"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a   :	Dict 					=     logging.get_logger(__name__)
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    Dict	      =	["pixel_values"]






     def __init__(	self       ,  a__ = True       ,  a__ = 32       ,  a__=PILImageResampling.BILINEAR       ,  a__ = True       ,  **a__       ,  ):
       _lowerCAmelCase		: Optional[Any]						    =  do_resize
       _lowerCAmelCase		: Optional[Any]						    =  do_rescale
       _lowerCAmelCase		: List[str]						    =  size_divisor
       _lowerCAmelCase		: Optional[Any]						    =  resample
       super().__init__(**a__						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__ = None       ,  **a__						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  get_image_size(a__						)
       # Rounds the height and width down to the closest multiple of size_divisor
       _lowerCAmelCase		: List[Any]						    =  height // size_divisor * size_divisor
       _lowerCAmelCase		: Any						    =  width // size_divisor * size_divisor
       _lowerCAmelCase		: Tuple						    =  resize(a__       ,  (new_h, new_w)       ,  resample=a__       ,  data_format=a__       ,  **a__						)
       return image






     def 					__A						(	self       ,  a__       ,  a__       ,  a__ = None       ,  **a__						):
       return rescale(image=a__       ,  scale=a__       ,  data_format=a__       ,  **a__						)






     def 					__A						(	self       ,  a__       ,  a__ = None       ,  a__ = None       ,  a__=None       ,  a__ = None       ,  a__ = None       ,  a__ = ChannelDimension.FIRST       ,  **a__       ,  ):
       _lowerCAmelCase		: Tuple						    =  do_resize if do_resize is not None else self.do_resize
       _lowerCAmelCase		: Dict						    =  do_rescale if do_rescale is not None else self.do_rescale
       _lowerCAmelCase		: Dict						    =  size_divisor if size_divisor is not None else self.size_divisor
       _lowerCAmelCase		: List[Any]						    =  resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
         raise ValueError("""size_divisor is required for resizing"""						)

       _lowerCAmelCase		: List[str]						    =  make_list_of_images(a__						)

       if not valid_images(a__						):
         raise ValueError("""Invalid image(s)"""						)

       # All transformations expect numpy arrays.
       _lowerCAmelCase		: Union[str, Any]						    =  [to_numpy_array(a__						) for img in images]

       if do_resize:
         _lowerCAmelCase		: int						    =  [self.resize(a__       ,  size_divisor=a__       ,  resample=a__						) for image in images]

       if do_rescale:
         _lowerCAmelCase		: Tuple						    =  [self.rescale(a__       ,  scale=1 / 255						) for image in images]

       _lowerCAmelCase		: int						    =  [to_channel_dimension_format(a__       ,  a__						) for image in images]

       _lowerCAmelCase		: Optional[int]						    =  {"""pixel_values""": images}
       return BatchFeature(data=a__       ,  tensor_type=a__						)





"""simple docstring"""




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 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    str	      =	CpmAntTokenizer
     _UpperCamelCase							:    List[Any]	      =	False






     def 					__A						(	self						):
       super().setUp()

       _lowerCAmelCase		: Dict						    =  [
           """<d>""",
           """</d>""",
           """<s>""",
           """</s>""",
           """</_>""",
           """<unk>""",
           """<pad>""",
           """</n>""",
           """我""",
           """是""",
           """C""",
           """P""",
           """M""",
           """A""",
           """n""",
           """t""",
       ]
       _lowerCAmelCase		: 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 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  CpmAntTokenizer.from_pretrained("""openbmb/cpm-ant-10b"""						)
       _lowerCAmelCase		: Optional[Any]						    =  """今天天气真好！"""
       _lowerCAmelCase		: Any						    =  ["""今天""", """天气""", """真""", """好""", """！"""]
       _lowerCAmelCase		: str						    =  tokenizer.tokenize(a__						)
       self.assertListEqual(a__       ,  a__						)
       _lowerCAmelCase		: Tuple						    =  """今天天气真好！"""
       _lowerCAmelCase		: Optional[Any]						    =  [tokenizer.bos_token] + tokens

       _lowerCAmelCase		: Optional[int]						    =  [6, 9802, 14962, 2082, 831, 244]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)

       _lowerCAmelCase		: Tuple						    =  tokenizer.decode(a__						)
       self.assertEqual(a__       ,  a__						)




