


"""simple docstring"""







import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
       import torch

       from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class  UpperCamelCase	:
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase=13  ,__UpperCamelCase=7  ,__UpperCamelCase=True  ,__UpperCamelCase=True  ,__UpperCamelCase=False  ,__UpperCamelCase=True  ,__UpperCamelCase=99  ,__UpperCamelCase=32  ,__UpperCamelCase=5  ,__UpperCamelCase=4  ,__UpperCamelCase=37  ,__UpperCamelCase="gelu"  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=512  ,__UpperCamelCase=16  ,__UpperCamelCase=2  ,__UpperCamelCase=0.02  ,__UpperCamelCase=3  ,__UpperCamelCase=4  ,__UpperCamelCase=None  ,)			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       Tuple									=			parent
        lowercase_			:       Optional[Any]									=			batch_size
        lowercase_			:       str									=			seq_length
        lowercase_			:       Optional[int]									=			is_training
        lowercase_			:       List[str]									=			use_input_mask
        lowercase_			:       List[str]									=			use_token_type_ids
        lowercase_			:       int									=			use_labels
        lowercase_			:       Union[str, Any]									=			vocab_size
        lowercase_			:       List[str]									=			hidden_size
        lowercase_			:       str									=			num_hidden_layers
        lowercase_			:       Any									=			num_attention_heads
        lowercase_			:       Union[str, Any]									=			intermediate_size
        lowercase_			:       int									=			hidden_act
        lowercase_			:       Optional[int]									=			hidden_dropout_prob
        lowercase_			:       Tuple									=			attention_probs_dropout_prob
        lowercase_			:       List[str]									=			max_position_embeddings
        lowercase_			:       List[str]									=			type_vocab_size
        lowercase_			:       Union[str, Any]									=			type_sequence_label_size
        lowercase_			:       Union[str, Any]									=			initializer_range
        lowercase_			:       Any									=			num_labels
        lowercase_			:       Any									=			num_choices
        lowercase_			:       Any									=			scope
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Tuple									=			ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size    )

        lowercase_			:       Optional[int]									=			None
        if self.use_input_mask:
             lowercase_			:       str									=			random_attention_mask([self.batch_size, self.seq_length]    )

        lowercase_			:       List[Any]									=			None
        if self.use_token_type_ids:
             lowercase_			:       Dict									=			ids_tensor([self.batch_size, self.seq_length]  ,self.type_vocab_size    )

        lowercase_			:       Optional[Any]									=			None
        lowercase_			:       int									=			None
        lowercase_			:       List[str]									=			None
        if self.use_labels:
             lowercase_			:       int									=			ids_tensor([self.batch_size]  ,self.type_sequence_label_size    )
             lowercase_			:       Union[str, Any]									=			ids_tensor([self.batch_size, self.seq_length]  ,self.num_labels    )
             lowercase_			:       int									=			ids_tensor([self.batch_size]  ,self.num_choices    )

        lowercase_			:       List[str]									=			self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        return LlamaConfig(
            vocab_size=self.vocab_size  ,hidden_size=self.hidden_size  ,num_hidden_layers=self.num_hidden_layers  ,num_attention_heads=self.num_attention_heads  ,intermediate_size=self.intermediate_size  ,hidden_act=self.hidden_act  ,hidden_dropout_prob=self.hidden_dropout_prob  ,attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,max_position_embeddings=self.max_position_embeddings  ,type_vocab_size=self.type_vocab_size  ,is_decoder=_a  ,initializer_range=self.initializer_range  ,)
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			LlamaModel(config=_a    )
        model.to(_a    )
        model.eval()
        lowercase_			:       Optional[Any]									=			model(_a  ,attention_mask=_a    )
        lowercase_			:       Optional[Any]									=			model(_a    )
        self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,)			->   Any:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			True
        lowercase_			:       Optional[Any]									=			LlamaModel(_a    )
        model.to(_a    )
        model.eval()
        lowercase_			:       Dict									=			model(
            _a  ,attention_mask=_a  ,encoder_hidden_states=_a  ,encoder_attention_mask=_a  ,)
        lowercase_			:       Dict									=			model(
            _a  ,attention_mask=_a  ,encoder_hidden_states=_a  ,)
        lowercase_			:       Union[str, Any]									=			model(_a  ,attention_mask=_a    )
        self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,)			->   int:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			LlamaForCausalLM(config=_a    )
        model.to(_a    )
        model.eval()
        lowercase_			:       int									=			model(_a  ,attention_mask=_a  ,labels=_a    )
        self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,)			->   Tuple:


