


"""simple docstring"""






import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
							from test_module.custom_tokenization_fast import CustomTokenizerFast





class      lowerCamelCase__   (     unittest.TestCase				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					0






					@slow
					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
																			__UpperCAmelCase   :						Dict      							=					AutoTokenizer.from_pretrained(UpperCamelCase  )
																			self.assertIsNotNone(UpperCamelCase  )
																			self.assertIsInstance(UpperCamelCase ,       (BertTokenizer, BertTokenizerFast)  )
																			self.assertGreater(len(UpperCamelCase  ) ,       0  )

												for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
																			__UpperCAmelCase   :						str      							=					AutoTokenizer.from_pretrained(UpperCamelCase  )
																			self.assertIsNotNone(UpperCamelCase  )
																			self.assertIsInstance(UpperCamelCase ,       (GPTaTokenizer, GPTaTokenizerFast)  )
																			self.assertGreater(len(UpperCamelCase  ) ,       0  )






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					AutoTokenizer.from_pretrained(UpperCamelCase  )
												self.assertIsInstance(UpperCamelCase ,       (BertTokenizer, BertTokenizerFast)  )
												self.assertEqual(tokenizer.vocab_size ,       12  )






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					AutoTokenizer.from_pretrained(UpperCamelCase  )
												self.assertIsInstance(UpperCamelCase ,       (RobertaTokenizer, RobertaTokenizerFast)  )
												self.assertEqual(tokenizer.vocab_size ,       20  )






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[int]      							=					AutoConfig.from_pretrained(UpperCamelCase  )
												self.assertIsInstance(UpperCamelCase ,       UpperCamelCase  )
												# Check that tokenizer_type ≠ model_type
												__UpperCAmelCase   :						Tuple      							=					AutoTokenizer.from_pretrained(UpperCamelCase ,       config=UpperCamelCase  )
												self.assertIsInstance(UpperCamelCase ,       (BertTokenizer, BertTokenizerFast)  )
												self.assertEqual(tokenizer.vocab_size ,       12  )






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												with tempfile.TemporaryDirectory() as tmp_dir:
																			shutil.copy("""./tests/fixtures/vocab.txt""" ,       os.path.join(UpperCamelCase ,       """vocab.txt"""  )  )

																			__UpperCAmelCase   :						Union[str, Any]      							=					AutoTokenizer.from_pretrained(UpperCamelCase ,       tokenizer_type="""bert""" ,       use_fast=UpperCamelCase  )
																			self.assertIsInstance(UpperCamelCase ,       UpperCamelCase  )

												with tempfile.TemporaryDirectory() as tmp_dir:
																			shutil.copy("""./tests/fixtures/vocab.json""" ,       os.path.join(UpperCamelCase ,       """vocab.json"""  )  )
																			shutil.copy("""./tests/fixtures/merges.txt""" ,       os.path.join(UpperCamelCase ,       """merges.txt"""  )  )

																			__UpperCAmelCase   :						Any      							=					AutoTokenizer.from_pretrained(UpperCamelCase ,       tokenizer_type="""gpt2""" ,       use_fast=UpperCamelCase  )
																			self.assertIsInstance(UpperCamelCase ,       UpperCamelCase  )






					@require_tokenizers
					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






												with tempfile.TemporaryDirectory() as tmp_dir:
																			shutil.copy("""./tests/fixtures/vocab.txt""" ,       os.path.join(UpperCamelCase ,       """vocab.txt"""  )  )

																			__UpperCAmelCase   :						Dict      							=					AutoTokenizer.from_pretrained(UpperCamelCase ,       tokenizer_type="""bert"""  )
																			self.assertIsInstance(UpperCamelCase ,       UpperCamelCase  )

												with tempfile.TemporaryDirectory() as tmp_dir:
																			shutil.copy("""./tests/fixtures/vocab.json""" ,       os.path.join(UpperCamelCase ,       """vocab.json"""  )  )
																			shutil.copy("""./tests/fixtures/merges.txt""" ,       os.path.join(UpperCamelCase ,       """merges.txt"""  )  )

