





'''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 lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Union[str, Any]=0.999					,       __lowerCamelCase :    Dict="cosine"					,       )						->Tuple:
    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__lowerCamelCase :    List[str]    ):
            return math.exp(t * -12.0    )

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

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






class 					a_     (				snake_case_      ,    snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = [e.name for e in KarrasDiffusionSchedulers]
    UpperCamelCase     = 2


    @register_to_config
    def __init__(		self ,		A = 1000 ,		A = 0.0_0085 ,		A = 0.012 ,		A = "linear" ,		A = None ,		A = "epsilon" ,		A = "linspace" ,		A = 0 ,		)		->   Dict:
        if trained_betas is not None:
            _SCREAMING_SNAKE_CASE									=			torch.tensor(A ,		dtype=torch.floataa       )
        elif beta_schedule == "linear":
            _SCREAMING_SNAKE_CASE									=			torch.linspace(A ,		A ,		A ,		dtype=torch.floataa       )
        elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            _SCREAMING_SNAKE_CASE									=			(
                torch.linspace(beta_start**0.5 ,		beta_end**0.5 ,		A ,		dtype=torch.floataa       ) ** 2
            )
        elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            _SCREAMING_SNAKE_CASE									=			betas_for_alpha_bar(A       )
        else:
            raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'       )

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

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


    def 			snake_case_(		self ,		A ,		A=None       )		->   Union[str, Any]:
        if schedule_timesteps is None:
            _SCREAMING_SNAKE_CASE									=			self.timesteps

        _SCREAMING_SNAKE_CASE									=			(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:
            _SCREAMING_SNAKE_CASE									=			1 if len(A       ) > 1 else 0
        else:
            _SCREAMING_SNAKE_CASE									=			timestep.cpu().item() if torch.is_tensor(A       ) else timestep
            _SCREAMING_SNAKE_CASE									=			self._index_counter[timestep_int]

        return indices[pos].item()


    @property
    def 			snake_case_(		self       )		->   Tuple:
        # 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 			snake_case_(		self ,		A ,		A ,		)		->   torch.FloatTensor:
        _SCREAMING_SNAKE_CASE									=			self.index_for_timestep(A       )

        if self.state_in_first_order:
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index]
        else:
            _SCREAMING_SNAKE_CASE									=			self.sigmas_interpol[step_index]

        _SCREAMING_SNAKE_CASE									=			sample / ((sigma**2 + 1) ** 0.5)
        return sample


    def 			snake_case_(		self ,		A ,		A = None ,		A = None ,		)		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			num_inference_steps

        _SCREAMING_SNAKE_CASE									=			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":
            _SCREAMING_SNAKE_CASE									=			np.linspace(0 ,		num_train_timesteps - 1 ,		A ,		dtype=A       )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
            _SCREAMING_SNAKE_CASE									=			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
            _SCREAMING_SNAKE_CASE									=			(np.arange(0 ,		A       ) * step_ratio).round()[::-1].copy().astype(A       )
            timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
            _SCREAMING_SNAKE_CASE									=			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
            _SCREAMING_SNAKE_CASE									=			(np.arange(A ,		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\'.'       )

        _SCREAMING_SNAKE_CASE									=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
        _SCREAMING_SNAKE_CASE									=			torch.from_numpy(np.log(A       )       ).to(A       )

        _SCREAMING_SNAKE_CASE									=			np.interp(A ,		np.arange(0 ,		len(A       )       ) ,		A       )
        _SCREAMING_SNAKE_CASE									=			np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
        _SCREAMING_SNAKE_CASE									=			torch.from_numpy(A       ).to(device=A       )

        # interpolate sigmas
        _SCREAMING_SNAKE_CASE									=			sigmas.log().lerp(sigmas.roll(1       ).log() ,		0.5       ).exp()

        _SCREAMING_SNAKE_CASE									=			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
        _SCREAMING_SNAKE_CASE									=			torch.cat(
            [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

        if str(A       ).startswith("""mps"""       ):
            # mps does not support float64
            _SCREAMING_SNAKE_CASE									=			torch.from_numpy(A       ).to(A ,		dtype=torch.floataa       )
        else:
            _SCREAMING_SNAKE_CASE									=			torch.from_numpy(A       ).to(A       )

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

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

        _SCREAMING_SNAKE_CASE									=			None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        _SCREAMING_SNAKE_CASE									=			defaultdict(A       )


    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        # get log sigma
        _SCREAMING_SNAKE_CASE									=			sigma.log()

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

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

        _SCREAMING_SNAKE_CASE									=			self.log_sigmas[low_idx]
        _SCREAMING_SNAKE_CASE									=			self.log_sigmas[high_idx]

