






'''simple docstring'''

import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MobileNetVaForImageClassification, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor






class     a_						(      lowerCamelCase    ):

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = self.config_class(**self.inputs_dict				)
        self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""tf_padding"""				)				)
        self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""depth_multiplier"""				)				)






class     a_						:

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=13      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=32      ,		_SCREAMING_SNAKE_CASE=0.2_5      ,		_SCREAMING_SNAKE_CASE=8      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=1024      ,		_SCREAMING_SNAKE_CASE=32      ,		_SCREAMING_SNAKE_CASE="relu6"      ,		_SCREAMING_SNAKE_CASE=0.1      ,		_SCREAMING_SNAKE_CASE=0.0_2      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=10      ,		_SCREAMING_SNAKE_CASE=None      ,		)  ->				str:



        """simple docstring"""






        UpperCamelCase      = parent
        UpperCamelCase      = batch_size
        UpperCamelCase      = num_channels
        UpperCamelCase      = image_size
        UpperCamelCase      = depth_multiplier
        UpperCamelCase      = min_depth
        UpperCamelCase      = tf_padding
        UpperCamelCase      = int(last_hidden_size * depth_multiplier				)
        UpperCamelCase      = output_stride
        UpperCamelCase      = hidden_act
        UpperCamelCase      = classifier_dropout_prob
        UpperCamelCase      = use_labels
        UpperCamelCase      = is_training
        UpperCamelCase      = num_labels
        UpperCamelCase      = initializer_range
        UpperCamelCase      = scope

  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        UpperCamelCase      = None
        UpperCamelCase      = None
        if self.use_labels:
              UpperCamelCase      = ids_tensor([self.batch_size]      ,		self.num_labels				)
              UpperCamelCase      = ids_tensor([self.batch_size, self.image_size, self.image_size]      ,		self.num_labels				)

        UpperCamelCase      = self.get_config()

        return config, pixel_values, labels, pixel_labels

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        return MobileNetVaConfig(
            num_channels=self.num_channels      ,		image_size=self.image_size      ,		depth_multiplier=self.depth_multiplier      ,		min_depth=self.min_depth      ,		tf_padding=self.tf_padding      ,		hidden_act=self.hidden_act      ,		classifier_dropout_prob=self.classifier_dropout_prob      ,		initializer_range=self.initializer_range      ,		)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = MobileNetVaModel(config=_SCREAMING_SNAKE_CASE				)
        model.to(_SCREAMING_SNAKE_CASE				)
        model.eval()
        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE				)
        self.parent.assertEqual(
            result.last_hidden_state.shape      ,		(
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )      ,		)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = self.num_labels
        UpperCamelCase      = MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE				)
        model.to(_SCREAMING_SNAKE_CASE				)
        model.eval()
        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE      ,		labels=_SCREAMING_SNAKE_CASE				)
        self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.num_labels)				)


  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.prepare_config_and_inputs()
        UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = config_and_inputs
        UpperCamelCase      = {"""pixel_values""": pixel_values}
        return config, inputs_dict






@require_torch
class     a_						(      lowerCamelCase     ,	lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
  lowercase							=				(
      {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
      if is_torch_available()
      else {}
  )

  lowercase							=				False
  lowercase							=				False
  lowercase							=				False
  lowercase							=				False

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = MobileNetVaModelTester(self				)
        UpperCamelCase      = MobileNetVaConfigTester(self      ,		config_class=_SCREAMING_SNAKE_CASE      ,		has_text_modality=_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        self.config_tester.run_common_tests()

  @unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""				)
  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        pass

  @unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""				)
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        pass

  @unittest.skip(reason="""MobileNetV1 does not output attentions"""				)
  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase  ,UpperCamelCase      = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase      = model_class(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCamelCase      = [*signature.parameters.keys()]

              UpperCamelCase      = ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]      ,		_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        def check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = model_class(_SCREAMING_SNAKE_CASE				)
              model.to(_SCREAMING_SNAKE_CASE				)
              model.eval()

              with torch.no_grad():
                    UpperCamelCase      = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)				)

              UpperCamelCase      = outputs.hidden_states

              UpperCamelCase      = 26
              self.assertEqual(len(_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase  ,UpperCamelCase      = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase      = True
              check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCamelCase      = True

              check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)


