





'''simple docstring'''






from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import numpy as np
     import tensorflow as tf

     from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
     from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor


class 	_lowercase    (				a					):


   def        snake_case					(			self       ):
       A							:							Any				=    self.config_class(**self.inputs_dict       )
       self.parent.assertTrue(hasattr(_UpperCAmelCase     , '''hidden_sizes'''       )       )
       self.parent.assertTrue(hasattr(_UpperCAmelCase     , '''neck_hidden_sizes'''       )       )
       self.parent.assertTrue(hasattr(_UpperCAmelCase     , '''num_attention_heads'''       )       )







class 	_lowercase    :


   def __init__(			self     , _UpperCAmelCase     , _UpperCAmelCase=13     , _UpperCAmelCase=32     , _UpperCAmelCase=2     , _UpperCAmelCase=3     , _UpperCAmelCase=640     , _UpperCAmelCase=4     , _UpperCAmelCase="silu"     , _UpperCAmelCase=3     , _UpperCAmelCase=32     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.02     , _UpperCAmelCase=True     , _UpperCAmelCase=True     , _UpperCAmelCase=10     , _UpperCAmelCase=None     , ):
       A							:							Union[str, Any]				=    parent
       A							:							Tuple				=    batch_size
       A							:							Optional[int]				=    image_size
       A							:							Dict				=    patch_size
       A							:							int				=    num_channels
       A							:							Dict				=    last_hidden_size
       A							:							Optional[Any]				=    num_attention_heads
       A							:							Dict				=    hidden_act
       A							:							Dict				=    conv_kernel_size
       A							:							Optional[int]				=    output_stride
       A							:							List[Any]				=    hidden_dropout_prob
       A							:							str				=    attention_probs_dropout_prob
       A							:							Any				=    classifier_dropout_prob
       A							:							Tuple				=    use_labels
       A							:							List[str]				=    is_training
       A							:							Any				=    num_labels
       A							:							List[Any]				=    initializer_range
       A							:							List[Any]				=    scope


   def        snake_case					(			self       ):
       A							:							Union[str, Any]				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

       A							:							List[str]				=    None
       A							:							Tuple				=    None
       if self.use_labels:
           A							:							str				=    ids_tensor([self.batch_size]     , self.num_labels       )
           A							:							int				=    ids_tensor([self.batch_size, self.image_size, self.image_size]     , self.num_labels       )

       A							:							Tuple				=    self.get_config()

       return config, pixel_values, labels, pixel_labels


   def        snake_case					(			self       ):
       return MobileViTConfig(
           image_size=self.image_size     , patch_size=self.patch_size     , num_channels=self.num_channels     , num_attention_heads=self.num_attention_heads     , hidden_act=self.hidden_act     , conv_kernel_size=self.conv_kernel_size     , output_stride=self.output_stride     , hidden_dropout_prob=self.hidden_dropout_prob     , attention_probs_dropout_prob=self.attention_probs_dropout_prob     , classifier_dropout_prob=self.classifier_dropout_prob     , initializer_range=self.initializer_range     , )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ):
       A							:							Union[str, Any]				=    TFMobileViTModel(config=_UpperCAmelCase       )
       A							:							Dict				=    model(_UpperCAmelCase     , training=_UpperCAmelCase       )
       A							:							Union[str, Any]				=    self.image_size // self.output_stride
       self.parent.assertEqual(
           result.last_hidden_state.shape     , (self.batch_size, self.last_hidden_size, expected_height, expected_width)       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ):
       A							:							Any				=    self.num_labels
       A							:							Optional[Any]				=    TFMobileViTForImageClassification(_UpperCAmelCase       )
       A							:							List[Any]				=    model(_UpperCAmelCase     , labels=_UpperCAmelCase     , training=_UpperCAmelCase       )
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ):
       A							:							List[Any]				=    self.num_labels
       A							:							Optional[Any]				=    TFMobileViTForSemanticSegmentation(_UpperCAmelCase       )
       A							:							Optional[Any]				=    self.image_size // self.output_stride

       A							:							Tuple				=    model(_UpperCAmelCase     , training=_UpperCAmelCase       )
       self.parent.assertEqual(
           result.logits.shape     , (self.batch_size, self.num_labels, expected_height, expected_width)       )

