

"""simple docstring"""






import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


lowerCamelCase__				:  Dict													=  logging.get_logger(__name__)

lowerCamelCase__				:  Optional[int]													=  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

lowerCamelCase__				:  str													=  {
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
}

lowerCamelCase__				:  List[Any]													=  {
    '''openai-gpt''': 5_12,
}
def       UpperCamelCase					(			_lowerCAmelCase     :						Tuple )				->			Union[str, Any]:
	_UpperCAmelCase    :							Any									=					text.strip()
	if not text:
		return []
	_UpperCAmelCase    :							List[Any]									=					text.split()
	return tokens






class     _UpperCAmelCase			(		UpperCamelCase_):
				def __init__(		self      ,		_A=True      ,		_A=None      ,		_A=True      ,		_A=None      ,		_A=True      ,		)    ->    str:






					'''simple docstring'''







					if never_split is None:
						_UpperCAmelCase    :							List[str]									=					[]
					_UpperCAmelCase    :							int									=					do_lower_case
					_UpperCAmelCase    :							Tuple									=					set(__SCREAMING_SNAKE_CASE     )
					_UpperCAmelCase    :							int									=					tokenize_chinese_chars
					_UpperCAmelCase    :							List[str]									=					strip_accents
					_UpperCAmelCase    :							int									=					do_split_on_punc
				def 							__snake_case (		self      ,		_A      ,		_A=None     )    ->    Any:






					'''simple docstring'''







					_UpperCAmelCase    :							List[str]									=					self.never_split.union(set(__SCREAMING_SNAKE_CASE     )     ) if never_split else self.never_split
					_UpperCAmelCase    :							str									=					self._clean_text(__SCREAMING_SNAKE_CASE     )

					# This was added on November 1st, 2018 for the multilingual and Chinese
					# models. This is also applied to the English models now, but it doesn't
					# matter since the English models were not trained on any Chinese data
					# and generally don't have any Chinese data in them (there are Chinese
					# characters in the vocabulary because Wikipedia does have some Chinese
					# words in the English Wikipedia.).
					if self.tokenize_chinese_chars:
						_UpperCAmelCase    :							Optional[Any]									=					self._tokenize_chinese_chars(__SCREAMING_SNAKE_CASE     )
					# prevents treating the same character with different unicode codepoints as different characters
					_UpperCAmelCase    :							str									=					unicodedata.normalize("""NFC"""      ,		__SCREAMING_SNAKE_CASE     )
					_UpperCAmelCase    :							Union[str, Any]									=					whitespace_tokenize(__SCREAMING_SNAKE_CASE     )
					_UpperCAmelCase    :							Optional[Any]									=					[]
					for token in orig_tokens:
						if token not in never_split:
							if self.do_lower_case:
								_UpperCAmelCase    :							List[str]									=					token.lower()
								if self.strip_accents is not False:
									_UpperCAmelCase    :							Tuple									=					self._run_strip_accents(__SCREAMING_SNAKE_CASE     )
							elif self.strip_accents:
								_UpperCAmelCase    :							Union[str, Any]									=					self._run_strip_accents(__SCREAMING_SNAKE_CASE     )
						split_tokens.extend(self._run_split_on_punc(__SCREAMING_SNAKE_CASE      ,		__SCREAMING_SNAKE_CASE     )     )

					_UpperCAmelCase    :							Optional[int]									=					whitespace_tokenize(""" """.join(__SCREAMING_SNAKE_CASE     )     )
					return output_tokens
				def 							__snake_case (		self      ,		_A     )    ->    int:






					'''simple docstring'''







					_UpperCAmelCase    :							int									=					unicodedata.normalize("""NFD"""      ,		__SCREAMING_SNAKE_CASE     )
					_UpperCAmelCase    :							Union[str, Any]									=					[]
					for char in text:
						_UpperCAmelCase    :							List[str]									=					unicodedata.category(__SCREAMING_SNAKE_CASE     )
						if cat == "Mn":
							continue
						output.append(__SCREAMING_SNAKE_CASE     )
					return "".join(__SCREAMING_SNAKE_CASE     )
				def 							__snake_case (		self      ,		_A      ,		_A=None     )    ->    List[Any]:






