


'''simple docstring'''



import tempfile
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel





class    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=99			,     SCREAMING_SNAKE_CASE_=13			,     SCREAMING_SNAKE_CASE_=7			,     SCREAMING_SNAKE_CASE_=9			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=32			,     SCREAMING_SNAKE_CASE_=5			,     SCREAMING_SNAKE_CASE_=4			,     SCREAMING_SNAKE_CASE_=37			,     SCREAMING_SNAKE_CASE_=8			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=0.0_0_2			,     SCREAMING_SNAKE_CASE_=1			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     )				->     List[str]:
         __lowerCamelCase   :	Optional[int]								=		parent
         __lowerCamelCase   :	Tuple								=		batch_size
         __lowerCamelCase   :	Union[str, Any]								=		encoder_seq_length
         __lowerCamelCase   :	List[Any]								=		decoder_seq_length
         # For common tests
         __lowerCamelCase   :	List[Any]								=		self.decoder_seq_length
         __lowerCamelCase   :	int								=		is_training
         __lowerCamelCase   :	str								=		use_attention_mask
         __lowerCamelCase   :	List[str]								=		use_labels
         __lowerCamelCase   :	Optional[int]								=		vocab_size
         __lowerCamelCase   :	int								=		hidden_size
         __lowerCamelCase   :	Any								=		num_hidden_layers
         __lowerCamelCase   :	Any								=		num_attention_heads
         __lowerCamelCase   :	str								=		d_ff
         __lowerCamelCase   :	Any								=		relative_attention_num_buckets
         __lowerCamelCase   :	Tuple								=		dropout_rate
         __lowerCamelCase   :	List[Any]								=		initializer_factor
         __lowerCamelCase   :	Optional[int]								=		eos_token_id
         __lowerCamelCase   :	Optional[Any]								=		pad_token_id
         __lowerCamelCase   :	Dict								=		decoder_start_token_id
         __lowerCamelCase   :	str								=		None
         __lowerCamelCase   :	Union[str, Any]								=		decoder_layers







    def       lowercase_				(							self	)				->     Tuple:
         return TaConfig.from_pretrained('google/umt5-base'	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     )				->     Union[str, Any]:
         if attention_mask is None:
              __lowerCamelCase   :	Tuple								=		input_ids.ne(config.pad_token_id	)
         if decoder_attention_mask is None:
              __lowerCamelCase   :	Any								=		decoder_input_ids.ne(config.pad_token_id	)
         if head_mask is None:
              __lowerCamelCase   :	int								=		torch.ones(config.num_hidden_layers			,     config.num_attention_heads			,     device=SCREAMING_SNAKE_CASE_	)
         if decoder_head_mask is None:
              __lowerCamelCase   :	int								=		torch.ones(config.num_decoder_layers			,     config.num_attention_heads			,     device=SCREAMING_SNAKE_CASE_	)
         if cross_attn_head_mask is None:
              __lowerCamelCase   :	Dict								=		torch.ones(
                  config.num_decoder_layers			,     config.num_attention_heads			,     device=SCREAMING_SNAKE_CASE_	)
         return {
             "input_ids": input_ids,
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "decoder_attention_mask": decoder_attention_mask,
             "head_mask": head_mask,
             "decoder_head_mask": decoder_head_mask,
             "cross_attn_head_mask": cross_attn_head_mask,
         }







    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	Union[str, Any]								=		ids_tensor([self.batch_size, self.encoder_seq_length]			,     self.vocab_size	)
         __lowerCamelCase   :	Optional[int]								=		ids_tensor([self.batch_size, self.decoder_seq_length]			,     self.vocab_size	)

         # we need to clamp the input ids here to avoid having pad token in between
         # this is because for NllbMoe the position_ids are prepared such that
         # all pad tokens have pos id = 2 and rest are between 2..seq_length
         # and the seq_length here is seq_length - num_pad_tokens
         # but when using past, there is no way of knowing if the past input ids had
         # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
         # position_ids being off by num_pad_tokens in past input
         __lowerCamelCase   :	Any								=		input_ids.clamp(self.pad_token_id + 1	)
         __lowerCamelCase   :	Union[str, Any]								=		decoder_input_ids.clamp(self.pad_token_id + 1	)

