'''simple docstring'''






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			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			int     )  ->			List[str]:
        # A mock response for an HTTP head request to emulate server down
        _A    		=  mock.Mock()
        _A    		=  5_00
        _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=UpperCamelCase__     ) 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 	__UpperCAmelCase   (      self      :			int     )  ->			List[Any]:
        # A mock response for an HTTP head request to emulate server down
        _A    		=  mock.Mock()
        _A    		=  5_00
        _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=UpperCamelCase__     ) as mock_head:
             _A    		=  GPTaTokenizerFast.from_pretrained('gpt2'     )
             # This check we did call the fake head request
             mock_head.assert_called()
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Any:
        # This test is for deprecated behavior and can be removed in v5
        try:
             _A    		=  tempfile.mktemp()
             with open(UpperCamelCase__, 'wb'     ) as f:
                  http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model', UpperCamelCase__     )

             _A    		=  AlbertTokenizer.from_pretrained(UpperCamelCase__     )
        finally:
             os.remove(UpperCamelCase__     )

        # 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', UpperCamelCase__     )
             _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, 10_00     )
             # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

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

   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[int]:
        # This test is for deprecated behavior and can be removed in v5
        _A    		=  AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'     )







@is_staging_test
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
   @classmethod
   def 	__UpperCAmelCase   (      cls      :			List[str]     )  ->			str:
        _A    		=  TOKEN
        HfFolder.save_token(UpperCamelCase__     )
   @classmethod
   def 	__UpperCAmelCase   (      cls      :			Union[str, Any]     )  ->			Optional[int]:
        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 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        with tempfile.TemporaryDirectory() as tmp_dir:
             _A    		=  os.path.join(UpperCamelCase__, 'vocab.txt'     )
             with open(UpperCamelCase__, 'w', encoding='utf-8'     ) as vocab_writer:
                  vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]     )     )
             _A    		=  BertTokenizer(UpperCamelCase__     )

        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(UpperCamelCase__, repo_id='test-tokenizer', push_to_hub=UpperCamelCase__, use_auth_token=self._token     )

        _A    		=  BertTokenizer.from_pretrained(f'{USER}/test-tokenizer'     )
        self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        with tempfile.TemporaryDirectory() as tmp_dir:
             _A    		=  os.path.join(UpperCamelCase__, 'vocab.txt'     )
             with open(UpperCamelCase__, 'w', encoding='utf-8'     ) as vocab_writer:
                  vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]     )     )
             _A    		=  BertTokenizer(UpperCamelCase__     )

        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(
                 UpperCamelCase__, repo_id='valid_org/test-tokenizer-org', push_to_hub=UpperCamelCase__, use_auth_token=self._token     )

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

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

        # 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=UpperCamelCase__     )
        # 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(UpperCamelCase__, 'vocab.txt'     )
             with open(UpperCamelCase__, 'w', encoding='utf-8'     ) as vocab_writer:
                  vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]     )     )

             _A    		=  BertTokenizerFast.from_pretrained(UpperCamelCase__     )
             bert_tokenizer.save_pretrained(UpperCamelCase__     )
             _A    		=  CustomTokenizerFast.from_pretrained(UpperCamelCase__     )

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

        _A    		=  AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer', trust_remote_code=UpperCamelCase__     )
        # 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=UpperCamelCase__, trust_remote_code=UpperCamelCase__     )
        # 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			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Tuple:
        _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 	__UpperCAmelCase   (      self      :			Any     )  ->			int:
        _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 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        _A    		=  Trie()
        trie.add('A'     )
        self.assertEqual(trie.split('ABC'     ), ['A', 'BC']     )
        self.assertEqual(trie.split('BCA'     ), ['BC', 'A']     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			str:
        _A    		=  Trie()
        trie.add('TOKEN]'     )
        trie.add('[SPECIAL_TOKEN]'     )
        self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'     ), ['This is something ', '[SPECIAL_TOKEN]']     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Any:
        _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 	__UpperCAmelCase   (      self      :			Dict     )  ->			Optional[int]:
        _A    		=  Trie()
        trie.add('AB'     )
        trie.add('B'     )
        trie.add('C'     )
        self.assertEqual(trie.split('ABC'     ), ['AB', 'C']     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Tuple:
        _A    		=  Trie()
        trie.add('ABC'     )
        trie.add('B'     )
        trie.add('CD'     )
        self.assertEqual(trie.split('ABCD'     ), ['ABC', 'D']     )

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




'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @slow
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        _A    		=  TFCamembertModel.from_pretrained('jplu/tf-camembert-base'     )

        _A    		=  tf.convert_to_tensor(
            [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

        _A    		=  model(UpperCamelCase__     )['last_hidden_state']
        _A    		=  tf.TensorShape((1, 10, 7_68)     )
        self.assertEqual(output.shape, UpperCamelCase__     )
        # compare the actual values for a slice.
        _A    		=  tf.convert_to_tensor(
            [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], dtype=tf.floataa, )
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4     )     )



