
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


A		:   Any         =    torch.cuda.is_available()
A		:   Dict         =    [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
A		:   List[Any]         =    ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
A		:   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   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Path		,      SCREAMING_SNAKE_CASE_  :  list					)     ->    Any:
						_lowercase									=				"""\n""".join(SCREAMING_SNAKE_CASE_					)
						Path(SCREAMING_SNAKE_CASE_					).open("""w"""					).writelines(SCREAMING_SNAKE_CASE_					)




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Optional[Any]					)     ->    str:
						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       a_   ( _a						):






			@classmethod
			def       UpperCamelCase_							(			cls     ):
									return cls






			@require_torch_multi_gpu
			def       UpperCamelCase_							(			self     ):
									_lowercase									=				{
									    """no_teacher""": True,
									    """freeze_encoder""": True,
									    """gpus""": 2,
									    """overwrite_output_dir""": True,
									    """sortish_sampler""": True,
									}
									self._test_distiller_cli_fork(__UpperCamelCase							, check_contents=__UpperCamelCase     )






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase=True     ):
									_lowercase									=				{
									    """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(__UpperCamelCase     )
									_lowercase									=				CHEAP_ARGS.copy()
									_lowercase									=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
									_lowercase									=				self.get_auto_remove_tmp_dir()
									args_d.update(data_dir=__UpperCamelCase							, output_dir=__UpperCamelCase							, **__UpperCamelCase     )

									def convert(__UpperCamelCase							, __UpperCamelCase     ):
															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}"""

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

									_lowercase									=				os.listdir(__UpperCamelCase     )
									_lowercase									=				{os.path.basename(__UpperCamelCase     ) for p in contents}
									_lowercase									=				[p for p in contents if p.endswith("""ckpt"""     )]
									assert len(__UpperCamelCase     ) > 0

									self.assertIn("""test_generations.txt"""							, __UpperCamelCase     )
									self.assertIn("""test_results.txt"""							, __UpperCamelCase     )

									# get the following from the module, (we don't have access to `model` here)
									_lowercase									=				os.path.join(__UpperCamelCase							, """metrics.json"""     )
									_lowercase									=				"""rouge2"""

									_lowercase									=				load_json(__UpperCamelCase     )
									# {'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(__UpperCamelCase     )
									_lowercase									=				metrics["""val"""][-1]
									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]							, 0.01     )
									self.assertIsInstance(last_step_stats[f"""val_avg_{val_metric}"""]							, __UpperCamelCase     )
									self.assertEqual(len(metrics["""test"""]     )							, 1     )
									_lowercase									=				int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1     )
									self.assertEqual(len(metrics["""val"""]     )							, __UpperCamelCase     )

import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


A		:   Optional[int]         =    TypeVar('''T''', bound='''TaskTemplate''')



@dataclass(frozen=_a						)
class       a_   ( abc.ABC						):
			# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
			a  : str
			a  : ClassVar[Features]
			a  : ClassVar[Features]






			def       UpperCamelCase_							(			self							, __UpperCamelCase     ):
									return copy.deepcopy(self     )






			@property
			def       UpperCamelCase_							(			self     ):
									return Features(**self.input_schema							, **self.label_schema     )






			@property
			@abc.abstractmethod
			def       UpperCamelCase_							(			self     ):
									raise NotImplementedError






			@classmethod
			def       UpperCamelCase_							(			cls							, __UpperCamelCase     ):
									_lowercase									=				{f.name for f in dataclasses.fields(cls     )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}     )