

from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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


if is_vision_available():
     import PIL

lowercase_       		=  logging.get_logger(__name__)
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int      )							->      List[List[ImageInput]]:





    '''simple docstring'''






    if isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      ) and isinstance(videos[0]    ,			(list, tuple)      ) and is_valid_image(videos[0][0]      ):
        return videos

    elif isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      ) and is_valid_image(videos[0]      ):
        return [videos]

    elif is_valid_image(SCREAMING_SNAKE_CASE__      ):
        return [[videos]]

    raise ValueError(f'Could not make batched video from {videos}'      )


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							['pixel_values']


    def __init__(      self :       List[Any],lowercase_ :       bool = True,lowercase_ :       Dict[str, int] = None,lowercase_ :       PILImageResampling = PILImageResampling.BILINEAR,lowercase_ :       bool = True,lowercase_ :       Dict[str, int] = None,lowercase_ :       bool = True,lowercase_ :       Union[int, float] = 1 / 2_5_5,lowercase_ :       bool = True,lowercase_ :       bool = True,lowercase_ :       Optional[Union[float, List[float]]] = None,lowercase_ :       Optional[Union[float, List[float]]] = None,**lowercase_ :       Any,)->    None:





        '''simple docstring'''







        super().__init__(**lowercase_		)
        A__          =   size if size is not None else {'shortest_edge': 2_5_6}
        A__          =   get_size_dict(lowercase_,default_to_square=lowercase_		)
        A__          =   crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
        A__          =   get_size_dict(lowercase_,param_name='crop_size'		)

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


    def 						snake_case__     (      self :       Any,lowercase_ :       np.ndarray,lowercase_ :       Dict[str, int],lowercase_ :       PILImageResampling = PILImageResampling.BILINEAR,lowercase_ :       Optional[Union[str, ChannelDimension]] = None,**lowercase_ :       Tuple,)->    np.ndarray:





        '''simple docstring'''







        A__          =   get_size_dict(lowercase_,default_to_square=lowercase_		)
        if "shortest_edge" in size:
            A__          =   get_resize_output_image_size(lowercase_,size['shortest_edge'],default_to_square=lowercase_		)
        elif "height" in size and "width" in size:
            A__          =   (size['height'], size['width'])
        else:
            raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'		)
        return resize(lowercase_,size=lowercase_,resample=lowercase_,data_format=lowercase_,**lowercase_		)


    def 						snake_case__     (      self :       str,lowercase_ :       np.ndarray,lowercase_ :       Dict[str, int],lowercase_ :       Optional[Union[str, ChannelDimension]] = None,**lowercase_ :       Tuple,)->    np.ndarray:





        '''simple docstring'''







        A__          =   get_size_dict(lowercase_		)
        if "height" not in size or "width" not in size:
            raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'		)
        return center_crop(lowercase_,size=(size['height'], size['width']),data_format=lowercase_,**lowercase_		)


    def 						snake_case__     (      self :       Tuple,lowercase_ :       np.ndarray,lowercase_ :       Union[int, float],lowercase_ :       bool = True,lowercase_ :       Optional[Union[str, ChannelDimension]] = None,**lowercase_ :       Tuple,)->    str:





        '''simple docstring'''







        A__          =   image.astype(np.floataa		)
        if offset:
            A__          =   image - (scale / 2)
        return rescale(lowercase_,scale=lowercase_,data_format=lowercase_,**lowercase_		)


    def 						snake_case__     (      self :       List[Any],lowercase_ :       np.ndarray,lowercase_ :       Union[float, List[float]],lowercase_ :       Union[float, List[float]],lowercase_ :       Optional[Union[str, ChannelDimension]] = None,**lowercase_ :       List[str],)->    np.ndarray:





        '''simple docstring'''







        return normalize(lowercase_,mean=lowercase_,std=lowercase_,data_format=lowercase_,**lowercase_		)


    def 						snake_case__     (      self :       List[Any],lowercase_ :       ImageInput,lowercase_ :       bool = None,lowercase_ :       Dict[str, int] = None,lowercase_ :       PILImageResampling = None,lowercase_ :       bool = None,lowercase_ :       Dict[str, int] = None,lowercase_ :       bool = None,lowercase_ :       float = None,lowercase_ :       bool = None,lowercase_ :       bool = None,lowercase_ :       Optional[Union[float, List[float]]] = None,lowercase_ :       Optional[Union[float, List[float]]] = None,lowercase_ :       Optional[ChannelDimension] = ChannelDimension.FIRST,)->    np.ndarray:





        '''simple docstring'''