					'''simple docstring'''







					if not self.do_split_on_punc or (never_split is not None and text in never_split):
						return [text]
					_UpperCAmelCase    :							Dict									=					list(__SCREAMING_SNAKE_CASE     )
					_UpperCAmelCase    :							str									=					0
					_UpperCAmelCase    :							Tuple									=					True
					_UpperCAmelCase    :							Tuple									=					[]
					while i < len(__SCREAMING_SNAKE_CASE     ):
						_UpperCAmelCase    :							int									=					chars[i]
						if _is_punctuation(__SCREAMING_SNAKE_CASE     ):
							output.append([char]     )
							_UpperCAmelCase    :							int									=					True
						else:
							if start_new_word:
								output.append([]     )
							_UpperCAmelCase    :							Any									=					False
							output[-1].append(__SCREAMING_SNAKE_CASE     )
						i += 1

					return ["".join(__SCREAMING_SNAKE_CASE     ) for x in output]
				def 							__snake_case (		self      ,		_A     )    ->    Any:






					'''simple docstring'''







					_UpperCAmelCase    :							str									=					[]
					for char in text:
						_UpperCAmelCase    :							Optional[Any]									=					ord(__SCREAMING_SNAKE_CASE     )
						if self._is_chinese_char(__SCREAMING_SNAKE_CASE     ):
							output.append(""" """     )
							output.append(__SCREAMING_SNAKE_CASE     )
							output.append(""" """     )
						else:
							output.append(__SCREAMING_SNAKE_CASE     )
					return "".join(__SCREAMING_SNAKE_CASE     )
				def 							__snake_case (		self      ,		_A     )    ->    Union[str, Any]:






					'''simple docstring'''







					if (
					    (cp >= 0X4E00 and cp <= 0X9FFF)
					    or (cp >= 0X3400 and cp <= 0X4DBF)  #
					    or (cp >= 0X2_0000 and cp <= 0X2_A6DF)  #
					    or (cp >= 0X2_A700 and cp <= 0X2_B73F)  #
					    or (cp >= 0X2_B740 and cp <= 0X2_B81F)  #
					    or (cp >= 0X2_B820 and cp <= 0X2_CEAF)  #
					    or (cp >= 0XF900 and cp <= 0XFAFF)
					    or (cp >= 0X2_F800 and cp <= 0X2_FA1F)  #
					):  #
						return True

					return False
				def 							__snake_case (		self      ,		_A     )    ->    List[Any]:






					'''simple docstring'''







					_UpperCAmelCase    :							Union[str, Any]									=					[]
					for char in text:
						_UpperCAmelCase    :							Dict									=					ord(__SCREAMING_SNAKE_CASE     )
						if cp == 0 or cp == 0XFFFD or _is_control(__SCREAMING_SNAKE_CASE     ):
							continue
						if _is_whitespace(__SCREAMING_SNAKE_CASE     ):
							output.append(""" """     )
						else:
							output.append(__SCREAMING_SNAKE_CASE     )
					return "".join(__SCREAMING_SNAKE_CASE     )
def       UpperCamelCase					(			_lowerCAmelCase     :						Any )				->			List[Any]:
	_UpperCAmelCase    :							int									=					set()
	_UpperCAmelCase    :							Union[str, Any]									=					word[0]
	for char in word[1:]:
		pairs.add((prev_char, char) )
		_UpperCAmelCase    :							Optional[int]									=					char
	return pairs
def       UpperCamelCase					(			_lowerCAmelCase     :						List[str] )				->			str:
	_UpperCAmelCase    :							List[Any]									=					text.replace("""—""",  """-""" )
	_UpperCAmelCase    :							List[Any]									=					text.replace("""–""",  """-""" )
	_UpperCAmelCase    :							Union[str, Any]									=					text.replace("""―""",  """-""" )
	_UpperCAmelCase    :							List[Any]									=					text.replace("""…""",  """...""" )
	_UpperCAmelCase    :							Optional[Any]									=					text.replace("""´""",  """\'""" )
	_UpperCAmelCase    :							Optional[int]									=					re.sub(R"""(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)""",  R""" \1 """,  snake_case__ )
	_UpperCAmelCase    :							List[Any]									=					re.sub(R"""\s*\n\s*""",  """ \n """,  snake_case__ )
	_UpperCAmelCase    :							Any									=					re.sub(R"""[^\S\n]+""",  """ """,  snake_case__ )
	return text.strip()






