



"""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    SCREAMING_SNAKE_CASE__						:

   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=0.002   ,  _SCREAMING_SNAKE_CASE=1   ,  _SCREAMING_SNAKE_CASE=0   ,  _SCREAMING_SNAKE_CASE=0   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			parent
    UpperCAmelCase			:      Tuple			=			batch_size
    UpperCAmelCase			:      Optional[int]			=			encoder_seq_length
    UpperCAmelCase			:      Dict			=			decoder_seq_length
    # For common tests
    UpperCAmelCase			:      Optional[Any]			=			self.decoder_seq_length
    UpperCAmelCase			:      Union[str, Any]			=			is_training
    UpperCAmelCase			:      List[Any]			=			use_attention_mask
    UpperCAmelCase			:      Optional[int]			=			use_labels
    UpperCAmelCase			:      str			=			vocab_size
    UpperCAmelCase			:      Dict			=			hidden_size
    UpperCAmelCase			:      List[str]			=			num_hidden_layers
    UpperCAmelCase			:      List[Any]			=			num_attention_heads
    UpperCAmelCase			:      int			=			d_ff
    UpperCAmelCase			:      Optional[int]			=			relative_attention_num_buckets
    UpperCAmelCase			:      Union[str, Any]			=			dropout_rate
    UpperCAmelCase			:      List[Any]			=			initializer_factor
    UpperCAmelCase			:      Union[str, Any]			=			eos_token_id
    UpperCAmelCase			:      List[Any]			=			pad_token_id
    UpperCAmelCase			:      Union[str, Any]			=			decoder_start_token_id
    UpperCAmelCase			:      int			=			None
    UpperCAmelCase			:      List[str]			=			decoder_layers

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    return TaConfig.from_pretrained("""google/umt5-base"""		)

   def 			SCREAMING_SNAKE_CASE  (     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   ,  )						->   int:





    '''simple docstring'''




    if attention_mask is None:
     UpperCAmelCase			:      Tuple			=			input_ids.ne(config.pad_token_id		)
    if decoder_attention_mask is None:
     UpperCAmelCase			:      int			=			decoder_input_ids.ne(config.pad_token_id		)
    if head_mask is None:
     UpperCAmelCase			:      str			=			torch.ones(config.num_hidden_layers   ,  config.num_attention_heads   ,  device=_SCREAMING_SNAKE_CASE		)
    if decoder_head_mask is None:
     UpperCAmelCase			:      List[str]			=			torch.ones(config.num_decoder_layers   ,  config.num_attention_heads   ,  device=_SCREAMING_SNAKE_CASE		)
    if cross_attn_head_mask is None:
     UpperCAmelCase			:      Optional[int]			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			ids_tensor([self.batch_size, self.encoder_seq_length]   ,  self.vocab_size		)
    UpperCAmelCase			:      Optional[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
    UpperCAmelCase			:      Dict			=			input_ids.clamp(self.pad_token_id + 1		)
    UpperCAmelCase			:      List[str]			=			decoder_input_ids.clamp(self.pad_token_id + 1		)

    UpperCAmelCase			:      Optional[Any]			=			self.get_config()
    UpperCAmelCase			:      Tuple			=			config.num_attention_heads
    UpperCAmelCase			:      Union[str, Any]			=			self.prepare_inputs_dict(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    return config, input_dict

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      str			=			self.prepare_config_and_inputs()
    return config, inputs_dict

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    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		)						->   Optional[int]:





    '''simple docstring'''




    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   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  )						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			UMTaModel(config=_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()
    UpperCAmelCase			:      Optional[Any]			=			model(
        input_ids=_SCREAMING_SNAKE_CASE   ,  decoder_input_ids=_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  decoder_attention_mask=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Union[str, Any]			=			model(input_ids=_SCREAMING_SNAKE_CASE   ,  decoder_input_ids=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			result.last_hidden_state
    UpperCAmelCase			:      Optional[int]			=			result.past_key_values
    UpperCAmelCase			:      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(_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 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  )						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			UMTaModel(config=_SCREAMING_SNAKE_CASE		).get_decoder().to(_SCREAMING_SNAKE_CASE		).eval()
    # first forward pass
    UpperCAmelCase			:      List[str]			=			model(_SCREAMING_SNAKE_CASE   ,  use_cache=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			model(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			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		)

