

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 					A__      (      unittest.TestCase			):



    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           A_						=      mock.Mock()
           A_						=      500
           A_						=      {}
           A_						=      HTTPError
           A_						=      {}

           # Download this model to make sure it's in the cache.
           A_						=      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_						=      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 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      mock.Mock()
           A_						=      500
           A_						=      {}
           A_						=      HTTPError
           A_						=      {}

           # Download this model to make sure it's in the cache.
           A_						=      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_						=      GPTaTokenizerFast.from_pretrained("""gpt2"""       )
                  # This check we did call the fake head request
                  mock_head.assert_called()



    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           # This test is for deprecated behavior and can be removed in v5
           try:
                  A_						=      tempfile.mktemp()
                  with open(A_		,				"""wb"""       ) as f:
                         http_get("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model"""		,				A_       )

                  A_						=      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_						=      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 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      AlbertTokenizer.from_pretrained("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model"""       )


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



    @classmethod
    def 					snake_case_			(					cls       )						->      Tuple:





           '''simple docstring'''
           A_						=      TOKEN
           HfFolder.save_token(A_       )



    @classmethod
    def 					snake_case_			(					cls       )						->      Optional[int]:





           '''simple docstring'''
           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 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           with tempfile.TemporaryDirectory() as tmp_dir:
                  A_						=      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_						=      BertTokenizer(A_       )

           tokenizer.push_to_hub("""test-tokenizer"""		,				use_auth_token=self._token       )
           A_						=      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_						=      BertTokenizer.from_pretrained(f'''{USER}/test-tokenizer'''       )
           self.assertDictEqual(new_tokenizer.vocab		,				tokenizer.vocab       )



    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           with tempfile.TemporaryDirectory() as tmp_dir:
                  A_						=      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_						=      BertTokenizer(A_       )

           tokenizer.push_to_hub("""valid_org/test-tokenizer-org"""		,				use_auth_token=self._token       )
           A_						=      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_						=      BertTokenizer.from_pretrained("""valid_org/test-tokenizer-org"""       )
           self.assertDictEqual(new_tokenizer.vocab		,				tokenizer.vocab       )







    @require_tokenizers
    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           CustomTokenizer.register_for_auto_class()
           with tempfile.TemporaryDirectory() as tmp_dir:
                  A_						=      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_						=      CustomTokenizer(A_       )

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

           A_						=      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_						=      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_						=      BertTokenizerFast.from_pretrained(A_       )
                  bert_tokenizer.save_pretrained(A_       )
                  A_						=      CustomTokenizerFast.from_pretrained(A_       )

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

           A_						=      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_						=      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 					A__      (      unittest.TestCase			):



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





           '''simple docstring'''
           A_						=      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 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           A_						=      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 					snake_case_			(					self       )						->      List[Any]:





           '''simple docstring'''
           A_						=      Trie()
           trie.add("""A"""       )
           self.assertEqual(trie.split("""ABC"""       )		,				["""A""", """BC"""]       )
           self.assertEqual(trie.split("""BCA"""       )		,				["""BC""", """A"""]       )



    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           A_						=      Trie()
           trie.add("""TOKEN]"""       )
           trie.add("""[SPECIAL_TOKEN]"""       )
           self.assertEqual(trie.split("""This is something [SPECIAL_TOKEN]"""       )		,				["""This is something """, """[SPECIAL_TOKEN]"""]       )



    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      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 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      Trie()
           trie.add("""AB"""       )
           trie.add("""B"""       )
           trie.add("""C"""       )
           self.assertEqual(trie.split("""ABC"""       )		,				["""AB""", """C"""]       )



    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           A_						=      Trie()
           trie.add("""ABC"""       )
           trie.add("""B"""       )
           trie.add("""CD"""       )
           self.assertEqual(trie.split("""ABCD"""       )		,				["""ABC""", """D"""]       )







    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      Trie()
           A_						=      trie.cut_text("""ABC"""		,				[0, 0, 2, 1, 2, 3]       )
           self.assertEqual(A_		,				["""AB""", """C"""]       )



'''simple docstring'''







import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    '''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionImg2ImgPipeline` instead.'''
)
