



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase_									=   logging.get_logger(__name__)


UpperCamelCase_									=   {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCamelCase_									=   {
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
}

UpperCamelCase_									=   {
    '''RUCAIBox/mvp''': 1024,
}







@lru_cache()
def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       List[Any]  		=					(
        list(range(ord("""!"""    )   ,		ord("""~"""    ) + 1    )    ) + list(range(ord("""¡"""    )   ,		ord("""¬"""    ) + 1    )    ) + list(range(ord("""®"""    )   ,		ord("""ÿ"""    ) + 1    )    )
    )
    UpperCAmelCase_     :       int  		=					bs[:]
    UpperCAmelCase_     :       Union[str, Any]  		=					0
    for b in range(2**8    ):
        if b not in bs:
            bs.append(_a    )
            cs.append(2**8 + n    )
            n += 1
    UpperCAmelCase_     :       Tuple  		=					[chr(_a    ) for n in cs]
    return dict(zip(_a   ,		_a    )    )







def       lowerCamelCase_  (    _a       :						Union[str, Any]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       str  		=					set()
    UpperCAmelCase_     :       Optional[Any]  		=					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)    )
        UpperCAmelCase_     :       Dict  		=					char
    return pairs







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : str       =						VOCAB_FILES_NAMES
   A__      : Optional[int]       =						PRETRAINED_VOCAB_FILES_MAP
   A__      : str       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__      : Tuple       =						["input_ids", "attention_mask"]


   def __init__(    self:     Union[str, Any]  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     str="replace"  ,lowerCamelCase_:     Optional[int]="<s>"  ,lowerCamelCase_:     Optional[int]="</s>"  ,lowerCamelCase_:     Any="</s>"  ,lowerCamelCase_:     Union[str, Any]="<s>"  ,lowerCamelCase_:     List[str]="<unk>"  ,lowerCamelCase_:     Union[str, Any]="<pad>"  ,lowerCamelCase_:     int="<mask>"  ,lowerCamelCase_:     List[Any]=False  ,**lowerCamelCase_:     List[Any]  ,)	->      int:
       UpperCAmelCase_     :       Optional[Any]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else bos_token
       UpperCAmelCase_     :       List[Any]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else eos_token
       UpperCAmelCase_     :       int  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else sep_token
       UpperCAmelCase_     :       Dict  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else cls_token
       UpperCAmelCase_     :       Union[str, Any]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else unk_token
       UpperCAmelCase_     :       Optional[int]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       UpperCAmelCase_     :       Union[str, Any]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else mask_token

       super().__init__(
           errors=lowerCamelCase_  ,bos_token=lowerCamelCase_  ,eos_token=lowerCamelCase_  ,unk_token=lowerCamelCase_  ,sep_token=lowerCamelCase_  ,cls_token=lowerCamelCase_  ,pad_token=lowerCamelCase_  ,mask_token=lowerCamelCase_  ,add_prefix_space=lowerCamelCase_  ,**lowerCamelCase_  ,)
       with open(lowerCamelCase_  ,encoding="""utf-8"""							) as vocab_handle:
           UpperCAmelCase_     :       Optional[int]  		=					json.load(lowerCamelCase_							)
       UpperCAmelCase_     :       List[Any]  		=					{v: k for k, v in self.encoder.items()}
       UpperCAmelCase_     :       str  		=					errors  # how to handle errors in decoding
       UpperCAmelCase_     :       Optional[Any]  		=					bytes_to_unicode()
       UpperCAmelCase_     :       str  		=					{v: k for k, v in self.byte_encoder.items()}
       with open(lowerCamelCase_  ,encoding="""utf-8"""							) as merges_handle:
           UpperCAmelCase_     :       int  		=					merges_handle.read().split("""\n"""							)[1:-1]
       UpperCAmelCase_     :       Optional[int]  		=					[tuple(merge.split()							) for merge in bpe_merges]
       UpperCAmelCase_     :       Dict  		=					dict(zip(lowerCamelCase_  ,range(len(lowerCamelCase_							)							)							)							)
       UpperCAmelCase_     :       Any  		=					{}
       UpperCAmelCase_     :       int  		=					add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       UpperCAmelCase_     :       Union[str, Any]  		=					re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)


