"""simple docstring"""



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

import black


SCREAMING_SNAKE_CASE__:List[str]			=	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.
SCREAMING_SNAKE_CASE__:str			=	"""    \"\"\"
    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   ):


 def 		a__ (				self		):
        __a     				=  tempfile.mkdtemp()
        os.makedirs(os.path.join(self.diffusers_dir  ,						"schedulers/"		)		)
        __a     				=  self.diffusers_dir
        shutil.copy(
            os.path.join(lowerCamelCase  ,						"src/diffusers/schedulers/scheduling_ddpm.py"		)  ,						os.path.join(self.diffusers_dir  ,						"schedulers/scheduling_ddpm.py"		)  ,						)


 def 		a__ (				self		):
        __a     				=  "src/diffusers"
        shutil.rmtree(self.diffusers_dir		)


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


 def 		a__ (				self		):
        __a     				=  check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput"		)
        self.assertEqual(lowerCamelCase  ,						lowerCamelCase		)






 def 		a__ (				self		):
        # 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"  ,						lowerCamelCase  ,						)

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

        # Copy consistency with a really long name
        __a     				=  "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"  ,						lowerCamelCase  ,						lowerCamelCase		)  ,						)

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

"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[Any]           =		["""pixel_values"""]


 def __init__(				self  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = PILImageResampling.BILINEAR  ,						lowerCamelCase = True  ,						lowerCamelCase = 1 / 255  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase		)
        __a     				=  size if size is not None else {"shortest_edge": 384}
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  do_resize
        __a     				=  size
        # Default value set here for backwards compatibility where the value in config is None
        __a     				=  crop_pct if crop_pct is not None else 224 / 256
        __a     				=  resample
        __a     				=  do_rescale
        __a     				=  rescale_factor
        __a     				=  do_normalize
        __a     				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a     				=  image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = PILImageResampling.BICUBIC  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)
        if "shortest_edge" not in size:
               raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"		)
        __a     				=  size["shortest_edge"]

        if shortest_edge < 384:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               __a     				=  int(shortest_edge / crop_pct		)
               __a     				=  get_resize_output_image_size(lowerCamelCase  ,						size=lowerCamelCase  ,						default_to_square=lowerCamelCase		)
               __a     				=  resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
        else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        return rescale(lowerCamelCase  ,						scale=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        return normalize(lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = ChannelDimension.FIRST  ,						**lowerCamelCase  ,						):
        __a     				=  do_resize if do_resize is not None else self.do_resize
        __a     				=  crop_pct if crop_pct is not None else self.crop_pct
        __a     				=  resample if resample is not None else self.resample
        __a     				=  do_rescale if do_rescale is not None else self.do_rescale
        __a     				=  rescale_factor if rescale_factor is not None else self.rescale_factor
        __a     				=  do_normalize if do_normalize is not None else self.do_normalize
        __a     				=  image_mean if image_mean is not None else self.image_mean
        __a     				=  image_std if image_std is not None else self.image_std

        __a     				=  size if size is not None else self.size
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  make_list_of_images(lowerCamelCase		)

        if not valid_images(lowerCamelCase		):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."		)

        if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."		)

        if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
               raise ValueError("crop_pct must be specified if size < 384."		)

        if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."		)

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."		)

        # All transformations expect numpy arrays.
        __a     				=  [to_numpy_array(lowerCamelCase		) for image in images]

        if do_resize:
               __a     				=  [self.resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						crop_pct=lowerCamelCase  ,						resample=lowerCamelCase		) for image in images]

        if do_rescale:
               __a     				=  [self.rescale(image=lowerCamelCase  ,						scale=lowerCamelCase		) for image in images]

        if do_normalize:
               __a     				=  [self.normalize(image=lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase		) for image in images]

        __a     				=  [to_channel_dimension_format(lowerCamelCase  ,						lowerCamelCase		) for image in images]

        __a     				=  {"pixel_values": images}
        return BatchFeature(data=lowerCamelCase  ,						tensor_type=lowerCamelCase		)
