


'''simple docstring'''






import inspect
import unittest

from transformers import ViTMSNConfig
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 torch import nn

  from transformers import ViTMSNForImageClassification, ViTMSNModel
  from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  Tuple   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  List[str]=13   ,						lowerCamelCase   :  str=30   ,						lowerCamelCase   :  Optional[int]=2   ,						lowerCamelCase   :  List[str]=3   ,						lowerCamelCase   :  Tuple=True   ,						lowerCamelCase   :  Any=True   ,						lowerCamelCase   :  List[str]=32   ,						lowerCamelCase   :  Optional[int]=5   ,						lowerCamelCase   :  Dict=4   ,						lowerCamelCase   :  int=37   ,						lowerCamelCase   :  Dict="gelu"   ,						lowerCamelCase   :  int=0.1   ,						lowerCamelCase   :  Optional[int]=0.1   ,						lowerCamelCase   :  Any=10   ,						lowerCamelCase   :  Union[str, Any]=0.02   ,						lowerCamelCase   :  Any=None   ,						)       ->  List[Any]:
      lowerCAmelCase_      :				List[Any]      =       parent
      lowerCAmelCase_      :				Tuple      =       batch_size
      lowerCAmelCase_      :				List[Any]      =       image_size
      lowerCAmelCase_      :				List[str]      =       patch_size
      lowerCAmelCase_      :				List[Any]      =       num_channels
      lowerCAmelCase_      :				Union[str, Any]      =       is_training
      lowerCAmelCase_      :				Union[str, Any]      =       use_labels
      lowerCAmelCase_      :				Optional[Any]      =       hidden_size
      lowerCAmelCase_      :				Optional[int]      =       num_hidden_layers
      lowerCAmelCase_      :				Tuple      =       num_attention_heads
      lowerCAmelCase_      :				Dict      =       intermediate_size
      lowerCAmelCase_      :				Union[str, Any]      =       hidden_act
      lowerCAmelCase_      :				int      =       hidden_dropout_prob
      lowerCAmelCase_      :				str      =       attention_probs_dropout_prob
      lowerCAmelCase_      :				int      =       type_sequence_label_size
      lowerCAmelCase_      :				Any      =       initializer_range
      lowerCAmelCase_      :				List[Any]      =       scope

      # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      lowerCAmelCase_      :				Union[str, Any]      =       (image_size // patch_size) ** 2
      lowerCAmelCase_      :				List[Any]      =       num_patches + 1

    def __lowercase       (       self   :  Any		)       ->  Optional[int]:
      lowerCAmelCase_      :				Dict      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

      lowerCAmelCase_      :				Optional[Any]      =       None
      if self.use_labels:
        lowerCAmelCase_      :				Optional[int]      =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)

      lowerCAmelCase_      :				Any      =       self.get_config()

      return config, pixel_values, labels

    def __lowercase       (       self   :  List[Any]		)       ->  List[str]:
      return ViTMSNConfig(
          image_size=self.image_size   ,						patch_size=self.patch_size   ,						num_channels=self.num_channels   ,						hidden_size=self.hidden_size   ,						num_hidden_layers=self.num_hidden_layers   ,						num_attention_heads=self.num_attention_heads   ,						intermediate_size=self.intermediate_size   ,						hidden_act=self.hidden_act   ,						hidden_dropout_prob=self.hidden_dropout_prob   ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,						initializer_range=self.initializer_range   ,						)

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Optional[int]		)       ->  Optional[Any]:
      lowerCAmelCase_      :				int      =       ViTMSNModel(config=lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				Any      =       model(lowerCamelCase		)
      self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)

    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  List[Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Any      =       self.type_sequence_label_size
      lowerCAmelCase_      :				Tuple      =       ViTMSNForImageClassification(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				Dict      =       model(lowerCamelCase   ,						labels=lowerCamelCase		)
      print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""		)
      print("""Labels: {labels}"""		)
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.type_sequence_label_size)		)

      # test greyscale images
      lowerCAmelCase_      :				Any      =       1
      lowerCAmelCase_      :				Tuple      =       ViTMSNForImageClassification(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()

      lowerCAmelCase_      :				List[Any]      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
      lowerCAmelCase_      :				int      =       model(lowerCamelCase		)
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.type_sequence_label_size)		)





    def __lowercase       (       self   :  Dict		)       ->  int:
      lowerCAmelCase_      :				Any      =       self.prepare_config_and_inputs()
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				List[str]      =       config_and_inputs
      lowerCAmelCase_      :				List[Any]      =       {"""pixel_values""": pixel_values}
      return config, inputs_dict







