"""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


_UpperCamelCase       :						int     =   logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 					UpperCAmelCase_     (   UpperCamelCase_):
						lowerCamelCase__		:   torch.FloatTensor
						lowerCamelCase__		:   Optional[torch.FloatTensor]			  =  None





def     a_					(				_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			Dict=0.9_9_9       ,			_lowerCAmelCase  :			Optional[Any]="cosine"       ,			):





							'''simple docstring'''

							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_lowerCAmelCase  :			Dict					):
																					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(_lowerCAmelCase  :			Tuple					):
																					return math.exp(t * -1_2.0					)

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

							lowercase__      :		Optional[int]          =     []
							for i in range(SCREAMING_SNAKE_CASE__					):
														lowercase__      :		Tuple          =     i / num_diffusion_timesteps
														lowercase__      :		Optional[Any]          =     (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__					) / alpha_bar_fn(SCREAMING_SNAKE_CASE__					)       ,			SCREAMING_SNAKE_CASE__					)					)
							return torch.tensor(SCREAMING_SNAKE_CASE__       ,			dtype=torch.floataa					)






class 					UpperCAmelCase_     (   UpperCamelCase_			,							UpperCamelCase_):
						lowerCamelCase__		:   List[Any]			  =  [e.name for e in KarrasDiffusionSchedulers]
						lowerCamelCase__		:   Dict			  =  1


						@register_to_config
						def __init__(       self		,      a = 1_0_0_0		,      a = 0.0_001		,      a = 0.02		,      a = "linear"		,      a = None		,      a = "epsilon"		,      a = "linspace"		,      a = 0		,      )	-> List[Any]:




													if trained_betas is not None:
																				lowercase__      :		str          =     torch.tensor(lowercase_		,      dtype=torch.floataa							)
													elif beta_schedule == "linear":
																				lowercase__      :		str          =     torch.linspace(lowercase_		,      lowercase_		,      lowercase_		,      dtype=torch.floataa							)
													elif beta_schedule == "scaled_linear":
																				# this schedule is very specific to the latent diffusion model.
																				lowercase__      :		int          =     (
																				    torch.linspace(beta_start**0.5		,      beta_end**0.5		,      lowercase_		,      dtype=torch.floataa							) ** 2
																				)
													elif beta_schedule == "squaredcos_cap_v2":
																				# Glide cosine schedule
																				lowercase__      :		Union[str, Any]          =     betas_for_alpha_bar(lowercase_							)
													else:
																				raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""							)

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

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

													# setable values
													lowercase__      :		Optional[int]          =     None
													lowercase__      :		int          =     np.linspace(0		,      num_train_timesteps - 1		,      lowercase_		,      dtype=lowercase_							)[::-1].copy()
													lowercase__      :		Optional[Any]          =     torch.from_numpy(lowercase_							)
													lowercase__      :		Tuple          =     False


						@property
						def 		_UpperCAmelCase	(       self							)	-> List[str]:




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

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


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




													if isinstance(lowercase_		,      torch.Tensor							):
																				lowercase__      :		Union[str, Any]          =     timestep.to(self.timesteps.device							)
													lowercase__      :		Optional[int]          =     (self.timesteps == timestep).nonzero().item()
													lowercase__      :		List[Any]          =     self.sigmas[step_index]
													lowercase__      :		Dict          =     sample / ((sigma**2 + 1) ** 0.5)
													lowercase__      :		Optional[Any]          =     True
													return sample


						def 		_UpperCAmelCase	(       self		,      a		,      a = None							)	-> Union[str, Any]:
													lowercase__      :		Union[str, Any]          =     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":
																				lowercase__      :		Tuple          =     np.linspace(0		,      self.config.num_train_timesteps - 1		,      lowercase_		,      dtype=lowercase_							)[
																				    ::-1
																				].copy()
													elif self.config.timestep_spacing == "leading":
																				lowercase__      :		int          =     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
																				lowercase__      :		List[str]          =     (np.arange(0		,      lowercase_							) * step_ratio).round()[::-1].copy().astype(lowercase_							)
																				timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																				lowercase__      :		Union[str, 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
																				lowercase__      :		List[Any]          =     (np.arange(self.config.num_train_timesteps		,      0		,      -step_ratio							)).round().copy().astype(lowercase_							)
																				timesteps -= 1
													else:
																				raise ValueError(
																				    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."""							)