         __lowerCamelCase   :	Optional[Any]								=		self.get_config()
         __lowerCamelCase   :	int								=		config.num_attention_heads
         __lowerCamelCase   :	str								=		self.prepare_inputs_dict(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
         return config, input_dict







    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase				,       __lowerCamelCase   :	Union[str, Any]								=		self.prepare_config_and_inputs()
         return config, inputs_dict







    def       lowercase_				(							self	)				->     List[Any]:
         return TaConfig(
             vocab_size=1_66			,     d_model=self.hidden_size			,     d_ff=self.d_ff			,     d_kv=self.hidden_size // self.num_attention_heads			,     num_layers=self.num_hidden_layers			,     num_decoder_layers=self.decoder_layers			,     num_heads=self.num_attention_heads			,     relative_attention_num_buckets=self.relative_attention_num_buckets			,     dropout_rate=self.dropout_rate			,     initializer_factor=self.initializer_factor			,     eos_token_id=self.eos_token_id			,     bos_token_id=self.pad_token_id			,     pad_token_id=self.pad_token_id			,     decoder_start_token_id=self.decoder_start_token_id			,     )







    def       lowercase_				(							self	)				->     int:
         return TaConfig(
             vocab_size=self.vocab_size			,     d_model=self.hidden_size			,     d_ff=self.d_ff			,     d_kv=self.hidden_size // self.num_attention_heads			,     num_layers=self.num_hidden_layers			,     num_decoder_layers=self.decoder_layers			,     num_heads=self.num_attention_heads			,     relative_attention_num_buckets=self.relative_attention_num_buckets			,     dropout_rate=self.dropout_rate			,     initializer_factor=self.initializer_factor			,     eos_token_id=self.eos_token_id			,     bos_token_id=self.pad_token_id			,     pad_token_id=self.pad_token_id			,     decoder_start_token_id=self.decoder_start_token_id			,     )







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     )				->     Tuple:
         __lowerCamelCase   :	Any								=		UMTaModel(config=SCREAMING_SNAKE_CASE_	)
         model.to(SCREAMING_SNAKE_CASE_	)
         model.eval()
         __lowerCamelCase   :	str								=		model(
             input_ids=SCREAMING_SNAKE_CASE_			,     decoder_input_ids=SCREAMING_SNAKE_CASE_			,     attention_mask=SCREAMING_SNAKE_CASE_			,     decoder_attention_mask=SCREAMING_SNAKE_CASE_			,     )
         __lowerCamelCase   :	Tuple								=		model(input_ids=SCREAMING_SNAKE_CASE_			,     decoder_input_ids=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		result.last_hidden_state
         __lowerCamelCase   :	Any								=		result.past_key_values
         __lowerCamelCase   :	int								=		result.encoder_last_hidden_state

         self.parent.assertEqual(encoder_output.size()			,     (self.batch_size, self.encoder_seq_length, self.hidden_size)	)
         self.parent.assertEqual(decoder_output.size()			,     (self.batch_size, self.decoder_seq_length, self.hidden_size)	)
         # There should be `num_layers` key value embeddings stored in decoder_past
         self.parent.assertEqual(len(SCREAMING_SNAKE_CASE_	)			,     config.num_layers	)
         # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
         self.parent.assertEqual(len(decoder_past[0]	)			,     4	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     )				->     Optional[int]:
         __lowerCamelCase   :	Union[str, Any]								=		UMTaModel(config=SCREAMING_SNAKE_CASE_	).get_decoder().to(SCREAMING_SNAKE_CASE_	).eval()
         # first forward pass
         __lowerCamelCase   :	Union[str, Any]								=		model(SCREAMING_SNAKE_CASE_			,     use_cache=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		model(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		model(SCREAMING_SNAKE_CASE_			,     use_cache=SCREAMING_SNAKE_CASE_	)

         self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_	) == len(SCREAMING_SNAKE_CASE_	)	)
         self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_	) == len(SCREAMING_SNAKE_CASE_	) + 1	)

         __lowerCamelCase				,       __lowerCamelCase   :	int								=		outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         __lowerCamelCase   :	Optional[Any]								=		ids_tensor((self.batch_size, 1)			,     config.vocab_size	)

