"""simple docstring"""




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__							(       __snake_case		):



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       tempfile.mkdtemp()
								__a		 =       5

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

								__a		 =       os.path.join(self.tmpdirname				,						'realm_block_records'   )
								os.makedirs(UpperCAmelCase				,						exist_ok=UpperCAmelCase   )



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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								shutil.rmtree(self.tmpdirname   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       RealmConfig(num_block_records=self.num_block_records   )
								return config



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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       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=UpperCAmelCase				,						)
								return block_records



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       RealmRetriever(
								    block_records=self.get_dummy_block_records()				,						tokenizer=self.get_tokenizer()				,						)
								return retriever



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       self.get_config()
								__a		 =       self.get_dummy_retriever()
								__a		 =       retriever.tokenizer

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

								__a ,				__a ,				__a ,				__a		 =       retriever(
								    UpperCAmelCase				,						UpperCAmelCase				,						answer_ids=UpperCAmelCase				,						max_length=UpperCAmelCase				,						return_tensors='np'   )

								self.assertEqual(len(UpperCAmelCase   )				,						2   )
								self.assertEqual(len(UpperCAmelCase   )				,						2   )
								self.assertEqual(len(UpperCAmelCase   )				,						2   )
								self.assertEqual(concat_inputs.input_ids.shape				,						(2, 1_0)   )
								self.assertEqual(concat_inputs.attention_mask.shape				,						(2, 1_0)   )
								self.assertEqual(concat_inputs.token_type_ids.shape				,						(2, 1_0)   )
								self.assertEqual(concat_inputs.special_tokens_mask.shape				,						(2, 1_0)   )
								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 __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       self.get_config()
								__a		 =       self.get_dummy_retriever()
								__a		 =       retriever.tokenizer

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

								__a ,				__a ,				__a ,				__a		 =       retriever(
								    UpperCAmelCase				,						UpperCAmelCase				,						answer_ids=UpperCAmelCase				,						max_length=UpperCAmelCase				,						return_tensors='np'   )

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







			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       self.get_dummy_retriever()
								retriever.save_pretrained(os.path.join(self.tmpdirname				,						'realm_block_records'   )   )

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

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :		Tuple							=	logging.get_logger(__name__)

lowerCamelCase_    :		List[Any]							=	{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}


class 						a__							(       __snake_case		):
			A__    :       Dict        =      'fnet'



			def __init__(						self				,						UpperCAmelCase=3_2_0_0_0				,						UpperCAmelCase=7_6_8				,						UpperCAmelCase=1_2				,						UpperCAmelCase=3_0_7_2				,						UpperCAmelCase="gelu_new"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=4				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1e-12				,						UpperCAmelCase=False				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=3				,						UpperCAmelCase=1				,						UpperCAmelCase=2				,						**UpperCAmelCase				,						)			->   Dict:
								super().__init__(pad_token_id=UpperCAmelCase				,						bos_token_id=UpperCAmelCase				,						eos_token_id=UpperCAmelCase				,						**UpperCAmelCase   )

								__a		 =       vocab_size
								__a		 =       max_position_embeddings
								__a		 =       hidden_size
								__a		 =       num_hidden_layers
								__a		 =       intermediate_size
								__a		 =       hidden_act
								__a		 =       hidden_dropout_prob
								__a		 =       initializer_range
								__a		 =       type_vocab_size
								__a		 =       layer_norm_eps
								__a		 =       use_tpu_fourier_optimizations
								__a		 =       tpu_short_seq_length