       A							:							List[Any]				=    model(_UpperCAmelCase     , labels=_UpperCAmelCase     , training=_UpperCAmelCase       )
       self.parent.assertEqual(
           result.logits.shape     , (self.batch_size, self.num_labels, expected_height, expected_width)       )


   def        snake_case					(			self       ):
       A							:							List[str]				=    self.prepare_config_and_inputs()
       A,						A,						A,						A							:							List[Any]				=    config_and_inputs
       A							:							List[Any]				=    {'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_tf
class 	_lowercase    (				a		,       a		,       unittest.TestCase					):
   _UpperCamelCase									= (
       (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
       if is_tf_available()
       else ()
   )
   _UpperCamelCase									= (
       {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
       if is_tf_available()
       else {}
   )

   _UpperCamelCase									= False
   _UpperCamelCase									= False
   _UpperCamelCase									= False
   _UpperCamelCase									= False
   _UpperCamelCase									= False


   def        snake_case					(			self       ):
       A							:							Any				=    TFMobileViTModelTester(self       )
       A							:							Tuple				=    TFMobileViTConfigTester(self     , config_class=_UpperCAmelCase     , has_text_modality=_UpperCAmelCase       )


   def        snake_case					(			self       ):
       self.config_tester.run_common_tests()


   @unittest.skip(reason='''MobileViT does not use inputs_embeds'''       )
   def        snake_case					(			self       ):
       pass


   @unittest.skip(reason='''MobileViT does not support input and output embeddings'''       )
   def        snake_case					(			self       ):
       pass


   @unittest.skip(reason='''MobileViT does not output attentions'''       )
   def        snake_case					(			self       ):
       pass


   def        snake_case					(			self       ):
       A,						A							:							Union[str, Any]				=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           A							:							Optional[int]				=    model_class(_UpperCAmelCase       )
           A							:							Dict				=    inspect.signature(model.call       )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           A							:							Tuple				=    [*signature.parameters.keys()]

           A							:							Tuple				=    ['''pixel_values''']
           self.assertListEqual(arg_names[:1]     , _UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							Optional[Any]				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase       )


   def        snake_case					(			self       ):
       def check_hidden_states_output(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ):
           A							:							Any				=    model_class(_UpperCAmelCase       )

           A							:							List[str]				=    model(**self._prepare_for_class(_UpperCAmelCase     , _UpperCAmelCase       )       )

           A							:							Optional[int]				=    outputs.hidden_states

           A							:							int				=    5
           self.assertEqual(len(_UpperCAmelCase       )     , _UpperCAmelCase       )

           # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
           # with the width and height being successively divided by 2.
           A							:							Any				=    2
           for i in range(len(_UpperCAmelCase       )       ):
               self.assertListEqual(
                   list(hidden_states[i].shape[-2:]       )     , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]     , )
               divisor *= 2

           self.assertEqual(self.model_tester.output_stride     , divisor // 2       )

       A,						A							:							Optional[Any]				=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           A							:							Optional[int]				=    True
           check_hidden_states_output(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           A							:							Dict				=    True

           check_hidden_states_output(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							str				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							Optional[Any]				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase       )


   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''       )       ) == 0     , reason='''TF does not support backprop for grouped convolutions on CPU.'''     , )
   def        snake_case					(			self       ):
       super().test_dataset_conversion()


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase=2E-1     , _UpperCAmelCase=2E-1       ):
       self.assertTrue(np.allclose(_UpperCAmelCase     , _UpperCAmelCase     , atol=_UpperCAmelCase     , rtol=_UpperCAmelCase       )       )


   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''       )       ) == 0     , reason='''TF does not support backprop for grouped convolutions on CPU.'''     , )
   @slow
   def        snake_case					(			self       ):
       A,						A							:							Union[str, Any]				=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
           if model_class.__name__ != "TFMobileViTModel":
               A							:							Dict				=    model_class(_UpperCAmelCase       )
               if getattr(_UpperCAmelCase     , '''hf_compute_loss'''     , _UpperCAmelCase       ):
                   super().test_keras_fit()


   def        snake_case					(			self       ):
       A,						A							:							Optional[int]				=    self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
           # set an ignore index to correctly test the masked loss used in
           # `TFMobileViTForSemanticSegmentation`.
           if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
               A							:							Any				=    5

