




import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
 from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor

class  _lowerCamelCase						:

      """simple docstring"""

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

          # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          __UpperCamelCase  		=						(image_size // patch_size) ** 2
          __UpperCamelCase  		=						num_patches + 1







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

          __UpperCamelCase  		=						None
          if self.use_labels:
              __UpperCamelCase  		=						ids_tensor([self.batch_size] ,       self.type_sequence_label_size				)

          __UpperCamelCase  		=						self.get_config()

          return config, pixel_values, labels







      def       snake_case	(     self					: Dict				):
          return ViTConfig(
              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 ,       is_decoder=snake_case ,       initializer_range=self.initializer_range ,       encoder_stride=self.encoder_stride ,       )







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Union[str, Any] ,       snake_case					: str ,       snake_case					: Tuple				):
          __UpperCamelCase  		=						ViTModel(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size)				)







      def       snake_case	(     self					: List[Any] ,       snake_case					: int ,       snake_case					: int ,       snake_case					: Any				):
          __UpperCamelCase  		=						ViTForMaskedImageModeling(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(
              result.reconstruction.shape ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

          # test greyscale images
          __UpperCamelCase  		=						1
          __UpperCamelCase  		=						ViTForMaskedImageModeling(snake_case				)
          model.to(snake_case				)
          model.eval()

          __UpperCamelCase  		=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.reconstruction.shape ,       (self.batch_size, 1, self.image_size, self.image_size)				)







      def       snake_case	(     self					: int ,       snake_case					: Optional[int] ,       snake_case					: List[str] ,       snake_case					: List[str]				):
          __UpperCamelCase  		=						self.type_sequence_label_size
          __UpperCamelCase  		=						ViTForImageClassification(snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       labels=snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size)				)

          # test greyscale images
          __UpperCamelCase  		=						1
          __UpperCamelCase  		=						ViTForImageClassification(snake_case				)
          model.to(snake_case				)
          model.eval()

          __UpperCamelCase  		=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size)				)







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()
          (
              (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      
          )  		=						config_and_inputs
          __UpperCamelCase  		=						{'''pixel_values''': pixel_values}
          return config, inputs_dict







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

      """simple docstring"""

      lowerCAmelCase__			: Optional[Any]    =					(
          (
              ViTModel,
              ViTForImageClassification,
              ViTForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__			: str    =					(
          {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
          if is_torch_available()
          else {}
      )
      lowerCAmelCase__			: Any    =					True

      lowerCAmelCase__			: List[Any]    =					False
      lowerCAmelCase__			: Dict    =					False
      lowerCAmelCase__			: List[Any]    =					False







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						ViTModelTester(self				)
          __UpperCamelCase  		=						ConfigTester(self ,       config_class=snake_case ,       has_text_modality=snake_case ,       hidden_size=37				)







      def       snake_case	(     self					: List[Any]				):
          self.config_tester.run_common_tests()







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







      def       snake_case	(     self					: str				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

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







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __UpperCamelCase  		=						model_class(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] ,       snake_case				)







      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case				)







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*snake_case				)







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case				)







      @slow
      def       snake_case	(     self					: List[Any]				):
          for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase  		=						ViTModel.from_pretrained(snake_case				)
              self.assertIsNotNone(snake_case				)







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

    """simple docstring"""


    __UpperCamelCase  		=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    return image


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

      """simple docstring"""

      @cached_property
      def       snake_case	(     self					: List[str]				):
          return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''				) if is_vision_available() else None







      @slow
      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''				).to(snake_case				)

          __UpperCamelCase  		=						self.default_image_processor
          __UpperCamelCase  		=						prepare_img()
          __UpperCamelCase  		=						image_processor(images=snake_case ,       return_tensors='''pt'''				).to(snake_case				)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase  		=						model(**snake_case				)

          # verify the logits
          __UpperCamelCase  		=						torch.Size((1, 1000)				)
          self.assertEqual(outputs.logits.shape ,       snake_case				)

          __UpperCamelCase  		=						torch.tensor([-0.27_44, 0.82_15, -0.08_36]				).to(snake_case				)

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







      @slow
      def       snake_case	(     self					: List[Any]				):
          # ViT models have an `interpolate_pos_encoding` argument in their forward method,
          # allowing to interpolate the pre-trained position embeddings in order to use
          # the model on higher resolutions. The DINO model by Facebook AI leverages this
          # to visualize self-attention on higher resolution images.
          __UpperCamelCase  		=						ViTModel.from_pretrained('''facebook/dino-vits8'''				).to(snake_case				)

          __UpperCamelCase  		=						ViTImageProcessor.from_pretrained('''facebook/dino-vits8''' ,       size=480				)
          __UpperCamelCase  		=						prepare_img()
          __UpperCamelCase  		=						image_processor(images=snake_case ,       return_tensors='''pt'''				)
          __UpperCamelCase  		=						inputs.pixel_values.to(snake_case				)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase  		=						model(snake_case ,       interpolate_pos_encoding=snake_case				)

          # verify the logits
          __UpperCamelCase  		=						torch.Size((1, 3601, 384)				)
          self.assertEqual(outputs.last_hidden_state.shape ,       snake_case				)

          __UpperCamelCase  		=						torch.tensor(
              [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]				).to(snake_case				)

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







      @slow
      @require_accelerate
      @require_torch_gpu
      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						ViTModel.from_pretrained('''facebook/dino-vits8''' ,       torch_dtype=torch.floataa ,       device_map='''auto'''				)
          __UpperCamelCase  		=						self.default_image_processor

          __UpperCamelCase  		=						prepare_img()
          __UpperCamelCase  		=						image_processor(images=snake_case ,       return_tensors='''pt'''				)
          __UpperCamelCase  		=						inputs.pixel_values.to(snake_case				)

          # forward pass to make sure inference works in fp16
          with torch.no_grad():
              __UpperCamelCase  		=						model(snake_case				)












from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: str    =					"big_bird"







      def __init__(     self					: str ,       snake_case					: Optional[Any]=50358 ,       snake_case					: Union[str, Any]=768 ,       snake_case					: Dict=12 ,       snake_case					: List[Any]=12 ,       snake_case					: Tuple=3072 ,       snake_case					: Tuple="gelu_new" ,       snake_case					: Dict=0.1 ,       snake_case					: Any=0.1 ,       snake_case					: List[Any]=4096 ,       snake_case					: Union[str, Any]=2 ,       snake_case					: Union[str, Any]=0.02 ,       snake_case					: Union[str, Any]=1E-12 ,       snake_case					: List[Any]=True ,       snake_case					: List[Any]=0 ,       snake_case					: int=1 ,       snake_case					: int=2 ,       snake_case					: Any=66 ,       snake_case					: str="block_sparse" ,       snake_case					: Any=True ,       snake_case					: Tuple=False ,       snake_case					: List[Any]=64 ,       snake_case					: Dict=3 ,       snake_case					: Union[str, Any]=None ,       **snake_case					: Any ,       ):
          super().__init__(
              pad_token_id=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case ,       sep_token_id=snake_case ,       **snake_case ,       )

          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						type_vocab_size
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						use_cache

          __UpperCamelCase  		=						rescale_embeddings
          __UpperCamelCase  		=						attention_type
          __UpperCamelCase  		=						use_bias
          __UpperCamelCase  		=						block_size
          __UpperCamelCase  		=						num_random_blocks
          __UpperCamelCase  		=						classifier_dropout







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @property
      def       snake_case	(     self					: int				):
          if self.task == "multiple-choice":
              __UpperCamelCase  		=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              __UpperCamelCase  		=						{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ]				)






