






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      42
	UpperCamelCase__								=      None



def __A    (      __lowerCAmelCase ,		__lowerCAmelCase=0.9_99 ,		__lowerCAmelCase="cosine" ,		)->  Union[str, Any]:





						"""simple docstring"""



						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

						_UpperCAmelCase									=			[]
						for i in range(__lowerCAmelCase	):
												_UpperCAmelCase									=			i / num_diffusion_timesteps
												_UpperCAmelCase									=			(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 __lowerCamelCase (      snake_case__     ,       snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      1

	@register_to_config
	def __init__(							self       ,    UpperCAmelCase = 1000       ,    UpperCAmelCase = 0.00_01       ,    UpperCAmelCase = 0.02       ,    UpperCAmelCase = "linear"       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    UpperCAmelCase = True       ,    UpperCAmelCase = 0       ,    UpperCAmelCase = "epsilon"       ,    UpperCAmelCase = 1.0       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							if kwargs.get('set_alpha_to_one'       ,    UpperCAmelCase    ) is not None:
													_UpperCAmelCase									=			(
													    'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
													)
													deprecate('set_alpha_to_one'       ,    '1.0.0'       ,    UpperCAmelCase       ,    standard_warn=UpperCAmelCase    )
													_UpperCAmelCase									=			kwargs['set_alpha_to_one']
							if trained_betas is not None:
													_UpperCAmelCase									=			torch.tensor(UpperCAmelCase       ,    dtype=torch.floataa    )
							elif beta_schedule == "linear":
													_UpperCAmelCase									=			torch.linspace(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    dtype=torch.floataa    )
							elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													_UpperCAmelCase									=			(
													    torch.linspace(beta_start**0.5       ,    beta_end**0.5       ,    UpperCAmelCase       ,    dtype=torch.floataa    ) ** 2
													)
							elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													_UpperCAmelCase									=			betas_for_alpha_bar(UpperCAmelCase    )
							else:
													raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

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

							# At every step in inverted ddim, we are looking into the next alphas_cumprod
							# For the final step, there is no next alphas_cumprod, and the index is out of bounds
							# `set_alpha_to_zero` decides whether we set this parameter simply to zero
							# in this case, self.step() just output the predicted noise
							# or whether we use the final alpha of the "non-previous" one.
							_UpperCAmelCase									=			torch.tensor(0.0    ) if set_alpha_to_zero else self.alphas_cumprod[-1]

							# standard deviation of the initial noise distribution
							_UpperCAmelCase									=			1.0

							# setable values
							_UpperCAmelCase									=			None
							_UpperCAmelCase									=			torch.from_numpy(np.arange(0       ,    UpperCAmelCase    ).copy().astype(np.intaa    )    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None    ):
							"""simple docstring"""







							return sample

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None    ):
							"""simple docstring"""







							if num_inference_steps > self.config.num_train_timesteps:
													raise ValueError(
													    F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
													    F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
													    F""" maximal {self.config.num_train_timesteps} timesteps."""    )

							_UpperCAmelCase									=			num_inference_steps
							_UpperCAmelCase									=			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
							_UpperCAmelCase									=			(np.arange(0       ,    UpperCAmelCase    ) * step_ratio).round().copy().astype(np.intaa    )
							_UpperCAmelCase									=			torch.from_numpy(UpperCAmelCase    ).to(UpperCAmelCase    )
							self.timesteps += self.config.steps_offset

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase = 0.0       ,    UpperCAmelCase = False       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			timestep + self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							# change original implementation to exactly match noise levels for analogous forward process
							_UpperCAmelCase									=			self.alphas_cumprod[timestep]
							_UpperCAmelCase									=			(
							    self.alphas_cumprod[prev_timestep]
							    if prev_timestep < self.config.num_train_timesteps
							    else self.final_alpha_cumprod
							)

							_UpperCAmelCase									=			1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							if self.config.prediction_type == "epsilon":
													_UpperCAmelCase									=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
													_UpperCAmelCase									=			model_output
							elif self.config.prediction_type == "sample":
													_UpperCAmelCase									=			model_output
													_UpperCAmelCase									=			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
													_UpperCAmelCase									=			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
													_UpperCAmelCase									=			(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
							else:
													raise ValueError(
													    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
													    ' `v_prediction`'    )

							# 4. Clip or threshold "predicted x_0"
							if self.config.clip_sample:
													_UpperCAmelCase									=			pred_original_sample.clamp(
													    -self.config.clip_sample_range       ,    self.config.clip_sample_range    )

							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_UpperCAmelCase									=			(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_UpperCAmelCase									=			alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if not return_dict:
													return (prev_sample, pred_original_sample)
							return DDIMSchedulerOutput(prev_sample=UpperCAmelCase       ,    pred_original_sample=UpperCAmelCase    )

	def __len__(							self    ):
							"""simple docstring"""







							return self.config.num_train_timesteps



import unittest

from transformers import DonutProcessor


snake_case_			  = '''naver-clova-ix/donut-base'''


class       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):





    def       snake_case_				(      self):
         lowercase__    :   Dict      =       DonutProcessor.from_pretrained(a)





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       {
             'name': 'John Doe',
             'age': '99',
             'city': 'Atlanta',
             'state': 'GA',
             'zip': '30301',
             'phone': '123-4567',
             'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
         }

         lowercase__    :   Tuple      =       (
             '<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
             '<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
             '<s_nicknames><s_nickname>Johnny</s_nickname>'
             '<sep/><s_nickname>JD</s_nickname></s_nicknames>'
         )
         lowercase__    :   str      =       self.processor.tokenajson(a)

         self.assertDictEqual(a						,   a)

