

'''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 ):


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

								# Download this model to make sure it's in the cache.
								UpperCAmelCase_		:      Union[str, 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=_snake_case) as mock_head:
									UpperCAmelCase_		:      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 						_snake_case							(			self)							->				str:
								# A mock response for an HTTP head request to emulate server down
								UpperCAmelCase_		:      Dict       	=					mock.Mock()
								UpperCAmelCase_		:      Dict       	=					500
								UpperCAmelCase_		:      int       	=					{}
								UpperCAmelCase_		:      Dict       	=					HTTPError
								UpperCAmelCase_		:      Dict       	=					{}

								# Download this model to make sure it's in the cache.
								UpperCAmelCase_		:      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=_snake_case) as mock_head:
									UpperCAmelCase_		:      int       	=					GPTaTokenizerFast.from_pretrained('gpt2')
									# This check we did call the fake head request
									mock_head.assert_called()


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

									UpperCAmelCase_		:      Optional[Any]       	=					AlbertTokenizer.from_pretrained(_snake_case)
								finally:
									os.remove(_snake_case)

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



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


							@classmethod
							def 						_snake_case							(			cls)							->				int:
								UpperCAmelCase_		:      List[str]       	=					TOKEN
								HfFolder.save_token(_snake_case)


							@classmethod
							def 						_snake_case							(			cls)							->				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 						_snake_case							(			self)							->				Optional[int]:
								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase_		:      str       	=					os.path.join(_snake_case    ,			'vocab.txt')
									with open(_snake_case    ,			'w'    ,			encoding='utf-8') as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]))
									UpperCAmelCase_		:      Optional[int]       	=					BertTokenizer(_snake_case)

								tokenizer.push_to_hub('test-tokenizer'    ,			use_auth_token=self._token)
								UpperCAmelCase_		:      List[str]       	=					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(_snake_case    ,			repo_id='test-tokenizer'    ,			push_to_hub=_snake_case    ,			use_auth_token=self._token)

								UpperCAmelCase_		:      int       	=					BertTokenizer.from_pretrained(F"""{USER}/test-tokenizer""")
								self.assertDictEqual(new_tokenizer.vocab    ,			tokenizer.vocab)


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

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

								UpperCAmelCase_		:      List[Any]       	=					BertTokenizer.from_pretrained('valid_org/test-tokenizer-org')
								self.assertDictEqual(new_tokenizer.vocab    ,			tokenizer.vocab)





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

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

								UpperCAmelCase_		:      str       	=					AutoTokenizer.from_pretrained(F"""{USER}/test-dynamic-tokenizer"""    ,			trust_remote_code=_snake_case)
								# 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:
									UpperCAmelCase_		:      Union[str, Any]       	=					os.path.join(_snake_case    ,			'vocab.txt')
									with open(_snake_case    ,			'w'    ,			encoding='utf-8') as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]))

									UpperCAmelCase_		:      str       	=					BertTokenizerFast.from_pretrained(_snake_case)
									bert_tokenizer.save_pretrained(_snake_case)
									UpperCAmelCase_		:      List[Any]       	=					CustomTokenizerFast.from_pretrained(_snake_case)

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

								UpperCAmelCase_		:      Any       	=					AutoTokenizer.from_pretrained(F"""{USER}/test-dynamic-tokenizer"""    ,			trust_remote_code=_snake_case)
								# 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')
								UpperCAmelCase_		:      str       	=					AutoTokenizer.from_pretrained(
								    F"""{USER}/test-dynamic-tokenizer"""    ,			use_fast=_snake_case    ,			trust_remote_code=_snake_case)
								# 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 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      str       	=					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)							->				Optional[int]:
								UpperCAmelCase_		:      Dict       	=					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)							->				Any:
								UpperCAmelCase_		:      str       	=					Trie()
								trie.add('A')
								self.assertEqual(trie.split('ABC')    ,			['A', 'BC'])
								self.assertEqual(trie.split('BCA')    ,			['BC', 'A'])


							def 						_snake_case							(			self)							->				Dict:
								UpperCAmelCase_		:      Optional[Any]       	=					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)							->				int:
								UpperCAmelCase_		:      List[str]       	=					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)							->				Dict:
								UpperCAmelCase_		:      Dict       	=					Trie()
								trie.add('AB')
								trie.add('B')
								trie.add('C')
								self.assertEqual(trie.split('ABC')    ,			['AB', 'C'])


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      List[Any]       	=					Trie()
								trie.add('ABC')
								trie.add('B')
								trie.add('CD')
								self.assertEqual(trie.split('ABCD')    ,			['ABC', 'D'])





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









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__										= {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__										= [
						    "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ClapModel",
						    "ClapPreTrainedModel",
						    "ClapTextModel",
						    "ClapTextModelWithProjection",
						    "ClapAudioModel",
						    "ClapAudioModelWithProjection",
						]
						lowerCAmelCase__										= ["ClapFeatureExtractor"]

if TYPE_CHECKING:
						from .configuration_clap import (
						    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ClapAudioConfig,
						    ClapConfig,
						    ClapTextConfig,
						)
						from .processing_clap import ClapProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_clap import ClapFeatureExtractor
												from .modeling_clap import (
												    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ClapAudioModel,
												    ClapAudioModelWithProjection,
												    ClapModel,
												    ClapPreTrainedModel,
												    ClapTextModel,
												    ClapTextModelWithProjection,
												)


else:
						import sys

						lowerCAmelCase__										= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






