





"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


_lowerCamelCase       =	logging.get_logger(__name__)





class 			snake_case		(   _snake_case   ):
      lowerCamelCase__						= ["pixel_values"]

      def __init__(			self   :Tuple						,							_lowerCamelCase   :bool = True						,							_lowerCamelCase   :Dict[str, int] = None						,							_lowerCamelCase   :PILImageResampling = PIL.Image.NEAREST						,							_lowerCamelCase   :bool = False						,							_lowerCamelCase   :Dict[str, int] = None						,							_lowerCamelCase   :Union[int, float] = 1 / 2_5_5						,							_lowerCamelCase   :bool = False						,							_lowerCamelCase   :bool = True						,							_lowerCamelCase   :bool = True						,							_lowerCamelCase   :Optional[Union[float, List[float]]] = None						,							_lowerCamelCase   :Optional[Union[float, List[float]]] = None						,							_lowerCamelCase   :bool = True						,							**_lowerCamelCase   :str						,							):
       super().__init__(**_lowercase						)
       __SCREAMING_SNAKE_CASE :			int												=       size if size is not None else {'''height''': 3_4_6, '''width''': 3_4_6}
       __SCREAMING_SNAKE_CASE :			Dict												=       get_size_dict(_lowercase						)
       __SCREAMING_SNAKE_CASE :			Any												=       crop_size if crop_size is not None else {'''height''': 2_8_9, '''width''': 2_8_9}
       __SCREAMING_SNAKE_CASE :			int												=       get_size_dict(_lowercase						,							param_name='''crop_size'''						)

       __SCREAMING_SNAKE_CASE :			Tuple												=       do_resize
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       size
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       resample
       __SCREAMING_SNAKE_CASE :			List[Any]												=       do_center_crop
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       crop_size
       __SCREAMING_SNAKE_CASE :			Dict												=       do_rescale
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       rescale_factor
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       rescale_offset
       __SCREAMING_SNAKE_CASE :			List[Any]												=       do_normalize
       __SCREAMING_SNAKE_CASE :			Any												=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       image_std if image_std is not None else IMAGENET_STANDARD_STD
       __SCREAMING_SNAKE_CASE :			Any												=       include_top

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						,							_lowerCamelCase   :np.ndarray						,							_lowerCamelCase   :Dict[str, int]						,							_lowerCamelCase   :PILImageResampling = PIL.Image.NEAREST						,							_lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None						,							**_lowerCamelCase   :Optional[int]						,							):
       __SCREAMING_SNAKE_CASE :			Dict												=       get_size_dict(_lowercase						)
       if "height" not in size or "width" not in size:
        raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
       return resize(
           _lowercase						,							size=(size['''height'''], size['''width'''])						,							resample=_lowercase						,							data_format=_lowercase						,							**_lowercase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						,							_lowerCamelCase   :np.ndarray						,							_lowerCamelCase   :Dict[str, int]						,							_lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None						,							**_lowerCamelCase   :Dict						,							):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       get_size_dict(_lowercase						)
       if "height" not in size or "width" not in size:
        raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
       return center_crop(_lowercase						,							size=(size['''height'''], size['''width'''])						,							data_format=_lowercase						,							**_lowercase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						,							_lowerCamelCase   :np.ndarray						,							_lowerCamelCase   :Union[int, float]						,							_lowerCamelCase   :bool = True						,							_lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None						,							**_lowerCamelCase   :Tuple						,							):
       if offset:
        __SCREAMING_SNAKE_CASE :			Any												=       (image - 1_2_7.5) * scale
        if data_format is not None:
         __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       to_channel_dimension_format(_lowercase						,							_lowercase						)
        __SCREAMING_SNAKE_CASE :			Tuple												=       rescaled_image.astype(np.floataa						)
       else:
        __SCREAMING_SNAKE_CASE :			Dict												=       rescale(_lowercase						,							scale=_lowercase						,							data_format=_lowercase						,							**_lowercase						)
       return rescaled_image

