




import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						RobertaTokenizer
     _a							 =						RobertaTokenizerFast
     _a							 =						True
     _a							 =						{"cls_token": "<s>"}

     def 					a__	(  self				)						->							Dict:
           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           _A							:     Any			=						[
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """\u0120""",
               """\u0120l""",
               """\u0120n""",
               """\u0120lo""",
               """\u0120low""",
               """er""",
               """\u0120lowest""",
               """\u0120newer""",
               """\u0120wider""",
               """<unk>""",
           ]
           _A							:     List[str]			=						dict(zip(_a		,     range(len(_a				)				)				)				)
           _A							:     Union[str, Any]			=						["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           _A							:     Optional[int]			=						{"""unk_token""": """<unk>"""}

           _A							:     Tuple			=						os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["""vocab_file"""]				)
           _A							:     int			=						os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["""merges_file"""]				)
           with open(self.vocab_file		,     """w"""		,     encoding="""utf-8"""				) as fp:
                 fp.write(json.dumps(_a				) + """\n"""				)
           with open(self.merges_file		,     """w"""		,     encoding="""utf-8"""				) as fp:
                 fp.write("""\n""".join(_a				)				)

     def 					a__	(  self		,     **_a				)						->							List[Any]:
           kwargs.update(self.special_tokens_map				)
           return self.tokenizer_class.from_pretrained(self.tmpdirname		,     **_a				)

     def 					a__	(  self		,     **_a				)						->							str:
           kwargs.update(self.special_tokens_map				)
           return RobertaTokenizerFast.from_pretrained(self.tmpdirname		,     **_a				)

     def 					a__	(  self		,     _a				)						->							Dict:
           _A							:     Optional[int]			=						"""lower newer"""
           _A							:     int			=						"""lower newer"""
           return input_text, output_text

     def 					a__	(  self				)						->							List[str]:
           _A							:     Tuple			=						self.tokenizer_class(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map				)
           _A							:     List[str]			=						"""lower newer"""
           _A							:     str			=						["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
           _A							:     str			=						tokenizer.tokenize(_a				)  # , add_prefix_space=True)
           self.assertListEqual(_a		,     _a				)

           _A							:     Optional[Any]			=						tokens + [tokenizer.unk_token]
           _A							:     Any			=						[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)		,     _a				)

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Optional[int]			=						self.get_tokenizer()

           self.assertListEqual(tokenizer.encode("""Hello world!"""		,     add_special_tokens=_a				)		,     [0, 3_1414, 232, 328, 2]				)
           self.assertListEqual(
               tokenizer.encode("""Hello world! cécé herlolip 418"""		,     add_special_tokens=_a				)		,     [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]		,     )

     @slow
     def 					a__	(  self				)						->							str:
           _A							:     Union[str, Any]			=						self.tokenizer_class.from_pretrained("""roberta-base"""				)

           _A							:     Optional[int]			=						tokenizer.encode("""sequence builders"""		,     add_special_tokens=_a				)
           _A							:     Union[str, Any]			=						tokenizer.encode("""multi-sequence build"""		,     add_special_tokens=_a				)

           _A							:     Optional[Any]			=						tokenizer.encode(
               """sequence builders"""		,     add_special_tokens=_a		,     add_prefix_space=_a				)
           _A							:     List[str]			=						tokenizer.encode(
               """sequence builders"""		,     """multi-sequence build"""		,     add_special_tokens=_a		,     add_prefix_space=_a				)

           _A							:     List[str]			=						tokenizer.build_inputs_with_special_tokens(_a				)
           _A							:     Dict			=						tokenizer.build_inputs_with_special_tokens(_a		,     _a				)

           assert encoded_sentence == encoded_text_from_decode
           assert encoded_pair == encoded_pair_from_decode

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Optional[int]			=						self.get_tokenizer()

           _A							:     List[Any]			=						"""Encode this sequence."""
           _A							:     Any			=						tokenizer.byte_encoder[""" """.encode("""utf-8"""				)[0]]

           # Testing encoder arguments
           _A							:     Dict			=						tokenizer.encode(_a		,     add_special_tokens=_a		,     add_prefix_space=_a				)
           _A							:     Dict			=						tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
           self.assertNotEqual(_a		,     _a				)

           _A							:     int			=						tokenizer.encode(_a		,     add_special_tokens=_a		,     add_prefix_space=_a				)
           _A							:     Tuple			=						tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
           self.assertEqual(_a		,     _a				)

           tokenizer.add_special_tokens({"""bos_token""": """<s>"""}				)
           _A							:     List[Any]			=						tokenizer.encode(_a		,     add_special_tokens=_a				)
           _A							:     int			=						tokenizer.convert_ids_to_tokens(encoded[1]				)[0]
           self.assertNotEqual(_a		,     _a				)

