
'''simple docstring'''





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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
      import PIL

if is_torch_available():
      import torch


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    int				=							["pixel_values"]







 def __init__(					self		:  Optional[Any]			,				snake_case__		:  bool = True			,				snake_case__		:  Dict[str, int] = None			,				snake_case__		:  PILImageResampling = PILImageResampling.BILINEAR			,				snake_case__		:  bool = True			,				snake_case__		:  Union[int, float] = 1 / 2_55			,				snake_case__		:  bool = True			,				snake_case__		:  Dict[str, int] = None			,				snake_case__		:  bool = True			,				**snake_case__		:  Union[str, Any]			,				):



   '''simple docstring'''




   super().__init__(**snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							size if size is not None else {"shortest_edge": 2_24}
   UpperCAmelCase__      :					List[str]				       =							get_size_dict(snake_case__			,				default_to_square=snake_case__ )
   UpperCAmelCase__      :					Dict				       =							crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
   UpperCAmelCase__      :					Dict				       =							get_size_dict(snake_case__			,				param_name="crop_size" )

   UpperCAmelCase__      :					List[str]				       =							do_resize
   UpperCAmelCase__      :					Tuple				       =							size
   UpperCAmelCase__      :					List[str]				       =							resample
   UpperCAmelCase__      :					str				       =							do_rescale
   UpperCAmelCase__      :					List[str]				       =							rescale_factor
   UpperCAmelCase__      :					str				       =							do_center_crop
   UpperCAmelCase__      :					Dict				       =							crop_size
   UpperCAmelCase__      :					List[str]				       =							do_flip_channel_order







 def 							UpperCamelCase  (					self		:  Any			,				snake_case__		:  np.ndarray			,				snake_case__		:  Dict[str, int]			,				snake_case__		:  PILImageResampling = PIL.Image.BILINEAR			,				snake_case__		:  Optional[Union[str, ChannelDimension]] = None			,				**snake_case__		:  Optional[Any]			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							get_size_dict(snake_case__			,				default_to_square=snake_case__ )
   if "shortest_edge" not in size:
     raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}""" )
   UpperCAmelCase__      :					Optional[int]				       =							get_resize_output_image_size(snake_case__			,				size=size["shortest_edge"]			,				default_to_square=snake_case__ )
   return resize(snake_case__			,				size=snake_case__			,				resample=snake_case__			,				data_format=snake_case__			,				**snake_case__ )







 def 							UpperCamelCase  (					self		:  List[Any]			,				snake_case__		:  np.ndarray			,				snake_case__		:  Dict[str, int]			,				snake_case__		:  Optional[Union[str, ChannelDimension]] = None			,				**snake_case__		:  Optional[int]			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							get_size_dict(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()}""" )
   return center_crop(snake_case__			,				size=(size["height"], size["width"])			,				data_format=snake_case__			,				**snake_case__ )







 def 							UpperCamelCase  (					self		:  Dict			,				snake_case__		:  np.ndarray			,				snake_case__		:  Union[int, float]			,				snake_case__		:  Optional[Union[str, ChannelDimension]] = None			,				**snake_case__		:  int			,				):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  np.ndarray			,				snake_case__		:  Optional[Union[str, ChannelDimension]] = None ):



   '''simple docstring'''




   return flip_channel_order(snake_case__			,				data_format=snake_case__ )







 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  ImageInput			,				snake_case__		:  bool = None			,				snake_case__		:  Dict[str, int] = None			,				snake_case__		:  PILImageResampling = None			,				snake_case__		:  bool = None			,				snake_case__		:  float = None			,				snake_case__		:  bool = None			,				snake_case__		:  Dict[str, int] = None			,				snake_case__		:  bool = None			,				snake_case__		:  Optional[Union[str, TensorType]] = None			,				snake_case__		:  ChannelDimension = ChannelDimension.FIRST			,				**snake_case__		:  Tuple			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase__      :					Union[str, Any]				       =							resample if resample is not None else self.resample
   UpperCAmelCase__      :					Optional[Any]				       =							do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase__      :					Union[str, Any]				       =							rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase__      :					int				       =							do_center_crop if do_center_crop is not None else self.do_center_crop
   UpperCAmelCase__      :					List[str]				       =							(
       do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
   )

