import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
	import torch

if is_vision_available():
	import PIL


_UpperCAmelCase          =						logging.get_logger(__name__)




def 							UpperCamelCase		(			__lowercase		:					List[str]		,__lowercase		:					Dict		,__lowercase		:					List[str]		,__lowercase		:					List[str]      ):
		'''simple docstring'''




		def constraint_to_multiple_of(__lowercase		:					Union[str, Any]		,__lowercase		:					Optional[int]		,__lowercase		:					Any=0		,__lowercase		:					Union[str, Any]=None      ):
				A_						:							Union[str, Any]      = round(val / multiple      ) * multiple

				if max_val is not None and x > max_val:
						A_						:							Optional[Any]      = math.floor(val / multiple      ) * multiple

				if x < min_val:
						A_						:							Union[str, Any]      = math.ceil(val / multiple      ) * multiple

				return x

		A_						:							List[Any]      = (output_size, output_size) if isinstance(__snake_case		,__snake_case      ) else output_size

		A_  ,     A_						:							Tuple      = get_image_size(__snake_case      )
		A_  ,     A_						:							Dict      = output_size

		# determine new height and width
		A_						:							Tuple      = output_height / input_height
		A_						:							List[Any]      = output_width / input_width

		if keep_aspect_ratio:
				# scale as little as possible
				if abs(1 - scale_width      ) < abs(1 - scale_height      ):
						# fit width
						A_						:							str      = scale_width
				else:
						# fit height
						A_						:							Optional[int]      = scale_height

		A_						:							List[str]      = constraint_to_multiple_of(scale_height * input_height		,multiple=__snake_case      )
		A_						:							List[str]      = constraint_to_multiple_of(scale_width * input_width		,multiple=__snake_case      )

		return (new_height, new_width)





class UpperCAmelCase		(   __lowercase    ):


				'''simple docstring'''






				lowerCamelCase_        =   ['''pixel_values''']
				def __init__(		self						,      lowercase = True						,      lowercase = None						,      lowercase = PILImageResampling.BILINEAR						,      lowercase = False						,      lowercase = 1						,      lowercase = True						,      lowercase = 1 / 2_5_5						,      lowercase = True						,      lowercase = None						,      lowercase = None						,      **lowercase						,      ):



						"""simple docstring"""





						super().__init__(**_A   )
						A_						:							Any      = size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
						A_						:							Dict      = get_size_dict(_A   )
						A_						:							Union[str, Any]      = do_resize
						A_						:							Optional[int]      = size
						A_						:							Optional[Any]      = keep_aspect_ratio
						A_						:							Union[str, Any]      = ensure_multiple_of
						A_						:							List[Any]      = resample
						A_						:							Optional[int]      = do_rescale
						A_						:							str      = rescale_factor
						A_						:							int      = do_normalize
						A_						:							Dict      = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						A_						:							Dict      = image_std if image_std is not None else IMAGENET_STANDARD_STD
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase = False						,      lowercase = 1						,      lowercase = PILImageResampling.BICUBIC						,      lowercase = None						,      **lowercase						,      ):



						"""simple docstring"""





						A_						:							List[str]      = get_size_dict(_A   )
						if "height" not in size or "width" not in size:
								raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''   )
						A_						:							Any      = get_resize_output_image_size(
						    _A						,      output_size=(size['height'], size['width'])						,      keep_aspect_ratio=_A						,      multiple=_A						,      )
						return resize(_A						,      size=_A						,      resample=_A						,      data_format=_A						,      **_A   )
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase = None						,      **lowercase						,      ):



						"""simple docstring"""





						return rescale(_A						,      scale=_A						,      data_format=_A						,      **_A   )
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      lowercase = None						,      **lowercase						,      ):



						"""simple docstring"""





						return normalize(_A						,      mean=_A						,      std=_A						,      data_format=_A						,      **_A   )
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = ChannelDimension.FIRST						,      **lowercase						,      ):



						"""simple docstring"""





						A_						:							str      = do_resize if do_resize is not None else self.do_resize
						A_						:							Union[str, Any]      = size if size is not None else self.size
						A_						:							Optional[int]      = get_size_dict(_A   )
						A_						:							int      = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						A_						:							Dict      = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						A_						:							Optional[int]      = resample if resample is not None else self.resample
						A_						:							List[Any]      = do_rescale if do_rescale is not None else self.do_rescale
						A_						:							Dict      = rescale_factor if rescale_factor is not None else self.rescale_factor
						A_						:							Optional[Any]      = do_normalize if do_normalize is not None else self.do_normalize
						A_						:							Optional[Any]      = image_mean if image_mean is not None else self.image_mean
						A_						:							str      = image_std if image_std is not None else self.image_std

						A_						:							Dict      = make_list_of_images(_A   )

						if not valid_images(_A   ):
								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_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_						:							Optional[Any]      = [to_numpy_array(_A   ) for image in images]

						if do_resize:
								A_						:							Dict      = [self.resize(image=_A						,      size=_A						,      resample=_A   ) for image in images]

						if do_rescale:
								A_						:							Dict      = [self.rescale(image=_A						,      scale=_A   ) for image in images]

						if do_normalize:
								A_						:							Dict      = [self.normalize(image=_A						,      mean=_A						,      std=_A   ) for image in images]

						A_						:							Any      = [to_channel_dimension_format(_A						,      _A   ) for image in images]

						A_						:							str      = {'pixel_values': images}
						return BatchFeature(data=_A						,      tensor_type=_A   )







				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase = None   ):



						"""simple docstring"""





						A_						:							int      = outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
								if len(_A   ) != len(_A   ):
										raise ValueError(
										    'Make sure that you pass in as many target sizes as the batch dimension of the logits'   )

								if is_torch_tensor(_A   ):
										A_						:							List[Any]      = target_sizes.numpy()

								A_						:							str      = []

								for idx in range(len(_A   )   ):
										A_						:							int      = torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0   )						,      size=target_sizes[idx]						,      mode='bilinear'						,      align_corners=_A   )
										A_						:							List[str]      = resized_logits[0].argmax(dim=0   )
										semantic_segmentation.append(_A   )
						else:
								A_						:							List[Any]      = logits.argmax(dim=1   )
								A_						:							Tuple      = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

						return semantic_segmentation







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		) ,			)
