



import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 						lowerCamelCase_     (			__a    ):






						'''simple docstring'''
						a__ =	CONFIG_NAME







						@register_to_config
						def __init__(						self   :      Dict						,						__lowerCamelCase   :      bool = True						,						__lowerCamelCase   :      int = 8						,						__lowerCamelCase   :      str = "lanczos"						,						__lowerCamelCase   :      bool = True						,						__lowerCamelCase   :      bool = False						,						)   ->					Tuple:
									super().__init__()







						@staticmethod
						def       SCREAMING_SNAKE_CASE__		(						__lowerCamelCase   :      np.ndarray			)   ->					Optional[int]:

									if images.ndim == 3:
												A				:       Union[str, Any]														=    images[None, ...]
									A				:       Any														=    (images * 2_55).round().astype("uint8"			)
									if images.shape[-1] == 1:
												# special case for grayscale (single channel) images
												A				:       Dict														=    [Image.fromarray(image.squeeze()						,						mode="L"			) for image in images]
									else:
												A				:       str														=    [Image.fromarray(__lowerCamelCase			) for image in images]

									return pil_images







						@staticmethod
						def       SCREAMING_SNAKE_CASE__		(						__lowerCamelCase   :      Union[List[PIL.Image.Image], PIL.Image.Image]			)   ->					List[str]:

									if not isinstance(__lowerCamelCase						,						__lowerCamelCase			):
												A				:       Optional[Any]														=    [images]
									A				:       str														=    [np.array(__lowerCamelCase			).astype(np.floataa			) / 255.0 for image in images]
									A				:       List[str]														=    np.stack(__lowerCamelCase						,						axis=0			)

									return images







						@staticmethod
						def       SCREAMING_SNAKE_CASE__		(						__lowerCamelCase   :      np.ndarray			)   ->					Any:

									if images.ndim == 3:
												A				:       Tuple														=    images[..., None]

									A				:       Any														=    torch.from_numpy(images.transpose(0						,						3						,						1						,						2			)			)
									return images







						@staticmethod
						def       SCREAMING_SNAKE_CASE__		(						__lowerCamelCase   :      torch.FloatTensor			)   ->					Optional[int]:
									A				:       List[Any]														=    images.cpu().permute(0						,						2						,						3						,						1			).float().numpy()
									return images







						@staticmethod
						def       SCREAMING_SNAKE_CASE__		(						__lowerCamelCase   :      Union[str, Any]			)   ->					Optional[int]:
									return 2.0 * images - 1.0







						@staticmethod
						def       SCREAMING_SNAKE_CASE__		(						__lowerCamelCase   :      int			)   ->					Tuple:
									return (images / 2 + 0.5).clamp(0						,						1			)







						@staticmethod
						def       SCREAMING_SNAKE_CASE__		(						__lowerCamelCase   :      PIL.Image.Image			)   ->					Any:
									A				:       Optional[Any]														=    image.convert("RGB"			)
									return image







						def       SCREAMING_SNAKE_CASE__		(						self   :      Dict						,						__lowerCamelCase   :      PIL.Image.Image						,						__lowerCamelCase   :      Optional[int] = None						,						__lowerCamelCase   :      Optional[int] = None						,						)   ->					str:

									if height is None:
												A				:       str														=    image.height
									if width is None:
												A				:       List[str]														=    image.width

									A							,			A				:       str														=    (
									    x - x % self.config.vae_scale_factor for x in (width, height)
									)  # resize to integer multiple of vae_scale_factor
									A				:       Dict														=    image.resize((width, height)						,						resample=PIL_INTERPOLATION[self.config.resample]			)
									return image







