





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a      :				int											=					{"""configuration_qdqbert""": ["""QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """QDQBertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a      :				Dict											=					[
	    """QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """QDQBertForMaskedLM""",
	    """QDQBertForMultipleChoice""",
	    """QDQBertForNextSentencePrediction""",
	    """QDQBertForQuestionAnswering""",
	    """QDQBertForSequenceClassification""",
	    """QDQBertForTokenClassification""",
	    """QDQBertLayer""",
	    """QDQBertLMHeadModel""",
	    """QDQBertModel""",
	    """QDQBertPreTrainedModel""",
	    """load_tf_weights_in_qdqbert""",
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

	_a      :				List[Any]											=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''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


_a      :				Optional[Any]											=					torch.cuda.is_available()
_a      :				Dict											=					[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_a      :				str											=					["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_a      :				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      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      Any:
			__lowerCAmelCase      		=       """\n""".join(lowercase			)
			Path(lowercase			).open("""w"""			).writelines(lowercase			)





def      _lowerCAmelCase		(		lowercase			)      ->      Optional[int]:
			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(lowercase      ,			f'{split}.source'			)      ,			lowercase			)
						_dump_articles(os.path.join(lowercase      ,			f'{split}.target'			)      ,			lowercase			)
			return tmp_dir







class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
		@classmethod
		def 						lowerCamelCase__    (						cls    ):



					'''simple docstring'''

					return cls
		@require_torch_multi_gpu
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       {
					    """no_teacher""": True,
					    """freeze_encoder""": True,
					    """gpus""": 2,
					    """overwrite_output_dir""": True,
					    """sortish_sampler""": True,
					}
					self._test_distiller_cli_fork(__SCREAMING_SNAKE_CASE,check_contents=__SCREAMING_SNAKE_CASE    )







		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=True    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       {
					    """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(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       CHEAP_ARGS.copy()
					__lowerCAmelCase      		=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
					__lowerCAmelCase      		=       self.get_auto_remove_tmp_dir()
					args_d.update(data_dir=__SCREAMING_SNAKE_CASE,output_dir=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )

					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}'

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

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

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

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

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