        '''simple docstring'''


        lowercase_			:       List[str]									=			True
        lowercase_			:       Any									=			True
        lowercase_			:       str									=			LlamaForCausalLM(config=_a    )
        model.to(_a    )
        model.eval()

        # first forward pass
        lowercase_			:       int									=			model(
            _a  ,attention_mask=_a  ,encoder_hidden_states=_a  ,encoder_attention_mask=_a  ,use_cache=_a  ,)
        lowercase_			:       Optional[int]									=			outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        lowercase_			:       str									=			ids_tensor((self.batch_size, 3)  ,config.vocab_size    )
        lowercase_			:       Union[str, Any]									=			ids_tensor((self.batch_size, 3)  ,vocab_size=2    )

        # append to next input_ids and
        lowercase_			:       List[str]									=			torch.cat([input_ids, next_tokens]  ,dim=-1    )
        lowercase_			:       Optional[Any]									=			torch.cat([input_mask, next_mask]  ,dim=-1    )

        lowercase_			:       Optional[Any]									=			model(
            _a  ,attention_mask=_a  ,encoder_hidden_states=_a  ,encoder_attention_mask=_a  ,output_hidden_states=_a  ,)["""hidden_states"""][0]
        lowercase_			:       List[Any]									=			model(
            _a  ,attention_mask=_a  ,encoder_hidden_states=_a  ,encoder_attention_mask=_a  ,past_key_values=_a  ,output_hidden_states=_a  ,)["""hidden_states"""][0]

        # select random slice
        lowercase_			:       Tuple									=			ids_tensor((1,)  ,output_from_past.shape[-1]    ).item()
        lowercase_			:       List[Any]									=			output_from_no_past[:, -3:, random_slice_idx].detach()
        lowercase_			:       List[Any]									=			output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

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







   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Any									=			self.prepare_config_and_inputs()
        (
            lowercase_     
        )			:       Optional[Any]									=			config_and_inputs
        lowercase_			:       Any									=			{"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict







@require_torch
class  UpperCamelCase	(    UpperCamelCase__		,   UpperCamelCase__		,   UpperCamelCase__		,   unittest.TestCase						):
   lowercase							=						(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
   lowercase							=						(LlamaForCausalLM,) if is_torch_available() else ()
   lowercase							=						(
       {
           'feature-extraction': LlamaModel,
           'text-classification': LlamaForSequenceClassification,
           'text-generation': LlamaForCausalLM,
           'zero-shot': LlamaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   lowercase							=						False
   lowercase							=						False
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Dict									=			LlamaModelTester(self    )
        lowercase_			:       Union[str, Any]									=			ConfigTester(self  ,config_class=_a  ,hidden_size=37    )
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a    )
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       Dict									=			self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
             lowercase_			:       Optional[Any]									=			type
             self.model_tester.create_and_check_model(*_a    )
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_			:       List[str]									=			3
        lowercase_			:       Tuple									=			input_dict["""input_ids"""]
        lowercase_			:       Tuple									=			input_ids.ne(1    ).to(_a    )
        lowercase_			:       Tuple									=			ids_tensor([self.model_tester.batch_size]  ,self.model_tester.type_sequence_label_size    )
        lowercase_			:       List[Any]									=			LlamaForSequenceClassification(_a    )
        model.to(_a    )
        model.eval()
        lowercase_			:       Tuple									=			model(_a  ,attention_mask=_a  ,labels=_a    )
        self.assertEqual(result.logits.shape  ,(self.model_tester.batch_size, self.model_tester.num_labels)    )
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        lowercase_			:       List[str]									=			self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_			:       Tuple									=			3
        lowercase_			:       Union[str, Any]									=			"""single_label_classification"""
        lowercase_			:       List[str]									=			input_dict["""input_ids"""]
        lowercase_			:       int									=			input_ids.ne(1    ).to(_a    )
        lowercase_			:       Tuple									=			ids_tensor([self.model_tester.batch_size]  ,self.model_tester.type_sequence_label_size    )
        lowercase_			:       Tuple									=			LlamaForSequenceClassification(_a    )
        model.to(_a    )
        model.eval()
        lowercase_			:       Optional[Any]									=			model(_a  ,attention_mask=_a  ,labels=_a    )
        self.assertEqual(result.logits.shape  ,(self.model_tester.batch_size, self.model_tester.num_labels)    )
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_			:       Tuple									=			3
        lowercase_			:       Dict									=			"""multi_label_classification"""
        lowercase_			:       Optional[Any]									=			input_dict["""input_ids"""]
        lowercase_			:       Any									=			input_ids.ne(1    ).to(_a    )
        lowercase_			:       str									=			ids_tensor(
            [self.model_tester.batch_size, config.num_labels]  ,self.model_tester.type_sequence_label_size    ).to(torch.float    )
        lowercase_			:       int									=			LlamaForSequenceClassification(_a    )
        model.to(_a    )
        model.eval()
        lowercase_			:       Optional[int]									=			model(_a  ,attention_mask=_a  ,labels=_a    )
        self.assertEqual(result.logits.shape  ,(self.model_tester.batch_size, self.model_tester.num_labels)    )
   @unittest.skip('LLaMA buffers include complex numbers, which breaks this test'    )
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        pass







   @parameterized.expand([('linear',), ('dynamic',)]    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Dict									=			self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_			:       Tuple									=			ids_tensor([1, 10]  ,config.vocab_size    )
        lowercase_			:       str									=			ids_tensor([1, int(config.max_position_embeddings * 1.5    )]  ,config.vocab_size    )

        set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
        lowercase_			:       int									=			LlamaModel(_a    )
        original_model.to(_a    )
        original_model.eval()
        lowercase_			:       List[str]									=			original_model(_a    ).last_hidden_state
        lowercase_			:       str									=			original_model(_a    ).last_hidden_state

        set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
        lowercase_			:       Union[str, Any]									=			{"""type""": scaling_type, """factor""": 10.0}
        lowercase_			:       Optional[Any]									=			LlamaModel(_a    )
        scaled_model.to(_a    )
        scaled_model.eval()
        lowercase_			:       List[str]									=			scaled_model(_a    ).last_hidden_state
        lowercase_			:       Any									=			scaled_model(_a    ).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
             self.assertTrue(torch.allclose(_a  ,_a  ,atol=1e-5    )    )
        else:
             self.assertFalse(torch.allclose(_a  ,_a  ,atol=1e-5    )    )

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(_a  ,_a  ,atol=1e-5    )    )







