


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

import numpy as np
import torch

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


UpperCAmelCase      :						List[Any]    =		logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 					__lowercase					(       a_     ):



		"""simple docstring"""

		UpperCamelCase    :				torch.FloatTensor
		UpperCamelCase    :				Optional[torch.FloatTensor] 					=		None






def   __lowerCamelCase	(       lowerCamelCase__		:	str     ,       lowerCamelCase__		:	Dict=0.9_9_9     ,       lowerCamelCase__		:	Optional[int]="cosine"     ,       ):





			'''simple docstring'''




			if alpha_transform_type == "cosine":

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

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

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







class 					__lowercase					(       a_ ,       a_     ):



		"""simple docstring"""

		UpperCamelCase    :				Tuple 					=		[e.name for e in KarrasDiffusionSchedulers]
		UpperCamelCase    :				str 					=		1







		@register_to_config
		def __init__(   self		,			A = 10_00		,			A = 0.0001		,			A = 0.02		,			A = "linear"		,			A = None		,			A = "epsilon"		,			A = "linspace"		,			A = 0		,			)     ->   Union[str, Any]:







					'''simple docstring'''







					if trained_betas is not None:
								lowerCamelCase								=     torch.tensor(A		,			dtype=torch.floataa  )
					elif beta_schedule == "linear":
								lowerCamelCase								=     torch.linspace(A		,			A		,			A		,			dtype=torch.floataa  )
					elif beta_schedule == "scaled_linear":
								# this schedule is very specific to the latent diffusion model.
								lowerCamelCase								=     (
								    torch.linspace(beta_start**0.5		,			beta_end**0.5		,			A		,			dtype=torch.floataa  ) ** 2
								)
					elif beta_schedule == "squaredcos_cap_v2":
								# Glide cosine schedule
								lowerCamelCase								=     betas_for_alpha_bar(A  )
					else:
								raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'  )

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

					lowerCamelCase								=     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
					lowerCamelCase								=     np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
					lowerCamelCase								=     torch.from_numpy(A  )

					# setable values
					lowerCamelCase								=     None
					lowerCamelCase								=     np.linspace(0		,			num_train_timesteps - 1		,			A		,			dtype=A  )[::-1].copy()
					lowerCamelCase								=     torch.from_numpy(A  )
					lowerCamelCase								=     False







		@property
		def 				__A      (   self  )     ->   int:







					'''simple docstring'''







					if self.config.timestep_spacing in ["linspace", "trailing"]:
								return self.sigmas.max()

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







		def 				__A      (   self		,			A		,			A  )     ->   torch.FloatTensor:







					'''simple docstring'''







					if isinstance(A		,			torch.Tensor  ):
								lowerCamelCase								=     timestep.to(self.timesteps.device  )
					lowerCamelCase								=     (self.timesteps == timestep).nonzero().item()
					lowerCamelCase								=     self.sigmas[step_index]
					lowerCamelCase								=     sample / ((sigma**2 + 1) ** 0.5)
					lowerCamelCase								=     True
					return sample







		def 				__A      (   self		,			A		,			A = None  )     ->   Tuple:







					'''simple docstring'''







					lowerCamelCase								=     num_inference_steps

					# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
					if self.config.timestep_spacing == "linspace":
								lowerCamelCase								=     np.linspace(0		,			self.config.num_train_timesteps - 1		,			A		,			dtype=A  )[
								    ::-1
								].copy()
					elif self.config.timestep_spacing == "leading":
								lowerCamelCase								=     self.config.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
								lowerCamelCase								=     (np.arange(0		,			A  ) * step_ratio).round()[::-1].copy().astype(A  )
								timesteps += self.config.steps_offset
					elif self.config.timestep_spacing == "trailing":
								lowerCamelCase								=     self.config.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
								lowerCamelCase								=     (np.arange(self.config.num_train_timesteps		,			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\'.'  )

					lowerCamelCase								=     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
					lowerCamelCase								=     np.interp(A		,			np.arange(0		,			len(A  )  )		,			A  )
					lowerCamelCase								=     np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
					lowerCamelCase								=     torch.from_numpy(A  ).to(device=A  )
					if str(A  ).startswith("""mps"""  ):
								# mps does not support float64
								lowerCamelCase								=     torch.from_numpy(A  ).to(A		,			dtype=torch.floataa  )
					else:
								lowerCamelCase								=     torch.from_numpy(A  ).to(device=A  )







		def 				__A      (   self		,			A		,			A		,			A		,			A = None		,			A = True		,			)     ->   Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:







					'''simple docstring'''







					if (
					    isinstance(A		,			A  )
					    or isinstance(A		,			torch.IntTensor  )
					    or isinstance(A		,			torch.LongTensor  )
					):
								raise ValueError(
								    (
								        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
								        """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
								        """ one of the `scheduler.timesteps` as a timestep."""
								    )		,			)

					if not self.is_scale_input_called:
								logger.warning(
								    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
								    """See `StableDiffusionPipeline` for a usage example."""  )

					if isinstance(A		,			torch.Tensor  ):
								lowerCamelCase								=     timestep.to(self.timesteps.device  )

					lowerCamelCase								=     (self.timesteps == timestep).nonzero().item()
					lowerCamelCase								=     self.sigmas[step_index]

