




'''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					       =torch.cuda.is_available()
A					       =[' Sam ate lunch today.', 'Sams lunch ingredients.']
A					       =['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
A					       ={
    '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_				(_a      :				Path       ,  _a      :				list	):
    UpperCAmelCase     			=						'''\n'''.join(_a	)
    Path(_a	).open('''w'''	).writelines(_a	)





def 		snake_case_				(_a      :				Optional[int]	):
    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      _a    (      __a					):
       @classmethod
       def        A (      cls		:      Optional[Any]							):



           '''simple docstring'''

           return cls
       @require_torch_multi_gpu
       def        A (      self		:      List[Any]							):



           '''simple docstring'''

           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		:      Tuple  ,							lowercase		:      Tuple  ,							lowercase		:      Optional[int]=True							):



           '''simple docstring'''

           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		:      Optional[Any]  ,							lowercase		:      List[Any]							):
               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					       =logging.get_logger(__name__)

A					       ={
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}


class      _a    (      __a					):
       __a							:	Optional[int]  							=   """regnet"""
       __a							:	Optional[Any]  							=   ["""x""", """y"""]
       def __init__(      self		:      int  ,							lowercase		:      List[Any]=3  ,							lowercase		:      Union[str, Any]=32  ,							lowercase		:      Tuple=[128, 192, 512, 1_088]  ,							lowercase		:      List[Any]=[2, 6, 12, 2]  ,							lowercase		:      Optional[Any]=64  ,							lowercase		:      Optional[int]="y"  ,							lowercase		:      int="relu"  ,							**lowercase		:      int  ,							):



           '''simple docstring'''

           super().__init__(**lowercase							)
           if layer_type not in self.layer_types:
               raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types							)}"							)
           UpperCAmelCase     			=						num_channels
           UpperCAmelCase     			=						embedding_size
           UpperCAmelCase     			=						hidden_sizes
           UpperCAmelCase     			=						depths
           UpperCAmelCase     			=						groups_width
           UpperCAmelCase     			=						layer_type
           UpperCAmelCase     			=						hidden_act
           # always downsample in the first stage
           UpperCAmelCase     			=						True




