






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      a_ (      _A      ,   _A=0.999      ,   _A="cosine"      ,   )			->			List[str]:







				"""simple docstring"""
				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

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


class   __SCREAMING_SNAKE_CASE(	a_    ,  a_	):
							_UpperCAmelCase     =       [e.name for e in KarrasDiffusionSchedulers]
							_UpperCAmelCase     =       2
							@register_to_config
							def __init__(     self:      int       ,	UpperCamelCase:      int = 10_00       ,	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       ,	)		->		Dict:
											if trained_betas is not None:
															snake_case__									=     torch.tensor(UpperCamelCase       ,	dtype=torch.floataa						)
											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						)
											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						)

											#  set all values
											self.set_timesteps(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Any=None						)		->		Optional[Any]:
											if schedule_timesteps is None:
															snake_case__									=     self.timesteps

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

											return indices[pos].item()
							@property
							def lowerCAmelCase_ (     self:      Any						)		->		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:      str       ,	UpperCamelCase:      torch.FloatTensor       ,	UpperCamelCase:      Union[float, torch.FloatTensor]       ,	)		->		torch.FloatTensor:
											snake_case__									=     self.index_for_timestep(UpperCamelCase						)

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

											snake_case__									=     sample / ((sigma**2 + 1) ** 0.5)
											return sample
							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Union[str, torch.device] = None       ,	UpperCamelCase:      Optional[int] = None       ,	)		->		Dict:
											snake_case__									=     num_inference_steps

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

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

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

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

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

											if str(UpperCamelCase						).startswith('mps'						):
															# mps does not support float64
															snake_case__									=     torch.from_numpy(UpperCamelCase						).to(UpperCamelCase       ,	dtype=torch.floataa						)
											else:
															snake_case__									=     torch.from_numpy(UpperCamelCase						).to(UpperCamelCase						)

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

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

											snake_case__									=     None

											# for exp beta schedules, such as the one for `pipeline_shap_e.py`
											# we need an index counter
											snake_case__									=     defaultdict(UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      Optional[int]						)		->		Tuple:
											# get log sigma
											snake_case__									=     sigma.log()

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

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

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

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

											# transform interpolation to time range
											snake_case__									=     (1 - w) * low_idx + w * high_idx
											snake_case__									=     t.view(sigma.shape						)
											return t
							@property
							def lowerCAmelCase_ (     self:      str						)		->		str:
											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       ,	)		->		Union[SchedulerOutput, Tuple]:
											snake_case__									=     self.index_for_timestep(UpperCamelCase						)

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

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

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

															# 3. delta timestep
															snake_case__									=     sigma_next - sigma_hat

															snake_case__									=     self.sample
															snake_case__									=     None

											snake_case__									=     sample + derivative * dt

											if not return_dict:
															return (prev_sample,)

											return SchedulerOutput(prev_sample=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      torch.FloatTensor       ,	UpperCamelCase:      torch.FloatTensor       ,	UpperCamelCase:      torch.FloatTensor       ,	)		->		torch.FloatTensor:
											# Make sure sigmas and timesteps have the same device and dtype as original_samples
											snake_case__									=     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
															snake_case__									=     self.timesteps.to(original_samples.device       ,	dtype=torch.floataa						)
															snake_case__									=     timesteps.to(original_samples.device       ,	dtype=torch.floataa						)
											else:
															snake_case__									=     self.timesteps.to(original_samples.device						)
															snake_case__									=     timesteps.to(original_samples.device						)

											snake_case__									=     [self.index_for_timestep(UpperCamelCase       ,	UpperCamelCase						) for t in timesteps]

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

											snake_case__									=     original_samples + noise * sigma
											return noisy_samples


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












import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


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

__UpperCamelCase					:		Union[str, Any]	  =						["""names""", """prefix"""]
__UpperCamelCase					:		List[str]	  =						["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
__UpperCamelCase					:		Dict	  =						["""encoding_errors""", """on_bad_lines"""]
__UpperCamelCase					:		Any	  =						["""date_format"""]


@dataclass
class   __SCREAMING_SNAKE_CASE(	datasets.BuilderConfig	):
							_UpperCAmelCase     =       ","
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       "infer"
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       "."
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       '"'
							_UpperCAmelCase     =       0
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       0
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       1_0_0_0_0
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       "strict"
							_UpperCAmelCase     =       "error"
							_UpperCAmelCase     =       None
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											if self.delimiter is not None:
															snake_case__									=     self.delimiter
											if self.column_names is not None:
															snake_case__									=     self.column_names