   @property
   def       A__    (    self:     int							)	->      Dict:
       return len(self.encoder							)


   def       A__    (    self:     List[str]							)	->      Any:
       return dict(self.encoder  ,**self.added_tokens_encoder							)


   def       A__    (    self:     int  ,lowerCamelCase_:     Optional[Any]							)	->      Union[str, Any]:
       if token in self.cache:
           return self.cache[token]
       UpperCAmelCase_     :       Optional[Any]  		=					tuple(lowerCamelCase_							)
       UpperCAmelCase_     :       List[Any]  		=					get_pairs(lowerCamelCase_							)

       if not pairs:
           return token

       while True:
           UpperCAmelCase_     :       Dict  		=					min(lowerCamelCase_  ,key=lambda lowerCamelCase_							: self.bpe_ranks.get(lowerCamelCase_  ,float("""inf"""							)							)							)
           if bigram not in self.bpe_ranks:
               break
           UpperCAmelCase_		, UpperCAmelCase_     :       str  		=					bigram
           UpperCAmelCase_     :       List[str]  		=					[]
           UpperCAmelCase_     :       Union[str, Any]  		=					0
           while i < len(lowerCamelCase_							):
               try:
                   UpperCAmelCase_     :       List[Any]  		=					word.index(lowerCamelCase_  ,lowerCamelCase_							)
               except ValueError:
                   new_word.extend(word[i:]							)
                   break
               else:
                   new_word.extend(word[i:j]							)
                   UpperCAmelCase_     :       Union[str, Any]  		=					j

               if word[i] == first and i < len(lowerCamelCase_							) - 1 and word[i + 1] == second:
                   new_word.append(first + second							)
                   i += 2
               else:
                   new_word.append(word[i]							)
                   i += 1
           UpperCAmelCase_     :       str  		=					tuple(lowerCamelCase_							)
           UpperCAmelCase_     :       str  		=					new_word
           if len(lowerCamelCase_							) == 1:
               break
           else:
               UpperCAmelCase_     :       int  		=					get_pairs(lowerCamelCase_							)
       UpperCAmelCase_     :       List[str]  		=					""" """.join(lowerCamelCase_							)
       UpperCAmelCase_     :       str  		=					word
       return word


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     Union[str, Any]							)	->      int:
       UpperCAmelCase_     :       Optional[Any]  		=					[]
       for token in re.findall(self.pat  ,lowerCamelCase_							):
           UpperCAmelCase_     :       int  		=					"""""".join(
               self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase_							).split(""" """							)							)
       return bpe_tokens


   def       A__    (    self:     Tuple  ,lowerCamelCase_:     List[str]							)	->      List[str]:
       return self.encoder.get(lowerCamelCase_  ,self.encoder.get(self.unk_token							)							)


   def       A__    (    self:     Any  ,lowerCamelCase_:     Optional[Any]							)	->      Optional[Any]:
       return self.decoder.get(lowerCamelCase_							)


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Any							)	->      Union[str, Any]:
       UpperCAmelCase_     :       Union[str, Any]  		=					"""""".join(lowerCamelCase_							)
       UpperCAmelCase_     :       List[Any]  		=					bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""  ,errors=self.errors							)
       return text


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Optional[str] = None							)	->      Tuple[str]:
       if not os.path.isdir(lowerCamelCase_							):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
       UpperCAmelCase_     :       List[str]  		=					os.path.join(
           lowerCamelCase_  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
       UpperCAmelCase_     :       List[Any]  		=					os.path.join(
           lowerCamelCase_  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

       with open(lowerCamelCase_  ,"""w"""  ,encoding="""utf-8"""							) as f:
           f.write(json.dumps(self.encoder  ,indent=2  ,sort_keys=lowerCamelCase_  ,ensure_ascii=lowerCamelCase_							) + """\n"""							)

       UpperCAmelCase_     :       str  		=					0
       with open(lowerCamelCase_  ,"""w"""  ,encoding="""utf-8"""							) as writer:
           writer.write("""#version: 0.2\n"""							)
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,key=lambda lowerCamelCase_							: kv[1]							):
               if index != token_index:
                   logger.warning(
                       F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                       """ Please check that the tokenizer is not corrupted!"""							)
                   UpperCAmelCase_     :       List[str]  		=					token_index
               writer.write(""" """.join(lowerCamelCase_							) + """\n"""							)
               index += 1

       return vocab_file, merge_file


   def       A__    (    self:     Any  ,lowerCamelCase_:     List[int]  ,lowerCamelCase_:     Optional[List[int]] = None							)	->      List[int]:
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCAmelCase_     :       Tuple  		=					[self.cls_token_id]
       UpperCAmelCase_     :       str  		=					[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


   def       A__    (    self:     int  ,lowerCamelCase_:     List[int]  ,lowerCamelCase_:     Optional[List[int]] = None  ,lowerCamelCase_:     bool = False							)	->      List[int]:
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=lowerCamelCase_  ,token_ids_a=lowerCamelCase_  ,already_has_special_tokens=lowerCamelCase_							)

       if token_ids_a is None:
           return [1] + ([0] * len(lowerCamelCase_							)) + [1]
       return [1] + ([0] * len(lowerCamelCase_							)) + [1, 1] + ([0] * len(lowerCamelCase_							)) + [1]


