


'''simple docstring'''

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

import numpy as np
import torch
from scipy import integrate

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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
 _lowerCamelCase :					torch.FloatTensor
 _lowerCamelCase :					Optional[torch.FloatTensor]    					=					None







def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				List[str]=0.9_9_9    ,		_UpperCamelCase      :				Optional[int]="cosine"    ,		)		->					Union[str, Any]:





   '''simple docstring'''







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(_UpperCamelCase      :				str   ):
         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      :				List[str]   ):
         return math.exp(t * -1_2.0   )

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

   UpperCamelCase__ 						=						[]
   for i in range(_UpperCamelCase   ):
      UpperCamelCase__ 						=						i / num_diffusion_timesteps
      UpperCamelCase__ 						=						(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__):
 _lowerCamelCase :					Union[str, Any]    					=					[e.name for e in KarrasDiffusionSchedulers]
 _lowerCamelCase :					List[Any]    					=					1

 @register_to_config
 def __init__( self :	Union[str, Any],					a_ :	int = 1000,					a_ :	float = 0.0_001,					a_ :	float = 0.02,					a_ :	str = "linear",					a_ :	Optional[Union[np.ndarray, List[float]]] = None,					a_ :	Optional[bool] = False,					a_ :	str = "epsilon",					a_ :	str = "linspace",					a_ :	int = 0,					):





    """simple docstring"""



    if trained_betas is not None:
       UpperCamelCase__ 						=						torch.tensor(a_,					dtype=torch.floataa					)
    elif beta_schedule == "linear":
       UpperCamelCase__ 						=						torch.linspace(a_,					a_,					a_,					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,					a_,					dtype=torch.floataa					) ** 2
       )
    elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       UpperCamelCase__ 						=						betas_for_alpha_bar(a_					)
    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					)

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

    # setable values
    UpperCamelCase__ 						=						None
    UpperCamelCase__ 						=						use_karras_sigmas
    self.set_timesteps(a_,					a_					)
    UpperCamelCase__ 						=						[]
    UpperCamelCase__ 						=						False

 @property
 def 		lowercase_						( self :	List[str]					):





    """simple docstring"""



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

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

 def 		lowercase_						( self :	Optional[Any],					a_ :	torch.FloatTensor,					a_ :	Union[float, torch.FloatTensor]					):





    """simple docstring"""



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

 def 		lowercase_						( self :	List[Any],					a_ :	Optional[Any],					a_ :	Dict,					a_ :	Tuple					):





    """simple docstring"""



    def lms_derivative(a_ :	int					):
       UpperCamelCase__ 						=						1.0
       for k in range(a_					):
          if current_order == k:
             continue
          prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
       return prod

    UpperCamelCase__ 						=						integrate.quad(a_,					self.sigmas[t],					self.sigmas[t + 1],					epsrel=1e-4					)[0]

    return integrated_coeff

 def 		lowercase_						( self :	str,					a_ :	int,					a_ :	Union[str, torch.device] = None					):





    """simple docstring"""



    UpperCamelCase__ 						=						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":
       UpperCamelCase__ 						=						np.linspace(0,					self.config.num_train_timesteps - 1,					a_,					dtype=a_					)[
           ::-1
       ].copy()
    elif self.config.timestep_spacing == "leading":
       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,					a_					) * step_ratio).round()[::-1].copy().astype(a_					)
       timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
       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(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\'.'					)

    UpperCamelCase__ 						=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
    UpperCamelCase__ 						=						np.log(a_					)
    UpperCamelCase__ 						=						np.interp(a_,					np.arange(0,					len(a_					)					),					a_					)

    if self.use_karras_sigmas:
       UpperCamelCase__ 						=						self._convert_to_karras(in_sigmas=a_					)
       UpperCamelCase__ 						=						np.array([self._sigma_to_t(a_,					a_					) for sigma in sigmas]					)

    UpperCamelCase__ 						=						np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)

    UpperCamelCase__ 						=						torch.from_numpy(a_					).to(device=a_					)
    if str(a_					).startswith("mps"					):
       # mps does not support float64
       UpperCamelCase__ 						=						torch.from_numpy(a_					).to(a_,					dtype=torch.floataa					)
    else:
       UpperCamelCase__ 						=						torch.from_numpy(a_					).to(device=a_					)

    UpperCamelCase__ 						=						[]

 def 		lowercase_						( self :	Optional[int],					a_ :	Tuple,					a_ :	Optional[Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						np.log(a_					)

    # get distribution
    UpperCamelCase__ 						=						log_sigma - log_sigmas[:, np.newaxis]

    # get sigmas range
    UpperCamelCase__ 						=						np.cumsum((dists >= 0),					axis=0					).argmax(axis=0					).clip(max=log_sigmas.shape[0] - 2					)
    UpperCamelCase__ 						=						low_idx + 1

    UpperCamelCase__ 						=						log_sigmas[low_idx]
    UpperCamelCase__ 						=						log_sigmas[high_idx]

    # interpolate sigmas
    UpperCamelCase__ 						=						(low - log_sigma) / (low - high)
    UpperCamelCase__ 						=						np.clip(a_,					0,					1					)

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

 def 		lowercase_						( self :	List[str],					a_ :	torch.FloatTensor					):





    """simple docstring"""



    UpperCamelCase__ 						=						in_sigmas[-1].item()
    UpperCamelCase__ 						=						in_sigmas[0].item()

    UpperCamelCase__ 						=						7.0  # 7.0 is the value used in the paper
    UpperCamelCase__ 						=						np.linspace(0,					1,					self.num_inference_steps					)
    UpperCamelCase__ 						=						sigma_min ** (1 / rho)
    UpperCamelCase__ 						=						sigma_max ** (1 / rho)
    UpperCamelCase__ 						=						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas

 def 		lowercase_						( self :	Union[str, Any],					a_ :	torch.FloatTensor,					a_ :	Union[float, torch.FloatTensor],					a_ :	torch.FloatTensor,					a_ :	int = 4,					a_ :	bool = True,					):





