


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)

SCREAMING_SNAKE_CASE__				:    List[Any]         =    get_tests_dir('fixtures')
SCREAMING_SNAKE_CASE__				:    Optional[Any]         =    os.path.join(dirname(get_tests_dir()))
SCREAMING_SNAKE_CASE__				:    int         =    dirname(os.path.abspath(__file__))

# default torch.distributed port
SCREAMING_SNAKE_CASE__				:    Dict         =    '10999'

SCREAMING_SNAKE_CASE__				:    Any         =    't5-small'

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
SCREAMING_SNAKE_CASE__				:    Optional[int]         =    'hf-internal-testing/tiny-random-mt5'
SCREAMING_SNAKE_CASE__				:    List[Any]         =    'hf-internal-testing/tiny-random-camembert'
SCREAMING_SNAKE_CASE__				:    Dict         =    'hf-internal-testing/tiny-random-openai-gpt'

# missing tokenizer files
SCREAMING_SNAKE_CASE__				:    str         =    'hf-internal-testing/tiny-random-convbert'
SCREAMING_SNAKE_CASE__				:    int         =    'hf-internal-testing/tiny-random-layoutlmv2'
SCREAMING_SNAKE_CASE__				:    Dict         =    'hf-internal-testing/tiny-random-hubert'

# issues with tokenizer
SCREAMING_SNAKE_CASE__				:    Union[str, Any]         =    'hf-internal-testing/tiny-random-ctrl'
SCREAMING_SNAKE_CASE__				:    Optional[int]         =    'hf-internal-testing/tiny-random-transfo-xl'  # same as ctrl

# other issues with tiny models
SCREAMING_SNAKE_CASE__				:    Optional[int]         =    'hf-internal-testing/tiny-random-ibert'  # multiple issues with either mlm/qa/clas
SCREAMING_SNAKE_CASE__				:    Optional[int]         =    'hf-internal-testing/tiny-random-reformer'  # multiple issues with either mlm/qa/clas

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




def       a__						(		snake_case__				:       int=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)
  _UpperCAmelCase		:       Dict       =    min(2	,      get_gpu_count()    ) if distributed else 1
  _UpperCAmelCase		:       List[Any]       =    os.environ.get("""DS_TEST_PORT"""	,      snake_case__    )
  return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()




def       a__						(		):
  _UpperCAmelCase		:       Any       =    f'''{FIXTURE_DIRECTORY}/tests_samples'''
  _UpperCAmelCase		:       Optional[Any]       =    f'''{data_dir_samples}/wmt_en_ro'''
  _UpperCAmelCase		:       Union[str, Any]       =    f'''{data_dir_samples}/xsum'''
  _UpperCAmelCase		:       str       =    """
        --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		:       str       =    {
      """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		:       List[Any]       =    f'''{ROOT_DIRECTORY}/examples/pytorch'''

  _UpperCAmelCase		:       Tuple       =    {
      """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
        ''',
  }

  _UpperCAmelCase		:       Optional[Any]       =    get_launcher(distributed=snake_case__    )

  _UpperCAmelCase		:       Union[str, Any]       =    {}
  for task, args in tasks.items():
    _UpperCAmelCase		:       str       =    args.split()
    for model in tasksamodels[task]:
      _UpperCAmelCase		:       List[str]       =    globals()[f'''{model.upper().replace("-"	,      "_"    )}_TINY''']
      _UpperCAmelCase		:       List[Any]       =    f'''--model_name_or_path {model_name}'''.split()
      _UpperCAmelCase		:       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


SCREAMING_SNAKE_CASE__				:    Union[str, Any]         =    make_task_cmds()

SCREAMING_SNAKE_CASE__				:    int         =    'zero2'
SCREAMING_SNAKE_CASE__				:    Optional[Any]         =    'zero3'

SCREAMING_SNAKE_CASE__				:    Tuple         =    [ZEROa, ZEROa]




def       a__						(		snake_case__				:       Optional[int]	,      snake_case__				:       str	,      snake_case__				:       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
  _UpperCAmelCase		:       str       =    parameterized.to_safe_name("""_""".join(str(snake_case__    ) for x in param.args    )    )
  return f'''{func.__name__}_{param_based_name}'''


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





@slow
@require_deepspeed
@require_torch_gpu
class 		_SCREAMING_SNAKE_CASE    (	A    ):


     def 					__snake_case(     self       ,				A_       ,				A_						):
       # 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		:       Any       =    task_cmds[task]
       _UpperCAmelCase		:       int       =    F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

       _UpperCAmelCase		:       Optional[int]       =    self.get_auto_remove_tmp_dir()
       _UpperCAmelCase		:       Union[str, Any]       =    F'''--output_dir {output_dir}'''.split()

       cmd += args_ds + args_out

       return cmd, output_dir




     @parameterized.expand(A_       ,				name_func=A_						)
     def 					__snake_case(     self       ,				A_       ,				A_						):
       # testing the ability to do a run followed by recovery of full fp32 weights

       _UpperCAmelCase,_UpperCAmelCase		:       Any       =    self.get_task_cmd(A_       ,				A_						)

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

       # 2. test that the fp32 weights get reconsolidated
       _UpperCAmelCase		:       List[Any]       =    F'''{output_dir}/checkpoint-1'''
       _UpperCAmelCase		:       Optional[int]       =    F'''{chkpt_dir}/out.bin'''
       _UpperCAmelCase		:       Dict       =    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(A_       ,				shell=A_						)
       assert os.path.exists(A_						), 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 Any


class 		_SCREAMING_SNAKE_CASE    :


     def __init__(     self						):
       _UpperCAmelCase		:       Union[str, Any]       =    []
       _UpperCAmelCase		:       Dict       =    0


     def __str__(     self						):
       _UpperCAmelCase		:       Union[str, Any]       =    """<""" + str(self.stack						)[1:-1] + """>"""
       return printed


     def 					__snake_case(     self       ,				A_						):
       self.stack.append(A_						)
       _UpperCAmelCase		:       Optional[Any]       =    self.length + 1


     def 					__snake_case(     self						):
       self.rotate(1						)
       _UpperCAmelCase		:       List[Any]       =    self.stack[self.length - 1]
       _UpperCAmelCase		:       Optional[Any]       =    self.stack[:-1]
       self.rotate(self.length - 1						)
       _UpperCAmelCase		:       Union[str, Any]       =    self.length - 1
       return dequeued


     def 					__snake_case(     self       ,				A_						):
       for _ in range(A_						):
         _UpperCAmelCase		:       Dict       =    self.stack[0]
         _UpperCAmelCase		:       List[Any]       =    self.stack[1:]
         self.put(A_						)
         _UpperCAmelCase		:       Dict       =    self.length - 1


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Tuple       =    self.get()
       self.put(A_						)
       self.rotate(self.length - 1						)
       return front


     def 					__snake_case(     self						):
       return self.length