																			__UpperCAmelCase   :						int      							=					AutoTokenizer.from_pretrained(UpperCamelCase ,       tokenizer_type="""gpt2"""  )
																			self.assertIsInstance(UpperCamelCase ,       UpperCamelCase  )






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												with pytest.raises(UpperCamelCase  ):
																			AutoTokenizer.from_pretrained("""./""" ,       tokenizer_type="""xxx"""  )






					@require_tokenizers
					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
																			__UpperCAmelCase   :						Dict      							=					tokenizer_class.from_pretrained("""wietsedv/bert-base-dutch-cased"""  )
																			self.assertIsInstance(UpperCamelCase ,       (BertTokenizer, BertTokenizerFast)  )

																			if isinstance(UpperCamelCase ,       UpperCamelCase  ):
																										self.assertEqual(tokenizer.basic_tokenizer.do_lower_case ,       UpperCamelCase  )
																			else:
																										self.assertEqual(tokenizer.do_lower_case ,       UpperCamelCase  )

																			self.assertEqual(tokenizer.model_max_length ,       512  )






					@require_tokenizers
					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
																			with self.assertRaisesRegex(
																			    UpperCamelCase ,       """julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier""" ,       ):
																										__UpperCAmelCase   :						int      							=					tokenizer_class.from_pretrained("""julien-c/herlolip-not-exists"""  )






					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					TOKENIZER_MAPPING.values()
												__UpperCAmelCase   :						str      							=					[]

												for slow_tok, fast_tok in tokenizers:
																			if slow_tok is not None:
																										tokenizer_names.append(slow_tok.__name__  )

																			if fast_tok is not None:
																										tokenizer_names.append(fast_tok.__name__  )

												for tokenizer_name in tokenizer_names:
																			# must find the right class
																			tokenizer_class_from_name(UpperCamelCase  )






					@require_tokenizers
					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased""" ,       use_fast=UpperCamelCase  ) ,       UpperCamelCase  )
												self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased"""  ) ,       UpperCamelCase  )






					@require_tokenizers
					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					AutoTokenizer.from_pretrained("""distilbert-base-uncased""" ,       do_lower_case=UpperCamelCase  )
												__UpperCAmelCase   :						Optional[Any]      							=					"""Hello, world. How are you?"""
												__UpperCAmelCase   :						Dict      							=					tokenizer.tokenize(UpperCamelCase  )
												self.assertEqual("""[UNK]""" ,       tokens[0]  )

												__UpperCAmelCase   :						Optional[Any]      							=					AutoTokenizer.from_pretrained("""microsoft/mpnet-base""" ,       do_lower_case=UpperCamelCase  )
												__UpperCAmelCase   :						Any      							=					tokenizer.tokenize(UpperCamelCase  )
												self.assertEqual("""[UNK]""" ,       tokens[0]  )






					@require_tokenizers
					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					AutoTokenizer.from_pretrained("""robot-test/dummy-tokenizer-fast-with-model-config"""  )
												self.assertEqual(type(UpperCamelCase  ) ,       UpperCamelCase  )
												self.assertEqual(tokenizer.model_max_length ,       512  )
												self.assertEqual(tokenizer.vocab_size ,       30_000  )
												self.assertEqual(tokenizer.unk_token ,       """[UNK]"""  )
												self.assertEqual(tokenizer.padding_side ,       """right"""  )
												self.assertEqual(tokenizer.truncation_side ,       """right"""  )






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					AutoTokenizer.from_pretrained(UpperCamelCase  )
												self.assertIsInstance(UpperCamelCase ,       (BertTokenizer, BertTokenizerFast)  )
												with tempfile.TemporaryDirectory() as tmp_dir:
																			tokenizer.save_pretrained(UpperCamelCase  )
																			__UpperCAmelCase   :						Dict      							=					AutoTokenizer.from_pretrained(UpperCamelCase  )

												self.assertIsInstance(UpperCamelCase ,       tokenizer.__class__  )
												self.assertEqual(tokenizera.vocab_size ,       12  )