@require_torch
class  UpperCamelCase	(    unittest.TestCase						):
   @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'    )
   @slow
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
        lowercase_			:       Tuple									=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'  ,device_map='auto'    )
        lowercase_			:       Optional[Any]									=			model(torch.tensor([input_ids]    )    )
        # Expected mean on dim = -1
        lowercase_			:       int									=			torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]    )
        torch.testing.assert_close(out.mean(-1    )  ,_a  ,atol=1e-2  ,rtol=1e-2    )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        lowercase_			:       str									=			torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]    )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,_a  ,atol=1e-5  ,rtol=1e-5    )
   @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'    )
   @slow
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        lowercase_			:       int									=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
        lowercase_			:       int									=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'  ,device_map='auto'    )
        lowercase_			:       List[str]									=			model(torch.tensor(_a    )    )
        # Expected mean on dim = -1
        lowercase_			:       str									=			torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]    )
        torch.testing.assert_close(out.mean(-1    )  ,_a  ,atol=1e-2  ,rtol=1e-2    )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        lowercase_			:       List[Any]									=			torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]    )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,_a  ,atol=1e-5  ,rtol=1e-5    )
   @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'    )
   @slow
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
        lowercase_			:       List[Any]									=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'  ,device_map='auto'    )
        lowercase_			:       Optional[Any]									=			model(torch.tensor(_a    )    )
        # Expected mean on dim = -1
        lowercase_			:       Union[str, Any]									=			torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]    )
        torch.testing.assert_close(out.mean(-1    )  ,_a  ,atol=1e-2  ,rtol=1e-2    )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        lowercase_			:       List[Any]									=			torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]    )
        # fmt: on
        torch.testing.assert_close(out.mean(-1    )  ,_a  ,atol=1e-2  ,rtol=1e-2    )
   @unittest.skip(
       'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'    )
   @slow
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       List[str]									=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
        lowercase_			:       Optional[Any]									=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'  ,device_map='auto'    )
        lowercase_			:       Any									=			model(torch.tensor(_a    )    )

        lowercase_			:       List[str]									=			torch.tensor(
            [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]  ,dtype=torch.floataa    )
        torch.testing.assert_close(out.mean(-1    )  ,_a  ,atol=1e-2  ,rtol=1e-2    )
        # fmt: off
        lowercase_			:       Any									=			torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]    )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,_a  ,atol=1e-5  ,rtol=1e-5    )







   @unittest.skip('Model is curently gated'    )
   @slow
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       str									=			"""Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
        lowercase_			:       Optional[int]									=			"""Simply put, the theory of relativity states that """
        lowercase_			:       List[str]									=			LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'    )
        lowercase_			:       Dict									=			tokenizer.encode(_a  ,return_tensors='pt'    )
        lowercase_			:       Any									=			LlamaForCausalLM.from_pretrained(
            'meta-llama/Llama-2-13b-chat-hf'  ,device_map='sequential'  ,use_safetensors=_a    )

        # greedy generation outputs
        lowercase_			:       Optional[Any]									=			model.generate(_a  ,max_new_tokens=64  ,top_p=_a  ,temperature=1  ,do_sample=_a    )
        lowercase_			:       List[Any]									=			tokenizer.decode(generated_ids[0]  ,skip_special_tokens=_a    )
        self.assertEqual(_a  ,_a    )


"""simple docstring"""







__SCREAMING_SNAKE_CASE								="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"



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

     lowercase_			:       List[Any]									=			''.join(bin(__SCREAMING_SNAKE_CASE   )[2:].zfill(8   ) for byte in data   )

     lowercase_			:       List[Any]									=			len(__SCREAMING_SNAKE_CASE   ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          lowercase_			:       Optional[int]									=			b'=' * ((6 - len(__SCREAMING_SNAKE_CASE   ) % 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(__SCREAMING_SNAKE_CASE   ) % 6)
     else:
          lowercase_			:       List[Any]									=			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(__SCREAMING_SNAKE_CASE   )  ,    6   )   ).encode()
         + padding
     )



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

     # 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(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          try:
               lowercase_			:       List[str]									=			encoded_data.decode('utf-8'   )
          except UnicodeDecodeError:
               raise ValueError('base64 encoded data should only contain ASCII characters'   )

     lowercase_			:       List[str]									=			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(__SCREAMING_SNAKE_CASE   ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          lowercase_			:       Union[str, Any]									=			encoded_data[:-padding]

          lowercase_			:       List[Any]									=			''.join(
              bin(B64_CHARSET.index(__SCREAMING_SNAKE_CASE   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
     else:
          lowercase_			:       int									=			''.join(
              bin(B64_CHARSET.index(__SCREAMING_SNAKE_CASE   )   )[2:].zfill(6   ) for char in encoded_data   )

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

     return bytes(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

