






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  (				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Any    =				1





	@register_to_config
	def __init__(self     ,     lowerCAmelCase_ = 1000     ,     lowerCAmelCase_ = None							):
		# set `betas`, `alphas`, `timesteps`
		self.set_timesteps(lowerCAmelCase_							)

		# standard deviation of the initial noise distribution
		A_		:						str	    =   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_		:						Dict	    =   4

		# running values
		A_		:						Optional[Any]	    =   []





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
		A_		:						Union[str, Any]	    =   num_inference_steps
		A_		:						List[str]	    =   torch.linspace(1     ,     0     ,     num_inference_steps + 1							)[:-1]
		A_		:						Union[str, Any]	    =   torch.cat([steps, torch.tensor([0.0]							)]							)

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

		A_		:						Any	    =   (1.0 - self.betas**2) ** 0.5

		A_		:						Optional[int]	    =   (torch.atana(self.betas     ,     self.alphas							) / math.pi * 2)[:-1]
		A_		:						Union[str, Any]	    =   timesteps.to(lowerCAmelCase_							)

		A_		:						Tuple	    =   []





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = True     ,     ):
		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_		:						Union[str, Any]	    =   (self.timesteps == timestep).nonzero().item()
		A_		:						Dict	    =   timestep_index + 1

		A_		:						List[str]	    =   sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
		self.ets.append(lowerCAmelCase_							)

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

		A_		:						Tuple	    =   self._get_prev_sample(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)

		if not return_dict:
			return (prev_sample,)

		return SchedulerOutput(prev_sample=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return sample





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						int	    =   self.alphas[timestep_index]
		A_		:						Optional[int]	    =   self.betas[timestep_index]

		A_		:						Tuple	    =   self.alphas[prev_timestep_index]
		A_		:						str	    =   self.betas[prev_timestep_index]

		A_		:						Union[str, Any]	    =   (sample - sigma * ets) / max(lowerCAmelCase_     ,     1e-8							)
		A_		:						str	    =   next_alpha * pred + ets * next_sigma

		return prev_sample





	def __len__(self							):
		return self.config.num_train_timesteps









"""simple docstring"""
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  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				int    =				PriorTransformer
	_A       :				Dict    =				"""hidden_states"""





	@property
	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   4
		A_		:						Dict	    =   8
		A_		:						str	    =   7

		A_		:						Dict	    =   floats_tensor((batch_size, embedding_dim)							).to(lowerCAmelCase_							)

		A_		:						str	    =   floats_tensor((batch_size, embedding_dim)							).to(lowerCAmelCase_							)
		A_		:						List[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    lowerCamelCase(self     ,     lowerCAmelCase_=0							):
		torch.manual_seed(lowerCAmelCase_							)
		A_		:						Optional[Any]	    =   4
		A_		:						Tuple	    =   8
		A_		:						Tuple	    =   7

		A_		:						int	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)

		A_		:						List[str]	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)
		A_		:						Tuple	    =   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    lowerCamelCase(self							):
		return (4, 8)





	@property
	def    lowerCamelCase(self							):
		return (4, 8)





	def    lowerCamelCase(self							):
		A_		:						int	    =   {
		    """num_attention_heads""": 2,
		    """attention_head_dim""": 4,
		    """num_layers""": 2,
		    """embedding_dim""": 8,
		    """num_embeddings""": 7,
		    """additional_embeddings""": 4,
		}
		A_		:						Dict	    =   self.dummy_input
		return init_dict, inputs_dict





	def    lowerCamelCase(self							):
		A_    ,     A_		:						Optional[int]	    =   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_							)
		A_		:						List[Any]	    =   model(**self.dummy_input							)[0]

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





	def    lowerCamelCase(self							):
		A_    ,     A_		:						Union[str, Any]	    =   self.prepare_init_args_and_inputs_for_common()

		A_		:						Union[str, Any]	    =   self.model_class(**lowerCAmelCase_							)
		A_		:						Tuple	    =   inspect.signature(model.forward							)
		# signature.parameters is an OrderedDict => so arg_names order is deterministic
		A_		:						Tuple	    =   [*signature.parameters.keys()]

		A_		:						List[str]	    =   ["""hidden_states""", """timestep"""]
		self.assertListEqual(arg_names[:2]     ,     lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						int	    =   PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""							)
		A_		:						Optional[Any]	    =   model.to(lowerCAmelCase_							)

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

		A_		:						int	    =   self.get_dummy_seed_input()

		with torch.no_grad():
			A_		:						int	    =   model(**lowerCAmelCase_							)[0]

		A_		:						Any	    =   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.
		A_		:						Optional[int]	    =   torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]							)
		self.assertTrue(torch_all_close(lowerCAmelCase_     ,     lowerCAmelCase_     ,     rtol=1e-2							)							)






@slow
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	def    lowerCamelCase(self     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=768     ,     lowerCAmelCase_=77     ,     lowerCAmelCase_=0							):
		torch.manual_seed(lowerCAmelCase_							)
		A_		:						Tuple	    =   batch_size
		A_		:						Any	    =   embedding_dim
		A_		:						Tuple	    =   num_embeddings

		A_		:						Any	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)

		A_		:						Union[str, Any]	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)
		A_		:						Tuple	    =   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    lowerCamelCase(self							):
		# clean up the VRAM after each test
		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     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""     ,     subfolder="""prior"""							)
		model.to(lowerCAmelCase_							)
		A_		:						int	    =   self.get_dummy_seed_input(seed=lowerCAmelCase_							)

		with torch.no_grad():
			A_		:						Tuple	    =   model(**lowerCAmelCase_							)[0]

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

		A_		:						Tuple	    =   sample[0, :8].flatten().cpu()
		print(lowerCAmelCase_							)
		A_		:						Optional[int]	    =   torch.tensor(lowerCAmelCase_							)

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

