'''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 lowercase_       :







   """simple docstring"""

   def __init__(      self      :			str, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			List[str]=13, UpperCamelCase__      :			Any=64, UpperCamelCase__      :			Optional[int]=2, UpperCamelCase__      :			Optional[Any]=3, UpperCamelCase__      :			List[str]=True, UpperCamelCase__      :			str=True, UpperCamelCase__      :			Tuple=32, UpperCamelCase__      :			str=5, UpperCamelCase__      :			Union[str, Any]=4, UpperCamelCase__      :			List[str]=37, UpperCamelCase__      :			int="gelu", UpperCamelCase__      :			Any=0.1, UpperCamelCase__      :			Union[str, Any]=0.1, UpperCamelCase__      :			Optional[int]=10, UpperCamelCase__      :			Union[str, Any]=0.02, UpperCamelCase__      :			str=[1, 16, 4, 4], UpperCamelCase__      :			Dict=None, )  ->			Dict:
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  image_size
        _A    		=  patch_size
        _A    		=  num_channels
        _A    		=  is_training
        _A    		=  use_labels
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  num_attention_heads
        _A    		=  intermediate_size
        _A    		=  hidden_act
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  type_sequence_label_size
        _A    		=  initializer_range
        _A    		=  scope
        _A    		=  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    		=  (self.image_size // 32) ** 2
        _A    		=  num_patches + 1
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			str:
        _A    		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

        _A    		=  self.get_config()

        return config, pixel_values, labels
   def 	__UpperCAmelCase   (      self      :			str     )  ->			List[Any]:
        _A    		=  {
            'global_padding': 'same',
            'layer_type': 'bottleneck',
            'depths': [3, 4, 9],
            'out_features': ['stage1', 'stage2', 'stage3'],
            'embedding_dynamic_padding': True,
            'hidden_sizes': [4, 8, 16, 32],
            '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=UpperCamelCase__, initializer_range=self.initializer_range, backbone_featmap_shape=self.backbone_featmap_shape, backbone_config=UpperCamelCase__, )
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Any     )  ->			Any:
        _A    		=  ViTHybridModel(config=UpperCamelCase__     )
        model.to(UpperCamelCase__     )
        model.eval()
        _A    		=  model(UpperCamelCase__     )
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Optional[int]     )  ->			List[str]:
        _A    		=  self.type_sequence_label_size
        _A    		=  ViTHybridForImageClassification(UpperCamelCase__     )
        model.to(UpperCamelCase__     )
        model.eval()
        _A    		=  model(UpperCamelCase__, labels=UpperCamelCase__     )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)     )

   def 	__UpperCAmelCase   (      self      :			Any     )  ->			int:
        _A    		=  self.prepare_config_and_inputs()
        _A		,       _A		,       _A    		=  config_and_inputs
        _A    		=  {'pixel_values': pixel_values}
        return config, inputs_dict







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







   """simple docstring"""

   __lowerCAmelCase         =   (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
   __lowerCAmelCase         =   (
       {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
       if is_torch_available()
       else {}
   )
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   def 	__UpperCAmelCase   (      self      :			int     )  ->			List[Any]:
        _A    		=  ViTHybridModelTester(self     )
        _A    		=  ConfigTester(self, config_class=UpperCamelCase__, has_text_modality=UpperCamelCase__, hidden_size=37     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			str:
        self.config_tester.run_common_tests()
   @unittest.skip(reason='ViT does not use inputs_embeds'     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Union[str, Any]:
        pass
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Tuple:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _A    		=  model_class(UpperCamelCase__     )
             self.assertIsInstance(model.get_input_embeddings(), (nn.Module)     )
             _A    		=  model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(UpperCamelCase__, nn.Linear     )     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Tuple:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()

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

             _A    		=  ['pixel_values']
             self.assertListEqual(arg_names[:1], UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			List[Any]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Any:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			List[str]:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()

        _A    		=  _config_zero_init(UpperCamelCase__     )
        for model_class in self.all_model_classes:
             _A    		=  model_class(config=UpperCamelCase__     )
             # Skip the check for the backbone
             for name, module in model.named_modules():
                  if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                       _A    		=  [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 	__UpperCAmelCase   (      self      :			List[str]     )  ->			List[str]:
        for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A    		=  ViTHybridModel.from_pretrained(UpperCamelCase__     )
             self.assertIsNotNone(UpperCamelCase__     )






def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
     return image







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







   """simple docstring"""

   @cached_property
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Union[str, Any]:
        return (
            ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
            if is_vision_available()
            else None
        )
   @slow
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        _A    		=  ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(
            UpperCamelCase__     )

        _A    		=  self.default_image_processor
        _A    		=  prepare_img()
        _A    		=  image_processor(images=UpperCamelCase__, return_tensors='pt'     ).to(UpperCamelCase__     )

        # forward pass
        with torch.no_grad():
             _A    		=  model(**UpperCamelCase__     )

        # verify the logits
        _A    		=  torch.Size((1, 10_00)     )
        self.assertEqual(outputs.logits.shape, UpperCamelCase__     )

