase			(		__a    ,     unittest.TestCase):
			__a :       Dict									=							SpeechaTextaTokenizer
			__a :       Optional[Any]									=							False
			def 							__snake_case (		self     )    ->    Dict:






				'''simple docstring'''







				super().setUp()

				_UpperCAmelCase    :							List[str]									=					"""<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab""".split(""" """     )
				_UpperCAmelCase    :							int									=					["""he re</w> 123""", """here a 1456"""]
				_UpperCAmelCase    :							List[Any]									=					dict(zip(_A      ,		range(len(_A     )     )     )     )

				_UpperCAmelCase    :							Optional[int]									=					{"""pad_token""": """<pad>""", """unk_token""": """<unk>""", """bos_token""": """<s>""", """eos_token""": """</s>"""}

				_UpperCAmelCase    :							List[str]									=					tempfile.mkdtemp()
				_UpperCAmelCase    :							Tuple									=					os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["""vocab_file"""]     )
				_UpperCAmelCase    :							Optional[int]									=					os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["""merges_file"""]     )

				with open(self.vocab_file      ,		"""w"""      ,		encoding="""utf-8"""     ) as fp:
					fp.write(json.dumps(_A     ) + """\n"""     )

				with open(self.merges_file      ,		"""w"""     ) as fp:
					fp.write("""\n""".join(_A     )     )
			def 							__snake_case (		self     )    ->    Tuple:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[int]									=					list(self.get_tokenizer().get_vocab().keys()     )

				self.assertEqual(vocab_keys[0]      ,		"""<s>"""     )
				self.assertEqual(vocab_keys[1]      ,		"""<pad>"""     )
				self.assertEqual(vocab_keys[-1]      ,		"""vocab"""     )
				self.assertEqual(len(_A     )      ,		14     )
			def 							__snake_case (		self     )    ->    Optional[int]:






				'''simple docstring'''







				self.assertEqual(self.get_tokenizer().vocab_size      ,		14     )
			def 							__snake_case (		self     )    ->    int:






				'''simple docstring'''







				_UpperCAmelCase    :							Union[str, Any]									=					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname     )

				# make sure @@ is correctly concatenated
				_UpperCAmelCase    :							Optional[Any]									=					[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
				_UpperCAmelCase    :							Tuple									=					tokenizer.decode(_A     )

				self.assertTrue(output_string == """herecouple words ofthe"""     )
			def 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[Any]									=					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname     )

				with tempfile.TemporaryDirectory() as tmp_dirname:
					tokenizer.save_pretrained(_A     )
					os.remove(os.path.join(_A      ,		"""merges.txt"""     )     )

					# load tokenizer without merges file should not throw an error
					_UpperCAmelCase    :							Optional[Any]									=					SpeechaTextaTokenizer.from_pretrained(_A     )

				with tempfile.TemporaryDirectory() as tmp_dirname:
					# save tokenizer and load again
					tokenizer.save_pretrained(_A     )
					_UpperCAmelCase    :							Any									=					SpeechaTextaTokenizer.from_pretrained(_A     )

				self.assertIsNotNone(_A     )
			def 							__snake_case (		self     )    ->    Tuple:






				'''simple docstring'''







				if not self.test_slow_tokenizer:
					return

				_UpperCAmelCase    :							Union[str, Any]									=					inspect.signature(self.tokenizer_class.__init__     )
				_UpperCAmelCase    :							int									=					self.get_tokenizer()

				for parameter_name, parameter in signature.parameters.items():
					if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
						self.assertIn(_A      ,		tokenizer.init_kwargs     )








"""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__				:  Any													=  torch.cuda.is_available()
lowerCamelCase__				:  Optional[Any]													=  [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCamelCase__				:  List[str]													=  ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCamelCase__				:  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       UpperCamelCase					(			_lowerCAmelCase     :						Path,  _lowerCAmelCase     :						list )				->			str:
	_UpperCAmelCase    :							Dict									=					"""\n""".join(_lowerCAmelCase )
	Path(_lowerCAmelCase ).open("""w""" ).writelines(_lowerCAmelCase )
def       UpperCamelCase					(			_lowerCAmelCase     :						Optional[int] )				->			Optional[Any]:
	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     _UpperCAmelCase			(		__a):
			@classmethod
			def 							__snake_case (		cls     )    ->    Optional[int]:






				'''simple docstring'''







				return cls
			@require_torch_multi_gpu
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[str]									=					{
				    """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      ,		_A      ,		_A=True     )    ->    int:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					{
				    """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    :							List[Any]									=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
				_UpperCAmelCase    :							Optional[Any]									=					self.get_auto_remove_tmp_dir()
				args_d.update(data_dir=_A      ,		output_dir=_A      ,		**_A     )

				def convert(_A      ,		_A     ):
					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    :							List[Any]									=					[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    :							Union[str, Any]									=					os.listdir(_A     )
				_UpperCAmelCase    :							Dict									=					{os.path.basename(_A     ) for p in contents}
				_UpperCAmelCase    :							str									=					[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[str]									=					os.path.join(_A      ,		"""metrics.json"""     )
				_UpperCAmelCase    :							List[Any]									=					"""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    :							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    :							Optional[int]									=					int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1     )
				self.assertEqual(len(metrics["""val"""]     )      ,		_A     )





