




import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
  from test_module.custom_tokenization_fast import CustomTokenizerFast







class 						lowercase							(   unittest.TestCase		):

     def 					a__	(  self				)						->							Any:
           # A mock response for an HTTP head request to emulate server down
           _A							:     Optional[int]			=						mock.Mock()
           _A							:     Optional[Any]			=						500
           _A							:     Dict			=						{}
           _A							:     Union[str, Any]			=						HTTPError
           _A							:     List[Any]			=						{}

           # Download this model to make sure it's in the cache.
           _A							:     int			=						BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""				)

           # Under the mock environment we get a 500 error when trying to reach the tokenizer.
           with mock.patch("""requests.Session.request"""		,     return_value=_a				) as mock_head:
                 _A							:     List[Any]			=						BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""				)
                 # This check we did call the fake head request
                 mock_head.assert_called()

     @require_tokenizers
     def 					a__	(  self				)						->							Optional[Any]:
           # A mock response for an HTTP head request to emulate server down
           _A							:     str			=						mock.Mock()
           _A							:     Any			=						500
           _A							:     Optional[int]			=						{}
           _A							:     List[str]			=						HTTPError
           _A							:     int			=						{}

           # Download this model to make sure it's in the cache.
           _A							:     int			=						GPTaTokenizerFast.from_pretrained("""gpt2"""				)

           # Under the mock environment we get a 500 error when trying to reach the tokenizer.
           with mock.patch("""requests.Session.request"""		,     return_value=_a				) as mock_head:
                 _A							:     str			=						GPTaTokenizerFast.from_pretrained("""gpt2"""				)
                 # This check we did call the fake head request
                 mock_head.assert_called()

     def 					a__	(  self				)						->							Optional[int]:
           # This test is for deprecated behavior and can be removed in v5
           try:
                 _A							:     Tuple			=						tempfile.mktemp()
                 with open(_a		,     """wb"""				) as f:
                       http_get("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model"""		,     _a				)

                 _A							:     str			=						AlbertTokenizer.from_pretrained(_a				)
           finally:
                 os.remove(_a				)

           # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
           # the current folder and have the right name.
           if os.path.isfile("""tokenizer.json"""				):
                 # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
                 return
           try:
                 with open("""tokenizer.json"""		,     """wb"""				) as f:
                       http_get("""https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"""		,     _a				)
                 _A							:     Optional[int]			=						AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""				)
                 # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
                 self.assertEqual(tokenizer.vocab_size		,     1000				)
                 # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

           finally:
                 os.remove("""tokenizer.json"""				)



     def 					a__	(  self				)						->							Union[str, Any]:
           # This test is for deprecated behavior and can be removed in v5
           _A							:     Dict			=						AlbertTokenizer.from_pretrained("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model"""				)







@is_staging_test
class 						lowercase							(   unittest.TestCase		):
     _a							 =						["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]

     @classmethod
     def 					a__	(  cls				)						->							Tuple:
           _A							:     int			=						TOKEN
           HfFolder.save_token(_a				)

     @classmethod
     def 					a__	(  cls				)						->							List[Any]:
           try:
                 delete_repo(token=cls._token		,     repo_id="""test-tokenizer"""				)
           except HTTPError:
                 pass

           try:
                 delete_repo(token=cls._token		,     repo_id="""valid_org/test-tokenizer-org"""				)
           except HTTPError:
                 pass

           try:
                 delete_repo(token=cls._token		,     repo_id="""test-dynamic-tokenizer"""				)
           except HTTPError:
                 pass

     def 					a__	(  self				)						->							Dict:
           with tempfile.TemporaryDirectory() as tmp_dir:
                 _A							:     Optional[Any]			=						os.path.join(_a		,     """vocab.txt"""				)
                 with open(_a		,     """w"""		,     encoding="""utf-8"""				) as vocab_writer:
                       vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]				)				)
                 _A							:     Optional[Any]			=						BertTokenizer(_a				)

           tokenizer.push_to_hub("""test-tokenizer"""		,     use_auth_token=self._token				)
           _A							:     List[Any]			=						BertTokenizer.from_pretrained(F'''{USER}/test-tokenizer'''				)
           self.assertDictEqual(new_tokenizer.vocab		,     tokenizer.vocab				)

