





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 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							str   ,				UpperCamelCase__:							str      ):
		SCREAMING_SNAKE_CASE__								= """\n""".join(__A      )
		Path(__A      ).open("""w"""      ).writelines(__A      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Optional[int]      ):



		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_    (       UpperCamelCase__      ):


							@classmethod
							def        _snake_case					(							cls      :List[Any]						)				->				List[Any]:



									"""simple docstring"""



									return cls


							@require_torch_multi_gpu
							def        _snake_case					(							self      :Any						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= {
									    """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        _snake_case					(							self      :Optional[int]							,			__A      :int							,			__A      :Optional[Any]=True						)				->				Union[str, Any]:



									"""simple docstring"""



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

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

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

									SCREAMING_SNAKE_CASE__								= 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						)
									SCREAMING_SNAKE_CASE__								= metrics["""val"""][-1]
									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]							,			0.0_1						)
									self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']							,			__A						)
									self.assertEqual(len(metrics["""test"""]						)							,			1						)
									SCREAMING_SNAKE_CASE__								= int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1						)
									self.assertEqual(len(metrics["""val"""]						)							,			__A						)






import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
	from tests.trainer.test_trainer import (  # noqa
	    RegressionModelConfig,
	    RegressionPreTrainedModel,
	    get_regression_trainer,
	)


set_seed(42)

_lowerCamelCase							=     get_tests_dir('fixtures')
_lowerCamelCase							=     os.path.join(dirname(get_tests_dir()))
_lowerCamelCase							=     dirname(os.path.abspath(__file__))

# default torch.distributed port
_lowerCamelCase							=     '10999'

_lowerCamelCase							=     't5-small'

# *** Working Models ***
_lowerCamelCase							=     'hf-internal-testing/tiny-albert'
_lowerCamelCase							=     'sshleifer/bart-tiny-random'
_lowerCamelCase							=     'hf-internal-testing/tiny-bert'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-bigbird_pegasus'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-big_bird'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-blenderbot'
_lowerCamelCase							=     'bigscience/bigscience-small-testing'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-deberta'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-deberta-v2'
_lowerCamelCase							=     'sshleifer/tiny-distilbert-base-cased'
_lowerCamelCase							=     'hf-internal-testing/tiny-electra'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-flaubert'
_lowerCamelCase							=     'stas/tiny-wmt19-en-de'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-funnel'
_lowerCamelCase							=     'sshleifer/tiny-gpt2'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-gptj'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-gpt_neo'
_lowerCamelCase							=     'hf-internal-testing/tiny-layoutlm'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-led'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-longformer'
_lowerCamelCase							=     'stas/tiny-m2m_100'  # hf tiny model is unsuitable
_lowerCamelCase							=     'sshleifer/tiny-marian-en-de'
_lowerCamelCase							=     'sshleifer/tiny-mbart'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-mobilebert'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-mpnet'
_lowerCamelCase							=     'stas/pegasus-cnn_dailymail-tiny-random'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-prophetnet'
_lowerCamelCase							=     'sshleifer/tiny-distilroberta-base'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-squeezebert'
_lowerCamelCase							=     'patrickvonplaten/t5-tiny-random'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-t5-v1.1'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-vit'
_lowerCamelCase							=     'hf-internal-testing/tiny-xlm-roberta'
_lowerCamelCase							=     'sshleifer/tiny-xlnet-base-cased'


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_lowerCamelCase							=     'hf-internal-testing/tiny-random-mt5'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-camembert'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-openai-gpt'

# missing tokenizer files
_lowerCamelCase							=     'hf-internal-testing/tiny-random-convbert'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-layoutlmv2'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-hubert'

# issues with tokenizer
_lowerCamelCase							=     'hf-internal-testing/tiny-random-ctrl'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-transfo-xl'  # same as ctrl

# other issues with tiny models
_lowerCamelCase							=     'hf-internal-testing/tiny-random-ibert'  # multiple issues with either mlm/qa/clas
_lowerCamelCase							=     'hf-internal-testing/tiny-random-reformer'  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
_lowerCamelCase							=     'hf-internal-testing/tiny-random-dpr'
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_lowerCamelCase							=     'hf-internal-testing/tiny-random-rag'
# - "rag" research_projects
_lowerCamelCase							=     ''
# - "luke" Entities classes - no plan to make such example
_lowerCamelCase							=     'hf-internal-testing/tiny-random-lxmert'
# - "lxmert" doesn't work with run_qa.py
_lowerCamelCase							=     'hf-internal-testing/tiny-random-clip'
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
_lowerCamelCase							=     'hf-internal-testing/tiny-random-speech_to_text'
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
_lowerCamelCase							=     'hf-internal-testing/tiny-random-tapas'






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Optional[Any]=False      ):
		# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
		# - it won't be able to handle that
		# 2. for now testing with just 2 gpus max (since some quality tests may give different
		# results with mode gpus because we use very little data)
		SCREAMING_SNAKE_CASE__								= min(2   ,				get_gpu_count()      ) if distributed else 1
		SCREAMING_SNAKE_CASE__								= os.environ.get("""DS_TEST_PORT"""   ,				UpperCamelCase__      )
		return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()






def 			SCREAMING_SNAKE_CASE__							(    ):
		SCREAMING_SNAKE_CASE__								= f'''{FIXTURE_DIRECTORY}/tests_samples'''
		SCREAMING_SNAKE_CASE__								= f'''{data_dir_samples}/wmt_en_ro'''
		SCREAMING_SNAKE_CASE__								= f'''{data_dir_samples}/xsum'''
		SCREAMING_SNAKE_CASE__								= """
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        """.split()

