


'''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)

__lowercase	:   str   		=					get_tests_dir("""fixtures""")
__lowercase	:   List[str]   		=					os.path.join(dirname(get_tests_dir()))
__lowercase	:   Optional[Any]   		=					dirname(os.path.abspath(__file__))

# default torch.distributed port
__lowercase	:   int   		=					"""10999"""

__lowercase	:   Tuple   		=					"""t5-small"""

# *** Working Models ***
__lowercase	:   Optional[int]   		=					"""hf-internal-testing/tiny-albert"""
__lowercase	:   List[str]   		=					"""sshleifer/bart-tiny-random"""
__lowercase	:   List[str]   		=					"""hf-internal-testing/tiny-bert"""
__lowercase	:   Union[str, Any]   		=					"""hf-internal-testing/tiny-random-bigbird_pegasus"""
__lowercase	:   List[Any]   		=					"""hf-internal-testing/tiny-random-big_bird"""
__lowercase	:   str   		=					"""hf-internal-testing/tiny-random-blenderbot"""
__lowercase	:   int   		=					"""bigscience/bigscience-small-testing"""
__lowercase	:   List[str]   		=					"""hf-internal-testing/tiny-random-deberta"""
__lowercase	:   int   		=					"""hf-internal-testing/tiny-random-deberta-v2"""
__lowercase	:   str   		=					"""sshleifer/tiny-distilbert-base-cased"""
__lowercase	:   Dict   		=					"""hf-internal-testing/tiny-electra"""
__lowercase	:   Any   		=					"""hf-internal-testing/tiny-random-flaubert"""
__lowercase	:   Tuple   		=					"""stas/tiny-wmt19-en-de"""
__lowercase	:   Union[str, Any]   		=					"""hf-internal-testing/tiny-random-funnel"""
__lowercase	:   int   		=					"""sshleifer/tiny-gpt2"""
__lowercase	:   str   		=					"""hf-internal-testing/tiny-random-gptj"""
__lowercase	:   Union[str, Any]   		=					"""hf-internal-testing/tiny-random-gpt_neo"""
__lowercase	:   List[Any]   		=					"""hf-internal-testing/tiny-layoutlm"""
__lowercase	:   Tuple   		=					"""hf-internal-testing/tiny-random-led"""
__lowercase	:   Any   		=					"""hf-internal-testing/tiny-random-longformer"""
__lowercase	:   Tuple   		=					"""stas/tiny-m2m_100"""  # hf tiny model is unsuitable
__lowercase	:   Dict   		=					"""sshleifer/tiny-marian-en-de"""
__lowercase	:   Optional[int]   		=					"""sshleifer/tiny-mbart"""
__lowercase	:   Optional[int]   		=					"""hf-internal-testing/tiny-random-mobilebert"""
__lowercase	:   int   		=					"""hf-internal-testing/tiny-random-mpnet"""
__lowercase	:   Union[str, Any]   		=					"""stas/pegasus-cnn_dailymail-tiny-random"""
__lowercase	:   int   		=					"""hf-internal-testing/tiny-random-prophetnet"""
__lowercase	:   Dict   		=					"""sshleifer/tiny-distilroberta-base"""
__lowercase	:   Optional[Any]   		=					"""hf-internal-testing/tiny-random-squeezebert"""
__lowercase	:   Dict   		=					"""patrickvonplaten/t5-tiny-random"""
__lowercase	:   List[str]   		=					"""hf-internal-testing/tiny-random-t5-v1.1"""
__lowercase	:   str   		=					"""hf-internal-testing/tiny-random-vit"""
__lowercase	:   Optional[int]   		=					"""hf-internal-testing/tiny-xlm-roberta"""
__lowercase	:   Any   		=					"""sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
__lowercase	:   int   		=					"""hf-internal-testing/tiny-random-mt5"""
__lowercase	:   Tuple   		=					"""hf-internal-testing/tiny-random-camembert"""
__lowercase	:   Tuple   		=					"""hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
__lowercase	:   Optional[Any]   		=					"""hf-internal-testing/tiny-random-convbert"""
__lowercase	:   Any   		=					"""hf-internal-testing/tiny-random-layoutlmv2"""
__lowercase	:   Optional[Any]   		=					"""hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
__lowercase	:   Dict   		=					"""hf-internal-testing/tiny-random-ctrl"""
__lowercase	:   Any   		=					"""hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
__lowercase	:   Any   		=					"""hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
__lowercase	:   Any   		=					"""hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

