


import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
    from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Union[str, Any]					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			Union[str, Any]=13					,      __lowerCamelCase							:			str=32					,      __lowerCamelCase							:			Optional[Any]=2					,      __lowerCamelCase							:			List[Any]=3					,      __lowerCamelCase							:			str=16					,      __lowerCamelCase							:			Tuple=[1, 2, 1]					,      __lowerCamelCase							:			Tuple=[2, 2, 4]					,      __lowerCamelCase							:			str=2					,      __lowerCamelCase							:			int=2.0					,      __lowerCamelCase							:			Union[str, Any]=True					,      __lowerCamelCase							:			Tuple=0.0					,      __lowerCamelCase							:			List[Any]=0.0					,      __lowerCamelCase							:			int=0.1					,      __lowerCamelCase							:			str="gelu"					,      __lowerCamelCase							:			int=False					,      __lowerCamelCase							:			List[Any]=True					,      __lowerCamelCase							:			Union[str, Any]=0.0_2					,      __lowerCamelCase							:			Optional[Any]=1E-5					,      __lowerCamelCase							:			Optional[int]=True					,      __lowerCamelCase							:			Any=None					,      __lowerCamelCase							:			List[str]=True					,      __lowerCamelCase							:			List[str]=10					,      __lowerCamelCase							:			Optional[int]=8					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  parent
             lowerCamelCase__		:				int    =  batch_size
             lowerCamelCase__		:				Union[str, Any]    =  image_size
             lowerCamelCase__		:				str    =  patch_size
             lowerCamelCase__		:				List[str]    =  num_channels
             lowerCamelCase__		:				Union[str, Any]    =  embed_dim
             lowerCamelCase__		:				Any    =  depths
             lowerCamelCase__		:				int    =  num_heads
             lowerCamelCase__		:				Tuple    =  window_size
             lowerCamelCase__		:				List[Any]    =  mlp_ratio
             lowerCamelCase__		:				Optional[int]    =  qkv_bias
             lowerCamelCase__		:				Dict    =  hidden_dropout_prob
             lowerCamelCase__		:				Dict    =  attention_probs_dropout_prob
             lowerCamelCase__		:				Any    =  drop_path_rate
             lowerCamelCase__		:				Dict    =  hidden_act
             lowerCamelCase__		:				Optional[int]    =  use_absolute_embeddings
             lowerCamelCase__		:				Union[str, Any]    =  patch_norm
             lowerCamelCase__		:				List[str]    =  layer_norm_eps
             lowerCamelCase__		:				Union[str, Any]    =  initializer_range
             lowerCamelCase__		:				Optional[int]    =  is_training
             lowerCamelCase__		:				List[Any]    =  scope
             lowerCamelCase__		:				Union[str, Any]    =  use_labels
             lowerCamelCase__		:				Dict    =  type_sequence_label_size
             lowerCamelCase__		:				Tuple    =  encoder_stride





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             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__		:				Any    =  ids_tensor([self.batch_size]					,      self.type_sequence_label_size       )

             lowerCamelCase__		:				Any    =  self.get_config()

             return config, pixel_values, labels





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             return SwinvaConfig(
                 image_size=self.image_size					,      patch_size=self.patch_size					,      num_channels=self.num_channels					,      embed_dim=self.embed_dim					,      depths=self.depths					,      num_heads=self.num_heads					,      window_size=self.window_size					,      mlp_ratio=self.mlp_ratio					,      qkv_bias=self.qkv_bias					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      drop_path_rate=self.drop_path_rate					,      hidden_act=self.hidden_act					,      use_absolute_embeddings=self.use_absolute_embeddings					,      path_norm=self.patch_norm					,      layer_norm_eps=self.layer_norm_eps					,      initializer_range=self.initializer_range					,      encoder_stride=self.encoder_stride					,      )





       def 						lowerCAmelCase     (   self							:			str					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  SwinvaModel(config=__SCREAMING_SNAKE_CASE       )
             model.to(__SCREAMING_SNAKE_CASE       )
             model.eval()
             lowerCamelCase__		:				int    =  model(__SCREAMING_SNAKE_CASE       )

             lowerCamelCase__		:				List[str]    =  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
             lowerCamelCase__		:				List[str]    =  int(config.embed_dim * 2 ** (len(config.depths       ) - 1)       )

             self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, expected_seq_len, expected_dim)       )





       def 						lowerCAmelCase     (   self							:			str					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  SwinvaForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE       )
             model.to(__SCREAMING_SNAKE_CASE       )
             model.eval()
             lowerCamelCase__		:				Optional[int]    =  model(__SCREAMING_SNAKE_CASE       )
             self.parent.assertEqual(
                 result.logits.shape					,      (self.batch_size, self.num_channels, self.image_size, self.image_size)       )