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


						def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a = None		,      a = True		,      )	-> Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:




													if (
													    isinstance(lowercase_		,      lowercase_							)
													    or isinstance(lowercase_		,      torch.IntTensor							)
													    or isinstance(lowercase_		,      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(lowercase_		,      torch.Tensor							):
																				lowercase__      :		Tuple          =     timestep.to(self.timesteps.device							)

													lowercase__      :		Optional[Any]          =     (self.timesteps == timestep).nonzero().item()
													lowercase__      :		Optional[int]          =     self.sigmas[step_index]

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

													lowercase__      :		int          =     self.sigmas[step_index]
													lowercase__      :		Optional[Any]          =     self.sigmas[step_index + 1]
													lowercase__      :		List[Any]          =     (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
													lowercase__      :		Optional[int]          =     (sigma_to**2 - sigma_up**2) ** 0.5

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

													lowercase__      :		Optional[int]          =     sigma_down - sigma

													lowercase__      :		List[str]          =     sample + derivative * dt

													lowercase__      :		Any          =     model_output.device
													lowercase__      :		List[Any]          =     randn_tensor(model_output.shape		,      dtype=model_output.dtype		,      device=lowercase_		,      generator=lowercase_							)

													lowercase__      :		List[Any]          =     prev_sample + noise * sigma_up

													if not return_dict:
																				return (prev_sample,)

													return EulerAncestralDiscreteSchedulerOutput(
													    prev_sample=lowercase_		,      pred_original_sample=lowercase_							)


						def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      )	-> torch.FloatTensor:
													lowercase__      :		Dict          =     self.sigmas.to(device=original_samples.device		,      dtype=original_samples.dtype							)
													if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_							):
																				# mps does not support float64
																				lowercase__      :		str          =     self.timesteps.to(original_samples.device		,      dtype=torch.floataa							)
																				lowercase__      :		Union[str, Any]          =     timesteps.to(original_samples.device		,      dtype=torch.floataa							)
													else:
																				lowercase__      :		Any          =     self.timesteps.to(original_samples.device							)
																				lowercase__      :		Tuple          =     timesteps.to(original_samples.device							)

													lowercase__      :		Union[str, Any]          =     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

													lowercase__      :		int          =     original_samples + noise * sigma
													return noisy_samples


						def __len__(       self							)	-> Optional[int]:
													return self.config.num_train_timesteps


"""simple docstring"""





import logging
from pathlib import Path
from typing import Any, List, Tuple

import yaml


# loading package files: https://stackoverflow.com/a/20885799
try:
       import importlib.resources as pkg_resources
except ImportError:
       # Try backported to PY<37 `importlib_resources`.
       import importlib_resources as pkg_resources

from . import resources


_UpperCamelCase       :						Optional[int]     =   "https://github.com/huggingface/datasets/tree/main/src/datasets/utils"
_UpperCamelCase       :						Union[str, Any]     =   f'''{BASE_REF_URL}/{__file__}'''
_UpperCamelCase       :						Any     =   logging.getLogger(__name__)





def     a_					(				_lowerCAmelCase  :			str					):





       '''simple docstring'''

       lowercase__      :		Optional[Any]          =     pkg_resources.read_text(_lowerCAmelCase       ,			_lowerCAmelCase					)
       return yaml.safe_load(_lowerCAmelCase					), f"""{BASE_REF_URL}/resources/{resource}"""


_UpperCamelCase   ,				_UpperCamelCase       :						int     =   load_yaml_resource("readme_structure.yaml")

_UpperCamelCase       :						Optional[Any]     =   [
    "[Needs More Information]",
    "[More Information Needed]",
    "(https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)",
]

# Dictionary representation of section/readme, error_list, warning_list
_UpperCamelCase       :						List[Any]     =   Tuple[dict, List[str], List[str]]


class 					UpperCAmelCase_     :


      def __init__(       self		,      a		,      a		,      a = None		,      a = False							)	-> str:
             lowercase__      :		List[str]          =     name
             lowercase__      :		int          =     level
             lowercase__      :		Dict          =     lines
             lowercase__      :		Optional[int]          =     ''
             lowercase__      :		List[str]          =     True
             lowercase__      :		Union[str, Any]          =     {}
             lowercase__      :		Any          =     []
             lowercase__      :		Optional[int]          =     []
             if self.lines is not None:
                    self.parse(suppress_parsing_errors=a							)


      def 		_UpperCAmelCase	(       self		,      a = False							)	-> Any:
             lowercase__      :		List[str]          =     ''
             lowercase__      :		int          =     []
             lowercase__      :		Any          =     False
             for line in self.lines:
                    if line.strip(' \n'							) == "":
                           continue
                    elif line.strip(' \n'							)[:3] == "```":
                           lowercase__      :		Optional[Any]          =     not code_start
                    elif line.split()[0] == self.level + "#" and not code_start:
                           if current_sub_level != "":
                                  lowercase__      :		Tuple          =     Section(a		,      self.level + '#'		,      a							)
                                  lowercase__      :		str          =     []
                           else:
                                  if current_lines != []:
                                         self.text += "".join(a							).strip()
                                         if self.text != "" and self.text not in FILLER_TEXT:
                                                lowercase__      :		List[Any]          =     False
                                         lowercase__      :		List[str]          =     []

