
'''simple docstring'''





import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import YolosForObjectDetection, YolosModel
      from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor






class 							UpperCAmelCase_							:

 '''simple docstring'''







 def __init__(					self		:  List[Any]			,				snake_case__		:  Optional[Any]			,				snake_case__		:  Tuple=13			,				snake_case__		:  Optional[int]=[30, 30]			,				snake_case__		:  Optional[int]=2			,				snake_case__		:  List[Any]=3			,				snake_case__		:  List[Any]=True			,				snake_case__		:  Dict=True			,				snake_case__		:  Union[str, Any]=32			,				snake_case__		:  Optional[Any]=5			,				snake_case__		:  Union[str, Any]=4			,				snake_case__		:  Dict=37			,				snake_case__		:  Union[str, Any]="gelu"			,				snake_case__		:  Optional[Any]=0.1			,				snake_case__		:  Any=0.1			,				snake_case__		:  Tuple=10			,				snake_case__		:  Tuple=0.02			,				snake_case__		:  List[Any]=3			,				snake_case__		:  Optional[Any]=None			,				snake_case__		:  int=8			,				snake_case__		:  int=10			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							parent
   UpperCAmelCase__      :					List[Any]				       =							batch_size
   UpperCAmelCase__      :					Tuple				       =							image_size
   UpperCAmelCase__      :					Optional[Any]				       =							patch_size
   UpperCAmelCase__      :					Optional[int]				       =							num_channels
   UpperCAmelCase__      :					Optional[Any]				       =							is_training
   UpperCAmelCase__      :					Tuple				       =							use_labels
   UpperCAmelCase__      :					Tuple				       =							hidden_size
   UpperCAmelCase__      :					Union[str, Any]				       =							num_hidden_layers
   UpperCAmelCase__      :					Union[str, Any]				       =							num_attention_heads
   UpperCAmelCase__      :					int				       =							intermediate_size
   UpperCAmelCase__      :					Union[str, Any]				       =							hidden_act
   UpperCAmelCase__      :					Optional[Any]				       =							hidden_dropout_prob
   UpperCAmelCase__      :					Union[str, Any]				       =							attention_probs_dropout_prob
   UpperCAmelCase__      :					Optional[Any]				       =							type_sequence_label_size
   UpperCAmelCase__      :					List[Any]				       =							initializer_range
   UpperCAmelCase__      :					int				       =							num_labels
   UpperCAmelCase__      :					Dict				       =							scope
   UpperCAmelCase__      :					List[str]				       =							n_targets
   UpperCAmelCase__      :					Optional[int]				       =							num_detection_tokens
   # we set the expected sequence length (which is used in several tests)
   # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
   UpperCAmelCase__      :					Dict				       =							(image_size[1] // patch_size) * (image_size[0] // patch_size)
   UpperCAmelCase__      :					Optional[int]				       =							num_patches + 1 + self.num_detection_tokens







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )

   UpperCAmelCase__      :					int				       =							None
   if self.use_labels:
     # labels is a list of Dict (each Dict being the labels for a given example in the batch)
     UpperCAmelCase__      :					int				       =							[]
     for i in range(self.batch_size ):
       UpperCAmelCase__      :					Optional[Any]				       =							{}
       UpperCAmelCase__      :					Dict				       =							torch.randint(
           high=self.num_labels			,				size=(self.n_targets,)			,				device=snake_case__ )
       UpperCAmelCase__      :					Tuple				       =							torch.rand(self.n_targets			,				4			,				device=snake_case__ )
       labels.append(snake_case__ )

   UpperCAmelCase__      :					Dict				       =							self.get_config()

   return config, pixel_values, labels







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   return YolosConfig(
       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			,				num_detection_tokens=self.num_detection_tokens			,				num_labels=self.num_labels			,				)







 def 							UpperCamelCase  (					self		:  int			,				snake_case__		:  Tuple			,				snake_case__		:  Any			,				snake_case__		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Union[str, Any]				       =							YolosModel(config=snake_case__ )
   model.to(snake_case__ )
   model.eval()
   UpperCAmelCase__      :					Optional[int]				       =							model(snake_case__ )
   self.parent.assertEqual(
       result.last_hidden_state.shape			,				(self.batch_size, self.expected_seq_len, self.hidden_size) )







 def 							UpperCamelCase  (					self		:  int			,				snake_case__		:  Optional[Any]			,				snake_case__		:  List[Any]			,				snake_case__		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Union[str, Any]				       =							YolosForObjectDetection(snake_case__ )
   model.to(snake_case__ )
   model.eval()

