




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_     :    Union[str, Any]      =			os.path.join(dirname(get_tests_dir()))
lowercase_     :    Optional[int]      =			dirname(os.path.abspath(__file__))

# default torch.distributed port
lowercase_     :    Optional[int]      =			'10999'

lowercase_     :    Optional[int]      =			't5-small'

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
lowercase_     :    Optional[Any]      =			'hf-internal-testing/tiny-random-mt5'
lowercase_     :    Optional[int]      =			'hf-internal-testing/tiny-random-camembert'
lowercase_     :    List[Any]      =			'hf-internal-testing/tiny-random-openai-gpt'

# missing tokenizer files
lowercase_     :    List[str]      =			'hf-internal-testing/tiny-random-convbert'
lowercase_     :    str      =			'hf-internal-testing/tiny-random-layoutlmv2'
lowercase_     :    Any      =			'hf-internal-testing/tiny-random-hubert'

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

# other issues with tiny models
lowercase_     :    List[Any]      =			'hf-internal-testing/tiny-random-ibert'  # multiple issues with either mlm/qa/clas
lowercase_     :    str      =			'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_     :    Optional[Any]      =			'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_     :    Tuple      =			''
# - "luke" Entities classes - no plan to make such example
lowercase_     :    List[str]      =			'hf-internal-testing/tiny-random-lxmert'
# - "lxmert" doesn't work with run_qa.py
lowercase_     :    Any      =			'hf-internal-testing/tiny-random-clip'
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
lowercase_     :    Dict      =			'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 							__SCREAMING_SNAKE_CASE  (      snake_case_=False						):

						'''simple docstring'''


						_UpperCAmelCase													=					min(2     ,   get_gpu_count()						) if distributed else 1
						_UpperCAmelCase													=					os.environ.get("DS_TEST_PORT"     ,   snake_case_						)
						return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()


def 							__SCREAMING_SNAKE_CASE  (      ):

						'''simple docstring'''


						_UpperCAmelCase													=					f"""{FIXTURE_DIRECTORY}/tests_samples"""
						_UpperCAmelCase													=					f"""{data_dir_samples}/wmt_en_ro"""
						_UpperCAmelCase													=					f"""{data_dir_samples}/xsum"""
						_UpperCAmelCase													=					"\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.
						_UpperCAmelCase													=					{
						    "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",
						    ],
						}

						_UpperCAmelCase													=					f"""{ROOT_DIRECTORY}/examples/pytorch"""

						_UpperCAmelCase													=					{
						    "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
        """,
						}

						_UpperCAmelCase													=					get_launcher(distributed=snake_case_						)

						_UpperCAmelCase													=					{}
						for task, args in tasks.items():
												_UpperCAmelCase													=					args.split()
												for model in tasksamodels[task]:
																		_UpperCAmelCase													=					globals()[f"""{model.upper().replace("-"     ,   "_"						)}_TINY"""]
																		_UpperCAmelCase													=					f"""--model_name_or_path {model_name}""".split()
																		_UpperCAmelCase													=					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_     :    Tuple      =			make_task_cmds()

lowercase_     :    int      =			'zero2'
lowercase_     :    List[str]      =			'zero3'

lowercase_     :    List[Any]      =			[ZEROa, ZEROa]


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					parameterized.to_safe_name("_".join(str(snake_case_						) 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    __lowerCAmelCase			(	UpperCAmelCase__			):







			def UpperCamelCase      (	self   :      str   ,   snake_case__   :      Dict   ,   snake_case__   :      List[str]				):




									"""simple docstring"""


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

									_UpperCAmelCase													=					task_cmds[task]
									_UpperCAmelCase													=					F"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

									_UpperCAmelCase													=					self.get_auto_remove_tmp_dir()
									_UpperCAmelCase													=					F"""--output_dir {output_dir}""".split()

									cmd += args_ds + args_out

									return cmd, output_dir







			@parameterized.expand(snake_case__   ,   name_func=snake_case__				)
			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      Any   ,   snake_case__   :      Dict				):




									"""simple docstring"""


									_UpperCAmelCase							,			_UpperCAmelCase													=					self.get_task_cmd(snake_case__   ,   snake_case__				)

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

									# 2. test that the fp32 weights get reconsolidated
									_UpperCAmelCase													=					F"""{output_dir}/checkpoint-1"""
									_UpperCAmelCase													=					F"""{chkpt_dir}/out.bin"""
									_UpperCAmelCase													=					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(snake_case__   ,   shell=snake_case__				)
									assert os.path.exists(snake_case__				), 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










class    __lowerCAmelCase			:







			def __init__(	self   :      Dict   ,   snake_case__   :      List[str]=None				):




									"""simple docstring"""


									_UpperCAmelCase													=					data
									_UpperCAmelCase													=					None







			def __repr__(	self   :      Union[str, Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					""
									_UpperCAmelCase													=					self
									while temp:
															string_rep += F"""<{temp.data}> ---> """
															_UpperCAmelCase													=					temp.next
									string_rep += "<END>"
									return string_rep




def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


						if not elements_list:
												raise Exception("The Elements List is empty"						)

						# Set first element as Head
						_UpperCAmelCase													=					Node(elements_list[0]						)
						_UpperCAmelCase													=					head
						# Loop through elements from position 1
						for data in elements_list[1:]:
												_UpperCAmelCase													=					Node(snake_case_						)
												_UpperCAmelCase													=					current.next
						return head


lowercase_     :    str      =			[1, 3, 5, 32, 44, 12, 43]
print(f"""List: {list_data}""")
print('Creating Linked List from List.')
lowercase_     :    Dict      =			make_linked_list(list_data)
print('Linked List:')





print(linked_list)