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					AutoTokenizer.from_pretrained("""ctrl"""  )
												# There is no fast CTRL so this always gives us a slow tokenizer.
												self.assertIsInstance(UpperCamelCase ,       UpperCamelCase  )






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					get_tokenizer_config("""bert-base-cased"""  )
												__UpperCAmelCase   :						Any      							=					config.pop("""_commit_hash""" ,       UpperCamelCase  )
												# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
												self.assertEqual(UpperCamelCase ,       {"""do_lower_case""": False}  )

												# This model does not have a tokenizer_config so we get back an empty dict.
												__UpperCAmelCase   :						List[Any]      							=					get_tokenizer_config(UpperCamelCase  )
												self.assertDictEqual(UpperCamelCase ,       {}  )

												# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
												__UpperCAmelCase   :						Union[str, Any]      							=					AutoTokenizer.from_pretrained(UpperCamelCase  )
												with tempfile.TemporaryDirectory() as tmp_dir:
																			tokenizer.save_pretrained(UpperCamelCase  )
																			__UpperCAmelCase   :						List[str]      							=					get_tokenizer_config(UpperCamelCase  )

												# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
												self.assertEqual(config["""tokenizer_class"""] ,       """BertTokenizer"""  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												try:
																			AutoConfig.register("""custom""" ,       UpperCamelCase  )

																			AutoTokenizer.register(UpperCamelCase ,       slow_tokenizer_class=UpperCamelCase  )
																			# Trying to register something existing in the Transformers library will raise an error
																			with self.assertRaises(UpperCamelCase  ):
																										AutoTokenizer.register(UpperCamelCase ,       slow_tokenizer_class=UpperCamelCase  )

																			__UpperCAmelCase   :						str      							=					CustomTokenizer.from_pretrained(UpperCamelCase  )
																			with tempfile.TemporaryDirectory() as tmp_dir:
																										tokenizer.save_pretrained(UpperCamelCase  )

																										__UpperCAmelCase   :						Tuple      							=					AutoTokenizer.from_pretrained(UpperCamelCase  )
																										self.assertIsInstance(UpperCamelCase ,       UpperCamelCase  )

												finally:
																			if "custom" in CONFIG_MAPPING._extra_content:
																										del CONFIG_MAPPING._extra_content["custom"]
																			if CustomConfig in TOKENIZER_MAPPING._extra_content:
																										del TOKENIZER_MAPPING._extra_content[CustomConfig]






					@require_tokenizers
					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												try:
																			AutoConfig.register("""custom""" ,       UpperCamelCase  )

																			# Can register in two steps
																			AutoTokenizer.register(UpperCamelCase ,       slow_tokenizer_class=UpperCamelCase  )
																			self.assertEqual(TOKENIZER_MAPPING[CustomConfig] ,       (CustomTokenizer, None)  )
																			AutoTokenizer.register(UpperCamelCase ,       fast_tokenizer_class=UpperCamelCase  )
																			self.assertEqual(TOKENIZER_MAPPING[CustomConfig] ,       (CustomTokenizer, CustomTokenizerFast)  )

																			del TOKENIZER_MAPPING._extra_content[CustomConfig]
																			# Can register in one step
																			AutoTokenizer.register(
																			    UpperCamelCase ,       slow_tokenizer_class=UpperCamelCase ,       fast_tokenizer_class=UpperCamelCase  )
																			self.assertEqual(TOKENIZER_MAPPING[CustomConfig] ,       (CustomTokenizer, CustomTokenizerFast)  )

																			# Trying to register something existing in the Transformers library will raise an error
																			with self.assertRaises(UpperCamelCase  ):
																										AutoTokenizer.register(UpperCamelCase ,       fast_tokenizer_class=UpperCamelCase  )

																			# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
																			# and that model does not have a tokenizer.json
																			with tempfile.TemporaryDirectory() as tmp_dir:
																										__UpperCAmelCase   :						List[str]      							=					BertTokenizerFast.from_pretrained(UpperCamelCase  )
																										bert_tokenizer.save_pretrained(UpperCamelCase  )
																										__UpperCAmelCase   :						Tuple      							=					CustomTokenizerFast.from_pretrained(UpperCamelCase  )

																			with tempfile.TemporaryDirectory() as tmp_dir:
																										tokenizer.save_pretrained(UpperCamelCase  )