							@property
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		List[str]:
											snake_case__									=     {
											    'sep': self.sep,
											    'header': self.header,
											    'names': self.names,
											    'index_col': self.index_col,
											    'usecols': self.usecols,
											    'prefix': self.prefix,
											    'mangle_dupe_cols': self.mangle_dupe_cols,
											    'engine': self.engine,
											    'converters': self.converters,
											    'true_values': self.true_values,
											    'false_values': self.false_values,
											    'skipinitialspace': self.skipinitialspace,
											    'skiprows': self.skiprows,
											    'nrows': self.nrows,
											    'na_values': self.na_values,
											    'keep_default_na': self.keep_default_na,
											    'na_filter': self.na_filter,
											    'verbose': self.verbose,
											    'skip_blank_lines': self.skip_blank_lines,
											    'thousands': self.thousands,
											    'decimal': self.decimal,
											    'lineterminator': self.lineterminator,
											    'quotechar': self.quotechar,
											    'quoting': self.quoting,
											    'escapechar': self.escapechar,
											    'comment': self.comment,
											    'encoding': self.encoding,
											    'dialect': self.dialect,
											    'error_bad_lines': self.error_bad_lines,
											    'warn_bad_lines': self.warn_bad_lines,
											    'skipfooter': self.skipfooter,
											    'doublequote': self.doublequote,
											    'memory_map': self.memory_map,
											    'float_precision': self.float_precision,
											    'chunksize': self.chunksize,
											    'encoding_errors': self.encoding_errors,
											    'on_bad_lines': self.on_bad_lines,
											    'date_format': self.date_format,
											}

											# some kwargs must not be passed if they don't have a default value
											# some others are deprecated and we can also not pass them if they are the default value
											for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
															if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()       ,	UpperCamelCase						):
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
											if not (datasets.config.PANDAS_VERSION.major >= 2):
															for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
											if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
															for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

											return pd_read_csv_kwargs


class   __SCREAMING_SNAKE_CASE(	datasets.ArrowBasedBuilder	):
							_UpperCAmelCase     =       CsvConfig
							def lowerCAmelCase_ (     self:      str						)		->		int:
											return datasets.DatasetInfo(features=self.config.features						)
							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      Dict						)		->		int:
											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}'''						)
											snake_case__									=     dl_manager.download_and_extract(self.config.data_files						)
											if isinstance(UpperCamelCase       ,	(str, list, tuple)						):
															snake_case__									=     data_files
															if isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			snake_case__									=     [files]
															snake_case__									=     [dl_manager.iter_files(UpperCamelCase						) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,	gen_kwargs={'files': files}						)]
											snake_case__									=     []
											for split_name, files in data_files.items():
															if isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			snake_case__									=     [files]
															snake_case__									=     [dl_manager.iter_files(UpperCamelCase						) for file in files]
															splits.append(datasets.SplitGenerator(name=UpperCamelCase       ,	gen_kwargs={'files': files}						)						)
											return splits
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      pa.Table						)		->		pa.Table:
											if self.config.features is not None:
															snake_case__									=     self.config.features.arrow_schema
															if all(not require_storage_cast(UpperCamelCase						) for feature in self.config.features.values()						):
																			# cheaper cast
																			snake_case__									=     pa.Table.from_arrays([pa_table[field.name] for field in schema]       ,	schema=UpperCamelCase						)
															else:
																			# more expensive cast; allows str <-> int/float or str to Audio for example
																			snake_case__									=     table_cast(UpperCamelCase       ,	UpperCamelCase						)
											return pa_table


							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      Tuple						)		->		Tuple:
											snake_case__									=     self.config.features.arrow_schema if self.config.features else None
											# dtype allows reading an int column as str
											snake_case__									=     (
											    {
											        name: dtype.to_pandas_dtype() if not require_storage_cast(UpperCamelCase						) else object
											        for name, dtype, feature in zip(schema.names       ,	schema.types       ,	self.config.features.values()						)
											    }
											    if schema is not None
											    else None
											)
											for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase						)						):
															snake_case__									=     pd.read_csv(UpperCamelCase       ,	iterator=UpperCamelCase       ,	dtype=UpperCamelCase       ,	**self.config.pd_read_csv_kwargs						)
															try:
																			for batch_idx, df in enumerate(UpperCamelCase						):
																							snake_case__									=     pa.Table.from_pandas(UpperCamelCase						)
																							# 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(UpperCamelCase						)
															except ValueError as e:
																			logger.error(F'''Failed to read file \'{file}\' with error {type(UpperCamelCase						)}: {e}'''						)
																			raise




