




'''simple docstring'''







import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
      import torch







@require_torch
@require_optimum
@slow
class 					_a (unittest.TestCase	):





     '''simple docstring'''



     def 			snake_case_      (				self							)      ->				int:
          snake_case			:      str        =			"""hf-internal-testing/tiny-random-t5"""
          snake_case			:      int        =			AutoTokenizer.from_pretrained(__a							)
          snake_case			:      Optional[Any]        =			AutoModelForSeqaSeqLM.from_pretrained(__a							)

          snake_case			:      Tuple        =			tokenizer("""This is me"""							,return_tensors="""pt"""							)

          snake_case			:      str        =			model.to_bettertransformer()

          self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

          snake_case			:      Dict        =			model.generate(**__a							)

          snake_case			:      Optional[int]        =			model.reverse_bettertransformer()

          self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

          with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(__a							)

               snake_case			:      int        =			AutoModelForSeqaSeqLM.from_pretrained(__a							)

               self.assertFalse(
                   any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()							)							)

               snake_case			:      Dict        =			model_reloaded.generate(**__a							)
               self.assertTrue(torch.allclose(__a							,__a							)							)







     def 			snake_case_      (				self							)      ->				int:
          snake_case			:      Optional[int]        =			"""hf-internal-testing/tiny-random-t5"""
          snake_case			:      Optional[int]        =			AutoModelForSeqaSeqLM.from_pretrained(__a							)

          snake_case			:      List[str]        =			model.to_bettertransformer()

          with tempfile.TemporaryDirectory() as tmpdirname:
               with self.assertRaises(__a							):
                    model.save_pretrained(__a							)

               snake_case			:      Tuple        =			model.reverse_bettertransformer()
               model.save_pretrained(__a							)









'''simple docstring'''







import numpy as np

lowercase      :   str      =       [
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]







class 					_a :





     '''simple docstring'''



     def __init__(				self							)      ->				None:
          snake_case			:      Dict        =			np.array(__a							)







     def 			snake_case_      (				self							,__a							)      ->				np.ndarray:
          snake_case ,		snake_case			:      Optional[Any]        =			np.where(letter == self.SQUARE							)
          snake_case			:      List[Any]        =			np.concatenate([indexa + 1, indexa + 1]							)
          return indexes







     def 			snake_case_      (				self							,__a							,__a							)      ->				str:
          snake_case			:      List[Any]        =			self.SQUARE[indexa - 1, indexa - 1]
          return letter







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      List[Any]        =			message.lower()
          snake_case			:      Dict        =			message.replace(""" """							,""""""							)
          snake_case			:      List[str]        =			message.replace("""j"""							,"""i"""							)

          snake_case			:      Optional[int]        =			np.empty((2, len(__a							))							)
          for letter_index in range(len(__a							)							):
               snake_case			:      Tuple        =			self.letter_to_numbers(message[letter_index]							)

               snake_case			:      Optional[Any]        =			numbers[0]
               snake_case			:      List[str]        =			numbers[1]

          snake_case			:      List[str]        =			first_step.reshape(2 * len(__a							)							)
          snake_case			:      List[Any]        =			""""""
          for numbers_index in range(len(__a							)							):
               snake_case			:      List[str]        =			int(second_step[numbers_index * 2]							)
               snake_case			:      List[Any]        =			int(second_step[(numbers_index * 2) + 1]							)
               snake_case			:      str        =			self.numbers_to_letter(__a							,__a							)
               snake_case			:      Any        =			encoded_message + letter

          return encoded_message







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      Any        =			message.lower()
          message.replace(""" """							,""""""							)
          snake_case			:      Dict        =			np.empty(2 * len(__a							)							)
          for letter_index in range(len(__a							)							):
               snake_case			:      str        =			self.letter_to_numbers(message[letter_index]							)
               snake_case			:      Any        =			numbers[0]
               snake_case			:      int        =			numbers[1]

          snake_case			:      List[str]        =			first_step.reshape((2, len(__a							))							)
          snake_case			:      Dict        =			""""""
          for numbers_index in range(len(__a							)							):
               snake_case			:      Optional[Any]        =			int(second_step[0, numbers_index]							)
               snake_case			:      Optional[int]        =			int(second_step[1, numbers_index]							)
               snake_case			:      Tuple        =			self.numbers_to_letter(__a							,__a							)
               snake_case			:      int        =			decoded_message + letter

          return decoded_message



