






'''simple docstring'''







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     :   int	      =		datasets.utils.logging.get_logger(__name__)

UpperCamelCase     :   List[str]	      =		['names', 'prefix']
UpperCamelCase     :   List[str]	      =		['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
UpperCamelCase     :   Optional[int]	      =		['encoding_errors', 'on_bad_lines']
UpperCamelCase     :   Optional[int]	      =		['date_format']




@dataclass
class 		UpperCamelCase__     (datasets.BuilderConfig      ):




		'''simple docstring'''





		_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						=       10000
		_UpperCamelCase						=       None
		_UpperCamelCase						=       "strict"
		_UpperCamelCase						=       "error"
		_UpperCamelCase						=       None






		def       UpperCamelCase_       (   self							):
								if self.delimiter is not None:
														lowerCamelCase__								=	self.delimiter
								if self.column_names is not None:
														lowerCamelCase__								=	self.column_names






		@property
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	{
								    """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() ,_lowerCAmelCase							):
																				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 		UpperCamelCase__     (datasets.ArrowBasedBuilder      ):




		'''simple docstring'''





		_UpperCamelCase						=       CsvConfig






		def       UpperCamelCase_       (   self							):
								return datasets.DatasetInfo(features=self.config.features							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								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}'''							)
								lowerCamelCase__								=	dl_manager.download_and_extract(self.config.data_files							)
								if isinstance(_lowerCAmelCase ,(str, list, tuple)							):
														lowerCamelCase__								=	data_files
														if isinstance(_lowerCAmelCase ,_lowerCAmelCase							):
																				lowerCamelCase__								=	[files]
														lowerCamelCase__								=	[dl_manager.iter_files(_lowerCAmelCase							) for file in files]
														return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={"""files""": files}							)]
								lowerCamelCase__								=	[]
								for split_name, files in data_files.items():
														if isinstance(_lowerCAmelCase ,_lowerCAmelCase							):
																				lowerCamelCase__								=	[files]
														lowerCamelCase__								=	[dl_manager.iter_files(_lowerCAmelCase							) for file in files]
														splits.append(datasets.SplitGenerator(name=_lowerCAmelCase ,gen_kwargs={"""files""": files}							)							)
								return splits






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								if self.config.features is not None:
														lowerCamelCase__								=	self.config.features.arrow_schema
														if all(not require_storage_cast(_lowerCAmelCase							) for feature in self.config.features.values()							):
																				# cheaper cast
																				lowerCamelCase__								=	pa.Table.from_arrays([pa_table[field.name] for field in schema] ,schema=_lowerCAmelCase							)
														else:
																				# more expensive cast; allows str <-> int/float or str to Audio for example
																				lowerCamelCase__								=	table_cast(_lowerCAmelCase ,_lowerCAmelCase							)
								return pa_table






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								lowerCamelCase__								=	self.config.features.arrow_schema if self.config.features else None
								# dtype allows reading an int column as str
								lowerCamelCase__								=	(
								    {
								        name: dtype.to_pandas_dtype() if not require_storage_cast(_lowerCAmelCase							) 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(_lowerCAmelCase							)							):
														lowerCamelCase__								=	pd.read_csv(_lowerCAmelCase ,iterator=_lowerCAmelCase ,dtype=_lowerCAmelCase ,**self.config.pd_read_csv_kwargs							)
														try:
																				for batch_idx, df in enumerate(_lowerCAmelCase							):
																										lowerCamelCase__								=	pa.Table.from_pandas(_lowerCAmelCase							)
																										# 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(_lowerCAmelCase							)
														except ValueError as e:
																				logger.error(F'''Failed to read file \'{file}\' with error {type(_lowerCAmelCase							)}: {e}'''							)
																				raise





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

import numpy as np
import torch

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




@dataclass
class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		Optional[torch.FloatTensor]										=						None






class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						2


	@register_to_config
	def __init__(    self				,     lowercase__ = 0.0_2				,     lowercase__ = 1_0_0				,     lowercase__ = 1.0_0_7				,     lowercase__ = 8_0				,     lowercase__ = 0.0_5				,     lowercase__ = 5_0				,     ):
							# standard deviation of the initial noise distribution
							__UpperCAmelCase			:							Union[str, Any]	    =	sigma_max

							# setable values
							__UpperCAmelCase			:							int	    =	None
							__UpperCAmelCase			:							np.IntTensor	    =	None
							__UpperCAmelCase			:							torch.FloatTensor	    =	None  # sigma(t_i)


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							return sample


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							int	    =	num_inference_steps
							__UpperCAmelCase			:							List[str]	    =	np.arange(0				,     self.num_inference_steps)[::-1].copy()
							__UpperCAmelCase			:							Any	    =	torch.from_numpy(lowercase__).to(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in self.timesteps
							]
							__UpperCAmelCase			:							Tuple	    =	torch.tensor(lowercase__				,     dtype=torch.floataa				,     device=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None):
							if self.config.s_min <= sigma <= self.config.s_max:
													__UpperCAmelCase			:							int	    =	min(self.config.s_churn / self.num_inference_steps				,     2**0.5 - 1)
							else:
													__UpperCAmelCase			:							int	    =	0

							# sample eps ~ N(0, S_noise^2 * I)
							__UpperCAmelCase			:							List[str]	    =	self.config.s_noise * randn_tensor(sample.shape				,     generator=lowercase__).to(sample.device)
							__UpperCAmelCase			:							Optional[int]	    =	sigma + gamma * sigma
							__UpperCAmelCase			:							List[Any]	    =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							str	    =	sample_hat + sigma_hat * model_output
							__UpperCAmelCase			:							Tuple	    =	(sample_hat - pred_original_sample) / sigma_hat
							__UpperCAmelCase			:							str	    =	sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							Any	    =	sample_prev + sigma_prev * model_output
							__UpperCAmelCase			:							List[str]	    =	(sample_prev - pred_original_sample) / sigma_prev
							__UpperCAmelCase			:							Union[str, Any]	    =	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							raise NotImplementedError()

