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					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     PriorTransformer
						lowercase					 =     'hidden_states'

						@property
						def        __a		(	self				:      int   )							->		List[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        __a		(	self				:      int							,							__lowerCamelCase				:      Union[str, Any]=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        __a		(	self				:      List[Any]   )							->		Optional[int]:


									'''simple docstring'''

									return (4, 8)

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


									'''simple docstring'''

									return (4, 8)

						def        __a		(	self				:      Dict   )							->		int:


									'''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        __a		(	self				:      str   )							->		Union[str, Any]:


									'''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        __a		(	self				:      str   )							->		Optional[int]:


									'''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        __a		(	self				:      Optional[int]   )							->		Optional[int]:


									'''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      __lowercase					(    unittest.TestCase    ):

						def        __a		(	self				:      int							,							__lowerCamelCase				:      Tuple=1							,							__lowerCamelCase				:      Dict=7_68							,							__lowerCamelCase				:      str=77							,							__lowerCamelCase				:      List[str]=0   )							->		str:


									'''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        __a		(	self				:      List[str]   )							->		str:


									'''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        __a		(	self				:      Tuple							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Optional[int]   )							->		Union[str, 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 __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt






def        __UpperCAmelCase    (       UpperCAmelCase       )->       bool:



			"""simple docstring"""







			lowercase							=		int(number**0.5       )
			return number == sq * sq






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       tuple[int, int]:



			"""simple docstring"""







			lowercase							=		x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
			lowercase							=		x_den * y_den * z_den
			lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
			top //= hcf
			bottom //= hcf
			return top, bottom






def        __UpperCAmelCase    (       UpperCAmelCase = 35       )->       int:



			"""simple docstring"""







			lowercase							=		set()
			lowercase							=		42
			lowercase							=		Fraction(0       )
			lowercase							=		42

			for x_num in range(1,		order + 1       ):
						for x_den in range(x_num + 1,		order + 1       ):
									for y_num in range(1,		order + 1       ):
												for y_den in range(y_num + 1,		order + 1       ):
															# n=1
															lowercase							=		x_num * y_den + x_den * y_num
															lowercase							=		x_den * y_den
															lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
															z_num //= hcf
															z_den //= hcf
															if 0 < z_num < z_den <= order:
																		lowercase							=		add_three(
																		    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
																		unique_s.add(UpperCAmelCase       )

															# n=2
															lowercase							=		(
															    x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
															)
															lowercase							=		x_den * x_den * y_den * y_den
															if is_sq(UpperCAmelCase       ) and is_sq(UpperCAmelCase       ):
																		lowercase							=		int(sqrt(UpperCAmelCase       )       )
																		lowercase							=		int(sqrt(UpperCAmelCase       )       )
																		lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
																		z_num //= hcf
																		z_den //= hcf
																		if 0 < z_num < z_den <= order:
																					lowercase							=		add_three(
																					    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
																					unique_s.add(UpperCAmelCase       )

                    # n=-1
															lowercase							=		x_num * y_num
															lowercase							=		x_den * y_num + x_num * y_den
															lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
															z_num //= hcf
															z_den //= hcf
															if 0 < z_num < z_den <= order:
																		lowercase							=		add_three(
																		    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
																		unique_s.add(UpperCAmelCase       )

															# n=2
															lowercase							=		x_num * x_num * y_num * y_num
															lowercase							=		(
															    x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
															)
															if is_sq(UpperCAmelCase       ) and is_sq(UpperCAmelCase       ):
																		lowercase							=		int(sqrt(UpperCAmelCase       )       )
																		lowercase							=		int(sqrt(UpperCAmelCase       )       )
																		lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
																		z_num //= hcf
																		z_den //= hcf
																		if 0 < z_num < z_den <= order:
																					lowercase							=		add_three(
																					    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
																					unique_s.add(UpperCAmelCase       )

			for num, den in unique_s:
						total += Fraction(UpperCAmelCase,		UpperCAmelCase       )

			return total.denominator + total.numerator


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