   UpperCAmelCase__      :					str				       =							size if size is not None else self.size
   UpperCAmelCase__      :					int				       =							get_size_dict(snake_case__			,				default_to_square=snake_case__ )
   UpperCAmelCase__      :					Dict				       =							crop_size if crop_size is not None else self.crop_size
   UpperCAmelCase__      :					Optional[int]				       =							get_size_dict(snake_case__			,				param_name="crop_size" )

   UpperCAmelCase__      :					Union[str, Any]				       =							make_list_of_images(snake_case__ )

   if not valid_images(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:
     raise ValueError("Size 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_center_crop and crop_size is None:
     raise ValueError("Crop size must be specified if do_center_crop is True." )

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

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

   if do_center_crop:
     UpperCAmelCase__      :					Union[str, Any]				       =							[self.center_crop(image=snake_case__			,				size=snake_case__ ) for image in images]

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

   # the pretrained checkpoints assume images are BGR, not RGB
   if do_flip_channel_order:
     UpperCAmelCase__      :					str				       =							[self.flip_channel_order(image=snake_case__ ) for image in images]

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

   UpperCAmelCase__      :					int				       =							{"pixel_values": images}
   return BatchFeature(data=snake_case__			,				tensor_type=snake_case__ )







 def 							UpperCamelCase  (					self		:  Optional[int]			,				snake_case__		:  Union[str, Any]			,				snake_case__		:  List[Tuple] = None ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							outputs.logits

   # Resize logits and compute semantic segmentation maps
   if target_sizes is not None:
     if len(snake_case__ ) != len(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(snake_case__ ):
       UpperCAmelCase__      :					Any				       =							target_sizes.numpy()

     UpperCAmelCase__      :					Optional[Any]				       =							[]

     for idx in range(len(snake_case__ ) ):
       UpperCAmelCase__      :					Union[str, Any]				       =							torch.nn.functional.interpolate(
           logits[idx].unsqueeze(dim=0 )			,				size=target_sizes[idx]			,				mode="bilinear"			,				align_corners=snake_case__ )
       UpperCAmelCase__      :					str				       =							resized_logits[0].argmax(dim=0 )
       semantic_segmentation.append(snake_case__ )
   else:
     UpperCAmelCase__      :					Optional[Any]				       =							logits.argmax(dim=1 )
     UpperCAmelCase__      :					Optional[Any]				       =							[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 snake_case_     (					lowercase__		):
  return x + 2






class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							"x = 3"
   UpperCAmelCase__      :					List[str]				       =							{}
   UpperCAmelCase__      :					Tuple				       =							evaluate(snake_case__			,				{}			,				state=snake_case__ )
   assert result == 3
   self.assertDictEqual(snake_case__			,				{"x": 3} )

   UpperCAmelCase__      :					Any				       =							"x = y"
   UpperCAmelCase__      :					List[str]				       =							{"y": 5}
   UpperCAmelCase__      :					Dict				       =							evaluate(snake_case__			,				{}			,				state=snake_case__ )
   # evaluate returns the value of the last assignment.
   assert result == 5
   self.assertDictEqual(snake_case__			,				{"x": 5, "y": 5} )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							"y = add_two(x)"
   UpperCAmelCase__      :					Tuple				       =							{"x": 3}
   UpperCAmelCase__      :					Union[str, Any]				       =							evaluate(snake_case__			,				{"add_two": add_two}			,				state=snake_case__ )
   assert result == 5
   self.assertDictEqual(snake_case__			,				{"x": 3, "y": 5} )

