# 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 torch
class     lowerCAmelCase_     (       TensorFormatter[Mapping, "torch.Tensor", Mapping] ):







							def __init__(   self      :     Any ,			_A      :     List[str]=None ,			**_A      :     Optional[int]		):
												super().__init__(features=_A		)
												_UpperCamelCase			     =     torch_tensor_kwargs
												import torch  # noqa import torch at initialization







							def  UpperCamelCase_						(   self      :     List[Any] ,			_A      :     int		):
												import torch

												if isinstance(_A ,			_A		) and column:
																	if all(
																	    isinstance(_A ,			torch.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype
																	    for x in column		):
																						return torch.stack(_A		)
												return column







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     str		):
												import torch

												if isinstance(_A ,			(str, bytes, type(_A		))		):
																	return value
												elif isinstance(_A ,			(np.character, np.ndarray)		) and np.issubdtype(value.dtype ,			np.character		):
																	return value.tolist()

												_UpperCamelCase			     =     {}

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

																	if isinstance(_A ,			PIL.Image.Image		):
																						_UpperCamelCase			     =     np.asarray(_A		)
												return torch.tensor(_A ,			**{**default_dtype, **self.torch_tensor_kwargs}		)







							def  UpperCamelCase_						(   self      :     List[Any] ,			_A      :     List[str]		):
												import torch

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







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     dict		):
												return map_nested(self._recursive_tensorize ,			_A ,			map_list=_A		)







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     pa.Table		):
												_UpperCamelCase			     =     self.numpy_arrow_extractor().extract_row(_A		)
												_UpperCamelCase			     =     self.python_features_decoder.decode_row(_A		)
												return self.recursive_tensorize(_A		)







							def  UpperCamelCase_						(   self      :     str ,			_A      :     pa.Table		):
												_UpperCamelCase			     =     self.numpy_arrow_extractor().extract_column(_A		)
												_UpperCamelCase			     =     self.python_features_decoder.decode_column(_A ,			pa_table.column_names[0]		)
												_UpperCamelCase			     =     self.recursive_tensorize(_A		)
												_UpperCamelCase			     =     self._consolidate(_A		)
												return column







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     pa.Table		):
												_UpperCamelCase			     =     self.numpy_arrow_extractor().extract_batch(_A		)
												_UpperCamelCase			     =     self.python_features_decoder.decode_batch(_A		)
												_UpperCamelCase			     =     self.recursive_tensorize(_A		)
												for column_name in batch:
																	_UpperCamelCase			     =     self._consolidate(batch[column_name]		)
												return batch

import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowerCAmelCase       =		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCAmelCase       =		"    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"
class     lowerCAmelCase_     (       unittest.TestCase ):







							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     tempfile.mkdtemp()
												os.makedirs(os.path.join(self.diffusers_dir ,			'''schedulers/'''		)		)
												_UpperCamelCase			     =     self.diffusers_dir
												shutil.copy(
												    os.path.join(_A ,			'''src/diffusers/schedulers/scheduling_ddpm.py'''		) ,			os.path.join(self.diffusers_dir ,			'''schedulers/scheduling_ddpm.py'''		) ,			)







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     '''src/diffusers'''
												shutil.rmtree(self.diffusers_dir		)







							def  UpperCamelCase_						(   self      :     str ,			_A      :     List[str] ,			_A      :     Optional[Any] ,			_A      :     List[str] ,			_A      :     Optional[int]=None		):
												_UpperCamelCase			     =     comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
												if overwrite_result is not None:
																	_UpperCamelCase			     =     comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
												_UpperCamelCase			     =     black.Mode(target_versions={black.TargetVersion.PYaa} ,			line_length=119		)
												_UpperCamelCase			     =     black.format_str(_A ,			mode=_A		)
												_UpperCamelCase			     =     os.path.join(self.diffusers_dir ,			'''new_code.py'''		)
												with open(_A ,			'''w''' ,			newline='''\n'''		) as f:
																	f.write(_A		)
												if overwrite_result is None:
																	self.assertTrue(len(check_copies.is_copy_consistent(_A		)		) == 0		)
												else:
																	check_copies.is_copy_consistent(f.name ,			overwrite=_A		)
																	with open(_A ,			'''r'''		) as f:
																						self.assertTrue(f.read() ,			_A		)







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''		)
												self.assertEqual(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     List[str]		):
												# Base copy consistency
												self.check_copy_consistency(
												    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,			'''DDPMSchedulerOutput''' ,			REFERENCE_CODE + '''\n''' ,			)

												# With no empty line at the end
												self.check_copy_consistency(
												    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,			'''DDPMSchedulerOutput''' ,			_A ,			)

												# Copy consistency with rename
												self.check_copy_consistency(
												    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,			'''TestSchedulerOutput''' ,			re.sub('''DDPM''' ,			'''Test''' ,			_A		) ,			)

												# Copy consistency with a really long name
												_UpperCamelCase			     =     '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
												self.check_copy_consistency(
												    F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" ,			F"""{long_class_name}SchedulerOutput""" ,			re.sub('''Bert''' ,			_A ,			_A		) ,			)

												# Copy consistency with overwrite
												self.check_copy_consistency(
												    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,			'''TestSchedulerOutput''' ,			_A ,			overwrite_result=re.sub('''DDPM''' ,			'''Test''' ,			_A		) ,			)
