
import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast





@dataclass
class 							UpperCAmelCase      (  datasets.BuilderConfig  ):




	'''simple docstring'''





	snake_case_					    =     None





class 							UpperCAmelCase      (  datasets.ArrowBasedBuilder  ):




	'''simple docstring'''





	snake_case_					    =     PandasConfig






	def 				UpperCamelCase_  (       self					: List[Any] ):
					return datasets.DatasetInfo(features=self.config.features )






	def 				UpperCamelCase_  (       self					: str							,A					: Dict ):
					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}''' )
					__A   			= dl_manager.download_and_extract(self.config.data_files )
					if isinstance(A							,(str, list, tuple) ):
									__A   			= data_files
									if isinstance(A							,A ):
													__A   			= [files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									__A   			= [dl_manager.iter_files(A ) for file in files]
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,gen_kwargs={"files": files} )]
					__A   			= []
					for split_name, files in data_files.items():
									if isinstance(A							,A ):
													__A   			= [files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									__A   			= [dl_manager.iter_files(A ) for file in files]
									splits.append(datasets.SplitGenerator(name=A							,gen_kwargs={"files": files} ) )
					return splits






	def 				UpperCamelCase_  (       self					: Dict							,A					: pa.Table ):
					if self.config.features is not None:
									# more expensive cast to support nested features with keys in a different order
									# allows str <-> int/float or str to Audio for example
									__A   			= table_cast(A							,self.config.features.arrow_schema )
					return pa_table







	def 				UpperCamelCase_  (       self					: Dict							,A					: Union[str, Any] ):
					for i, file in enumerate(itertools.chain.from_iterable(A ) ):
									with open(A							,"rb" ) as f:
													__A   			= pa.Table.from_pandas(pd.read_pickle(A ) )
													yield i, self._cast_table(A )


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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     None





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     2






	@register_to_config
	def __init__(       self					: str							,A					: float = 0.02							,A					: float = 1_00							,A					: float = 1.0_07							,A					: float = 80							,A					: float = 0.05							,A					: float = 50							,):
					# standard deviation of the initial noise distribution
					__A   			= sigma_max

					# setable values
					__A   			= None
					__A   			= None
					__A   			= None  # sigma(t_i)






	def 				UpperCamelCase_  (       self					: str							,A					: torch.FloatTensor							,A					: Optional[int] = None ):
					return sample






	def 				UpperCamelCase_  (       self					: Dict							,A					: int							,A					: Union[str, torch.device] = None ):
					__A   			= num_inference_steps
					__A   			= np.arange(0							,self.num_inference_steps )[::-1].copy()
					__A   			= torch.from_numpy(A ).to(A )
					__A   			= [
					    (
					        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
					]
					__A   			= torch.tensor(A							,dtype=torch.floataa							,device=A )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: torch.FloatTensor							,A					: float							,A					: Optional[torch.Generator] = None ):
					if self.config.s_min <= sigma <= self.config.s_max:
									__A   			= min(self.config.s_churn / self.num_inference_steps							,2**0.5 - 1 )
					else:
									__A   			= 0

					# sample eps ~ N(0, S_noise^2 * I)
					__A   			= self.config.s_noise * randn_tensor(sample.shape							,generator=A ).to(sample.device )
					__A   			= sigma + gamma * sigma
					__A   			= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

					return sample_hat, sigma_hat






	def 				UpperCamelCase_  (       self					: Dict							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_hat + sigma_hat * model_output
					__A   			= (sample_hat - pred_original_sample) / sigma_hat
					__A   			= sample_hat + (sigma_prev - sigma_hat) * derivative

					if not return_dict:
									return (sample_prev, derivative)

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






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_prev + sigma_prev * model_output
					__A   			= (sample_prev - pred_original_sample) / sigma_prev
					__A   			= 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=A							,derivative=A							,pred_original_sample=A )







	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Dict							,A					: List[str]							,A					: str ):
					raise NotImplementedError()
