





'''simple docstring'''

import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






@require_tokenizers
class      UpperCAmelCase			( unittest.TestCase       ):
 __lowercase     	=       EsmTokenizer
 def   UpperCAmelCase_							(       self   :Any							)->							int:
     super().setUp()
     A__       =    tempfile.mkdtemp()
     # fmt: off
     A__       =    ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
     # fmt: on
     A__       =    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]							)							)
 def   UpperCAmelCase_							(       self   :Any		, **lowercase_   :int							)->							List[PreTrainedTokenizerBase]:
     return [self.get_tokenizer(**lowercase_							)]
 def   UpperCAmelCase_							(       self   :Any		, **lowercase_   :Dict							)->							PreTrainedTokenizer:
     return self.tokenizer_class.from_pretrained(self.tmpdirname		, **lowercase_							)
 def   UpperCAmelCase_							(       self   :int							)->							Dict:
     A__       =    self.tokenizer_class(self.vocab_file							)

     A__       =    tokenizer.tokenize("LAGVS"							)
     self.assertListEqual(lowercase_		, ["L", "A", "G", "V", "S"]							)
     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)		, [4, 5, 6, 7, 8]							)
 def   UpperCAmelCase_							(       self   :Tuple							)->							Tuple:
     A__       =    self.tokenizer_class(self.vocab_file							)

     A__       =    "LAGVS"
     self.assertListEqual(tokenizer.encode(lowercase_							)		, [0, 4, 5, 6, 7, 8, 2]							)
 def   UpperCAmelCase_							(       self   :Any							)->							Tuple:
     A__       =    self.tokenizer_class(self.vocab_file							)

     A__       =    ["LAGVS", "WCB"]
     A__       =    tokenizer(lowercase_		, padding=lowercase_							)["input_ids"]

     self.assertListEqual(lowercase_		, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]							)
 def   UpperCAmelCase_							(       self   :str							)->							Any:
     A__       =    self.tokenizer_class(self.vocab_file							)

     A__       =    ["LAGVS", "WCB"]
     A__       =    tokenizer(lowercase_		, padding=lowercase_							)["input_ids"]

     self.assertListEqual(lowercase_		, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]							)



 def   UpperCAmelCase_							(       self   :Optional[Any]							)->							List[str]:
     A__       =    self.get_tokenizers(fast=lowercase_							)
     for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"							):
             A__       =    "<unk>"
             A__       =    "<mask>"

             A__       =    tokenizer.tokenize(lowercase_							)
             A__       =    tokenizer.tokenize(lowercase_							)

             self.assertEqual(len(lowercase_							)		, 1							)
             self.assertEqual(len(lowercase_							)		, 1							)
             self.assertEqual(token_a[0]		, lowercase_							)
             self.assertEqual(token_a[0]		, 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 )