        # interpolate sigmas
        _SCREAMING_SNAKE_CASE									=			(low - log_sigma) / (low - high)
        _SCREAMING_SNAKE_CASE									=			w.clamp(0 ,		1       )

        # transform interpolation to time range
        _SCREAMING_SNAKE_CASE									=			(1 - w) * low_idx + w * high_idx
        _SCREAMING_SNAKE_CASE									=			t.view(sigma.shape       )
        return t


    @property
    def 			snake_case_(		self       )		->   int:
        return self.sample is None


    def 			snake_case_(		self ,		A ,		A ,		A ,		A = True ,		)		->   Union[SchedulerOutput, Tuple]:
        _SCREAMING_SNAKE_CASE									=			self.index_for_timestep(A       )

        # advance index counter by 1
        _SCREAMING_SNAKE_CASE									=			timestep.cpu().item() if torch.is_tensor(A       ) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index]
            _SCREAMING_SNAKE_CASE									=			self.sigmas_interpol[step_index + 1]
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index + 1]
        else:
            # 2nd order / KDPM2's method
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index - 1]
            _SCREAMING_SNAKE_CASE									=			self.sigmas_interpol[step_index]
            _SCREAMING_SNAKE_CASE									=			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
        _SCREAMING_SNAKE_CASE									=			0
        _SCREAMING_SNAKE_CASE									=			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":
            _SCREAMING_SNAKE_CASE									=			sigma_hat if self.state_in_first_order else sigma_interpol
            _SCREAMING_SNAKE_CASE									=			sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
            _SCREAMING_SNAKE_CASE									=			sigma_hat if self.state_in_first_order else sigma_interpol
            _SCREAMING_SNAKE_CASE									=			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
            _SCREAMING_SNAKE_CASE									=			(sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            _SCREAMING_SNAKE_CASE									=			sigma_interpol - sigma_hat

            # store for 2nd order step
            _SCREAMING_SNAKE_CASE									=			sample
        else:
            # DPM-Solver-2
            # 2. Convert to an ODE derivative for 2nd order
            _SCREAMING_SNAKE_CASE									=			(sample - pred_original_sample) / sigma_interpol

            # 3. delta timestep
            _SCREAMING_SNAKE_CASE									=			sigma_next - sigma_hat

            _SCREAMING_SNAKE_CASE									=			self.sample
            _SCREAMING_SNAKE_CASE									=			None

        _SCREAMING_SNAKE_CASE									=			sample + derivative * dt

        if not return_dict:
            return (prev_sample,)

        return SchedulerOutput(prev_sample=A       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		)		->   torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        _SCREAMING_SNAKE_CASE									=			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
            _SCREAMING_SNAKE_CASE									=			self.timesteps.to(original_samples.device ,		dtype=torch.floataa       )
            _SCREAMING_SNAKE_CASE									=			timesteps.to(original_samples.device ,		dtype=torch.floataa       )
        else:
            _SCREAMING_SNAKE_CASE									=			self.timesteps.to(original_samples.device       )
            _SCREAMING_SNAKE_CASE									=			timesteps.to(original_samples.device       )

        _SCREAMING_SNAKE_CASE									=			[self.index_for_timestep(A ,		A       ) for t in timesteps]

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

        _SCREAMING_SNAKE_CASE									=			original_samples + noise * sigma
        return noisy_samples





    def __len__(		self       )		->   str:
        return self.config.num_train_timesteps








"""simple docstring"""
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


A:   Any								=		datasets.utils.logging.get_logger(__name__)



@dataclass
class       SCREAMING_SNAKE_CASE__  (					datasets.BuilderConfig ):
   __lowerCAmelCase  :   Optional[datasets.Features]						=						None
   __lowerCAmelCase  :   str						=						"utf-8"
   __lowerCAmelCase  :   Optional[str]						=						None
   __lowerCAmelCase  :   Optional[str]						=						None
   __lowerCAmelCase  :   bool						=						True  # deprecated
   __lowerCAmelCase  :   Optional[int]						=						None  # deprecated
   __lowerCAmelCase  :   int						=						10 << 20  # 10MB
   __lowerCAmelCase  :   Optional[bool]						=						None



class       SCREAMING_SNAKE_CASE__  (					datasets.ArrowBasedBuilder ):
   __lowerCAmelCase  :   int						=						JsonConfig

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      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	:					Any	=	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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Tuple:





    '''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	:					List[Any]	=	dl_manager.download_and_extract(self.config.data_files						)
    if isinstance(_SCREAMING_SNAKE_CASE      ,     (str, list, tuple)						):
     UpperCAmelCase	:					int	=	data_files
     if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
      UpperCAmelCase	:					Tuple	=	[files]
     UpperCAmelCase	:					List[str]	=	[dl_manager.iter_files(_SCREAMING_SNAKE_CASE						) for file in files]
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,     gen_kwargs={"""files""": files}						)]
    UpperCAmelCase	:					List[str]	=	[]
    for split_name, files in data_files.items():
     if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
      UpperCAmelCase	:					Dict	=	[files]
     UpperCAmelCase	:					str	=	[dl_manager.iter_files(_SCREAMING_SNAKE_CASE						) for file in files]
     splits.append(datasets.SplitGenerator(name=_SCREAMING_SNAKE_CASE      ,     gen_kwargs={"""files""": files}						)						)
    return splits

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      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	:					Tuple	=	self.config.features.arrow_schema.field(_SCREAMING_SNAKE_CASE						).type
      UpperCAmelCase	:					int	=	pa_table.append_column(_SCREAMING_SNAKE_CASE      ,     pa.array([None] * len(_SCREAMING_SNAKE_CASE						)      ,     type=_SCREAMING_SNAKE_CASE						)						)
     # more expensive cast to support nested structures with keys in a different order
     # allows str <-> int/float or str to Audio for example
     UpperCAmelCase	:					Any	=	table_cast(_SCREAMING_SNAKE_CASE      ,     self.config.features.arrow_schema						)
    return pa_table




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    for file_idx, file in enumerate(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE						)						):
     # 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(_SCREAMING_SNAKE_CASE      ,     encoding=self.config.encoding      ,     errors=self.config.encoding_errors						) as f:
       UpperCAmelCase	:					Union[str, Any]	=	json.load(_SCREAMING_SNAKE_CASE						)

      # We keep only the field we are interested in
      UpperCAmelCase	:					Dict	=	dataset[self.config.field]

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

     # If the file has one json object per line
     else:
      with open(_SCREAMING_SNAKE_CASE      ,     """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	:					int	=	max(self.config.chunksize // 32      ,     16 << 10						)
       UpperCAmelCase	:					Dict	=	(
           self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
       )
       while True:
        UpperCAmelCase	:					Tuple	=	f.read(self.config.chunksize						)
        if not batch:
         break
        # Finish current line
        try:
         batch += f.readline()
        except (AttributeError, io.UnsupportedOperation):
         batch += readline(_SCREAMING_SNAKE_CASE						)
        # PyArrow only accepts utf-8 encoded bytes
        if self.config.encoding != "utf-8":
         UpperCAmelCase	:					Tuple	=	batch.decode(self.config.encoding      ,     errors=_SCREAMING_SNAKE_CASE						).encode("""utf-8"""						)
        try:
         while True:
          try:
           UpperCAmelCase	:					int	=	paj.read_json(
               io.BytesIO(_SCREAMING_SNAKE_CASE						)      ,     read_options=paj.ReadOptions(block_size=_SCREAMING_SNAKE_CASE						)						)
           break
          except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
           if (
               isinstance(_SCREAMING_SNAKE_CASE      ,     pa.ArrowInvalid						)
               and "straddling" not in str(_SCREAMING_SNAKE_CASE						)
               or block_size > len(_SCREAMING_SNAKE_CASE						)
           ):
            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(_SCREAMING_SNAKE_CASE						)} 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(
              _SCREAMING_SNAKE_CASE      ,     encoding=self.config.encoding      ,     errors=self.config.encoding_errors						) as f:
           UpperCAmelCase	:					str	=	json.load(_SCREAMING_SNAKE_CASE						)
         except json.JSONDecodeError:
          logger.error(F"Failed to read file '{file}' with error {type(_SCREAMING_SNAKE_CASE						)}: {e}"						)
          raise e
         # If possible, parse the file as a list of json objects and exit the loop
         if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):  # list is the only sequence type supported in JSON
          try:
           UpperCAmelCase	:					Dict	=	set().union(*[row.keys() for row in dataset]						)
           UpperCAmelCase	:					Union[str, Any]	=	{col: [row.get(_SCREAMING_SNAKE_CASE						) for row in dataset] for col in keys}
           UpperCAmelCase	:					Dict	=	pa.Table.from_pydict(_SCREAMING_SNAKE_CASE						)
          except (pa.ArrowInvalid, AttributeError) as e:
           logger.error(F"Failed to read file '{file}' with error {type(_SCREAMING_SNAKE_CASE						)}: {e}"						)
           raise ValueError(F"Not able to read records in the JSON file at {file}."						) from None
          yield file_idx, self._cast_table(_SCREAMING_SNAKE_CASE						)
          break
         else:
          logger.error(F"Failed to read file '{file}' with error {type(_SCREAMING_SNAKE_CASE						)}: {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(_SCREAMING_SNAKE_CASE						)
        batch_idx += 1
