





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__ :			Any       	=			torch.cuda.is_available()
UpperCamelCase__ :			Any       	=			[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
UpperCamelCase__ :			List[Any]       	=			["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
UpperCamelCase__ :			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""": 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  A_(      A					,   A ):
	UpperCAmelCase_		    =  """\n""".join(A )
	Path(A ).open("""w""" ).writelines(A )




def  A_(      A ):
	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 	_UpperCamelCase	(							A_	):







					'''simple docstring'''

					@classmethod
					def 							SCREAMING_SNAKE_CASE				(    cls     :	str		):






						'''simple docstring'''







						return cls





					@require_torch_multi_gpu
					def 							SCREAMING_SNAKE_CASE				(    self     :	Any		):






						'''simple docstring'''







						UpperCAmelCase_		    =  {
						    """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 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]			,       __lowercase     :	Union[str, Any]			,       __lowercase     :	Union[str, Any]=True		):






						'''simple docstring'''







						UpperCAmelCase_		    =  {
						    """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		)
						UpperCAmelCase_		    =  CHEAP_ARGS.copy()
						UpperCAmelCase_		    =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
						UpperCAmelCase_		    =  self.get_auto_remove_tmp_dir()
						args_d.update(data_dir=__lowercase			,       output_dir=__lowercase			,       **__lowercase		)

						def convert(__lowercase     :	str			,       __lowercase     :	Optional[int]		):
							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}"""

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

						UpperCAmelCase_		    =  os.listdir(__lowercase		)
						UpperCAmelCase_		    =  {os.path.basename(__lowercase		) for p in contents}
						UpperCAmelCase_		    =  [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)
						UpperCAmelCase_		    =  os.path.join(__lowercase			,       """metrics.json"""		)
						UpperCAmelCase_		    =  """rouge2"""

						UpperCAmelCase_		    =  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		)
						UpperCAmelCase_		    =  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		)
						UpperCAmelCase_		    =  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_torch_available,
    is_vision_available,
)


UpperCamelCase__ :			Union[str, Any]       	=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			int       	=			[
				    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """MobileViTV2ForImageClassification""",
				    """MobileViTV2ForSemanticSegmentation""",
				    """MobileViTV2Model""",
				    """MobileViTV2PreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

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