                           lowercase__      :		Dict          =     ' '.join(line.split()[1:]							).strip(' \n'							)
                    else:
                           current_lines.append(a							)
             else:
                    if current_sub_level != "":
                           if current_sub_level in self.content:
                                  self.parsing_error_list.append(
                                      f"""Multiple sections with the same heading `{current_sub_level}` have been found. Please keep only one of these sections."""							)
                           lowercase__      :		Tuple          =     Section(a		,      self.level + '#'		,      a							)
                    else:
                           if current_lines != []:
                                  self.text += "".join(a							).strip()
                                  if self.text != "" and self.text not in FILLER_TEXT:
                                         lowercase__      :		Dict          =     False

             if self.level == "" and not suppress_parsing_errors:
                    if self.parsing_error_list != [] or self.parsing_warning_list != []:
                           lowercase__      :		Optional[Any]          =     '\n'.join('-\t' + x for x in self.parsing_error_list + self.parsing_warning_list							)
                           lowercase__      :		int          =     f"""The following issues were found while parsing the README at `{self.name}`:\n""" + errors
                           raise ValueError(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> ReadmeValidatorOutput:
             lowercase__      :		Tuple          =     []
             lowercase__      :		int          =     []
             if structure["allow_empty"] is False:
                    # If content is expected
                    if self.is_empty_text and self.content == {}:
                           # If no content is found, mention it in the error_list
                           error_list.append(f"""Expected some content in section `{self.name}` but it is empty."""							)

             if structure["allow_empty_text"] is False:
                    # If some text is expected
                    if self.is_empty_text:
                           # If no text is found, mention it in the error_list
                           error_list.append(
                               f"""Expected some text in section `{self.name}` but it is empty (text in subsections are ignored)."""							)
        # Subsections Validation
             if structure["subsections"] is not None:
                    # If subsections are expected
                    if self.content == {}:
                           # If no subsections are present
                           lowercase__      :		str          =     [subsection['name'] for subsection in structure['subsections']]
                           # Mention the expected values in the error_list
                           error_list.append(
                               f"""Section `{self.name}` expected the following subsections: {", ".join(["`"+x+"`" for x in values]							)}. Found 'None'."""							)
                    else:
                           # If some subsections are present
                           lowercase__      :		Union[str, Any]          =     [subsection['name'] for subsection in structure['subsections']]
                           lowercase__      :		Any          =     False
                           for idx, name in enumerate(a							):
                                  if name not in self.content:
                                         # If the expected subsection is not present
                                         error_list.append(f"""Section `{self.name}` is missing subsection: `{name}`."""							)
                                         lowercase__      :		str          =     True
                                  else:
                                         # If the subsection is present, validate subsection, return the result
                                         # and concat the errors from subsection to section error_list

                                         # Skip sublevel validation if current level is `###`
                                         if self.level == "###":
                                                continue
                                         else:
                                                lowercase__   ,				lowercase__   ,				lowercase__      :		Optional[Any]          =     self.content[name].validate(
                                                    structure['subsections'][idx]							)
                                         error_list += subsec_error_list
                                         warning_list += subsec_warning_list

                           if has_missing_subsections:  # we only allow to have extra subsections if all the other ones are here
                                  for name in self.content:
                                         if name not in structure_names:
                                                # If an extra subsection is present
                                                warning_list.append(
                                                    f"""`{self.name}` has an extra subsection: `{name}`. Skipping further validation checks for this subsection as expected structure is unknown."""							)
             if error_list:
                    # If there are errors, do not return the dictionary as it is invalid
                    return {}, error_list, warning_list
             else:
                    return self.to_dict(), error_list, warning_list


      def 		_UpperCAmelCase	(       self							)	-> dict:
             return {
                 "name": self.name,
                 "text": self.text,
                 "is_empty_text": self.is_empty_text,
                 "subsections": [value.to_dict() for value in self.content.values()],
             }





class 					UpperCAmelCase_     (   _a):  # Level 0


      def __init__(       self		,      a		,      a		,      a = None		,      a = False							)	-> Any:
             super().__init__(name=a		,      level=''							)  # Not using lines here as we need to use a child class parse
             lowercase__      :		List[Any]          =     structure
             lowercase__      :		Dict          =     -2
             lowercase__      :		Union[str, Any]          =     0
             lowercase__      :		Dict          =     lines
             if self.lines is not None:
                    self.parse(suppress_parsing_errors=a							)


