





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


snake_case_				=      torch.cuda.is_available()
snake_case_				=      [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
snake_case_				=      ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
snake_case_				=      {
    '''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        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Optional[Any]:
		lowerCamelCase :			Any    		='''\n'''.join(a__		)
		Path(a__		).open('''w'''		).writelines(a__		)


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  Tuple:
		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 							snake_case_		(      __SCREAMING_SNAKE_CASE):







		@classmethod
		def       __lowercase (		cls )					-> Union[str, Any]:
				return cls







		@require_torch_multi_gpu
		def       __lowercase (		self )					-> Optional[int]:
				lowerCamelCase :			Tuple    		={
				    '''no_teacher''': True,
				    '''freeze_encoder''': True,
				    '''gpus''': 2,
				    '''overwrite_output_dir''': True,
				    '''sortish_sampler''': True,
				}
				self._test_distiller_cli_fork(__UpperCamelCase      ,  check_contents=__UpperCamelCase )







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase=True )					-> Dict:
				lowerCamelCase :			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(__UpperCamelCase )
				lowerCamelCase :			Tuple    		=CHEAP_ARGS.copy()
				lowerCamelCase :			Optional[Any]    		=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
				lowerCamelCase :			str    		=self.get_auto_remove_tmp_dir()
				args_d.update(data_dir=__UpperCamelCase      ,  output_dir=__UpperCamelCase      ,  **__UpperCamelCase )

				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}"

				lowerCamelCase :			Union[str, Any]    		=[x for x in (convert(__UpperCamelCase      ,  __UpperCamelCase ) for k, v in args_d.items()) if len(__UpperCamelCase )]
				lowerCamelCase :			Dict    		=[sys.executable, F"{self.test_file_dir}/distillation.py"] + cli_args
				execute_subprocess_async(__UpperCamelCase      ,  env=self.get_env() )

				lowerCamelCase :			Optional[Any]    		=os.listdir(__UpperCamelCase )
				lowerCamelCase :			Union[str, Any]    		={os.path.basename(__UpperCamelCase ) for p in contents}
				lowerCamelCase :			Any    		=[p for p in contents if p.endswith('''ckpt''' )]
				assert len(__UpperCamelCase ) > 0

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

				# get the following from the module, (we don't have access to `model` here)
				lowerCamelCase :			int    		=os.path.join(__UpperCamelCase      ,  '''metrics.json''' )
				lowerCamelCase :			Any    		='''rouge2'''

				lowerCamelCase :			List[Any]    		=load_json(__UpperCamelCase )
				# {'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(__UpperCamelCase )
				lowerCamelCase :			Dict    		=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}"]      ,  __UpperCamelCase )
				self.assertEqual(len(metrics['''test'''] )      ,  1 )
				lowerCamelCase :			Tuple    		=int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1 )
				self.assertEqual(len(metrics['''val'''] )      ,  __UpperCamelCase )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A							=       {'''configuration_dinat''': ['''DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DinatConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _A							=       [
          '''DINAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DinatForImageClassification''',
          '''DinatModel''',
          '''DinatPreTrainedModel''',
          '''DinatBackbone''',
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

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



