






'''simple docstring'''







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=0.999       ,    UpperCamelCase="cosine"       ,    )   -> Tuple:
							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}'''   )

							lowerCamelCase__      :			Optional[Any] 						= []
							for i in range(UpperCamelCase   ):
														lowerCamelCase__      :			Any 						= i / num_diffusion_timesteps
														lowerCamelCase__      :			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			(       _lowercase       ,   _lowercase	):
							a          = [e.name for e in KarrasDiffusionSchedulers]
							a          = 2




							@register_to_config
							def __init__(							self:       Tuple    ,							UpperCamelCase__:       int = 1_000    ,							UpperCamelCase__:       float = 0.00_085    ,							UpperCamelCase__:       float = 0.012    ,							UpperCamelCase__:       str = "linear"    ,							UpperCamelCase__:       Optional[Union[np.ndarray, List[float]]] = None    ,							UpperCamelCase__:       str = "epsilon"    ,							UpperCamelCase__:       str = "linspace"    ,							UpperCamelCase__:       int = 0    ,							):
														if trained_betas is not None:
																					lowerCamelCase__      :			List[Any] 						= torch.tensor(UpperCamelCase__    ,							dtype=torch.floataa  )
														elif beta_schedule == "linear":
																					lowerCamelCase__      :			Dict 						= torch.linspace(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							dtype=torch.floataa  )
														elif beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					lowerCamelCase__      :			int 						= (
																					    torch.linspace(beta_start**0.5    ,							beta_end**0.5    ,							UpperCamelCase__    ,							dtype=torch.floataa  ) ** 2
																					)
														elif beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					lowerCamelCase__      :			Any 						= betas_for_alpha_bar(UpperCamelCase__  )
														else:
																					raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''  )

														lowerCamelCase__      :			Union[str, Any] 						= 1.0 - self.betas
														lowerCamelCase__      :			Optional[Any] 						= torch.cumprod(self.alphas    ,							dim=0  )

														#  set all values
														self.set_timesteps(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Any=None  ):
														if schedule_timesteps is None:
																					lowerCamelCase__      :			int 						= self.timesteps

														lowerCamelCase__      :			Optional[Any] 						= (schedule_timesteps == timestep).nonzero()

														# The sigma index that is taken for the **very** first `step`
														# is always the second index (or the last index if there is only 1)
														# This way we can ensure we don't accidentally skip a sigma in
														# case we start in the middle of the denoising schedule (e.g. for image-to-image)
														if len(self._index_counter  ) == 0:
																					lowerCamelCase__      :			Any 						= 1 if len(UpperCamelCase__  ) > 1 else 0
														else:
																					lowerCamelCase__      :			Optional[Any] 						= timestep.cpu().item() if torch.is_tensor(UpperCamelCase__  ) else timestep
																					lowerCamelCase__      :			str 						= self._index_counter[timestep_int]

														return indices[pos].item()




							@property
							def 	lowerCamelCase_       (							self:       List[str]  ):
														# 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]    ,							UpperCamelCase__:       torch.FloatTensor    ,							UpperCamelCase__:       Union[float, torch.FloatTensor]    ,							):
														lowerCamelCase__      :			Any 						= self.index_for_timestep(UpperCamelCase__  )

														if self.state_in_first_order:
																					lowerCamelCase__      :			Tuple 						= self.sigmas[step_index]
														else:
																					lowerCamelCase__      :			str 						= self.sigmas_interpol[step_index]

														lowerCamelCase__      :			Any 						= sample / ((sigma**2 + 1) ** 0.5)
														return sample




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Union[str, torch.device] = None    ,							UpperCamelCase__:       Optional[int] = None    ,							):
														lowerCamelCase__      :			List[Any] 						= num_inference_steps

														lowerCamelCase__      :			List[str] 						= num_train_timesteps or self.config.num_train_timesteps

														# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
														if self.config.timestep_spacing == "linspace":
																					lowerCamelCase__      :			Any 						= np.linspace(0    ,							num_train_timesteps - 1    ,							UpperCamelCase__    ,							dtype=UpperCamelCase__  )[::-1].copy()
														elif self.config.timestep_spacing == "leading":
																					lowerCamelCase__      :			Any 						= 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
																					lowerCamelCase__      :			Any 						= (np.arange(0    ,							UpperCamelCase__  ) * step_ratio).round()[::-1].copy().astype(UpperCamelCase__  )
																					timesteps += self.config.steps_offset
														elif self.config.timestep_spacing == "trailing":
																					lowerCamelCase__      :			Any 						= 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
																					lowerCamelCase__      :			Any 						= (np.arange(UpperCamelCase__    ,							0    ,							-step_ratio  )).round().copy().astype(UpperCamelCase__  )
																					timesteps -= 1
														else:
																					raise ValueError(
																					    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''  )

														lowerCamelCase__      :			Dict 						= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
														lowerCamelCase__      :			Union[str, Any] 						= torch.from_numpy(np.log(UpperCamelCase__  )  ).to(UpperCamelCase__  )

														lowerCamelCase__      :			List[Any] 						= np.interp(UpperCamelCase__    ,							np.arange(0    ,							len(UpperCamelCase__  )  )    ,							UpperCamelCase__  )
														lowerCamelCase__      :			List[str] 						= np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
														lowerCamelCase__      :			Dict 						= torch.from_numpy(UpperCamelCase__  ).to(device=UpperCamelCase__  )

														# interpolate sigmas
														lowerCamelCase__      :			List[str] 						= sigmas.log().lerp(sigmas.roll(1  ).log()    ,							0.5  ).exp()

														lowerCamelCase__      :			List[Any] 						= torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2  ), sigmas[-1:]]  )
														lowerCamelCase__      :			Union[str, Any] 						= torch.cat(
														    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2  ), sigmas_interpol[-1:]]  )

														if str(UpperCamelCase__  ).startswith("""mps"""  ):
																					# mps does not support float64
																					lowerCamelCase__      :			Tuple 						= torch.from_numpy(UpperCamelCase__  ).to(UpperCamelCase__    ,							dtype=torch.floataa  )
														else:
																					lowerCamelCase__      :			int 						= torch.from_numpy(UpperCamelCase__  ).to(UpperCamelCase__  )

														# interpolate timesteps
														lowerCamelCase__      :			Tuple 						= self.sigma_to_t(UpperCamelCase__  ).to(UpperCamelCase__    ,							dtype=timesteps.dtype  )
														lowerCamelCase__      :			Tuple 						= torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])    ,							dim=-1  ).flatten()

														lowerCamelCase__      :			Dict 						= torch.cat([timesteps[:1], interleaved_timesteps]  )

														lowerCamelCase__      :			List[str] 						= None

														# for exp beta schedules, such as the one for `pipeline_shap_e.py`
														# we need an index counter
														lowerCamelCase__      :			Tuple 						= defaultdict(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       str  ):
														# get log sigma
														lowerCamelCase__      :			int 						= sigma.log()

														# get distribution
														lowerCamelCase__      :			Dict 						= log_sigma - self.log_sigmas[:, None]

														# get sigmas range
														lowerCamelCase__      :			Tuple 						= dists.ge(0  ).cumsum(dim=0  ).argmax(dim=0  ).clamp(max=self.log_sigmas.shape[0] - 2  )
														lowerCamelCase__      :			List[str] 						= low_idx + 1

														lowerCamelCase__      :			Optional[int] 						= self.log_sigmas[low_idx]
														lowerCamelCase__      :			str 						= self.log_sigmas[high_idx]

														# interpolate sigmas
														lowerCamelCase__      :			Optional[int] 						= (low - log_sigma) / (low - high)
														lowerCamelCase__      :			int 						= w.clamp(0    ,							1  )

														# transform interpolation to time range
														lowerCamelCase__      :			List[Any] 						= (1 - w) * low_idx + w * high_idx
														lowerCamelCase__      :			List[Any] 						= t.view(sigma.shape  )
														return t




							@property
							def 	lowerCamelCase_       (							self:       Any  ):
														return self.sample is None




							def 	lowerCamelCase_       (							self:       Tuple    ,							UpperCamelCase__:       Union[torch.FloatTensor, np.ndarray]    ,							UpperCamelCase__:       Union[float, torch.FloatTensor]    ,							UpperCamelCase__:       Union[torch.FloatTensor, np.ndarray]    ,							UpperCamelCase__:       bool = True    ,							):
														lowerCamelCase__      :			int 						= self.index_for_timestep(UpperCamelCase__  )

