

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


lowerCamelCase									= torch.cuda.is_available()
lowerCamelCase									= [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCamelCase									= ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCamelCase									= {
    '''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       UpperCAmelCase__    (      _A				:			Path     ,				_A				:			list							):





  '''simple docstring'''







  a__  ='''\n'''.join(_A							)
  Path(_A							).open('''w'''							).writelines(_A							)




def       UpperCAmelCase__    (      _A				:			List[str]							):





  '''simple docstring'''







  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 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   @classmethod
   def 			_UpperCAmelCase			(    cls	)							->   Dict:






     """simple docstring"""






     return cls



   @require_torch_multi_gpu
   def 			_UpperCAmelCase			(    self	)							->   Dict:






     """simple docstring"""






     a__  ={
         '''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 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_=True	)							->   Union[str, Any]:






     """simple docstring"""






     a__  ={
         '''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_	)
     a__  =CHEAP_ARGS.copy()
     a__  =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
     a__  =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}"""

     a__  =[x for x in (convert(lowercase_,   lowercase_	) for k, v in args_d.items()) if len(lowercase_	)]
     a__  =[sys.executable, F"""{self.test_file_dir}/distillation.py"""] + cli_args
     execute_subprocess_async(lowercase_,   env=self.get_env()	)

     a__  =os.listdir(lowercase_	)
     a__  ={os.path.basename(lowercase_	) for p in contents}
     a__  =[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)
     a__  =os.path.join(lowercase_,   '''metrics.json'''	)
     a__  ='''rouge2'''

     a__  =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_	)
     a__  =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	)
     a__  =int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1	)
     self.assertEqual(len(metrics['''val''']	),   lowercase_	)



from typing import TYPE_CHECKING

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


lowerCamelCase									= {
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase									= [
           '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''FlaubertForMultipleChoice''',
           '''FlaubertForQuestionAnswering''',
           '''FlaubertForQuestionAnsweringSimple''',
           '''FlaubertForSequenceClassification''',
           '''FlaubertForTokenClassification''',
           '''FlaubertModel''',
           '''FlaubertWithLMHeadModel''',
           '''FlaubertPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase									= [
           '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFFlaubertForMultipleChoice''',
           '''TFFlaubertForQuestionAnsweringSimple''',
           '''TFFlaubertForSequenceClassification''',
           '''TFFlaubertForTokenClassification''',
           '''TFFlaubertModel''',
           '''TFFlaubertPreTrainedModel''',
           '''TFFlaubertWithLMHeadModel''',
       ]


if TYPE_CHECKING:
       from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
       from .tokenization_flaubert import FlaubertTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flaubert import (
                  FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaubertForMultipleChoice,
                  FlaubertForQuestionAnswering,
                  FlaubertForQuestionAnsweringSimple,
                  FlaubertForSequenceClassification,
                  FlaubertForTokenClassification,
                  FlaubertModel,
                  FlaubertPreTrainedModel,
                  FlaubertWithLMHeadModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_flaubert import (
                  TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFFlaubertForMultipleChoice,
                  TFFlaubertForQuestionAnsweringSimple,
                  TFFlaubertForSequenceClassification,
                  TFFlaubertForTokenClassification,
                  TFFlaubertModel,
                  TFFlaubertPreTrainedModel,
                  TFFlaubertWithLMHeadModel,
              )

else:
       import sys

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