

A__						=							"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"




def 					_lowercase     (				a_				:					bytes				)				->	bytes:




					'''simple docstring'''







					if not isinstance(a_					,a_				):
										__magic_name__							=    F'a bytes-like object is required, not \'{data.__class__.__name__}\''
										raise TypeError(a_				)

					__magic_name__							=    ''.join(bin(a_				)[2:].zfill(8				) for byte in data				)

					__magic_name__							=    len(a_				) % 6 != 0

					if padding_needed:
										# The padding that will be added later
										__magic_name__							=    B'=' * ((6 - len(a_				) % 6) // 2)

										# Append binary_stream with arbitrary binary digits (0's by default) to make its
										# length a multiple of 6.
										binary_stream += "0" * (6 - len(a_				) % 6)
					else:
										__magic_name__							=    B''

					# Encode every 6 binary digits to their corresponding Base64 character
					return (
					    "".join(
					        B64_CHARSET[int(binary_stream[index : index + 6]					,2				)]
					        for index in range(0					,len(a_				)					,6				)				).encode()
					    + padding
					)




def 					_lowercase     (				a_				:					str				)				->	bytes:




					'''simple docstring'''







					if not isinstance(a_					,a_				) and not isinstance(a_					,a_				):
										__magic_name__							=    (
										    'argument should be a bytes-like object or ASCII string, '
										    F'not \'{encoded_data.__class__.__name__}\''
										)
										raise TypeError(a_				)

					# In case encoded_data is a bytes-like object, make sure it contains only
					# ASCII characters so we convert it to a string object
					if isinstance(a_					,a_				):
										try:
															__magic_name__							=    encoded_data.decode('utf-8'				)
										except UnicodeDecodeError:
															raise ValueError('base64 encoded data should only contain ASCII characters'				)

					__magic_name__							=    encoded_data.count('='				)

					# Check if the encoded string contains non base64 characters
					if padding:
										assert all(
										    char in B64_CHARSET for char in encoded_data[:-padding]				), "Invalid base64 character(s) found."
					else:
										assert all(
										    char in B64_CHARSET for char in encoded_data				), "Invalid base64 character(s) found."

					# Check the padding
					assert len(a_				) % 4 == 0 and padding < 3, "Incorrect padding"

					if padding:
										# Remove padding if there is one
										__magic_name__							=    encoded_data[:-padding]

										__magic_name__							=    ''.join(
										    bin(B64_CHARSET.index(a_				)				)[2:].zfill(6				) for char in encoded_data				)[: -padding * 2]
					else:
										__magic_name__							=    ''.join(
										    bin(B64_CHARSET.index(a_				)				)[2:].zfill(6				) for char in encoded_data				)

					__magic_name__							=    [
					    int(binary_stream[index : index + 8]					,2				)
					    for index in range(0					,len(a_				)					,8				)
					]

					return bytes(a_				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  , return_dict=__UpperCamelCase      ).to(__UpperCamelCase      )
						__magic_name__							=    AutoTokenizer.from_pretrained('google/mt5-small'      )

						__magic_name__							=    tokenizer('Hello there'  , return_tensors='pt'      ).input_ids
						__magic_name__							=    tokenizer('Hi I am'  , return_tensors='pt'      ).input_ids

						__magic_name__							=    model(input_ids.to(__UpperCamelCase      )  , labels=labels.to(__UpperCamelCase      )      ).loss
						__magic_name__							=    -(labels.shape[-1] * loss.item())

						__magic_name__							=    -84.9127
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1E-4      )




