






"""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  :						Dict					       =							torch.cuda.is_available()
a  :						List[str]					       =							[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
a  :						Tuple					       =							['''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''': 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       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->int:
 UpperCAmelCase__ 						=				"""\n""".join(_SCREAMING_SNAKE_CASE			)
 Path(_SCREAMING_SNAKE_CASE			).open("""w"""			).writelines(_SCREAMING_SNAKE_CASE			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->List[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 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    @classmethod
    def 		A__      (      cls			):
     return cls


    @require_torch_multi_gpu
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				{
         """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 		A__      (      self   ,			__lowercase   ,			__lowercase=True			):
     UpperCAmelCase__ 						=				{
         """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			)
     UpperCAmelCase__ 						=				CHEAP_ARGS.copy()
     UpperCAmelCase__ 						=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
     UpperCAmelCase__ 						=				self.get_auto_remove_tmp_dir()
     args_d.update(data_dir=__lowercase   ,			output_dir=__lowercase   ,			**__lowercase			)

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

     UpperCAmelCase__ 						=				os.listdir(__lowercase			)
     UpperCAmelCase__ 						=				{os.path.basename(__lowercase			) for p in contents}
     UpperCAmelCase__ 						=				[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)
     UpperCAmelCase__ 						=				os.path.join(__lowercase   ,			"""metrics.json"""			)
     UpperCAmelCase__ 						=				"""rouge2"""

     UpperCAmelCase__ 						=				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			)
     UpperCAmelCase__ 						=				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			)
     UpperCAmelCase__ 						=				int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1			)
     self.assertEqual(len(metrics["""val"""]			)   ,			__lowercase			)











"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :						Optional[Any]					       =							logging.get_logger(__name__)

a  :						int					       =							{
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}


class 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   Optional[Any]											=						'opt'
    __lowercase    :   str											=						['past_key_values']


    def __init__(      self   ,			__lowercase=50272   ,			__lowercase=768   ,			__lowercase=12   ,			__lowercase=3072   ,			__lowercase=2048   ,			__lowercase=True   ,			__lowercase=False   ,			__lowercase=None   ,			__lowercase=0.1   ,			__lowercase=0.0   ,			__lowercase=12   ,			__lowercase="relu"   ,			__lowercase=0.0   ,			__lowercase=0.02   ,			__lowercase=True   ,			__lowercase=1   ,			__lowercase=2   ,			__lowercase=2   ,			__lowercase=True   ,			__lowercase=True   ,			**__lowercase   ,			):
     super().__init__(
         pad_token_id=__lowercase   ,			bos_token_id=__lowercase   ,			eos_token_id=__lowercase   ,			**__lowercase   ,			)
     UpperCAmelCase__ 						=				vocab_size
     UpperCAmelCase__ 						=				max_position_embeddings
     UpperCAmelCase__ 						=				num_attention_heads
     UpperCAmelCase__ 						=				word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
     UpperCAmelCase__ 						=				ffn_dim
     UpperCAmelCase__ 						=				hidden_size
     UpperCAmelCase__ 						=				num_hidden_layers
     UpperCAmelCase__ 						=				dropout
     UpperCAmelCase__ 						=				attention_dropout
     UpperCAmelCase__ 						=				activation_function
     UpperCAmelCase__ 						=				init_std
     UpperCAmelCase__ 						=				layerdrop
     UpperCAmelCase__ 						=				use_cache
     UpperCAmelCase__ 						=				do_layer_norm_before
     # We keep these variables at `True` for backward compatibility.
     UpperCAmelCase__ 						=				enable_bias
     UpperCAmelCase__ 						=				layer_norm_elementwise_affine

     # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
     # with checkpoints that have been fine-tuned before transformers v4.20.1
     # see https://github.com/facebookresearch/metaseq/pull/164
     UpperCAmelCase__ 						=				_remove_final_layer_norm