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





def 				a_							(    lowercase=False     )      ->Dict:






				'''simple docstring'''

				__lowerCamelCase		:   List[Any]           =       min(2				,    get_gpu_count()     ) if distributed else 1
				__lowerCamelCase		:   List[Any]           =       os.environ.get("DS_TEST_PORT"				,    lowercase     )
				return F'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()





def 				a_							(    )      ->Dict:






				'''simple docstring'''

				__lowerCamelCase		:   Dict           =       F'{FIXTURE_DIRECTORY}/tests_samples'
				__lowerCamelCase		:   Any           =       F'{data_dir_samples}/wmt_en_ro'
				__lowerCamelCase		:   Any           =       F'{data_dir_samples}/xsum'
				__lowerCamelCase		:   Dict           =       "\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.
				__lowerCamelCase		:   Dict           =       {
				    "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",
				    ],
				}

				__lowerCamelCase		:   Optional[Any]           =       F'{ROOT_DIRECTORY}/examples/pytorch'

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

				__lowerCamelCase		:   Optional[int]           =       get_launcher(distributed=lowercase     )

				__lowerCamelCase		:   List[Any]           =       {}
				for task, args in tasks.items():
								__lowerCamelCase		:   Tuple           =       args.split()
								for model in tasksamodels[task]:
												__lowerCamelCase		:   Tuple           =       globals()[F'{model.upper().replace("-"				,    "_"     )}_TINY']
												__lowerCamelCase		:   Any           =       F'--model_name_or_path {model_name}'.split()
												__lowerCamelCase		:   List[Any]           =       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


__lowercase	:   Dict   		=					make_task_cmds()

__lowercase	:   str   		=					"""zero2"""
__lowercase	:   List[str]   		=					"""zero3"""

__lowercase	:   Optional[int]   		=					[ZEROa, ZEROa]





def 				a_							(    lowercase				,    lowercase				,    lowercase     )      ->Optional[Any]:






				'''simple docstring'''

				__lowerCamelCase		:   Optional[Any]           =       parameterized.to_safe_name("_".join(str(lowercase     ) for x in param.args     )     )
				return F'{func.__name__}_{param_based_name}'


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





@slow
@require_deepspeed
@require_torch_gpu
class _snake_case (			a__ ):
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->Optional[Any]:



											"""simple docstring"""







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

											__lowerCamelCase		:   List[str]           =       task_cmds[task]
											__lowerCamelCase		:   Optional[Any]           =       f'--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'.split()

											__lowerCamelCase		:   List[Any]           =       self.get_auto_remove_tmp_dir()
											__lowerCamelCase		:   Any           =       f'--output_dir {output_dir}'.split()

											cmd += args_ds + args_out

											return cmd, output_dir







							@parameterized.expand(__A      ,					name_func=__A						)
							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->Optional[Any]:



											"""simple docstring"""







											__lowerCamelCase    ,       __lowerCamelCase		:   Optional[int]           =       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
											__lowerCamelCase		:   str           =       f'{output_dir}/checkpoint-1'
											__lowerCamelCase		:   Any           =       f'{chkpt_dir}/out.bin'
											__lowerCamelCase		:   Optional[Any]           =       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









'''simple docstring'''




import os





def 				a_							(    )      ->int:






				'''simple docstring'''

				__lowerCamelCase		:   Dict           =       os.path.dirname(os.path.realpath(lowercase     )     )
				__lowerCamelCase		:   Any           =       os.path.join(lowercase				,    "triangle.txt"     )

				with open(lowercase     ) as in_file:
								__lowerCamelCase		:   List[str]           =       [[int(lowercase     ) for i in line.split()] for line in in_file]

				while len(lowercase     ) != 1:
								__lowerCamelCase		:   List[str]           =       triangle.pop()
								__lowerCamelCase		:   Union[str, Any]           =       triangle[-1]
								for j in range(len(lowercase     ) - 1     ):
												curr_row[j] += max(last_row[j]				,    last_row[j + 1]     )
				return triangle[0][0]


if __name__ == "__main__":
				print(solution())