class     _UpperCAmelCase			(		UpperCamelCase_):
				__a :       Optional[Any]									=							VOCAB_FILES_NAMES
				__a :       Optional[int]									=							PRETRAINED_VOCAB_FILES_MAP
				__a :       int									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a :       str									=							["""input_ids""", """attention_mask"""]
				def __init__(		self      ,		_A      ,		_A      ,		_A="<unk>"      ,		**_A     )    ->    Union[str, Any]:






					'''simple docstring'''







					super().__init__(unk_token=__SCREAMING_SNAKE_CASE      ,		**__SCREAMING_SNAKE_CASE     )

					try:
						import ftfy
						from spacy.lang.en import English

						_UpperCAmelCase    :							str									=					English()
						_UpperCAmelCase    :							List[str]									=					_nlp.tokenizer
						_UpperCAmelCase    :							Tuple									=					ftfy.fix_text
					except ImportError:
						logger.warning("""ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."""     )
						_UpperCAmelCase    :							Optional[int]									=					BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE     )
						_UpperCAmelCase    :							Optional[Any]									=					None

					with open(__SCREAMING_SNAKE_CASE      ,		encoding="""utf-8"""     ) as vocab_handle:
						_UpperCAmelCase    :							Union[str, Any]									=					json.load(__SCREAMING_SNAKE_CASE     )
					_UpperCAmelCase    :							Dict									=					{v: k for k, v in self.encoder.items()}
					with open(__SCREAMING_SNAKE_CASE      ,		encoding="""utf-8"""     ) as merges_handle:
						_UpperCAmelCase    :							List[str]									=					merges_handle.read().split("""\n"""     )[1:-1]
					_UpperCAmelCase    :							Optional[int]									=					[tuple(merge.split()     ) for merge in merges]
					_UpperCAmelCase    :							int									=					dict(zip(__SCREAMING_SNAKE_CASE      ,		range(len(__SCREAMING_SNAKE_CASE     )     )     )     )
					_UpperCAmelCase    :							List[str]									=					{}
				@property
				def 							__snake_case (		self     )    ->    Tuple:






					'''simple docstring'''







					return True
				@property
				def 							__snake_case (		self     )    ->    int:






					'''simple docstring'''







					return len(self.encoder     )
				def 							__snake_case (		self     )    ->    List[Any]:






					'''simple docstring'''







					return dict(self.encoder      ,		**self.added_tokens_encoder     )
				def 							__snake_case (		self      ,		_A     )    ->    str:






					'''simple docstring'''







					_UpperCAmelCase    :							Union[str, Any]									=					tuple(token[:-1]     ) + (token[-1] + """</w>""",)
					if token in self.cache:
						return self.cache[token]
					_UpperCAmelCase    :							Dict									=					get_pairs(__SCREAMING_SNAKE_CASE     )

					if not pairs:
						return token + "</w>"

