





'''simple docstring'''

from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class      UpperCAmelCase			( UpperCamelCase__       ):
 def   UpperCAmelCase_							(       self   :int		, lowercase_   :Optional[int]		, lowercase_   :int		, lowercase_   :Union[str, Any]		, lowercase_   :str		, lowercase_   :Dict							)->							Optional[Any]:
     return True



 def   UpperCAmelCase_							(       self   :Dict							)->							Dict:
     A__       =    CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"							)
     A__       =    "Hugging Face大法好，谁用谁知道。"
     A__       =    "Hugging Face大法好,谁用谁知道。<unk>"
     A__       =    "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

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

     A__       =    tokens + [tokenizer.unk_token]

     A__       =    [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)		, lowercase_							)

     A__       =    tokenizer.decode(lowercase_							)
     self.assertEqual(lowercase_		, lowercase_							)










'''simple docstring'''

import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image





def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Union[str, Any]=2_56 ):
    A__       =    min(img.size )

    if s < target_image_size:
        raise ValueError(F"min dim for image {s} < {target_image_size}" )

    A__       =    target_image_size / s
    A__       =    (round(r * img.size[1] ), round(r * img.size[0] ))
    A__       =    TF.resize(_lowerCamelCase		, _lowerCamelCase		, interpolation=PIL.Image.LANCZOS )
    A__       =    TF.center_crop(_lowerCamelCase		, output_size=2 * [target_image_size] )
    A__       =    torch.unsqueeze(T.ToTensor()(_lowerCamelCase )		, 0 )
    return img





def 		UpperCamelCase       (							_lowerCamelCase      :      int ):
    A__       =    2.0 * x - 1.0
    return x





def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Tuple=True		, _lowerCamelCase      :      int="RGB" ):
    A__       =    x.detach().cpu()
    if process:
        A__       =    post_process_tensor(_lowerCamelCase )
    A__       =    x.numpy()
    if process:
        A__       =    (2_55 * x).astype(np.uinta )
    A__       =    Image.fromarray(_lowerCamelCase )
    if not x.mode == mode:
        A__       =    x.convert(_lowerCamelCase )
    return x





def 		UpperCamelCase       (							_lowerCamelCase      :      Any ):
    A__       =    torch.clamp(_lowerCamelCase		, -1.0		, 1.0 )
    A__       =    (x + 1.0) / 2.0
    A__       =    x.permute(1		, 2		, 0 )
    return x





def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any] ):
    A__       =    post_process_tensor(x.squeeze() )
    return x.permute(2		, 0		, 1 ).unsqueeze(0 )



