




"""simple docstring"""







import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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


if is_torch_available():
		import torch

		from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
		    SpeechaTextaDecoder,
		    SpeechaTextaForCausalLM,
		)






@require_torch
class  SCREAMING_SNAKE_CASE__   :


						def __init__(							self			:     List[Any]  ,  lowerCAmelCase			:     Dict  ,  lowerCAmelCase			:     Dict=99  ,  lowerCAmelCase			:     Union[str, Any]=13  ,  lowerCAmelCase			:     Optional[int]=16  ,  lowerCAmelCase			:     Any=7  ,  lowerCAmelCase			:     Dict=True  ,  lowerCAmelCase			:     List[Any]=True  ,  lowerCAmelCase			:     Union[str, Any]=True  ,  lowerCAmelCase			:     List[str]=False  ,  lowerCAmelCase			:     Dict=True  ,  lowerCAmelCase			:     List[str]=2  ,  lowerCAmelCase			:     Optional[int]=32  ,  lowerCAmelCase			:     List[Any]=4  ,  lowerCAmelCase			:     Tuple=4  ,  lowerCAmelCase			:     Optional[Any]=30  ,  lowerCAmelCase			:     Union[str, Any]=0  ,  lowerCAmelCase			:     Union[str, Any]=1  ,  lowerCAmelCase			:     str=2  ,  lowerCAmelCase			:     Optional[Any]=None  ,  ):
										lowerCAmelCase		=			parent
										lowerCAmelCase		=			batch_size
										lowerCAmelCase		=			decoder_seq_length
										# For common tests
										lowerCAmelCase		=			self.decoder_seq_length
										lowerCAmelCase		=			is_training
										lowerCAmelCase		=			use_attention_mask
										lowerCAmelCase		=			use_labels

										lowerCAmelCase		=			vocab_size
										lowerCAmelCase		=			d_model
										lowerCAmelCase		=			d_model
										lowerCAmelCase		=			decoder_layers
										lowerCAmelCase		=			decoder_layers
										lowerCAmelCase		=			decoder_ffn_dim
										lowerCAmelCase		=			decoder_attention_heads
										lowerCAmelCase		=			decoder_attention_heads
										lowerCAmelCase		=			eos_token_id
										lowerCAmelCase		=			bos_token_id
										lowerCAmelCase		=			pad_token_id
										lowerCAmelCase		=			decoder_start_token_id
										lowerCAmelCase		=			use_cache
										lowerCAmelCase		=			max_position_embeddings

										lowerCAmelCase		=			None
										lowerCAmelCase		=			decoder_seq_length
										lowerCAmelCase		=			2
										lowerCAmelCase		=			1


						def 					__lowercase	(							self			:     str   ):
										lowerCAmelCase		=			ids_tensor([self.batch_size, self.decoder_seq_length]  ,  self.vocab_size   )

										lowerCAmelCase		=			None
										if self.use_attention_mask:
														lowerCAmelCase		=			ids_tensor([self.batch_size, self.decoder_seq_length]  ,  vocab_size=2   )

										lowerCAmelCase		=			None
										if self.use_labels:
														lowerCAmelCase		=			ids_tensor([self.batch_size, self.decoder_seq_length]  ,  self.vocab_size   )

										lowerCAmelCase		=			SpeechaTextaConfig(
										    vocab_size=self.vocab_size  ,  d_model=self.d_model  ,  decoder_layers=self.decoder_layers  ,  decoder_ffn_dim=self.decoder_ffn_dim  ,  decoder_attention_heads=self.decoder_attention_heads  ,  eos_token_id=self.eos_token_id  ,  bos_token_id=self.bos_token_id  ,  use_cache=self.use_cache  ,  pad_token_id=self.pad_token_id  ,  decoder_start_token_id=self.decoder_start_token_id  ,  max_position_embeddings=self.max_position_embeddings  ,  )

										return (
										    config,
										    input_ids,
										    attention_mask,
										    lm_labels,
										)


						def 					__lowercase	(							self			:     str  ,  lowerCAmelCase			:     Tuple  ,  lowerCAmelCase			:     Union[str, Any]  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     Union[str, Any]  ,  ):
										lowerCAmelCase		=			True
										lowerCAmelCase		=			SpeechaTextaDecoder(config=lowerCAmelCase   ).to(lowerCAmelCase   ).eval()
										lowerCAmelCase		=			input_ids[:2]

										input_ids[input_ids == 0] += 1
										# first forward pass
										lowerCAmelCase		=			model(lowerCAmelCase  ,  use_cache=lowerCAmelCase   )
										lowerCAmelCase		=			model(lowerCAmelCase   )
										lowerCAmelCase		=			model(lowerCAmelCase  ,  use_cache=lowerCAmelCase   )

