


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(4_2)

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

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

__lowerCAmelCase     :			str      ='t5-small'

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


# *** To Fix ***


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

# missing tokenizer files
__lowerCAmelCase     :			str      ='hf-internal-testing/tiny-random-convbert'
__lowerCAmelCase     :			Any      ='hf-internal-testing/tiny-random-layoutlmv2'
__lowerCAmelCase     :			Any      ='hf-internal-testing/tiny-random-hubert'

# issues with tokenizer
__lowerCAmelCase     :			Optional[Any]      ='hf-internal-testing/tiny-random-ctrl'
__lowerCAmelCase     :			Any      ='hf-internal-testing/tiny-random-transfo-xl'  # same as ctrl

# other issues with tiny models
__lowerCAmelCase     :			Optional[int]      ='hf-internal-testing/tiny-random-ibert'  # multiple issues with either mlm/qa/clas
__lowerCAmelCase     :			List[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
__lowerCAmelCase     :			Tuple      ='hf-internal-testing/tiny-random-dpr'
# - "dpr"  examples/research_projects/rag-end2end-retriever/
__lowerCAmelCase     :			Optional[Any]      ='hf-internal-testing/tiny-random-rag'
# - "rag" research_projects
__lowerCAmelCase     :			Tuple      =''
# - "luke" Entities classes - no plan to make such example
__lowerCAmelCase     :			Union[str, Any]      ='hf-internal-testing/tiny-random-lxmert'
# - "lxmert" doesn't work with run_qa.py
__lowerCAmelCase     :			Optional[int]      ='hf-internal-testing/tiny-random-clip'
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
__lowerCAmelCase     :			Optional[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
__lowerCAmelCase     :			Optional[int]      ='hf-internal-testing/tiny-random-tapas'


def 	_UpperCamelCase							(				lowercase__=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						:					Optional[int]		      =		min(2   ,	get_gpu_count()				) if distributed else 1
						__SCREAMING_SNAKE_CASE						:					Any		      =		os.environ.get('''DS_TEST_PORT'''   ,	lowercase__				)
						return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()


def 	_UpperCamelCase							(				):
						__SCREAMING_SNAKE_CASE						:					str		      =		F'''{FIXTURE_DIRECTORY}/tests_samples'''
						__SCREAMING_SNAKE_CASE						:					Any		      =		F'''{data_dir_samples}/wmt_en_ro'''
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		F'''{data_dir_samples}/xsum'''
						__SCREAMING_SNAKE_CASE						:					str		      =		'''
        --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						:					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''',
						    ],
						}

						__SCREAMING_SNAKE_CASE						:					Any		      =		F'''{ROOT_DIRECTORY}/examples/pytorch'''

						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		{
						    '''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						:					str		      =		get_launcher(distributed=lowercase__				)

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

__lowerCAmelCase     :			Tuple      ='zero2'
__lowerCAmelCase     :			str      ='zero3'

__lowerCAmelCase     :			str      =[ZEROa, ZEROa]


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						# 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						:					Optional[int]		      =		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
__lowerCAmelCase     :			Optional[Any]      =list(itertools.product(stages, task_cmds.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def 		__magic_name__(	self      :List[Any]      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :Union[str, Any]     )		->      Optional[int]:
										# 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()}'''     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		task_cmds[task]
										__SCREAMING_SNAKE_CASE						:					Dict		      =		f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.get_auto_remove_tmp_dir()
										__SCREAMING_SNAKE_CASE						:					Any		      =		f'''--output_dir {output_dir}'''.split()

										cmd += args_ds + args_out

										return cmd, output_dir



				@parameterized.expand(lowerCAmelCase__      ,      name_func=lowerCAmelCase__     )
				def 		__magic_name__(	self      :Tuple      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      List[str]:
										# testing the ability to do a run followed by recovery of full fp32 weights

										__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Tuple		      =		self.get_task_cmd(lowerCAmelCase__      ,      lowerCAmelCase__     )

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

										# 2. test that the fp32 weights get reconsolidated
										__SCREAMING_SNAKE_CASE						:					Dict		      =		f'''{output_dir}/checkpoint-1'''
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		f'''{chkpt_dir}/out.bin'''
										__SCREAMING_SNAKE_CASE						:					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(lowerCAmelCase__      ,      shell=lowerCAmelCase__     )
										assert os.path.exists(lowerCAmelCase__     ), 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




def 	_UpperCamelCase							(				lowercase__				):
						barcode //= 10  # exclude the last digit
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		False
						__SCREAMING_SNAKE_CASE						:					Dict		      =		0

						# extract and check each digit
						while barcode != 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		1 if checker else 3
												s += mult * (barcode % 10)
												barcode //= 10
												__SCREAMING_SNAKE_CASE						:					Dict		      =		not checker

						return (10 - (s % 10)) % 10


def 	_UpperCamelCase							(				lowercase__				):
						return len(str(lowercase__				)				) == 13 and get_check_digit(lowercase__				) == barcode % 10


def 	_UpperCamelCase							(				lowercase__				):
						if str(lowercase__				).isalpha():
												__SCREAMING_SNAKE_CASE						:					str		      =		F'''Barcode \'{barcode}\' has alphabetic characters.'''
												raise ValueError(lowercase__				)
						elif int(lowercase__				) < 0:
												raise ValueError('''The entered barcode has a negative value. Try again.'''				)
						else:
												return int(lowercase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__lowerCAmelCase     :			Union[str, Any]      =get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(f"""'{barcode}' is a valid barcode.""")
						else:
												print(f"""'{barcode}' is NOT a valid barcode.""")
