



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


__snake_case		:Optional[Any]   	=  logging.get_logger(__name__)

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					List[Any]		      =		['''pixel_values''']



       def __init__(	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int] = None  ,	__SCREAMING_SNAKE_CASE							:  PILImageResampling = PILImageResampling.BILINEAR  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  Union[int, float] = 1 / 255  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int] = None  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	**__SCREAMING_SNAKE_CASE							:  List[Any]  ,	):


           '''simple docstring'''

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

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



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


           '''simple docstring'''

           __a		 =       get_size_dict(__SCREAMING_SNAKE_CASE  ,	default_to_square=__SCREAMING_SNAKE_CASE)
           if "shortest_edge" not in size:
               raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}')
           __a		 =       get_resize_output_image_size(__SCREAMING_SNAKE_CASE  ,	size=size['''shortest_edge''']  ,	default_to_square=__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 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  np.ndarray  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[str, ChannelDimension]] = None  ,	**__SCREAMING_SNAKE_CASE							:  Any  ,	):


           '''simple docstring'''

           __a		 =       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()}')
           return center_crop(__SCREAMING_SNAKE_CASE  ,	size=(size['''height'''], size['''width'''])  ,	data_format=__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  np.ndarray  ,	__SCREAMING_SNAKE_CASE							:  Union[int, float]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[str, ChannelDimension]] = None  ,	**__SCREAMING_SNAKE_CASE							:  List[Any]  ,	):


           '''simple docstring'''

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



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


           '''simple docstring'''

           return flip_channel_order(__SCREAMING_SNAKE_CASE  ,	data_format=__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           __a		 =       do_resize if do_resize is not None else self.do_resize
           __a		 =       resample if resample is not None else self.resample
           __a		 =       do_rescale if do_rescale is not None else self.do_rescale
           __a		 =       rescale_factor if rescale_factor is not None else self.rescale_factor
           __a		 =       do_center_crop if do_center_crop is not None else self.do_center_crop
           __a		 =       (
               do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
           )

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

           __a		 =       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:
               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.
           __a		 =       [to_numpy_array(__SCREAMING_SNAKE_CASE) for image in images]

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

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

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

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

           __a		 =       [to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE) for image in images]

           __a		 =       {'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE  ,	tensor_type=__SCREAMING_SNAKE_CASE)





       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  List[Tuple] = None):


           '''simple docstring'''

           __a		 =       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):
                   __a		 =       target_sizes.numpy()

               __a		 =       []

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

           return semantic_segmentation










import os
import sys
import unittest


__snake_case		:Union[str, Any]   	=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__snake_case		:List[str]   	=  os.path.join(git_repo_path, '''src''', '''transformers''')

__snake_case		:Any   	=  '''
{0} = None
'''

__snake_case		:Dict   	=  '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


__snake_case		:str   	=  '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''

class 					_A (			unittest.TestCase					):



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")''')
           self.assertIsNone(__SCREAMING_SNAKE_CASE)

           __a		 =       find_backend('''    if not is_tokenizers_available():''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''tokenizers''')

           __a		 =       find_backend('''    if not is_tensorflow_text_available():''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''tensorflow_text''')

           __a		 =       find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''sentencepiece_and_tokenizers''')

           __a		 =       find_backend(
               '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''sentencepiece_and_tensorflow_text''')

           __a		 =       find_backend(
               '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''sentencepiece_and_tokenizers_and_vision''')



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       read_init()
           # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
           self.assertIn('''torch'''  ,	__SCREAMING_SNAKE_CASE)
           self.assertIn('''tensorflow_text'''  ,	__SCREAMING_SNAKE_CASE)
           self.assertIn('''sentencepiece_and_tokenizers'''  ,	__SCREAMING_SNAKE_CASE)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn('''BertModel'''  ,	objects['''torch'''])
           self.assertIn('''TFBertModel'''  ,	objects['''tf'''])
           self.assertIn('''FlaxBertModel'''  ,	objects['''flax'''])
           self.assertIn('''BertModel'''  ,	objects['''torch'''])
           self.assertIn('''TFBertTokenizer'''  ,	objects['''tensorflow_text'''])
           self.assertIn('''convert_slow_tokenizer'''  ,	objects['''sentencepiece_and_tokenizers'''])



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       create_dummy_object('''CONSTANT'''  ,	'''\'torch\'''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''\nCONSTANT = None\n''')

           __a		 =       create_dummy_object('''function'''  ,	'''\'torch\'''')
           self.assertEqual(
               __SCREAMING_SNAKE_CASE  ,	'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n''')

           __a		 =       '''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
           __a		 =       create_dummy_object('''FakeClass'''  ,	'''\'torch\'''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)





       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
           __a		 =       create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']})
           self.assertEqual(dummy_files['''torch''']  ,	__SCREAMING_SNAKE_CASE)





