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      (	lowercase_			,      unittest.TestCase  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				PriorTransformer
     SCREAMING_SNAKE_CASE   						=				"hidden_states"




     @property
     def 		lowerCAmelCase_     (				self       :			int				):


         """simple docstring"""



         __A         =	4
         __A         =	8
         __A         =	7

         __A         =	floats_tensor((batch_size, embedding_dim)				).to(UpperCamelCase_				)

         __A         =	floats_tensor((batch_size, embedding_dim)				).to(UpperCamelCase_				)
         __A         =	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       :			Optional[Any] ,	UpperCamelCase_       :			List[Any]=0				):


         """simple docstring"""



         torch.manual_seed(UpperCamelCase_				)
         __A         =	4
         __A         =	8
         __A         =	7

         __A         =	torch.randn((batch_size, embedding_dim)				).to(UpperCamelCase_				)

         __A         =	torch.randn((batch_size, embedding_dim)				).to(UpperCamelCase_				)
         __A         =	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				):


         """simple docstring"""



         return (4, 8)




     @property
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         return (4, 8)




     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A    ,	__A         =	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_				)
         __A         =	model(**self.dummy_input				)[0]

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




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


         """simple docstring"""



         __A    ,	__A         =	self.prepare_init_args_and_inputs_for_common()

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

         __A         =	["""hidden_states""", """timestep"""]
         self.assertListEqual(arg_names[:2] ,	UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



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

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

         __A         =	self.get_dummy_seed_input()

         with torch.no_grad():
             __A         =	model(**UpperCamelCase_				)[0]

         __A         =	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.
         __A         =	torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]				)
         self.assertTrue(torch_all_close(UpperCamelCase_ ,	UpperCamelCase_ ,	rtol=1e-2				)				)


@slow
class       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			List[str]=1 ,	UpperCamelCase_       :			Any=768 ,	UpperCamelCase_       :			Dict=77 ,	UpperCamelCase_       :			List[Any]=0				):


         """simple docstring"""



         torch.manual_seed(UpperCamelCase_				)
         __A         =	batch_size
         __A         =	embedding_dim
         __A         =	num_embeddings

         __A         =	torch.randn((batch_size, embedding_dim)				).to(UpperCamelCase_				)

         __A         =	torch.randn((batch_size, embedding_dim)				).to(UpperCamelCase_				)
         __A         =	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       :			Any				):


         """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 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Any				):


         """simple docstring"""



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

         with torch.no_grad():
             __A         =	model(**UpperCamelCase_				)[0]

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

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

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












def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Dict			,				__lowercase   :			str    )						->      Dict:





    """simple docstring"""

    if n == 0:
        return 1

    elif n % 2 == 1:
        return (binary_exponentiation(__lowercase			,				n - 1			,				__lowercase    ) * a) % mod

    else:
        __A         =	binary_exponentiation(__lowercase			,				n / 2			,				__lowercase    )
        return (b * b) % mod


# a prime number
__a      :       Union[str, Any]				  =   701

__a      :       Dict				  =   1000000000
__a      :       Union[str, Any]				  =   10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)






print((a / b) % p == (a * b ** (p - 2)) % p)




