


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''': 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  __SCREAMING_SNAKE_CASE       (				lowercase__  :Path      ,						lowercase__  :list				)       ->  Dict:
						__magic_name__				:							Union[str, Any]				=      '\n'.join(lowercase__				)
						Path(lowercase__				).open('w'				).writelines(lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]				)       ->  Optional[int]:







						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     snake_case__  (      lowercase__  ):
				@classmethod
				def  UpperCamelCase_						(  cls      ):




										"""simple docstring"""




										return cls
				@require_torch_multi_gpu
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      {
										    'no_teacher': True,
										    'freeze_encoder': True,
										    'gpus': 2,
										    'overwrite_output_dir': True,
										    'sortish_sampler': True,
										}
										self._test_distiller_cli_fork(_A     ,      check_contents=_A      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=True      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      {
										    '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(_A      )
										__magic_name__				:							dict				=      CHEAP_ARGS.copy()
										__magic_name__				:							Tuple				=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
										__magic_name__				:							Optional[int]				=      self.get_auto_remove_tmp_dir()
										args_d.update(data_dir=_A     ,      output_dir=_A     ,      **_A      )

										def convert(_UpperCAmelCase     ,      _UpperCAmelCase      ):
																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}"

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

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

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

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

										__magic_name__				:							Optional[Any]				=      load_json(_A      )
										# {'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(_A      )
										__magic_name__				:							Any				=      metrics['val'][-1]
										self.assertGreaterEqual(last_step_stats['val_avg_gen_time']     ,      0.01      )
										self.assertIsInstance(last_step_stats[F"val_avg_{val_metric}"]     ,      _A      )
										self.assertEqual(len(metrics['test']      )     ,      1      )
										__magic_name__				:							str				=      int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1      )
										self.assertEqual(len(metrics['val']      )     ,      _A      )






from typing import TYPE_CHECKING

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


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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    '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:
			snake_case__     							= [
			    '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

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


