






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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__lowerCamelCase :      Dict										=				logging.get_logger(__name__)


if is_vision_available():
	import PIL



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["pixel_values"]



					def __init__( self					:  Optional[Any]							,		__A					:  bool = True							,		__A					:  Dict[str, int] = None							,		__A					:  PILImageResampling = PILImageResampling.BICUBIC							,		__A					:  bool = True							,		__A					:  Dict[str, int] = None							,		__A					:  bool = True							,		__A					:  Union[int, float] = 1 / 2_5_5							,		__A					:  bool = True							,		__A					:  Optional[Union[float, List[float]]] = None							,		__A					:  Optional[Union[float, List[float]]] = None							,		__A					:  bool = True							,		**__A					:  str							,		):
						super().__init__(**__A	)
						snake_case__			:				Optional[Any]       =  size if size is not None else {"shortest_edge": 2_2_4}
						snake_case__			:				Optional[int]       =  get_size_dict(__A							,		default_to_square=__A	)
						snake_case__			:				List[Any]       =  crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						snake_case__			:				Dict       =  get_size_dict(__A							,		default_to_square=__A							,		param_name="crop_size"	)

						snake_case__			:				Tuple       =  do_resize
						snake_case__			:				Any       =  size
						snake_case__			:				List[Any]       =  resample
						snake_case__			:				Any       =  do_center_crop
						snake_case__			:				Tuple       =  crop_size
						snake_case__			:				int       =  do_rescale
						snake_case__			:				Optional[Any]       =  rescale_factor
						snake_case__			:				List[Any]       =  do_normalize
						snake_case__			:				str       =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						snake_case__			:				Tuple       =  image_std if image_std is not None else OPENAI_CLIP_STD
						snake_case__			:				Optional[int]       =  do_convert_rgb



					def 	_lowercase					( self					:  List[str]							,		__A					:  np.ndarray							,		__A					:  Dict[str, int]							,		__A					:  PILImageResampling = PILImageResampling.BICUBIC							,		__A					:  Optional[Union[str, ChannelDimension]] = None							,		**__A					:  Dict							,		):
						snake_case__			:				Tuple       =  get_size_dict(__A							,		default_to_square=__A	)
						if "shortest_edge" not in size:
							raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''	)
						snake_case__			:				List[Any]       =  get_resize_output_image_size(__A							,		size=size["shortest_edge"]							,		default_to_square=__A	)
						return resize(__A							,		size=__A							,		resample=__A							,		data_format=__A							,		**__A	)



					def 	_lowercase					( self					:  Dict							,		__A					:  np.ndarray							,		__A					:  Dict[str, int]							,		__A					:  Optional[Union[str, ChannelDimension]] = None							,		**__A					:  List[str]							,		):
						snake_case__			:				Optional[int]       =  get_size_dict(__A	)
						if "height" not in size or "width" not in size:
							raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''	)
						return center_crop(__A							,		size=(size["height"], size["width"])							,		data_format=__A							,		**__A	)



					def 	_lowercase					( self					:  int							,		__A					:  np.ndarray							,		__A					:  Union[int, float]							,		__A					:  Optional[Union[str, ChannelDimension]] = None							,		**__A					:  Any							,		):
						return rescale(__A							,		scale=__A							,		data_format=__A							,		**__A	)



					def 	_lowercase					( self					:  Any							,		__A					:  np.ndarray							,		__A					:  Union[float, List[float]]							,		__A					:  Union[float, List[float]]							,		__A					:  Optional[Union[str, ChannelDimension]] = None							,		**__A					:  Union[str, Any]							,		):
						return normalize(__A							,		mean=__A							,		std=__A							,		data_format=__A							,		**__A	)






					def 	_lowercase					( self					:  Tuple							,		__A					:  ImageInput							,		__A					:  bool = None							,		__A					:  Dict[str, int] = None							,		__A					:  PILImageResampling = None							,		__A					:  bool = None							,		__A					:  int = None							,		__A					:  bool = None							,		__A					:  float = None							,		__A					:  bool = None							,		__A					:  Optional[Union[float, List[float]]] = None							,		__A					:  Optional[Union[float, List[float]]] = None							,		__A					:  bool = None							,		__A					:  Optional[Union[str, TensorType]] = None							,		__A					:  Optional[ChannelDimension] = ChannelDimension.FIRST							,		**__A					:  Optional[int]							,		):
						snake_case__			:				int       =  do_resize if do_resize is not None else self.do_resize
						snake_case__			:				Optional[int]       =  size if size is not None else self.size
						snake_case__			:				int       =  get_size_dict(__A							,		param_name="size"							,		default_to_square=__A	)
						snake_case__			:				Tuple       =  resample if resample is not None else self.resample
						snake_case__			:				List[Any]       =  do_center_crop if do_center_crop is not None else self.do_center_crop
						snake_case__			:				Dict       =  crop_size if crop_size is not None else self.crop_size
						snake_case__			:				List[str]       =  get_size_dict(__A							,		param_name="crop_size"							,		default_to_square=__A	)
						snake_case__			:				Any       =  do_rescale if do_rescale is not None else self.do_rescale
						snake_case__			:				Union[str, Any]       =  rescale_factor if rescale_factor is not None else self.rescale_factor
						snake_case__			:				str       =  do_normalize if do_normalize is not None else self.do_normalize
						snake_case__			:				Union[str, Any]       =  image_mean if image_mean is not None else self.image_mean
						snake_case__			:				Tuple       =  image_std if image_std is not None else self.image_std
						snake_case__			:				Any       =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						snake_case__			:				Optional[int]       =  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:
							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."	)

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
							snake_case__			:				Optional[Any]       =  [convert_to_rgb(__A	) for image in images]

						# All transformations expect numpy arrays.
						snake_case__			:				int       =  [to_numpy_array(__A	) for image in images]

						if do_resize:
							snake_case__			:				Union[str, Any]       =  [self.resize(image=__A							,		size=__A							,		resample=__A	) for image in images]

						if do_center_crop:
							snake_case__			:				Optional[int]       =  [self.center_crop(image=__A							,		size=__A	) for image in images]

						if do_rescale:
							snake_case__			:				Tuple       =  [self.rescale(image=__A							,		scale=__A	) for image in images]

						if do_normalize:
							snake_case__			:				Union[str, Any]       =  [self.normalize(image=__A							,		mean=__A							,		std=__A	) for image in images]

						snake_case__			:				Union[str, Any]       =  [to_channel_dimension_format(__A							,		__A	) for image in images]

						snake_case__			:				List[str]       =  {"pixel_values": images}
						return BatchFeature(data=__A							,		tensor_type=__A	)











from datetime import datetime

import matplotlib.pyplot as plt
import torch







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	for param in module.parameters():
		snake_case__			:				Tuple       =  False







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				Any       =  "cuda" if torch.cuda.is_available() else "cpu"
	if torch.backends.mps.is_available() and torch.backends.mps.is_built():
		snake_case__			:				List[Any]       =  "mps"
	if device == "mps":
		print(
		    "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
		    " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
		    " with generations."       )
	return device







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				List[str]       =  plt.imshow(snake_case_       )
	fig.axes.get_xaxis().set_visible(snake_case_       )
	fig.axes.get_yaxis().set_visible(snake_case_       )
	plt.show()







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				str       =  datetime.now()
	snake_case__			:				Optional[Any]       =  current_time.strftime("%H:%M:%S"       )
	return timestamp