    """simple docstring"""



    if not self.is_scale_input_called:
       warnings.warn(
           "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					):
       UpperCamelCase__ 						=						timestep.to(self.timesteps.device					)
    UpperCamelCase__ 						=						(self.timesteps == timestep).nonzero().item()
    UpperCamelCase__ 						=						self.sigmas[step_index]

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
       UpperCamelCase__ 						=						sample - sigma * model_output
    elif self.config.prediction_type == "v_prediction":
       # * c_out + input * c_skip
       UpperCamelCase__ 						=						model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
    elif self.config.prediction_type == "sample":
       UpperCamelCase__ 						=						model_output
    else:
       raise ValueError(
           f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'					)

    # 2. Convert to an ODE derivative
    UpperCamelCase__ 						=						(sample - pred_original_sample) / sigma
    self.derivatives.append(a_					)
    if len(self.derivatives					) > order:
       self.derivatives.pop(0					)

    # 3. Compute linear multistep coefficients
    UpperCamelCase__ 						=						min(step_index + 1,					a_					)
    UpperCamelCase__ 						=						[self.get_lms_coefficient(a_,					a_,					a_					) for curr_order in range(a_					)]

    # 4. Compute previous sample based on the derivatives path
    UpperCamelCase__ 						=						sample + sum(
        coeff * derivative for coeff, derivative in zip(a_,					reversed(self.derivatives					)					)					)

    if not return_dict:
       return (prev_sample,)

    return LMSDiscreteSchedulerOutput(prev_sample=a_,					pred_original_sample=a_					)

 def 		lowercase_						( self :	int,					a_ :	torch.FloatTensor,					a_ :	torch.FloatTensor,					a_ :	torch.FloatTensor,					):





    """simple docstring"""



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

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

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

    UpperCamelCase__ 						=						original_samples + noise * sigma
    return noisy_samples

 def __len__( self :	Optional[int]					):





    """simple docstring"""



    return self.config.num_train_timesteps



'''simple docstring'''

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast







@require_vision
class  UpperCAmelCase (       unittest.TestCase):

 def 		lowercase_						( self :	Optional[Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						tempfile.mkdtemp()

    UpperCamelCase__ 						=						CLIPImageProcessor()
    UpperCamelCase__ 						=						BertTokenizer.from_pretrained(
        "hf-internal-testing/tiny-random-BertModel",					model_input_names=["input_ids", "attention_mask"]					)

    UpperCamelCase__ 						=						GitProcessor(a_,					a_					)

    processor.save_pretrained(self.tmpdirname					)

 def 		lowercase_						( self :	Union[str, Any],					**a_ :	str					):





    """simple docstring"""



    return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).tokenizer

 def 		lowercase_						( self :	Union[str, Any],					**a_ :	List[str]					):





    """simple docstring"""



    return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).image_processor

 def 		lowercase_						( self :	int					):





    """simple docstring"""



    shutil.rmtree(self.tmpdirname					)

 def 		lowercase_						( self :	str					):





    """simple docstring"""



    UpperCamelCase__ 						=						[np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta					)]

    UpperCamelCase__ 						=						[Image.fromarray(np.moveaxis(a_,					0,					-1					)					) for x in image_inputs]

    return image_inputs

 def 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						GitProcessor(tokenizer=self.get_tokenizer(),					image_processor=self.get_image_processor()					)
    processor.save_pretrained(self.tmpdirname					)

    UpperCamelCase__ 						=						self.get_tokenizer(bos_token="(BOS)",					eos_token="(EOS)"					)
    UpperCamelCase__ 						=						self.get_image_processor(do_normalize=a_,					padding_value=1.0					)

    UpperCamelCase__ 						=						GitProcessor.from_pretrained(
        self.tmpdirname,					bos_token="(BOS)",					eos_token="(EOS)",					do_normalize=a_,					padding_value=1.0					)

    self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()					)
    self.assertIsInstance(processor.tokenizer,					a_					)

    self.assertEqual(processor.image_processor.to_json_string(),					image_processor_add_kwargs.to_json_string()					)
    self.assertIsInstance(processor.image_processor,					a_					)

 def 		lowercase_						( self :	int					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						self.prepare_image_inputs()

    UpperCamelCase__ 						=						image_processor(a_,					return_tensors="np"					)
    UpperCamelCase__ 						=						processor(images=a_,					return_tensors="np"					)

    for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum(),					input_processor[key].sum(),					delta=1e-2					)

 def 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						"lower newer"

    UpperCamelCase__ 						=						processor(text=a_					)

    UpperCamelCase__ 						=						tokenizer(a_,					return_token_type_ids=a_					)

    for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key],					encoded_processor[key]					)

 def 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						"lower newer"
    UpperCamelCase__ 						=						self.prepare_image_inputs()

    UpperCamelCase__ 						=						processor(text=a_,					images=a_					)

    self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)

    # test if it raises when no input is passed
    with pytest.raises(a_					):
       processor()

 def 		lowercase_						( self :	Union[str, Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    UpperCamelCase__ 						=						processor.batch_decode(a_					)
    UpperCamelCase__ 						=						tokenizer.batch_decode(a_					)

    self.assertListEqual(a_,					a_					)

 def 		lowercase_						( self :	Union[str, Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						"lower newer"
    UpperCamelCase__ 						=						self.prepare_image_inputs()

    UpperCamelCase__ 						=						processor(text=a_,					images=a_					)

    # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
    self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)