'''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(42)

snake_case__	:     Union[str, Any]      =    get_tests_dir('''fixtures''')
snake_case__	:     int      =    os.path.join(dirname(get_tests_dir()))
snake_case__	:     Union[str, Any]      =    dirname(os.path.abspath(__file__))

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

snake_case__	:     str      =    '''t5-small'''

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


# *** To Fix ***


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

# missing tokenizer files
snake_case__	:     Optional[int]      =    '''hf-internal-testing/tiny-random-convbert'''
snake_case__	:     Optional[Any]      =    '''hf-internal-testing/tiny-random-layoutlmv2'''
snake_case__	:     Union[str, Any]      =    '''hf-internal-testing/tiny-random-hubert'''

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

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


def    _lowerCamelCase       ( lowerCamelCase_   :				int=False  ):



     """simple docstring"""





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


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Tuple  = F'''{FIXTURE_DIRECTORY}/tests_samples'''
     UpperCAmelCase_				:  List[Any]  = F'''{data_dir_samples}/wmt_en_ro'''
     UpperCAmelCase_				:  Optional[int]  = F'''{data_dir_samples}/xsum'''
     UpperCAmelCase_				:  Optional[int]  = '\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.
     UpperCAmelCase_				:  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',
         ],
     }

     UpperCAmelCase_				:  List[Any]  = F'''{ROOT_DIRECTORY}/examples/pytorch'''

     UpperCAmelCase_				:  Optional[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
        ''',
     }

     UpperCAmelCase_				:  Union[str, Any]  = get_launcher(distributed=lowerCamelCase_  )

     UpperCAmelCase_				:  Optional[int]  = {}
     for task, args in tasks.items():
          UpperCAmelCase_				:  Optional[int]  = args.split()
          for model in tasksamodels[task]:
               UpperCAmelCase_				:  Any  = globals()[F'''{model.upper().replace('-'      ,	'_'  )}_TINY''']
               UpperCAmelCase_				:  Optional[int]  = F'''--model_name_or_path {model_name}'''.split()
               UpperCAmelCase_				:  Dict  = 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


snake_case__	:     Optional[int]      =    make_task_cmds()

snake_case__	:     str      =    '''zero2'''
snake_case__	:     str      =    '''zero3'''

snake_case__	:     Optional[int]      =    [ZEROa, ZEROa]


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[Any]      ,	lowerCamelCase_   :				Union[str, Any]      ,	lowerCamelCase_   :				List[str]  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[str]  = 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
snake_case__	:     Union[str, Any]      =    list(itertools.product(stages, task_cmds.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

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

       UpperCAmelCase_				:  Optional[int]  = task_cmds[task]
       UpperCAmelCase_				:  List[str]  = F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

       UpperCAmelCase_				:  List[str]  = self.get_auto_remove_tmp_dir()
       UpperCAmelCase_				:  List[Any]  = F'''--output_dir {output_dir}'''.split()

       cmd += args_ds + args_out

       return cmd, output_dir






  @parameterized.expand(snake_case_ ,						name_func=snake_case_		)
  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[int]  = self.get_task_cmd(snake_case_ ,						snake_case_		)

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

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











import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {}







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : List[str]       =						"fsmt_decoder"


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     Tuple=0  ,lowerCamelCase_:     List[str]=0							)	->      Any:
       super().__init__()
       UpperCAmelCase_     :       Optional[Any]  		=					vocab_size
       UpperCAmelCase_     :       Union[str, Any]  		=					bos_token_id







