


import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    min(re.search('''^ +'''	,			_UpperCamelCase ).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(_UpperCamelCase )] )


__a			 =   "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

__a			 =   "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


__a			 =   "\\n# Begin of markdown\n\nSome cool dataset card\n"


class 							lowerCamelCase       (			unittest.TestCase ):


							"""simple docstring"""






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

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

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






							def 					lowerCAmelCase     (						self :     int				):
													UpperCAmelCase__		=    _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(lowerCamelCase_				)

													UpperCAmelCase__		=    _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(lowerCamelCase_				):
																			DatasetMetadata.from_yaml_string(lowerCamelCase_				)

													UpperCAmelCase__		=    _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(lowerCamelCase_				)



"""simple docstring"""







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


__a			 =   logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      42
							SCREAMING_SNAKE_CASE										=      None






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=0.999	,			_UpperCamelCase="cosine"	,			):





						"""simple docstring"""

						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

						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 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      [e.name for e in KarrasDiffusionSchedulers]
							SCREAMING_SNAKE_CASE										=      1






							@register_to_config
							def __init__(						self :     Dict		, lowerCamelCase_ :     int = 1000		, lowerCamelCase_ :     float = 0.0001		, lowerCamelCase_ :     float = 0.02		, lowerCamelCase_ :     str = "linear"		, lowerCamelCase_ :     Optional[Union[np.ndarray, List[float]]] = None		, lowerCamelCase_ :     str = "epsilon"		, lowerCamelCase_ :     str = "linspace"		, lowerCamelCase_ :     int = 0		, ):
													if trained_betas is not None:
																			UpperCAmelCase__		=    torch.tensor(lowerCamelCase_		, dtype=torch.floataa				)
													elif beta_schedule == "linear":
																			UpperCAmelCase__		=    torch.linspace(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, 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		, lowerCamelCase_		, dtype=torch.floataa				) ** 2
																			)
													elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			UpperCAmelCase__		=    betas_for_alpha_bar(lowerCamelCase_				)
													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(lowerCamelCase_				)

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






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													# 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 					lowerCAmelCase     (						self :     Union[str, Any]		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     Union[float, torch.FloatTensor]				):
													if isinstance(lowerCamelCase_		, 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 					lowerCAmelCase     (						self :     List[Any]		, lowerCamelCase_ :     int		, lowerCamelCase_ :     Union[str, torch.device] = None				):
													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		, lowerCamelCase_		, dtype=lowerCamelCase_				)[
																			    ::-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		, lowerCamelCase_				) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_				)
																			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(lowerCamelCase_				)
																			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.interp(lowerCamelCase_		, np.arange(0		, len(lowerCamelCase_				)				)		, lowerCamelCase_				)
													UpperCAmelCase__		=    np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
													UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				).to(device=lowerCamelCase_				)
													if str(lowerCamelCase_				).startswith('''mps'''				):
																			# mps does not support float64
																			UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				).to(lowerCamelCase_		, dtype=torch.floataa				)
													else:
																			UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				).to(device=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     List[str]		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     Union[float, torch.FloatTensor]		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     Optional[torch.Generator] = None		, lowerCamelCase_ :     bool = True		, ):

													if (
													    isinstance(lowerCamelCase_		, lowerCamelCase_				)
													    or isinstance(lowerCamelCase_		, torch.IntTensor				)
													    or isinstance(lowerCamelCase_		, 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(lowerCamelCase_		, 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":
																			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`"""				)

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

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

													UpperCAmelCase__		=    sigma_down - sigma

													UpperCAmelCase__		=    sample + derivative * dt

													UpperCAmelCase__		=    model_output.device
													UpperCAmelCase__		=    randn_tensor(model_output.shape		, dtype=model_output.dtype		, device=lowerCamelCase_		, generator=lowerCamelCase_				)

													UpperCAmelCase__		=    prev_sample + noise * sigma_up

													if not return_dict:
																			return (prev_sample,)

													return EulerAncestralDiscreteSchedulerOutput(
													    prev_sample=lowerCamelCase_		, pred_original_sample=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     int		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     torch.FloatTensor		, ):
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													UpperCAmelCase__		=    self.sigmas.to(device=original_samples.device		, dtype=original_samples.dtype				)
													if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_				):
																			# 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]				):
													return self.config.num_train_timesteps