



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:     Dict								=  {
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     int								=  [
        """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CamembertForCausalLM""",
        """CamembertForMaskedLM""",
        """CamembertForMultipleChoice""",
        """CamembertForQuestionAnswering""",
        """CamembertForSequenceClassification""",
        """CamembertForTokenClassification""",
        """CamembertModel""",
        """CamembertPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     str								=  [
        """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCamembertForCausalLM""",
        """TFCamembertForMaskedLM""",
        """TFCamembertForMultipleChoice""",
        """TFCamembertForQuestionAnswering""",
        """TFCamembertForSequenceClassification""",
        """TFCamembertForTokenClassification""",
        """TFCamembertModel""",
        """TFCamembertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert import CamembertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert_fast import CamembertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_camembert import (
            CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CamembertForCausalLM,
            CamembertForMaskedLM,
            CamembertForMultipleChoice,
            CamembertForQuestionAnswering,
            CamembertForSequenceClassification,
            CamembertForTokenClassification,
            CamembertModel,
            CamembertPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_camembert import (
            TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCamembertForCausalLM,
            TFCamembertForMaskedLM,
            TFCamembertForMultipleChoice,
            TFCamembertForQuestionAnswering,
            TFCamembertForSequenceClassification,
            TFCamembertForTokenClassification,
            TFCamembertModel,
            TFCamembertPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase				:     List[Any]								=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





"""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(4_2)

lowerCamelCase				:     Union[str, Any]								=  get_tests_dir("""fixtures""")
lowerCamelCase				:     str								=  os.path.join(dirname(get_tests_dir()))
lowerCamelCase				:     Union[str, Any]								=  dirname(os.path.abspath(__file__))

# default torch.distributed port
lowerCamelCase				:     Optional[Any]								=  """10999"""

lowerCamelCase				:     Optional[Any]								=  """t5-small"""

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


# *** To Fix ***


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

# missing tokenizer files
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-convbert"""
lowerCamelCase				:     str								=  """hf-internal-testing/tiny-random-layoutlmv2"""
lowerCamelCase				:     List[Any]								=  """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
lowerCamelCase				:     List[str]								=  """hf-internal-testing/tiny-random-ctrl"""
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
lowerCamelCase				:     Tuple								=  """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
lowerCamelCase				:     Dict								=  """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				:     List[str]								=  """hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
lowerCamelCase				:     str								=  """hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
lowerCamelCase				:     List[Any]								=  """"""
# - "luke" Entities classes - no plan to make such example
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
lowerCamelCase				:     Dict								=  """hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
lowerCamelCase				:     Optional[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
lowerCamelCase				:     Union[str, Any]								=  """hf-internal-testing/tiny-random-tapas"""


def 							A__ (     UpperCamelCase__=False    ):


  '''simple docstring'''


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


def 							A__ (     ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			F'''{FIXTURE_DIRECTORY}/tests_samples'''
  _SCREAMING_SNAKE_CASE        =			F'''{data_dir_samples}/wmt_en_ro'''
  _SCREAMING_SNAKE_CASE        =			F'''{data_dir_samples}/xsum'''
  _SCREAMING_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.
  _SCREAMING_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''',
      ],
  }

  _SCREAMING_SNAKE_CASE        =			F'''{ROOT_DIRECTORY}/examples/pytorch'''

  _SCREAMING_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
        ''',
  }

  _SCREAMING_SNAKE_CASE        =			get_launcher(distributed=UpperCamelCase__    )

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

lowerCamelCase				:     Any								=  """zero2"""
lowerCamelCase				:     List[Any]								=  """zero3"""

lowerCamelCase				:     int								=  [ZEROa, ZEROa]


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			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
lowerCamelCase				:     Optional[Any]								=  list(itertools.product(stages, task_cmds.keys()))







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




    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



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

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

      _SCREAMING_SNAKE_CASE        =			self.get_auto_remove_tmp_dir()
      _SCREAMING_SNAKE_CASE        =			F'''--output_dir {output_dir}'''.split()

      cmd += args_ds + args_out

      return cmd, output_dir




    @parameterized.expand(A_    ,							name_func=A_			)
    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			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
      _SCREAMING_SNAKE_CASE        =			F'''{output_dir}/checkpoint-1'''
      _SCREAMING_SNAKE_CASE        =			F'''{chkpt_dir}/out.bin'''
      _SCREAMING_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(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
