

'''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


UpperCAmelCase							=					torch.cuda.is_available()
UpperCAmelCase							=					[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
UpperCAmelCase							=					['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
UpperCAmelCase							=					{
    '''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-0_8,
    '''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							(		lowercase__  :					Union[str, Any],   lowercase__  :					Optional[Any]	):






     '''simple docstring'''





     __lowercase     						='\n'.join(lowercase__	)
     Path(lowercase__	).open('w'	).writelines(lowercase__	)

def 			__UpperCamelCase							(		lowercase__  :					List[str]	):






     '''simple docstring'''





     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 					lowerCAmelCase       ( __UpperCAmelCase				):







       @classmethod
       def      snake_case				(							cls     :	Any ):







            """simple docstring"""







            return cls







       @require_torch_multi_gpu
       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						={
                'no_teacher': True,
                'freeze_encoder': True,
                'gpus': 2,
                'overwrite_output_dir': True,
                'sortish_sampler': True,
            }
            self._test_distiller_cli_fork(lowerCamelCase__  , check_contents=lowerCamelCase__ )







       def      snake_case				(							self     :	List[str]  , __lowercase     :	str  , __lowercase     :	Optional[int]=True ):







            """simple docstring"""







            __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(lowerCamelCase__ )
            __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=lowerCamelCase__  , output_dir=lowerCamelCase__  , **lowerCamelCase__ )

            def convert(__lowercase     :	List[str]  , __lowercase     :	Union[str, 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}'''

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

            __lowercase     						=os.listdir(lowerCamelCase__ )
            __lowercase     						={os.path.basename(lowerCamelCase__ ) for p in contents}
            __lowercase     						=[p for p in contents if p.endswith('ckpt' )]
            assert len(lowerCamelCase__ ) > 0

            self.assertIn('test_generations.txt'  , lowerCamelCase__ )
            self.assertIn('test_results.txt'  , lowerCamelCase__ )

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

            __lowercase     						=load_json(lowerCamelCase__ )
            # {'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(lowerCamelCase__ )
            __lowercase     						=metrics['val'][-1]
            self.assertGreaterEqual(last_step_stats['val_avg_gen_time']  , 0.0_1 )
            self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']  , lowerCamelCase__ )
            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'] )  , lowerCamelCase__ )





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase							=					{'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					[
           '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ViTHybridForImageClassification''',
           '''ViTHybridModel''',
           '''ViTHybridPreTrainedModel''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
       from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit_hybrid import (
                  VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTHybridForImageClassification,
                  ViTHybridModel,
                  ViTHybridPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
       import sys

       UpperCAmelCase							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