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     List[int]  ,lowerCamelCase_:     Optional[List[int]] = None							)	->      List[int]:
       UpperCAmelCase_     :       List[Any]  		=					[self.sep_token_id]
       UpperCAmelCase_     :       int  		=					[self.cls_token_id]

       if token_ids_a is None:
           return len(cls + token_ids_a + sep							) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]






   def       A__    (    self:     Tuple  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Tuple=False  ,**lowerCamelCase_:     List[Any]							)	->      Any:
       UpperCAmelCase_     :       List[str]  		=					kwargs.pop("""add_prefix_space"""  ,self.add_prefix_space							)
       if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase_							) > 0 and not text[0].isspace()):
           UpperCAmelCase_     :       List[str]  		=					""" """ + text
       return (text, kwargs)





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)

UpperCamelCase_									=   get_tests_dir('''fixtures''')
UpperCamelCase_									=   os.path.join(dirname(get_tests_dir()))
UpperCamelCase_									=   dirname(os.path.abspath(__file__))

# default torch.distributed port
UpperCamelCase_									=   '''10999'''

UpperCamelCase_									=   '''t5-small'''

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-mt5'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-camembert'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-openai-gpt'''

# missing tokenizer files
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-convbert'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-layoutlmv2'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-hubert'''

# issues with tokenizer
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-ctrl'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-transfo-xl'''  # same as ctrl

# other issues with tiny models
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-ibert'''  # multiple issues with either mlm/qa/clas
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-reformer'''  # multiple issues with either mlm/qa/clas

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







def       lowerCamelCase_  (    _a       :						Any=False    ):



    '''simple docstring'''




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







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Dict  		=					F'''{FIXTURE_DIRECTORY}/tests_samples'''
    UpperCAmelCase_     :       Optional[int]  		=					F'''{data_dir_samples}/wmt_en_ro'''
    UpperCAmelCase_     :       Dict  		=					F'''{data_dir_samples}/xsum'''
    UpperCAmelCase_     :       Optional[Any]  		=					"""
        --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_     :       List[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_     :       Union[str, Any]  		=					F'''{ROOT_DIRECTORY}/examples/pytorch'''

    UpperCAmelCase_     :       List[str]  		=					{
        """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_     :       List[Any]  		=					get_launcher(distributed=_a    )

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


UpperCamelCase_									=   make_task_cmds()

UpperCamelCase_									=   '''zero2'''
UpperCamelCase_									=   '''zero3'''

UpperCamelCase_									=   [ZEROa, ZEROa]







def       lowerCamelCase_  (    _a       :						List[str]   ,		_a       :						int   ,		_a       :						List[Any]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[Any]  		=					parameterized.to_safe_name("""_""".join(str(_a    ) for x in param.args    )    )
    return F'''{func.__name__}_{param_based_name}'''


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







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






   '''simple docstring'''






   def       A__    (    self:     Any  ,lowerCamelCase_:     int  ,lowerCamelCase_:     int							)	->      int:
       # 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_     :       List[Any]  		=					task_cmds[task]
       UpperCAmelCase_     :       Union[str, Any]  		=					F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

       UpperCAmelCase_     :       int  		=					self.get_auto_remove_tmp_dir()
       UpperCAmelCase_     :       str  		=					F'''--output_dir {output_dir}'''.split()

       cmd += args_ds + args_out

       return cmd, output_dir






   @parameterized.expand(lowerCamelCase_  ,name_func=lowerCamelCase_							)
   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     str							)	->      Tuple:
       # testing the ability to do a run followed by recovery of full fp32 weights

       UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					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
       UpperCAmelCase_     :       str  		=					F'''{output_dir}/checkpoint-1'''
       UpperCAmelCase_     :       Optional[int]  		=					F'''{chkpt_dir}/out.bin'''
       UpperCAmelCase_     :       int  		=					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
