


import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer

class 						__a (							UpperCAmelCase     ):
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     tempfile.mkdtemp()
									_UpperCAmelCase											=     5

									# Realm tok
									_UpperCAmelCase											=     [
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    '[PAD]',
									    '[MASK]',
									    'test',
									    'question',
									    'this',
									    'is',
									    'the',
									    'first',
									    'second',
									    'third',
									    'fourth',
									    'fifth',
									    'record',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									_UpperCAmelCase											=     os.path.join(self.tmpdirname     ,       'realm_tokenizer'		)
									os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     os.path.join(_SCREAMING_SNAKE_CASE     ,       VOCAB_FILES_NAMES['vocab_file']		)
									with open(self.vocab_file     ,       'w'     ,       encoding='utf-8'		) as vocab_writer:
																vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)

									_UpperCAmelCase											=     os.path.join(self.tmpdirname     ,       'realm_block_records'		)
									os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			RealmTokenizer:




									"""simple docstring"""





									return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,       'realm_tokenizer'		)		)
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									shutil.rmtree(self.tmpdirname		)
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     RealmConfig(num_block_records=self.num_block_records		)
									return config
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     Dataset.from_dict(
									    {
									        'id': ['0', '1'],
									        'question': ['foo', 'bar'],
									        'answers': [['Foo', 'Bar'], ['Bar']],
									    }		)
									return dataset
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     np.array(
									    [
									        b'This is the first record',
									        b'This is the second record',
									        b'This is the third record',
									        b'This is the fourth record',
									        b'This is the fifth record',
									        b'This is a longer longer longer record',
									    ]     ,       dtype=_SCREAMING_SNAKE_CASE     ,       )
									return block_records
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     RealmRetriever(
									    block_records=self.get_dummy_block_records()     ,       tokenizer=self.get_tokenizer()     ,       )
									return retriever
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_config()
									_UpperCAmelCase											=     self.get_dummy_retriever()
									_UpperCAmelCase											=     retriever.tokenizer

									_UpperCAmelCase											=     np.array([0, 3]     ,       dtype='long'		)
									_UpperCAmelCase											=     tokenizer(['Test question']		).input_ids
									_UpperCAmelCase											=     tokenizer(
									    ['the fourth']     ,       add_special_tokens=_SCREAMING_SNAKE_CASE     ,       return_token_type_ids=_SCREAMING_SNAKE_CASE     ,       return_attention_mask=_SCREAMING_SNAKE_CASE     ,       ).input_ids
									_UpperCAmelCase											=     config.reader_seq_len

									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     retriever(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       answer_ids=_SCREAMING_SNAKE_CASE     ,       max_length=_SCREAMING_SNAKE_CASE     ,       return_tensors='np'		)

									self.assertEqual(len(_SCREAMING_SNAKE_CASE		)     ,       2		)
									self.assertEqual(len(_SCREAMING_SNAKE_CASE		)     ,       2		)
									self.assertEqual(len(_SCREAMING_SNAKE_CASE		)     ,       2		)
									self.assertEqual(concat_inputs.input_ids.shape     ,       (2, 10)		)
									self.assertEqual(concat_inputs.attention_mask.shape     ,       (2, 10)		)
									self.assertEqual(concat_inputs.token_type_ids.shape     ,       (2, 10)		)
									self.assertEqual(concat_inputs.special_tokens_mask.shape     ,       (2, 10)		)
									self.assertEqual(
									    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]		)     ,       ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]']     ,       )
									self.assertEqual(
									    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]		)     ,       ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]']     ,       )
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_config()
									_UpperCAmelCase											=     self.get_dummy_retriever()
									_UpperCAmelCase											=     retriever.tokenizer

									_UpperCAmelCase											=     np.array([0, 3, 5]     ,       dtype='long'		)
									_UpperCAmelCase											=     tokenizer(['Test question']		).input_ids
									_UpperCAmelCase											=     tokenizer(
									    ['the fourth', 'longer longer']     ,       add_special_tokens=_SCREAMING_SNAKE_CASE     ,       return_token_type_ids=_SCREAMING_SNAKE_CASE     ,       return_attention_mask=_SCREAMING_SNAKE_CASE     ,       ).input_ids
									_UpperCAmelCase											=     config.reader_seq_len

									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     retriever(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       answer_ids=_SCREAMING_SNAKE_CASE     ,       max_length=_SCREAMING_SNAKE_CASE     ,       return_tensors='np'		)

									self.assertEqual([False, True, True]     ,       _SCREAMING_SNAKE_CASE		)
									self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]     ,       _SCREAMING_SNAKE_CASE		)
									self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]     ,       _SCREAMING_SNAKE_CASE		)






		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_dummy_retriever()
									retriever.save_pretrained(os.path.join(self.tmpdirname     ,       'realm_block_records'		)		)

									# Test local path
									_UpperCAmelCase											=     retriever.from_pretrained(os.path.join(self.tmpdirname     ,       'realm_block_records'		)		)
									self.assertEqual(retriever.block_records[0]     ,       b'This is the first record'		)

									# Test mocked remote path
									with patch('transformers.models.realm.retrieval_realm.hf_hub_download'		) as mock_hf_hub_download:
																_UpperCAmelCase											=     os.path.join(
																    os.path.join(self.tmpdirname     ,       'realm_block_records'		)     ,       _REALM_BLOCK_RECORDS_FILENAME		)
																_UpperCAmelCase											=     RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa'		)

									self.assertEqual(retriever.block_records[0]     ,       b'This is the first record'		)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase__    :Tuple       =				{
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Dict       =				['''SpeechT5Tokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[str]       =				[
		    '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SpeechT5ForSpeechToText''',
		    '''SpeechT5ForSpeechToSpeech''',
		    '''SpeechT5ForTextToSpeech''',
		    '''SpeechT5Model''',
		    '''SpeechT5PreTrainedModel''',
		    '''SpeechT5HifiGan''',
		]

if TYPE_CHECKING:
		from .configuration_speechta import (
		    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
		    SpeechTaConfig,
		    SpeechTaHifiGanConfig,
		)
		from .feature_extraction_speechta import SpeechTaFeatureExtractor
		from .processing_speechta import SpeechTaProcessor

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_speechta import SpeechTaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speechta import (
				    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechTaForSpeechToSpeech,
				    SpeechTaForSpeechToText,
				    SpeechTaForTextToSpeech,
				    SpeechTaHifiGan,
				    SpeechTaModel,
				    SpeechTaPreTrainedModel,
				)

else:
		import sys

		lowerCAmelCase__    :str       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