        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.'		)

        if offset and not do_rescale:
            raise ValueError('For offset, do_rescale must also be set to True.'		)

        # All transformations expect numpy arrays.
        A__          =   to_numpy_array(lowercase_		)

        if do_resize:
            A__          =   self.resize(image=lowercase_,size=lowercase_,resample=lowercase_		)

        if do_center_crop:
            A__          =   self.center_crop(lowercase_,size=lowercase_		)

        if do_rescale:
            A__          =   self.rescale(image=lowercase_,scale=lowercase_,offset=lowercase_		)

        if do_normalize:
            A__          =   self.normalize(image=lowercase_,mean=lowercase_,std=lowercase_		)

        A__          =   to_channel_dimension_format(lowercase_,lowercase_		)
        return image


    def 						snake_case__     (      self :       Any,lowercase_ :       ImageInput,lowercase_ :       bool = None,lowercase_ :       Dict[str, int] = None,lowercase_ :       PILImageResampling = None,lowercase_ :       bool = None,lowercase_ :       Dict[str, int] = None,lowercase_ :       bool = None,lowercase_ :       float = None,lowercase_ :       bool = None,lowercase_ :       bool = None,lowercase_ :       Optional[Union[float, List[float]]] = None,lowercase_ :       Optional[Union[float, List[float]]] = None,lowercase_ :       Optional[Union[str, TensorType]] = None,lowercase_ :       ChannelDimension = ChannelDimension.FIRST,**lowercase_ :       Tuple,)->    PIL.Image.Image:





        '''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_center_crop if do_center_crop is not None else self.do_center_crop
        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__          =   offset if offset is not None else self.offset
        A__          =   do_normalize if do_normalize is not None else self.do_normalize
        A__          =   image_mean if image_mean is not None else self.image_mean
        A__          =   image_std if image_std is not None else self.image_std

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

        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.'		)

        A__          =   make_batched(lowercase_		)

        A__          =   [
            [
                self._preprocess_image(
                    image=lowercase_,do_resize=lowercase_,size=lowercase_,resample=lowercase_,do_center_crop=lowercase_,crop_size=lowercase_,do_rescale=lowercase_,rescale_factor=lowercase_,offset=lowercase_,do_normalize=lowercase_,image_mean=lowercase_,image_std=lowercase_,data_format=lowercase_,)
                for img in video
            ]
            for video in videos
        ]

        A__          =   {'pixel_values': videos}
        return BatchFeature(data=lowercase_,tensor_type=lowercase_		)




import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_       		=  get_tests_dir("fixtures/spiece.model")


@require_sentencepiece
@require_tokenizers
class  A						(							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							AlbertTokenizer
    lowerCamelCase			=							AlbertTokenizerFast
    lowerCamelCase			=							True
    lowerCamelCase			=							True
    lowerCamelCase			=							True


    def 						snake_case__     (      self :       Dict		)->    Any:





        '''simple docstring'''







        super().setUp()

        # We have a SentencePiece fixture for testing
        A__          =   AlbertTokenizer(lowercase_		)
        tokenizer.save_pretrained(self.tmpdirname		)


    def 						snake_case__     (      self :       List[str],lowercase_ :       str		)->    Any:





        '''simple docstring'''







        A__          =   'this is a test'
        A__          =   'this is a test'
        return input_text, output_text


    def 						snake_case__     (      self :       List[Any]		)->    Optional[int]:





        '''simple docstring'''







        A__          =   '<pad>'
        A__          =   0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_		),lowercase_		)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_		),lowercase_		)


    def 						snake_case__     (      self :       List[str]		)->    str:





        '''simple docstring'''







        A__          =   list(self.get_tokenizer().get_vocab().keys()		)

        self.assertEqual(vocab_keys[0],'<pad>'		)
        self.assertEqual(vocab_keys[1],'<unk>'		)
        self.assertEqual(vocab_keys[-1],'▁eloquent'		)
        self.assertEqual(len(lowercase_		),3_0_0_0_0		)


    def 						snake_case__     (      self :       int		)->    List[Any]:





        '''simple docstring'''







        self.assertEqual(self.get_tokenizer().vocab_size,3_0_0_0_0		)


    def 						snake_case__     (      self :       Union[str, Any]		)->    List[Any]:





        '''simple docstring'''







        if not self.test_rust_tokenizer:
            return

        A__          =   self.get_tokenizer()
        A__          =   self.get_rust_tokenizer()

        A__          =   'I was born in 92000, and this is falsé.'