             # test greyscale images
             lowerCamelCase__		:				str    =  1
             lowerCamelCase__		:				Tuple    =  SwinvaForMaskedImageModeling(__SCREAMING_SNAKE_CASE       )
             model.to(__SCREAMING_SNAKE_CASE       )
             model.eval()

             lowerCamelCase__		:				Optional[int]    =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
             lowerCamelCase__		:				Tuple    =  model(__SCREAMING_SNAKE_CASE       )
             self.parent.assertEqual(result.logits.shape					,      (self.batch_size, 1, self.image_size, self.image_size)       )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  self.type_sequence_label_size
             lowerCamelCase__		:				str    =  SwinvaForImageClassification(__SCREAMING_SNAKE_CASE       )
             model.to(__SCREAMING_SNAKE_CASE       )
             model.eval()
             lowerCamelCase__		:				List[Any]    =  model(__SCREAMING_SNAKE_CASE					,      labels=__SCREAMING_SNAKE_CASE       )
             self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)       )





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  self.prepare_config_and_inputs()
             lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  config_and_inputs
             lowerCamelCase__		:				str    =  {"pixel_values": pixel_values}
             return config, inputs_dict







@require_torch
class 			_lowercase (							__UpperCAmelCase							,  __UpperCAmelCase							,  unittest.TestCase):


       """simple docstring"""




       A__											=			(
           (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
       )
       A__											=			(
           {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
           if is_torch_available()
           else {}
       )

       A__											=			False
       A__											=			False
       A__											=			False
       A__											=			False





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  SwinvaModelTester(self       )
             lowerCamelCase__		:				List[Any]    =  ConfigTester(self					,      config_class=__SCREAMING_SNAKE_CASE					,      embed_dim=37       )





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             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 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )





       @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."       )
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             pass





       @unittest.skip(reason="Swinv2 does not use inputs_embeds"       )
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



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

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





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				str    =  self.model_tester.prepare_config_and_inputs_for_common()

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

                   lowerCamelCase__		:				Optional[int]    =  ["pixel_values"]
                   self.assertListEqual(arg_names[:1]					,      __SCREAMING_SNAKE_CASE       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				int    =  self.model_tester.prepare_config_and_inputs_for_common()
             lowerCamelCase__		:				Any    =  True

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				int    =  True
                   lowerCamelCase__		:				Tuple    =  False
                   lowerCamelCase__		:				Optional[Any]    =  True
                   lowerCamelCase__		:				Any    =  model_class(__SCREAMING_SNAKE_CASE       )
                   model.to(__SCREAMING_SNAKE_CASE       )
                   model.eval()
                   with torch.no_grad():
                         lowerCamelCase__		:				Tuple    =  model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE       )       )
                   lowerCamelCase__		:				Dict    =  outputs.attentions
                   lowerCamelCase__		:				List[str]    =  len(self.model_tester.depths       )
                   self.assertEqual(len(__SCREAMING_SNAKE_CASE       )					,      __SCREAMING_SNAKE_CASE       )

                   # check that output_attentions also work using config
                   del inputs_dict["output_attentions"]
                   lowerCamelCase__		:				List[str]    =  True
                   lowerCamelCase__		:				Optional[int]    =  config.window_size**2
                   lowerCamelCase__		:				Any    =  model_class(__SCREAMING_SNAKE_CASE       )
                   model.to(__SCREAMING_SNAKE_CASE       )
                   model.eval()
                   with torch.no_grad():
                         lowerCamelCase__		:				List[Any]    =  model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE       )       )
                   lowerCamelCase__		:				Dict    =  outputs.attentions
                   self.assertEqual(len(__SCREAMING_SNAKE_CASE       )					,      __SCREAMING_SNAKE_CASE       )

                   self.assertListEqual(
                       list(attentions[0].shape[-3:]       )					,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,      )
                   lowerCamelCase__		:				Union[str, Any]    =  len(__SCREAMING_SNAKE_CASE       )

                   # Check attention is always last and order is fine
                   lowerCamelCase__		:				Dict    =  True
                   lowerCamelCase__		:				List[str]    =  True
                   lowerCamelCase__		:				Dict    =  model_class(__SCREAMING_SNAKE_CASE       )
                   model.to(__SCREAMING_SNAKE_CASE       )
                   model.eval()
                   with torch.no_grad():
                         lowerCamelCase__		:				Optional[Any]    =  model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE       )       )

                   if hasattr(self.model_tester					,      "num_hidden_states_types"       ):
                         lowerCamelCase__		:				Union[str, Any]    =  self.model_tester.num_hidden_states_types
                   else:
                         # also another +1 for reshaped_hidden_states
                         lowerCamelCase__		:				List[Any]    =  2
                   self.assertEqual(out_len + added_hidden_states					,      len(__SCREAMING_SNAKE_CASE       )       )

                   lowerCamelCase__		:				int    =  outputs.attentions

                   self.assertEqual(len(__SCREAMING_SNAKE_CASE       )					,      __SCREAMING_SNAKE_CASE       )

                   self.assertListEqual(
                       list(self_attentions[0].shape[-3:]       )					,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,      )





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  model_class(__SCREAMING_SNAKE_CASE       )
             model.to(__SCREAMING_SNAKE_CASE       )
             model.eval()

             with torch.no_grad():
                   lowerCamelCase__		:				Union[str, Any]    =  model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE       )       )

             lowerCamelCase__		:				List[str]    =  outputs.hidden_states

             lowerCamelCase__		:				str    =  getattr(
                 self.model_tester					,      "expected_num_hidden_layers"					,      len(self.model_tester.depths       ) + 1       )
             self.assertEqual(len(__SCREAMING_SNAKE_CASE       )					,      __SCREAMING_SNAKE_CASE       )

