

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   UpperCamelCase(   lowercase_=False				)      ->							Dict:





  '''simple docstring'''





  snake_case_					= min(2     ,   get_gpu_count()				) if distributed else 1
  snake_case_					= os.environ.get("""DS_TEST_PORT"""     ,   lowercase_				)
  return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()



def   UpperCamelCase(   )      ->							Union[str, 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_					= """
        --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.
  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=lowercase_				)

  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


lowerCamelCase_					  =   make_task_cmds()

lowerCamelCase_					  =   '''zero2'''
lowerCamelCase_					  =   '''zero3'''

lowerCamelCase_					  =   [ZEROa, ZEROa]



def   UpperCamelCase(   lowercase_     ,   lowercase_     ,   lowercase_				)      ->							Union[str, Any]:





  '''simple docstring'''





  snake_case_					= 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_					  =   list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 					__lowerCamelCase							(      __snake_case      ):







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase  )		->			Tuple:
        # 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()}'''  )

        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(lowerCamelCase				,    name_func=lowerCamelCase  )
      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase  )		->			List[str]:
        # testing the ability to do a run followed by recovery of full fp32 weights

        snake_case_		,							snake_case_					= 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
        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(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_     ,   lowercase_				)      ->							Union[str, Any]:





  '''simple docstring'''





  if len(str(lowercase_				)				) == 1 or len(str(lowercase_				)				) == 1:
    return a * b

  snake_case_					= max(len(str(lowercase_				)				)     ,   len(str(lowercase_				)				)				)
  snake_case_					= ma // 2

  snake_case_		,							snake_case_					= divmod(lowercase_     ,   10**ma				)
  snake_case_		,							snake_case_					= divmod(lowercase_     ,   10**ma				)

  snake_case_					= karatsuba(lowercase_     ,   lowercase_				)
  snake_case_					= karatsuba((aa + aa)     ,   (ba + ba)				)
  snake_case_					= karatsuba(lowercase_     ,   lowercase_				)

  return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)



def   UpperCamelCase(   )      ->							Dict:





  '''simple docstring'''





  print(karatsuba(15463     ,   23489				)				)


if __name__ == "__main__":
    main()