         # append to next input_ids and
         __lowerCamelCase   :	Any								=		torch.cat([input_ids, next_tokens]			,     dim=-1	)

         __lowerCamelCase   :	Union[str, Any]								=		model(SCREAMING_SNAKE_CASE_	)['last_hidden_state']
         __lowerCamelCase   :	Optional[Any]								=		model(SCREAMING_SNAKE_CASE_			,     past_key_values=SCREAMING_SNAKE_CASE_	)['last_hidden_state']

         # select random slice
         __lowerCamelCase   :	Dict								=		ids_tensor((1,)			,     output_from_past.shape[-1]	).item()
         __lowerCamelCase   :	Tuple								=		output_from_no_past[:, -1, random_slice_idx].detach()
         __lowerCamelCase   :	List[Any]								=		output_from_past[:, 0, random_slice_idx].detach()

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-3	)	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     )				->     List[Any]:
         __lowerCamelCase   :	Any								=		UMTaModel(config=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	).half().eval()
         __lowerCamelCase   :	Any								=		model(**SCREAMING_SNAKE_CASE_	)['last_hidden_state']
         self.parent.assertFalse(torch.isnan(SCREAMING_SNAKE_CASE_	).any().item()	)







@require_torch
class    UpperCAmelCase_  (_UpperCAmelCase					,		_UpperCAmelCase					,		_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  :					int							     =			(
        (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
    )
    lowerCamelCase  :					str							     =			(UMTaForConditionalGeneration,) if is_torch_available() else ()
    lowerCamelCase  :					List[Any]							     =			(
        {
            'conversational': UMTaForConditionalGeneration,
            'feature-extraction': UMTaModel,
            'summarization': UMTaForConditionalGeneration,
            'text2text-generation': UMTaForConditionalGeneration,
            'translation': UMTaForConditionalGeneration,
            'question-answering': UMTaForQuestionAnswering,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase  :					str							     =			True
    lowerCamelCase  :					Dict							     =			False
    lowerCamelCase  :					Optional[int]							     =			False
    lowerCamelCase  :					int							     =			True
    lowerCamelCase  :					Dict							     =			True
    # The small UMT5 model needs higher percentages for CPU/MP tests
    lowerCamelCase  :					Optional[int]							     =			[0.8, 0.9]







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	str								=		UMTaModelTester(self	)







    @unittest.skip('Test has a segmentation fault on torch 1.8.0'	)
    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	Any								=		self.model_tester.prepare_config_and_inputs()
         __lowerCamelCase   :	Dict								=		UMTaModel(config_and_inputs[0]	).to(SCREAMING_SNAKE_CASE_	)
         with tempfile.TemporaryDirectory() as tmpdirname:
              torch.onnx.export(
                  SCREAMING_SNAKE_CASE_			,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])			,     f'{tmpdirname}/t5_test.onnx'			,     export_params=SCREAMING_SNAKE_CASE_			,     opset_version=9			,     input_names=['input_ids', 'decoder_input_ids']			,     )







    @unittest.skipIf(torch_device == 'cpu'			,     'Cant do half precision'	)
    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Tuple								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model_fpaa_forward(*SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	Any								=		['encoder_attentions', 'decoder_attentions', 'cross_attentions']
         __lowerCamelCase   :	str								=		self.model_tester.prepare_config_and_inputs()
         __lowerCamelCase   :	Tuple								=		config_and_inputs[0]
         __lowerCamelCase   :	str								=		UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE_	).eval()
         model.to(SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[Any]								=		{
             'head_mask': torch.zeros(config.num_layers			,     config.num_heads			,     device=SCREAMING_SNAKE_CASE_	),
             'decoder_head_mask': torch.zeros(config.num_decoder_layers			,     config.num_heads			,     device=SCREAMING_SNAKE_CASE_	),
             'cross_attn_head_mask': torch.zeros(config.num_decoder_layers			,     config.num_heads			,     device=SCREAMING_SNAKE_CASE_	),
         }

         for attn_name, (name, mask) in zip(SCREAMING_SNAKE_CASE_			,     head_masking.items()	):
              __lowerCamelCase   :	Union[str, Any]								=		{name: mask}
              # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
              if name == "head_mask":
                   __lowerCamelCase   :	str								=		torch.ones(
                       config.num_decoder_layers			,     config.num_heads			,     device=SCREAMING_SNAKE_CASE_	)