        A__          =   tokenizer.tokenize(lowercase_		)
        A__          =   rust_tokenizer.tokenize(lowercase_		)
        self.assertListEqual(lowercase_,lowercase_		)

        A__          =   tokenizer.encode(lowercase_,add_special_tokens=lowercase_		)
        A__          =   rust_tokenizer.encode(lowercase_,add_special_tokens=lowercase_		)
        self.assertListEqual(lowercase_,lowercase_		)

        A__          =   self.get_rust_tokenizer()
        A__          =   tokenizer.encode(lowercase_		)
        A__          =   rust_tokenizer.encode(lowercase_		)
        self.assertListEqual(lowercase_,lowercase_		)


    def 						snake_case__     (      self :       int		)->    int:





        '''simple docstring'''







        A__          =   AlbertTokenizer(lowercase_,keep_accents=lowercase_		)

        A__          =   tokenizer.tokenize('This is a test'		)
        self.assertListEqual(lowercase_,['▁this', '▁is', '▁a', '▁test']		)

        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_		),[4_8, 2_5, 2_1, 1_2_8_9]		)

        A__          =   tokenizer.tokenize('I was born in 92000, and this is falsé.'		)
        self.assertListEqual(
            lowercase_,['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']		)
        A__          =   tokenizer.convert_tokens_to_ids(lowercase_		)
        self.assertListEqual(lowercase_,[3_1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]		)

        A__          =   tokenizer.convert_ids_to_tokens(lowercase_		)
        self.assertListEqual(
            lowercase_,['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.'],)


    def 						snake_case__     (      self :       Union[str, Any]		)->    str:





        '''simple docstring'''







        A__          =   AlbertTokenizer(lowercase_		)

        A__          =   tokenizer.encode('sequence builders'		)
        A__          =   tokenizer.encode('multi-sequence build'		)

        A__          =   tokenizer.build_inputs_with_special_tokens(lowercase_		)
        A__          =   tokenizer.build_inputs_with_special_tokens(lowercase_,lowercase_		)

        assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
        assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
            tokenizer.sep_token_id
        ]


    @slow
    def 						snake_case__     (      self :       Any		)->    Tuple:





        '''simple docstring'''







        A__          =   {'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'input_ids': [[2, 2_1_9_7_0, 1_3, 5, 6_0_9_2, 1_6_7, 2_8, 7_1_0_3, 2_1_5_3, 6_7_3, 8, 7_0_2_8, 1_2_0_5_1, 1_8, 1_7, 7_1_0_3, 2_1_5_3, 6_7_3, 8, 3_5_1_5, 1_8_6_8_4, 8, 4_4_6_1, 6, 1_9_2_7, 2_9_7, 8, 1_2_0_6_0, 2_6_0_7, 1_8, 1_3, 5, 4_4_6_1, 1_5, 1_0_5_3_8, 3_8, 8, 1_3_5, 1_5, 8_2_2, 5_8, 1_5, 9_9_3, 1_0_3_6_3, 1_5, 1_4_6_0, 8_0_0_5, 4_4_6_1, 1_5, 9_9_3, 2_5_5, 2_3_2_8, 9, 9, 9, 6, 2_6, 1_1_1_2, 8_1_6, 3_2_6_0, 1_3, 5, 1_0_3, 2_3_7_7, 6, 1_7, 1_1_1_2, 8_1_6, 2_7_8_2, 1_3, 5, 1_0_3, 1_0_6_4_1, 6, 2_9, 8_4, 2_5_1_2, 2_4_3_0, 7_8_2, 1_8_6_8_4, 2_7_6_1, 1_9, 8_0_8, 2_4_3_0, 2_5_5_6, 1_7, 8_5_5, 1_4_8_0, 9_4_7_7, 4_0_9_1, 1_2_8, 1_1_7_1_2, 1_5, 7_1_0_3, 2_1_5_3, 6_7_3, 1_7, 2_4_8_8_3, 9_9_9_0, 9, 3], [2, 1_1_5_0_2, 2_5, 1_0_0_6, 2_0, 7_8_2, 8, 1_1_8_0_9, 8_5_5, 1_7_3_2, 1_9_3_9_3, 1_8_6_6_7, 3_7, 3_6_7, 2_1_0_1_8, 6_9, 1_8_5_4, 3_4, 1_1_8_6_0, 1_9_1_2_4, 2_7, 1_5_6, 2_2_5, 1_7, 1_9_3, 4_1_4_1, 1_9, 6_5, 9_1_2_4, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 1_4, 2_2_3_1, 8_8_6, 2_3_8_5, 1_7_6_5_9, 8_4, 1_4, 1_6_7_9_2, 1_9_5_2, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowercase_,model_name='albert-base-v2',revision='6b6560eaf5ff2e250b00c50f380c5389a9c2d82e',)

