
'''simple docstring'''

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


snake_case_			=     logging.get_logger(__name__)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :np.ndarray    ,	_SCREAMING_SNAKE_CASE      :Union[int, Iterable[int]]    ,	_SCREAMING_SNAKE_CASE      :bool    ,	_SCREAMING_SNAKE_CASE      :int       ):
				def constraint_to_multiple_of(_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Any=0    ,	_SCREAMING_SNAKE_CASE      :str=None       ):
								SCREAMING_SNAKE_CASE  :						Tuple      					= round(val / multiple       ) * multiple

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

								if x < min_val:
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= math.ceil(val / multiple       ) * multiple

								return x

				SCREAMING_SNAKE_CASE  :						Tuple      					= (output_size, output_size) if isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ) else output_size

				SCREAMING_SNAKE_CASE  :						str      					= get_image_size(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= output_size

				# determine new height and width
				SCREAMING_SNAKE_CASE  :						int      					= output_height / input_height
				SCREAMING_SNAKE_CASE  :						Optional[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
												SCREAMING_SNAKE_CASE  :						int      					= scale_width
								else:
												# fit height
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= scale_height

				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= constraint_to_multiple_of(scale_height * input_height    ,	multiple=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Tuple      					= constraint_to_multiple_of(scale_width * input_width    ,	multiple=_SCREAMING_SNAKE_CASE       )

				return (new_height, new_width)




class 	a__     (				_lowercase	):
	__magic_name__		:    Dict	    =     ["pixel_values"]


	def __init__(self : Dict,					__UpperCAmelCase : bool = True,					__UpperCAmelCase : Dict[str, int] = None,					__UpperCAmelCase : PILImageResampling = PILImageResampling.BILINEAR,					__UpperCAmelCase : bool = False,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : bool = True,					__UpperCAmelCase : Union[int, float] = 1 / 255,					__UpperCAmelCase : bool = True,					__UpperCAmelCase : Optional[Union[float, List[float]]] = None,					__UpperCAmelCase : Optional[Union[float, List[float]]] = None,					**__UpperCAmelCase : Optional[int],					)     ->			None:

					"""simple docstring"""

					super().__init__(**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= size if size is not None else {'''height''': 384, '''width''': 384}
					SCREAMING_SNAKE_CASE  :						Dict      					= get_size_dict(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= do_resize
					SCREAMING_SNAKE_CASE  :						Dict      					= size
					SCREAMING_SNAKE_CASE  :						Any      					= keep_aspect_ratio
					SCREAMING_SNAKE_CASE  :						Tuple      					= ensure_multiple_of
					SCREAMING_SNAKE_CASE  :						Dict      					= resample
					SCREAMING_SNAKE_CASE  :						int      					= do_rescale
					SCREAMING_SNAKE_CASE  :						Dict      					= rescale_factor
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= do_normalize
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					SCREAMING_SNAKE_CASE  :						str      					= image_std if image_std is not None else IMAGENET_STANDARD_STD


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : np.ndarray,					__UpperCAmelCase : Dict[str, int],					__UpperCAmelCase : bool = False,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC,					__UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None,					**__UpperCAmelCase : Optional[Any],					)     ->			np.ndarray:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= get_size_dict(__UpperCAmelCase      )
					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()}'''      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= get_resize_output_image_size(
					    __UpperCAmelCase,					output_size=(size['''height'''], size['''width''']),					keep_aspect_ratio=__UpperCAmelCase,					multiple=__UpperCAmelCase,					)
					return resize(__UpperCAmelCase,					size=__UpperCAmelCase,					resample=__UpperCAmelCase,					data_format=__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : np.ndarray,					__UpperCAmelCase : Union[int, float],					__UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None,					**__UpperCAmelCase : List[str],					)     ->			Any:

					"""simple docstring"""

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


	def 				lowercase__  (self : str,					__UpperCAmelCase : np.ndarray,					__UpperCAmelCase : Union[float, List[float]],					__UpperCAmelCase : Union[float, List[float]],					__UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None,					**__UpperCAmelCase : int,					)     ->			np.ndarray:

					"""simple docstring"""

