




'''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)

a__         =     get_tests_dir('''fixtures''')
a__         =     os.path.join(dirname(get_tests_dir()))
a__         =     dirname(os.path.abspath(__file__))

# default torch.distributed port
a__         =     '''10999'''

a__         =     '''t5-small'''

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
a__         =     '''hf-internal-testing/tiny-random-mt5'''
a__         =     '''hf-internal-testing/tiny-random-camembert'''
a__         =     '''hf-internal-testing/tiny-random-openai-gpt'''

# missing tokenizer files
a__         =     '''hf-internal-testing/tiny-random-convbert'''
a__         =     '''hf-internal-testing/tiny-random-layoutlmv2'''
a__         =     '''hf-internal-testing/tiny-random-hubert'''

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

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

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




def    snake_case__     (  a=False	)			->       int:





			'''simple docstring'''






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




def    snake_case__     (  )			->       Optional[Any]:





			'''simple docstring'''






			snake_case__          =						F"""{FIXTURE_DIRECTORY}/tests_samples"""
			snake_case__          =						F"""{data_dir_samples}/wmt_en_ro"""
			snake_case__          =						F"""{data_dir_samples}/xsum"""
			snake_case__          =						'\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.
			snake_case__          =						{
			    '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',
			    ],
			}

			snake_case__          =						F"""{ROOT_DIRECTORY}/examples/pytorch"""

			snake_case__          =						{
			    '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
        """,
			}

			snake_case__          =						get_launcher(distributed=lowerCamelCase_	)

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


a__         =     make_task_cmds()

a__         =     '''zero2'''
a__         =     '''zero3'''

a__         =     [ZEROa, ZEROa]




def    snake_case__     (  a       ,      a       ,      a	)			->       Union[str, Any]:





			'''simple docstring'''






			snake_case__          =						parameterized.to_safe_name("""_""".join(str(lowerCamelCase_	) for x in param.args	)	)
			return F"""{func.__name__}_{param_based_name}"""


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





@slow
@require_deepspeed
@require_torch_gpu
class __magic_name__(						lowerCamelCase_      ):






						def       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						Union[str, Any]					,   __UpperCamelCase						:						Dict       ):



									'''simple docstring'''






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

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

									snake_case__          =						self.get_auto_remove_tmp_dir()
									snake_case__          =						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						:						str					,   __UpperCamelCase						:						Tuple					,   __UpperCamelCase						:						Optional[Any]       ):



									'''simple docstring'''






									snake_case__          =						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
									snake_case__          =						f"""{output_dir}/checkpoint-1"""
									snake_case__          =						f"""{chkpt_dir}/out.bin"""
									snake_case__          =						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
'''simple docstring'''


def    _lowerCamelCase       ( lowerCamelCase_   :				list  ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[int]  = len(lowerCamelCase_  )

     # Set the gap value to be decreased by a factor of 1.3
     # after each iteration
     UpperCAmelCase_				:  Optional[Any]  = 1.3

     # Continue sorting until the gap is 1
     while gap > 1:
          # Decrease the gap value
          UpperCAmelCase_				:  Optional[Any]  = int(gap / shrink  )

          # Sort the elements using insertion sort
          for i in range(lowerCamelCase_      ,	len(lowerCamelCase_  )  ):
               UpperCAmelCase_				:  Optional[int]  = collection[i]
               UpperCAmelCase_				:  Tuple  = i
               while j >= gap and collection[j - gap] > temp:
                    UpperCAmelCase_				:  Tuple  = collection[j - gap]
                    j -= gap
               UpperCAmelCase_				:  Optional[int]  = temp

     return collection


if __name__ == "__main__":
 import doctest

 doctest.testmod()