  @slow
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase      = MobileNetVaModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)







def    lowercase__			(				)->			Optional[Any]:
      UpperCamelCase      = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
      return image






@require_torch
@require_vision
class     a_						(      unittest.TestCase    ):

  @cached_property
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        return (
            MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""				) if is_vision_available() else None
        )


  @slow
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""				).to(_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = self.default_image_processor
        UpperCamelCase      = prepare_img()
        UpperCamelCase      = image_processor(images=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).to(_SCREAMING_SNAKE_CASE				)

        # forward pass
        with torch.no_grad():
              UpperCamelCase      = model(**_SCREAMING_SNAKE_CASE				)

        # verify the logits
        UpperCamelCase      = torch.Size((1, 1001)				)
        self.assertEqual(outputs.logits.shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]				).to(_SCREAMING_SNAKE_CASE				)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)








'''simple docstring'''

import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(				)->			Tuple:
      # Get the sagemaker specific mp parameters from smp_options variable.
      UpperCamelCase      = os.getenv("""SM_HP_MP_PARAMETERS"""		,						"""{}"""   )
      try:
            # Parse it and check the field "partitions" is included, it is required for model parallel.
            UpperCamelCase      = json.loads(__UpperCamelCase   )
            if "partitions" not in smp_options:
                  return False
      except json.JSONDecodeError:
            return False

      # Get the sagemaker specific framework parameters from mpi_options variable.
      UpperCamelCase      = os.getenv("""SM_FRAMEWORK_PARAMS"""		,						"""{}"""   )
      try:
            # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
            UpperCamelCase      = json.loads(__UpperCamelCase   )
            if not mpi_options.get("""sagemaker_mpi_enabled"""		,						__UpperCamelCase   ):
                  return False
      except json.JSONDecodeError:
            return False
      # Lastly, check if the `smdistributed` module is present.
      return importlib.util.find_spec("""smdistributed"""   ) is not None


if is_sagemaker_model_parallel_available():
     import smdistributed.modelparallel.torch as smp

     smp.init()






@dataclass
class     a_						(      lowerCamelCase    ):
  lowercase							=				field(
      default=""""""     ,	metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}     ,	)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        super().__post_init__()
        warnings.warn(
            """`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
            """`TrainingArguments` instead."""      ,		_SCREAMING_SNAKE_CASE      ,		)

  @cached_property
  def   A__						(				self				)  ->				"torch.device":



        """simple docstring"""






        logger.info("""PyTorch: setting up devices"""				)
        if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
              logger.warning(
                  """torch.distributed process group is initialized, but local_rank == -1. """
                  """In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"""				)
        if self.no_cuda:
              UpperCamelCase      = torch.device("""cpu"""				)
              UpperCamelCase      = 0
        elif is_sagemaker_model_parallel_available():
              UpperCamelCase      = smp.local_rank()
              UpperCamelCase      = torch.device("""cuda"""      ,		_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = 1
        elif is_sagemaker_dp_enabled():
              import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

              torch.distributed.init_process_group(backend="""smddp"""      ,		timeout=self.ddp_timeout_delta				)
              UpperCamelCase      = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""				)				)
              UpperCamelCase      = torch.device("""cuda"""      ,		self.local_rank				)
              UpperCamelCase      = 1
        elif self.local_rank == -1:
              # if n_gpu is > 1 we'll use nn.DataParallel.
              # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
              # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
              # trigger an error that a device index is missing. Index 0 takes into account the
              # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
              # will use the first GPU in that env, i.e. GPU#1
              UpperCamelCase      = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""				)
              # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
              # the default value.
              UpperCamelCase      = torch.cuda.device_count()
        else:
              # Here, we'll use torch.distributed.
              # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
              if not torch.distributed.is_initialized():
                    torch.distributed.init_process_group(backend="""nccl"""      ,		timeout=self.ddp_timeout_delta				)
              UpperCamelCase      = torch.device("""cuda"""      ,		self.local_rank				)
              UpperCamelCase      = 1

        if device.type == "cuda":
              torch.cuda.set_device(_SCREAMING_SNAKE_CASE				)

        return device

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        if is_sagemaker_model_parallel_available():
              return smp.dp_size()

        return super().world_size

  @property
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        return not is_sagemaker_model_parallel_available()


  @property
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        return False
