




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 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Optional[int]=False      )  ->							int:
    # 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)
    UpperCAmelCase_							 =     min(2       ,     get_gpu_count()      ) if distributed else 1
    UpperCAmelCase_							 =     os.environ.get('''DS_TEST_PORT'''       ,     __UpperCamelCase      )
    return f'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()




def 					SCREAMING_SNAKE_CASE	(       )  ->							Tuple:
    UpperCAmelCase_							 =     f'{FIXTURE_DIRECTORY}/tests_samples'
    UpperCAmelCase_							 =     f'{data_dir_samples}/wmt_en_ro'
    UpperCAmelCase_							 =     f'{data_dir_samples}/xsum'
    UpperCAmelCase_							 =     '''
        --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.
    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'\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        ',
    }

    UpperCAmelCase_							 =     get_launcher(distributed=__UpperCamelCase      )

    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


_lowerCamelCase			 =						make_task_cmds()

_lowerCamelCase			 =						'zero2'
_lowerCamelCase			 =						'zero3'

_lowerCamelCase			 =						[ZEROa, ZEROa]




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Union[str, Any]       ,     __UpperCamelCase			:						List[str]       ,     __UpperCamelCase			:						str      )  ->							Dict:
    # 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
    UpperCAmelCase_							 =     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
_lowerCamelCase			 =						list(itertools.product(stages, task_cmds.keys()))



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

     '''simple docstring'''





     def   lowerCamelCase_   (	self   :			int					,    __snake_case   :			List[Any]					,    __snake_case   :			List[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()}'				)

         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   lowerCamelCase_   (	self   :			int					,    __snake_case   :			List[str]					,    __snake_case   :			Tuple				):
         # testing the ability to do a run followed by recovery of full fp32 weights

         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











from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


_lowerCamelCase			 =						datasets.utils.logging.get_logger(__name__)



class 			a     (   folder_based_builder.FolderBasedBuilderConfig			):

     '''simple docstring'''





     lowerCAmelCase				:       bool   		=  None
     lowerCAmelCase				:       bool   		=  None



class 			a     (   folder_based_builder.FolderBasedBuilder			):

     '''simple docstring'''





     lowerCAmelCase				:       List[Any]   		=  datasets.Image()
     lowerCAmelCase				:       List[Any]   		=  'image'
     lowerCAmelCase				:       List[Any]   		=  ImageFolderConfig
     lowerCAmelCase				:       List[str]  # definition at the bottom of the script
     lowerCAmelCase				:       Dict   		=  ImageClassification(image_column='image'	,   label_column='label'			)


_lowerCamelCase			 =						[
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
_lowerCamelCase			 =						IMAGE_EXTENSIONS





