



"""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


lowercase  							=					logging.get_logger(__name__)




class 			lowercase__			(      __a    ):
       '''simple docstring'''







       _UpperCAmelCase      				=					["pixel_values"]






       def __init__(  self	,       snake_case = True	,       snake_case = None	,       snake_case = PIL.Image.BICUBIC	,       snake_case = True	,       snake_case = None	,       snake_case = 1 / 255	,       snake_case = True	,       snake_case = True	,       snake_case = None	,       snake_case = None	,       **snake_case	,       )			->							None:
         super().__init__(**lowerCAmelCase_						)
         _UpperCAmelCase							 =		size if size is not None else {'height': 256, 'width': 256}
         _UpperCAmelCase							 =		get_size_dict(lowerCAmelCase_						)
         _UpperCAmelCase							 =		crop_size if crop_size is not None else {'height': 224, 'width': 224}
         _UpperCAmelCase							 =		get_size_dict(lowerCAmelCase_	,       param_name='crop_size'						)

         _UpperCAmelCase							 =		do_resize
         _UpperCAmelCase							 =		size
         _UpperCAmelCase							 =		resample
         _UpperCAmelCase							 =		do_center_crop
         _UpperCAmelCase							 =		crop_size
         _UpperCAmelCase							 =		do_rescale
         _UpperCAmelCase							 =		rescale_factor
         _UpperCAmelCase							 =		do_normalize
         _UpperCAmelCase							 =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         _UpperCAmelCase							 =		image_std if image_std is not None else IMAGENET_STANDARD_STD






       def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case = PIL.Image.BICUBIC	,       snake_case = None	,       **snake_case	,       )			->							np.ndarray:
         _UpperCAmelCase							 =		get_size_dict(lowerCAmelCase_						)
         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(
             lowerCAmelCase_	,       size=(size['height'], size['width'])	,       resample=lowerCAmelCase_	,       data_format=lowerCAmelCase_	,       **lowerCAmelCase_						)






       def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case = None	,       **snake_case	,       )			->							np.ndarray:
         _UpperCAmelCase							 =		get_size_dict(lowerCAmelCase_						)
         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(lowerCAmelCase_	,       size=(size['height'], size['width'])	,       data_format=lowerCAmelCase_	,       **lowerCAmelCase_						)






       def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case = None	,       **snake_case	,       )			->							List[Any]:
         return rescale(lowerCAmelCase_	,       scale=lowerCAmelCase_	,       data_format=lowerCAmelCase_	,       **lowerCAmelCase_						)






       def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case = None	,       **snake_case	,       )			->							np.ndarray:
         return normalize(lowerCAmelCase_	,       mean=lowerCAmelCase_	,       std=lowerCAmelCase_	,       data_format=lowerCAmelCase_	,       **lowerCAmelCase_						)






       def        lowerCamelCase_   (  self	,       snake_case	,       snake_case = None	,       snake_case = None	,       snake_case=None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = ChannelDimension.FIRST	,       **snake_case	,       )			->							PIL.Image.Image:
         _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_center_crop if do_center_crop is not None else self.do_center_crop
         _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_normalize if do_normalize is not None else self.do_normalize
         _UpperCAmelCase							 =		image_mean if image_mean is not None else self.image_mean
         _UpperCAmelCase							 =		image_std if image_std is not None else self.image_std

         _UpperCAmelCase							 =		size if size is not None else self.size
         _UpperCAmelCase							 =		get_size_dict(lowerCAmelCase_						)
         _UpperCAmelCase							 =		crop_size if crop_size is not None else self.crop_size
         _UpperCAmelCase							 =		get_size_dict(lowerCAmelCase_	,       param_name='crop_size'						)

         _UpperCAmelCase							 =		make_list_of_images(lowerCAmelCase_						)

         if not valid_images(lowerCAmelCase_						):
           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.
         _UpperCAmelCase							 =		[to_numpy_array(lowerCAmelCase_						) for image in images]

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

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

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

         if do_normalize:
           _UpperCAmelCase							 =		[self.normalize(image=lowerCAmelCase_	,       mean=lowerCAmelCase_	,       std=lowerCAmelCase_						) for image in images]

         _UpperCAmelCase							 =		[to_channel_dimension_format(lowerCAmelCase_	,       lowerCAmelCase_						) for image in images]

         _UpperCAmelCase							 =		{'pixel_values': images}
         return BatchFeature(data=lowerCAmelCase_	,       tensor_type=lowerCAmelCase_						)







