





"""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      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def __init__(		self   :       Any					,   __a   :       Dict					,   __a   :       Optional[int]=3					,   __a   :       Optional[Any]=32					,   __a   :       int=3					,   __a   :       Tuple=10					,   __a   :       List[Any]=[10, 20, 30, 40]					,   __a   :       Tuple=[1, 1, 2, 1]					,   __a   :       Optional[int]=True					,   __a   :       List[str]=True					,   __a   :       List[Any]="relu"					,   __a   :       Union[str, Any]=3					,   __a   :       List[Any]=None					,   )		->				Dict:
        _UpperCamelCase      :	int						=       parent
        _UpperCamelCase      :	int						=       batch_size
        _UpperCamelCase      :	Union[str, Any]						=       image_size
        _UpperCamelCase      :	Union[str, Any]						=       num_channels
        _UpperCamelCase      :	List[Any]						=       embeddings_size
        _UpperCamelCase      :	List[Any]						=       hidden_sizes
        _UpperCamelCase      :	Optional[Any]						=       depths
        _UpperCamelCase      :	Dict						=       is_training
        _UpperCamelCase      :	Optional[int]						=       use_labels
        _UpperCamelCase      :	List[str]						=       hidden_act
        _UpperCamelCase      :	str						=       num_labels
        _UpperCamelCase      :	List[Any]						=       scope
        _UpperCamelCase      :	Tuple						=       len(__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Optional[Any]:
        _UpperCamelCase      :	Any						=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

        _UpperCamelCase      :	Tuple						=       None
        if self.use_labels:
              _UpperCamelCase      :	Union[str, Any]						=       ids_tensor([self.batch_size]					,   self.num_labels	)

        _UpperCamelCase      :	List[Any]						=       self.get_config()

        return config, pixel_values, labels

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Optional[int]:
        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    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       List[Any]					,   __a   :       List[Any]					,   __a   :       List[str]	)		->				List[str]:
        _UpperCamelCase      :	Union[str, Any]						=       RegNetModel(config=__a	)
        model.to(__a	)
        model.eval()
        _UpperCamelCase      :	str						=       model(__a	)
        # 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    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       List[Any]					,   __a   :       Optional[int]					,   __a   :       Optional[Any]	)		->				str:
        _UpperCamelCase      :	Tuple						=       self.num_labels
        _UpperCamelCase      :	Optional[int]						=       RegNetForImageClassification(__a	)
        model.to(__a	)
        model.eval()
        _UpperCamelCase      :	List[str]						=       model(__a					,   labels=__a	)
        self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_labels)	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				int:
        _UpperCamelCase      :	Any						=       self.prepare_config_and_inputs()
        _UpperCamelCase      :	int						=       config_and_inputs
        _UpperCamelCase      :	int						=       {"pixel_values": pixel_values}
        return config, inputs_dict





@require_torch
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  _UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:int	     =					(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__							:Tuple	     =					(
      {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE__							:Tuple	     =					False
  SCREAMING_SNAKE_CASE__							:Tuple	     =					False
  SCREAMING_SNAKE_CASE__							:List[Any]	     =					False
  SCREAMING_SNAKE_CASE__							:List[Any]	     =					False

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				List[str]:
        _UpperCamelCase      :	List[Any]						=       RegNetModelTester(self	)
        _UpperCamelCase      :	List[str]						=       ConfigTester(self					,   config_class=__a					,   has_text_modality=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				List[Any]:
        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    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				List[Any]:
        return

  @unittest.skip(reason="RegNet does not use inputs_embeds"	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				str:
        pass

  @unittest.skip(reason="RegNet does not support input and output embeddings"	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Dict:
        pass

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				List[str]:
        _UpperCamelCase      :	List[Any]						=       self.model_tester.prepare_config_and_inputs_for_common()

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

              _UpperCamelCase      :	Union[str, Any]						=       ["pixel_values"]
              self.assertListEqual(arg_names[:1]					,   __a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				List[str]:
        _UpperCamelCase      :	Optional[int]						=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				List[Any]:
        _UpperCamelCase      :	List[Any]						=       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _UpperCamelCase      :	Any						=       model_class(config=__a	)
              for name, module in model.named_modules():
                    if isinstance(__a					,   (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    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				str:
        def check_hidden_states_output(__a   :       Union[str, Any]					,   __a   :       Any					,   __a   :       int	):
              _UpperCamelCase      :	Tuple						=       model_class(__a	)
              model.to(__a	)
              model.eval()

              with torch.no_grad():
                    _UpperCamelCase      :	int						=       model(**self._prepare_for_class(__a					,   __a	)	)

              _UpperCamelCase      :	List[str]						=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              _UpperCamelCase      :	List[str]						=       self.model_tester.num_stages
              self.assertEqual(len(__a	)					,   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]					,   )

        _UpperCamelCase      :	Optional[Any]						=       self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCamelCase      :	int						=       ["basic", "bottleneck"]
        for model_class in self.all_model_classes:
              for layer_type in layers_type:
                    _UpperCamelCase      :	Tuple						=       layer_type
                    _UpperCamelCase      :	Union[str, Any]						=       True
                    check_hidden_states_output(__a					,   __a					,   __a	)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    _UpperCamelCase      :	Optional[int]						=       True

                    check_hidden_states_output(__a					,   __a					,   __a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Tuple:
        _UpperCamelCase      :	Dict						=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__a	)


  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				List[str]:
        for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _UpperCamelCase      :	List[Any]						=       RegNetModel.from_pretrained(__a	)
              self.assertIsNotNone(__a	)





def 					lowercase__    (					)					->		Tuple:




      """simple docstring"""

      _UpperCamelCase      :	Any						=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
      return image



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







  '''simple docstring'''


  @cached_property
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Any:
        return (
            AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
            if is_vision_available()
            else None
        )


  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Optional[int]:
        _UpperCamelCase      :	Union[str, Any]						=       RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(__a	)

        _UpperCamelCase      :	str						=       self.default_image_processor
        _UpperCamelCase      :	Dict						=       prepare_img()
        _UpperCamelCase      :	Any						=       image_processor(images=__a					,   return_tensors="pt"	).to(__a	)

        # forward pass
        with torch.no_grad():
              _UpperCamelCase      :	Dict						=       model(**__a	)

        # verify the logits
        _UpperCamelCase      :	Optional[int]						=       torch.Size((1, 1000)	)
        self.assertEqual(outputs.logits.shape					,   __a	)

        _UpperCamelCase      :	Union[str, Any]						=       torch.tensor([-0.41_80, -1.50_51, -3.48_36]	).to(__a	)

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












"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase__				  =   {
    "configuration_owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTOnnxConfig",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "processing_owlvit": ["OwlViTProcessor"],
}


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   ["OwlViTFeatureExtractor"]
  lowerCamelCase__				  =   ["OwlViTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   [
      "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "OwlViTModel",
      "OwlViTPreTrainedModel",
      "OwlViTTextModel",
      "OwlViTVisionModel",
      "OwlViTForObjectDetection",
  ]

if TYPE_CHECKING:
  from .configuration_owlvit import (
      OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      OwlViTConfig,
      OwlViTOnnxConfig,
      OwlViTTextConfig,
      OwlViTVisionConfig,
  )
  from .processing_owlvit import OwlViTProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_owlvit import OwlViTFeatureExtractor
    from .image_processing_owlvit import OwlViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_owlvit import (
        OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OwlViTForObjectDetection,
        OwlViTModel,
        OwlViTPreTrainedModel,
        OwlViTTextModel,
        OwlViTVisionModel,
    )

else:
  import sys

  lowerCamelCase__				  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