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







    """simple docstring"""



    lowercase       			= (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    lowercase       			= (
        {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

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

    def __lowercase       (       self   :  str		)       ->  List[str]:
      lowerCAmelCase_      :				Tuple      =       ViTMSNModelTester(self		)
      lowerCAmelCase_      :				List[Any]      =       ConfigTester(self   ,						config_class=lowerCamelCase   ,						has_text_modality=lowerCamelCase   ,						hidden_size=37		)

    def __lowercase       (       self   :  Dict		)       ->  Optional[int]:
      self.config_tester.run_common_tests()

    @unittest.skip(reason="""ViTMSN does not use inputs_embeds"""		)
    def __lowercase       (       self   :  Any		)       ->  Union[str, Any]:
      pass

    def __lowercase       (       self   :  Dict		)       ->  str:
      lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				str      =       model_class(lowerCamelCase		)
        self.assertIsInstance(model.get_input_embeddings()   ,						(nn.Module)		)
        lowerCAmelCase_      :				Tuple      =       model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCamelCase   ,						nn.Linear		)		)

    def __lowercase       (       self   :  List[Any]		)       ->  Optional[int]:
      lowerCAmelCase_,							lowerCAmelCase_      :				str      =       self.model_tester.prepare_config_and_inputs_for_common()

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

        lowerCAmelCase_      :				Optional[Any]      =       ["""pixel_values"""]
        self.assertListEqual(arg_names[:1]   ,						lowerCamelCase		)

    def __lowercase       (       self   :  Optional[int]		)       ->  List[str]:
      lowerCAmelCase_      :				int      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase		)

    def __lowercase       (       self   :  Optional[Any]		)       ->  List[Any]:
      lowerCAmelCase_      :				List[str]      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCamelCase		)





    @slow
    def __lowercase       (       self   :  List[Any]		)       ->  Optional[Any]:
      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCAmelCase_      :				Any      =       ViTMSNModel.from_pretrained(lowerCamelCase		)
        self.assertIsNotNone(lowerCamelCase		)







def     UpperCamelCase_      (   ):
  '''simple docstring'''





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






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







    """simple docstring"""



    @cached_property
    def __lowercase       (       self   :  Any		)       ->  List[Any]:
      return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""		) if is_vision_available() else None





    @slow
    def __lowercase       (       self   :  Optional[Any]		)       ->  Tuple:
      torch.manual_seed(2		)
      lowerCAmelCase_      :				Optional[Any]      =       ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""		).to(lowerCamelCase		)

      lowerCAmelCase_      :				int      =       self.default_image_processor
      lowerCAmelCase_      :				str      =       prepare_img()
      lowerCAmelCase_      :				str      =       image_processor(images=lowerCamelCase   ,						return_tensors="""pt"""		).to(lowerCamelCase		)

      # forward pass
      with torch.no_grad():
        lowerCAmelCase_      :				Optional[Any]      =       model(**lowerCamelCase		)

      # verify the logits
      lowerCAmelCase_      :				List[str]      =       torch.Size((1, 10_00)		)
      self.assertEqual(outputs.logits.shape   ,						lowerCamelCase		)

      lowerCAmelCase_      :				int      =       torch.tensor([-0.0_803, -0.4_454, -0.2_375]		).to(lowerCamelCase		)

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







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A :  List[Any]    							=				{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  pass

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Union[str, Any]    							=				[
      "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoCBertForCausalLM",
      "RoCBertForMaskedLM",
      "RoCBertForMultipleChoice",
      "RoCBertForPreTraining",
      "RoCBertForQuestionAnswering",
      "RoCBertForSequenceClassification",
      "RoCBertForTokenClassification",
      "RoCBertLayer",
      "RoCBertModel",
      "RoCBertPreTrainedModel",
      "load_tf_weights_in_roc_bert",
  ]

if TYPE_CHECKING:
  from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
  from .tokenization_roc_bert import RoCBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    raise OptionalDependencyNotAvailable()

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roc_bert import (
        ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertLayer,
        RoCBertModel,
        RoCBertPreTrainedModel,
        load_tf_weights_in_roc_bert,
    )


else:
  import sys

  __A :  int    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