"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
					import jax.numpy as jnp

if is_tf_available():
					import tensorflow as tf

if is_torch_available():
					import torch




class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		{
									    'task_specific_params': {
									        'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
									        'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
									        'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
									    }
									}
									_UpperCAmelCase							 =		{
									    'task_specific_params.summarization.length_penalty': 1.0,
									    'task_specific_params.summarization.max_length': 128,
									    'task_specific_params.summarization.min_length': 12,
									    'task_specific_params.summarization.num_beams': 4,
									    'task_specific_params.summarization_cnn.length_penalty': 2.0,
									    'task_specific_params.summarization_cnn.max_length': 142,
									    'task_specific_params.summarization_cnn.min_length': 56,
									    'task_specific_params.summarization_cnn.num_beams': 4,
									    'task_specific_params.summarization_xsum.length_penalty': 1.0,
									    'task_specific_params.summarization_xsum.max_length': 62,
									    'task_specific_params.summarization_xsum.min_length': 11,
									    'task_specific_params.summarization_xsum.num_beams': 6,
									}

									self.assertEqual(flatten_dict(snake_case						)	,       snake_case						)






							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									self.assertTrue(np.allclose(transpose(snake_case						)	,       x.transpose()						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									self.assertTrue(np.allclose(transpose(snake_case	,       axes=(1, 2, 0)						)	,       x.transpose((1, 2, 0)						)						)						)






							@require_torch
							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case						)	,       transpose(snake_case						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case	,       axes=(1, 2, 0)						)	,       transpose(snake_case	,       axes=(1, 2, 0)						).numpy()						)						)






							@require_tf
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case						)	,       transpose(snake_case						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case	,       axes=(1, 2, 0)						)	,       transpose(snake_case	,       axes=(1, 2, 0)						).numpy()						)						)






							@require_flax
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case						)	,       np.asarray(transpose(snake_case						)						)						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(transpose(snake_case	,       axes=(1, 2, 0)						)	,       np.asarray(transpose(snake_case	,       axes=(1, 2, 0)						)						)						)						)






							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (4, 3)						)	,       np.reshape(snake_case	,       (4, 3)						)						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (12, 5)						)	,       np.reshape(snake_case	,       (12, 5)						)						)						)






							@require_torch
							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (4, 3)						)	,       reshape(snake_case	,       (4, 3)						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (12, 5)						)	,       reshape(snake_case	,       (12, 5)						).numpy()						)						)






							@require_tf
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (4, 3)						)	,       reshape(snake_case	,       (4, 3)						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (12, 5)						)	,       reshape(snake_case	,       (12, 5)						).numpy()						)						)






							@require_flax
							def        lowerCamelCase_   (  self						)			->							Tuple:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (4, 3)						)	,       np.asarray(reshape(snake_case	,       (4, 3)						)						)						)						)

									_UpperCAmelCase							 =		np.random.randn(3	,       4	,       5						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(reshape(snake_case	,       (12, 5)						)	,       np.asarray(reshape(snake_case	,       (12, 5)						)						)						)						)






							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		np.random.randn(1	,       3	,       4						)
									self.assertTrue(np.allclose(squeeze(snake_case						)	,       np.squeeze(snake_case						)						)						)

									_UpperCAmelCase							 =		np.random.randn(1	,       4	,       1	,       5						)
									self.assertTrue(np.allclose(squeeze(snake_case	,       axis=2						)	,       np.squeeze(snake_case	,       axis=2						)						)						)






							@require_torch
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(1	,       3	,       4						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case						)	,       squeeze(snake_case						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(1	,       4	,       1	,       5						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case	,       axis=2						)	,       squeeze(snake_case	,       axis=2						).numpy()						)						)






							@require_tf
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									_UpperCAmelCase							 =		np.random.randn(1	,       3	,       4						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case						)	,       squeeze(snake_case						).numpy()						)						)

									_UpperCAmelCase							 =		np.random.randn(1	,       4	,       1	,       5						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case	,       axis=2						)	,       squeeze(snake_case	,       axis=2						).numpy()						)						)






							@require_flax
							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		np.random.randn(1	,       3	,       4						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case						)	,       np.asarray(squeeze(snake_case						)						)						)						)

									_UpperCAmelCase							 =		np.random.randn(1	,       4	,       1	,       5						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(squeeze(snake_case	,       axis=2						)	,       np.asarray(squeeze(snake_case	,       axis=2						)						)						)						)






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									self.assertTrue(np.allclose(expand_dims(snake_case	,       axis=1						)	,       np.expand_dims(snake_case	,       axis=1						)						)						)






							@require_torch
							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		torch.tensor(snake_case						)
									self.assertTrue(np.allclose(expand_dims(snake_case	,       axis=1						)	,       expand_dims(snake_case	,       axis=1						).numpy()						)						)






							@require_tf
							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		tf.constant(snake_case						)
									self.assertTrue(np.allclose(expand_dims(snake_case	,       axis=1						)	,       expand_dims(snake_case	,       axis=1						).numpy()						)						)






							@require_flax
							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		np.random.randn(3	,       4						)
									_UpperCAmelCase							 =		jnp.array(snake_case						)
									self.assertTrue(np.allclose(expand_dims(snake_case	,       axis=1						)	,       np.asarray(expand_dims(snake_case	,       axis=1						)						)						)						)


