




import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 			a__    (					_UpperCAmelCase      ):
						_a							:    str					=    4_2
						_a							:    Any					=    None







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				str=0.9_99      ,      SCREAMING_SNAKE_CASE_							:				List[str]="cosine"      ,      ):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(SCREAMING_SNAKE_CASE_							:				Tuple     ):
															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2     ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(SCREAMING_SNAKE_CASE_							:				int     ):
															return math.exp(t * -12.0     )

					else:
										raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""     )

					__lowerCAmelCase				   =       []
					for i in range(UpperCAmelCase__     ):
										__lowerCAmelCase				   =       i / num_diffusion_timesteps
										__lowerCAmelCase				   =       (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(UpperCAmelCase__     ) / alpha_bar_fn(UpperCAmelCase__     )      ,      UpperCAmelCase__     )     )
					return torch.tensor(UpperCAmelCase__      ,      dtype=torch.floataa     )


class 			a__    (					_UpperCAmelCase ,				_UpperCAmelCase      ):






						@register_to_config
						def __init__(   self		,			_A = 1_0_0_0		,			_A = "fixed_small_log"		,			_A = True		,			_A = 1.0		,			_A = "epsilon"		,			_A = "squaredcos_cap_v2"		,			):




											"""simple docstring"""



											if beta_schedule != "squaredcos_cap_v2":
																raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"			)

											__lowerCAmelCase				   =       betas_for_alpha_bar(lowercase_			)

											__lowerCAmelCase				   =       1.0 - self.betas
											__lowerCAmelCase				   =       torch.cumprod(self.alphas		,			dim=0			)
											__lowerCAmelCase				   =       torch.tensor(1.0			)

											# standard deviation of the initial noise distribution
											__lowerCAmelCase				   =       1.0

											# setable values
											__lowerCAmelCase				   =       None
											__lowerCAmelCase				   =       torch.from_numpy(np.arange(0		,			lowercase_			)[::-1].copy()			)

											__lowerCAmelCase				   =       variance_type






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None			):




											"""simple docstring"""



											return sample






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None			):




											"""simple docstring"""



											__lowerCAmelCase				   =       num_inference_steps
											__lowerCAmelCase				   =       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
											__lowerCAmelCase				   =       (np.arange(0		,			lowercase_			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
											__lowerCAmelCase				   =       torch.from_numpy(lowercase_			).to(lowercase_			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=None		,			_A=None		,			_A=None			):




											"""simple docstring"""



											if prev_timestep is None:
																__lowerCAmelCase				   =       t - 1

											__lowerCAmelCase				   =       self.alphas_cumprod[t]
											__lowerCAmelCase				   =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
											__lowerCAmelCase				   =       1 - alpha_prod_t
											__lowerCAmelCase				   =       1 - alpha_prod_t_prev

											if prev_timestep == t - 1:
																__lowerCAmelCase				   =       self.betas[t]
											else:
																__lowerCAmelCase				   =       1 - alpha_prod_t / alpha_prod_t_prev

											# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
											# and sample from it to get previous sample
											# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
											__lowerCAmelCase				   =       beta_prod_t_prev / beta_prod_t * beta

											if variance_type is None:
																__lowerCAmelCase				   =       self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small_log":
																__lowerCAmelCase				   =       torch.log(torch.clamp(lowercase_		,			min=1E-2_0			)			)
																__lowerCAmelCase				   =       torch.exp(0.5 * variance			)
											elif variance_type == "learned_range":
																# NOTE difference with DDPM scheduler
																__lowerCAmelCase				   =       variance.log()
																__lowerCAmelCase				   =       beta.log()

																__lowerCAmelCase				   =       (predicted_variance + 1) / 2
																__lowerCAmelCase				   =       frac * max_log + (1 - frac) * min_log

											return variance






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A = None		,			_A=None		,			_A = True		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       timestep

											if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																__lowerCAmelCase				   =       torch.split(lowercase_		,			sample.shape[1]		,			dim=1			)
											else:
																__lowerCAmelCase				   =       None

											# 1. compute alphas, betas
											if prev_timestep is None:
																__lowerCAmelCase				   =       t - 1

											__lowerCAmelCase				   =       self.alphas_cumprod[t]
											__lowerCAmelCase				   =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
											__lowerCAmelCase				   =       1 - alpha_prod_t
											__lowerCAmelCase				   =       1 - alpha_prod_t_prev

											if prev_timestep == t - 1:
																__lowerCAmelCase				   =       self.betas[t]
																__lowerCAmelCase				   =       self.alphas[t]
											else:
																__lowerCAmelCase				   =       1 - alpha_prod_t / alpha_prod_t_prev
																__lowerCAmelCase				   =       1 - beta

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											if self.config.prediction_type == "epsilon":
																__lowerCAmelCase				   =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																__lowerCAmelCase				   =       model_output
											else:
																raise ValueError(
																    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
																    " for the UnCLIPScheduler."			)

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																__lowerCAmelCase				   =       torch.clamp(
																    lowercase_		,			-self.config.clip_sample_range		,			self.config.clip_sample_range			)

											# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											__lowerCAmelCase				   =       (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
											__lowerCAmelCase				   =       alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

											# 5. Compute predicted previous sample µ_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											__lowerCAmelCase				   =       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											__lowerCAmelCase				   =       0
											if t > 0:
																__lowerCAmelCase				   =       randn_tensor(
																    model_output.shape		,			dtype=model_output.dtype		,			generator=lowercase_		,			device=model_output.device			)

																__lowerCAmelCase				   =       self._get_variance(
																    lowercase_		,			predicted_variance=lowercase_		,			prev_timestep=lowercase_		,			)

																if self.variance_type == "fixed_small_log":
																					__lowerCAmelCase				   =       variance
																elif self.variance_type == "learned_range":
																					__lowerCAmelCase				   =       (0.5 * variance).exp()
																else:
																					raise ValueError(
																					    f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																					    " for the UnCLIPScheduler."			)

																__lowerCAmelCase				   =       variance * variance_noise

											__lowerCAmelCase				   =       pred_prev_sample + variance

											if not return_dict:
																return (pred_prev_sample,)

											return UnCLIPSchedulerOutput(prev_sample=lowercase_		,			pred_original_sample=lowercase_			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.alphas_cumprod.to(device=original_samples.device		,			dtype=original_samples.dtype			)
											__lowerCAmelCase				   =       timesteps.to(original_samples.device			)

											__lowerCAmelCase				   =       alphas_cumprod[timesteps] ** 0.5
											__lowerCAmelCase				   =       sqrt_alpha_prod.flatten()
											while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
																__lowerCAmelCase				   =       sqrt_alpha_prod.unsqueeze(-1			)

											__lowerCAmelCase				   =       (1 - alphas_cumprod[timesteps]) ** 0.5
											__lowerCAmelCase				   =       sqrt_one_minus_alpha_prod.flatten()
											while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
																__lowerCAmelCase				   =       sqrt_one_minus_alpha_prod.unsqueeze(-1			)

											__lowerCAmelCase				   =       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
											return noisy_samples





'''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     __magic_name__						(    _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       PriorTransformer
			UpperCamelCase__				=       '''hidden_states'''







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[Any]										=       4
										lowercase_					:			int										=       8
										lowercase_					:			Union[str, Any]										=       7

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

										lowercase_					:			Tuple										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[str]										=       floats_tensor((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[Any]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			int										=       4
										lowercase_					:			Any										=       8
										lowercase_					:			Tuple										=       7

										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

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







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										return (4, 8)







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return (4, 8)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			Optional[Any]										=       {
										    """num_attention_heads""": 2,
										    """attention_head_dim""": 4,
										    """num_layers""": 2,
										    """embedding_dim""": 8,
										    """num_embeddings""": 7,
										    """additional_embeddings""": 4,
										}
										lowercase_					:			List[Any]										=       self.dummy_input
										return init_dict, inputs_dict







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_						,						lowercase_					:			Tuple										=       PriorTransformer.from_pretrained(
										    """hf-internal-testing/prior-dummy"""				,			output_loading_info=lowercase_    )
										self.assertIsNotNone(lowercase_    )
										self.assertEqual(len(loading_info["""missing_keys"""]    )				,			0    )

										model.to(lowercase_    )
										lowercase_					:			Optional[int]										=       model(**self.dummy_input    )[0]

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_						,						lowercase_					:			str										=       self.prepare_init_args_and_inputs_for_common()

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

										lowercase_					:			Optional[Any]										=       ["""hidden_states""", """timestep"""]
										self.assertListEqual(arg_names[:2]				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_					:			Optional[Any]										=       PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""    )
										lowercase_					:			Any										=       model.to(lowercase_    )

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

										lowercase_					:			Any										=       self.get_dummy_seed_input()

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

										lowercase_					:			Tuple										=       output[0, :5].flatten().cpu()
										print(lowercase_    )

										# 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_					:			Dict										=       torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]    )
										self.assertTrue(torch_all_close(lowercase_				,			lowercase_				,			rtol=1E-2    )    )





@slow
class     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Union[str, Any]=1				,			lowercase_				:  Optional[Any]=768				,			lowercase_				:  Tuple=77				,			lowercase_				:  Optional[int]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			Optional[Any]										=       batch_size
										lowercase_					:			int										=       embedding_dim
										lowercase_					:			int										=       num_embeddings

										lowercase_					:			Dict										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Any										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
			        [37, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
			        # fmt: on
			    ]    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Any				,			lowercase_				:  List[str]    ):
										lowercase_					:			List[Any]										=       PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""				,			subfolder="""prior"""    )
										model.to(lowercase_    )
										lowercase_					:			Optional[Any]										=       self.get_dummy_seed_input(seed=lowercase_    )

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

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

										lowercase_					:			Union[str, Any]										=       sample[0, :8].flatten().cpu()
										print(lowercase_    )
										lowercase_					:			Optional[Any]										=       torch.tensor(lowercase_    )

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





