



'''simple docstring'''

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   __lowerCamelCase  (   lowerCAmelCase_=False						)    ->   str:
		# 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)
		_a  :   List[Any]				 =							min(2		,    get_gpu_count()						) if distributed else 1
		_a  :   List[str]				 =							os.environ.get('DS_TEST_PORT'		,    lowerCAmelCase_						)
		return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()



def   __lowerCamelCase  (   )    ->   Dict:
		_a  :   List[str]				 =							f"""{FIXTURE_DIRECTORY}/tests_samples"""
		_a  :   Dict				 =							f"""{data_dir_samples}/wmt_en_ro"""
		_a  :   Dict				 =							f"""{data_dir_samples}/xsum"""
		_a  :   Optional[Any]				 =							'\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        '.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.
		_a  :   List[Any]				 =							{
		    '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',
		    ],
		}

		_a  :   Union[str, Any]				 =							f"""{ROOT_DIRECTORY}/examples/pytorch"""

		_a  :   Any				 =							{
		    '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
        """,
		}

		_a  :   Tuple				 =							get_launcher(distributed=lowerCAmelCase_						)

		_a  :   List[str]				 =							{}
		for task, args in tasks.items():
				_a  :   List[str]				 =							args.split()
				for model in tasksamodels[task]:
						_a  :   Dict				 =							globals()[f"""{model.upper().replace('-'		,    '_'						)}_TINY"""]
						_a  :   List[str]				 =							f"""--model_name_or_path {model_name}""".split()
						_a  :   Tuple				 =							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   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   int:
		# 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
		_a  :   Tuple				 =							parameterized.to_safe_name('_'.join(str(lowerCAmelCase_						) 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 			__magic_name__		(					_UpperCamelCase     ):







							def 							__lowercase    (							self      : List[str]			,_UpperCAmelCase      : List[Any]			,_UpperCAmelCase      : Optional[Any]	):
									# return a ready to run train cmd
									if task not in task_cmds:
											raise ValueError(F"""don't know of task {task}, have {task_cmds.keys()}"""	)

									_a  :   List[str]				 =							task_cmds[task]
									_a  :   Tuple				 =							F"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

									_a  :   List[Any]				 =							self.get_auto_remove_tmp_dir()
									_a  :   str				 =							F"""--output_dir {output_dir}""".split()

									cmd += args_ds + args_out

									return cmd, output_dir







							@parameterized.expand(_UpperCAmelCase			,name_func=_UpperCAmelCase	)
							def 							__lowercase    (							self      : Any			,_UpperCAmelCase      : Dict			,_UpperCAmelCase      : Dict	):
									# testing the ability to do a run followed by recovery of full fp32 weights

									_a  :   str				 =							self.get_task_cmd(_UpperCAmelCase			,_UpperCAmelCase	)

									# 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(_UpperCAmelCase			,env=self.get_env()	)

									# 2. test that the fp32 weights get reconsolidated
									_a  :   Optional[int]				 =							F"""{output_dir}/checkpoint-1"""
									_a  :   List[str]				 =							F"""{chkpt_dir}/out.bin"""
									_a  :   List[str]				 =							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(_UpperCAmelCase			,shell=_UpperCAmelCase	)
									assert os.path.exists(_UpperCAmelCase	), 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









'''simple docstring'''

import math



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   list:
		if n <= 0:
				raise ValueError('Only positive integers have prime factors'						)
		_a  :   Tuple				 =							[]
		while n % 2 == 0:
				pf.append(2						)
				_a  :   str				 =							int(n / 2						)
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				while n % i == 0:
						pf.append(lowerCAmelCase_						)
						_a  :   List[Any]				 =							int(n / i						)
		if n > 2:
				pf.append(lowerCAmelCase_						)
		return pf



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		if n <= 0:
				raise ValueError('Only positive numbers are accepted'						)
		_a  :   int				 =							1
		_a  :   Union[str, Any]				 =							1
		while n % 2 == 0:
				temp += 1
				_a  :   str				 =							int(n / 2						)
		div *= temp
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				_a  :   List[str]				 =							1
				while n % i == 0:
						temp += 1
						_a  :   List[str]				 =							int(n / i						)
				div *= temp
		if n > 1:
				div *= 2
		return div



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		if n <= 0:
				raise ValueError('Only positive numbers are accepted'						)
		_a  :   List[str]				 =							1
		_a  :   Union[str, Any]				 =							1
		while n % 2 == 0:
				temp += 1
				_a  :   Tuple				 =							int(n / 2						)
		if temp > 1:
				s *= (2**temp - 1) / (2 - 1)
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				_a  :   str				 =							1
				while n % i == 0:
						temp += 1
						_a  :   Any				 =							int(n / i						)
				if temp > 1:
						s *= (i**temp - 1) / (i - 1)
		return int(lowerCAmelCase_						)



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		_a  :   Dict				 =							n
		for x in set(prime_factors(lowerCAmelCase_						)						):
				s *= (x - 1) / x
		return int(lowerCAmelCase_						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




