





'''simple docstring'''

import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class      UpperCAmelCase			( unittest.TestCase       ):
 @cached_property
 def   UpperCAmelCase_							(       self   :Optional[Any]							)->							Union[str, Any]:
     return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"							)
 @cached_property
 def   UpperCAmelCase_							(       self   :Dict							)->							List[Any]:
     return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"							)
 def   UpperCAmelCase_							(       self   :str							)->							Tuple:
     A__       =    self.tokenizer_ab
     A__       =    " I am a small frog."
     A__       =    tok([src_text]		, padding=__lowerCamelCase		, truncation=__lowerCamelCase							)["input_ids"]
     A__       =    tok.batch_decode(__lowerCamelCase		, skip_special_tokens=__lowerCamelCase		, clean_up_tokenization_spaces=__lowerCamelCase							)[0]
     assert src_text == decoded
 def   UpperCAmelCase_							(       self   :Dict							)->							Tuple:
     A__       =    self.rust_tokenizer_ab
     A__       =    " I am a small frog."
     A__       =    tok([src_text]		, padding=__lowerCamelCase		, truncation=__lowerCamelCase							)["input_ids"]
     A__       =    tok.batch_decode(__lowerCamelCase		, skip_special_tokens=__lowerCamelCase		, clean_up_tokenization_spaces=__lowerCamelCase							)[0]
     assert src_text == decoded
 def   UpperCAmelCase_							(       self   :Tuple							)->							str:
     assert self.tokenizer_ab.add_prefix_space
     assert self.tokenizer_ab([" Sam", "Sam"]							).input_ids == [[55_02, 2], [55_02, 2]]



 def   UpperCAmelCase_							(       self   :int							)->							Optional[Any]:
     assert self.rust_tokenizer_ab.add_prefix_space
     assert self.rust_tokenizer_ab([" Sam", "Sam"]							).input_ids == [[55_02, 2], [55_02, 2]]













'''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 )



