




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 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		PriorTransformer
     _a											=		"hidden_states"

     @property
     def       a__			(						self       )		->							str:
           _A							:  Optional[Any]    =		4
           _A							:  List[str]    =		8
           _A							:  Optional[Any]    =		7

           _A							:  Union[str, Any]    =		floats_tensor((batch_size, embedding_dim)       ).to(_a       )

           _A							:  Tuple    =		floats_tensor((batch_size, embedding_dim)       ).to(_a       )
           _A							:  Any    =		floats_tensor((batch_size, num_embeddings, embedding_dim)       ).to(_a       )

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

     def       a__			(						self	,  _a=0       )		->							Optional[Any]:
           torch.manual_seed(_a       )
           _A							:  Dict    =		4
           _A							:  str    =		8
           _A							:  Optional[Any]    =		7

           _A							:  Any    =		torch.randn((batch_size, embedding_dim)       ).to(_a       )

           _A							:  Tuple    =		torch.randn((batch_size, embedding_dim)       ).to(_a       )
           _A							:  List[Any]    =		torch.randn((batch_size, num_embeddings, embedding_dim)       ).to(_a       )

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

     @property
     def       a__			(						self       )		->							Union[str, Any]:
           return (4, 8)

     @property
     def       a__			(						self       )		->							int:
           return (4, 8)

     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  List[Any]    =		{
               """num_attention_heads""": 2,
               """attention_head_dim""": 4,
               """num_layers""": 2,
               """embedding_dim""": 8,
               """num_embeddings""": 7,
               """additional_embeddings""": 4,
           }
           _A							:  Optional[int]    =		self.dummy_input
           return init_dict, inputs_dict

     def       a__			(						self       )		->							Union[str, Any]:
           _A    ,     _A							:  Dict    =		PriorTransformer.from_pretrained(
               """hf-internal-testing/prior-dummy"""	,  output_loading_info=_a       )
           self.assertIsNotNone(_a       )
           self.assertEqual(len(loading_info["""missing_keys"""]       )	,  0       )

           model.to(_a       )
           _A							:  Optional[int]    =		model(**self.dummy_input       )[0]

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

     def       a__			(						self       )		->							Any:
           _A    ,     _A							:  List[Any]    =		self.prepare_init_args_and_inputs_for_common()

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

           _A							:  List[Any]    =		["""hidden_states""", """timestep"""]
           self.assertListEqual(arg_names[:2]	,  _a       )



     def       a__			(						self       )		->							Any:
           _A							:  Union[str, Any]    =		PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""       )
           _A							:  Optional[int]    =		model.to(_a       )

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

           _A							:  Tuple    =		self.get_dummy_seed_input()

           with torch.no_grad():
                 _A							:  Dict    =		model(**_a       )[0]

           _A							:  List[str]    =		output[0, :5].flatten().cpu()
           print(_a       )

           # 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.
           _A							:  Optional[int]    =		torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]       )
           self.assertTrue(torch_all_close(_a	,  _a	,  rtol=1e-2       )       )







@slow
class 		lowercase							(     unittest.TestCase	):

     def       a__			(						self	,  _a=1	,  _a=768	,  _a=77	,  _a=0       )		->							List[str]:
           torch.manual_seed(_a       )
           _A							:  List[str]    =		batch_size
           _A							:  Optional[int]    =		embedding_dim
           _A							:  List[str]    =		num_embeddings

           _A							:  Optional[int]    =		torch.randn((batch_size, embedding_dim)       ).to(_a       )

           _A							:  Optional[Any]    =		torch.randn((batch_size, embedding_dim)       ).to(_a       )
           _A							:  Dict    =		torch.randn((batch_size, num_embeddings, embedding_dim)       ).to(_a       )

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

     def       a__			(						self       )		->							Any:
           # clean up the VRAM after each test
           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       a__			(						self	,  _a	,  _a       )		->							Any:
           _A							:  List[Any]    =		PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""	,  subfolder="""prior"""       )
           model.to(_a       )
           _A							:  Optional[Any]    =		self.get_dummy_seed_input(seed=_a       )

           with torch.no_grad():
                 _A							:  Dict    =		model(**_a       )[0]

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

           _A							:  Optional[Any]    =		sample[0, :8].flatten().cpu()
           print(_a       )
           _A							:  Tuple    =		torch.tensor(_a       )

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







def        lowerCAmelCase_			(  snake_case_ = 1000000  ):
      _A							:  Any    =		limit + 1
      _A							:  Tuple    =		[0] * limit
      for first_term in range(1,snake_case_  ):
            for n in range(snake_case_,snake_case_,snake_case_  ):
                  _A							:  Optional[int]    =		first_term + n / first_term
                  if common_difference % 4:  # d must be divisble by 4
                        continue
                  else:
                        common_difference /= 4
                        if (
                            first_term > common_difference
                            and first_term < 4 * common_difference
                        ):  # since x,y,z are positive integers
                              frequency[n] += 1  # so z>0 and a>d ,also 4d<a

      _A							:  List[str]    =		sum(1 for x in frequency[1:limit] if x == 10  )

      return count


if __name__ == "__main__":
  print(f"""{solution() = }""")