        _A    		=  torch.tensor([-1.9_090, -0.4_993, -0.2_389]     ).to(UpperCamelCase__     )

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

   @slow
   @require_accelerate
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Tuple:
        _A    		=  ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'     )
        _A    		=  ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384', device_map='auto'     )

        _A    		=  prepare_img()

        _A    		=  image_processor(images=UpperCamelCase__, return_tensors='pt'     )
        _A    		=  model(**UpperCamelCase__     )
        _A    		=  outputs.logits
        # model predicts one of the 1000 ImageNet classes
        _A    		=  logits.argmax(-1     ).item()

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




'''simple docstring'''






import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible







class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Dict:
        _A    		=  [
            'safety_checker/pytorch_model.bin',
            'safety_checker/model.safetensors',
            'vae/diffusion_pytorch_model.bin',
            'vae/diffusion_pytorch_model.safetensors',
            'text_encoder/pytorch_model.bin',
            'text_encoder/model.safetensors',
            'unet/diffusion_pytorch_model.bin',
            'unet/diffusion_pytorch_model.safetensors',
        ]
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        _A    		=  [
            'unet/diffusion_pytorch_model.bin',
            'unet/diffusion_pytorch_model.safetensors',
        ]
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Union[str, Any]:
        _A    		=  [
            'safety_checker/pytorch_model.bin',
            'safety_checker/model.safetensors',
            'vae/diffusion_pytorch_model.bin',
            'vae/diffusion_pytorch_model.safetensors',
            'text_encoder/pytorch_model.bin',
            'text_encoder/model.safetensors',
            'unet/diffusion_pytorch_model.bin',
            # Removed: 'unet/diffusion_pytorch_model.safetensors',
        ]
        self.assertFalse(is_safetensors_compatible(UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Dict:
        _A    		=  [
            'text_encoder/pytorch_model.bin',
            'text_encoder/model.safetensors',
        ]
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			str:
        _A    		=  [
            'safety_checker/pytorch_model.bin',
            'safety_checker/model.safetensors',
            'vae/diffusion_pytorch_model.bin',
            'vae/diffusion_pytorch_model.safetensors',
            'text_encoder/pytorch_model.bin',
            # Removed: 'text_encoder/model.safetensors',
            'unet/diffusion_pytorch_model.bin',
            'unet/diffusion_pytorch_model.safetensors',
        ]
        self.assertFalse(is_safetensors_compatible(UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Dict:
        _A    		=  [
            'safety_checker/pytorch_model.fp16.bin',
            'safety_checker/model.fp16.safetensors',
            'vae/diffusion_pytorch_model.fp16.bin',
            'vae/diffusion_pytorch_model.fp16.safetensors',
            'text_encoder/pytorch_model.fp16.bin',
            'text_encoder/model.fp16.safetensors',
            'unet/diffusion_pytorch_model.fp16.bin',
            'unet/diffusion_pytorch_model.fp16.safetensors',
        ]
        _A    		=  'fp16'
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			List[str]:
        _A    		=  [
            'unet/diffusion_pytorch_model.fp16.bin',
            'unet/diffusion_pytorch_model.fp16.safetensors',
        ]
        _A    		=  'fp16'
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			List[str]:
        # pass variant but use the non-variant filenames
        _A    		=  [
            'unet/diffusion_pytorch_model.bin',
            'unet/diffusion_pytorch_model.safetensors',
        ]
        _A    		=  'fp16'
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[Any]:
        _A    		=  [
            'safety_checker/pytorch_model.fp16.bin',
            'safety_checker/model.fp16.safetensors',
            'vae/diffusion_pytorch_model.fp16.bin',
            'vae/diffusion_pytorch_model.fp16.safetensors',
            'text_encoder/pytorch_model.fp16.bin',
            'text_encoder/model.fp16.safetensors',
            'unet/diffusion_pytorch_model.fp16.bin',
            # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
        ]
        _A    		=  'fp16'
        self.assertFalse(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Union[str, Any]:
        _A    		=  [
            'text_encoder/pytorch_model.fp16.bin',
            'text_encoder/model.fp16.safetensors',
        ]
        _A    		=  'fp16'
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			str:
        # pass variant but use the non-variant filenames
        _A    		=  [
            'text_encoder/pytorch_model.bin',
            'text_encoder/model.safetensors',
        ]
        _A    		=  'fp16'
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )

   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			int:
        _A    		=  [
            'safety_checker/pytorch_model.fp16.bin',
            'safety_checker/model.fp16.safetensors',
            'vae/diffusion_pytorch_model.fp16.bin',
            'vae/diffusion_pytorch_model.fp16.safetensors',
            'text_encoder/pytorch_model.fp16.bin',
            # 'text_encoder/model.fp16.safetensors',
            'unet/diffusion_pytorch_model.fp16.bin',
            'unet/diffusion_pytorch_model.fp16.safetensors',
        ]
        _A    		=  'fp16'
        self.assertFalse(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )



