
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 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Union[str, Any]				,    __UpperCamelCase			:    Dict=0.9_9_9				,    __UpperCamelCase			:    Any="cosine"				,    )			->							Optional[int]:

				"""simple docstring"""







				if alpha_transform_type == "cosine":

								def alpha_bar_fn(__UpperCamelCase			:    Union[str, Any]							):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2							) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(__UpperCamelCase			:    Dict							):
												return math.exp(t * -1_2.0							)

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

				A__	:						str								=				[]
				for i in range(__UpperCamelCase							):
								A__	:						Union[str, Any]								=				i / num_diffusion_timesteps
								A__	:						Any								=				(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 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					[e.name for e in KarrasDiffusionSchedulers]
	_lowerCAmelCase											=					2





	@register_to_config
	def __init__(      self					,							UpperCamelCase__ = 1000					,							UpperCamelCase__ = 0.0_0_0_8_5					,							UpperCamelCase__ = 0.0_1_2					,							UpperCamelCase__ = "linear"					,							UpperCamelCase__ = None					,							UpperCamelCase__ = "epsilon"					,							UpperCamelCase__ = "linspace"					,							UpperCamelCase__ = 0					,							):
					if trained_betas is not None:
									A__	:						List[str]								=				torch.tensor(UpperCamelCase__					,							dtype=torch.floataa		)
					elif beta_schedule == "linear":
									A__	:						Dict								=				torch.linspace(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							dtype=torch.floataa		)
					elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									A__	:						str								=				(
									    torch.linspace(beta_start**0.5					,							beta_end**0.5					,							UpperCamelCase__					,							dtype=torch.floataa		) ** 2
									)
					elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									A__	:						Dict								=				betas_for_alpha_bar(UpperCamelCase__		)
					else:
									raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"		)

					A__	:						str								=				1.0 - self.betas
					A__	:						str								=				torch.cumprod(self.alphas					,							dim=0		)

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





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__=None		):
					if schedule_timesteps is None:
									A__	:						int								=				self.timesteps

					A__	:						Any								=				(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__	:						str								=				1 if len(UpperCamelCase__		) > 1 else 0
					else:
									A__	:						List[str]								=				timestep.cpu().item() if torch.is_tensor(UpperCamelCase__		) else timestep
									A__	:						str								=				self._index_counter[timestep_int]

					return indices[pos].item()





	@property
	def 	__snake_case					(      self		):
					# 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 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							):
					A__	:						str								=				self.index_for_timestep(UpperCamelCase__		)

					if self.state_in_first_order:
									A__	:						Dict								=				self.sigmas[step_index]
					else:
									A__	:						int								=				self.sigmas_interpol[step_index - 1]

					A__	:						Any								=				sample / ((sigma**2 + 1) ** 0.5)
					return sample





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__ = None					,							UpperCamelCase__ = None					,							):
					A__	:						Optional[int]								=				num_inference_steps

					A__	:						int								=				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__	:						Any								=				np.linspace(0					,							num_train_timesteps - 1					,							UpperCamelCase__					,							dtype=UpperCamelCase__		)[::-1].copy()
					elif self.config.timestep_spacing == "leading":
									A__	:						Tuple								=				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__	:						Union[str, Any]								=				(np.arange(0					,							UpperCamelCase__		) * step_ratio).round()[::-1].copy().astype(UpperCamelCase__		)
									timesteps += self.config.steps_offset
					elif self.config.timestep_spacing == "trailing":
									A__	:						Optional[int]								=				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__	:						Tuple								=				(np.arange(UpperCamelCase__					,							0					,							-step_ratio		)).round().copy().astype(UpperCamelCase__		)
									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__	:						Optional[int]								=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
					A__	:						List[str]								=				torch.from_numpy(np.log(UpperCamelCase__		)		).to(UpperCamelCase__		)

					A__	:						List[Any]								=				np.interp(UpperCamelCase__					,							np.arange(0					,							len(UpperCamelCase__		)		)					,							UpperCamelCase__		)
					A__	:						int								=				np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
					A__	:						Union[str, Any]								=				torch.from_numpy(UpperCamelCase__		).to(device=UpperCamelCase__		)

					# compute up and down sigmas
					A__	:						List[Any]								=				sigmas.roll(-1		)
					A__	:						List[str]								=				0.0
					A__	:						Union[str, Any]								=				(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
					A__	:						Union[str, Any]								=				(sigmas_next**2 - sigmas_up**2) ** 0.5
					A__	:						Any								=				0.0

					# compute interpolated sigmas
					A__	:						List[str]								=				sigmas.log().lerp(sigmas_down.log()					,							0.5		).exp()
					A__	:						Any								=				0.0

					# set sigmas
					A__	:						Optional[Any]								=				torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2		), sigmas[-1:]]		)
					A__	:						Tuple								=				torch.cat(
					    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2		), sigmas_interpol[-1:]]		)
					A__	:						List[Any]								=				torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2		), sigmas_up[-1:]]		)
					A__	:						Optional[Any]								=				torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2		), sigmas_down[-1:]]		)

					if str(UpperCamelCase__		).startswith('''mps'''		):
									# mps does not support float64
									A__	:						Optional[Any]								=				torch.from_numpy(UpperCamelCase__		).to(UpperCamelCase__					,							dtype=torch.floataa		)
					else:
									A__	:						Tuple								=				torch.from_numpy(UpperCamelCase__		).to(UpperCamelCase__		)

					A__	:						List[str]								=				self.sigma_to_t(UpperCamelCase__		).to(UpperCamelCase__					,							dtype=timesteps.dtype		)
					A__	:						List[str]								=				torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])					,							dim=-1		).flatten()

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

					A__	:						Any								=				None

					# for exp beta schedules, such as the one for `pipeline_shap_e.py`
					# we need an index counter
					A__	:						int								=				defaultdict(UpperCamelCase__		)





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					# get log sigma
					A__	:						Union[str, Any]								=				sigma.log()

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

					# get sigmas range
					A__	:						List[str]								=				dists.ge(0		).cumsum(dim=0		).argmax(dim=0		).clamp(max=self.log_sigmas.shape[0] - 2		)
					A__	:						Union[str, Any]								=				low_idx + 1

					A__	:						Dict								=				self.log_sigmas[low_idx]
					A__	:						Optional[Any]								=				self.log_sigmas[high_idx]

					# interpolate sigmas
					A__	:						Union[str, Any]								=				(low - log_sigma) / (low - high)
					A__	:						Union[str, Any]								=				w.clamp(0					,							1		)

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





	@property
	def 	__snake_case					(      self		):
					return self.sample is None





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__ = None					,							UpperCamelCase__ = True					,							):
					A__	:						Tuple								=				self.index_for_timestep(UpperCamelCase__		)

					# advance index counter by 1
					A__	:						Optional[Any]								=				timestep.cpu().item() if torch.is_tensor(UpperCamelCase__		) else timestep
					self._index_counter[timestep_int] += 1

					if self.state_in_first_order:
									A__	:						Optional[Any]								=				self.sigmas[step_index]
									A__	:						List[str]								=				self.sigmas_interpol[step_index]
									A__	:						Any								=				self.sigmas_up[step_index]
									A__	:						Dict								=				self.sigmas_down[step_index - 1]
					else:
									# 2nd order / KPDM2's method
									A__	:						str								=				self.sigmas[step_index - 1]
									A__	:						List[str]								=				self.sigmas_interpol[step_index - 1]
									A__	:						Union[str, Any]								=				self.sigmas_up[step_index - 1]
									A__	:						Any								=				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__	:						Any								=				0
					A__	:						Any								=				sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

					A__	:						Dict								=				model_output.device
					A__	:						List[Any]								=				randn_tensor(model_output.shape					,							dtype=model_output.dtype					,							device=UpperCamelCase__					,							generator=UpperCamelCase__		)

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
									A__	:						int								=				sigma_hat if self.state_in_first_order else sigma_interpol
									A__	:						str								=				sample - sigma_input * model_output
					elif self.config.prediction_type == "v_prediction":
									A__	:						List[Any]								=				sigma_hat if self.state_in_first_order else sigma_interpol
									A__	:						List[str]								=				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__	:						int								=				(sample - pred_original_sample) / sigma_hat
									# 3. delta timestep
									A__	:						int								=				sigma_interpol - sigma_hat

									# store for 2nd order step
									A__	:						Tuple								=				sample
									A__	:						str								=				dt
									A__	:						Any								=				sample + derivative * dt
					else:
									# DPM-Solver-2
									# 2. Convert to an ODE derivative for 2nd order
									A__	:						Optional[int]								=				(sample - pred_original_sample) / sigma_interpol
									# 3. delta timestep
									A__	:						Optional[int]								=				sigma_down - sigma_hat

									A__	:						int								=				self.sample
									A__	:						Optional[Any]								=				None

									A__	:						Union[str, Any]								=				sample + derivative * dt
									A__	:						int								=				prev_sample + noise * sigma_up

					if not return_dict:
									return (prev_sample,)

					return SchedulerOutput(prev_sample=UpperCamelCase__		)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							):
					# Make sure sigmas and timesteps have the same device and dtype as original_samples
					A__	:						int								=				self.sigmas.to(device=original_samples.device					,							dtype=original_samples.dtype		)
					if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase__		):
									# mps does not support float64
									A__	:						Dict								=				self.timesteps.to(original_samples.device					,							dtype=torch.floataa		)
									A__	:						Any								=				timesteps.to(original_samples.device					,							dtype=torch.floataa		)
					else:
									A__	:						int								=				self.timesteps.to(original_samples.device		)
									A__	:						Union[str, Any]								=				timesteps.to(original_samples.device		)

					A__	:						List[Any]								=				[self.index_for_timestep(UpperCamelCase__					,							UpperCamelCase__		) for t in timesteps]

					A__	:						Dict								=				sigmas[step_indices].flatten()
					while len(sigma.shape		) < len(original_samples.shape		):
									A__	:						Optional[Any]								=				sigma.unsqueeze(-1		)

					A__	:						int								=				original_samples + noise * sigma
					return noisy_samples





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

import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
					from PIL import Image







class 			UpperCamelCase__		(     unittest.TestCase,      SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''





	def 	__snake_case					(      self		):
					A__	:						Dict								=				load_tool('''image-question-answering'''		)
					self.tool.setup()
					A__	:						List[Any]								=				load_tool('''image-question-answering'''					,							remote=UpperCamelCase__		)





	def 	__snake_case					(      self		):
					A__	:						str								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						List[str]								=				self.tool(UpperCamelCase__					,							'''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Tuple								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						Dict								=				self.remote_tool(UpperCamelCase__					,							'''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Dict								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						int								=				self.tool(image=UpperCamelCase__					,							question='''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Tuple								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						str								=				self.remote_tool(image=UpperCamelCase__					,							question='''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)