   UpperCAmelCase__      :					int				       =							model(pixel_values=snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							model(snake_case__ )

   self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
   self.parent.assertEqual(result.pred_boxes.shape			,				(self.batch_size, self.num_detection_tokens, 4) )

   UpperCAmelCase__      :					List[Any]				       =							model(pixel_values=snake_case__			,				labels=snake_case__ )

   self.parent.assertEqual(result.loss.shape			,				() )
   self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
   self.parent.assertEqual(result.pred_boxes.shape			,				(self.batch_size, self.num_detection_tokens, 4) )







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							self.prepare_config_and_inputs()
   UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					Optional[Any]				       =							config_and_inputs
   UpperCAmelCase__      :					Tuple				       =							{"pixel_values": pixel_values}
   return config, inputs_dict






@require_torch
class 							UpperCAmelCase_							(    A					,				A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    List[str]				=							(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
 lowercase_					:    Tuple				=							(
     {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
 )

 lowercase_					:    Any				=							False
 lowercase_					:    Any				=							False
 lowercase_					:    List[Any]				=							False
 lowercase_					:    List[Any]				=							False







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  str			,				snake_case__		:  int			,				snake_case__		:  Union[str, Any]=False ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							super()._prepare_for_class(snake_case__			,				snake_case__			,				return_labels=snake_case__ )

   if return_labels:
     if model_class.__name__ == "YolosForObjectDetection":
       UpperCAmelCase__      :					List[Any]				       =							[]
       for i in range(self.model_tester.batch_size ):
         UpperCAmelCase__      :					str				       =							{}
         UpperCAmelCase__      :					List[Any]				       =							torch.ones(
             size=(self.model_tester.n_targets,)			,				device=snake_case__			,				dtype=torch.long )
         UpperCAmelCase__      :					str				       =							torch.ones(
             self.model_tester.n_targets			,				4			,				device=snake_case__			,				dtype=torch.float )
         labels.append(snake_case__ )
       UpperCAmelCase__      :					Union[str, Any]				       =							labels

   return inputs_dict







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							YolosModelTester(self )
   UpperCAmelCase__      :					Optional[int]				       =							ConfigTester(self			,				config_class=snake_case__			,				has_text_modality=snake_case__			,				hidden_size=37 )







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



   '''simple docstring'''




   self.config_tester.run_common_tests()







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



   '''simple docstring'''




   pass







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					int				       =							self.model_tester.prepare_config_and_inputs_for_common()

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







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							self.model_tester.prepare_config_and_inputs_for_common()

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

     UpperCAmelCase__      :					Union[str, Any]				       =							["pixel_values"]
     self.assertListEqual(arg_names[:1]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*snake_case__ )







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




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

   # in YOLOS, the seq_len is different
   UpperCAmelCase__      :					List[Any]				       =							self.model_tester.expected_seq_len
   for model_class in self.all_model_classes:
     UpperCAmelCase__      :					List[str]				       =							True
     UpperCAmelCase__      :					Optional[Any]				       =							False
     UpperCAmelCase__      :					Optional[int]				       =							True
     UpperCAmelCase__      :					Tuple				       =							model_class(snake_case__ )
     model.to(snake_case__ )
     model.eval()
     with torch.no_grad():
       UpperCAmelCase__      :					List[Any]				       =							model(**self._prepare_for_class(snake_case__			,				snake_case__ ) )
     UpperCAmelCase__      :					List[str]				       =							outputs.attentions
     self.assertEqual(len(snake_case__ )			,				self.model_tester.num_hidden_layers )

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     UpperCAmelCase__      :					int				       =							True
     UpperCAmelCase__      :					Any				       =							model_class(snake_case__ )
     model.to(snake_case__ )
     model.eval()
     with torch.no_grad():
       UpperCAmelCase__      :					List[Any]				       =							model(**self._prepare_for_class(snake_case__			,				snake_case__ ) )
     UpperCAmelCase__      :					Any				       =							outputs.attentions
     self.assertEqual(len(snake_case__ )			,				self.model_tester.num_hidden_layers )

     self.assertListEqual(
         list(attentions[0].shape[-3:] )			,				[self.model_tester.num_attention_heads, seq_len, seq_len]			,				)
     UpperCAmelCase__      :					Optional[int]				       =							len(snake_case__ )