																										__UpperCAmelCase   :						int      							=					AutoTokenizer.from_pretrained(UpperCamelCase  )
																										self.assertIsInstance(UpperCamelCase ,       UpperCamelCase  )

																										__UpperCAmelCase   :						Optional[Any]      							=					AutoTokenizer.from_pretrained(UpperCamelCase ,       use_fast=UpperCamelCase  )
																										self.assertIsInstance(UpperCamelCase ,       UpperCamelCase  )

												finally:
																			if "custom" in CONFIG_MAPPING._extra_content:
																										del CONFIG_MAPPING._extra_content["custom"]
																			if CustomConfig in TOKENIZER_MAPPING._extra_content:
																										del TOKENIZER_MAPPING._extra_content[CustomConfig]






					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												with self.assertRaises(UpperCamelCase  ):
																			__UpperCAmelCase   :						int      							=					AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""  )
												# If remote code is disabled, we can't load this config.
												with self.assertRaises(UpperCamelCase  ):
																			__UpperCAmelCase   :						Optional[int]      							=					AutoTokenizer.from_pretrained(
																			    """hf-internal-testing/test_dynamic_tokenizer""" ,       trust_remote_code=UpperCamelCase  )

												__UpperCAmelCase   :						Tuple      							=					AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer""" ,       trust_remote_code=UpperCamelCase  )
												self.assertTrue(tokenizer.special_attribute_present  )
												# Test tokenizer can be reloaded.
												with tempfile.TemporaryDirectory() as tmp_dir:
																			tokenizer.save_pretrained(UpperCamelCase  )
																			__UpperCAmelCase   :						Optional[int]      							=					AutoTokenizer.from_pretrained(UpperCamelCase ,       trust_remote_code=UpperCamelCase  )
												self.assertTrue(reloaded_tokenizer.special_attribute_present  )

												if is_tokenizers_available():
																			self.assertEqual(tokenizer.__class__.__name__ ,       """NewTokenizerFast"""  )
																			self.assertEqual(reloaded_tokenizer.__class__.__name__ ,       """NewTokenizerFast"""  )

																			# Test we can also load the slow version
																			__UpperCAmelCase   :						List[Any]      							=					AutoTokenizer.from_pretrained(
																			    """hf-internal-testing/test_dynamic_tokenizer""" ,       trust_remote_code=UpperCamelCase ,       use_fast=UpperCamelCase  )
																			self.assertTrue(tokenizer.special_attribute_present  )
																			self.assertEqual(tokenizer.__class__.__name__ ,       """NewTokenizer"""  )
																			# Test tokenizer can be reloaded.
																			with tempfile.TemporaryDirectory() as tmp_dir:
																										tokenizer.save_pretrained(UpperCamelCase  )
																										__UpperCAmelCase   :						Optional[Any]      							=					AutoTokenizer.from_pretrained(UpperCamelCase ,       trust_remote_code=UpperCamelCase ,       use_fast=UpperCamelCase  )
																			self.assertEqual(reloaded_tokenizer.__class__.__name__ ,       """NewTokenizer"""  )
																			self.assertTrue(reloaded_tokenizer.special_attribute_present  )
												else:
																			self.assertEqual(tokenizer.__class__.__name__ ,       """NewTokenizer"""  )
																			self.assertEqual(reloaded_tokenizer.__class__.__name__ ,       """NewTokenizer"""  )






					@require_tokenizers
					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												class      lowerCamelCase__   (     A				):







																	"""simple docstring"""


																	__a	       =							False






												class      lowerCamelCase__   (     A				):







																	"""simple docstring"""