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
								lowerCamelCase								=     sample - sigma * model_output
					elif self.config.prediction_type == "v_prediction":
								# * c_out + input * c_skip
								lowerCamelCase								=     model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`'  )

					lowerCamelCase								=     self.sigmas[step_index]
					lowerCamelCase								=     self.sigmas[step_index + 1]
					lowerCamelCase								=     (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
					lowerCamelCase								=     (sigma_to**2 - sigma_up**2) ** 0.5

					# 2. Convert to an ODE derivative
					lowerCamelCase								=     (sample - pred_original_sample) / sigma

					lowerCamelCase								=     sigma_down - sigma

					lowerCamelCase								=     sample + derivative * dt

					lowerCamelCase								=     model_output.device
					lowerCamelCase								=     randn_tensor(model_output.shape		,			dtype=model_output.dtype		,			device=A		,			generator=A  )

					lowerCamelCase								=     prev_sample + noise * sigma_up

					if not return_dict:
								return (prev_sample,)

					return EulerAncestralDiscreteSchedulerOutput(
					    prev_sample=A		,			pred_original_sample=A  )







		def 				__A      (   self		,			A		,			A		,			A		,			)     ->   torch.FloatTensor:







					'''simple docstring'''







					lowerCamelCase								=     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
								lowerCamelCase								=     self.timesteps.to(original_samples.device		,			dtype=torch.floataa  )
								lowerCamelCase								=     timesteps.to(original_samples.device		,			dtype=torch.floataa  )
					else:
								lowerCamelCase								=     self.timesteps.to(original_samples.device  )
								lowerCamelCase								=     timesteps.to(original_samples.device  )

					lowerCamelCase								=     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

					lowerCamelCase								=     original_samples + noise * sigma
					return noisy_samples







		def __len__(   self  )     ->   Union[str, Any]:







					'''simple docstring'''







					return self.config.num_train_timesteps





import unittest

from cer import CER


UpperCAmelCase      :						Union[str, Any]    =		CER()







class 					__lowercase					(       unittest.TestCase     ):



		"""simple docstring"""

		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     ["""White House"""]
					lowerCamelCase								=     ["""white house"""]
					# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.1818181818  ) < 1e-6  )







		def 				__A      (   self  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     ["""were wolf"""]
					lowerCamelCase								=     ["""werewolf"""]
					# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.1111111  ) < 1e-6  )

					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""weae     wolf"""]
					# S = 1, D = 1, I = 0, N = 8, CER = 0.25
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.25  ) < 1e-6  )

					# consecutive whitespaces case 1
					lowerCamelCase								=     ["""were wolf"""]
					lowerCamelCase								=     ["""were               wolf"""]
					# S = 0, D = 0, I = 0, N = 9, CER = 0
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.0  ) < 1e-6  )

					# consecutive whitespaces case 2
					lowerCamelCase								=     ["""were   wolf"""]
					lowerCamelCase								=     ["""were               wolf"""]
					# S = 0, D = 0, I = 0, N = 9, CER = 0
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.0  ) < 1e-6  )







		def 				__A      (   self  )     ->   str:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""weaewolf"""]
					# S = 1, D = 0, I = 0, N = 8, CER = 0.125
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.125  ) < 1e-6  )







		def 				__A      (   self  )     ->   Tuple:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""wereawolf"""]
					# S = 0, D = 1, I = 0, N = 8, CER = 0.125
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.125  ) < 1e-6  )







		def 				__A      (   self  )     ->   List[Any]:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""wereolf"""]
					# S = 0, D = 0, I = 1, N = 8, CER = 0.125
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.125  ) < 1e-6  )







		def 				__A      (   self  )     ->   Tuple:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertEqual(A		,			0.0  )







		def 				__A      (   self  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf""", """I am your father"""]
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertEqual(A		,			0.0  )

					lowerCamelCase								=     ["""werewolf""", """I am your father""", """doge"""]
					lowerCamelCase								=     ["""werxwolf""", """I       am your father""", """doge"""]
					# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.03571428  ) < 1e-6  )







		def 				__A      (   self  )     ->   List[Any]:







					'''simple docstring'''







					lowerCamelCase								=     ["""My hovercraft""", """is full of eels"""]
					lowerCamelCase								=     ["""My hovercraft is full""", """ of eels"""]
					# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
					# whitespace at the front of " of eels" will be strip during preporcessing
					# so need to insert 2 whitespaces
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A		,			concatenate_texts=A  )
					self.assertTrue(abs(char_error_rate - 0.071428  ) < 1e-6  )







		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     ["""我能吞下玻璃而不伤身体"""]
					lowerCamelCase								=     [""" 能吞虾玻璃而 不霜身体啦"""]
					# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.4545454545  ) < 1e-6  )

					lowerCamelCase								=     ["""我能吞下玻璃""", """而不伤身体"""]
					lowerCamelCase								=     ["""我    能 吞 下 玻 璃""", """而不伤身体"""]
					# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.454545454545  ) < 1e-6  )

					lowerCamelCase								=     ["""我能吞下玻璃而不伤身体"""]
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertFalse(A		,			0.0  )







		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     [""""""]
					lowerCamelCase								=     ["""Hypothesis"""]
					with self.assertRaises(A  ):
								cer.compute(predictions=A		,			references=A  )


if __name__ == "__main__":
		unittest.main()

