






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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def    __A    (   _lowercase    ,	_lowercase=0.9_99    ,	_lowercase="cosine"    ,	):

					'''simple docstring'''







					if alpha_transform_type == "cosine":

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

					elif alpha_transform_type == "exp":

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

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

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





class   SCREAMING_SNAKE_CASE (       snake_case     ,		snake_case    ):





							"""simple docstring"""

							A_       =    [e.name for e in KarrasDiffusionSchedulers]
							A_       =    2

							@register_to_config
							def __init__(     self:      int						,   __A:      int = 10_00						,   __A:      float = 0.00_085						,   __A:      float = 0.012						,   __A:      str = "linear"						,   __A:      Optional[Union[np.ndarray, List[float]]] = None						,   __A:      str = "epsilon"						,   __A:      str = "linspace"						,   __A:      int = 0						,   )			-> Any:
												if trained_betas is not None:
																	_A									=       torch.tensor(__A						,   dtype=torch.floataa      )
												elif beta_schedule == "linear":
																	_A									=       torch.linspace(__A						,   __A						,   __A						,   dtype=torch.floataa      )
												elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	_A									=       (
																	    torch.linspace(beta_start**0.5						,   beta_end**0.5						,   __A						,   dtype=torch.floataa      ) ** 2
																	)
												elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	_A									=       betas_for_alpha_bar(__A      )
												else:
																	raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""      )

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

												#  set all values
												self.set_timesteps(__A						,   __A						,   __A      )

							def 					__A       (     self:      List[Any]						,   __A:      Any						,   __A:      int=None      )			-> Union[str, Any]:
												if schedule_timesteps is None:
																	_A									=       self.timesteps

												_A									=       (schedule_timesteps == timestep).nonzero()

												# The sigma index that is taken for the **very** first `step`
												# is always the second index (or the last index if there is only 1)
												# This way we can ensure we don't accidentally skip a sigma in
												# case we start in the middle of the denoising schedule (e.g. for image-to-image)
												if len(self._index_counter      ) == 0:
																	_A									=       1 if len(__A      ) > 1 else 0
												else:
																	_A									=       timestep.cpu().item() if torch.is_tensor(__A      ) else timestep
																	_A									=       self._index_counter[timestep_int]

												return indices[pos].item()

							@property
							def 					__A       (     self:      Dict      )			-> str:
												# standard deviation of the initial noise distribution
												if self.config.timestep_spacing in ["linspace", "trailing"]:
																	return self.sigmas.max()

												return (self.sigmas.max() ** 2 + 1) ** 0.5

							def 					__A       (     self:      Any						,   __A:      torch.FloatTensor						,   __A:      Union[float, torch.FloatTensor]						,   )			-> torch.FloatTensor:
												_A									=       self.index_for_timestep(__A      )

												if self.state_in_first_order:
																	_A									=       self.sigmas[step_index]
												else:
																	_A									=       self.sigmas_interpol[step_index - 1]

												_A									=       sample / ((sigma**2 + 1) ** 0.5)
												return sample

							def 					__A       (     self:      str						,   __A:      int						,   __A:      Union[str, torch.device] = None						,   __A:      Optional[int] = None						,   )			-> Dict:
												_A									=       num_inference_steps

												_A									=       num_train_timesteps or self.config.num_train_timesteps

												# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
												if self.config.timestep_spacing == "linspace":
																	_A									=       np.linspace(0						,   num_train_timesteps - 1						,   __A						,   dtype=__A      )[::-1].copy()
												elif self.config.timestep_spacing == "leading":
																	_A									=       num_train_timesteps // self.num_inference_steps
																	# creates integer timesteps by multiplying by ratio
																	# casting to int to avoid issues when num_inference_step is power of 3
																	_A									=       (np.arange(0						,   __A      ) * step_ratio).round()[::-1].copy().astype(__A      )
																	timesteps += self.config.steps_offset
												elif self.config.timestep_spacing == "trailing":
																	_A									=       num_train_timesteps / self.num_inference_steps
																	# creates integer timesteps by multiplying by ratio
																	# casting to int to avoid issues when num_inference_step is power of 3
																	_A									=       (np.arange(__A						,   0						,   -step_ratio      )).round().copy().astype(__A      )
																	timesteps -= 1
												else:
																	raise ValueError(
																	    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""      )

												_A									=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
												_A									=       torch.from_numpy(np.log(__A      )      ).to(__A      )

												_A									=       np.interp(__A						,   np.arange(0						,   len(__A      )      )						,   __A      )
												_A									=       np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
												_A									=       torch.from_numpy(__A      ).to(device=__A      )

												# compute up and down sigmas
												_A									=       sigmas.roll(-1      )
												_A									=       0.0
												_A									=       (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
												_A									=       (sigmas_next**2 - sigmas_up**2) ** 0.5
												_A									=       0.0