    UpperCAmelCase			,	UpperCAmelCase			:      Union[str, Any]			=			outputs.to_tuple()

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

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

    UpperCAmelCase			:      Union[str, Any]			=			model(_SCREAMING_SNAKE_CASE		)["""last_hidden_state"""]
    UpperCAmelCase			:      Dict			=			model(_SCREAMING_SNAKE_CASE   ,  past_key_values=_SCREAMING_SNAKE_CASE		)["""last_hidden_state"""]

    # select random slice
    UpperCAmelCase			:      List[str]			=			ids_tensor((1,)   ,  output_from_past.shape[-1]		).item()
    UpperCAmelCase			:      Tuple			=			output_from_no_past[:, -1, random_slice_idx].detach()
    UpperCAmelCase			:      List[str]			=			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 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  )						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			UMTaModel(config=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		).half().eval()
    UpperCAmelCase			:      Optional[Any]			=			model(**_SCREAMING_SNAKE_CASE		)["""last_hidden_state"""]
    self.parent.assertFalse(torch.isnan(_SCREAMING_SNAKE_CASE		).any().item()		)



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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			UMTaModelTester(self		)

   @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""		)
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			self.model_tester.prepare_config_and_inputs()
    UpperCAmelCase			:      Optional[Any]			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model_fpaa_forward(*_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
    UpperCAmelCase			:      Optional[int]			=			self.model_tester.prepare_config_and_inputs()
    UpperCAmelCase			:      Any			=			config_and_inputs[0]
    UpperCAmelCase			:      List[Any]			=			UMTaForConditionalGeneration(_SCREAMING_SNAKE_CASE		).eval()
    model.to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			{
        """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()		):
     UpperCAmelCase			:      List[str]			=			{name: mask}
     # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
     if name == "head_mask":
      UpperCAmelCase			:      Union[str, Any]			=			torch.ones(
          config.num_decoder_layers   ,  config.num_heads   ,  device=_SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      Optional[Any]			=			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
     UpperCAmelCase			:      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 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    pass



@require_torch
@require_sentencepiece
@require_tokenizers
class    SCREAMING_SNAKE_CASE__						(      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		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""   ,  return_dict=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			AutoTokenizer.from_pretrained("""google/umt5-small"""   ,  use_fast=_SCREAMING_SNAKE_CASE   ,  legacy=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			[
        """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>.""",
    ]
    UpperCAmelCase			:      Dict			=			tokenizer(_SCREAMING_SNAKE_CASE   ,  return_tensors="""pt"""   ,  padding=_SCREAMING_SNAKE_CASE		).input_ids
    # fmt: off
    UpperCAmelCase			:      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(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Union[str, Any]			=			model.generate(input_ids.to(_SCREAMING_SNAKE_CASE		)		)
    UpperCAmelCase			:      Tuple			=			[
        """<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>""",
    ]
    UpperCAmelCase			:      Optional[Any]			=			tokenizer.batch_decode(_SCREAMING_SNAKE_CASE		)
    self.assertEqual(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)





"""simple docstring"""
import requests
from bsa import BeautifulSoup

def    _snake_case		(		UpperCamelCase		:	str = "AAPL"		):
 UpperCAmelCase			:      Any			=			F"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
 UpperCAmelCase			:      Optional[int]			=			BeautifulSoup(requests.get(UpperCamelCase		).text    ,	"""html.parser"""		)
 UpperCAmelCase			:      int			=			"""My(6px) Pos(r) smartphone_Mt(6px)"""
 return soup.find("""div"""    ,	class_=class_		).find("""span"""		).text


if __name__ == "__main__":
      for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
            print(f"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")
