





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 			lowerCamelCase_		(					lowerCAmelCase:		Union[str, Any]=False )->						str:
  # 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)
  _snake_case      : List[str]														=		min(2			,							get_gpu_count() ) if distributed else 1
  _snake_case      : Tuple														=		os.environ.get('DS_TEST_PORT'			,							lowerCAmelCase )
  return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()


def 			lowerCamelCase_		(					)->						Optional[int]:
  _snake_case      : Optional[Any]														=		F"""{FIXTURE_DIRECTORY}/tests_samples"""
  _snake_case      : List[Any]														=		F"""{data_dir_samples}/wmt_en_ro"""
  _snake_case      : Any														=		F"""{data_dir_samples}/xsum"""
  _snake_case      : Optional[Any]														=		'\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      : Optional[Any]														=		{
      '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      : List[Any]														=		F"""{ROOT_DIRECTORY}/examples/pytorch"""

  _snake_case      : int														=		{
      '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      : List[str]														=		get_launcher(distributed=lowerCAmelCase )

  _snake_case      : str														=		{}
  for task, args in tasks.items():
    _snake_case      : Any														=		args.split()
    for model in tasksamodels[task]:
      _snake_case      : Any														=		globals()[F"""{model.upper().replace('-'			,							'_' )}_TINY"""]
      _snake_case      : Optional[int]														=		F"""--model_name_or_path {model_name}""".split()
      _snake_case      : int														=		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 			lowerCamelCase_		(					lowerCAmelCase:		int			,							lowerCAmelCase:		Union[str, Any]			,							lowerCAmelCase:		Optional[int] )->						List[str]:
  # 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
  _snake_case      : Any														=		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
lowerCAmelCase_        =			list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  def UpperCamelCase_       (					self					:    Optional[int]					, UpperCamelCase					:    Optional[int]					, UpperCamelCase					:    int							):


    '''simple docstring'''





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

    _snake_case      : Tuple														=		task_cmds[task]
    _snake_case      : List[str]														=		f"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

    _snake_case      : List[Any]														=		self.get_auto_remove_tmp_dir()
    _snake_case      : Union[str, Any]														=		f"""--output_dir {output_dir}""".split()

    cmd += args_ds + args_out

    return cmd, output_dir







  @parameterized.expand(UpperCamelCase					, name_func=UpperCamelCase							)
  def UpperCamelCase_       (					self					:    int					, UpperCamelCase					:    str					, UpperCamelCase					:    Union[str, Any]							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : Tuple														=		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
    _snake_case      : str														=		f"""{output_dir}/checkpoint-1"""
    _snake_case      : Dict														=		f"""{chkpt_dir}/out.bin"""
    _snake_case      : str														=		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









from __future__ import annotations



def        __a     (    lowerCAmelCase_   :					int				)      -> int | None:






						'''simple docstring'''



						for r in range(1			,lowerCAmelCase_				):
												UpperCAmelCase_=   []
												for x in range(n - 1				):
																		UpperCAmelCase_=   pow(lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_				)
																		if val in li:
																								break
																		li.append(lowerCAmelCase_				)
												else:
																		return r
						return None


if __name__ == "__main__":
	__A				    =						int(input('''Enter a prime number q: '''))
	__A				    =						find_primitive(q)
	if a is None:
		print(f'Cannot find the primitive for the value: {a!r}')
	else:
		__A				    =						int(input('''Enter private key of A: '''))
		__A				    =						pow(a, a_private, q)
		__A				    =						int(input('''Enter private key of B: '''))
		__A				    =						pow(a, b_private, q)

		__A				    =						pow(b_public, a_private, q)
		__A				    =						pow(a_public, b_private, q)

		print('''The key value generated by A is: ''', a_secret)
		print('''The key value generated by B is: ''', b_secret)




