'''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


_lowerCAmelCase  :List[Any]  				=    logging.get_logger(__name__)






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   int							=						["pixel_values"]






    def __init__(       self    ,							lowercase__ = True    ,							lowercase__ = None    ,							lowercase__ = PILImageResampling.BILINEAR    ,							lowercase__ = True    ,							lowercase__ = 1 / 255    ,							lowercase__ = True    ,							lowercase__ = None    ,							lowercase__ = True    ,							**lowercase__    ,							)							-> None:
         super().__init__(**lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       size if size is not None else {'shortest_edge': 224}
         SCREAMING_SNAKE_CASE					:			Dict    	=       get_size_dict(lowercase__    ,							default_to_square=lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       crop_size if crop_size is not None else {'height': 256, 'width': 256}
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       get_size_dict(lowercase__    ,							param_name='crop_size' )

         SCREAMING_SNAKE_CASE					:			str    	=       do_resize
         SCREAMING_SNAKE_CASE					:			Dict    	=       size
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       resample
         SCREAMING_SNAKE_CASE					:			Any    	=       do_rescale
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       rescale_factor
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       do_center_crop
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       crop_size
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       do_flip_channel_order






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = PIL.Image.BILINEAR    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> np.ndarray:
         SCREAMING_SNAKE_CASE					:			int    	=       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()}""" )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> np.ndarray:
         SCREAMING_SNAKE_CASE					:			str    	=       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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> Any:
         return rescale(lowercase__    ,							scale=lowercase__    ,							data_format=lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> np.ndarray:
         return flip_channel_order(lowercase__    ,							data_format=lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = ChannelDimension.FIRST    ,							**lowercase__    ,							)							-> PIL.Image.Image:
         SCREAMING_SNAKE_CASE					:			Any    	=       do_resize if do_resize is not None else self.do_resize
         SCREAMING_SNAKE_CASE					:			Dict    	=       resample if resample is not None else self.resample
         SCREAMING_SNAKE_CASE					:			int    	=       do_rescale if do_rescale is not None else self.do_rescale
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       rescale_factor if rescale_factor is not None else self.rescale_factor
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       do_center_crop if do_center_crop is not None else self.do_center_crop
         SCREAMING_SNAKE_CASE					:			List[str]    	=       (
             do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
         )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       size if size is not None else self.size
         SCREAMING_SNAKE_CASE					:			Any    	=       get_size_dict(lowercase__    ,							default_to_square=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Any    	=       crop_size if crop_size is not None else self.crop_size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       get_size_dict(lowercase__    ,							param_name='crop_size' )

         SCREAMING_SNAKE_CASE					:			int    	=       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.
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [to_numpy_array(lowercase__ ) for image in images]

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

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

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

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

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [to_channel_dimension_format(lowercase__    ,							lowercase__ ) for image in images]

         SCREAMING_SNAKE_CASE					:			int    	=       {'pixel_values': images}
         return BatchFeature(data=lowercase__    ,							tensor_type=lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> str:
         SCREAMING_SNAKE_CASE					:			Dict    	=       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__ ):
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       target_sizes.numpy()

              SCREAMING_SNAKE_CASE					:			int    	=       []

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

         return semantic_segmentation




'''simple docstring'''





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 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         self.assertTrue(torch.allclose(gelu_python(lowercase__ )    ,							torch_builtin(lowercase__ ) ) )
         self.assertFalse(torch.allclose(gelu_python(lowercase__ )    ,							gelu_new(lowercase__ ) ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			Dict    	=       get_activation('gelu_10' )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch_builtin(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       geluaa(lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.where(y_gelu_aa < 1_0.0    ,							1    ,							0 )

         self.assertTrue(torch.max(lowercase__ ).item() == 1_0.0 )
         self.assertTrue(torch.allclose(y_gelu * clipped_mask    ,							y_gelu_aa * clipped_mask ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         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(lowercase__ ):
              get_activation('bogus' )
         with self.assertRaises(lowercase__ ):
              get_activation(lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			str    	=       1
         SCREAMING_SNAKE_CASE					:			List[str]    	=       get_activation('gelu' )
         self.assertEqual(acta.a    ,							1 )
         with self.assertRaises(lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Dict    	=       acta.a