           A							:							Tuple				=    model_class(_UpperCAmelCase       )
           if getattr(_UpperCAmelCase     , '''hf_compute_loss'''     , _UpperCAmelCase       ):
               # The number of elements in the loss should be the same as the number of elements in the label
               A							:							Tuple				=    self._prepare_for_class(inputs_dict.copy()     , _UpperCAmelCase     , return_labels=_UpperCAmelCase       )
               A							:							Optional[Any]				=    prepared_for_class[
                   sorted(prepared_for_class.keys() - inputs_dict.keys()     , reverse=_UpperCAmelCase       )[0]
               ]
               A							:							Optional[int]				=    added_label.shape.as_list()[:1]

               # Test that model correctly compute the loss with kwargs
               A							:							List[str]				=    self._prepare_for_class(inputs_dict.copy()     , _UpperCAmelCase     , return_labels=_UpperCAmelCase       )
               A							:							Union[str, Any]				=    {'''input_ids''', '''pixel_values''', '''input_features'''}
               A							:							Union[str, Any]				=    possible_input_names.intersection(set(_UpperCAmelCase       )       ).pop()
               A							:							Tuple				=    prepared_for_class.pop(_UpperCAmelCase       )

               A							:							str				=    model(_UpperCAmelCase     , **_UpperCAmelCase       )[0]
               self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )

               # Test that model correctly compute the loss when we mask some positions
               A							:							Dict				=    self._prepare_for_class(inputs_dict.copy()     , _UpperCAmelCase     , return_labels=_UpperCAmelCase       )
               A							:							int				=    {'''input_ids''', '''pixel_values''', '''input_features'''}
               A							:							List[Any]				=    possible_input_names.intersection(set(_UpperCAmelCase       )       ).pop()
               A							:							List[Any]				=    prepared_for_class.pop(_UpperCAmelCase       )
               if "labels" in prepared_for_class:
                   A							:							Dict				=    prepared_for_class['''labels'''].numpy()
                   if len(labels.shape       ) > 1 and labels.shape[1] != 1:
                       # labels[0] = -100
                       A							:							Optional[int]				=    tf.convert_to_tensor(_UpperCAmelCase       )
                       A							:							List[Any]				=    model(_UpperCAmelCase     , **_UpperCAmelCase       )[0]
                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )
                       self.assertTrue(not np.any(np.isnan(loss.numpy()       )       )       )

                # Test that model correctly compute the loss with a dict
               A							:							List[str]				=    self._prepare_for_class(inputs_dict.copy()     , _UpperCAmelCase     , return_labels=_UpperCAmelCase       )
               A							:							Optional[int]				=    model(_UpperCAmelCase       )[0]
               self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )

               # Test that model correctly compute the loss with a tuple
               A							:							str				=    self._prepare_for_class(inputs_dict.copy()     , _UpperCAmelCase     , return_labels=_UpperCAmelCase       )

               # Get keys that were added with the _prepare_for_class function
               A							:							Tuple				=    prepared_for_class.keys() - inputs_dict.keys()
               A							:							Optional[Any]				=    inspect.signature(model.call       ).parameters
               A							:							Union[str, Any]				=    list(signature.keys()       )

               # Create a dictionary holding the location of the tensors in the tuple
               A							:							Any				=    {0: input_name}
               for label_key in label_keys:
                   A							:							Tuple				=    signature_names.index(_UpperCAmelCase       )
                   A							:							Optional[int]				=    label_key
               A							:							List[Any]				=    sorted(tuple_index_mapping.items()       )
               # Initialize a list with their default values, update the values and convert to a tuple
               A							:							Optional[Any]				=    []

               for name in signature_names:
                   if name != "kwargs":
                       list_input.append(signature[name].default       )

               for index, value in sorted_tuple_index_mapping:
                   A							:							str				=    prepared_for_class[value]

               A							:							List[Any]				=    tuple(_UpperCAmelCase       )

               # Send to model
               A							:							Dict				=    model(tuple_input[:-1]       )[0]

               self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )


   @slow
   def        snake_case					(			self       ):
       for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           A							:							Dict				=    TFMobileViTModel.from_pretrained(_UpperCAmelCase       )
           self.assertIsNotNone(_UpperCAmelCase       )







def 				_lowerCamelCase(	)		->	Union[str, Any]:
    A							:							Optional[Any]				=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
    return image





@require_tf
class 	_lowercase    (				unittest.TestCase					):


   @slow
   def        snake_case					(			self       ):
       A							:							List[str]				=    TFMobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''       )

       A							:							List[Any]				=    MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''       )
       A							:							Dict				=    prepare_img()
       A							:							Optional[int]				=    image_processor(images=_UpperCAmelCase     , return_tensors='''tf'''       )

