

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 						__magic_name__      (							_A							):

   '''simple docstring'''




   lowerCamelCase__			:     List[str]    =       42
   lowerCamelCase__			:     Dict    =       None





def       UpperCAmelCase__    (      _A				:			Optional[int]     ,				_A				:			List[str]=0.999     ,				_A				:			int="cosine"     ,				):





  '''simple docstring'''







  if alpha_transform_type == "cosine":

    def alpha_bar_fn(_A				:			str							):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(_A				:			List[str]							):
      return math.exp(t * -12.0							)

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

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







class 						__magic_name__      (							_A		,	_A							):

   '''simple docstring'''




   lowerCamelCase__			:     List[Any]    =       [e.name for e in KarrasDiffusionSchedulers]
   lowerCamelCase__			:     Dict    =       1



   @register_to_config
   def __init__(    self,   lowercase_ = 1000,   lowercase_ = 0.0001,   lowercase_ = 0.02,   lowercase_ = "linear",   lowercase_ = None,   lowercase_ = False,   lowercase_ = "epsilon",   lowercase_ = "linspace",   lowercase_ = 0,   )							->   Any:






     """simple docstring"""






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

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

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



   @property
   def 			_UpperCAmelCase			(    self	)							->   Tuple:






     """simple docstring"""






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

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   torch.FloatTensor:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_	)							->   Any:






     """simple docstring"""






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

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

     return integrated_coeff



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = None	)							->   Dict:






     """simple docstring"""






     a__  =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":
       a__  =np.linspace(0,   self.config.num_train_timesteps - 1,   UpperCamelCase__,   dtype=UpperCamelCase__	)[
           ::-1
       ].copy()
     elif self.config.timestep_spacing == "leading":
       a__  =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
       a__  =(np.arange(0,   UpperCamelCase__	) * step_ratio).round()[::-1].copy().astype(UpperCamelCase__	)
       timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
       a__  =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
       a__  =(np.arange(self.config.num_train_timesteps,   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__  =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
     a__  =np.log(UpperCamelCase__	)
     a__  =np.interp(UpperCamelCase__,   np.arange(0,   len(UpperCamelCase__	)	),   UpperCamelCase__	)

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

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

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

     a__  =[]



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






     """simple docstring"""






     a__  =np.log(UpperCamelCase__	)

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

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

     a__  =log_sigmas[low_idx]
     a__  =log_sigmas[high_idx]

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

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



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   torch.FloatTensor:






     """simple docstring"""






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

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = 4,   lowercase_ = True,   )							->   Union[LMSDiscreteSchedulerOutput, Tuple]:






     """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(UpperCamelCase__,   torch.Tensor	):
       a__  =timestep.to(self.timesteps.device	)
     a__  =(self.timesteps == timestep).nonzero().item()
     a__  =self.sigmas[step_index]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
       a__  =sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
       # * c_out + input * c_skip
       a__  =model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     elif self.config.prediction_type == "sample":
       a__  =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
     a__  =(sample - pred_original_sample) / sigma
     self.derivatives.append(UpperCamelCase__	)
     if len(self.derivatives	) > order:
       self.derivatives.pop(0	)

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

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

     if not return_dict:
       return (prev_sample,)

     return LMSDiscreteSchedulerOutput(prev_sample=UpperCamelCase__,   pred_original_sample=UpperCamelCase__	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   )							->   torch.FloatTensor:






     """simple docstring"""






     a__  =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__  =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__  =[(schedule_timesteps == t).nonzero().item() 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	)							->   Dict:






     """simple docstring"""






     return self.config.num_train_timesteps


import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata




def       UpperCAmelCase__    (      _A				:			str							):





  '''simple docstring'''







  a__  =min(re.search('''^ +'''     ,				_A							).end() if t.startswith(''' '''							) else 0 for t in string.splitlines()							)
  return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_A							)]							)


lowerCamelCase									= '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

lowerCamelCase									= '''\
---
---
# Begin of markdown

Some cool dataset card
'''


lowerCamelCase									= '''\
# Begin of markdown

Some cool dataset card
'''







class 						__magic_name__      (							unittest.TestCase							):

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmp_dir:
       a__  =Path(lowercase_	) / '''README.md'''
       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertDictEqual(lowercase_,   {'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}	)

       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertDictEqual(lowercase_,   {}	)

       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertEqual(lowercase_,   {}	)



   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            '''	)
     assert DatasetMetadata.from_yaml_string(lowercase_	)

     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            '''	)
     with self.assertRaises(lowercase_	):
       DatasetMetadata.from_yaml_string(lowercase_	)

     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            '''	)
     assert DatasetMetadata.from_yaml_string(lowercase_	)