		# try to cover as many models as possible once (it's enough to run on one task per model)
		# but need a tiny model for each
		#
		# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
		SCREAMING_SNAKE_CASE__								= {
		    """trans""": [
		        """bart""",
		        """fsmt""",
		        """m2m_100""",
		        """marian""",
		        """mbart""",
		        """t5""",
		        """t5_v1""",
		        # "mt5", missing model files
		    ],
		    """sum""": [
		        """pegasus""",
		    ],
		    """clm""": [
		        """big_bird""",
		        """bigbird_pegasus""",
		        """blenderbot""",
		        """bloom""",
		        """gpt2""",
		        """gpt_neo""",
		        """gptj""",
		        """xlm-roberta""",
		        """prophetnet""",
		        # "camembert", missing model files
		    ],
		    """mlm""": [
		        """albert""",
		        """deberta""",
		        """deberta-v2""",
		        """distilbert""",
		        """electra""",
		        """flaubert""",
		        """funnel""",
		        """layoutlm""",
		        # "reformer", # multiple issues with either mlm/qa/clas
		    ],
		    """qa""": [
		        """led""",
		        """longformer""",
		        """mobilebert""",
		        """mpnet""",
		        """roberta""",
		        """squeezebert""",
		        # "convbert", # missing tokenizer files
		        # "layoutlmv2", missing model files
		    ],
		    """clas""": [
		        """bert""",
		        """xlnet""",
		        # "hubert", # missing tokenizer files
		        # "ibert", # multiple issues with either mlm/qa/clas
		        # "transfo-xl", # tokenizer issues as ctrl
		        # "ctrl", # tokenizer issues
		        # "openai-gpt", missing model files
		        # "tapas", multiple issues
		    ],
		    """img_clas""": [
		        """vit""",
		    ],
		}

		SCREAMING_SNAKE_CASE__								= f'''{ROOT_DIRECTORY}/examples/pytorch'''

		SCREAMING_SNAKE_CASE__								= {
		    """trans""": f'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
		    """sum""": f'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
		    """clm""": f'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
		    """mlm""": f'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
		    """qa""": f'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
		    """clas""": f'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
		    """img_clas""": f'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
		}

		SCREAMING_SNAKE_CASE__								= get_launcher(distributed=UpperCamelCase__      )

		SCREAMING_SNAKE_CASE__								= {}
		for task, args in tasks.items():
				SCREAMING_SNAKE_CASE__								= args.split()
				for model in tasksamodels[task]:
						SCREAMING_SNAKE_CASE__								= globals()[f'''{model.upper().replace('-'   ,				'_'      )}_TINY''']
						SCREAMING_SNAKE_CASE__								= f'''--model_name_or_path {model_name}'''.split()
						SCREAMING_SNAKE_CASE__								= launcher + args + args_model + args_main

						# # generation special case
						# if task == "gen":
						#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
						#     args_model += f"--model_type {model}".split()
						#     cmds[f"{task}_{model}"] = launcher + args + args_model
						# else:

		return cmds


_lowerCamelCase							=     make_task_cmds()

_lowerCamelCase							=     'zero2'
_lowerCamelCase							=     'zero3'

_lowerCamelCase							=     [ZEROa, ZEROa]






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[str]   ,				UpperCamelCase__:							str   ,				UpperCamelCase__:							Tuple      ):
		# customize the test name generator function as we want both params to appear in the sub-test
		# name, as by default it shows only the first param
		SCREAMING_SNAKE_CASE__								= parameterized.to_safe_name("""_""".join(str(UpperCamelCase__      ) for x in param.args      )      )
		return f'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
_lowerCamelCase							=     list(itertools.product(stages, task_cmds.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class 					UpperCamelCase_    (       UpperCamelCase__      ):


							def        _snake_case					(							self      :Tuple							,			__A      :int							,			__A      :str						)				->				List[Any]:



									"""simple docstring"""



									if task not in task_cmds:
											raise ValueError(f'''don\'t know of task {task}, have {task_cmds.keys()}'''						)

									SCREAMING_SNAKE_CASE__								= task_cmds[task]
									SCREAMING_SNAKE_CASE__								= f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

									SCREAMING_SNAKE_CASE__								= self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__								= f'''--output_dir {output_dir}'''.split()

									cmd += args_ds + args_out

									return cmd, output_dir


							@parameterized.expand(__A							,			name_func=__A						)
							def        _snake_case					(							self      :List[Any]							,			__A      :Tuple							,			__A      :int						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self.get_task_cmd(__A							,			__A						)

									# 1. generate the checkpoint
									cmd += "--save_steps 1".split()
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
									execute_subprocess_async(__A							,			env=self.get_env()						)

									# 2. test that the fp32 weights get reconsolidated
									SCREAMING_SNAKE_CASE__								= f'''{output_dir}/checkpoint-1'''
									SCREAMING_SNAKE_CASE__								= f'''{chkpt_dir}/out.bin'''
									SCREAMING_SNAKE_CASE__								= f'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
									subprocess.check_call(__A							,			shell=__A						)
									assert os.path.exists(__A						), f'''{recovered_model_path} was not found'''

									# possibly could also test that the resulting saved model is usable but given that we use
									# random models we won't know if it's any good