


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        a					:





     def __init__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=99    ,	SCREAMING_SNAKE_CASE_					:				Dict=13    ,	SCREAMING_SNAKE_CASE_					:				int=7    ,	SCREAMING_SNAKE_CASE_					:				Any=9    ,	SCREAMING_SNAKE_CASE_					:				Dict=True    ,	SCREAMING_SNAKE_CASE_					:				int=True    ,	SCREAMING_SNAKE_CASE_					:				Tuple=False    ,	SCREAMING_SNAKE_CASE_					:				Tuple=32    ,	SCREAMING_SNAKE_CASE_					:				Dict=5    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=4    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=37    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=8    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=0.1    ,	SCREAMING_SNAKE_CASE_					:				str=0.002    ,	SCREAMING_SNAKE_CASE_					:				str=1    ,	SCREAMING_SNAKE_CASE_					:				Any=0    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=0    ,	SCREAMING_SNAKE_CASE_					:				Dict=None    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=None    ,	):
        __lowerCamelCase:				Union[str, Any]     =	parent
        __lowerCamelCase:				Union[str, Any]     =	batch_size
        __lowerCamelCase:				Any     =	encoder_seq_length
        __lowerCamelCase:				str     =	decoder_seq_length
        # For common tests
        __lowerCamelCase:				Optional[int]     =	self.decoder_seq_length
        __lowerCamelCase:				Optional[Any]     =	is_training
        __lowerCamelCase:				List[Any]     =	use_attention_mask
        __lowerCamelCase:				Union[str, Any]     =	use_labels
        __lowerCamelCase:				Any     =	vocab_size
        __lowerCamelCase:				Optional[int]     =	hidden_size
        __lowerCamelCase:				List[str]     =	num_hidden_layers
        __lowerCamelCase:				Union[str, Any]     =	num_attention_heads
        __lowerCamelCase:				Any     =	d_ff
        __lowerCamelCase:				Any     =	relative_attention_num_buckets
        __lowerCamelCase:				Optional[Any]     =	dropout_rate
        __lowerCamelCase:				int     =	initializer_factor
        __lowerCamelCase:				Optional[Any]     =	eos_token_id
        __lowerCamelCase:				Dict     =	pad_token_id
        __lowerCamelCase:				Optional[Any]     =	decoder_start_token_id
        __lowerCamelCase:				Union[str, Any]     =	None
        __lowerCamelCase:				List[str]     =	decoder_layers





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        return TaConfig.from_pretrained("""google/umt5-base"""							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=None    ,	SCREAMING_SNAKE_CASE_					:				Dict=None    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=None    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=None    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=None    ,	):
        if attention_mask is None:
           __lowerCamelCase:				Union[str, Any]     =	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:				List[Any]     =	torch.ones(config.num_hidden_layers    ,	config.num_attention_heads    ,	device=UpperCAmelCase__							)
        if decoder_head_mask is None:
           __lowerCamelCase:				Tuple     =	torch.ones(config.num_decoder_layers    ,	config.num_attention_heads    ,	device=UpperCAmelCase__							)
        if cross_attn_head_mask is None:
           __lowerCamelCase:				Union[str, Any]     =	torch.ones(
               config.num_decoder_layers    ,	config.num_attention_heads    ,	device=UpperCAmelCase__							)
        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  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Union[str, Any]     =	ids_tensor([self.batch_size, self.encoder_seq_length]    ,	self.vocab_size							)
        __lowerCamelCase:				Union[str, Any]     =	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:				List[str]     =	input_ids.clamp(self.pad_token_id + 1							)
        __lowerCamelCase:				List[str]     =	decoder_input_ids.clamp(self.pad_token_id + 1							)

        __lowerCamelCase:				str     =	self.get_config()
        __lowerCamelCase:				Tuple     =	config.num_attention_heads
        __lowerCamelCase:				List[Any]     =	self.prepare_inputs_dict(UpperCAmelCase__    ,	UpperCAmelCase__    ,	UpperCAmelCase__							)
        return config, input_dict





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase:				List[str]     =	self.prepare_config_and_inputs()
        return config, inputs_dict





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        return TaConfig(
            vocab_size=166    ,	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  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        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  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	):
        __lowerCamelCase:				str     =	UMTaModel(config=UpperCAmelCase__							)
        model.to(UpperCAmelCase__							)
        model.eval()
        __lowerCamelCase:				str     =	model(
            input_ids=UpperCAmelCase__    ,	decoder_input_ids=UpperCAmelCase__    ,	attention_mask=UpperCAmelCase__    ,	decoder_attention_mask=UpperCAmelCase__    ,	)
        __lowerCamelCase:				int     =	model(input_ids=UpperCAmelCase__    ,	decoder_input_ids=UpperCAmelCase__							)
        __lowerCamelCase:				int     =	result.last_hidden_state
        __lowerCamelCase:				Dict     =	result.past_key_values
        __lowerCamelCase:				Dict     =	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(UpperCAmelCase__							)    ,	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  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	):
        __lowerCamelCase:				int     =	UMTaModel(config=UpperCAmelCase__							).get_decoder().to(UpperCAmelCase__							).eval()
        # first forward pass
        __lowerCamelCase:				List[Any]     =	model(UpperCAmelCase__    ,	use_cache=UpperCAmelCase__							)
        __lowerCamelCase:				List[Any]     =	model(UpperCAmelCase__							)
        __lowerCamelCase:				Any     =	model(UpperCAmelCase__    ,	use_cache=UpperCAmelCase__							)

