
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


lowercase__							:							int									=			logging.get_logger(__name__)




def 	A_ (							snake_case							:   np.ndarray ,      snake_case							:   Union[int, Iterable[int]] ,      snake_case							:   bool ,      snake_case							:   int )      ->    Tuple[int, int]:



	'''simple docstring'''

	def constraint_to_multiple_of(snake_case							:   Any ,      snake_case							:   Optional[int] ,      snake_case							:   List[str]=0 ,      snake_case							:   str=None ):
		__UpperCamelCase       =			round(val / multiple ) * multiple

		if max_val is not None and x > max_val:
			__UpperCamelCase       =			math.floor(val / multiple ) * multiple

		if x < min_val:
			__UpperCamelCase       =			math.ceil(val / multiple ) * multiple

		return x

	__UpperCamelCase       =			(output_size, output_size) if isinstance(snake_case ,      snake_case ) else output_size

	__UpperCamelCase  ,							__UpperCamelCase       =			get_image_size(snake_case )
	__UpperCamelCase  ,							__UpperCamelCase       =			output_size

	# determine new height and width
	__UpperCamelCase       =			output_height / input_height
	__UpperCamelCase       =			output_width / input_width

	if keep_aspect_ratio:
		# scale as little as possible
		if abs(1 - scale_width ) < abs(1 - scale_height ):
			# fit width
			__UpperCamelCase       =			scale_width
		else:
			# fit height
			__UpperCamelCase       =			scale_height

	__UpperCamelCase       =			constraint_to_multiple_of(scale_height * input_height ,      multiple=snake_case )
	__UpperCamelCase       =			constraint_to_multiple_of(scale_width * input_width ,      multiple=snake_case )

	return (new_height, new_width)






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  ['pixel_values']



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = 1 / 255     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   None:





								'''simple docstring'''






								super().__init__(**SCREAMING_SNAKE_CASE_			)
								__UpperCamelCase       =			size if size is not None else {'''height''': 384, '''width''': 384}
								__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_			)
								__UpperCamelCase       =			do_resize
								__UpperCamelCase       =			size
								__UpperCamelCase       =			keep_aspect_ratio
								__UpperCamelCase       =			ensure_multiple_of
								__UpperCamelCase       =			resample
								__UpperCamelCase       =			do_rescale
								__UpperCamelCase       =			rescale_factor
								__UpperCamelCase       =			do_normalize
								__UpperCamelCase       =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__UpperCamelCase       =			image_std if image_std is not None else IMAGENET_STANDARD_STD



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   np.ndarray:





								'''simple docstring'''






								__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_			)
								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()}"			)
								__UpperCamelCase       =			get_resize_output_image_size(
								    SCREAMING_SNAKE_CASE_     ,	output_size=(size['''height'''], size['''width'''])     ,	keep_aspect_ratio=SCREAMING_SNAKE_CASE_     ,	multiple=SCREAMING_SNAKE_CASE_     ,	)
								return resize(SCREAMING_SNAKE_CASE_     ,	size=SCREAMING_SNAKE_CASE_     ,	resample=SCREAMING_SNAKE_CASE_     ,	data_format=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   str:





								'''simple docstring'''






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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   np.ndarray:





								'''simple docstring'''






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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST     ,	**SCREAMING_SNAKE_CASE_     ,	)->   PIL.Image.Image:





								'''simple docstring'''






								__UpperCamelCase       =			do_resize if do_resize is not None else self.do_resize
								__UpperCamelCase       =			size if size is not None else self.size
								__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_			)
								__UpperCamelCase       =			keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
								__UpperCamelCase       =			ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
								__UpperCamelCase       =			resample if resample is not None else self.resample
								__UpperCamelCase       =			do_rescale if do_rescale is not None else self.do_rescale
								__UpperCamelCase       =			rescale_factor if rescale_factor is not None else self.rescale_factor
								__UpperCamelCase       =			do_normalize if do_normalize is not None else self.do_normalize
								__UpperCamelCase       =			image_mean if image_mean is not None else self.image_mean
								__UpperCamelCase       =			image_std if image_std is not None else self.image_std

								__UpperCamelCase       =			make_list_of_images(SCREAMING_SNAKE_CASE_			)

								if not valid_images(SCREAMING_SNAKE_CASE_			):
									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.
								__UpperCamelCase       =			[to_numpy_array(SCREAMING_SNAKE_CASE_			) for image in images]

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

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

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

								__UpperCamelCase       =			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) for image in images]

								__UpperCamelCase       =			{'''pixel_values''': images}
								return BatchFeature(data=SCREAMING_SNAKE_CASE_     ,	tensor_type=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   Tuple:





								'''simple docstring'''






								__UpperCamelCase       =			outputs.logits

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

									if is_torch_tensor(SCREAMING_SNAKE_CASE_			):
										__UpperCamelCase       =			target_sizes.numpy()

									__UpperCamelCase       =			[]

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

								return semantic_segmentation






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__							:							int									=			{
    "configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
    "processing_git": ["GitProcessor"],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			[
				    "GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "GitForCausalLM",
				    "GitModel",
				    "GitPreTrainedModel",
				    "GitVisionModel",
				]

if TYPE_CHECKING:
				from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
				from .processing_git import GitProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_git import (
								    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GitForCausalLM,
								    GitModel,
								    GitPreTrainedModel,
								    GitVisionModel,
								)

else:
				import sys

				lowercase__							:							Optional[Any]									=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




