



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 							lowerCAmelCase_						(    a__			,       unittest.TestCase				):
       UpperCAmelCase__							:   int  	=    PriorTransformer
       UpperCAmelCase__							:   Optional[Any]  	=    "hidden_states"
       @property
       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase       :    List[str]		      =      4
          UpperCamelCase       :    Dict		      =      8
          UpperCamelCase       :    int		      =      7

          UpperCamelCase       :    Dict		      =      floats_tensor((batch_size, embedding_dim)			).to(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    int		      =      floats_tensor((batch_size, embedding_dim)			).to(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[Any]		      =      floats_tensor((batch_size, num_embeddings, embedding_dim)			).to(SCREAMING_SNAKE_CASE_			)

          return {
              "hidden_states": hidden_states,
              "timestep": 2,
              "proj_embedding": proj_embedding,
              "encoder_hidden_states": encoder_hidden_states,
          }
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_=0			)	->						Optional[Any]:
          torch.manual_seed(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      4
          UpperCamelCase       :    int		      =      8
          UpperCamelCase       :    Tuple		      =      7

          UpperCamelCase       :    Tuple		      =      torch.randn((batch_size, embedding_dim)			).to(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[Any]		      =      torch.randn((batch_size, embedding_dim)			).to(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    str		      =      torch.randn((batch_size, num_embeddings, embedding_dim)			).to(SCREAMING_SNAKE_CASE_			)

          return {
              "hidden_states": hidden_states,
              "timestep": 2,
              "proj_embedding": proj_embedding,
              "encoder_hidden_states": encoder_hidden_states,
          }
       @property
       def    snake_case_			(     self			)	->						Optional[Any]:
          return (4, 8)
       @property
       def    snake_case_			(     self			)	->						str:
          return (4, 8)
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    List[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    snake_case_			(     self			)	->						str:
          UpperCamelCase      ,	UpperCamelCase       :    Dict		      =      PriorTransformer.from_pretrained(
              'hf-internal-testing/prior-dummy',     output_loading_info=SCREAMING_SNAKE_CASE_			)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
          self.assertEqual(len(loading_info['missing_keys']			),     0			)

          model.to(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[str]		      =      model(**self.dummy_input			)[0]

          assert hidden_states is not None, "Make sure output is not None"
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase      ,	UpperCamelCase       :    Optional[int]		      =      self.prepare_init_args_and_inputs_for_common()

          UpperCamelCase       :    Union[str, Any]		      =      self.model_class(**SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Tuple		      =      inspect.signature(model.forward			)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          UpperCamelCase       :    Tuple		      =      [*signature.parameters.keys()]

          UpperCamelCase       :    List[Any]		      =      ['hidden_states', 'timestep']
          self.assertListEqual(arg_names[:2],     SCREAMING_SNAKE_CASE_			)






       def    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    List[Any]		      =      PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'			)
          UpperCamelCase       :    List[str]		      =      model.to(SCREAMING_SNAKE_CASE_			)

          if hasattr(SCREAMING_SNAKE_CASE_,     'set_default_attn_processor'			):
             model.set_default_attn_processor()

          UpperCamelCase       :    int		      =      self.get_dummy_seed_input()

          with torch.no_grad():
             UpperCamelCase       :    Optional[Any]		      =      model(**SCREAMING_SNAKE_CASE_			)[0]

          UpperCamelCase       :    Dict		      =      output[0, :5].flatten().cpu()
          print(SCREAMING_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([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]			)
          self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     rtol=1e-2			)			)






@slow
class 							lowerCAmelCase_						(    unittest.TestCase				):
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_=1,     SCREAMING_SNAKE_CASE_=768,     SCREAMING_SNAKE_CASE_=77,     SCREAMING_SNAKE_CASE_=0			)	->						Any:
          torch.manual_seed(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    str		      =      batch_size
          UpperCamelCase       :    Any		      =      embedding_dim
          UpperCamelCase       :    Any		      =      num_embeddings

          UpperCamelCase       :    Dict		      =      torch.randn((batch_size, embedding_dim)			).to(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    int		      =      torch.randn((batch_size, embedding_dim)			).to(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Union[str, Any]		      =      torch.randn((batch_size, num_embeddings, embedding_dim)			).to(SCREAMING_SNAKE_CASE_			)

          return {
              "hidden_states": hidden_states,
              "timestep": 2,
              "proj_embedding": proj_embedding,
              "encoder_hidden_states": encoder_hidden_states,
          }
       def    snake_case_			(     self			)	->						Optional[int]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






       @parameterized.expand(
           [
               # fmt: off
               [13, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
               [37, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
               # fmt: on
           ]			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          UpperCamelCase       :    Optional[Any]		      =      PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior',     subfolder='prior'			)
          model.to(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[Any]		      =      self.get_dummy_seed_input(seed=SCREAMING_SNAKE_CASE_			)

          with torch.no_grad():
             UpperCamelCase       :    List[Any]		      =      model(**SCREAMING_SNAKE_CASE_			)[0]

          assert list(sample.shape			) == [1, 768]

          UpperCamelCase       :    List[str]		      =      sample[0, :8].flatten().cpu()
          print(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[str]		      =      torch.tensor(SCREAMING_SNAKE_CASE_			)

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





"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_A     :			Optional[int]  						=			logging.get_logger(__name__)

class 						a__		( a_		):
   def __init__(     self      ,       *_a      ,       **_a							):
          warnings.warn(
              "The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
              " use OwlViTImageProcessor instead."      ,       _a      ,       )
          super().__init__(*_a      ,       **_a							)


