

import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata







def     A_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 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					)]					)


lowerCAmelCase							:						List[str]     			=		"""\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
"""

lowerCAmelCase							:						Optional[int]     			=		"""\
---
---
# Begin of markdown

Some cool dataset card
"""


lowerCAmelCase							:						Tuple     			=		"""\
# Begin of markdown

Some cool dataset card
"""



class 						__lowercase       (				unittest.TestCase			):




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									with tempfile.TemporaryDirectory() as tmp_dir:
															SCREAMING_SNAKE_CASE_:				Tuple							= Path(lowerCAmelCase__) / "README.md"
															with open(lowerCAmelCase__     ,		"w+") as readme_file:
																					readme_file.write(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:				List[str]							= 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__)
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= DatasetMetadata.from_readme(lowerCAmelCase__)
															self.assertDictEqual(lowerCAmelCase__     ,		{})

															with open(lowerCAmelCase__     ,		"w+") as readme_file:
																					readme_file.write(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:				Dict							= DatasetMetadata.from_readme(lowerCAmelCase__)
															self.assertEqual(lowerCAmelCase__     ,		{})




			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= _dedent(
									    "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            ")
									assert DatasetMetadata.from_yaml_string(lowerCAmelCase__)

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

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









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


lowerCAmelCase							:						Union[str, Any]     			=		logging.get_logger(__name__)  # pylint: disable=invalid-name



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




			"""simple docstring"""







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







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase=0.9_9_9       ,			_UpperCAmelCase="cosine"       ,			):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_UpperCAmelCase					):
																		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					):
																		return math.exp(t * -1_2.0					)

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

						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= []
						for i in range(_UpperCAmelCase					):
												SCREAMING_SNAKE_CASE_:				List[str]							= i / num_diffusion_timesteps
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= (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 						__lowercase       (				UpperCAmelCase_	, UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							Tuple    							=  [e.name for e in KarrasDiffusionSchedulers]
			_UpperCAmelCase     :							Optional[Any]    							=  1



			@register_to_config
			def __init__(				self						:  Optional[Any]     ,		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:
															SCREAMING_SNAKE_CASE_:				Tuple							= torch.tensor(lowerCAmelCase__     ,		dtype=torch.floataa)
									elif beta_schedule == "linear":
															SCREAMING_SNAKE_CASE_:				List[str]							= torch.linspace(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		dtype=torch.floataa)
									elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															SCREAMING_SNAKE_CASE_:				str							= (
															    torch.linspace(beta_start**0.5     ,		beta_end**0.5     ,		lowerCAmelCase__     ,		dtype=torch.floataa) ** 2
															)
									elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															SCREAMING_SNAKE_CASE_:				int							= betas_for_alpha_bar(lowerCAmelCase__)
									else:
															raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}")

									SCREAMING_SNAKE_CASE_:				List[Any]							= 1.0 - self.betas
									SCREAMING_SNAKE_CASE_:				Tuple							= torch.cumprod(self.alphas     ,		dim=0)

									SCREAMING_SNAKE_CASE_:				Optional[int]							= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
									SCREAMING_SNAKE_CASE_:				str							= torch.from_numpy(lowerCAmelCase__)

									# setable values
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= None
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= np.linspace(0     ,		num_train_timesteps - 1     ,		lowerCAmelCase__     ,		dtype=lowerCAmelCase__)[::-1].copy()
									SCREAMING_SNAKE_CASE_:				Optional[int]							= torch.from_numpy(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				str							= False



			@property
			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):
									# 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  _SCREAMING_SNAKE_CASE    (				self						:  Any     ,		lowerCAmelCase__						:  torch.FloatTensor     ,		lowerCAmelCase__						:  Union[float, torch.FloatTensor]):
									if isinstance(lowerCAmelCase__     ,		torch.Tensor):
															SCREAMING_SNAKE_CASE_:				Dict							= timestep.to(self.timesteps.device)
									SCREAMING_SNAKE_CASE_:				List[Any]							= (self.timesteps == timestep).nonzero().item()
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= self.sigmas[step_index]
									SCREAMING_SNAKE_CASE_:				List[str]							= sample / ((sigma**2 + 1) ** 0.5)
									SCREAMING_SNAKE_CASE_:				int							= True
									return sample



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  Union[str, torch.device] = None):
									SCREAMING_SNAKE_CASE_:				Dict							= 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":
															SCREAMING_SNAKE_CASE_:				str							= np.linspace(0     ,		self.config.num_train_timesteps - 1     ,		lowerCAmelCase__     ,		dtype=lowerCAmelCase__)[
															    ::-1
															].copy()
									elif self.config.timestep_spacing == "leading":
															SCREAMING_SNAKE_CASE_:				List[Any]							= 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
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= (np.arange(0     ,		lowerCAmelCase__) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__)
															timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= 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
															SCREAMING_SNAKE_CASE_:				int							= (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'.")

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



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]     ,		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):
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= timestep.to(self.timesteps.device)

									SCREAMING_SNAKE_CASE_:				int							= (self.timesteps == timestep).nonzero().item()
									SCREAMING_SNAKE_CASE_:				str							= self.sigmas[step_index]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
															SCREAMING_SNAKE_CASE_:				List[Any]							= sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= 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`")

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.sigmas[step_index]
									SCREAMING_SNAKE_CASE_:				Tuple							= self.sigmas[step_index + 1]
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
									SCREAMING_SNAKE_CASE_:				List[str]							= (sigma_to**2 - sigma_up**2) ** 0.5

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

									SCREAMING_SNAKE_CASE_:				List[str]							= sigma_down - sigma

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= sample + derivative * dt

									SCREAMING_SNAKE_CASE_:				Any							= model_output.device
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= randn_tensor(model_output.shape     ,		dtype=model_output.dtype     ,		device=lowerCAmelCase__     ,		generator=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Dict							= prev_sample + noise * sigma_up

									if not return_dict:
															return (prev_sample,)

									return EulerAncestralDiscreteSchedulerOutput(
									    prev_sample=lowerCAmelCase__     ,		pred_original_sample=lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any     ,		lowerCAmelCase__						:  torch.FloatTensor     ,		lowerCAmelCase__						:  torch.FloatTensor     ,		lowerCAmelCase__						:  torch.FloatTensor     ,		):
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									SCREAMING_SNAKE_CASE_:				Optional[int]							= 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
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.timesteps.to(original_samples.device     ,		dtype=torch.floataa)
															SCREAMING_SNAKE_CASE_:				List[str]							= timesteps.to(original_samples.device     ,		dtype=torch.floataa)
									else:
															SCREAMING_SNAKE_CASE_:				int							= self.timesteps.to(original_samples.device)
															SCREAMING_SNAKE_CASE_:				Dict							= timesteps.to(original_samples.device)

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= [(schedule_timesteps == t).nonzero().item() for t in timesteps]

									SCREAMING_SNAKE_CASE_:				List[Any]							= sigmas[step_indices].flatten()
									while len(sigma.shape) < len(original_samples.shape):
															SCREAMING_SNAKE_CASE_:				int							= sigma.unsqueeze(-1)

									SCREAMING_SNAKE_CASE_:				str							= original_samples + noise * sigma
									return noisy_samples




			def __len__(				self						:  int):
									return self.config.num_train_timesteps