             # Swinv2 has a different seq_length
             lowerCamelCase__		:				Union[str, Any]    =  (
                 config.patch_size
                 if isinstance(config.patch_size					,      collections.abc.Iterable       )
                 else (config.patch_size, config.patch_size)
             )

             lowerCamelCase__		:				Optional[int]    =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]       )					,      [num_patches, self.model_tester.embed_dim]					,      )

             lowerCamelCase__		:				Optional[int]    =  outputs.reshaped_hidden_states
             self.assertEqual(len(__SCREAMING_SNAKE_CASE       )					,      __SCREAMING_SNAKE_CASE       )

             lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  reshaped_hidden_states[0].shape
             lowerCamelCase__		:				int    =  (
                 reshaped_hidden_states[0].view(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE					,      height * width       ).permute(0					,      2					,      1       )
             )
             self.assertListEqual(
                 list(reshaped_hidden_states.shape[-2:]       )					,      [num_patches, self.model_tester.embed_dim]					,      )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				Optional[Any]    =  self.model_tester.prepare_config_and_inputs_for_common()

             lowerCamelCase__		:				List[str]    =  (
                 self.model_tester.image_size
                 if isinstance(self.model_tester.image_size					,      collections.abc.Iterable       )
                 else (self.model_tester.image_size, self.model_tester.image_size)
             )

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				int    =  True
                   self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE       )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   lowerCamelCase__		:				Tuple    =  True

                   self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE       )





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				Dict    =  self.model_tester.prepare_config_and_inputs_for_common()
             lowerCamelCase__		:				List[Any]    =  3

             lowerCamelCase__		:				Dict    =  (
                 self.model_tester.image_size
                 if isinstance(self.model_tester.image_size					,      collections.abc.Iterable       )
                 else (self.model_tester.image_size, self.model_tester.image_size)
             )
             lowerCamelCase__		:				List[Any]    =  (
                 config.patch_size
                 if isinstance(config.patch_size					,      collections.abc.Iterable       )
                 else (config.patch_size, config.patch_size)
             )

             lowerCamelCase__		:				str    =  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
             lowerCamelCase__		:				List[Any]    =  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				int    =  True
                   self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE					,      (padded_height, padded_width)       )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   lowerCamelCase__		:				Union[str, Any]    =  True
                   self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE					,      (padded_height, padded_width)       )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE       )





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE       )





       @slow
       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__		:				str    =  SwinvaModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
                   self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



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

             lowerCamelCase__		:				Union[str, Any]    =  _config_zero_init(__SCREAMING_SNAKE_CASE       )
             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				Tuple    =  model_class(config=__SCREAMING_SNAKE_CASE       )
                   for name, param in model.named_parameters():
                         if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                               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"					,      )







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


       """simple docstring"""




       @cached_property
       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             return (
                 AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"       )
                 if is_vision_available()
                 else None
             )





       @slow
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"       ).to(
                 __SCREAMING_SNAKE_CASE       )
             lowerCamelCase__		:				Optional[int]    =  self.default_image_processor

             lowerCamelCase__		:				Dict    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
             lowerCamelCase__		:				Any    =  image_processor(images=__SCREAMING_SNAKE_CASE					,      return_tensors="pt"       ).to(__SCREAMING_SNAKE_CASE       )

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

             # verify the logits
             lowerCamelCase__		:				Dict    =  torch.Size((1, 1000)       )
             self.assertEqual(outputs.logits.shape					,      __SCREAMING_SNAKE_CASE       )
             lowerCamelCase__		:				Optional[Any]    =  torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]       ).to(__SCREAMING_SNAKE_CASE       )
             self.assertTrue(torch.allclose(outputs.logits[0, :3]					,      __SCREAMING_SNAKE_CASE					,      atol=1E-4       )       )







import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible

class     _A       (	unittest.TestCase    ):



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __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(__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				[
               '''unet/diffusion_pytorch_model.bin''',
               '''unet/diffusion_pytorch_model.safetensors''',
           ]
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __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(__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				[
               '''text_encoder/pytorch_model.bin''',
               '''text_encoder/model.safetensors''',
           ]
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __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(__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __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(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				[
               '''unet/diffusion_pytorch_model.fp16.bin''',
               '''unet/diffusion_pytorch_model.fp16.safetensors''',
           ]
           __a						       =				'''fp16'''
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				[
               '''unet/diffusion_pytorch_model.bin''',
               '''unet/diffusion_pytorch_model.safetensors''',
           ]
           __a						       =				'''fp16'''
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __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(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				[
               '''text_encoder/pytorch_model.fp16.bin''',
               '''text_encoder/model.fp16.safetensors''',
           ]
           __a						       =				'''fp16'''
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				[
               '''text_encoder/pytorch_model.bin''',
               '''text_encoder/model.safetensors''',
           ]
           __a						       =				'''fp16'''
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __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(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))





