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

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available





@dataclass
class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
						from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
						from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline


import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter





class      UpperCamelCase_			(			TensorFormatter[Mapping, np.ndarray, Mapping]		):







						'''simple docstring'''



						def __init__( self       :int		,						lowerCAmelCase__       :Any=None		,						**lowerCAmelCase__       :Union[str, Any]    )     ->int:
										super().__init__(features=lowerCAmelCase__    )
										lowercase						    =			np_array_kwargs





						def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]		,						lowerCAmelCase__       :str    )     ->Optional[Any]:
										if isinstance(lowerCAmelCase__		,						lowerCAmelCase__    ):
														if column and all(
														    isinstance(lowerCAmelCase__		,						np.ndarray    ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column    ):
																		return np.stack(lowerCAmelCase__    )
														else:
																		# don't use np.array(column, dtype=object)
																		# since it fails in certain cases
																		# see https://stackoverflow.com/q/51005699
																		lowercase						    =			np.empty(len(lowerCAmelCase__    )		,						dtype=lowerCAmelCase__    )
																		lowercase						    =			column
																		return out
										return column





						def 					SCREAMING_SNAKE_CASE( self       :str		,						lowerCAmelCase__       :List[str]    )     ->Union[str, Any]:
										if isinstance(lowerCAmelCase__		,						(str, bytes, type(lowerCAmelCase__    ))    ):
														return value
										elif isinstance(lowerCAmelCase__		,						(np.character, np.ndarray)    ) and np.issubdtype(value.dtype		,						np.character    ):
														return value
										elif isinstance(lowerCAmelCase__		,						np.number    ):
														return value

										lowercase						    =			{}

										if isinstance(lowerCAmelCase__		,						np.ndarray    ) and np.issubdtype(value.dtype		,						np.integer    ):
														lowercase						    =			{"dtype": np.intaa}
										elif isinstance(lowerCAmelCase__		,						np.ndarray    ) and np.issubdtype(value.dtype		,						np.floating    ):
														lowercase						    =			{"dtype": np.floataa}
										elif config.PIL_AVAILABLE and "PIL" in sys.modules:
														import PIL.Image

														if isinstance(lowerCAmelCase__		,						PIL.Image.Image    ):
																		return np.asarray(lowerCAmelCase__		,						**self.np_array_kwargs    )

										return np.asarray(lowerCAmelCase__		,						**{**default_dtype, **self.np_array_kwargs}    )





						def 					SCREAMING_SNAKE_CASE( self       :int		,						lowerCAmelCase__       :Dict    )     ->int:
										# support for torch, tf, jax etc.
										if config.TORCH_AVAILABLE and "torch" in sys.modules:
														import torch

														if isinstance(lowerCAmelCase__		,						torch.Tensor    ):
																		return self._tensorize(data_struct.detach().cpu().numpy()[()]    )
										if hasattr(lowerCAmelCase__		,						"__array__"    ) and not isinstance(lowerCAmelCase__		,						(np.ndarray, np.character, np.number)    ):
														lowercase						    =			data_struct.__array__()
										# support for nested types like struct of list of struct
										if isinstance(lowerCAmelCase__		,						np.ndarray    ):
														if data_struct.dtype == object:
																		return self._consolidate([self.recursive_tensorize(lowerCAmelCase__    ) for substruct in data_struct]    )
										if isinstance(lowerCAmelCase__		,						(list, tuple)    ):
														return self._consolidate([self.recursive_tensorize(lowerCAmelCase__    ) for substruct in data_struct]    )
										return self._tensorize(lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :int		,						lowerCAmelCase__       :dict    )     ->str:
										return map_nested(self._recursive_tensorize		,						lowerCAmelCase__		,						map_list=lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :pa.Table    )     ->Mapping:
										lowercase						    =			self.numpy_arrow_extractor().extract_row(lowerCAmelCase__    )
										lowercase						    =			self.python_features_decoder.decode_row(lowerCAmelCase__    )
										return self.recursive_tensorize(lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :str		,						lowerCAmelCase__       :pa.Table    )     ->np.ndarray:
										lowercase						    =			self.numpy_arrow_extractor().extract_column(lowerCAmelCase__    )
										lowercase						    =			self.python_features_decoder.decode_column(lowerCAmelCase__		,						pa_table.column_names[0]    )
										lowercase						    =			self.recursive_tensorize(lowerCAmelCase__    )
										lowercase						    =			self._consolidate(lowerCAmelCase__    )
										return column





						def 					SCREAMING_SNAKE_CASE( self       :List[Any]		,						lowerCAmelCase__       :pa.Table    )     ->Mapping:
										lowercase						    =			self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__    )
										lowercase						    =			self.python_features_decoder.decode_batch(lowerCAmelCase__    )
										lowercase						    =			self.recursive_tensorize(lowerCAmelCase__    )
										for column_name in batch:
														lowercase						    =			self._consolidate(batch[column_name]    )
										return batch