														# advance index counter by 1
														lowerCamelCase__      :			List[str] 						= timestep.cpu().item() if torch.is_tensor(UpperCamelCase__  ) else timestep
														self._index_counter[timestep_int] += 1

														if self.state_in_first_order:
																					lowerCamelCase__      :			Optional[Any] 						= self.sigmas[step_index]
																					lowerCamelCase__      :			Tuple 						= self.sigmas_interpol[step_index + 1]
																					lowerCamelCase__      :			Optional[Any] 						= self.sigmas[step_index + 1]
														else:
																					# 2nd order / KDPM2's method
																					lowerCamelCase__      :			Optional[int] 						= self.sigmas[step_index - 1]
																					lowerCamelCase__      :			Optional[Any] 						= self.sigmas_interpol[step_index]
																					lowerCamelCase__      :			List[str] 						= self.sigmas[step_index]

														# currently only gamma=0 is supported. This usually works best anyways.
														# We can support gamma in the future but then need to scale the timestep before
														# passing it to the model which requires a change in API
														lowerCamelCase__      :			Optional[int] 						= 0
														lowerCamelCase__      :			List[Any] 						= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

														# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
														if self.config.prediction_type == "epsilon":
																					lowerCamelCase__      :			Union[str, Any] 						= sigma_hat if self.state_in_first_order else sigma_interpol
																					lowerCamelCase__      :			Optional[Any] 						= sample - sigma_input * model_output
														elif self.config.prediction_type == "v_prediction":
																					lowerCamelCase__      :			Tuple 						= sigma_hat if self.state_in_first_order else sigma_interpol
																					lowerCamelCase__      :			Optional[Any] 						= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																					    sample / (sigma_input**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`'''  )

														if self.state_in_first_order:
																					# 2. Convert to an ODE derivative for 1st order
																					lowerCamelCase__      :			str 						= (sample - pred_original_sample) / sigma_hat
																					# 3. delta timestep
																					lowerCamelCase__      :			Union[str, Any] 						= sigma_interpol - sigma_hat

																					# store for 2nd order step
																					lowerCamelCase__      :			Any 						= sample
														else:
																					# DPM-Solver-2
																					# 2. Convert to an ODE derivative for 2nd order
																					lowerCamelCase__      :			Union[str, Any] 						= (sample - pred_original_sample) / sigma_interpol

																					# 3. delta timestep
																					lowerCamelCase__      :			List[str] 						= sigma_next - sigma_hat

																					lowerCamelCase__      :			Dict 						= self.sample
																					lowerCamelCase__      :			str 						= None

														lowerCamelCase__      :			Any 						= sample + derivative * dt

														if not return_dict:
																					return (prev_sample,)

														return SchedulerOutput(prev_sample=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       torch.FloatTensor    ,							UpperCamelCase__:       torch.FloatTensor    ,							UpperCamelCase__:       torch.FloatTensor    ,							):
														# Make sure sigmas and timesteps have the same device and dtype as original_samples
														lowerCamelCase__      :			Dict 						= self.sigmas.to(device=original_samples.device    ,							dtype=original_samples.dtype  )
														if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase__  ):
																					# mps does not support float64
																					lowerCamelCase__      :			Optional[Any] 						= self.timesteps.to(original_samples.device    ,							dtype=torch.floataa  )
																					lowerCamelCase__      :			Optional[Any] 						= timesteps.to(original_samples.device    ,							dtype=torch.floataa  )
														else:
																					lowerCamelCase__      :			List[str] 						= self.timesteps.to(original_samples.device  )
																					lowerCamelCase__      :			List[Any] 						= timesteps.to(original_samples.device  )

														lowerCamelCase__      :			Tuple 						= [self.index_for_timestep(UpperCamelCase__    ,							UpperCamelCase__  ) for t in timesteps]

														lowerCamelCase__      :			Union[str, Any] 						= sigmas[step_indices].flatten()
														while len(sigma.shape  ) < len(original_samples.shape  ):
																					lowerCamelCase__      :			List[Any] 						= sigma.unsqueeze(-1  )

														lowerCamelCase__      :			List[Any] 						= original_samples + noise * sigma
														return noisy_samples