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :np.ndarray						,							_lowerCamelCase   :Union[float, List[float]]						,							_lowerCamelCase   :Union[float, List[float]]						,							_lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None						,							**_lowerCamelCase   :Union[str, Any]						,							):
       return normalize(_lowercase						,							mean=_lowercase						,							std=_lowercase						,							data_format=_lowercase						,							**_lowercase						)



      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :ImageInput						,							_lowerCamelCase   :bool = None						,							_lowerCamelCase   :Dict[str, int] = None						,							_lowerCamelCase   :List[Any]=None						,							_lowerCamelCase   :bool = None						,							_lowerCamelCase   :Dict[str, int] = None						,							_lowerCamelCase   :bool = None						,							_lowerCamelCase   :float = None						,							_lowerCamelCase   :bool = None						,							_lowerCamelCase   :bool = None						,							_lowerCamelCase   :Optional[Union[float, List[float]]] = None						,							_lowerCamelCase   :Optional[Union[float, List[float]]] = None						,							_lowerCamelCase   :bool = None						,							_lowerCamelCase   :Optional[Union[str, TensorType]] = None						,							_lowerCamelCase   :ChannelDimension = ChannelDimension.FIRST						,							**_lowerCamelCase   :str						,							):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       do_resize if do_resize is not None else self.do_resize
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       resample if resample is not None else self.resample
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       do_center_crop if do_center_crop is not None else self.do_center_crop
       __SCREAMING_SNAKE_CASE :			str												=       do_rescale if do_rescale is not None else self.do_rescale
       __SCREAMING_SNAKE_CASE :			Dict												=       rescale_factor if rescale_factor is not None else self.rescale_factor
       __SCREAMING_SNAKE_CASE :			Any												=       rescale_offset if rescale_offset is not None else self.rescale_offset
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       do_normalize if do_normalize is not None else self.do_normalize
       __SCREAMING_SNAKE_CASE :			Dict												=       image_mean if image_mean is not None else self.image_mean
       __SCREAMING_SNAKE_CASE :			int												=       image_std if image_std is not None else self.image_std
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       include_top if include_top is not None else self.include_top

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

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       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 or resample is None:
        raise ValueError('''Size and resample must be specified if do_resize is True.'''						)

       if do_center_crop and crop_size is None:
        raise ValueError('''Crop size must be specified if do_center_crop 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 :			List[str]												=       [to_numpy_array(_lowercase						) for image in images]

       if do_resize:
        __SCREAMING_SNAKE_CASE :			List[str]												=       [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 :			Union[str, Any]												=       [self.rescale(image=_lowercase						,							scale=_lowercase						,							offset=_lowercase						) for image in images]

       if do_normalize:
        __SCREAMING_SNAKE_CASE :			int												=       [self.normalize(image=_lowercase						,							mean=_lowercase						,							std=_lowercase						) for image in images]

       if include_top:
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       [self.normalize(image=_lowercase						,							mean=[0, 0, 0]						,							std=_lowercase						) for image in images]

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       [to_channel_dimension_format(_lowercase						,							_lowercase						) for image in images]

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






import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
							from PIL import Image







class 							a_  (		unittest.TestCase					, _snake_case				):

						def 	__a       (    self	:Union[str, Any])		->					Optional[Any]:
												UpperCAmelCase_     							=     load_tool('''image-question-answering''')
												self.tool.setup()
												UpperCAmelCase_     							=     load_tool('''image-question-answering'''   ,						remote=_lowercase)

						def 	__a       (    self	:int)		->					Union[str, Any]:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.tool(_lowercase   ,						'''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')

						def 	__a       (    self	:int)		->					Dict:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.remote_tool(_lowercase   ,						'''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')

						def 	__a       (    self	:Optional[int])		->					int:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.tool(image=_lowercase   ,						question='''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')




						def 	__a       (    self	:Optional[Any])		->					List[str]:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.remote_tool(image=_lowercase   ,						question='''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')