              __lowerCamelCase   :	int								=		model.generate(
                  config_and_inputs[1]['input_ids']			,     num_beams=1			,     max_length=3			,     output_attentions=SCREAMING_SNAKE_CASE_			,     return_dict_in_generate=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_			,     )
              # We check the state of decoder_attentions and cross_attentions just from the last step
              __lowerCamelCase   :	Optional[int]								=		out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
              self.assertEqual(sum([w.sum().item() for w in attn_weights]	)			,     0.0	)







    @unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'	)
    def       lowercase_				(							self	)				->     Union[str, Any]:
         pass







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





    """simple docstring"""






    @slow
    @unittest.skip(
        'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'	)
    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	str								=		UMTaForConditionalGeneration.from_pretrained('google/umt5-small'			,     return_dict=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Optional[Any]								=		AutoTokenizer.from_pretrained('google/umt5-small'			,     use_fast=SCREAMING_SNAKE_CASE_			,     legacy=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		[
             'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
             'No se como puedo <extra_id_0>.',
             'This is the reason why we <extra_id_0> them.',
             'The <extra_id_0> walks in <extra_id_1>, seats',
             'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
         ]
         __lowerCamelCase   :	List[Any]								=		tokenizer(SCREAMING_SNAKE_CASE_			,     return_tensors='pt'			,     padding=SCREAMING_SNAKE_CASE_	).input_ids
         # fmt: off
         __lowerCamelCase   :	Any								=		torch.tensor(
             [
                 [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
                 [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
             ]	)
         # fmt: on
         torch.testing.assert_allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[str]								=		model.generate(input_ids.to(SCREAMING_SNAKE_CASE_	)	)
         __lowerCamelCase   :	str								=		[
             '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
             '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
             '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
             '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
             '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
         ]
         __lowerCamelCase   :	str								=		tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)










'''simple docstring'''



A__		:					Optional[int]												=  """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				bytes							)     ->		bytes:
     # Make sure the supplied data is a bytes-like object
     if not isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							):
          __lowerCamelCase   :	Dict								=		F'a bytes-like object is required, not \'{data.__class__.__name__}\''
          raise TypeError(UpperCAmelCase_							)

     __lowerCamelCase   :	Optional[int]								=		''.join(bin(UpperCAmelCase_							)[2:].zfill(8							) for byte in data							)

     __lowerCamelCase   :	Any								=		len(UpperCAmelCase_							) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          __lowerCamelCase   :	int								=		B'=' * ((6 - len(UpperCAmelCase_							) % 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(UpperCAmelCase_							) % 6)
     else:
          __lowerCamelCase   :	str								=		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(UpperCAmelCase_							)      ,    6							)							).encode()
         + padding
     )



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str							)     ->		bytes:
     # Make sure encoded_data is either a string or a bytes-like object
     if not isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							) and not isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							):
          __lowerCamelCase   :	Dict								=		(
              'argument should be a bytes-like object or ASCII string, '
              F'not \'{encoded_data.__class__.__name__}\''
          )
          raise TypeError(UpperCAmelCase_							)

     # 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(UpperCAmelCase_      ,    UpperCAmelCase_							):
          try:
               __lowerCamelCase   :	int								=		encoded_data.decode('utf-8'							)
          except UnicodeDecodeError:
               raise ValueError('base64 encoded data should only contain ASCII characters'							)

     __lowerCamelCase   :	Union[str, Any]								=		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(UpperCAmelCase_							) % 4 == 0 and padding < 3, "Incorrect padding"

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

          __lowerCamelCase   :	Optional[Any]								=		''.join(
              bin(B64_CHARSET.index(UpperCAmelCase_							)							)[2:].zfill(6							) for char in encoded_data							)[: -padding * 2]
     else:
          __lowerCamelCase   :	Any								=		''.join(
              bin(B64_CHARSET.index(UpperCAmelCase_							)							)[2:].zfill(6							) for char in encoded_data							)

     __lowerCamelCase   :	str								=		[
         int(binary_stream[index : index + 8]      ,    2							)
         for index in range(0      ,    len(UpperCAmelCase_							)      ,    8							)
     ]

     return bytes(UpperCAmelCase_							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






