





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       :					Optional[Any]     					=     logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 			A_ (a_    ):
	UpperCAmelCase__      =    42
	UpperCAmelCase__      =    None





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__=0.999    ,    UpperCamelCase__="cosine"    ,    )						->       Tuple:







			'''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 			A_ (a_  ,							a_    ):
	UpperCAmelCase__      =    [e.name for e in KarrasDiffusionSchedulers]
	UpperCAmelCase__      =    1
	@register_to_config
	def __init__(					self							,      _A = 1_0_0_0							,      _A = 0.00_01							,      _A = 0.02							,      _A = "linear"							,      _A = None							,      _A = "epsilon"							,      _A = "linear"							,      _A = False							,      _A = "linspace"							,      _A = 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     	=       np.linspace(0							,      num_train_timesteps - 1							,      _A							,      dtype=_A	)[::-1].copy()
				UpperCAmelCase     	=       torch.from_numpy(_A	)
				UpperCAmelCase     	=       False
				UpperCAmelCase     	=       use_karras_sigmas
	@property
	def   _lowercase       (					self	):






				'''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							,      _A							,      _A	):






				'''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							,      _A							,      _A = 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	)

				if self.config.interpolation_type == "linear":
							UpperCAmelCase     	=       np.interp(_A							,      np.arange(0							,      len(_A	)	)							,      _A	)
				elif self.config.interpolation_type == "log_linear":
							UpperCAmelCase     	=       torch.linspace(np.log(sigmas[-1]	)							,      np.log(sigmas[0]	)							,      num_inference_steps + 1	).exp()
				else:
							raise ValueError(
							    F"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
							    ''' \'linear\' or \'log_linear\''''	)

				if self.use_karras_sigmas:
							UpperCAmelCase     	=       self._convert_to_karras(in_sigmas=_A							,      num_inference_steps=self.num_inference_steps	)
							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	)
	def   _lowercase       (					self							,      _A							,      _A	):






				'''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							,      _A							,      _A	):






				'''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							,      _A	)
				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							,      _A							,      _A							,      _A							,      _A = 0.0							,      _A = 0.0							,      _A = float('''inf'''	)							,      _A = 1.0							,      _A = None							,      _A = True							,      ):






				'''simple docstring'''







				if (
				    isinstance(_A							,      _A	)
				    or isinstance(_A							,      torch.IntTensor	)
				    or isinstance(_A							,      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(_A							,      torch.Tensor	):
							UpperCAmelCase     	=       timestep.to(self.timesteps.device	)

				UpperCAmelCase     	=       (self.timesteps == timestep).nonzero().item()
				UpperCAmelCase     	=       self.sigmas[step_index]

				UpperCAmelCase     	=       min(s_churn / (len(self.sigmas	) - 1)							,      2**0.5 - 1	) if s_tmin <= sigma <= s_tmax else 0.0

				UpperCAmelCase     	=       randn_tensor(
				    model_output.shape							,      dtype=model_output.dtype							,      device=model_output.device							,      generator=_A	)

				UpperCAmelCase     	=       noise * s_noise
				UpperCAmelCase     	=       sigma * (gamma + 1)

				if gamma > 0:
							UpperCAmelCase     	=       sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				# NOTE: "original_sample" should not be an expected prediction_type but is left in for
				# backwards compatibility
				if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
							UpperCAmelCase     	=       model_output
				elif self.config.prediction_type == "epsilon":
							UpperCAmelCase     	=       sample - sigma_hat * 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))
				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_hat

				UpperCAmelCase     	=       self.sigmas[step_index + 1] - sigma_hat

				UpperCAmelCase     	=       sample + derivative * dt

				if not return_dict:
							return (prev_sample,)

				return EulerDiscreteSchedulerOutput(prev_sample=_A							,      pred_original_sample=_A	)
	def   _lowercase       (					self							,      _A							,      _A							,      _A							,      ):






				'''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	):






				'''simple docstring'''







				return self.config.num_train_timesteps










import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


__A       :					List[Any]     					=     get_logger(__name__)


if TYPE_CHECKING:
				from .builder import DatasetBuilder





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__ = None				)						->       str:







			'''simple docstring'''

			UpperCAmelCase     	=       importlib.import_module(UpperCamelCase__				)

			# TODO(QL): always update the module to add subsequent new authentication
			if hasattr(UpperCamelCase__    ,    '''_patched_for_streaming'''				) and module._patched_for_streaming:
						return

			def wrap_auth(UpperCamelCase__				):
						@wraps(UpperCamelCase__				)
						def wrapper(*UpperCamelCase__    ,    **UpperCamelCase__				):
									return function(*UpperCamelCase__    ,    use_auth_token=UpperCamelCase__    ,    **UpperCamelCase__				)

