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    _UpperCamelCase      (						_UpperCAmelCase						,unittest.TestCase ):


			"""simple docstring"""






			__a  : str			 =						PriorTransformer
			__a  : Tuple			 =						'''hidden_states'''





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

							'''simple docstring'''


							__lowercase 			=							4
							__lowercase 			=							8
							__lowercase 			=							7

							__lowercase 			=							floats_tensor((batch_size, embedding_dim)						).to(lowerCAmelCase__						)

							__lowercase 			=							floats_tensor((batch_size, embedding_dim)						).to(lowerCAmelCase__						)
							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__=0						)     ->					Union[str, Any]:

							'''simple docstring'''


							torch.manual_seed(lowerCAmelCase__						)
							__lowercase 			=							4
							__lowercase 			=							8
							__lowercase 			=							7

							__lowercase 			=							torch.randn((batch_size, embedding_dim)						).to(lowerCAmelCase__						)

							__lowercase 			=							torch.randn((batch_size, embedding_dim)						).to(lowerCAmelCase__						)
							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							return (4, 8)





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

							'''simple docstring'''


							return (4, 8)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[int]:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							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__						)
							__lowercase 			=							model(**self.dummy_input						)[0]

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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.prepare_init_args_and_inputs_for_common()

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

							__lowercase 			=							['''hidden_states''', '''timestep''']
							self.assertListEqual(arg_names[:2]      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							__lowercase 			=							PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''						)
							__lowercase 			=							model.to(lowerCAmelCase__						)

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

							__lowercase 			=							self.get_dummy_seed_input()

							with torch.no_grad():
											__lowercase 			=							model(**lowerCAmelCase__						)[0]

							__lowercase 			=							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.
							__lowercase 			=							torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]						)
							self.assertTrue(torch_all_close(lowerCAmelCase__      ,   lowerCAmelCase__      ,   rtol=1E-2						)						)




@slow
class    _UpperCamelCase      (						unittest.TestCase ):


			"""simple docstring"""






			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=77      ,   lowerCAmelCase__=0						)     ->					Union[str, Any]:

							'''simple docstring'''


							torch.manual_seed(lowerCAmelCase__						)
							__lowercase 			=							batch_size
							__lowercase 			=							embedding_dim
							__lowercase 			=							num_embeddings

							__lowercase 			=							torch.randn((batch_size, embedding_dim)						).to(lowerCAmelCase__						)

							__lowercase 			=							torch.randn((batch_size, embedding_dim)						).to(lowerCAmelCase__						)
							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''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        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					Any:

							'''simple docstring'''


							__lowercase 			=							PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''      ,   subfolder='''prior'''						)
							model.to(lowerCAmelCase__						)
							__lowercase 			=							self.get_dummy_seed_input(seed=lowerCAmelCase__						)

							with torch.no_grad():
											__lowercase 			=							model(**lowerCAmelCase__						)[0]

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

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

							assert torch_all_close(lowerCAmelCase__      ,   lowerCAmelCase__      ,   atol=1E-3						)
from decimal import Decimal, getcontext
from math import ceil, factorial





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				if not isinstance(lowercase							,			lowercase							):
								raise TypeError('''Undefined for non-integers'''							)
				elif precision < 1:
								raise ValueError('''Undefined for non-natural numbers'''							)

				__lowercase 			=							precision
				__lowercase 			=							ceil(precision / 14							)
				__lowercase 			=							426880 * Decimal(10005							).sqrt()
				__lowercase 			=							1
				__lowercase 			=							13591409
				__lowercase 			=							Decimal(lowercase							)
				for k in range(1							,			lowercase							):
								__lowercase 			=							factorial(6 * k							) // (factorial(3 * k							) * factorial(lowercase							) ** 3)
								linear_term += 545140134
								exponential_term *= -262537412640768000
								partial_sum += Decimal(multinomial_term * linear_term							) / exponential_term
				return str(constant_term / partial_sum							)[:-1]


if __name__ == "__main__":
		__a  :     Optional[Any]       =  5_0
		print(F'''The first {n} digits of pi is: {pi(n)}''')