

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 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any] )		->							str:
       SCREAMING_SNAKE_CASE__   =   tempfile.mkdtemp()
       SCREAMING_SNAKE_CASE__   =   5

       # Realm tok
       SCREAMING_SNAKE_CASE__   =   [
           '''[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''',
       ]
       SCREAMING_SNAKE_CASE__   =   os.path.join(self.tmpdirname     ,       '''realm_tokenizer''' )
       os.makedirs(__lowerCamelCase     ,       exist_ok=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   os.path.join(__lowerCamelCase     ,       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] ) )

       SCREAMING_SNAKE_CASE__   =   os.path.join(self.tmpdirname     ,       '''realm_block_records''' )
       os.makedirs(__lowerCamelCase     ,       exist_ok=__lowerCamelCase )






  def 				lowercase_		(							self						:		Dict )		->							RealmTokenizer:
       return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,       '''realm_tokenizer''' ) )






  def 				lowercase_		(							self						:		Optional[Any] )		->							Tuple:
       shutil.rmtree(self.tmpdirname )






  def 				lowercase_		(							self						:		str )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   RealmConfig(num_block_records=self.num_block_records )
       return config






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






  def 				lowercase_		(							self						:		Tuple )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   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=__lowerCamelCase     ,       )
       return block_records






  def 				lowercase_		(							self						:		Union[str, Any] )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   RealmRetriever(
           block_records=self.get_dummy_block_records()     ,       tokenizer=self.get_tokenizer()     ,       )
       return retriever






  def 				lowercase_		(							self						:		str )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   self.get_config()
       SCREAMING_SNAKE_CASE__   =   self.get_dummy_retriever()
       SCREAMING_SNAKE_CASE__   =   retriever.tokenizer

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

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   retriever(
           __lowerCamelCase     ,       __lowerCamelCase     ,       answer_ids=__lowerCamelCase     ,       max_length=__lowerCamelCase     ,       return_tensors='''np''' )

       self.assertEqual(len(__lowerCamelCase )     ,       2 )
       self.assertEqual(len(__lowerCamelCase )     ,       2 )
       self.assertEqual(len(__lowerCamelCase )     ,       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 				lowercase_		(							self						:		Optional[int] )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   self.get_config()
       SCREAMING_SNAKE_CASE__   =   self.get_dummy_retriever()
       SCREAMING_SNAKE_CASE__   =   retriever.tokenizer

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

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   retriever(
           __lowerCamelCase     ,       __lowerCamelCase     ,       answer_ids=__lowerCamelCase     ,       max_length=__lowerCamelCase     ,       return_tensors='''np''' )

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







  def 				lowercase_		(							self						:		Dict )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   self.get_dummy_retriever()
       retriever.save_pretrained(os.path.join(self.tmpdirname     ,       '''realm_block_records''' ) )

       # Test local path
       SCREAMING_SNAKE_CASE__   =   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:
            SCREAMING_SNAKE_CASE__   =   os.path.join(
                os.path.join(self.tmpdirname     ,       '''realm_block_records''' )     ,       _REALM_BLOCK_RECORDS_FILENAME )
            SCREAMING_SNAKE_CASE__   =   RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa''' )

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





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
       from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