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


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : ImageInput,					__UpperCAmelCase : bool = None,					__UpperCAmelCase : int = None,					__UpperCAmelCase : bool = None,					__UpperCAmelCase : int = None,					__UpperCAmelCase : PILImageResampling = None,					__UpperCAmelCase : bool = None,					__UpperCAmelCase : float = None,					__UpperCAmelCase : bool = None,					__UpperCAmelCase : Optional[Union[float, List[float]]] = None,					__UpperCAmelCase : Optional[Union[float, List[float]]] = None,					__UpperCAmelCase : Optional[Union[str, TensorType]] = None,					__UpperCAmelCase : ChannelDimension = ChannelDimension.FIRST,					**__UpperCAmelCase : int,					)     ->			PIL.Image.Image:

					"""simple docstring"""

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

					SCREAMING_SNAKE_CASE  :						str      					= make_list_of_images(__UpperCAmelCase      )

					if not valid_images(__UpperCAmelCase      ):
									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.
					SCREAMING_SNAKE_CASE  :						int      					= [to_numpy_array(__UpperCAmelCase      ) for image in images]

					if do_resize:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= [self.resize(image=__UpperCAmelCase,					size=__UpperCAmelCase,					resample=__UpperCAmelCase      ) for image in images]

					if do_rescale:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= [self.rescale(image=__UpperCAmelCase,					scale=__UpperCAmelCase      ) for image in images]

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

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [to_channel_dimension_format(__UpperCAmelCase,					__UpperCAmelCase      ) for image in images]

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= {'''pixel_values''': images}
					return BatchFeature(data=__UpperCAmelCase,					tensor_type=__UpperCAmelCase      )






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Dict,					__UpperCAmelCase : List[Tuple] = None      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= outputs.logits

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

									if is_torch_tensor(__UpperCAmelCase      ):
													SCREAMING_SNAKE_CASE  :						Optional[int]      					= target_sizes.numpy()

									SCREAMING_SNAKE_CASE  :						Optional[int]      					= []

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

					return semantic_segmentation









'''simple docstring'''

import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]       ):
				return x + 2




class 	a__     (				unittest.TestCase	):


	def 				lowercase__  (self : List[str]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''x = 3'''
					SCREAMING_SNAKE_CASE  :						Dict      					= {}
					SCREAMING_SNAKE_CASE  :						Any      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3}      )

					SCREAMING_SNAKE_CASE  :						str      					= '''x = y'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''y''': 5}
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 5, '''y''': 5}      )


	def 				lowercase__  (self : Optional[int]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= '''y = add_two(x)'''
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 5}      )

					# Won't work without the tool
					with CaptureStdout() as out:
									SCREAMING_SNAKE_CASE  :						Tuple      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result is None
					assert "tried to execute add_two" in out.out


	def 				lowercase__  (self : Any      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''x = 3'''
					SCREAMING_SNAKE_CASE  :						Any      					= {}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3}      )


	def 				lowercase__  (self : Optional[int]      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''test_dict = {\'x\': x, \'y\': add_two(x)}'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						List[Any]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 5}      )
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}      )


	def 				lowercase__  (self : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''x = 3\ny = 5'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {}
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 5}      )


	def 				lowercase__  (self : Tuple      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''text = f\'This is x: {x}.\''''
					SCREAMING_SNAKE_CASE  :						List[str]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Dict      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == "This is x: 3."
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''text''': '''This is x: 3.'''}      )


	def 				lowercase__  (self : int      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''if x <= 3:\n    y = 2\nelse:\n    y = 5'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						List[str]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 2
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 2}      )

					SCREAMING_SNAKE_CASE  :						Any      					= {'''x''': 8}
					SCREAMING_SNAKE_CASE  :						int      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 8, '''y''': 5}      )


	def 				lowercase__  (self : Any      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''test_list = [x, add_two(x)]'''
					SCREAMING_SNAKE_CASE  :						List[str]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Tuple      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					self.assertListEqual(__UpperCAmelCase,					[3, 5]      )
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_list''': [3, 5]}      )


	def 				lowercase__  (self : int      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= '''y = x'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 3}      )


	def 				lowercase__  (self : Optional[Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= '''test_list = [x, add_two(x)]\ntest_list[1]'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_list''': [3, 5]}      )

					SCREAMING_SNAKE_CASE  :						Dict      					= '''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}      )






	def 				lowercase__  (self : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''x = 0\nfor i in range(3):\n    x = i'''
					SCREAMING_SNAKE_CASE  :						List[Any]      					= {}
					SCREAMING_SNAKE_CASE  :						List[str]      					= evaluate(__UpperCAmelCase,					{'''range''': range},					state=__UpperCAmelCase      )
					assert result == 2
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 2, '''i''': 2}      )




