




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


__magic_name__								=logging.get_logger(__name__)



class     _A   (    _UpperCAmelCase							):
  SCREAMING_SNAKE_CASE_	:	int						=["pixel_values"]






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





       '''simple docstring'''





       super().__init__(**lowercase__	)
       UpperCamelCase__										=      size if size is not None else {"shortest_edge": 224}
       UpperCamelCase__										=      get_size_dict(lowercase__   ,				default_to_square=lowercase__	)
       UpperCamelCase__										=      crop_size if crop_size is not None else {"height": 256, "width": 256}
       UpperCamelCase__										=      get_size_dict(lowercase__   ,				param_name='''crop_size'''	)

       UpperCamelCase__										=      do_resize
       UpperCamelCase__										=      size
       UpperCamelCase__										=      resample
       UpperCamelCase__										=      do_rescale
       UpperCamelCase__										=      rescale_factor
       UpperCamelCase__										=      do_center_crop
       UpperCamelCase__										=      crop_size
       UpperCamelCase__										=      do_flip_channel_order






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = PIL.Image.BILINEAR   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Union[str, Any]:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      get_size_dict(lowercase__	)
       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(lowercase__   ,				size=(size['''height'''], size['''width'''])   ,				data_format=lowercase__   ,				**lowercase__	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[Any]:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      Union[str, Any]:





       '''simple docstring'''





       return flip_channel_order(lowercase__   ,				data_format=lowercase__	)






  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_ = ChannelDimension.FIRST   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      do_resize if do_resize is not None else self.do_resize
       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_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase__										=      (
           do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
       )

       UpperCamelCase__										=      size if size is not None else self.size
       UpperCamelCase__										=      get_size_dict(lowercase__   ,				default_to_square=lowercase__	)
       UpperCamelCase__										=      crop_size if crop_size is not None else self.crop_size
       UpperCamelCase__										=      get_size_dict(lowercase__   ,				param_name='''crop_size'''	)

       UpperCamelCase__										=      make_list_of_images(lowercase__	)

       if not valid_images(lowercase__	):
            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__										=      [to_numpy_array(lowercase__	) for image in images]

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

       if do_center_crop:
            UpperCamelCase__										=      [self.center_crop(image=lowercase__   ,				size=lowercase__	) for image in images]

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

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

       UpperCamelCase__										=      [to_channel_dimension_format(lowercase__   ,				lowercase__	) for image in images]

       UpperCamelCase__										=      {"pixel_values": images}
       return BatchFeature(data=lowercase__   ,				tensor_type=lowercase__	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      outputs.logits

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

            if is_torch_tensor(lowercase__	):
                 UpperCamelCase__										=      target_sizes.numpy()

            UpperCamelCase__										=      []

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

       return semantic_segmentation




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch

     from transformers.activations import gelu_new, gelu_python, get_activation



@require_torch
class     _A   (    unittest.TestCase							):






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]	)
       UpperCamelCase__										=      get_activation('''gelu'''	)
       self.assertTrue(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE_	)   ,				torch_builtin(SCREAMING_SNAKE_CASE_	)	)	)
       self.assertFalse(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE_	)   ,				gelu_new(SCREAMING_SNAKE_CASE_	)	)	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]	)
       UpperCamelCase__										=      get_activation('''gelu'''	)
       UpperCamelCase__										=      get_activation('''gelu_10'''	)

       UpperCamelCase__										=      torch_builtin(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      geluaa(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.where(y_gelu_aa < 10.0   ,				1   ,				0	)

       self.assertTrue(torch.max(SCREAMING_SNAKE_CASE_	).item() == 10.0	)
       self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,				y_gelu_aa * clipped_mask	)	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       get_activation('''gelu'''	)
       get_activation('''gelu_10'''	)
       get_activation('''gelu_fast'''	)
       get_activation('''gelu_new'''	)
       get_activation('''gelu_python'''	)
       get_activation('''gelu_pytorch_tanh'''	)
       get_activation('''linear'''	)
       get_activation('''mish'''	)
       get_activation('''quick_gelu'''	)
       get_activation('''relu'''	)
       get_activation('''sigmoid'''	)
       get_activation('''silu'''	)
       get_activation('''swish'''	)
       get_activation('''tanh'''	)
       with self.assertRaises(SCREAMING_SNAKE_CASE_	):
            get_activation('''bogus'''	)
       with self.assertRaises(SCREAMING_SNAKE_CASE_	):
            get_activation(SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      get_activation('''gelu'''	)
       UpperCamelCase__										=      1
       UpperCamelCase__										=      get_activation('''gelu'''	)
       self.assertEqual(acta.a   ,				1	)
       with self.assertRaises(SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      acta.a