     # Check attention is always last and order is fine
     UpperCAmelCase__      :					Union[str, Any]				       =							True
     UpperCAmelCase__      :					Any				       =							True
     UpperCAmelCase__      :					Tuple				       =							model_class(snake_case__ )
     model.to(snake_case__ )
     model.eval()
     with torch.no_grad():
       UpperCAmelCase__      :					Optional[Any]				       =							model(**self._prepare_for_class(snake_case__			,				snake_case__ ) )

     UpperCAmelCase__      :					List[Any]				       =							1
     self.assertEqual(out_len + added_hidden_states			,				len(snake_case__ ) )

     UpperCAmelCase__      :					Dict				       =							outputs.attentions

     self.assertEqual(len(snake_case__ )			,				self.model_tester.num_hidden_layers )
     self.assertListEqual(
         list(self_attentions[0].shape[-3:] )			,				[self.model_tester.num_attention_heads, seq_len, seq_len]			,				)







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   def check_hidden_states_output(snake_case__		:  List[Any]			,				snake_case__		:  Union[str, Any]			,				snake_case__		:  Any ):
     UpperCAmelCase__      :					int				       =							model_class(snake_case__ )
     model.to(snake_case__ )
     model.eval()

     with torch.no_grad():
       UpperCAmelCase__      :					str				       =							model(**self._prepare_for_class(snake_case__			,				snake_case__ ) )

     UpperCAmelCase__      :					Union[str, Any]				       =							outputs.hidden_states

     UpperCAmelCase__      :					int				       =							getattr(
         self.model_tester			,				"expected_num_hidden_layers"			,				self.model_tester.num_hidden_layers + 1 )
     self.assertEqual(len(snake_case__ )			,				snake_case__ )

     # YOLOS has a different seq_length
     UpperCAmelCase__      :					Tuple				       =							self.model_tester.expected_seq_len

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

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

   for model_class in self.all_model_classes:
     UpperCAmelCase__      :					Optional[int]				       =							True
     check_hidden_states_output(snake_case__			,				snake_case__			,				snake_case__ )

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

     check_hidden_states_output(snake_case__			,				snake_case__			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_object_detection(*snake_case__ )







 @slow
 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase__      :					str				       =							YolosModel.from_pretrained(snake_case__ )
     self.assertIsNotNone(snake_case__ )







def snake_case_     (					):
  UpperCAmelCase__      :					str				       =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
  return image






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

 '''simple docstring'''







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



   '''simple docstring'''




   return AutoImageProcessor.from_pretrained("hustvl/yolos-small" ) if is_vision_available() else None







 @slow
 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							YolosForObjectDetection.from_pretrained("hustvl/yolos-small" ).to(snake_case__ )

   UpperCAmelCase__      :					str				       =							self.default_image_processor
   UpperCAmelCase__      :					Optional[Any]				       =							prepare_img()
   UpperCAmelCase__      :					Optional[int]				       =							image_processor(images=snake_case__			,				return_tensors="pt" ).to(snake_case__ )

   # forward pass
   with torch.no_grad():
     UpperCAmelCase__      :					Optional[int]				       =							model(inputs.pixel_values )

   # verify outputs
   UpperCAmelCase__      :					int				       =							torch.Size((1, 1_00, 92) )
   self.assertEqual(outputs.logits.shape			,				snake_case__ )

   UpperCAmelCase__      :					Dict				       =							torch.tensor(
       [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]			,				device=snake_case__			,				)
   UpperCAmelCase__      :					Dict				       =							torch.tensor(
       [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]			,				device=snake_case__ )
   self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]			,				snake_case__			,				atol=1e-4 ) )
   self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]			,				snake_case__			,				atol=1e-4 ) )

   # verify postprocessing
   UpperCAmelCase__      :					List[str]				       =							image_processor.post_process_object_detection(
       snake_case__			,				threshold=0.3			,				target_sizes=[image.size[::-1]] )[0]
   UpperCAmelCase__      :					Tuple				       =							torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861] ).to(snake_case__ )
   UpperCAmelCase__      :					Dict				       =							[75, 75, 17, 63, 17]
   UpperCAmelCase__      :					str				       =							torch.tensor([335.0609, 79.3848, 375.4216, 187.2495] ).to(snake_case__ )

   self.assertEqual(len(results["scores"] )			,				5 )
   self.assertTrue(torch.allclose(results["scores"]			,				snake_case__			,				atol=1e-4 ) )
   self.assertSequenceEqual(results["labels"].tolist()			,				snake_case__ )
   self.assertTrue(torch.allclose(results["boxes"][0, :]			,				snake_case__ ) )





