




'''simple docstring'''







import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = 42
					lowercase__    = 42






def 		_lowercase		(	lowercase_						,							lowercase_=0.999						,							lowercase_="cosine"						,							):



		'''simple docstring'''







		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

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

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

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





class      lowerCamelCase_							(       UpperCAmelCase_,       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = 1






					@register_to_config
					def __init__(    self,  __UpperCamelCase = 1_000,  __UpperCamelCase = 0.00_01,  __UpperCamelCase = 0.02,  __UpperCamelCase = "linear",  __UpperCamelCase = 0.0,  __UpperCamelCase = None,  __UpperCamelCase = True,  ):
							if trained_betas is not None:
									_snake_case	       =       torch.from_numpy(__UpperCamelCase )
							elif beta_schedule == "linear":
									_snake_case	       =       torch.linspace(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  dtype=torch.floataa )
							elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									_snake_case	       =       (
									    torch.linspace(beta_start**0.5,  beta_end**0.5,  __UpperCamelCase,  dtype=torch.floataa ) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									_snake_case	       =       betas_for_alpha_bar(__UpperCamelCase )
							elif beta_schedule == "sigmoid":
									# GeoDiff sigmoid schedule
									_snake_case	       =       torch.linspace(-6,  6,  __UpperCamelCase )
									_snake_case	       =       torch.sigmoid(__UpperCamelCase ) * (beta_end - beta_start) + beta_start
							else:
									raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

							_snake_case	       =       1.0 - self.betas
							_snake_case	       =       torch.cumprod(self.alphas,  dim=0 )
							_snake_case	       =       torch.tensor(1.0 )

							_snake_case	       =       torch.tensor(1.0 )

							# standard deviation of the initial noise distribution
							_snake_case	       =       1.0

							# setable values
							_snake_case	       =       None
							_snake_case	       =       torch.from_numpy(np.arange(0,  __UpperCamelCase )[::-1].copy() )

							_snake_case	       =       eta






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):
							return sample






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = 10,  __UpperCamelCase = 10,  __UpperCamelCase = None,  ):
							_snake_case	       =       min(self.config.num_train_timesteps,  __UpperCamelCase )
							_snake_case	       =       num_inference_steps

							_snake_case	       =       []

							_snake_case	       =       {}
							for j in range(0,  num_inference_steps - jump_length,  __UpperCamelCase ):
									_snake_case	       =       jump_n_sample - 1

							_snake_case	       =       num_inference_steps
							while t >= 1:
									_snake_case	       =       t - 1
									timesteps.append(__UpperCamelCase )

									if jumps.get(__UpperCamelCase,  0 ) > 0:
											_snake_case	       =       jumps[t] - 1
											for _ in range(__UpperCamelCase ):
													_snake_case	       =       t + 1
													timesteps.append(__UpperCamelCase )

							_snake_case	       =       np.array(__UpperCamelCase ) * (self.config.num_train_timesteps // self.num_inference_steps)
							_snake_case	       =       torch.from_numpy(__UpperCamelCase ).to(__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							_snake_case	       =       t - self.config.num_train_timesteps // self.num_inference_steps

							_snake_case	       =       self.alphas_cumprod[t]
							_snake_case	       =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							_snake_case	       =       1 - alpha_prod_t
							_snake_case	       =       1 - alpha_prod_t_prev

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from
							# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
							# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
							# variance to pred_sample
							# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
							# without eta.
							# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
							_snake_case	       =       (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase = None,  __UpperCamelCase = True,  ):
							_snake_case	       =       timestep
							_snake_case	       =       timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 1. compute alphas, betas
							_snake_case	       =       self.alphas_cumprod[t]
							_snake_case	       =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							_snake_case	       =       1 - alpha_prod_t

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							_snake_case	       =       (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
									_snake_case	       =       torch.clamp(__UpperCamelCase,  -1,  1 )

							# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
							# substitute formula (7) in the algorithm coming from DDPM paper
							# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
							# DDIM schedule gives the same results as DDPM with eta = 1.0
							# Noise is being reused in 7. and 8., but no impact on quality has
							# been observed.

							# 5. Add noise
							_snake_case	       =       model_output.device
							_snake_case	       =       randn_tensor(model_output.shape,  generator=__UpperCamelCase,  device=__UpperCamelCase,  dtype=model_output.dtype )
							_snake_case	       =       self.eta * self._get_variance(__UpperCamelCase ) ** 0.5

							_snake_case	       =       0
							if t > 0 and self.eta > 0:
									_snake_case	       =       std_dev_t * noise

							# 6. compute "direction pointing to x_t" of formula (12)
							# from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case	       =       (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case	       =       alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

							# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
							_snake_case	       =       (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

							# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
							_snake_case	       =       mask * prev_known_part + (1.0 - mask) * prev_unknown_part

							if not return_dict:
									return (
									    pred_prev_sample,
									    pred_original_sample,
									)

							return RePaintSchedulerOutput(prev_sample=__UpperCamelCase,  pred_original_sample=__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase=None ):
							_snake_case	       =       self.config.num_train_timesteps // self.num_inference_steps

							for i in range(__UpperCamelCase ):
									_snake_case	       =       self.betas[timestep + i]
									if sample.device.type == "mps":
											# randn does not work reproducibly on mps
											_snake_case	       =       randn_tensor(sample.shape,  dtype=sample.dtype,  generator=__UpperCamelCase )
											_snake_case	       =       noise.to(sample.device )
									else:
											_snake_case	       =       randn_tensor(sample.shape,  generator=__UpperCamelCase,  device=sample.device,  dtype=sample.dtype )

									# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
									_snake_case	       =       (1 - beta) ** 0.5 * sample + beta**0.5 * noise

							return sample






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  ):
							raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint." )






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











'''simple docstring'''







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


_lowerCamelCase							:						int								=  get_logger(__name__)


if TYPE_CHECKING:
						from .builder import DatasetBuilder






def 		_lowercase		(	lowercase_						,							lowercase_ = None  ):



		'''simple docstring'''







		_snake_case	       =       importlib.import_module(lowercase_  )

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

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

				_snake_case	       =       "wrap_auth"
				return wrapper

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






def 		_lowercase		(	lowercase_  ):



		'''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":
								_snake_case	       =       imports[1]
								_snake_case	       =       ".".join(builder.__module__.split("."  )[:-1] + [internal_import_name]  )
								extend_module_for_streaming(lowercase_						,							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

		_snake_case	       =       [
		    cls.__module__
		    for cls in type(lowercase_  ).__mro__[1:]  # make sure it's not the same module we've already patched
		    if issubclass(cls						,							lowercase_  ) 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(lowercase_						,							use_auth_token=builder.use_auth_token  )





