




"""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 _lowerCamelCase    (					a_       ,	unittest.TestCase						):
   _lowerCamelCase    :Any     				=     PriorTransformer
   _lowerCamelCase    :List[Any]     				=     "hidden_states"






   @property
   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	4
         lowerCAmelCase__      :			Dict   =	8
         lowerCAmelCase__      :			Dict   =	7

         lowerCAmelCase__      :			str   =	floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )

         lowerCAmelCase__      :			str   =	floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )
         lowerCAmelCase__      :			Any   =	floats_tensor((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

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






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		Optional[int]=0   )			->						Tuple:

         """simple docstring"""
         torch.manual_seed(UpperCamelCase   )
         lowerCAmelCase__      :			Dict   =	4
         lowerCAmelCase__      :			Union[str, Any]   =	8
         lowerCAmelCase__      :			List[Any]   =	7

         lowerCAmelCase__      :			str   =	torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

         lowerCAmelCase__      :			Optional[int]   =	torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
         lowerCAmelCase__      :			Optional[int]   =	torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

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






   @property
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Optional[Any]:

         """simple docstring"""
         return (4, 8)






   @property
   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						List[Any]:

         """simple docstring"""
         return (4, 8)






   def 	_lowerCAmelCase   (		self       :		Any   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	{
             """num_attention_heads""": 2,
             """attention_head_dim""": 4,
             """num_layers""": 2,
             """embedding_dim""": 8,
             """num_embeddings""": 7,
             """additional_embeddings""": 4,
         }
         lowerCAmelCase__      :			Dict   =	self.dummy_input
         return init_dict, inputs_dict






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[Any]   =	PriorTransformer.from_pretrained(
             """hf-internal-testing/prior-dummy"""   ,     output_loading_info=UpperCamelCase   )
         self.assertIsNotNone(UpperCamelCase   )
         self.assertEqual(len(loading_info["""missing_keys"""]   )   ,     0   )

         model.to(UpperCamelCase   )
         lowerCAmelCase__      :			int   =	model(**self.dummy_input   )[0]

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






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			Dict   =	self.prepare_init_args_and_inputs_for_common()

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

         lowerCAmelCase__      :			str   =	["""hidden_states""", """timestep"""]
         self.assertListEqual(arg_names[:2]   ,     UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""   )
         lowerCAmelCase__      :			Union[str, Any]   =	model.to(UpperCamelCase   )

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

         lowerCAmelCase__      :			str   =	self.get_dummy_seed_input()

         with torch.no_grad():
               lowerCAmelCase__      :			Union[str, Any]   =	model(**UpperCamelCase   )[0]

         lowerCAmelCase__      :			Any   =	output[0, :5].flatten().cpu()
         print(UpperCamelCase   )

         # 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.
         lowerCAmelCase__      :			List[str]   =	torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]   )
         self.assertTrue(torch_all_close(UpperCamelCase   ,     UpperCamelCase   ,     rtol=1E-2   )   )






@slow
class _lowerCamelCase    (					unittest.TestCase						):






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		Dict=1   ,     UpperCamelCase       :		Tuple=7_68   ,     UpperCamelCase       :		Tuple=77   ,     UpperCamelCase       :		Optional[int]=0   )			->						Optional[Any]:

         """simple docstring"""
         torch.manual_seed(UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	batch_size
         lowerCAmelCase__      :			List[Any]   =	embedding_dim
         lowerCAmelCase__      :			Union[str, Any]   =	num_embeddings

         lowerCAmelCase__      :			Optional[int]   =	torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

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

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






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Tuple:

         """simple docstring"""
         # 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 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		Any   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""   ,     subfolder="""prior"""   )
         model.to(UpperCamelCase   )
         lowerCAmelCase__      :			Optional[int]   =	self.get_dummy_seed_input(seed=UpperCamelCase   )

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

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

         lowerCAmelCase__      :			Optional[int]   =	sample[0, :8].flatten().cpu()
         print(UpperCamelCase   )
         lowerCAmelCase__      :			Dict   =	torch.tensor(UpperCamelCase   )

         assert torch_all_close(UpperCamelCase   ,     UpperCamelCase   ,     atol=1E-3   )











"""simple docstring"""


def 	lowercase_				(			__UpperCAmelCase				)						->    str:
      if not all(char in """01""" for char in bin_string				):
            raise ValueError("""Non-binary value was passed to the function"""				)
      if not bin_string:
            raise ValueError("""Empty string was passed to the function"""				)
      lowerCAmelCase__      :			List[str]   =	""""""
      while len(__UpperCAmelCase				) % 3 != 0:
            lowerCAmelCase__      :			Optional[Any]   =	"""0""" + bin_string
      lowerCAmelCase__      :			List[Any]   =	[
          bin_string[index : index + 3]
          for index in range(len(__UpperCAmelCase				)				)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            lowerCAmelCase__      :			Optional[int]   =	0
            for index, val in enumerate(__UpperCAmelCase				):
                  oct_val += int(2 ** (2 - index) * int(__UpperCAmelCase				)				)
            oct_string += str(__UpperCAmelCase				)
      return oct_string


if __name__ == "__main__":
  from doctest import testmod

  testmod()