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







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							"x = 3"
   UpperCAmelCase__      :					List[Any]				       =							{}
   UpperCAmelCase__      :					Tuple				       =							evaluate(snake_case__			,				{}			,				state=snake_case__ )
   assert result == 3
   self.assertDictEqual(snake_case__			,				{"x": 3} )







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							"test_dict = {'x': x, 'y': add_two(x)}"
   UpperCAmelCase__      :					List[Any]				       =							{"x": 3}
   UpperCAmelCase__      :					int				       =							evaluate(snake_case__			,				{"add_two": add_two}			,				state=snake_case__ )
   self.assertDictEqual(snake_case__			,				{"x": 3, "y": 5} )
   self.assertDictEqual(snake_case__			,				{"x": 3, "test_dict": {"x": 3, "y": 5}} )







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							"x = 3\ny = 5"
   UpperCAmelCase__      :					Dict				       =							{}
   UpperCAmelCase__      :					Union[str, Any]				       =							evaluate(snake_case__			,				{}			,				state=snake_case__ )
   # evaluate returns the value of the last assignment.
   assert result == 5
   self.assertDictEqual(snake_case__			,				{"x": 3, "y": 5} )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							"text = f'This is x: {x}.'"
   UpperCAmelCase__      :					Dict				       =							{"x": 3}
   UpperCAmelCase__      :					Dict				       =							evaluate(snake_case__			,				{}			,				state=snake_case__ )
   # evaluate returns the value of the last assignment.
   assert result == "This is x: 3."
   self.assertDictEqual(snake_case__			,				{"x": 3, "text": "This is x: 3."} )







 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							"if x <= 3:\n    y = 2\nelse:\n    y = 5"
   UpperCAmelCase__      :					Dict				       =							{"x": 3}
   UpperCAmelCase__      :					Dict				       =							evaluate(snake_case__			,				{}			,				state=snake_case__ )
   # evaluate returns the value of the last assignment.
   assert result == 2
   self.assertDictEqual(snake_case__			,				{"x": 3, "y": 2} )

   UpperCAmelCase__      :					Optional[Any]				       =							{"x": 8}
   UpperCAmelCase__      :					str				       =							evaluate(snake_case__			,				{}			,				state=snake_case__ )
   # evaluate returns the value of the last assignment.
   assert result == 5
   self.assertDictEqual(snake_case__			,				{"x": 8, "y": 5} )







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							"test_list = [x, add_two(x)]"
   UpperCAmelCase__      :					Any				       =							{"x": 3}
   UpperCAmelCase__      :					Any				       =							evaluate(snake_case__			,				{"add_two": add_two}			,				state=snake_case__ )
   self.assertListEqual(snake_case__			,				[3, 5] )
   self.assertDictEqual(snake_case__			,				{"x": 3, "test_list": [3, 5]} )







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							"y = x"
   UpperCAmelCase__      :					str				       =							{"x": 3}
   UpperCAmelCase__      :					Any				       =							evaluate(snake_case__			,				{}			,				state=snake_case__ )
   assert result == 3
   self.assertDictEqual(snake_case__			,				{"x": 3, "y": 3} )







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							"test_list = [x, add_two(x)]\ntest_list[1]"
   UpperCAmelCase__      :					List[Any]				       =							{"x": 3}
   UpperCAmelCase__      :					List[Any]				       =							evaluate(snake_case__			,				{"add_two": add_two}			,				state=snake_case__ )
   assert result == 5
   self.assertDictEqual(snake_case__			,				{"x": 3, "test_list": [3, 5]} )

   UpperCAmelCase__      :					Union[str, Any]				       =							"test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
   UpperCAmelCase__      :					str				       =							{"x": 3}
   UpperCAmelCase__      :					Any				       =							evaluate(snake_case__			,				{"add_two": add_two}			,				state=snake_case__ )
   assert result == 5
   self.assertDictEqual(snake_case__			,				{"x": 3, "test_dict": {"x": 3, "y": 5}} )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




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