           # Testing spaces after special tokens
           _A							:     Dict			=						"""<mask>"""
           tokenizer.add_special_tokens(
               {"""mask_token""": AddedToken(_a		,     lstrip=_a		,     rstrip=_a				)}				)  # mask token has a left space
           _A							:     Any			=						tokenizer.convert_tokens_to_ids(_a				)

           _A							:     Union[str, Any]			=						"""Encode <mask> sequence"""
           _A							:     Optional[Any]			=						"""Encode <mask>sequence"""

           _A							:     List[Any]			=						tokenizer.encode(_a				)
           _A							:     int			=						encoded.index(_a				)
           _A							:     Any			=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
           self.assertEqual(_a		,     _a				)

           _A							:     Optional[Any]			=						tokenizer.encode(_a				)
           _A							:     Optional[Any]			=						encoded.index(_a				)
           _A							:     List[str]			=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
           self.assertNotEqual(_a		,     _a				)

     def 					a__	(  self				)						->							Optional[int]:
           pass

     def 					a__	(  self				)						->							Tuple:
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
                       _A							:     Any			=						self.rust_tokenizer_class.from_pretrained(_a		,     **_a				)
                       _A							:     int			=						self.tokenizer_class.from_pretrained(_a		,     **_a				)
                       _A							:     Union[str, Any]			=						"""A, <mask> AllenNLP sentence."""
                       _A							:     List[Any]			=						tokenizer_r.encode_plus(_a		,     add_special_tokens=_a		,     return_token_type_ids=_a				)
                       _A							:     Optional[int]			=						tokenizer_p.encode_plus(_a		,     add_special_tokens=_a		,     return_token_type_ids=_a				)

                       # token_type_ids should put 0 everywhere
                       self.assertEqual(sum(tokens_r["""token_type_ids"""]				)		,     sum(tokens_p["""token_type_ids"""]				)				)

                       # attention_mask should put 1 everywhere, so sum over length should be 1
                       self.assertEqual(
                           sum(tokens_r["""attention_mask"""]				) / len(tokens_r["""attention_mask"""]				)		,     sum(tokens_p["""attention_mask"""]				) / len(tokens_p["""attention_mask"""]				)		,     )

                       _A							:     str			=						tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]				)
                       _A							:     Optional[Any]			=						tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]				)

                       # Rust correctly handles the space before the mask while python doesnt
                       self.assertSequenceEqual(tokens_p["""input_ids"""]		,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]				)
                       self.assertSequenceEqual(tokens_r["""input_ids"""]		,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]				)

                       self.assertSequenceEqual(
                           _a		,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)
                       self.assertSequenceEqual(
                           _a		,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)

     def 					a__	(  self				)						->							Any:
           for trim_offsets, add_prefix_space in itertools.product([True, False]		,     repeat=2				):
                 _A							:     List[Any]			=						self.rust_tokenizer_class.from_pretrained(
                     self.tmpdirname		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)

                 _A							:     Union[str, Any]			=						json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()				)
                 _A							:     Union[str, Any]			=						json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()				)

                 self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]		,     _a				)

                 self.assertEqual(post_processor_state["""add_prefix_space"""]		,     _a				)
                 self.assertEqual(post_processor_state["""trim_offsets"""]		,     _a				)



     def 					a__	(  self				)						->							Union[str, Any]:
           # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
           # `trim_offsets`
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
                       _A							:     Optional[int]			=						"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                       _A							:     Dict			=						F'''{text_of_1_token} {text_of_1_token}'''

                       _A							:     Tuple			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     Optional[int]			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (0, len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (len(_a				) + 1, len(_a				) + 1 + len(_a				))		,     )

                       _A							:     List[str]			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     str			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (0, len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (len(_a				) + 1, len(_a				) + 1 + len(_a				))		,     )

                       _A							:     Dict			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     List[str]			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (0, len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (len(_a				), len(_a				) + 1 + len(_a				))		,     )

                       _A							:     int			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     List[Any]			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (0, len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (len(_a				), len(_a				) + 1 + len(_a				))		,     )

                       _A							:     Dict			=						F''' {text}'''

                       # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                       #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                       # )
                       # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                       # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                       # self.assertEqual(
                       #     encoding.offset_mapping[1],
                       #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                       # )

                       _A							:     Optional[Any]			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     Union[str, Any]			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (1, 1 + len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (1 + len(_a				) + 1, 1 + len(_a				) + 1 + len(_a				))		,     )

                       _A							:     Optional[int]			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     int			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (0, 1 + len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (1 + len(_a				), 1 + len(_a				) + 1 + len(_a				))		,     )

                       _A							:     Optional[int]			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     Optional[Any]			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (0, 1 + len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (1 + len(_a				), 1 + len(_a				) + 1 + len(_a				))		,     )







import requests
from bsa import BeautifulSoup







def 				lowerCAmelCase_	(     snake_case_ = "AAPL"     ):
      _A							:     str			=						f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
      _A							:     List[Any]			=						BeautifulSoup(requests.get(snake_case_     ).text,"""html.parser"""     )
      _A							:     Union[str, Any]			=						"""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}""")