												# compute interpolated sigmas
												_A									=       sigmas.log().lerp(sigmas_down.log()						,   0.5      ).exp()
												_A									=       0.0

												# set sigmas
												_A									=       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
												_A									=       torch.cat(
												    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )
												_A									=       torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2      ), sigmas_up[-1:]]      )
												_A									=       torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2      ), sigmas_down[-1:]]      )

												if str(__A      ).startswith('''mps'''      ):
																	# mps does not support float64
																	_A									=       torch.from_numpy(__A      ).to(__A						,   dtype=torch.floataa      )
												else:
																	_A									=       torch.from_numpy(__A      ).to(__A      )

												_A									=       self.sigma_to_t(__A      ).to(__A						,   dtype=timesteps.dtype      )
												_A									=       torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						,   dim=-1      ).flatten()

												_A									=       torch.cat([timesteps[:1], interleaved_timesteps]      )

												_A									=       None

												# for exp beta schedules, such as the one for `pipeline_shap_e.py`
												# we need an index counter
												_A									=       defaultdict(__A      )

							def 					__A       (     self:      str						,   __A:      List[str]      )			-> Dict:
												# get log sigma
												_A									=       sigma.log()

												# get distribution
												_A									=       log_sigma - self.log_sigmas[:, None]

												# get sigmas range
												_A									=       dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
												_A									=       low_idx + 1

												_A									=       self.log_sigmas[low_idx]
												_A									=       self.log_sigmas[high_idx]

												# interpolate sigmas
												_A									=       (low - log_sigma) / (low - high)
												_A									=       w.clamp(0						,   1      )

												# transform interpolation to time range
												_A									=       (1 - w) * low_idx + w * high_idx
												_A									=       t.view(sigma.shape      )
												return t

							@property
							def 					__A       (     self:      int      )			-> Union[str, Any]:
												return self.sample is None

							def 					__A       (     self:      int						,   __A:      Union[torch.FloatTensor, np.ndarray]						,   __A:      Union[float, torch.FloatTensor]						,   __A:      Union[torch.FloatTensor, np.ndarray]						,   __A:      Optional[torch.Generator] = None						,   __A:      bool = True						,   )			-> Union[SchedulerOutput, Tuple]:
												_A									=       self.index_for_timestep(__A      )

												# advance index counter by 1
												_A									=       timestep.cpu().item() if torch.is_tensor(__A      ) else timestep
												self._index_counter[timestep_int] += 1

												if self.state_in_first_order:
																	_A									=       self.sigmas[step_index]
																	_A									=       self.sigmas_interpol[step_index]
																	_A									=       self.sigmas_up[step_index]
																	_A									=       self.sigmas_down[step_index - 1]
												else:
																	# 2nd order / KPDM2's method
																	_A									=       self.sigmas[step_index - 1]
																	_A									=       self.sigmas_interpol[step_index - 1]
																	_A									=       self.sigmas_up[step_index - 1]
																	_A									=       self.sigmas_down[step_index - 1]

												# currently only gamma=0 is supported. This usually works best anyways.
												# We can support gamma in the future but then need to scale the timestep before
												# passing it to the model which requires a change in API
												_A									=       0
												_A									=       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

												_A									=       model_output.device
												_A									=       randn_tensor(model_output.shape						,   dtype=model_output.dtype						,   device=__A						,   generator=__A      )

												# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
												if self.config.prediction_type == "epsilon":
																	_A									=       sigma_hat if self.state_in_first_order else sigma_interpol
																	_A									=       sample - sigma_input * model_output
												elif self.config.prediction_type == "v_prediction":
																	_A									=       sigma_hat if self.state_in_first_order else sigma_interpol
																	_A									=       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																	    sample / (sigma_input**2 + 1)
																	)
												elif self.config.prediction_type == "sample":
																	raise NotImplementedError('''prediction_type not implemented yet: sample'''      )
												else:
																	raise ValueError(
																	    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""      )

												if self.state_in_first_order:
																	# 2. Convert to an ODE derivative for 1st order
																	_A									=       (sample - pred_original_sample) / sigma_hat
																	# 3. delta timestep
																	_A									=       sigma_interpol - sigma_hat

																	# store for 2nd order step
																	_A									=       sample
																	_A									=       dt
																	_A									=       sample + derivative * dt
												else:
																	# DPM-Solver-2
																	# 2. Convert to an ODE derivative for 2nd order
																	_A									=       (sample - pred_original_sample) / sigma_interpol
																	# 3. delta timestep
																	_A									=       sigma_down - sigma_hat

