
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__       :   Union[str, Any]   						=					torch.cuda.is_available()
A__       :   Union[str, Any]   						=					[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
A__       :   int   						=					["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
A__       :   Union[str, Any]   						=					{
    """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     (   __UpperCamelCase       :  Dict	,__UpperCamelCase       :  Dict    ):
   lowerCAmelCase__ :    Tuple				      =			'''\n'''.join(lowercase__    )
   Path(lowercase__    ).open('''w'''    ).writelines(lowercase__    )



def      _a     (   __UpperCamelCase       :  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 						lowercase       (  __UpperCAmelCase					):
     @classmethod
     def lowercase_			(		cls    ):
        """simple docstring"""







        return cls
     @require_torch_multi_gpu
     def lowercase_			(		self    ):
        """simple docstring"""







        lowerCAmelCase__ :    Dict				      =			{
            '''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 lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=True    ):
        """simple docstring"""







        lowerCAmelCase__ :    str				      =			{
            '''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__    )
        lowerCAmelCase__ :    Union[str, Any]				      =			CHEAP_ARGS.copy()
        lowerCAmelCase__ :    Union[str, Any]				      =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
        lowerCAmelCase__ :    Dict				      =			self.get_auto_remove_tmp_dir()
        args_d.update(data_dir=lowerCamelCase__ ,	output_dir=lowerCamelCase__ ,	**lowerCamelCase__    )

        def convert(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
           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__ :    Optional[Any]				      =			[x for x in (convert(lowerCamelCase__ ,	lowerCamelCase__    ) for k, v in args_d.items()) if len(lowerCamelCase__    )]
        lowerCAmelCase__ :    Optional[int]				      =			[sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
        execute_subprocess_async(lowerCamelCase__ ,	env=self.get_env()    )

        lowerCAmelCase__ :    Optional[int]				      =			os.listdir(lowerCamelCase__    )
        lowerCAmelCase__ :    List[str]				      =			{os.path.basename(lowerCamelCase__    ) for p in contents}
        lowerCAmelCase__ :    Optional[int]				      =			[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)
        lowerCAmelCase__ :    Tuple				      =			os.path.join(lowerCamelCase__ ,	'''metrics.json'''    )
        lowerCAmelCase__ :    Optional[Any]				      =			'''rouge2'''

        lowerCAmelCase__ :    List[str]				      =			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__    )
        lowerCAmelCase__ :    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}'''] ,	lowerCamelCase__    )
        self.assertEqual(len(metrics['''test''']    ) ,	1    )
        lowerCAmelCase__ :    Optional[int]				      =			int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1    )
        self.assertEqual(len(metrics['''val''']    ) ,	lowerCamelCase__    )





from typing import TYPE_CHECKING

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


A__       :   Union[str, Any]   						=					{"""configuration_cvt""": ["""CVT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CvtConfig"""]}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   Union[str, Any]   						=					[
							    """CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CvtForImageClassification""",
							    """CvtModel""",
							    """CvtPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   str   						=					[
							    """TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFCvtForImageClassification""",
							    """TFCvtModel""",
							    """TFCvtPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cvt import (
														    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CvtForImageClassification,
														    CvtModel,
														    CvtPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_cvt import (
														    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCvtForImageClassification,
														    TFCvtModel,
														    TFCvtPreTrainedModel,
														)


else:
							import sys

							A__       :   Optional[Any]   						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



