



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 						a__     (						_UpperCamelCase     :	List[str]=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)
   __lowerCamelCase  							=      min(2	,get_gpu_count()							) if distributed else 1
   __lowerCamelCase  							=      os.environ.get('''DS_TEST_PORT'''	,_UpperCamelCase							)
   return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()





def 						a__     (						):
   __lowerCamelCase  							=      F"""{FIXTURE_DIRECTORY}/tests_samples"""
   __lowerCamelCase  							=      F"""{data_dir_samples}/wmt_en_ro"""
   __lowerCamelCase  							=      F"""{data_dir_samples}/xsum"""
   __lowerCamelCase  							=      '''
        --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.
   __lowerCamelCase  							=      {
       '''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''',
       ],
   }

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

   __lowerCamelCase  							=      {
       '''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
        """,
   }

   __lowerCamelCase  							=      get_launcher(distributed=_UpperCamelCase							)

   __lowerCamelCase  							=      {}
   for task, args in tasks.items():
      __lowerCamelCase  							=      args.split()
      for model in tasksamodels[task]:
         __lowerCamelCase  							=      globals()[F"""{model.upper().replace("-"	,"_"							)}_TINY"""]
         __lowerCamelCase  							=      F"""--model_name_or_path {model_name}""".split()
         __lowerCamelCase  							=      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 						a__     (						_UpperCamelCase     :	Dict	,_UpperCamelCase     :	Any	,_UpperCamelCase     :	Tuple							):
   # 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
   __lowerCamelCase  							=      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
a_								=   list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 	__lowerCAmelCase       (		lowerCAmelCase__				):




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase			):



    '''simple docstring'''
    # 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()}"""			)

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

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

    cmd += args_ds + args_out

    return cmd, output_dir




 @parameterized.expand(__UpperCAmelCase		,			name_func=__UpperCAmelCase			)
 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase			):



    '''simple docstring'''
    # testing the ability to do a run followed by recovery of full fp32 weights

    __lowerCamelCase							,__lowerCamelCase  							=      self.get_task_cmd(__UpperCAmelCase		,			__UpperCAmelCase			)

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

    # 2. test that the fp32 weights get reconsolidated
    __lowerCamelCase  							=      F"""{output_dir}/checkpoint-1"""
    __lowerCamelCase  							=      F"""{chkpt_dir}/out.bin"""
    __lowerCamelCase  							=      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(__UpperCAmelCase		,			shell=__UpperCAmelCase			)
    assert os.path.exists(__UpperCAmelCase			), 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 						a__     (						_UpperCamelCase     :	list	,_UpperCamelCase     :	list	,_UpperCamelCase     :	list	,_UpperCamelCase     :	int	,_UpperCamelCase     :	int							):
   if target == 0:
      answer.append(path.copy()							)
   else:
      for index in range(_UpperCamelCase	,len(_UpperCamelCase							)							):
         if target >= candidates[index]:
            path.append(candidates[index]							)
            backtrack(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,target - candidates[index]	,_UpperCamelCase							)
            path.pop(len(_UpperCamelCase							) - 1							)





def 						a__     (						_UpperCamelCase     :	list	,_UpperCamelCase     :	int							):
   __lowerCamelCase  							=      []  # type: list[int]
   __lowerCamelCase  							=      []  # type: list[int]
   backtrack(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,0							)
   return answer





def 						a__     (						):
   print(combination_sum([-8, 2.3, 0]	,1							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()