      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             if self.structure is None:
                    lowercase__   ,				lowercase__   ,				lowercase__      :		Any          =     self._validate(a							)
             else:
                    lowercase__   ,				lowercase__   ,				lowercase__      :		Dict          =     self._validate(self.structure							)
             if error_list != [] or warning_list != []:
                    lowercase__      :		Optional[Any]          =     '\n'.join(['-\t' + x for x in error_list + warning_list]							)
                    lowercase__      :		Union[str, Any]          =     f"""The following issues were found for the README at `{self.name}`:\n""" + errors
                    raise ValueError(a							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      a		,      a = None		,      a = False							)	-> Tuple:
             with open(a		,      encoding='utf-8'							) as f:
                    lowercase__      :		List[Any]          =     f.readlines()
             return cls(a		,      a		,      a		,      suppress_parsing_errors=a							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      a		,      a = None		,      a = "root"		,      a = False							)	-> int:
             lowercase__      :		Optional[Any]          =     string.split('\n'							)
             return cls(a		,      a		,      a		,      suppress_parsing_errors=a							)


      def 		_UpperCAmelCase	(       self		,      a = False							)	-> List[Any]:
             # Skip Tags
             lowercase__      :		Tuple          =     0

             for line in self.lines:
                    self.yaml_tags_line_count += 1
                    if line.strip(' \n'							) == "---":
                           self.tag_count += 1
                           if self.tag_count == 2:
                                  break
                    line_count += 1
             if self.tag_count == 2:
                    lowercase__      :		Optional[int]          =     self.lines[line_count + 1 :]  # Get the last + 1 th item.
             else:
                    lowercase__      :		List[str]          =     self.lines[self.tag_count :]
             super().parse(suppress_parsing_errors=a							)


      def __str__(       self							)	-> Any:
             return str(self.to_dict()							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> str:
             lowercase__      :		Any          =     []
             lowercase__      :		Optional[int]          =     []
             if self.yaml_tags_line_count == 0:
                    warning_list.append('Empty YAML markers are present in the README.'							)
             elif self.tag_count == 0:
                    warning_list.append('No YAML markers are present in the README.'							)
             elif self.tag_count == 1:
                    warning_list.append('Only the start of YAML tags present in the README.'							)
             # Check how many first level sections are present.
             lowercase__      :		int          =     len(self.content.keys()							)
             if num_first_level_keys > 1:
                    # If more than one, add to the error list, continue
                    error_list.append(
                        f"""The README has several first-level headings: {", ".join(["`"+x+"`" for x in list(self.content.keys()							)]							)}. Only one heading is expected. Skipping further validation for this README."""							)
             elif num_first_level_keys < 1:
                    # If less than one, append error.
                    error_list.append(
                        'The README has no first-level headings. One heading is expected. Skipping further validation for this README.'							)

             else:
                    # If one exactly
                    lowercase__      :		Dict          =     list(self.content.keys()							)[0]  # Get the key
                    if start_key.startswith('Dataset Card for'							):  # Check correct start
                           # If the starting is correct, validate all the sections
                           lowercase__   ,				lowercase__   ,				lowercase__      :		Any          =     self.content[start_key].validate(
                               readme_structure['subsections'][0]							)
                           error_list += sec_error_list
                           warning_list += sec_warning_list
                    else:
                           # If not found, append error
                           error_list.append(
                               'No first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README.'							)
             if error_list:
                    # If there are errors, do not return the dictionary as it is invalid
                    return {}, error_list, warning_list
             else:
                    return self.to_dict(), error_list, warning_list


if __name__ == "__main__":
       from argparse import ArgumentParser

       _UpperCamelCase       :						Tuple     =   ArgumentParser(usage="Validate the content (excluding YAML tags) of a README.md file.")
       ap.add_argument("readme_filepath")
       _UpperCamelCase       :						str     =   ap.parse_args()
       _UpperCamelCase       :						Optional[int]     =   Path(args.readme_filepath)
       _UpperCamelCase       :						Dict     =   ReadMe.from_readme(readme_filepath)

