"""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


lowercase__ 	=     logging.get_logger(__name__)







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

  # Get the sagemaker specific framework parameters from mpi_options variable.
  a__:					Optional[int]      							=     os.getenv('SM_FRAMEWORK_PARAMS'		,					'{}'       )
  try:
    # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
    a__:					Any      							=     json.loads(_SCREAMING_SNAKE_CASE       )
    if not mpi_options.get('sagemaker_mpi_enabled'		,					_SCREAMING_SNAKE_CASE       ):
      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        __snake_case	(					__lowerCAmelCase    ):
      a__				=		field(
          default=""""""	,				metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}	,				)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





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



      @cached_property
      def   lowerCamelCase_       (						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:
          a__:					Union[str, Any]      							=     torch.device('cpu')
          a__:					Union[str, Any]      							=     0
        elif is_sagemaker_model_parallel_available():
          a__:					str      							=     smp.local_rank()
          a__:					Union[str, Any]      							=     torch.device('cuda'   ,      lowercase)
          a__:					Dict      							=     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)
          a__:					Tuple      							=     int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'))
          a__:					Tuple      							=     torch.device('cuda'   ,      self.local_rank)
          a__:					int      							=     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
          a__:					Optional[int]      							=     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.
          a__:					List[str]      							=     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)
          a__:					List[Any]      							=     torch.device('cuda'   ,      self.local_rank)
          a__:					List[str]      							=     1

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

        return device



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





        '''simple docstring'''





        if is_sagemaker_model_parallel_available():
          return smp.dp_size()

        return super().world_size



      @property
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        return not is_sagemaker_model_parallel_available()



      @property
      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        return False



"""simple docstring"""
import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 torch import nn

 from transformers import RegNetForImageClassification, RegNetModel
 from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




class        __snake_case	:



      def __init__(						self   ,      lowercase   ,      lowercase=3   ,      lowercase=32   ,      lowercase=3   ,      lowercase=10   ,      lowercase=[10, 20, 30, 40]   ,      lowercase=[1, 1, 2, 1]   ,      lowercase=True   ,      lowercase=True   ,      lowercase="relu"   ,      lowercase=3   ,      lowercase=None   ,      )     ->      Any:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     parent
        a__:					int      							=     batch_size
        a__:					List[Any]      							=     image_size
        a__:					Any      							=     num_channels
        a__:					Dict      							=     embeddings_size
        a__:					str      							=     hidden_sizes
        a__:					List[Any]      							=     depths
        a__:					Optional[int]      							=     is_training
        a__:					Optional[int]      							=     use_labels
        a__:					Tuple      							=     hidden_act
        a__:					Any      							=     num_labels
        a__:					Union[str, Any]      							=     scope
        a__:					Tuple      							=     len(lowercase)



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					Optional[int]      							=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        a__:					str      							=     None
        if self.use_labels:
          a__:					Dict      							=     ids_tensor([self.batch_size]   ,      self.num_labels)

        a__:					int      							=     self.get_config()

        return config, pixel_values, labels



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        return RegNetConfig(
            num_channels=self.num_channels   ,      embeddings_size=self.embeddings_size   ,      hidden_sizes=self.hidden_sizes   ,      depths=self.depths   ,      hidden_act=self.hidden_act   ,      num_labels=self.num_labels   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      Any:





        '''simple docstring'''





        a__:					Dict      							=     RegNetModel(config=lowercase)
        model.to(lowercase)
        model.eval()
        a__:					List[str]      							=     model(lowercase)
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape   ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        a__:					Tuple      							=     self.num_labels
        a__:					Tuple      							=     RegNetForImageClassification(lowercase)
        model.to(lowercase)
        model.eval()
        a__:					Any      							=     model(lowercase   ,      labels=lowercase)
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels))



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					int      							=     self.prepare_config_and_inputs()
        a__							,   a__							,   a__:					List[Any]      							=     config_and_inputs
        a__:					Union[str, Any]      							=     {'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class        __snake_case	(					__lowerCAmelCase	,				__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
      a__				=		(
          {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
          if is_torch_available()
          else {}
      )

      a__				=		False
      a__				=		False
      a__				=		False
      a__				=		False



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					Dict      							=     RegNetModelTester(self)
        a__:					Dict      							=     ConfigTester(self   ,      config_class=lowercase   ,      has_text_modality=lowercase)



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()



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





        '''simple docstring'''





        return



      @unittest.skip(reason='RegNet does not use inputs_embeds')
      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        a__							,   a__:					str      							=     self.model_tester.prepare_config_and_inputs_for_common()

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

          a__:					List[str]      							=     ['pixel_values']
          self.assertListEqual(arg_names[:1]   ,      lowercase)



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





        '''simple docstring'''





        a__:					Any      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase)



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__							,   a__:					int      							=     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          a__:					int      							=     model_class(config=lowercase)
          for name, module in model.named_modules():
            if isinstance(lowercase   ,      (nn.BatchNormad, nn.GroupNorm)):
              self.assertTrue(
                  torch.all(module.weight == 1)   ,      msg=f'Parameter {name} of model {model_class} seems not properly initialized'   ,      )
              self.assertTrue(
                  torch.all(module.bias == 0)   ,      msg=f'Parameter {name} of model {model_class} seems not properly initialized'   ,      )



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        def check_hidden_states_output(lowercase   ,      lowercase   ,      lowercase):
          a__:					int      							=     model_class(lowercase)
          model.to(lowercase)
          model.eval()

          with torch.no_grad():
            a__:					int      							=     model(**self._prepare_for_class(lowercase   ,      lowercase))

          a__:					Any      							=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          a__:					List[str]      							=     self.model_tester.num_stages
          self.assertEqual(len(lowercase)   ,      expected_num_stages + 1)

          # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:])   ,      [self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,      )

        a__							,   a__:					Optional[Any]      							=     self.model_tester.prepare_config_and_inputs_for_common()
        a__:					Dict      							=     ['basic', 'bottleneck']
        for model_class in self.all_model_classes:
          for layer_type in layers_type:
            a__:					Dict      							=     layer_type
            a__:					Dict      							=     True
            check_hidden_states_output(lowercase   ,      lowercase   ,      lowercase)

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

            check_hidden_states_output(lowercase   ,      lowercase   ,      lowercase)



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





        '''simple docstring'''





        a__:					Optional[Any]      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:					Tuple      							=     RegNetModel.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)







def 						__a       (     )     ->Dict:
  a__:					str      							=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  return image




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



      @cached_property
      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        return (
            AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
            if is_vision_available()
            else None
        )



      @slow
      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					Tuple      							=     RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowercase)

        a__:					Tuple      							=     self.default_image_processor
        a__:					str      							=     prepare_img()
        a__:					Optional[int]      							=     image_processor(images=lowercase   ,      return_tensors='pt').to(lowercase)

        # forward pass
        with torch.no_grad():
          a__:					List[Any]      							=     model(**lowercase)

        # verify the logits
        a__:					Tuple      							=     torch.Size((1, 10_00))
        self.assertEqual(outputs.logits.shape   ,      lowercase)

        a__:					Optional[Any]      							=     torch.tensor([-0.4180, -1.5051, -3.4836]).to(lowercase)

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


