






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 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					PriorTransformer
							A_						  =					"hidden_states"

							@property
							def       __A (  self:   List[str]   )    ->				Dict:
												_A								= 4
												_A								= 8
												_A								= 7

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

												_A								= floats_tensor((batch_size, embedding_dim)   ).to(__A   )
												_A								= floats_tensor((batch_size, num_embeddings, embedding_dim)   ).to(__A   )

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

							def       __A (  self:   Union[str, Any]		,     __A:   List[Any]=0   )    ->				str:
												torch.manual_seed(__A   )
												_A								= 4
												_A								= 8
												_A								= 7

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

												_A								= torch.randn((batch_size, embedding_dim)   ).to(__A   )
												_A								= torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(__A   )

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

							@property
							def       __A (  self:   Dict   )    ->				int:
												return (4, 8)

							@property
							def       __A (  self:   Any   )    ->				Dict:
												return (4, 8)

							def       __A (  self:   List[Any]   )    ->				List[str]:
												_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       __A (  self:   Optional[Any]   )    ->				str:
												_A							,_A								= PriorTransformer.from_pretrained(
												    '''hf-internal-testing/prior-dummy'''		,     output_loading_info=__A   )
												self.assertIsNotNone(__A   )
												self.assertEqual(len(loading_info['''missing_keys''']   )		,     0   )

												model.to(__A   )
												_A								= model(**self.dummy_input   )[0]

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

							def       __A (  self:   Tuple   )    ->				List[Any]:
												_A							,_A								= self.prepare_init_args_and_inputs_for_common()

												_A								= self.model_class(**__A   )
												_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]		,     __A   )



							def       __A (  self:   Optional[Any]   )    ->				int:
												_A								= PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''   )
												_A								= model.to(__A   )

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

												_A								= self.get_dummy_seed_input()

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

												_A								= output[0, :5].flatten().cpu()
												print(__A   )

												# 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([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]   )
												self.assertTrue(torch_all_close(__A		,     __A		,     rtol=1e-2   )   )





@slow
class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							def       __A (  self:   int		,     __A:   Optional[Any]=1		,     __A:   List[Any]=7_68		,     __A:   List[str]=77		,     __A:   List[Any]=0   )    ->				Any:
												torch.manual_seed(__A   )
												_A								= batch_size
												_A								= embedding_dim
												_A								= num_embeddings

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

												_A								= torch.randn((batch_size, embedding_dim)   ).to(__A   )
												_A								= torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(__A   )

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

							def       __A (  self:   Optional[Any]   )    ->				Any:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



							@parameterized.expand(
							    [
							        # fmt: off
							        [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
							        [37, [-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       __A (  self:   Any		,     __A:   Tuple		,     __A:   int   )    ->				Dict:
												_A								= PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''		,     subfolder='''prior'''   )
												model.to(__A   )
												_A								= self.get_dummy_seed_input(seed=__A   )

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

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

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

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











import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class 						SCREAMING_SNAKE_CASE				( snake_case  ,	snake_case					):





							"""simple docstring"""

							A_						  =					1

							@register_to_config
							def __init__(  self:   Any		,     __A:   int = 10_00		,     __A:   Optional[Union[np.ndarray, List[float]]] = None   )    ->				List[str]:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(__A   )

												# standard deviation of the initial noise distribution
												_A								= 1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												_A								= 4

												# running values
												_A								= []

							def       __A (  self:   str		,     __A:   int		,     __A:   Union[str, torch.device] = None   )    ->				int:
												_A								= num_inference_steps
												_A								= torch.linspace(1		,     0		,     num_inference_steps + 1   )[:-1]
												_A								= torch.cat([steps, torch.tensor([0.0]   )]   )

												if self.config.trained_betas is not None:
																	_A								= torch.tensor(self.config.trained_betas		,     dtype=torch.floataa   )
												else:
																	_A								= torch.sin(steps * math.pi / 2   ) ** 2

												_A								= (1.0 - self.betas**2) ** 0.5

												_A								= (torch.atana(self.betas		,     self.alphas   ) / math.pi * 2)[:-1]
												_A								= timesteps.to(__A   )

												_A								= []

							def       __A (  self:   Tuple		,     __A:   torch.FloatTensor		,     __A:   int		,     __A:   torch.FloatTensor		,     __A:   bool = True		,     )    ->				Union[SchedulerOutput, Tuple]:
												if self.num_inference_steps is None:
																	raise ValueError(
																	    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''   )

												_A								= (self.timesteps == timestep).nonzero().item()
												_A								= timestep_index + 1

												_A								= sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(__A   )

												if len(self.ets   ) == 1:
																	_A								= self.ets[-1]
												elif len(self.ets   ) == 2:
																	_A								= (3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets   ) == 3:
																	_A								= (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																	_A								= (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												_A								= self._get_prev_sample(__A		,     __A		,     __A		,     __A   )

												if not return_dict:
																	return (prev_sample,)

												return SchedulerOutput(prev_sample=__A   )

							def       __A (  self:   Optional[int]		,     __A:   torch.FloatTensor		,     *__A:   Tuple		,     **__A:   List[Any]   )    ->				torch.FloatTensor:
												return sample

							def       __A (  self:   List[str]		,     __A:   Optional[Any]		,     __A:   Optional[Any]		,     __A:   Any		,     __A:   List[Any]   )    ->				List[Any]:
												_A								= self.alphas[timestep_index]
												_A								= self.betas[timestep_index]

												_A								= self.alphas[prev_timestep_index]
												_A								= self.betas[prev_timestep_index]

												_A								= (sample - sigma * ets) / max(__A		,     1e-8   )
												_A								= next_alpha * pred + ets * next_sigma

												return prev_sample



							def __len__(  self:   List[str]   )    ->				Dict:
												return self.config.num_train_timesteps