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

										lowerCAmelCase		=			outputs["""past_key_values"""]

										# create hypothetical next token and extent to next_input_ids
										lowerCAmelCase		=			ids_tensor((2, 1)  ,  config.vocab_size - 1   ) + 1

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

										print(lowerCAmelCase   )

										lowerCAmelCase		=			model(lowerCAmelCase   )["""last_hidden_state"""]
										lowerCAmelCase		=			model(lowerCAmelCase  ,  past_key_values=lowerCAmelCase   )["""last_hidden_state"""]

										# select random slice
										lowerCAmelCase		=			ids_tensor((1,)  ,  output_from_past.shape[-1]   ).item()
										lowerCAmelCase		=			output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
										lowerCAmelCase		=			output_from_past[:, 0, random_slice_idx].detach()

										# test that outputs are equal for slice
										assert torch.allclose(lowerCAmelCase  ,  lowerCAmelCase  ,  atol=1e-3   )




						def 					__lowercase	(							self			:     Any   ):
										lowerCAmelCase		=			self.prepare_config_and_inputs()
										(
										    (
										    lowerCAmelCase
										)	,  (
										    lowerCAmelCase
										)	,  (
										    lowerCAmelCase
										)	,  (
										    lowerCAmelCase
										)	,  
										)		=			config_and_inputs

										lowerCAmelCase		=			{
										    """input_ids""": input_ids,
										    """attention_mask""": attention_mask,
										}
										return config, inputs_dict






@require_torch
class  SCREAMING_SNAKE_CASE__   (   _a  ,			_a  ,			_a  ,			unittest.TestCase   ):
						_a        =  (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
						_a        =  (SpeechaTextaForCausalLM,) if is_torch_available() else ()
						_a        =  {'text-generation': SpeechaTextaForCausalLM} if is_torch_available() else {}
						_a        =  True
						_a        =  False


						def 					__lowercase	(							self			:     List[str]  ,  ):
										lowerCAmelCase		=			SpeechaTextaStandaloneDecoderModelTester(self  ,  is_training=lowerCAmelCase   )
										lowerCAmelCase		=			ConfigTester(self  ,  config_class=lowerCAmelCase   )


						def 					__lowercase	(							self			:     Any   ):
										pass


						def 					__lowercase	(							self			:     List[str]   ):
										pass


						def 					__lowercase	(							self			:     Any   ):
										pass


						def 					__lowercase	(							self			:     int   ):
										self.config_tester.run_common_tests()


						def 					__lowercase	(							self			:     int   ):
										lowerCAmelCase		=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_decoder_model_past(*lowerCAmelCase   )




						def 					__lowercase	(							self			:     int   ):
										return











"""simple docstring"""







def 							lowercase   (snake_case__				:	int	)       ->					int:


				'''simple docstring'''






				barcode //= 10  # exclude the last digit
				lowerCAmelCase		=			False
				lowerCAmelCase		=			0

				# extract and check each digit
				while barcode != 0:
								lowerCAmelCase		=			1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								lowerCAmelCase		=			not checker

				return (10 - (s % 10)) % 10





def 							lowercase   (snake_case__				:	int	)       ->					bool:


				'''simple docstring'''






				return len(str(snake_case__	)	) == 13 and get_check_digit(snake_case__	) == barcode % 10





def 							lowercase   (snake_case__				:	str	)       ->					int:


				'''simple docstring'''






				if str(snake_case__	).isalpha():
								lowerCAmelCase		=			f'''Barcode \'{barcode}\' has alphabetic characters.'''
								raise ValueError(snake_case__	)
				elif int(snake_case__	) < 0:
								raise ValueError("""The entered barcode has a negative value. Try again."""	)
				else:
								return int(snake_case__	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		a								=							get_barcode(input('Barcode: ').strip())

		if is_valid(barcode):
				print(f"""'{barcode}' is a valid barcode.""")
		else:
				print(f"""'{barcode}' is NOT a valid barcode.""")