																	__a	       =							NewTokenizer
																	__a	       =							False

												try:
																			AutoConfig.register("""custom""" ,       UpperCamelCase  )
																			AutoTokenizer.register(UpperCamelCase ,       slow_tokenizer_class=UpperCamelCase  )
																			AutoTokenizer.register(UpperCamelCase ,       fast_tokenizer_class=UpperCamelCase  )
																			# If remote code is not set, the default is to use local
																			__UpperCAmelCase   :						Union[str, Any]      							=					AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""  )
																			self.assertEqual(tokenizer.__class__.__name__ ,       """NewTokenizerFast"""  )
																			self.assertFalse(tokenizer.special_attribute_present  )
																			__UpperCAmelCase   :						int      							=					AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer""" ,       use_fast=UpperCamelCase  )
																			self.assertEqual(tokenizer.__class__.__name__ ,       """NewTokenizer"""  )
																			self.assertFalse(tokenizer.special_attribute_present  )

																			# If remote code is disabled, we load the local one.
																			__UpperCAmelCase   :						str      							=					AutoTokenizer.from_pretrained(
																			    """hf-internal-testing/test_dynamic_tokenizer""" ,       trust_remote_code=UpperCamelCase  )
																			self.assertEqual(tokenizer.__class__.__name__ ,       """NewTokenizerFast"""  )
																			self.assertFalse(tokenizer.special_attribute_present  )
																			__UpperCAmelCase   :						Tuple      							=					AutoTokenizer.from_pretrained(
																			    """hf-internal-testing/test_dynamic_tokenizer""" ,       trust_remote_code=UpperCamelCase ,       use_fast=UpperCamelCase  )
																			self.assertEqual(tokenizer.__class__.__name__ ,       """NewTokenizer"""  )
																			self.assertFalse(tokenizer.special_attribute_present  )

																			# If remote is enabled, we load from the Hub
																			__UpperCAmelCase   :						Union[str, Any]      							=					AutoTokenizer.from_pretrained(
																			    """hf-internal-testing/test_dynamic_tokenizer""" ,       trust_remote_code=UpperCamelCase  )
																			self.assertEqual(tokenizer.__class__.__name__ ,       """NewTokenizerFast"""  )
																			self.assertTrue(tokenizer.special_attribute_present  )
																			__UpperCAmelCase   :						Dict      							=					AutoTokenizer.from_pretrained(
																			    """hf-internal-testing/test_dynamic_tokenizer""" ,       trust_remote_code=UpperCamelCase ,       use_fast=UpperCamelCase  )
																			self.assertEqual(tokenizer.__class__.__name__ ,       """NewTokenizer"""  )
																			self.assertTrue(tokenizer.special_attribute_present  )

												finally:
																			if "custom" in CONFIG_MAPPING._extra_content:
																										del CONFIG_MAPPING._extra_content["custom"]
																			if CustomConfig in TOKENIZER_MAPPING._extra_content:
																										del TOKENIZER_MAPPING._extra_content[CustomConfig]






					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					AutoTokenizer.from_pretrained(
												    """hf-internal-testing/test_dynamic_tokenizer_legacy""" ,       trust_remote_code=UpperCamelCase  )
												self.assertTrue(tokenizer.special_attribute_present  )
												if is_tokenizers_available():
																			self.assertEqual(tokenizer.__class__.__name__ ,       """NewTokenizerFast"""  )

																			# Test we can also load the slow version
																			__UpperCAmelCase   :						Any      							=					AutoTokenizer.from_pretrained(
																			    """hf-internal-testing/test_dynamic_tokenizer_legacy""" ,       trust_remote_code=UpperCamelCase ,       use_fast=UpperCamelCase  )
																			self.assertTrue(tokenizer.special_attribute_present  )
																			self.assertEqual(tokenizer.__class__.__name__ ,       """NewTokenizer"""  )
												else:
																			self.assertEqual(tokenizer.__class__.__name__ ,       """NewTokenizer"""  )






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												with self.assertRaisesRegex(
												    UpperCamelCase ,       """bert-base is not a local folder and is not a valid model identifier"""  ):
																			__UpperCAmelCase   :						str      							=					AutoTokenizer.from_pretrained("""bert-base"""  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												with self.assertRaisesRegex(
												    UpperCamelCase ,       R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""  ):
																			__UpperCAmelCase   :						int      							=					AutoTokenizer.from_pretrained(UpperCamelCase ,       revision="""aaaaaa"""  )