'''simple docstring'''





import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()






class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Tuple				=							PriorTransformer
 lowercase_					:    Dict				=							"hidden_states"







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							4
   UpperCAmelCase__      :					List[Any]				       =							8
   UpperCAmelCase__      :					Union[str, Any]				       =							7

   UpperCAmelCase__      :					Tuple				       =							floats_tensor((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					List[Any]				       =							floats_tensor((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 def 							UpperCamelCase  (					self		:  int			,				snake_case__		:  List[str]=0 ):



   '''simple docstring'''




   torch.manual_seed(snake_case__ )
   UpperCAmelCase__      :					str				       =							4
   UpperCAmelCase__      :					int				       =							8
   UpperCAmelCase__      :					Optional[int]				       =							7

   UpperCAmelCase__      :					Any				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					Union[str, Any]				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 @property
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   return (4, 8)







 @property
 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   return (4, 8)







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							{
       "num_attention_heads": 2,
       "attention_head_dim": 4,
       "num_layers": 2,
       "embedding_dim": 8,
       "num_embeddings": 7,
       "additional_embeddings": 4,
   }
   UpperCAmelCase__      :					Optional[Any]				       =							self.dummy_input
   return init_dict, inputs_dict







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



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					List[str]				       =							PriorTransformer.from_pretrained(
       "hf-internal-testing/prior-dummy"			,				output_loading_info=snake_case__ )
   self.assertIsNotNone(snake_case__ )
   self.assertEqual(len(loading_info["missing_keys"] )			,				0 )

   model.to(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							model(**self.dummy_input )[0]

   assert hidden_states is not None, "Make sure output is not None"







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					int				       =							self.prepare_init_args_and_inputs_for_common()

   UpperCAmelCase__      :					Tuple				       =							self.model_class(**snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							inspect.signature(model.forward )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   UpperCAmelCase__      :					int				       =							[*signature.parameters.keys()]

   UpperCAmelCase__      :					Optional[Any]				       =							["hidden_states", "timestep"]
   self.assertListEqual(arg_names[:2]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy" )
   UpperCAmelCase__      :					List[Any]				       =							model.to(snake_case__ )

   if hasattr(snake_case__			,				"set_default_attn_processor" ):
     model.set_default_attn_processor()

   UpperCAmelCase__      :					int				       =							self.get_dummy_seed_input()

   with torch.no_grad():
     UpperCAmelCase__      :					Tuple				       =							model(**snake_case__ )[0]

   UpperCAmelCase__      :					Any				       =							output[0, :5].flatten().cpu()
   print(snake_case__ )

   # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
   # the expected output slices are not the same for CPU and GPU.
   UpperCAmelCase__      :					Optional[int]				       =							torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239] )
   self.assertTrue(torch_all_close(snake_case__			,				snake_case__			,				rtol=1e-2 ) )






@slow
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  Optional[int]=1			,				snake_case__		:  Any=7_68			,				snake_case__		:  List[Any]=77			,				snake_case__		:  Tuple=0 ):



   '''simple docstring'''




   torch.manual_seed(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							batch_size
   UpperCAmelCase__      :					Any				       =							embedding_dim
   UpperCAmelCase__      :					Any				       =							num_embeddings

   UpperCAmelCase__      :					str				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					Optional[Any]				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					Any				       =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
         [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
         # fmt: on
     ] )
 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  List[str]			,				snake_case__		:  Optional[int] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"			,				subfolder="prior" )
   model.to(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							self.get_dummy_seed_input(seed=snake_case__ )

   with torch.no_grad():
     UpperCAmelCase__      :					Any				       =							model(**snake_case__ )[0]

   assert list(sample.shape ) == [1, 7_68]

   UpperCAmelCase__      :					List[str]				       =							sample[0, :8].flatten().cpu()
   print(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							torch.tensor(snake_case__ )

   assert torch_all_close(snake_case__			,				snake_case__			,				atol=1e-3 )