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









'''simple docstring'''







import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE

class _lowercase			(       _lowercase	):
							a          = """"""
							a          = (
							    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
							)
							a          = None  # compression type in fsspec. ex: "gzip"
							a          = None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz




							def __init__(							self:       str    ,							UpperCamelCase__:       str = ""    ,							UpperCamelCase__:       Optional[str] = None    ,							UpperCamelCase__:       Optional[dict] = None    ,							**UpperCamelCase__:       List[Any]  ):
														super().__init__(self    ,							**UpperCamelCase__  )
														# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
														lowerCamelCase__      :			List[Any] 						= fsspec.open(
														    UpperCamelCase__    ,							mode="""rb"""    ,							protocol=UpperCamelCase__    ,							compression=self.compression    ,							client_kwargs={
														        """requote_redirect_url""": False,  # see https://github.com/huggingface/datasets/pull/5459
														        """trust_env""": True,  # Enable reading proxy env variables.
														        **(target_options or {}).pop("""client_kwargs"""    ,							{}  ),  # To avoid issues if it was already passed.
														    }    ,							**(target_options or {})    ,							)
														lowerCamelCase__      :			str 						= os.path.basename(self.file.path.split("""::"""  )[0]  )
														lowerCamelCase__      :			Union[str, Any] 						= (
														    self.compressed_name[: self.compressed_name.rindex("""."""  )]
														    if """.""" in self.compressed_name
														    else self.compressed_name
														)
														lowerCamelCase__      :			Tuple 						= None




							@classmethod
							def 	lowerCamelCase_       (							cls:       Optional[int]    ,							UpperCamelCase__:       Optional[int]  ):
														# compressed file paths are always relative to the archive root
														return super()._strip_protocol(UpperCamelCase__  ).lstrip("""/"""  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														if self.dir_cache is None:
																					lowerCamelCase__      :			Dict 						= {**self.file.fs.info(self.file.path  ), """name""": self.uncompressed_name}
																					lowerCamelCase__      :			int 						= {f["""name"""]: f}




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       str  ):
														return self.file.open().read()




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str = "rb"    ,							UpperCamelCase__:       Optional[int]=None    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       Tuple=None    ,							**UpperCamelCase__:       Optional[Any]    ,							):
														lowerCamelCase__      :			Union[str, Any] 						= self._strip_protocol(UpperCamelCase__  )
														if mode != "rb":
																					raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''''  )
														return self.file.open()



class _lowercase			(       _lowercase	):
							a          = """bz2"""
							a          = """bz2"""
							a          = """.bz2"""



class _lowercase			(       _lowercase	):
							a          = """gzip"""
							a          = """gzip"""
							a          = """.gz"""



class _lowercase			(       _lowercase	):
							a          = """lz4"""
							a          = """lz4"""
							a          = """.lz4"""



class _lowercase			(       _lowercase	):
							a          = """xz"""
							a          = """xz"""
							a          = """.xz"""



class _lowercase			(       _lowercase	):
							a          = """zstd"""
							a          = """zstd"""
							a          = """.zst"""




							def __init__(							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str = "rb"    ,							UpperCamelCase__:       Optional[str] = None    ,							UpperCamelCase__:       Optional[dict] = None    ,							UpperCamelCase__:       int = DEFAULT_BLOCK_SIZE    ,							**UpperCamelCase__:       Dict    ,							):
														super().__init__(
														    fo=UpperCamelCase__    ,							mode=UpperCamelCase__    ,							target_protocol=UpperCamelCase__    ,							target_options=UpperCamelCase__    ,							block_size=UpperCamelCase__    ,							**UpperCamelCase__    ,							)
														# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
														#
														# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
														#     out.close = close
														# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
														#
														# see https://github.com/intake/filesystem_spec/issues/725
														lowerCamelCase__      :			Tuple 						= self.file.__enter__

														class _lowercase			:




																					def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Any  ):
																												lowerCamelCase__      :			Optional[int] 						= file_




																					def __enter__(							self:       List[Any]  ):
																												self._file.__enter__()
																												return self




																					def __exit__(							self:       Any    ,							*UpperCamelCase__:       str    ,							**UpperCamelCase__:       Any  ):
																												self._file.__exit__(*UpperCamelCase__    ,							**UpperCamelCase__  )




																					def __iter__(							self:       Any  ):
																												return iter(self._file  )




																					def 	lowerCamelCase_       (							self:       List[Any]  ):
																												return next(self._file  )




																					def __getattr__(							self:       List[str]    ,							UpperCamelCase__:       Dict  ):
																												return getattr(self._file    ,							UpperCamelCase__  )

														def fixed_enter(*UpperCamelCase__:       Union[str, Any]    ,							**UpperCamelCase__:       List[str]  ):
																					return WrappedFile(_enter(*UpperCamelCase__    ,							**UpperCamelCase__  )  )

														lowerCamelCase__      :			Optional[Any] 						= fixed_enter