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Dict       =						"fsmt"
   A__      : int       =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(    self:     int  ,lowerCamelCase_:     Optional[Any]=["en", "de"]  ,lowerCamelCase_:     Any=42024  ,lowerCamelCase_:     Any=42024  ,lowerCamelCase_:     List[str]="relu"  ,lowerCamelCase_:     Any=1024  ,lowerCamelCase_:     Optional[int]=200  ,lowerCamelCase_:     str=1024  ,lowerCamelCase_:     Any=4096  ,lowerCamelCase_:     Dict=12  ,lowerCamelCase_:     Tuple=16  ,lowerCamelCase_:     List[Any]=0.0  ,lowerCamelCase_:     str=4096  ,lowerCamelCase_:     Union[str, Any]=12  ,lowerCamelCase_:     Any=16  ,lowerCamelCase_:     List[str]=0.0  ,lowerCamelCase_:     str=0.0  ,lowerCamelCase_:     Optional[int]=0.1  ,lowerCamelCase_:     List[Any]=0.0  ,lowerCamelCase_:     Tuple=0.0_2  ,lowerCamelCase_:     str=2  ,lowerCamelCase_:     List[str]=True  ,lowerCamelCase_:     Dict=True  ,lowerCamelCase_:     Any=False  ,lowerCamelCase_:     Any=5  ,lowerCamelCase_:     Optional[int]=1.0  ,lowerCamelCase_:     List[str]=False  ,lowerCamelCase_:     List[Any]=True  ,lowerCamelCase_:     List[str]=1  ,lowerCamelCase_:     int=0  ,lowerCamelCase_:     Optional[Any]=2  ,lowerCamelCase_:     Any=2  ,**lowerCamelCase_:     Optional[Any]  ,)	->      int:
       UpperCAmelCase_     :       Dict  		=					langs
       UpperCAmelCase_     :       Tuple  		=					src_vocab_size
       UpperCAmelCase_     :       Optional[int]  		=					tgt_vocab_size
       UpperCAmelCase_     :       Optional[Any]  		=					d_model  # encoder_embed_dim and decoder_embed_dim

       UpperCAmelCase_     :       List[str]  		=					encoder_ffn_dim
       UpperCAmelCase_     :       List[Any]  		=					encoder_layers
       UpperCAmelCase_     :       List[Any]  		=					encoder_attention_heads
       UpperCAmelCase_     :       Union[str, Any]  		=					encoder_layerdrop
       UpperCAmelCase_     :       List[Any]  		=					decoder_layerdrop
       UpperCAmelCase_     :       str  		=					decoder_ffn_dim
       UpperCAmelCase_     :       Optional[int]  		=					decoder_layers
       UpperCAmelCase_     :       int  		=					decoder_attention_heads
       UpperCAmelCase_     :       List[Any]  		=					max_position_embeddings
       UpperCAmelCase_     :       Dict  		=					init_std  # Normal(0, this parameter)
       UpperCAmelCase_     :       Dict  		=					activation_function

       UpperCAmelCase_     :       List[str]  		=					DecoderConfig(vocab_size=lowerCamelCase_  ,bos_token_id=lowerCamelCase_							)
       if "decoder" in common_kwargs:
           del common_kwargs["decoder"]

       UpperCAmelCase_     :       Any  		=					scale_embedding  # scale factor will be sqrt(d_model) if True

       # 3 Types of Dropout
       UpperCAmelCase_     :       int  		=					attention_dropout
       UpperCAmelCase_     :       int  		=					activation_dropout
       UpperCAmelCase_     :       Any  		=					dropout

       UpperCAmelCase_     :       Any  		=					use_cache
       super().__init__(
           pad_token_id=lowerCamelCase_  ,bos_token_id=lowerCamelCase_  ,eos_token_id=lowerCamelCase_  ,decoder_start_token_id=lowerCamelCase_  ,is_encoder_decoder=lowerCamelCase_  ,tie_word_embeddings=lowerCamelCase_  ,forced_eos_token_id=lowerCamelCase_  ,max_length=lowerCamelCase_  ,num_beams=lowerCamelCase_  ,length_penalty=lowerCamelCase_  ,early_stopping=lowerCamelCase_  ,**lowerCamelCase_  ,)






   def       A__    (    self:     List[str]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       str  		=					copy.deepcopy(self.__dict__							)
       UpperCAmelCase_     :       int  		=					self.decoder.to_dict()
       UpperCAmelCase_     :       Dict  		=					self.__class__.model_type
       return output
