





"""simple docstring"""




import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
   from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

class  _UpperCAmelCase  :



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_=1_3					,      snake_case_=6_4					,      snake_case_=2					,      snake_case_=3					,      snake_case_=True					,      snake_case_=True					,      snake_case_=3_2					,      snake_case_=5					,      snake_case_=4					,      snake_case_=3_7					,      snake_case_="gelu"					,      snake_case_=0.1					,      snake_case_=0.1					,      snake_case_=1_0					,      snake_case_=0.02					,      snake_case_=[1, 1_6, 4, 4]					,      snake_case_=None					,      ):

        """simple docstring"""

        A_  :       List[str]										=  parent
        A_  :       Any										=  batch_size
        A_  :       Any										=  image_size
        A_  :       Optional[int]										=  patch_size
        A_  :       Union[str, Any]										=  num_channels
        A_  :       Optional[Any]										=  is_training
        A_  :       Union[str, Any]										=  use_labels
        A_  :       List[Any]										=  hidden_size
        A_  :       int										=  num_hidden_layers
        A_  :       str										=  num_attention_heads
        A_  :       Dict										=  intermediate_size
        A_  :       int										=  hidden_act
        A_  :       Any										=  hidden_dropout_prob
        A_  :       List[Any]										=  attention_probs_dropout_prob
        A_  :       Any										=  type_sequence_label_size
        A_  :       Any										=  initializer_range
        A_  :       Tuple										=  scope
        A_  :       List[str]										=  backbone_featmap_shape

        # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        # the number of patches is based on the feature map of the backbone, which by default uses an output stride
        # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
        A_  :       Optional[int]										=  (self.image_size // 3_2) ** 2
        A_  :       int										=  num_patches + 1

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[str]										=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        A_  :       Union[str, Any]										=  None
        if self.use_labels:
          A_  :       List[str]										=  ids_tensor([self.batch_size]					,      self.type_sequence_label_size  )

        A_  :       Dict										=  self.get_config()

        return config, pixel_values, labels

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[str]										=  {
            'global_padding': 'same',
            'layer_type': 'bottleneck',
            'depths': [3, 4, 9],
            'out_features': ['stage1', 'stage2', 'stage3'],
            'embedding_dynamic_padding': True,
            'hidden_sizes': [4, 8, 1_6, 3_2],
            'num_groups': 2,
        }

        return ViTHybridConfig(
            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					,      backbone_featmap_shape=self.backbone_featmap_shape					,      backbone_config=snake_case_					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Optional[int]										=  ViTHybridModel(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       str										=  model(snake_case_  )
        self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Optional[int]										=  self.type_sequence_label_size
        A_  :       str										=  ViTHybridForImageClassification(snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       int										=  model(snake_case_					,      labels=snake_case_  )
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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


@require_torch
class  _UpperCAmelCase  (   UpperCAmelCase__			,   UpperCAmelCase__			,   unittest.TestCase		):



      '''simple docstring'''
      lowercase_      : List[str]	    =   (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
      lowercase_      : Any	    =   (
          {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
          if is_torch_available()
          else {}
      )
      lowercase_      : Optional[int]	    =   False
      lowercase_      : List[str]	    =   False
      lowercase_      : Optional[int]	    =   False

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       int										=  ViTHybridModelTester(self  )
        A_  :       str										=  ConfigTester(self					,      config_class=snake_case_					,      has_text_modality=snake_case_					,      hidden_size=3_7  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        self.config_tester.run_common_tests()

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

        """simple docstring"""

        pass

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       Dict										=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          A_  :       List[Any]										=  model_class(snake_case_  )
          self.assertIsInstance(model.get_input_embeddings()					,      (nn.Module)  )
          A_  :       int										=  model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(snake_case_					,      nn.Linear  )  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       Dict										=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          A_  :       Dict										=  model_class(snake_case_  )
          A_  :       Union[str, Any]										=  inspect.signature(model.forward  )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          A_  :       str										=  [*signature.parameters.keys()]

          A_  :       Dict										=  ['pixel_values']
          self.assertListEqual(arg_names[:1]					,      snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Optional[int]										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       Tuple										=  self.model_tester.prepare_config_and_inputs_for_common()

        A_  :       Any										=  _config_zero_init(snake_case_  )
        for model_class in self.all_model_classes:
          A_  :       List[str]										=  model_class(config=snake_case_  )
          # Skip the check for the backbone
          for name, module in model.named_modules():
            if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
              A_  :       str										=  [F"""{name}.{key}""" for key in module.state_dict().keys()]
              break

          for name, param in model.named_parameters():
            if param.requires_grad:
              if name in backbone_params:
                continue
              self.assertIn(
                  ((param.data.mean() * 1E9).round() / 1E9).item()					,      [0.0, 1.0]					,      msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,      )

      @slow
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          A_  :       Tuple										=  ViTHybridModel.from_pretrained(snake_case_  )
          self.assertIsNotNone(snake_case_  )





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





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





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



      '''simple docstring'''

      @cached_property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return (
            ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
            if is_vision_available()
            else None
        )

      @slow
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       int										=  ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(
            snake_case_  )

        A_  :       int										=  self.default_image_processor
        A_  :       Dict										=  prepare_img()
        A_  :       List[Any]										=  image_processor(images=snake_case_					,      return_tensors='pt'  ).to(snake_case_  )

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

        # verify the logits
        A_  :       Dict										=  torch.Size((1, 1_0_0_0)  )
        self.assertEqual(outputs.logits.shape					,      snake_case_  )

        A_  :       Union[str, Any]										=  torch.tensor([-1.90_90, -0.49_93, -0.23_89]  ).to(snake_case_  )

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

      @slow
      @require_accelerate
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       str										=  ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'  )
        A_  :       Optional[Any]										=  ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'					,      device_map='auto'  )

        A_  :       Optional[Any]										=  prepare_img()

        A_  :       List[Any]										=  image_processor(images=snake_case_					,      return_tensors='pt'  )
        A_  :       Optional[Any]										=  model(**snake_case_  )
        A_  :       str										=  outputs.logits
        # model predicts one of the 1000 ImageNet classes
        A_  :       Union[str, Any]										=  logits.argmax(-1  ).item()

        self.assertTrue(model.config.idalabel[predicted_class_idx]					,      'tabby, tabby cat'  )






"""simple docstring"""




import torch

from diffusers import DiffusionPipeline

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        super().__init__()

        self.register_modules(unet=snake_case_					,      scheduler=snake_case_  )

      def __call__(     self  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  torch.randn(
            (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      )
        A_  :       List[str]										=  1

        A_  :       List[str]										=  self.unet(snake_case_					,      snake_case_  ).sample
        A_  :       Optional[int]										=  self.scheduler.step(snake_case_					,      snake_case_					,      snake_case_  ).prev_sample

        A_  :       List[Any]										=  scheduler_output - scheduler_output + torch.ones_like(snake_case_  )

        return result