





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    _UpperCAmelCase							(		unittest.TestCase     ):
      def 		_lowerCamelCase      (		self       ):
         # A mock response for an HTTP head request to emulate server down
         A_     :				Tuple			      =	mock.Mock()
         A_     :				List[str]			      =	500
         A_     :				Any			      =	{}
         A_     :				Union[str, Any]			      =	HTTPError
         A_     :				Tuple			      =	{}

         # Download this model to make sure it's in the cache.
         A_     :				Any			      =	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_     :				Dict			      =	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 		_lowerCamelCase      (		self       ):
         # A mock response for an HTTP head request to emulate server down
         A_     :				Union[str, Any]			      =	mock.Mock()
         A_     :				int			      =	500
         A_     :				Any			      =	{}
         A_     :				Dict			      =	HTTPError
         A_     :				List[Any]			      =	{}

         # Download this model to make sure it's in the cache.
         A_     :				Optional[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_     :				Any			      =	GPTaTokenizerFast.from_pretrained("""gpt2"""       )
            # This check we did call the fake head request
            mock_head.assert_called()
      def 		_lowerCamelCase      (		self       ):
         # This test is for deprecated behavior and can be removed in v5
         try:
            A_     :				Union[str, Any]			      =	tempfile.mktemp()
            with open(a__      ,       """wb"""       ) as f:
               http_get("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model"""      ,       a__       )

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






@is_staging_test
class    _UpperCAmelCase							(		unittest.TestCase     ):
      a						       =							['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
      @classmethod
      def 		_lowerCamelCase      (		cls       ):
         A_     :				List[str]			      =	TOKEN
         HfFolder.save_token(a__       )
      @classmethod
      def 		_lowerCamelCase      (		cls       ):
         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 		_lowerCamelCase      (		self       ):
         with tempfile.TemporaryDirectory() as tmp_dir:
            A_     :				List[str]			      =	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			      =	BertTokenizer(a__       )

         tokenizer.push_to_hub("""test-tokenizer"""      ,       use_auth_token=self._token       )
         A_     :				Dict			      =	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 		_lowerCamelCase      (		self       ):
         with tempfile.TemporaryDirectory() as tmp_dir:
            A_     :				List[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_     :				Any			      =	BertTokenizer(a__       )

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






      @require_tokenizers
      def 		_lowerCamelCase      (		self       ):
         CustomTokenizer.register_for_auto_class()
         with tempfile.TemporaryDirectory() as tmp_dir:
            A_     :				str			      =	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[int]			      =	CustomTokenizer(a__       )

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

         A_     :				Union[str, Any]			      =	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_     :				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			      =	BertTokenizerFast.from_pretrained(a__       )
            bert_tokenizer.save_pretrained(a__       )
            A_     :				List[Any]			      =	CustomTokenizerFast.from_pretrained(a__       )

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

         A_     :				List[Any]			      =	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_     :				List[str]			      =	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    _UpperCAmelCase							(		unittest.TestCase     ):
      def 		_lowerCamelCase      (		self       ):
         A_     :				int			      =	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 		_lowerCamelCase      (		self       ):
         A_     :				List[str]			      =	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 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	Trie()
         trie.add("""A"""       )
         self.assertEqual(trie.split("""ABC"""       )      ,       ["""A""", """BC"""]       )
         self.assertEqual(trie.split("""BCA"""       )      ,       ["""BC""", """A"""]       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	Trie()
         trie.add("""TOKEN]"""       )
         trie.add("""[SPECIAL_TOKEN]"""       )
         self.assertEqual(trie.split("""This is something [SPECIAL_TOKEN]"""       )      ,       ["""This is something """, """[SPECIAL_TOKEN]"""]       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				List[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 		_lowerCamelCase      (		self       ):
         A_     :				str			      =	Trie()
         trie.add("""AB"""       )
         trie.add("""B"""       )
         trie.add("""C"""       )
         self.assertEqual(trie.split("""ABC"""       )      ,       ["""AB""", """C"""]       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Dict			      =	Trie()
         trie.add("""ABC"""       )
         trie.add("""B"""       )
         trie.add("""CD"""       )
         self.assertEqual(trie.split("""ABCD"""       )      ,       ["""ABC""", """D"""]       )






      def 		_lowerCamelCase      (		self       ):
         # Even if the offsets are wrong, we necessarily output correct string
         # parts.
         A_     :				Optional[int]			      =	Trie()
         A_     :				Union[str, Any]			      =	trie.cut_text("""ABC"""      ,       [0, 0, 2, 1, 2, 3]       )
         self.assertEqual(a__      ,       ["""AB""", """C"""]       )













def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	[0] * len(_lowerCAmelCase     )
   A_     :				Optional[int]			      =	[]
   A_     :				str			      =	[]
   A_     :				Dict			      =	0

   for values in graph.values():
      for i in values:
         indegree[i] += 1

   for i in range(len(_lowerCAmelCase     )     ):
      if indegree[i] == 0:
         queue.append(_lowerCAmelCase     )

   while queue:
      A_     :				List[str]			      =	queue.pop(0     )
      cnt += 1
      topo.append(_lowerCAmelCase     )
      for x in graph[vertex]:
         indegree[x] -= 1
         if indegree[x] == 0:
            queue.append(_lowerCAmelCase     )

   if cnt != len(_lowerCAmelCase     ):
      print("""Cycle exists"""     )
   else:
      print(_lowerCAmelCase     )


# Adjacency List of Graph
_lowerCAmelCase 				=     {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)