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]						,						__lowerCamelCase   :      Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]						,						__lowerCamelCase   :      Optional[int] = None						,						__lowerCamelCase   :      Optional[int] = None						,						)   ->					Any:
									A				:       Optional[Any]														=    (PIL.Image.Image, np.ndarray, torch.Tensor)
									if isinstance(__lowerCamelCase						,						__lowerCamelCase			):
												A				:       List[Any]														=    [image]
									elif not (isinstance(__lowerCamelCase						,						__lowerCamelCase			) and all(isinstance(__lowerCamelCase						,						__lowerCamelCase			) for i in image			)):
												raise ValueError(
												    F"""Input is in incorrect format: {[type(__lowerCamelCase			) for i in image]}. Currently, we only support {", ".join(__lowerCamelCase			)}"""			)

									if isinstance(image[0]						,						PIL.Image.Image			):
												if self.config.do_convert_rgb:
															A				:       List[Any]														=    [self.convert_to_rgb(__lowerCamelCase			) for i in image]
												if self.config.do_resize:
															A				:       int														=    [self.resize(__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			) for i in image]
												A				:       str														=    self.pil_to_numpy(__lowerCamelCase			)  # to np
												A				:       Union[str, Any]														=    self.numpy_to_pt(__lowerCamelCase			)  # to pt

									elif isinstance(image[0]						,						np.ndarray			):
												A				:       List[Any]														=    np.concatenate(__lowerCamelCase						,						axis=0			) if image[0].ndim == 4 else np.stack(__lowerCamelCase						,						axis=0			)
												A				:       Tuple														=    self.numpy_to_pt(__lowerCamelCase			)
												A							,			A							,			A							,			A				:       Optional[int]														=    image.shape
												if self.config.do_resize and (
												    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
												):
															raise ValueError(
															    F"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
															    F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""			)

									elif isinstance(image[0]						,						torch.Tensor			):
												A				:       List[Any]														=    torch.cat(__lowerCamelCase						,						axis=0			) if image[0].ndim == 4 else torch.stack(__lowerCamelCase						,						axis=0			)
												A							,			A							,			A							,			A				:       Optional[Any]														=    image.shape

												# don't need any preprocess if the image is latents
												if channel == 4:
															return image

												if self.config.do_resize and (
												    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
												):
															raise ValueError(
															    F"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
															    F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""			)

        # expected range [0,1], normalize to [-1,1]
									A				:       Optional[int]														=    self.config.do_normalize
									if image.min() < 0:
												warnings.warn(
												    "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
												    F"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""						,						__lowerCamelCase						,						)
												A				:       Optional[int]														=    False

									if do_normalize:
												A				:       int														=    self.normalize(__lowerCamelCase			)

									return image







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple						,						__lowerCamelCase   :      torch.FloatTensor						,						__lowerCamelCase   :      str = "pil"						,						__lowerCamelCase   :      Optional[List[bool]] = None						,						)   ->					Optional[Any]:

									if not isinstance(__lowerCamelCase						,						torch.Tensor			):
												raise ValueError(
												    F"""Input for postprocessing is in incorrect format: {type(__lowerCamelCase			)}. We only support pytorch tensor"""			)
									if output_type not in ["latent", "pt", "np", "pil"]:
												A				:       Optional[int]														=    (
												    F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
												    "`pil`, `np`, `pt`, `latent`"
												)
												deprecate("Unsupported output_type"						,						"1.0.0"						,						__lowerCamelCase						,						standard_warn=__lowerCamelCase			)
												A				:       List[Any]														=    "np"

									if output_type == "latent":
												return image

									if do_denormalize is None:
												A				:       List[Any]														=    [self.config.do_normalize] * image.shape[0]

									A				:       List[Any]														=    torch.stack(
									    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

									if output_type == "pt":
												return image

									A				:       Optional[Any]														=    self.pt_to_numpy(__lowerCamelCase			)

									if output_type == "np":
												return image

									if output_type == "pil":
												return self.numpy_to_pil(__lowerCamelCase			)







class 						lowerCamelCase_     (			__a    ):






						'''simple docstring'''
						a__ =	CONFIG_NAME







						@register_to_config
						def __init__(						self   :      Optional[Any]						,						__lowerCamelCase   :      bool = True						,						__lowerCamelCase   :      int = 8						,						__lowerCamelCase   :      str = "lanczos"						,						__lowerCamelCase   :      bool = True						,						)   ->					Optional[Any]:
									super().__init__()







