



import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
      import torch


UpperCamelCase_									=   random.Random()







def       lowerCamelCase_  (    _a       :						Any   ,		_a       :						List[Any]=1.0   ,		_a       :						Tuple=None   ,		_a       :						Optional[int]=None    ):



    '''simple docstring'''




    if rng is None:
        UpperCAmelCase_     :       int  		=					global_rng

    UpperCAmelCase_     :       Tuple  		=					[]
    for batch_idx in range(shape[0]    ):
        values.append([]    )
        for _ in range(shape[1]    ):
            values[-1].append(rng.random() * scale    )

    return values







@require_torch
class       _snake_case				(     unittest.TestCase ):






   '''simple docstring'''






   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     str  ,lowerCamelCase_:     List[str]=7  ,lowerCamelCase_:     int=400  ,lowerCamelCase_:     Tuple=2000  ,lowerCamelCase_:     Optional[Any]=1  ,lowerCamelCase_:     Optional[int]=0.0  ,lowerCamelCase_:     Dict=24000  ,lowerCamelCase_:     Dict=True  ,)	->      Any:
       UpperCAmelCase_     :       List[str]  		=					parent
       UpperCAmelCase_     :       List[str]  		=					batch_size
       UpperCAmelCase_     :       List[str]  		=					min_seq_length
       UpperCAmelCase_     :       Optional[int]  		=					max_seq_length
       UpperCAmelCase_     :       int  		=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       UpperCAmelCase_     :       int  		=					feature_size
       UpperCAmelCase_     :       List[str]  		=					padding_value
       UpperCAmelCase_     :       Optional[Any]  		=					sampling_rate
       UpperCAmelCase_     :       Optional[int]  		=					return_attention_mask


   def       A__    (    self:     Optional[int]							)	->      Optional[Any]:
       return {
           "feature_size": self.feature_size,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "return_attention_mask": self.return_attention_mask,
       }






   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     List[Any]=False  ,lowerCamelCase_:     Dict=False							)	->      List[str]:
       def _flatten(lowerCamelCase_:     Optional[int]							):
           return list(itertools.chain(*lowerCamelCase_							)							)

       if equal_length:
           UpperCAmelCase_     :       Optional[int]  		=					floats_list((self.batch_size, self.max_seq_length)							)
       else:
           # make sure that inputs increase in size
           UpperCAmelCase_     :       List[Any]  		=					[
               _flatten(floats_list((x, self.feature_size)							)							)
               for x in range(self.min_seq_length  ,self.max_seq_length  ,self.seq_length_diff							)
           ]

       if numpify:
           UpperCAmelCase_     :       str  		=					[np.asarray(lowerCamelCase_							) for x in audio_inputs]

       return audio_inputs







@require_torch
class       _snake_case				(     __snake_case ,							unittest.TestCase ):






   '''simple docstring'''






   A__      : List[Any]       =						EncodecFeatureExtractor


   def       A__    (    self:     Union[str, Any]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       List[str]  		=					EnCodecFeatureExtractionTester(self							)


   def       A__    (    self:     List[Any]							)	->      Dict:
       # Tests that all call wrap to encode_plus and batch_encode_plus
       UpperCAmelCase_     :       List[Any]  		=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
       # create three inputs of length 800, 1000, and 1200
       UpperCAmelCase_     :       str  		=					[floats_list((1, x)							)[0] for x in range(800  ,1400  ,200							)]
       UpperCAmelCase_     :       int  		=					[np.asarray(lowerCamelCase_							) for audio_input in audio_inputs]

       # Test not batched input
       UpperCAmelCase_     :       Union[str, Any]  		=					feat_extract(audio_inputs[0]  ,return_tensors="""np"""							).input_values
       UpperCAmelCase_     :       Dict  		=					feat_extract(np_audio_inputs[0]  ,return_tensors="""np"""							).input_values
       self.assertTrue(np.allclose(lowerCamelCase_  ,lowerCamelCase_  ,atol=1e-3							)							)

       # Test batched
       UpperCAmelCase_     :       Tuple  		=					feat_extract(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""np"""							).input_values
       UpperCAmelCase_     :       Any  		=					feat_extract(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""np"""							).input_values
       for enc_seq_a, enc_seq_a in zip(lowerCamelCase_  ,lowerCamelCase_							):
           self.assertTrue(np.allclose(lowerCamelCase_  ,lowerCamelCase_  ,atol=1e-3							)							)


   def       A__    (    self:     str							)	->      List[str]:
       UpperCAmelCase_     :       Union[str, Any]  		=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
       UpperCAmelCase_     :       Union[str, Any]  		=					np.random.rand(100							).astype(np.floataa							)
       UpperCAmelCase_     :       Union[str, Any]  		=					np_audio_inputs.tolist()

       for inputs in [py_audio_inputs, np_audio_inputs]:
           UpperCAmelCase_     :       Dict  		=					feature_extractor.pad([{"""input_values""": inputs}]  ,return_tensors="""np"""							)
           self.assertTrue(np_processed.input_values.dtype == np.floataa							)
           UpperCAmelCase_     :       Dict  		=					feature_extractor.pad([{"""input_values""": inputs}]  ,return_tensors="""pt"""							)
           self.assertTrue(pt_processed.input_values.dtype == torch.floataa							)


