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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Optional[Any]      			=   ["""pixel_values"""]







    def __init__(			self				,				__lowercase = True				,				__lowercase = None				,				__lowercase = PILImageResampling.BICUBIC				,				__lowercase = True				,				__lowercase = 1 / 255				,				__lowercase = True				,				__lowercase = None				,				__lowercase = None				,				__lowercase = True				,				**__lowercase				,				) ->			None:
        super().__init__(**__lowercase)
        __UpperCamelCase  :Optional[int]    						=	size if size is not None else {'''height''': 384, '''width''': 384}
        __UpperCamelCase  :List[Any]    						=	get_size_dict(__lowercase				,				default_to_square=__lowercase)

        __UpperCamelCase  :Optional[Any]    						=	do_resize
        __UpperCamelCase  :int    						=	size
        __UpperCamelCase  :Dict    						=	resample
        __UpperCamelCase  :List[str]    						=	do_rescale
        __UpperCamelCase  :Optional[Any]    						=	rescale_factor
        __UpperCamelCase  :str    						=	do_normalize
        __UpperCamelCase  :List[str]    						=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        __UpperCamelCase  :List[Any]    						=	image_std if image_std is not None else OPENAI_CLIP_STD
        __UpperCamelCase  :Optional[int]    						=	do_convert_rgb







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase = PILImageResampling.BICUBIC				,				__lowercase = None				,				**__lowercase				,				) ->			np.ndarray:
        __UpperCamelCase  :Any    						=	get_size_dict(__lowercase				,				default_to_square=__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()}""")
        __UpperCamelCase  :Union[str, Any]    						=	(size['''height'''], size['''width'''])
        return resize(__lowercase				,				size=__lowercase				,				resample=__lowercase				,				data_format=__lowercase				,				**__lowercase)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase = None				,				**__lowercase				,				) ->			Optional[int]:
        return rescale(__lowercase				,				scale=__lowercase				,				data_format=__lowercase				,				**__lowercase)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase = None				,				**__lowercase				,				) ->			np.ndarray:
        return normalize(__lowercase				,				mean=__lowercase				,				std=__lowercase				,				data_format=__lowercase				,				**__lowercase)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = None				,				__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:
        __UpperCamelCase  :int    						=	do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase  :Tuple    						=	resample if resample is not None else self.resample
        __UpperCamelCase  :Tuple    						=	do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase  :List[Any]    						=	rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCamelCase  :List[str]    						=	do_normalize if do_normalize is not None else self.do_normalize
        __UpperCamelCase  :Optional[Any]    						=	image_mean if image_mean is not None else self.image_mean
        __UpperCamelCase  :Optional[Any]    						=	image_std if image_std is not None else self.image_std
        __UpperCamelCase  :int    						=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        __UpperCamelCase  :Dict    						=	size if size is not None else self.size
        __UpperCamelCase  :Optional[int]    						=	get_size_dict(__lowercase				,				default_to_square=__lowercase)

        __UpperCamelCase  :Dict    						=	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_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.''')

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
            __UpperCamelCase  :Dict    						=	[convert_to_rgb(__lowercase) for image in images]

        # All transformations expect numpy arrays.
        __UpperCamelCase  :int    						=	[to_numpy_array(__lowercase) for image in images]

        if do_resize:
            __UpperCamelCase  :Tuple    						=	[self.resize(image=__lowercase				,				size=__lowercase				,				resample=__lowercase) for image in images]

        if do_rescale:
            __UpperCamelCase  :Tuple    						=	[self.rescale(image=__lowercase				,				scale=__lowercase) for image in images]

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

        __UpperCamelCase  :List[str]    						=	[to_channel_dimension_format(__lowercase				,				__lowercase) for image in images]

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature(data={'''pixel_values''': images}				,				tensor_type=__lowercase)

        return encoded_outputs







import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin


@require_jieba
class 			lowerCamelCase_  (     UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		Any      			=   CpmAntTokenizer
    a__			:		Optional[Any]      			=   False







    def     UpperCamelCase__     (			self) ->			Any:
        super().setUp()

        __UpperCamelCase  :Optional[int]    						=	[
            '''<d>''',
            '''</d>''',
            '''<s>''',
            '''</s>''',
            '''</_>''',
            '''<unk>''',
            '''<pad>''',
            '''</n>''',
            '''我''',
            '''是''',
            '''C''',
            '''P''',
            '''M''',
            '''A''',
            '''n''',
            '''t''',
        ]
        __UpperCamelCase  :Optional[int]    						=	os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES['''vocab_file'''])
        with open(self.vocab_file				,				'''w'''				,				encoding='''utf-8''') as vocab_writer:
            vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))







    @tooslow
    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Tuple    						=	CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b''')
        __UpperCamelCase  :Dict    						=	'''今天天气真好！'''
        __UpperCamelCase  :Tuple    						=	['''今天''', '''天气''', '''真''', '''好''', '''！''']
        __UpperCamelCase  :Optional[Any]    						=	tokenizer.tokenize(__lowercase)
        self.assertListEqual(__lowercase				,				__lowercase)
        __UpperCamelCase  :int    						=	'''今天天气真好！'''
        __UpperCamelCase  :List[str]    						=	[tokenizer.bos_token] + tokens

        __UpperCamelCase  :List[str]    						=	[6, 9_802, 14_962, 2_082, 831, 244]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase)				,				__lowercase)

        __UpperCamelCase  :Dict    						=	tokenizer.decode(__lowercase)
        self.assertEqual(__lowercase				,				__lowercase)





