





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      A       (	__lowercase		,      unittest.TestCase						):
      _snake_case							     =PriorTransformer
      _snake_case							     ='''hidden_states'''



      @property
      def 	lowerCAmelCase__    (	self:						Dict       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=4
         UpperCAmelCase_											=8
         UpperCAmelCase_											=7

         UpperCAmelCase_											=floats_tensor((batch_size, embedding_dim)       ).to(_lowerCAmelCase       )

         UpperCAmelCase_											=floats_tensor((batch_size, embedding_dim)       ).to(_lowerCAmelCase       )
         UpperCAmelCase_											=floats_tensor((batch_size, num_embeddings, embedding_dim)       ).to(_lowerCAmelCase       )

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



      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						Tuple=0       )						->    Optional[int]:
         '''simple docstring'''

         torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=4
         UpperCAmelCase_											=8
         UpperCAmelCase_											=7

         UpperCAmelCase_											=torch.randn((batch_size, embedding_dim)       ).to(_lowerCAmelCase       )

         UpperCAmelCase_											=torch.randn((batch_size, embedding_dim)       ).to(_lowerCAmelCase       )
         UpperCAmelCase_											=torch.randn((batch_size, num_embeddings, embedding_dim)       ).to(_lowerCAmelCase       )

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



      @property
      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Dict:
         '''simple docstring'''

         return (4, 8)



      @property
      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Any:
         '''simple docstring'''

         return (4, 8)



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											={
             "num_attention_heads": 2,
             "attention_head_dim": 4,
             "num_layers": 2,
             "embedding_dim": 8,
             "num_embeddings": 7,
             "additional_embeddings": 4,
         }
         UpperCAmelCase_											=self.dummy_input
         return init_dict, inputs_dict



      def 	lowerCAmelCase__    (	self:						Dict       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=PriorTransformer.from_pretrained(
             "hf-internal-testing/prior-dummy"		,  output_loading_info=_lowerCAmelCase       )
         self.assertIsNotNone(_lowerCAmelCase       )
         self.assertEqual(len(loading_info["missing_keys"]       )		,  0       )

         model.to(_lowerCAmelCase       )
         UpperCAmelCase_											=model(**self.dummy_input       )[0]

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



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=self.prepare_init_args_and_inputs_for_common()

         UpperCAmelCase_											=self.model_class(**_lowerCAmelCase       )
         UpperCAmelCase_											=inspect.signature(model.forward       )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         UpperCAmelCase_											=[*signature.parameters.keys()]

         UpperCAmelCase_											=["hidden_states", "timestep"]
         self.assertListEqual(arg_names[:2]		,  _lowerCAmelCase       )






      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"       )
         UpperCAmelCase_											=model.to(_lowerCAmelCase       )

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

         UpperCAmelCase_											=self.get_dummy_seed_input()

         with torch.no_grad():
            UpperCAmelCase_											=model(**_lowerCAmelCase       )[0]

         UpperCAmelCase_											=output[0, :5].flatten().cpu()
         print(_lowerCAmelCase       )

         # 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_											=torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]       )
         self.assertTrue(torch_all_close(_lowerCAmelCase		,  _lowerCAmelCase		,  rtol=1e-2       )       )

@slow
class      A       (	unittest.TestCase						):



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						Tuple=1		,  _lowerCAmelCase:						Optional[int]=768		,  _lowerCAmelCase:						Optional[Any]=77		,  _lowerCAmelCase:						List[str]=0       )						->    Optional[int]:
         '''simple docstring'''

         torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=batch_size
         UpperCAmelCase_											=embedding_dim
         UpperCAmelCase_											=num_embeddings

         UpperCAmelCase_											=torch.randn((batch_size, embedding_dim)       ).to(_lowerCAmelCase       )

         UpperCAmelCase_											=torch.randn((batch_size, embedding_dim)       ).to(_lowerCAmelCase       )
         UpperCAmelCase_											=torch.randn((batch_size, num_embeddings, embedding_dim)       ).to(_lowerCAmelCase       )

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



      def 	lowerCAmelCase__    (	self:						int       )						->    str:
         '''simple docstring'''

         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 	lowerCAmelCase__    (	self:						List[Any]		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						int       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"		,  subfolder="prior"       )
         model.to(_lowerCAmelCase       )
         UpperCAmelCase_											=self.get_dummy_seed_input(seed=_lowerCAmelCase       )

         with torch.no_grad():
            UpperCAmelCase_											=model(**_lowerCAmelCase       )[0]

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

         UpperCAmelCase_											=sample[0, :8].flatten().cpu()
         print(_lowerCAmelCase       )
         UpperCAmelCase_											=torch.tensor(_lowerCAmelCase       )

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase  :   Any						      ={
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   Tuple						      =[
         """UperNetForSemanticSegmentation""",
         """UperNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_upernet import UperNetConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
     import sys

     __lowercase  :   Any						      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