       # forward pass
       A							:							Optional[Any]				=    model(**_UpperCAmelCase     , training=_UpperCAmelCase       )

       # verify the logits
       A							:							Optional[Any]				=    tf.TensorShape((1, 1_000)       )
       self.assertEqual(outputs.logits.shape     , _UpperCAmelCase       )

       A							:							Dict				=    tf.constant([-1.9364, -1.2327, -0.4653]       )

       tf.debugging.assert_near(outputs.logits[0, :3]     , _UpperCAmelCase     , atol=1E-4     , rtol=1E-0_4       )


   @slow
   def        snake_case					(			self       ):
       # `from_pt` will be removed
       A							:							int				=    TFMobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''       )

       A							:							List[str]				=    MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''       )

       A							:							Optional[int]				=    prepare_img()
       A							:							Optional[Any]				=    image_processor(images=_UpperCAmelCase     , return_tensors='''tf'''       )

       # forward pass
       A							:							List[str]				=    model(inputs.pixel_values     , training=_UpperCAmelCase       )
       A							:							Any				=    outputs.logits

       # verify the logits
       A							:							List[str]				=    tf.TensorShape((1, 21, 32, 32)       )
       self.assertEqual(logits.shape     , _UpperCAmelCase       )

       A							:							List[str]				=    tf.constant(
           [
               [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
               [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
               [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
           ]       )

       tf.debugging.assert_near(logits[0, :3, :3, :3]     , _UpperCAmelCase     , rtol=1E-4     , atol=1E-4       )







'''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_     			=						get_tests_dir("""fixtures""")
snake_case_     			=						os.path.join(dirname(get_tests_dir()))
snake_case_     			=						dirname(os.path.abspath(__file__))

# default torch.distributed port
snake_case_     			=						"""10999"""

snake_case_     			=						"""t5-small"""

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


# *** To Fix ***


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

# missing tokenizer files
snake_case_     			=						"""hf-internal-testing/tiny-random-convbert"""
snake_case_     			=						"""hf-internal-testing/tiny-random-layoutlmv2"""
snake_case_     			=						"""hf-internal-testing/tiny-random-hubert"""

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

# other issues with tiny models
snake_case_     			=						"""hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
snake_case_     			=						"""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_     			=						"""hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
snake_case_     			=						"""hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
snake_case_     			=						""""""
# - "luke" Entities classes - no plan to make such example
snake_case_     			=						"""hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
snake_case_     			=						"""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_     			=						"""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_     			=						"""hf-internal-testing/tiny-random-tapas"""
def 				_lowerCamelCase(	UpperCamelCase__     :			str=False		)		->	List[str]:
    # 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)
    A							:							int				=    min(2     ,							get_gpu_count()		) if distributed else 1
    A							:							Dict				=    os.environ.get('''DS_TEST_PORT'''     ,							UpperCamelCase__		)
    return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()
def 				_lowerCamelCase(	)		->	Any:
    A							:							Tuple				=    F'''{FIXTURE_DIRECTORY}/tests_samples'''
    A							:							int				=    F'''{data_dir_samples}/wmt_en_ro'''
    A							:							List[str]				=    F'''{data_dir_samples}/xsum'''
    A							:							int				=    '''
        --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.
    A							:							List[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''',
        ],
    }

    A							:							int				=    F'''{ROOT_DIRECTORY}/examples/pytorch'''

    A							:							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
        ''',
    }

    A							:							int				=    get_launcher(distributed=UpperCamelCase__		)

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


snake_case_     			=						make_task_cmds()

snake_case_     			=						"""zero2"""
snake_case_     			=						"""zero3"""

snake_case_     			=						[ZEROa, ZEROa]
def 				_lowerCamelCase(	UpperCamelCase__     :			Tuple     ,							UpperCamelCase__     :			int     ,							UpperCamelCase__     :			Optional[int]		)		->	Any:
    # 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
    A							:							Union[str, Any]				=    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
snake_case_     			=						list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 	_lowercase    (				a					):


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       # 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()}'''       )

       A							:							List[str]				=    task_cmds[task]
       A							:							Dict				=    f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

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

       cmd += args_ds + args_out

       return cmd, output_dir


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

       A,						A							:							List[str]				=    self.get_task_cmd(_UpperCAmelCase     , _UpperCAmelCase       )

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

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