


'''simple docstring'''


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_	:     Union[str, Any]							     =	[' Sam ate lunch today.', 'Sams lunch ingredients.']
UpperCAmelCase_	:     Tuple							     =	['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
UpperCAmelCase_	:     str							     =	{
    '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     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   """\n""".join(SCREAMING_SNAKE_CASE__  )
				Path(SCREAMING_SNAKE_CASE__  ).open("""w"""  ).writelines(SCREAMING_SNAKE_CASE__  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(SCREAMING_SNAKE_CASE__							,   f"""{split}.source"""  )							,   SCREAMING_SNAKE_CASE__  )
								_dump_articles(os.path.join(SCREAMING_SNAKE_CASE__							,   f"""{split}.target"""  )							,   SCREAMING_SNAKE_CASE__  )
				return tmp_dir







class 					lowercase__						(		_snake_case      ):





							'''simple docstring'''


							@classmethod
							def UpperCAmelCase_			(					cls						):
											return cls






							@require_torch_multi_gpu
							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: List[str]  	=   {
											    """no_teacher""": True,
											    """freeze_encoder""": True,
											    """gpus""": 2,
											    """overwrite_output_dir""": True,
											    """sortish_sampler""": True,
											}
											self._test_distiller_cli_fork(__snake_case					,      check_contents=__snake_case						)






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case=True						):
											_SCREAMING_SNAKE_CASE				: 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(__snake_case						)
											_SCREAMING_SNAKE_CASE				: dict  	=   CHEAP_ARGS.copy()
											_SCREAMING_SNAKE_CASE				: Dict  	=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   self.get_auto_remove_tmp_dir()
											args_d.update(data_dir=__snake_case					,      output_dir=__snake_case					,      **__snake_case						)

											def convert(__snake_case					,      __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}"""

											_SCREAMING_SNAKE_CASE				: Dict  	=   [x for x in (convert(__snake_case					,      __snake_case						) for k, v in args_d.items()) if len(__snake_case						)]
											_SCREAMING_SNAKE_CASE				: List[str]  	=   [sys.executable, f"""{self.test_file_dir}/distillation.py"""] + cli_args
											execute_subprocess_async(__snake_case					,      env=self.get_env()						)

											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   os.listdir(__snake_case						)
											_SCREAMING_SNAKE_CASE				: List[str]  	=   {os.path.basename(__snake_case						) for p in contents}
											_SCREAMING_SNAKE_CASE				: Tuple  	=   [p for p in contents if p.endswith("""ckpt"""						)]
											assert len(__snake_case						) > 0

											self.assertIn("""test_generations.txt"""					,      __snake_case						)
											self.assertIn("""test_results.txt"""					,      __snake_case						)

											# get the following from the module, (we don't have access to `model` here)
											_SCREAMING_SNAKE_CASE				: Dict  	=   os.path.join(__snake_case					,      """metrics.json"""						)
											_SCREAMING_SNAKE_CASE				: Tuple  	=   """rouge2"""

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








'''simple docstring'''


import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: str  	=   set()
				# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
				for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
								_SCREAMING_SNAKE_CASE				: Any  	=   getattr(transformers.models.auto							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
								if module is None:
												continue
								# all mappings in a single auto modeling file
								_SCREAMING_SNAKE_CASE				: str  	=   [
								    x
								    for x in dir(SCREAMING_SNAKE_CASE__  )
								    if x.endswith("""_MAPPING_NAMES"""  )
								    and (x.startswith("""MODEL_"""  ) or x.startswith("""TF_MODEL_"""  ) or x.startswith("""FLAX_MODEL_"""  ))
								]
								for name in mapping_names:
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   getattr(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												if mapping is not None:
																for v in mapping.values():
																				if isinstance(SCREAMING_SNAKE_CASE__							,   (list, tuple)  ):
																								model_names.update(SCREAMING_SNAKE_CASE__  )
																				elif isinstance(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
																								model_names.add(SCREAMING_SNAKE_CASE__  )

				return sorted(SCREAMING_SNAKE_CASE__  )







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   set(get_all_model_names()  )

				with open("""tests/utils/tiny_model_summary.json"""  ) as fp:
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   json.load(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: str  	=   set()
				for model_base_name in tiny_model_info:
								tiny_models_names.update(tiny_model_info[model_base_name]["""model_classes"""]  )

				# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
				_SCREAMING_SNAKE_CASE				: str  	=   model_names.difference(SCREAMING_SNAKE_CASE__  )
				for model_name in copy.copy(SCREAMING_SNAKE_CASE__  ):
								if not model_name.startswith("""TF"""  ) and f"""TF{model_name}""" in not_on_hub:
												tiny_models_names.remove(SCREAMING_SNAKE_CASE__  )
								elif model_name.startswith("""TF"""  ) and model_name[2:] in not_on_hub:
												tiny_models_names.remove(SCREAMING_SNAKE_CASE__  )

				return sorted(SCREAMING_SNAKE_CASE__  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   COMPOSITE_MODELS.values()

				# All tiny model base names on Hub
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   get_all_model_names()
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   hf_api.list_models(
				    filter=ModelFilter(
				        author="""hf-internal-testing"""							,   )  )
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   set()
				for x in models:
								_SCREAMING_SNAKE_CASE				: Tuple  	=   x.modelId
								_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   model.split("""/"""  )
								if not model.startswith("""tiny-random-"""  ):
												continue
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   model.replace("""tiny-random-"""							,   """"""  )
								if not model[0].isupper():
												continue
								if model not in model_names and model not in special_models:
												continue
								_models.add(SCREAMING_SNAKE_CASE__  )

				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   sorted(_models  )
				# All tiny model names on Hub
				_SCREAMING_SNAKE_CASE				: Dict  	=   {}
				for model in models:
								_SCREAMING_SNAKE_CASE				: str  	=   f"""hf-internal-testing/tiny-random-{model}"""
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   model.split("""-"""  )[0]
								try:
												_SCREAMING_SNAKE_CASE				: Tuple  	=   hf_api.repo_info(SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   {
												    """tokenizer_classes""": set(),
												    """processor_classes""": set(),
												    """model_classes""": set(),
												    """sha""": repo_info.sha,
												}
								except Exception:
												continue
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: int  	=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__							,   use_fast=SCREAMING_SNAKE_CASE__  )
												content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: str  	=   AutoImageProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["processor_classes"].add(img_p.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: Any  	=   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__  )
												if not isinstance(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
																content["processor_classes"].add(feat_p.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: int  	=   getattr(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: Tuple  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["model_classes"].add(m.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   getattr(SCREAMING_SNAKE_CASE__							,   f"""TF{model}"""  )
												_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["model_classes"].add(m.__class__.__name__  )
								except Exception:
												pass

								_SCREAMING_SNAKE_CASE				: List[Any]  	=   sorted(content["""tokenizer_classes"""]  )
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   sorted(content["""processor_classes"""]  )
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   sorted(content["""model_classes"""]  )

								_SCREAMING_SNAKE_CASE				: List[Any]  	=   content
								with open(os.path.join(SCREAMING_SNAKE_CASE__							,   """hub_tiny_model_summary.json"""  )							,   """w"""  ) as fp:
												json.dump(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   ensure_ascii=SCREAMING_SNAKE_CASE__							,   indent=4  )


if __name__ == "__main__":
				UpperCAmelCase_	:     Any							     =	argparse.ArgumentParser()
				parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
				UpperCAmelCase_	:     Optional[int]							     =	parser.parse_args()

				# This has to be `spawn` to avoid hanging forever!
				multiprocessing.set_start_method('spawn')

				UpperCAmelCase_	:     Any							     =	'tiny_models'
				UpperCAmelCase_	:     Tuple							     =	True
				UpperCAmelCase_	:     Tuple							     =	None
				UpperCAmelCase_	:     Optional[int]							     =	get_tiny_model_names_from_repo()
				UpperCAmelCase_	:     Dict							     =	True
				UpperCAmelCase_	:     str							     =	True
				UpperCAmelCase_	:     Optional[int]							     =	'hf-internal-testing'

				create_tiny_models(
				    output_path,
				    all,
				    model_types,
				    models_to_skip,
				    no_check,
				    upload,
				    organization,
				    token=os.environ.get('TOKEN', None),
				    num_workers=args.num_workers,
				)




