



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)

__snake_case		:str 						=	get_tests_dir('''fixtures''')
__snake_case		:Any 						=	os.path.join(dirname(get_tests_dir()))
__snake_case		:Optional[Any] 						=	dirname(os.path.abspath(__file__))

# default torch.distributed port
__snake_case		:List[str] 						=	'''10999'''

__snake_case		:int 						=	'''t5-small'''

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


# *** To Fix ***


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

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

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

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

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





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





def 	__snake_case      (     ):
    __a					=							f'{FIXTURE_DIRECTORY}/tests_samples'
    __a					=							f'{data_dir_samples}/wmt_en_ro'
    __a					=							f'{data_dir_samples}/xsum'
    __a					=							'''
        --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.
    __a					=							{
        '''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					=							f'{ROOT_DIRECTORY}/examples/pytorch'

    __a					=							{
        '''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        ',
    }

    __a					=							get_launcher(distributed=_UpperCAmelCase							)

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


__snake_case		:Union[str, Any] 						=	make_task_cmds()

__snake_case		:Tuple 						=	'''zero2'''
__snake_case		:Tuple 						=	'''zero3'''

__snake_case		:Dict 						=	[ZEROa, ZEROa]





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							):
    # 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					=							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
__snake_case		:List[str] 						=	list(itertools.product(stages, task_cmds.keys()))

@slow
@require_deepspeed
@require_torch_gpu
class        _A			(      __UpperCAmelCase       ):



       def 						_lowerCamelCase     (						self				:	str	,						__SCREAMING_SNAKE_CASE				:	List[Any]	,						__SCREAMING_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()}')

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

           __a					=							self.get_auto_remove_tmp_dir()
           __a					=							F'--output_dir {output_dir}'.split()

           cmd += args_ds + args_out

           return cmd, output_dir





       @parameterized.expand(__SCREAMING_SNAKE_CASE	,						name_func=__SCREAMING_SNAKE_CASE)
       def 						_lowerCamelCase     (						self				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	List[Any]):


           '''simple docstring'''

           __a			,  __a					=							self.get_task_cmd(__SCREAMING_SNAKE_CASE	,						__SCREAMING_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(__SCREAMING_SNAKE_CASE	,						env=self.get_env())

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










def 	__snake_case      (     _UpperCAmelCase							):
    __a					=							1
    __a					=							0
    __a					=							1
    __a					=							1
    __a					=							3
    __a					=							3
    __a					=							limit
    __a					=							0

    __a					=							''''''
    while counter != decimal + 1:
        if 4 * q + r - t < n * t:
            result += str(_UpperCAmelCase							)
            if counter == 0:
                result += "."

            if decimal == counter:
                break

            counter += 1
            __a					=							10 * (r - n * t)
            __a					=							((10 * (3 * q + r)) // t) - 10 * n
            q *= 10
            __a					=							nr
        else:
            __a					=							(2 * q + r) * l
            __a					=							(q * (7 * k) + 2 + (r * l)) // (t * l)
            q *= k
            t *= l
            l += 2
            k += 1
            __a					=							nn
            __a					=							nr
    return result





def 	__snake_case      (     ):
    print(f'{calculate_pi(50							) = }'							)
    import doctest

    doctest.testmod()


if __name__ == "__main__":
 main()





