



'''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


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

snake_case_			=     ["""names""", """prefix"""]
snake_case_			=     ["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
snake_case_			=     ["""encoding_errors""", """on_bad_lines"""]
snake_case_			=     ["""date_format"""]


@dataclass
class 	a__     (				datasets.BuilderConfig	):
	__magic_name__		:    str	    =     ","
	__magic_name__		:    Optional[str]	    =     None
	__magic_name__		:    Optional[Union[int, List[int], str]]	    =     "infer"
	__magic_name__		:    Optional[List[str]]	    =     None
	__magic_name__		:    Optional[List[str]]	    =     None
	__magic_name__		:    Optional[Union[int, str, List[int], List[str]]]	    =     None
	__magic_name__		:    Optional[Union[List[int], List[str]]]	    =     None
	__magic_name__		:    Optional[str]	    =     None
	__magic_name__		:    bool	    =     True
	__magic_name__		:    Optional[Literal["c", "python", "pyarrow"]]	    =     None
	__magic_name__		:    Dict[Union[int, str], Callable[[Any], Any]]	    =     None
	__magic_name__		:    Optional[list]	    =     None
	__magic_name__		:    Optional[list]	    =     None
	__magic_name__		:    bool	    =     False
	__magic_name__		:    Optional[Union[int, List[int]]]	    =     None
	__magic_name__		:    Optional[int]	    =     None
	__magic_name__		:    Optional[Union[str, List[str]]]	    =     None
	__magic_name__		:    bool	    =     True
	__magic_name__		:    bool	    =     True
	__magic_name__		:    bool	    =     False
	__magic_name__		:    bool	    =     True
	__magic_name__		:    Optional[str]	    =     None
	__magic_name__		:    str	    =     "."
	__magic_name__		:    Optional[str]	    =     None
	__magic_name__		:    str	    =     '"'
	__magic_name__		:    int	    =     0
	__magic_name__		:    Optional[str]	    =     None
	__magic_name__		:    Optional[str]	    =     None
	__magic_name__		:    Optional[str]	    =     None
	__magic_name__		:    Optional[str]	    =     None
	__magic_name__		:    bool	    =     True
	__magic_name__		:    bool	    =     True
	__magic_name__		:    int	    =     0
	__magic_name__		:    bool	    =     True
	__magic_name__		:    bool	    =     False
	__magic_name__		:    Optional[str]	    =     None
	__magic_name__		:    int	    =     1_00_00
	__magic_name__		:    Optional[datasets.Features]	    =     None
	__magic_name__		:    Optional[str]	    =     "strict"
	__magic_name__		:    Literal["error", "warn", "skip"]	    =     "error"
	__magic_name__		:    Optional[str]	    =     None


	def 				lowercase__  (self : int      )     ->			Optional[int]:

					"""simple docstring"""

					if self.delimiter is not None:
									SCREAMING_SNAKE_CASE  :						int      					= self.delimiter
					if self.column_names is not None:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.column_names






	@property
	def 				lowercase__  (self : Tuple      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= {
					    '''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 	a__     (				datasets.ArrowBasedBuilder	):
	__magic_name__		:    int	    =     CsvConfig


	def 				lowercase__  (self : str      )     ->			List[Any]:

					"""simple docstring"""

					return datasets.DatasetInfo(features=self.config.features      )


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Dict      )     ->			str:

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


	def 				lowercase__  (self : Any,					__UpperCAmelCase : pa.Table      )     ->			pa.Table:

					"""simple docstring"""

					if self.config.features is not None:
									SCREAMING_SNAKE_CASE  :						Tuple      					= self.config.features.arrow_schema
									if all(not require_storage_cast(__UpperCAmelCase      ) for feature in self.config.features.values()      ):
													# cheaper cast
													SCREAMING_SNAKE_CASE  :						Any      					= 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
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= table_cast(__UpperCAmelCase,					__UpperCAmelCase      )
					return pa_table






	def 				lowercase__  (self : Dict,					__UpperCAmelCase : Tuple      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= self.config.features.arrow_schema if self.config.features else None
					# dtype allows reading an int column as str
					SCREAMING_SNAKE_CASE  :						int      					= (
					    {
					        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      )      ):
									SCREAMING_SNAKE_CASE  :						Any      					= pd.read_csv(__UpperCAmelCase,					iterator=__UpperCAmelCase,					dtype=__UpperCAmelCase,					**self.config.pd_read_csv_kwargs      )
									try:
													for batch_idx, df in enumerate(__UpperCAmelCase      ):
																	SCREAMING_SNAKE_CASE  :						Any      					= 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









'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 	a__     (				_lowercase	):
	__magic_name__		:    UNetaDModel
	__magic_name__		:    ScoreSdeVeScheduler


	def __init__(self : List[str],					__UpperCAmelCase : UNetaDModel,					__UpperCAmelCase : ScoreSdeVeScheduler      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()
					self.register_modules(unet=__UpperCAmelCase,					scheduler=__UpperCAmelCase      )






	@torch.no_grad()
	def __call__(self : int,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : int = 2000,					__UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__UpperCAmelCase : Optional[str] = "pil",					__UpperCAmelCase : bool = True,					**__UpperCAmelCase : List[str],					)     ->			Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.unet.config.sample_size
					SCREAMING_SNAKE_CASE  :						Dict      					= (batch_size, 3, img_size, img_size)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.unet

					SCREAMING_SNAKE_CASE  :						str      					= randn_tensor(__UpperCAmelCase,					generator=__UpperCAmelCase      ) * self.scheduler.init_noise_sigma
					SCREAMING_SNAKE_CASE  :						Any      					= sample.to(self.device      )

					self.scheduler.set_timesteps(__UpperCAmelCase      )
					self.scheduler.set_sigmas(__UpperCAmelCase      )

					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
									SCREAMING_SNAKE_CASE  :						int      					= self.scheduler.sigmas[i] * torch.ones(shape[0],					device=self.device      )

									# correction step
									for _ in range(self.scheduler.config.correct_steps      ):
													SCREAMING_SNAKE_CASE  :						Any      					= self.unet(__UpperCAmelCase,					__UpperCAmelCase      ).sample
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.scheduler.step_correct(__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# prediction step
									SCREAMING_SNAKE_CASE  :						str      					= model(__UpperCAmelCase,					__UpperCAmelCase      ).sample
									SCREAMING_SNAKE_CASE  :						Dict      					= self.scheduler.step_pred(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= output.prev_sample, output.prev_sample_mean

					SCREAMING_SNAKE_CASE  :						Dict      					= sample_mean.clamp(0,					1      )
					SCREAMING_SNAKE_CASE  :						str      					= sample.cpu().permute(0,					2,					3,					1      ).numpy()
					if output_type == "pil":
									SCREAMING_SNAKE_CASE  :						int      					= self.numpy_to_pil(__UpperCAmelCase      )

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=__UpperCAmelCase      )




