





import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


UpperCAmelCase_  							=				datasets.utils.logging.get_logger(__name__)






@dataclass
class 					lowercase__ (			datasets.BuilderConfig       ):

   '''simple docstring'''


   a		:							Optional[datasets.Features]     	=	None
   a		:							str     	=	"utf-8"
   a		:							Optional[str]     	=	None
   a		:							Optional[str]     	=	None
   a		:							bool     	=	True  # deprecated
   a		:							Optional[int]     	=	None  # deprecated
   a		:							int     	=	10 << 20  # 10MB
   a		:							Optional[bool]     	=	None






class 					lowercase__ (			datasets.ArrowBasedBuilder       ):

   '''simple docstring'''


   a		:							int     	=	JsonConfig




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       if self.config.block_size is not None:
           logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''							)
           UpperCamelCase__							:       str      =   self.config.block_size
       if self.config.use_threads is not True:
           logger.warning(
               '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''							)
       if self.config.newlines_in_values is not None:
           raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''							)
       return datasets.DatasetInfo(features=self.config.features							)




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   Union[str, Any]:






       """simple docstring"""
       if not self.config.data_files:
           raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"							)
       UpperCamelCase__							:       int      =   dl_manager.download_and_extract(self.config.data_files							)
       if isinstance(__magic_name__,	(str, list, tuple)							):
           UpperCamelCase__							:       List[str]      =   data_files
           if isinstance(__magic_name__,	__magic_name__							):
               UpperCamelCase__							:       Optional[Any]      =   [files]
           UpperCamelCase__							:       Tuple      =   [dl_manager.iter_files(__magic_name__							) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN,	gen_kwargs={'''files''': files}							)]
       UpperCamelCase__							:       Union[str, Any]      =   []
       for split_name, files in data_files.items():
           if isinstance(__magic_name__,	__magic_name__							):
               UpperCamelCase__							:       Tuple      =   [files]
           UpperCamelCase__							:       List[str]      =   [dl_manager.iter_files(__magic_name__							) for file in files]
           splits.append(datasets.SplitGenerator(name=__magic_name__,	gen_kwargs={'''files''': files}							)							)
       return splits




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   pa.Table:






       """simple docstring"""
       if self.config.features is not None:
           # adding missing columns
           for column_name in set(self.config.features							) - set(pa_table.column_names							):
               UpperCamelCase__							:       Any      =   self.config.features.arrow_schema.field(__magic_name__							).type
               UpperCamelCase__							:       Tuple      =   pa_table.append_column(__magic_name__,	pa.array([None] * len(__magic_name__							),	type=__magic_name__							)							)
           # more expensive cast to support nested structures with keys in a different order
           # allows str <-> int/float or str to Audio for example
           UpperCamelCase__							:       int      =   table_cast(__magic_name__,	self.config.features.arrow_schema							)
       return pa_table




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   Optional[int]:






       """simple docstring"""
       for file_idx, file in enumerate(itertools.chain.from_iterable(__magic_name__							)							):
           # If the file is one json object and if we need to look at the list of items in one specific field
           if self.config.field is not None:
               with open(__magic_name__,	encoding=self.config.encoding,	errors=self.config.encoding_errors							) as f:
                   UpperCamelCase__							:       Dict      =   json.load(__magic_name__							)

               # We keep only the field we are interested in
               UpperCamelCase__							:       Union[str, Any]      =   dataset[self.config.field]

               # We accept two format: a list of dicts or a dict of lists
               if isinstance(__magic_name__,	(list, tuple)							):
                   UpperCamelCase__							:       Any      =   set().union(*[row.keys() for row in dataset]							)
                   UpperCamelCase__							:       Optional[int]      =   {col: [row.get(__magic_name__							) for row in dataset] for col in keys}
               else:
                   UpperCamelCase__							:       int      =   dataset
               UpperCamelCase__							:       Union[str, Any]      =   pa.Table.from_pydict(__magic_name__							)
               yield file_idx, self._cast_table(__magic_name__							)