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     Union[str, Any]							)	->      Optional[Any]:
       from datasets import load_dataset

       UpperCAmelCase_     :       int  		=					load_dataset("""hf-internal-testing/librispeech_asr_dummy"""  ,"""clean"""  ,split="""validation"""							)
       # automatic decoding with librispeech
       UpperCAmelCase_     :       int  		=					ds.sort("""id"""							).select(range(lowerCamelCase_							)							)[:num_samples]["""audio"""]

       return [x["array"] for x in audio_samples]


   def       A__    (    self:     Optional[int]							)	->      Union[str, Any]:
       # fmt: off
       UpperCAmelCase_     :       Dict  		=					torch.tensor(
           [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
            3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
            2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
            4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
            7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
            4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]							)
       # fmt: on
       UpperCAmelCase_     :       Optional[Any]  		=					self._load_datasamples(1							)
       UpperCAmelCase_     :       Union[str, Any]  		=					EncodecFeatureExtractor()
       UpperCAmelCase_     :       List[Any]  		=					feature_extractor(lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(input_values.shape  ,(1, 1, 93680)							)
       self.assertTrue(torch.allclose(input_values[0, 0, :30]  ,lowerCamelCase_  ,atol=1e-6							)							)


   def       A__    (    self:     List[str]							)	->      Dict:
       # fmt: off
       UpperCAmelCase_     :       Optional[int]  		=					torch.tensor(
           [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
            3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
            2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
            4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
            7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
            4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]							)
       # fmt: on
       UpperCAmelCase_     :       Tuple  		=					self._load_datasamples(1							)
       UpperCAmelCase_     :       List[Any]  		=					[np.tile(input_audio[0][None]  ,reps=(2, 1)							)]
       input_audio[0][1] *= 0.5
       UpperCAmelCase_     :       int  		=					EncodecFeatureExtractor(feature_size=2							)
       UpperCAmelCase_     :       int  		=					feature_extractor(lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(input_values.shape  ,(1, 2, 93680)							)
       self.assertTrue(torch.allclose(input_values[0, 0, :30]  ,lowerCamelCase_  ,atol=1e-6							)							)
       self.assertTrue(torch.allclose(input_values[0, 1, :30]  ,EXPECTED_INPUT_VALUES * 0.5  ,atol=1e-6							)							)






   def       A__    (    self:     Optional[int]							)	->      List[str]:
       UpperCAmelCase_     :       Dict  		=					self._load_datasamples(2							)
       # would be easier if the stride was like
       UpperCAmelCase_     :       Any  		=					EncodecFeatureExtractor(feature_size=1  ,chunk_length_s=1  ,overlap=0.0_1							)

       # pad and trunc raise an error ?
       with self.assertRaisesRegex(
           lowerCamelCase_  ,"""^Both padding and truncation were set. Make sure you only set one.$"""  ,):
           UpperCAmelCase_     :       Optional[int]  		=					feature_extractor(
               lowerCamelCase_  ,padding="""max_length"""  ,truncation=lowerCamelCase_  ,return_tensors="""pt"""							).input_values

       # truncate to chunk
       UpperCAmelCase_     :       str  		=					feature_extractor(lowerCamelCase_  ,truncation=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(2, 1, 71520)							)  # 2 chunks

       # force truncate to max_length
       UpperCAmelCase_     :       Optional[Any]  		=					feature_extractor(
           lowerCamelCase_  ,truncation=lowerCamelCase_  ,max_length=48000  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(2, 1, 48000)							)

       # pad to chunk
       UpperCAmelCase_     :       Dict  		=					feature_extractor(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(padded_outputs.shape  ,(2, 1, 95280)							)

       # pad to chunk
       UpperCAmelCase_     :       Optional[Any]  		=					feature_extractor(lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(2, 1, 95280)							)

       # force pad to max length
       UpperCAmelCase_     :       str  		=					feature_extractor(
           lowerCamelCase_  ,padding="""max_length"""  ,max_length=100000  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(2, 1, 100000)							)

       # force no pad
       with self.assertRaisesRegex(
           lowerCamelCase_  ,"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""  ,):
           UpperCAmelCase_     :       Dict  		=					feature_extractor(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values

       UpperCAmelCase_     :       Any  		=					feature_extractor(input_audio[0]  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(1, 1, 93680)							)

       # no pad if no chunk_length_s
       UpperCAmelCase_     :       str  		=					None
       with self.assertRaisesRegex(
           lowerCamelCase_  ,"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""  ,):
           UpperCAmelCase_     :       Dict  		=					feature_extractor(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values

       UpperCAmelCase_     :       Any  		=					feature_extractor(input_audio[0]  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(1, 1, 93680)							)

       # no pad if no overlap
       UpperCAmelCase_     :       int  		=					2
       UpperCAmelCase_     :       Optional[Any]  		=					None
       with self.assertRaisesRegex(
           lowerCamelCase_  ,"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""  ,):
           UpperCAmelCase_     :       int  		=					feature_extractor(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values

       UpperCAmelCase_     :       Optional[Any]  		=					feature_extractor(input_audio[0]  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(1, 1, 93680)							)





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