           # Reset repo
           delete_repo(token=self._token		,     repo_id="""test-tokenizer"""				)

           # Push to hub via save_pretrained
           with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer.save_pretrained(_a		,     repo_id="""test-tokenizer"""		,     push_to_hub=_a		,     use_auth_token=self._token				)

           _A							:     List[str]			=						BertTokenizer.from_pretrained(F'''{USER}/test-tokenizer'''				)
           self.assertDictEqual(new_tokenizer.vocab		,     tokenizer.vocab				)

     def 					a__	(  self				)						->							Optional[int]:
           with tempfile.TemporaryDirectory() as tmp_dir:
                 _A							:     Optional[int]			=						os.path.join(_a		,     """vocab.txt"""				)
                 with open(_a		,     """w"""		,     encoding="""utf-8"""				) as vocab_writer:
                       vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]				)				)
                 _A							:     Tuple			=						BertTokenizer(_a				)

           tokenizer.push_to_hub("""valid_org/test-tokenizer-org"""		,     use_auth_token=self._token				)
           _A							:     List[Any]			=						BertTokenizer.from_pretrained("""valid_org/test-tokenizer-org"""				)
           self.assertDictEqual(new_tokenizer.vocab		,     tokenizer.vocab				)

           # Reset repo
           delete_repo(token=self._token		,     repo_id="""valid_org/test-tokenizer-org"""				)

           # Push to hub via save_pretrained
           with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer.save_pretrained(
                     _a		,     repo_id="""valid_org/test-tokenizer-org"""		,     push_to_hub=_a		,     use_auth_token=self._token				)

           _A							:     Dict			=						BertTokenizer.from_pretrained("""valid_org/test-tokenizer-org"""				)
           self.assertDictEqual(new_tokenizer.vocab		,     tokenizer.vocab				)



     @require_tokenizers
     def 					a__	(  self				)						->							str:
           CustomTokenizer.register_for_auto_class()
           with tempfile.TemporaryDirectory() as tmp_dir:
                 _A							:     Union[str, Any]			=						os.path.join(_a		,     """vocab.txt"""				)
                 with open(_a		,     """w"""		,     encoding="""utf-8"""				) as vocab_writer:
                       vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]				)				)
                 _A							:     str			=						CustomTokenizer(_a				)

           # No fast custom tokenizer
           tokenizer.push_to_hub("""test-dynamic-tokenizer"""		,     use_auth_token=self._token				)

           _A							:     Optional[int]			=						AutoTokenizer.from_pretrained(F'''{USER}/test-dynamic-tokenizer'''		,     trust_remote_code=_a				)
           # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
           self.assertEqual(tokenizer.__class__.__name__		,     """CustomTokenizer"""				)

           # Fast and slow custom tokenizer
           CustomTokenizerFast.register_for_auto_class()
           with tempfile.TemporaryDirectory() as tmp_dir:
                 _A							:     Any			=						os.path.join(_a		,     """vocab.txt"""				)
                 with open(_a		,     """w"""		,     encoding="""utf-8"""				) as vocab_writer:
                       vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]				)				)

                 _A							:     Dict			=						BertTokenizerFast.from_pretrained(_a				)
                 bert_tokenizer.save_pretrained(_a				)
                 _A							:     Dict			=						CustomTokenizerFast.from_pretrained(_a				)

           tokenizer.push_to_hub("""test-dynamic-tokenizer"""		,     use_auth_token=self._token				)

           _A							:     Tuple			=						AutoTokenizer.from_pretrained(F'''{USER}/test-dynamic-tokenizer'''		,     trust_remote_code=_a				)
           # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
           self.assertEqual(tokenizer.__class__.__name__		,     """CustomTokenizerFast"""				)
           _A							:     Tuple			=						AutoTokenizer.from_pretrained(
               F'''{USER}/test-dynamic-tokenizer'''		,     use_fast=_a		,     trust_remote_code=_a				)
           # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
           self.assertEqual(tokenizer.__class__.__name__		,     """CustomTokenizer"""				)







class 						lowercase							(   unittest.TestCase		):

