


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    (__SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						PriorTransformer
							_UpperCAmelCase      =						"""hidden_states"""






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					4
										SCREAMING_SNAKE_CASE_		:     int					=					8
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					7

										SCREAMING_SNAKE_CASE_		:     List[str]					=					floats_tensor((batch_size, embedding_dim)       ).to(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					floats_tensor((batch_size, embedding_dim)       ).to(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					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 					UpperCamelCase__     (      self			,						lowerCAmelCase__=0       ):





										"""simple docstring"""




										torch.manual_seed(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					4
										SCREAMING_SNAKE_CASE_		:     Tuple					=					8
										SCREAMING_SNAKE_CASE_		:     List[str]					=					7

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

										SCREAMING_SNAKE_CASE_		:     Dict					=					torch.randn((batch_size, embedding_dim)       ).to(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					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 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return (4, 8)






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return (4, 8)






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{
										    'num_attention_heads': 2,
										    'attention_head_dim': 4,
										    'num_layers': 2,
										    'embedding_dim': 8,
										    'num_embeddings': 7,
										    'additional_embeddings': 4,
										}
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.dummy_input
										return init_dict, inputs_dict






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Tuple					=					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__       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					model(**self.dummy_input       )[0]

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Tuple					=					self.prepare_init_args_and_inputs_for_common()

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

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					model.to(lowerCAmelCase__       )

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

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.get_dummy_seed_input()

										with torch.no_grad():
													SCREAMING_SNAKE_CASE_		:     List[str]					=					model(**lowerCAmelCase__       )[0]

										SCREAMING_SNAKE_CASE_		:     List[str]					=					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.
										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]       )
										self.assertTrue(torch_all_close(lowerCAmelCase__			,						lowerCAmelCase__			,						rtol=1E-2       )       )







@slow
class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__=1			,						lowerCAmelCase__=7_6_8			,						lowerCAmelCase__=7_7			,						lowerCAmelCase__=0       ):





										"""simple docstring"""




										torch.manual_seed(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					batch_size
										SCREAMING_SNAKE_CASE_		:     List[str]					=					embedding_dim
										SCREAMING_SNAKE_CASE_		:     Dict					=					num_embeddings

										SCREAMING_SNAKE_CASE_		:     Tuple					=					torch.randn((batch_size, embedding_dim)       ).to(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Any					=					torch.randn((batch_size, embedding_dim)       ).to(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					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 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






							@parameterized.expand(
							    [
							        # fmt: off
							        [1_3, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
							        [3_7, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
							        # fmt: on
							    ]       )
							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




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

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

										assert list(sample.shape       ) == [1, 7_6_8]

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					sample[0, :8].flatten().cpu()
										print(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.tensor(lowerCAmelCase__       )

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











'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						__snake_case						 =   int(a							)
						if n_element < 1:
												__snake_case						 =   ValueError('a should be a positive number'							)
												raise my_error

						__snake_case						 =   [1]
						__snake_case ,       __snake_case ,       __snake_case						 =   (0, 0, 0)
						__snake_case						 =   1
						while index < n_element:
												while hamming_list[i] * 2 <= hamming_list[-1]:
																		i += 1
												while hamming_list[j] * 3 <= hamming_list[-1]:
																		j += 1
												while hamming_list[k] * 5 <= hamming_list[-1]:
																		k += 1
												hamming_list.append(
												    min(hamming_list[i] * 2    ,	hamming_list[j] * 3    ,	hamming_list[k] * 5							)							)
												index += 1
						return hamming_list


if __name__ == "__main__":
	_lowercase  =			input("""Enter the last number (nth term) of the Hamming Number Series: """)
	print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
	_lowercase  =			hamming(int(n))
	print("""-----------------------------------------------------""")
	print(f'''The list with nth numbers is: {hamming_numbers}''')
	print("""-----------------------------------------------------""")