					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""  )
												with RequestCounter() as counter:
																			__UpperCAmelCase   :						Optional[int]      							=					AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""  )
																			self.assertEqual(counter.get_request_count ,       0  )
																			self.assertEqual(counter.head_request_count ,       1  )
																			self.assertEqual(counter.other_request_count ,       0  )




"""simple docstring"""






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class      lowerCamelCase__   (     unittest.TestCase				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					"""ylacombe/bark-small"""
												__UpperCAmelCase   :						List[Any]      							=					tempfile.mkdtemp()
												__UpperCAmelCase   :						Optional[Any]      							=					"""en_speaker_1"""
												__UpperCAmelCase   :						Union[str, Any]      							=					"""This is a test string"""
												__UpperCAmelCase   :						Dict      							=					"""speaker_embeddings_path.json"""
												__UpperCAmelCase   :						Any      							=					"""speaker_embeddings"""






					def lowerCamelCase__							(       self    : Dict ,       **UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






												return AutoTokenizer.from_pretrained(self.checkpoint ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												shutil.rmtree(self.tmpdirname  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Any      							=					BarkProcessor(tokenizer=UpperCamelCase  )

												processor.save_pretrained(self.tmpdirname  )
												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor.from_pretrained(self.tmpdirname  )

												self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer.get_vocab()  )






					@slow
					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       )
												processor.save_pretrained(
												    self.tmpdirname ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       speaker_embeddings_directory=self.speaker_embeddings_directory ,       )

												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer(bos_token="""(BOS)""" ,       eos_token="""(EOS)"""  )

												__UpperCAmelCase   :						Any      							=					BarkProcessor.from_pretrained(
												    self.tmpdirname ,       self.speaker_embeddings_dict_path ,       bos_token="""(BOS)""" ,       eos_token="""(EOS)""" ,       )

												self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()  )






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       )

												__UpperCAmelCase   :						List[str]      							=					35
												__UpperCAmelCase   :						Tuple      							=					2
												__UpperCAmelCase   :						Union[str, Any]      							=					8

												__UpperCAmelCase   :						Optional[Any]      							=					{
												    """semantic_prompt""": np.ones(UpperCamelCase  ),
												    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)  ),
												    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)  ),
												}

												# test providing already loaded voice_preset
												__UpperCAmelCase   :						Dict      							=					processor(text=self.input_string ,       voice_preset=UpperCamelCase  )

												__UpperCAmelCase   :						Union[str, Any]      							=					inputs["""history_prompt"""]
												for key in voice_preset:
																			self.assertListEqual(voice_preset[key].tolist() ,       processed_voice_preset.get(UpperCamelCase ,       np.array([]  )  ).tolist()  )

												# test loading voice preset from npz file
												__UpperCAmelCase   :						Union[str, Any]      							=					os.path.join(self.tmpdirname ,       """file.npz"""  )
												np.savez(UpperCamelCase ,       **UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					processor(text=self.input_string ,       voice_preset=UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					inputs["""history_prompt"""]

												for key in voice_preset:
																			self.assertListEqual(voice_preset[key].tolist() ,       processed_voice_preset.get(UpperCamelCase ,       np.array([]  )  ).tolist()  )

												# test loading voice preset from the hub
												__UpperCAmelCase   :						Dict      							=					processor(text=self.input_string ,       voice_preset=self.voice_preset  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor(tokenizer=UpperCamelCase  )

												__UpperCAmelCase   :						List[str]      							=					processor(text=self.input_string  )

												__UpperCAmelCase   :						Tuple      							=					tokenizer(
												    self.input_string ,       padding="""max_length""" ,       max_length=256 ,       add_special_tokens=UpperCamelCase ,       return_attention_mask=UpperCamelCase ,       return_token_type_ids=UpperCamelCase ,       )

												for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key] ,       encoded_processor[key].squeeze().tolist()  )
