
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


UpperCAmelCase__				:   Any   							=			torch.cuda.is_available()
UpperCAmelCase__				:   Dict   							=			[' Sam ate lunch today.', 'Sams lunch ingredients.']
UpperCAmelCase__				:   List[str]   							=			['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
UpperCAmelCase__				:   Optional[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': 4_2,
    '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': 1_2,
    'max_target_length': 1_2,
    'val_max_target_length': 1_2,
    'test_max_target_length': 1_2,
    '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 lowercase_  (       _snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       """\n""".join(_A						)
							Path(_A						).open("""w"""						).writelines(_A						)

def lowercase_  (       _snake_case						):







							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      lowerCAmelCase_						(lowerCamelCase__  ):


				"""simple docstring"""



				@classmethod
				def      __magic_name__   (cls   ) ->		Dict:



											"""simple docstring"""




											return cls



				@require_torch_multi_gpu
				def      __magic_name__   (self   ) ->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[str]									=       {
											    """no_teacher""": True,
											    """freeze_encoder""": True,
											    """gpus""": 2,
											    """overwrite_output_dir""": True,
											    """sortish_sampler""": True,
											}
											self._test_distiller_cli_fork(lowercase_       ,	check_contents=lowercase_   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=True   ) ->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       {
											    """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(lowercase_   )
											SCREAMING_SNAKE_CASE__						:	Any									=       CHEAP_ARGS.copy()
											SCREAMING_SNAKE_CASE__						:	Tuple									=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       self.get_auto_remove_tmp_dir()
											args_d.update(data_dir=lowercase_       ,	output_dir=lowercase_       ,	**lowercase_   )

											def convert(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																		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__						:	int									=       [x for x in (convert(lowercase_       ,	lowercase_   ) for k, v in args_d.items()) if len(lowercase_   )]
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
											execute_subprocess_async(lowercase_       ,	env=self.get_env()   )

											SCREAMING_SNAKE_CASE__						:	int									=       os.listdir(lowercase_   )
											SCREAMING_SNAKE_CASE__						:	List[str]									=       {os.path.basename(lowercase_   ) for p in contents}
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       [p for p in contents if p.endswith("""ckpt"""   )]
											assert len(lowercase_   ) > 0

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

											# get the following from the module, (we don't have access to `model` here)
											SCREAMING_SNAKE_CASE__						:	int									=       os.path.join(lowercase_       ,	"""metrics.json"""   )
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       """rouge2"""

											SCREAMING_SNAKE_CASE__						:	List[str]									=       load_json(lowercase_   )
											# {'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(lowercase_   )
											SCREAMING_SNAKE_CASE__						:	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}''']       ,	lowercase_   )
											self.assertEqual(len(metrics["""test"""]   )       ,	1   )
											SCREAMING_SNAKE_CASE__						:	List[str]									=       int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1   )
											self.assertEqual(len(metrics["""val"""]   )       ,	lowercase_   )






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__				:   Optional[Any]   							=			{'configuration_umt5': ['UMT5Config', 'UMT5OnnxConfig']}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   Tuple   							=			[
					    'UMT5EncoderModel',
					    'UMT5ForConditionalGeneration',
					    'UMT5ForQuestionAnswering',
					    'UMT5Model',
					    'UMT5PreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_umta import UMTaConfig, UMTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_umta import (
										    UMTaEncoderModel,
										    UMTaForConditionalGeneration,
										    UMTaForQuestionAnswering,
										    UMTaModel,
										    UMTaPreTrainedModel,
										)
else:
					import sys

					UpperCAmelCase__				:   Optional[Any]   							=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