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Optional[Any]			=						Trie()
           trie.add("""Hello 友達"""				)
           self.assertEqual(trie.data		,     {"""H""": {"""e""": {"""l""": {"""l""": {"""o""": {""" """: {"""友""": {"""達""": {"""""": 1}}}}}}}}}				)
           trie.add("""Hello"""				)
           trie.data
           self.assertEqual(trie.data		,     {"""H""": {"""e""": {"""l""": {"""l""": {"""o""": {"""""": 1, """ """: {"""友""": {"""達""": {"""""": 1}}}}}}}}}				)

     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     List[Any]			=						Trie()
           self.assertEqual(trie.split("""[CLS] This is a extra_id_100"""				)		,     ["""[CLS] This is a extra_id_100"""]				)
           trie.add("""[CLS]"""				)
           trie.add("""extra_id_1"""				)
           trie.add("""extra_id_100"""				)
           self.assertEqual(trie.split("""[CLS] This is a extra_id_100"""				)		,     ["""[CLS]""", """ This is a """, """extra_id_100"""]				)

     def 					a__	(  self				)						->							Dict:
           _A							:     List[Any]			=						Trie()
           trie.add("""A"""				)
           self.assertEqual(trie.split("""ABC"""				)		,     ["""A""", """BC"""]				)
           self.assertEqual(trie.split("""BCA"""				)		,     ["""BC""", """A"""]				)

     def 					a__	(  self				)						->							Dict:
           _A							:     Dict			=						Trie()
           trie.add("""TOKEN]"""				)
           trie.add("""[SPECIAL_TOKEN]"""				)
           self.assertEqual(trie.split("""This is something [SPECIAL_TOKEN]"""				)		,     ["""This is something """, """[SPECIAL_TOKEN]"""]				)

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Optional[Any]			=						Trie()
           trie.add("""A"""				)
           trie.add("""P"""				)
           trie.add("""[SPECIAL_TOKEN]"""				)
           self.assertEqual(trie.split("""This is something [SPECIAL_TOKEN]"""				)		,     ["""This is something """, """[SPECIAL_TOKEN]"""]				)

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Dict			=						Trie()
           trie.add("""AB"""				)
           trie.add("""B"""				)
           trie.add("""C"""				)
           self.assertEqual(trie.split("""ABC"""				)		,     ["""AB""", """C"""]				)

     def 					a__	(  self				)						->							int:
           _A							:     Any			=						Trie()
           trie.add("""ABC"""				)
           trie.add("""B"""				)
           trie.add("""CD"""				)
           self.assertEqual(trie.split("""ABCD"""				)		,     ["""ABC""", """D"""]				)



     def 					a__	(  self				)						->							List[Any]:
           # Even if the offsets are wrong, we necessarily output correct string
           # parts.
           _A							:     Tuple			=						Trie()
           _A							:     int			=						trie.cut_text("""ABC"""		,     [0, 0, 2, 1, 2, 3]				)
           self.assertEqual(_a		,     ["""AB""", """C"""]				)


"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      List[Any]:  # noqa: E741
  _lowerCAmelCase						:       Optional[int]  			=     len(_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     0
  _lowerCAmelCase						:       Any  			=     [0] * n
  _lowerCAmelCase						:       str  			=     [False] * n
  _lowerCAmelCase						:       str  			=     [False] * n

  def dfs(_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       str	):
    if parent == root:
      out_edge_count += 1
    _lowerCAmelCase						:       Any  			=     True
    _lowerCAmelCase						:       int  			=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _lowerCAmelCase						:       Union[str, Any]  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
        _lowerCAmelCase						:       Optional[int]  			=     min(low[at]			,low[to]	)

        # AP found via bridge
        if at < low[to]:
          _lowerCAmelCase						:       int  			=     True
        # AP found via cycle
        if at == low[to]:
          _lowerCAmelCase						:       Tuple  			=     True
      else:
        _lowerCAmelCase						:       Union[str, Any]  			=     min(low[at]			,_lowerCamelCase	)
    return out_edge_count

  for i in range(_lowerCamelCase	):
    if not visited[i]:
      _lowerCAmelCase						:       int  			=     0
      _lowerCAmelCase						:       Dict  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,-1			,_lowerCamelCase	)
      _lowerCAmelCase						:       List[str]  			=     out_edge_count > 1

  for x in range(len(_lowerCamelCase	)	):
    if is_art[x] is True:
      print(_lowerCamelCase	)


# Adjacency list of graph
_a      :		Optional[Any]           =   {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}






compute_ap(data)