					while True:
						_UpperCAmelCase    :							List[Any]									=					min(__SCREAMING_SNAKE_CASE      ,		key=lambda _A     : self.bpe_ranks.get(__SCREAMING_SNAKE_CASE      ,		float("""inf"""     )     )     )
						if bigram not in self.bpe_ranks:
							break
						_UpperCAmelCase						,   _UpperCAmelCase    :							Tuple									=					bigram
						_UpperCAmelCase    :							List[str]									=					[]
						_UpperCAmelCase    :							List[Any]									=					0
						while i < len(__SCREAMING_SNAKE_CASE     ):
							try:
								_UpperCAmelCase    :							Tuple									=					word.index(__SCREAMING_SNAKE_CASE      ,		__SCREAMING_SNAKE_CASE     )
							except ValueError:
								new_word.extend(word[i:]     )
								break
							else:
								new_word.extend(word[i:j]     )
								_UpperCAmelCase    :							int									=					j

							if word[i] == first and i < len(__SCREAMING_SNAKE_CASE     ) - 1 and word[i + 1] == second:
								new_word.append(first + second     )
								i += 2
							else:
								new_word.append(word[i]     )
								i += 1
						_UpperCAmelCase    :							int									=					tuple(__SCREAMING_SNAKE_CASE     )
						_UpperCAmelCase    :							Optional[int]									=					new_word
						if len(__SCREAMING_SNAKE_CASE     ) == 1:
							break
						else:
							_UpperCAmelCase    :							Optional[Any]									=					get_pairs(__SCREAMING_SNAKE_CASE     )
					_UpperCAmelCase    :							Optional[int]									=					""" """.join(__SCREAMING_SNAKE_CASE     )
					if word == "\n  </w>":
						_UpperCAmelCase    :							Dict									=					"""\n</w>"""
					_UpperCAmelCase    :							Union[str, Any]									=					word
					return word
				def 							__snake_case (		self      ,		_A     )    ->    Union[str, Any]:






					'''simple docstring'''







					_UpperCAmelCase    :							Union[str, Any]									=					[]
					if self.fix_text is None:
						# Using BERT's BasicTokenizer
						_UpperCAmelCase    :							str									=					self.nlp.tokenize(__SCREAMING_SNAKE_CASE     )
						for token in text:
							split_tokens.extend(list(self.bpe(__SCREAMING_SNAKE_CASE     ).split(""" """     )     )     )
					else:
						# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
						_UpperCAmelCase    :							List[str]									=					self.nlp(text_standardize(self.fix_text(__SCREAMING_SNAKE_CASE     )     )     )
						for token in text:
							split_tokens.extend(list(self.bpe(token.text.lower()     ).split(""" """     )     )     )
					return split_tokens
				def 							__snake_case (		self      ,		_A     )    ->    Optional[Any]:






					'''simple docstring'''







					return self.encoder.get(__SCREAMING_SNAKE_CASE      ,		self.encoder.get(self.unk_token     )     )
				def 							__snake_case (		self      ,		_A     )    ->    Any:






					'''simple docstring'''







					return self.decoder.get(__SCREAMING_SNAKE_CASE      ,		self.unk_token     )
				def 							__snake_case (		self      ,		_A     )    ->    Union[str, Any]:






					'''simple docstring'''







					_UpperCAmelCase    :							List[Any]									=					"""""".join(__SCREAMING_SNAKE_CASE     ).replace("""</w>"""      ,		""" """     ).strip()
					return out_string
				def 							__snake_case (		self      ,		_A      ,		_A = None     )    ->    Tuple[str]:






					'''simple docstring'''







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

					with open(__SCREAMING_SNAKE_CASE      ,		"""w"""      ,		encoding="""utf-8"""     ) as f:
						f.write(json.dumps(self.encoder      ,		indent=2      ,		sort_keys=__SCREAMING_SNAKE_CASE      ,		ensure_ascii=__SCREAMING_SNAKE_CASE     ) + """\n"""     )

					_UpperCAmelCase    :							int									=					0
					with open(__SCREAMING_SNAKE_CASE      ,		"""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    :							Tuple									=					token_index
							writer.write(""" """.join(__SCREAMING_SNAKE_CASE     ) + """\n"""     )
							index += 1

					return vocab_file, merge_file








"""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     )





