






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__       		=  """    \"\"\"
    Output class for the scheduler's step function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    \"\"\"

    prev_sample: torch.FloatTensor
    pred_original_sample: Optional[torch.FloatTensor] = None
"""




class       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            __snake_case      :List[str] 							=    tempfile.mkdtemp()
            os.makedirs(os.path.join(self.diffusers_dir      ,    """schedulers/"""     )     )
            __snake_case      :List[Any] 							=    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        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :int 							=    """src/diffusers"""
            shutil.rmtree(self.diffusers_dir     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__=None     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :List[str] 							=    comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
            if overwrite_result is not None:
                 __snake_case      :Optional[int] 							=    comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
            __snake_case      :Optional[Any] 							=    black.Mode(target_versions={black.TargetVersion.PYaa}      ,    line_length=1_19     )
            __snake_case      :Optional[int] 							=    black.format_str(a__      ,    mode=a__     )
            __snake_case      :Union[str, Any] 							=    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        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :Dict 							=    check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput"""     )
            self.assertEqual(a__      ,    a__     )



       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            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
            __snake_case      :Tuple 							=    """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__     )      ,    )










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

       '''simple docstring'''



       def __init__(				self      ,    a__=None      ,    **a__     )			->   Tuple:






            '''simple docstring'''





            super().__init__(features=a__     )
            __snake_case      :int 							=    torch_tensor_kwargs
            import torch  # noqa import torch at initialization



       def        __lowercase    (				self      ,    a__     )			->   List[str]:






            '''simple docstring'''





            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        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            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()

            __snake_case      :Optional[int] 							=    {}

            if isinstance(a__      ,    (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.integer     ):
                 __snake_case      :List[Any] 							=    {"""dtype""": torch.intaa}
            elif isinstance(a__      ,    (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.floating     ):
                 __snake_case      :Tuple 							=    {"""dtype""": torch.floataa}
            elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(a__      ,    PIL.Image.Image     ):
                      __snake_case      :Union[str, Any] 							=    np.asarray(a__     )
            return torch.tensor(a__      ,    **{**default_dtype, **self.torch_tensor_kwargs}     )



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            import torch

            # support for torch, tf, jax etc.
            if hasattr(a__      ,    """__array__"""     ) and not isinstance(a__      ,    torch.Tensor     ):
                 __snake_case      :Optional[Any] 							=    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        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            return map_nested(self._recursive_tensorize      ,    a__      ,    map_list=a__     )



       def        __lowercase    (				self      ,    a__     )			->   Mapping:






            '''simple docstring'''





            __snake_case      :Tuple 							=    self.numpy_arrow_extractor().extract_row(a__     )
            __snake_case      :Any 							=    self.python_features_decoder.decode_row(a__     )
            return self.recursive_tensorize(a__     )



       def        __lowercase    (				self      ,    a__     )			->   "torch.Tensor":






            '''simple docstring'''





            __snake_case      :List[str] 							=    self.numpy_arrow_extractor().extract_column(a__     )
            __snake_case      :List[Any] 							=    self.python_features_decoder.decode_column(a__      ,    pa_table.column_names[0]     )
            __snake_case      :Optional[Any] 							=    self.recursive_tensorize(a__     )
            __snake_case      :Any 							=    self._consolidate(a__     )
            return column



       def        __lowercase    (				self      ,    a__     )			->   Mapping:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self.numpy_arrow_extractor().extract_batch(a__     )
            __snake_case      :Tuple 							=    self.python_features_decoder.decode_batch(a__     )
            __snake_case      :Tuple 							=    self.recursive_tensorize(a__     )
            for column_name in batch:
                 __snake_case      :str 							=    self._consolidate(batch[column_name]     )
            return batch