																	_A									=       self.sample
																	_A									=       None

																	_A									=       sample + derivative * dt
																	_A									=       prev_sample + noise * sigma_up

												if not return_dict:
																	return (prev_sample,)

												return SchedulerOutput(prev_sample=__A      )

							def 					__A       (     self:      str						,   __A:      torch.FloatTensor						,   __A:      torch.FloatTensor						,   __A:      torch.FloatTensor						,   )			-> torch.FloatTensor:
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												_A									=       self.sigmas.to(device=original_samples.device						,   dtype=original_samples.dtype      )
												if original_samples.device.type == "mps" and torch.is_floating_point(__A      ):
																	# mps does not support float64
																	_A									=       self.timesteps.to(original_samples.device						,   dtype=torch.floataa      )
																	_A									=       timesteps.to(original_samples.device						,   dtype=torch.floataa      )
												else:
																	_A									=       self.timesteps.to(original_samples.device      )
																	_A									=       timesteps.to(original_samples.device      )

												_A									=       [self.index_for_timestep(__A						,   __A      ) for t in timesteps]

												_A									=       sigmas[step_indices].flatten()
												while len(sigma.shape      ) < len(original_samples.shape      ):
																	_A									=       sigma.unsqueeze(-1      )

												_A									=       original_samples + noise * sigma
												return noisy_samples



							def __len__(     self:      int      )			-> Tuple:
												return self.config.num_train_timesteps











import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor





class   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							def 					__A       (     self:      Optional[Any]						,   __A:      bool = False      )			-> Union[str, Any]:
												_A									=       10

												if only_cross_attention:
																	_A									=       12
												else:
																	# when only cross attention is not set, the cross attention dim must be the same as the query dim
																	_A									=       query_dim

												return {
												    "query_dim": query_dim,
												    "cross_attention_dim": cross_attention_dim,
												    "heads": 2,
												    "dim_head": 4,
												    "added_kv_proj_dim": 6,
												    "norm_num_groups": 1,
												    "only_cross_attention": only_cross_attention,
												    "processor": AttnAddedKVProcessor(),
												}

							def 					__A       (     self:      List[Any]						,   __A:      str						,   __A:      Union[str, Any]      )			-> Union[str, Any]:
												_A									=       2

												_A									=       torch.rand(__A						,   __A						,   3						,   2      )
												_A									=       torch.rand(__A						,   4						,   __A      )
												_A									=       None

												return {
												    "hidden_states": hidden_states,
												    "encoder_hidden_states": encoder_hidden_states,
												    "attention_mask": attention_mask,
												}



							def 					__A       (     self:      Optional[int]      )			-> Any:
												# self and cross attention

												torch.manual_seed(0      )

												_A									=       self.get_constructor_arguments(only_cross_attention=__A      )
												_A									=       Attention(**__A      )

												self.assertTrue(attn.to_k is not None      )
												self.assertTrue(attn.to_v is not None      )

												_A									=       self.get_forward_arguments(
												    query_dim=constructor_args['''query_dim''']						,   added_kv_proj_dim=constructor_args['''added_kv_proj_dim''']      )

												_A									=       attn(**__A      )

												# only self attention

												torch.manual_seed(0      )

												_A									=       self.get_constructor_arguments(only_cross_attention=__A      )
												_A									=       Attention(**__A      )

												self.assertTrue(attn.to_k is None      )
												self.assertTrue(attn.to_v is None      )

												_A									=       self.get_forward_arguments(
												    query_dim=constructor_args['''query_dim''']						,   added_kv_proj_dim=constructor_args['''added_kv_proj_dim''']      )

												_A									=       attn(**__A      )

												self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()      )





class   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							def 					__A       (     self:      Any      )			-> Dict:
												_A									=       DiffusionPipeline.from_pretrained('''hf-internal-testing/tiny-stable-diffusion-pipe'''						,   safety_checker=__A      )

												_A									=       pipe(
												    '''foo'''						,   num_inference_steps=2						,   generator=torch.Generator('''cpu'''      ).manual_seed(0      )						,   output_type='''np'''						,   ).images

												# the initial conversion succeeds
												_A									=       DiffusionPipeline.from_pretrained(
												    '''hf-internal-testing/tiny-stable-diffusion-pipe'''						,   device_map='''sequential'''						,   safety_checker=__A      )

												_A									=       pipe(
												    '''foo'''						,   num_inference_steps=2						,   generator=torch.Generator('''cpu'''      ).manual_seed(0      )						,   output_type='''np'''						,   ).images

												with tempfile.TemporaryDirectory() as tmpdir:
																	# save the converted model
																	pipe.save_pretrained(__A      )

																	# can also load the converted weights
																	_A									=       DiffusionPipeline.from_pretrained(__A						,   device_map='''sequential'''						,   safety_checker=__A      )

												_A									=       pipe(
												    '''foo'''						,   num_inference_steps=2						,   generator=torch.Generator('''cpu'''      ).manual_seed(0      )						,   output_type='''np'''						,   ).images

												self.assertTrue(np.allclose(__A						,   __A						,   atol=1e-5      )      )
												self.assertTrue(np.allclose(__A						,   __A						,   atol=1e-5      )      )