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

        __lowerCamelCase:				List[str]     =	outputs.to_tuple()

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

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

        __lowerCamelCase:				Any     =	model(UpperCAmelCase__							)['''last_hidden_state''']
        __lowerCamelCase:				Optional[Any]     =	model(UpperCAmelCase__    ,	past_key_values=UpperCAmelCase__							)['''last_hidden_state''']

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

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







     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	):
        __lowerCamelCase:				int     =	UMTaModel(config=UpperCAmelCase__							).to(UpperCAmelCase__							).half().eval()
        __lowerCamelCase:				str     =	model(**UpperCAmelCase__							)['''last_hidden_state''']
        self.parent.assertFalse(torch.isnan(UpperCAmelCase__							).any().item()							)



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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Union[str, Any]     =	UMTaModelTester(self							)





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





     @unittest.skipIf(torch_device == """cpu"""    ,	"""Cant do half precision"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase__							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Optional[int]     =	['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
        __lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs()
        __lowerCamelCase:				int     =	config_and_inputs[0]
        __lowerCamelCase:				Union[str, Any]     =	UMTaForConditionalGeneration(UpperCAmelCase__							).eval()
        model.to(UpperCAmelCase__							)

        __lowerCamelCase:				str     =	{
            '''head_mask''': torch.zeros(config.num_layers    ,	config.num_heads    ,	device=UpperCAmelCase__							),
            '''decoder_head_mask''': torch.zeros(config.num_decoder_layers    ,	config.num_heads    ,	device=UpperCAmelCase__							),
            '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers    ,	config.num_heads    ,	device=UpperCAmelCase__							),
        }

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

           __lowerCamelCase:				Union[str, Any]     =	model.generate(
               config_and_inputs[1]["""input_ids"""]    ,	num_beams=1    ,	max_length=3    ,	output_attentions=UpperCAmelCase__    ,	return_dict_in_generate=UpperCAmelCase__    ,	**UpperCAmelCase__    ,	)
           # We check the state of decoder_attentions and cross_attentions just from the last step
           __lowerCamelCase:				List[str]     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        pass



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





     @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  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Optional[Any]     =	UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""    ,	return_dict=UpperCAmelCase__							).to(UpperCAmelCase__							)
        __lowerCamelCase:				int     =	AutoTokenizer.from_pretrained("""google/umt5-small"""    ,	use_fast=UpperCAmelCase__    ,	legacy=UpperCAmelCase__							)
        __lowerCamelCase:				List[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:				Dict     =	tokenizer(UpperCAmelCase__    ,	return_tensors="""pt"""    ,	padding=UpperCAmelCase__							).input_ids
        # fmt: off
        __lowerCamelCase:				Optional[Any]     =	torch.tensor(
            [
                [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
            ]							)
        # fmt: on
        torch.testing.assert_allclose(UpperCAmelCase__    ,	UpperCAmelCase__							)

        __lowerCamelCase:				List[Any]     =	model.generate(input_ids.to(UpperCAmelCase__							)							)
        __lowerCamelCase:				int     =	[
            '''<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:				Tuple     =	tokenizer.batch_decode(UpperCAmelCase__							)
        self.assertEqual(UpperCAmelCase__    ,	UpperCAmelCase__							)






_A		:				Tuple									=       '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''



def     __lowerCAmelCase						(							)       ->							None:
   __lowerCamelCase:				Optional[int]     =	input("""Enter message: """ )
   __lowerCamelCase:				Dict     =	input("""Enter key [alphanumeric]: """ )
   __lowerCamelCase:				List[Any]     =	input("""Encrypt/Decrypt [e/d]: """ )

   if mode.lower().startswith("""e""" ):
      __lowerCamelCase:				Optional[int]     =	"""encrypt"""
      __lowerCamelCase:				Optional[int]     =	encrypt_message(snake_case						,      snake_case )
   elif mode.lower().startswith("""d""" ):
      __lowerCamelCase:				Union[str, Any]     =	"""decrypt"""
      __lowerCamelCase:				Optional[Any]     =	decrypt_message(snake_case						,      snake_case )

   print(f'\n{mode.title()}ed message:' )
   print(snake_case )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str )       ->							str:
   return translate_message(snake_case						,      snake_case						,      """encrypt""" )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str )       ->							str:
   return translate_message(snake_case						,      snake_case						,      """decrypt""" )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str						,      snake_case	:    str )       ->							str:
   __lowerCamelCase:				Any     =	[]
   __lowerCamelCase:				Optional[int]     =	0
   __lowerCamelCase:				Any     =	key.upper()

   for symbol in message:
      __lowerCamelCase:				int     =	LETTERS.find(symbol.upper() )
      if num != -1:
         if mode == "encrypt":
            num += LETTERS.find(key[key_index] )
         elif mode == "decrypt":
            num -= LETTERS.find(key[key_index] )

         num %= len(snake_case )

         if symbol.isupper():
            translated.append(LETTERS[num] )
         elif symbol.islower():
            translated.append(LETTERS[num].lower() )

         key_index += 1
         if key_index == len(snake_case ):
            __lowerCamelCase:				Union[str, Any]     =	0
      else:
         translated.append(snake_case )
   return "".join(snake_case )


if __name__ == "__main__":
   main()


