
from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
	import torch


lowerCAmelCase__	   = logging.get_logger(__name__)

class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       ['pixel_values']


			def __init__(    self      ,      lowercase = True      ,      lowercase = None      ,      lowercase = PILImageResampling.BILINEAR      ,      lowercase = True      ,      lowercase = None      ,      lowercase = True      ,      lowercase = 1 / 255      ,      lowercase = True      ,      lowercase = None      ,      lowercase = None      ,      **lowercase      ,      )  ->						None:
							'''simple docstring'''






							super().__init__(**lowercase       )
							A__        =			size if size is not None else {"shortest_edge": 256}
							A__        =			get_size_dict(lowercase      ,      default_to_square=lowercase       )
							A__        =			crop_size if crop_size is not None else {"height": 224, "width": 224}
							A__        =			get_size_dict(lowercase      ,      param_name="crop_size"       )
							A__        =			do_resize
							A__        =			size
							A__        =			resample
							A__        =			do_center_crop
							A__        =			crop_size
							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      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = PILImageResampling.BICUBIC      ,      lowercase = None      ,      **lowercase      ,      )  ->						np.ndarray:
							'''simple docstring'''






							A__        =			get_size_dict(lowercase      ,      default_to_square=lowercase       )
							if "shortest_edge" not in size:
											raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'       )
							A__        =			get_resize_output_image_size(lowercase      ,      size=size["shortest_edge"]      ,      default_to_square=lowercase       )
							return resize(lowercase      ,      size=lowercase      ,      resample=lowercase      ,      data_format=lowercase      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase      ,      )  ->						np.ndarray:
							'''simple docstring'''






							A__        =			get_size_dict(lowercase       )
							if "height" not in size or "width" not in size:
											raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'       )
							return center_crop(lowercase      ,      size=(size["height"], size["width"])      ,      data_format=lowercase      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase       )  ->						np.ndarray:
							'''simple docstring'''






							return rescale(lowercase      ,      scale=lowercase      ,      data_format=lowercase      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase      ,      )  ->						np.ndarray:
							'''simple docstring'''






							return normalize(lowercase      ,      mean=lowercase      ,      std=lowercase      ,      data_format=lowercase      ,      **lowercase       )


			def      UpperCamelCase		(    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      ,      )  ->						Any:
							'''simple docstring'''






							A__        =			do_resize if do_resize is not None else self.do_resize
							A__        =			size if size is not None else self.size
							A__        =			get_size_dict(lowercase      ,      default_to_square=lowercase       )
							A__        =			resample if resample is not None else self.resample
							A__        =			do_center_crop if do_center_crop is not None else self.do_center_crop
							A__        =			crop_size if crop_size is not None else self.crop_size
							A__        =			get_size_dict(lowercase      ,      param_name="crop_size"       )
							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__        =			make_list_of_images(lowercase       )

							if not valid_images(lowercase       ):
											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:
											raise ValueError("Size must be specified if do_resize is True."       )

							if do_center_crop and crop_size is None:
											raise ValueError("Crop size must be specified if do_center_crop 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__        =			[to_numpy_array(lowercase       ) for image in images]

							if do_resize:
											A__        =			[self.resize(image=lowercase      ,      size=lowercase      ,      resample=lowercase       ) for image in images]

							if do_center_crop:
											A__        =			[self.center_crop(image=lowercase      ,      size=lowercase       ) for image in images]

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

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

							A__        =			[to_channel_dimension_format(lowercase      ,      lowercase       ) for image in images]

							A__        =			{"pixel_values": images}
							return BatchFeature(data=lowercase      ,      tensor_type=lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None       )  ->						str:
							'''simple docstring'''






							A__        =			outputs.logits

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

											if is_torch_tensor(lowercase       ):
															A__        =			target_sizes.numpy()

											A__        =			[]

											for idx in range(len(lowercase       )       ):
															A__        =			torch.nn.functional.interpolate(
															    logits[idx].unsqueeze(dim=0       )      ,      size=target_sizes[idx]      ,      mode="bilinear"      ,      align_corners=lowercase       )
															A__        =			resized_logits[0].argmax(dim=0       )
															semantic_segmentation.append(lowercase       )
							else:
											A__        =			logits.argmax(dim=1       )
											A__        =			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

							return semantic_segmentation




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
	from .autoencoder_kl import AutoencoderKL
	from .controlnet import ControlNetModel
	from .dual_transformer_ad import DualTransformeraDModel
	from .modeling_utils import ModelMixin
	from .prior_transformer import PriorTransformer
	from .ta_film_transformer import TaFilmDecoder
	from .transformer_ad import TransformeraDModel
	from .unet_ad import UNetaDModel
	from .unet_ad import UNetaDModel
	from .unet_ad_condition import UNetaDConditionModel
	from .unet_ad_condition import UNetaDConditionModel
	from .vq_model import VQModel

if is_flax_available():
	from .controlnet_flax import FlaxControlNetModel
	from .unet_ad_condition_flax import FlaxUNetaDConditionModel
	from .vae_flax import FlaxAutoencoderKL