						@staticmethod
						def       SCREAMING_SNAKE_CASE__		(						__lowerCamelCase   :      List[Any]			)   ->					List[str]:

									if images.ndim == 3:
												A				:       Any														=    images[None, ...]
									A				:       Dict														=    (images * 2_55).round().astype("uint8"			)
									if images.shape[-1] == 1:
												# special case for grayscale (single channel) images
												A				:       List[Any]														=    [Image.fromarray(image.squeeze()						,						mode="L"			) for image in images]
									else:
												A				:       Tuple														=    [Image.fromarray(image[:, :, :3]			) for image in images]

									return pil_images







						@staticmethod
						def       SCREAMING_SNAKE_CASE__		(						__lowerCamelCase   :      Dict			)   ->					Tuple:
									return image[:, :, 1] * 2**8 + image[:, :, 2]







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]						,						__lowerCamelCase   :      Dict			)   ->					Optional[int]:

									if images.ndim == 3:
												A				:       Union[str, Any]														=    images[None, ...]
									A				:       List[str]														=    images[:, :, :, 3:]
									if images.shape[-1] == 6:
												A				:       List[str]														=    (images_depth * 2_55).round().astype("uint8"			)
												A				:       Tuple														=    [
												    Image.fromarray(self.rgblike_to_depthmap(__lowerCamelCase			)						,						mode="I;16"			) for image_depth in images_depth
												]
									elif images.shape[-1] == 4:
												A				:       List[Any]														=    (images_depth * 6_55_35.0).astype(np.uintaa			)
												A				:       Tuple														=    [Image.fromarray(__lowerCamelCase						,						mode="I;16"			) for image_depth in images_depth]
									else:
												raise Exception("Not supported"			)

									return pil_images







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      torch.FloatTensor						,						__lowerCamelCase   :      str = "pil"						,						__lowerCamelCase   :      Optional[List[bool]] = None						,						)   ->					List[str]:

									if not isinstance(__lowerCamelCase						,						torch.Tensor			):
												raise ValueError(
												    F"""Input for postprocessing is in incorrect format: {type(__lowerCamelCase			)}. We only support pytorch tensor"""			)
									if output_type not in ["latent", "pt", "np", "pil"]:
												A				:       Any														=    (
												    F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
												    "`pil`, `np`, `pt`, `latent`"
												)
												deprecate("Unsupported output_type"						,						"1.0.0"						,						__lowerCamelCase						,						standard_warn=__lowerCamelCase			)
												A				:       List[str]														=    "np"

									if do_denormalize is None:
												A				:       Any														=    [self.config.do_normalize] * image.shape[0]

									A				:       Any														=    torch.stack(
									    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

									A				:       Optional[Any]														=    self.pt_to_numpy(__lowerCamelCase			)

									if output_type == "np":
												if image.shape[-1] == 6:
															A				:       Tuple														=    np.stack([self.rgblike_to_depthmap(im[:, :, 3:]			) for im in image]						,						axis=0			)
												else:
															A				:       Dict														=    image[:, :, :, 3:]
												return image[:, :, :, :3], image_depth

									if output_type == "pil":
												return self.numpy_to_pil(__lowerCamelCase			), self.numpy_to_depth(__lowerCamelCase			)
									else:
												raise Exception(F"""This type {output_type} is not supported"""			)





'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A					       =logging.get_logger(__name__)

A					       ={
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}


class      _a    (      __a					):
       __a							:	Optional[int]  							=   """regnet"""
       __a							:	Optional[Any]  							=   ["""x""", """y"""]
       def __init__(      self		:      int  ,							lowercase		:      List[Any]=3  ,							lowercase		:      Union[str, Any]=32  ,							lowercase		:      Tuple=[128, 192, 512, 1_088]  ,							lowercase		:      List[Any]=[2, 6, 12, 2]  ,							lowercase		:      Optional[Any]=64  ,							lowercase		:      Optional[int]="y"  ,							lowercase		:      int="relu"  ,							**lowercase		:      int  ,							):



           '''simple docstring'''

           super().__init__(**lowercase							)
           if layer_type not in self.layer_types:
               raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types							)}"							)
           UpperCAmelCase     			=						num_channels
           UpperCAmelCase     			=						embedding_size
           UpperCAmelCase     			=						hidden_sizes
           UpperCAmelCase     			=						depths
           UpperCAmelCase     			=						groups_width
           UpperCAmelCase     			=						layer_type
           UpperCAmelCase     			=						hidden_act
           # always downsample in the first stage
           UpperCAmelCase     			=						True