           # If the file has one json object per line
           else:
               with open(__magic_name__,	'''rb'''							) as f:
                   UpperCamelCase__							:       Union[str, Any]      =   0
                   # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                   # Set a default minimum value of 16kB if the chunk size is really small
                   UpperCamelCase__							:       Union[str, Any]      =   max(self.config.chunksize // 32,	16 << 10							)
                   UpperCamelCase__							:       Optional[Any]      =   (
                       self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
                   )
                   while True:
                       UpperCamelCase__							:       Union[str, Any]      =   f.read(self.config.chunksize							)
                       if not batch:
                           break
                       # Finish current line
                       try:
                           batch += f.readline()
                       except (AttributeError, io.UnsupportedOperation):
                           batch += readline(__magic_name__							)
                       # PyArrow only accepts utf-8 encoded bytes
                       if self.config.encoding != "utf-8":
                           UpperCamelCase__							:       List[Any]      =   batch.decode(self.config.encoding,	errors=__magic_name__							).encode('''utf-8'''							)
                       try:
                           while True:
                               try:
                                   UpperCamelCase__							:       Union[str, Any]      =   paj.read_json(
                                       io.BytesIO(__magic_name__							),	read_options=paj.ReadOptions(block_size=__magic_name__							)							)
                                   break
                               except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                   if (
                                       isinstance(__magic_name__,	pa.ArrowInvalid							)
                                       and "straddling" not in str(__magic_name__							)
                                       or block_size > len(__magic_name__							)
                                   ):
                                       raise
                                   else:
                                       # Increase the block size in case it was too small.
                                       # The block size will be reset for the next file.
                                       logger.debug(
                                           f"Batch of {len(__magic_name__							)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."							)
                                       block_size *= 2
                       except pa.ArrowInvalid as e:
                           try:
                               with open(
                                   __magic_name__,	encoding=self.config.encoding,	errors=self.config.encoding_errors							) as f:
                                   UpperCamelCase__							:       List[str]      =   json.load(__magic_name__							)
                           except json.JSONDecodeError:
                               logger.error(f"Failed to read file '{file}' with error {type(__magic_name__							)}: {e}"							)
                               raise e
                           # If possible, parse the file as a list of json objects and exit the loop
                           if isinstance(__magic_name__,	__magic_name__							):  # list is the only sequence type supported in JSON
                               try:
                                   UpperCamelCase__							:       str      =   set().union(*[row.keys() for row in dataset]							)
                                   UpperCamelCase__							:       List[Any]      =   {col: [row.get(__magic_name__							) for row in dataset] for col in keys}
                                   UpperCamelCase__							:       Optional[Any]      =   pa.Table.from_pydict(__magic_name__							)
                               except (pa.ArrowInvalid, AttributeError) as e:
                                   logger.error(f"Failed to read file '{file}' with error {type(__magic_name__							)}: {e}"							)
                                   raise ValueError(f"Not able to read records in the JSON file at {file}."							) from None
                               yield file_idx, self._cast_table(__magic_name__							)
                               break
                           else:
                               logger.error(f"Failed to read file '{file}' with error {type(__magic_name__							)}: {e}"							)
                               raise ValueError(
                                   f"Not able to read records in the JSON file at {file}. "
                                   f"You should probably indicate the field of the JSON file containing your records. "
                                   f"This JSON file contain the following fields: {str(list(dataset.keys()							)							)}. "
                                   f"Select the correct one and provide it as `field='XXX'` to the dataset loading method. "							) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                       yield (file_idx, batch_idx), self._cast_table(__magic_name__							)
                       batch_idx += 1








# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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






@dataclass
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							torch.FloatTensor
   a		:							torch.FloatTensor






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase       ):

   '''simple docstring'''


   a		:							List[Any]     	=	1




   @register_to_config
   def __init__(					self,	__magic_name__ = 2000,	__magic_name__ = 0.15,	__magic_name__ = 0.01,	__magic_name__ = 1348.0,	__magic_name__ = 1E-5,	__magic_name__ = 1,	)				->   int:






       """simple docstring"""
       # standard deviation of the initial noise distribution
       UpperCamelCase__							:       int      =   sigma_max

       # setable values
       UpperCamelCase__							:       Optional[int]      =   None

       self.set_sigmas(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   torch.FloatTensor:






       """simple docstring"""
       return sample




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

       UpperCamelCase__							:       List[Any]      =   torch.linspace(1,	__magic_name__,	__magic_name__,	device=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   sigma_min if sigma_min is not None else self.config.sigma_min
       UpperCamelCase__							:       str      =   sigma_max if sigma_max is not None else self.config.sigma_max
       UpperCamelCase__							:       Optional[int]      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
           self.set_timesteps(__magic_name__,	__magic_name__							)

       UpperCamelCase__							:       str      =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       UpperCamelCase__							:       Optional[Any]      =   torch.exp(torch.linspace(math.log(__magic_name__							),	math.log(__magic_name__							),	__magic_name__							)							)
       UpperCamelCase__							:       Any      =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   str:






       """simple docstring"""
       return torch.where(
           timesteps == 0,	torch.zeros_like(t.to(timesteps.device							)							),	self.discrete_sigmas[timesteps - 1].to(timesteps.device							),	)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SdeVeOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

       UpperCamelCase__							:       Any      =   timestep * torch.ones(
           sample.shape[0],	device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
       UpperCamelCase__							:       Any      =   (timestep * (len(self.timesteps							) - 1)).long()

       # mps requires indices to be in the same device, so we use cpu as is the default with cuda
       UpperCamelCase__							:       List[Any]      =   timesteps.to(self.discrete_sigmas.device							)

       UpperCamelCase__							:       str      =   self.discrete_sigmas[timesteps].to(sample.device							)
       UpperCamelCase__							:       List[Any]      =   self.get_adjacent_sigma(__magic_name__,	__magic_name__							).to(sample.device							)
       UpperCamelCase__							:       Optional[Any]      =   torch.zeros_like(__magic_name__							)
       UpperCamelCase__							:       str      =   (sigma**2 - adjacent_sigma**2) ** 0.5

       # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
       # also equation 47 shows the analog from SDE models to ancestral sampling methods
       UpperCamelCase__							:       Union[str, Any]      =   diffusion.flatten()
       while len(diffusion.shape							) < len(sample.shape							):
           UpperCamelCase__							:       Any      =   diffusion.unsqueeze(-1							)
       UpperCamelCase__							:       str      =   drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       UpperCamelCase__							:       Union[str, Any]      =   randn_tensor(
           sample.shape,	layout=sample.layout,	generator=__magic_name__,	device=sample.device,	dtype=sample.dtype							)
       UpperCamelCase__							:       Optional[Any]      =   sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       UpperCamelCase__							:       str      =   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
           return (prev_sample, prev_sample_mean)

       return SdeVeOutput(prev_sample=__magic_name__,	prev_sample_mean=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SchedulerOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

       # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
       # sample noise for correction
       UpperCamelCase__							:       List[str]      =   randn_tensor(sample.shape,	layout=sample.layout,	generator=__magic_name__							).to(sample.device							)

       # compute step size from the model_output, the noise, and the snr
       UpperCamelCase__							:       str      =   torch.norm(model_output.reshape(model_output.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Tuple      =   torch.norm(noise.reshape(noise.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Union[str, Any]      =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
       UpperCamelCase__							:       Optional[Any]      =   step_size * torch.ones(sample.shape[0]							).to(sample.device							)
       # self.repeat_scalar(step_size, sample.shape[0])

       # compute corrected sample: model_output term and noise term
       UpperCamelCase__							:       Tuple      =   step_size.flatten()
       while len(step_size.shape							) < len(sample.shape							):
           UpperCamelCase__							:       int      =   step_size.unsqueeze(-1							)
       UpperCamelCase__							:       int      =   sample + step_size * model_output
       UpperCamelCase__							:       List[Any]      =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   torch.FloatTensor:






       """simple docstring"""
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__							:       Any      =   timesteps.to(original_samples.device							)
       UpperCamelCase__							:       List[str]      =   self.discrete_sigmas.to(original_samples.device							)[timesteps]
       UpperCamelCase__							:       Any      =   (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(__magic_name__							) * sigmas[:, None, None, None]
       )
       UpperCamelCase__							:       int      =   noise + original_samples
       return noisy_samples




   def __len__(					self							)				->   Union[str, Any]:






       """simple docstring"""
       return self.config.num_train_timesteps