						UpperCAmelCase     	=       '''wrap_auth'''
						return wrapper

			# open files in a streaming fashion
			patch_submodule(UpperCamelCase__    ,    '''open'''    ,    wrap_auth(UpperCamelCase__				)				).start()
			patch_submodule(UpperCamelCase__    ,    '''os.listdir'''    ,    wrap_auth(UpperCamelCase__				)				).start()
			patch_submodule(UpperCamelCase__    ,    '''os.walk'''    ,    wrap_auth(UpperCamelCase__				)				).start()
			patch_submodule(UpperCamelCase__    ,    '''glob.glob'''    ,    wrap_auth(UpperCamelCase__				)				).start()
			# allow to navigate in remote zip files
			patch_submodule(UpperCamelCase__    ,    '''os.path.join'''    ,    UpperCamelCase__				).start()
			patch_submodule(UpperCamelCase__    ,    '''os.path.dirname'''    ,    UpperCamelCase__				).start()
			patch_submodule(UpperCamelCase__    ,    '''os.path.basename'''    ,    UpperCamelCase__				).start()
			patch_submodule(UpperCamelCase__    ,    '''os.path.relpath'''    ,    UpperCamelCase__				).start()
			patch_submodule(UpperCamelCase__    ,    '''os.path.split'''    ,    UpperCamelCase__				).start()
			patch_submodule(UpperCamelCase__    ,    '''os.path.splitext'''    ,    UpperCamelCase__				).start()
			# allow checks on paths
			patch_submodule(UpperCamelCase__    ,    '''os.path.exists'''    ,    wrap_auth(UpperCamelCase__				)				).start()
			patch_submodule(UpperCamelCase__    ,    '''os.path.isdir'''    ,    wrap_auth(UpperCamelCase__				)				).start()
			patch_submodule(UpperCamelCase__    ,    '''os.path.isfile'''    ,    wrap_auth(UpperCamelCase__				)				).start()
			patch_submodule(UpperCamelCase__    ,    '''os.path.getsize'''    ,    wrap_auth(UpperCamelCase__				)				).start()
			patch_submodule(UpperCamelCase__    ,    '''pathlib.Path'''    ,    UpperCamelCase__				).start()
			# file readers
			patch_submodule(UpperCamelCase__    ,    '''gzip.open'''    ,    wrap_auth(UpperCamelCase__				)				).start()
			patch_submodule(UpperCamelCase__    ,    '''numpy.load'''    ,    wrap_auth(UpperCamelCase__				)				).start()
			patch_submodule(UpperCamelCase__    ,    '''pandas.read_csv'''    ,    wrap_auth(UpperCamelCase__				)    ,    attrs=['''__version__''']				).start()
			patch_submodule(UpperCamelCase__    ,    '''pandas.read_excel'''    ,    wrap_auth(UpperCamelCase__				)    ,    attrs=['''__version__''']				).start()
			patch_submodule(UpperCamelCase__    ,    '''scipy.io.loadmat'''    ,    wrap_auth(UpperCamelCase__				)    ,    attrs=['''__version__''']				).start()
			patch_submodule(UpperCamelCase__    ,    '''xml.etree.ElementTree.parse'''    ,    wrap_auth(UpperCamelCase__				)				).start()
			patch_submodule(UpperCamelCase__    ,    '''xml.dom.minidom.parse'''    ,    wrap_auth(UpperCamelCase__				)				).start()
			UpperCAmelCase     	=       True






def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__				)						->       Dict:







			'''simple docstring'''

			extend_module_for_streaming(builder.__module__    ,    use_auth_token=builder.use_auth_token				)
			# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
			if not builder.__module__.startswith('''datasets.'''				):  # check that it's not a packaged builder like csv
						for imports in get_imports(inspect.getfile(builder.__class__				)				):
									if imports[0] == "internal":
												UpperCAmelCase     	=       imports[1]
												UpperCAmelCase     	=       '''.'''.join(builder.__module__.split('''.'''				)[:-1] + [internal_import_name]				)
												extend_module_for_streaming(UpperCamelCase__    ,    use_auth_token=builder.use_auth_token				)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
			from .builder import DatasetBuilder

			UpperCAmelCase     	=       [
			    cls.__module__
			    for cls in type(UpperCamelCase__				).__mro__[1:]  # make sure it's not the same module we've already patched
			    if issubclass(cls    ,    UpperCamelCase__				) and cls.__module__ != DatasetBuilder.__module__
			]  # check it's not a standard builder from datasets.builder
			for module in parent_builder_modules:
						extend_module_for_streaming(UpperCamelCase__    ,    use_auth_token=builder.use_auth_token				)



