

# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowerCAmelCase      :    Any						       =    abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)






def  UpperCamelCase_(							_snake_case     :						int			):




			"""simple docstring"""






			from diffusers.utils.testing_utils import pytest_addoption_shared

			pytest_addoption_shared(_snake_case			)






def  UpperCamelCase_(							_snake_case     :						Optional[Any]			):




			"""simple docstring"""






			from diffusers.utils.testing_utils import pytest_terminal_summary_main

			__a						   =terminalreporter.config.getoption('--make-reports'			)
			if make_reports:
						pytest_terminal_summary_main(_snake_case ,  id=_snake_case			)








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
						import tensorflow as tf






class 			__magic_name__					(    TensorFormatter[Mapping, 'tf.Tensor', Mapping]   ):





			def __init__(	self      ,						__snake_case=None      ,						**__snake_case      )     ->				Union[str, Any]:




						'''simple docstring'''
						super().__init__(features=__snake_case      )
						__a						   =tf_tensor_kwargs
						import tensorflow as tf  # noqa: import tf at initialization





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				int:




						'''simple docstring'''
						import tensorflow as tf

						if isinstance(__snake_case      ,						__snake_case      ) and column:
									if all(
									    isinstance(__snake_case      ,						tf.Tensor      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
												return tf.stack(__snake_case      )
									elif all(
									    isinstance(__snake_case      ,						(tf.Tensor, tf.RaggedTensor)      ) and x.ndim == 1 and x.dtype == column[0].dtype
									    for x in column      ):
												# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
												return tf.ragged.stack(__snake_case      )

						return column





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Any:




						'''simple docstring'''
						import tensorflow as tf

						if value is None:
									return value

						__a						   ={}

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

									if isinstance(__snake_case      ,						PIL.Image.Image      ):
												__a						   =np.asarray(__snake_case      )

						return tf.convert_to_tensor(__snake_case      ,						**{**default_dtype, **self.tf_tensor_kwargs}      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Dict:




						'''simple docstring'''
						import tensorflow as tf

						# support for torch, tf, jax etc.
						if config.TORCH_AVAILABLE and "torch" in sys.modules:
									import torch

									if isinstance(__snake_case      ,						torch.Tensor      ):
												return self._tensorize(data_struct.detach().cpu().numpy()[()]      )
						if hasattr(__snake_case      ,						'__array__'      ) and not isinstance(__snake_case      ,						tf.Tensor      ):
									__a						   =data_struct.__array__()
						# support for nested types like struct of list of struct
						if isinstance(__snake_case      ,						np.ndarray      ):
									if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
												return self._consolidate([self.recursive_tensorize(__snake_case      ) for substruct in data_struct]      )
						elif isinstance(__snake_case      ,						(list, tuple)      ):
									return self._consolidate([self.recursive_tensorize(__snake_case      ) for substruct in data_struct]      )
						return self._tensorize(__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Optional[Any]:




						'''simple docstring'''
						return map_nested(self._recursive_tensorize      ,						__snake_case      ,						map_list=__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Mapping:




						'''simple docstring'''
						__a						   =self.numpy_arrow_extractor().extract_row(__snake_case      )
						__a						   =self.python_features_decoder.decode_row(__snake_case      )
						return self.recursive_tensorize(__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				"tf.Tensor":




						'''simple docstring'''
						__a						   =self.numpy_arrow_extractor().extract_column(__snake_case      )
						__a						   =self.python_features_decoder.decode_column(__snake_case      ,						pa_table.column_names[0]      )
						__a						   =self.recursive_tensorize(__snake_case      )
						__a						   =self._consolidate(__snake_case      )
						return column





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Mapping:




						'''simple docstring'''
						__a						   =self.numpy_arrow_extractor().extract_batch(__snake_case      )
						__a						   =self.python_features_decoder.decode_batch(__snake_case      )
						__a						   =self.recursive_tensorize(__snake_case      )
						for column_name in batch:
									__a						   =self._consolidate(batch[column_name]      )
						return batch






