
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        lowerCAmelCase__      (   __lowercase	):

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


								'''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(a       ,							exist_ok=a				)
								_UpperCamelCase						    =	os.path.join(a       ,							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(a       ,							exist_ok=a				)

						def  A_				(			self				)					->      RealmTokenizer:


								'''simple docstring'''

								return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,							"""realm_tokenizer"""				)				)

						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								shutil.rmtree(self.tmpdirname				)

						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	RealmConfig(num_block_records=self.num_block_records				)
								return config

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	Dataset.from_dict(
								    {
								        """id""": ["""0""", """1"""],
								        """question""": ["""foo""", """bar"""],
								        """answers""": [["""Foo""", """Bar"""], ["""Bar"""]],
								    }				)
								return dataset

						def  A_				(			self				)					->      Optional[int]:


								'''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=a       ,							)
								return block_records

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	RealmRetriever(
								    block_records=self.get_dummy_block_records()       ,							tokenizer=self.get_tokenizer()       ,							)
								return retriever

						def  A_				(			self				)					->      List[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=a       ,							return_token_type_ids=a       ,							return_attention_mask=a       ,							).input_ids
								_UpperCamelCase						    =	config.reader_seq_len

								_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	retriever(
								    a       ,							a       ,							answer_ids=a       ,							max_length=a       ,							return_tensors="""np"""				)

								self.assertEqual(len(a				)       ,							2				)
								self.assertEqual(len(a				)       ,							2				)
								self.assertEqual(len(a				)       ,							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  A_				(			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=a       ,							return_token_type_ids=a       ,							return_attention_mask=a       ,							).input_ids
								_UpperCamelCase						    =	config.reader_seq_len

								_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	retriever(
								    a       ,							a       ,							answer_ids=a       ,							max_length=a       ,							return_tensors="""np"""				)

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







						def  A_				(			self				)					->      Optional[Any]:


								'''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"""				)





import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase__	=							logging.get_logger(__name__)







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							*a       ,							**a				)					->      None:


								'''simple docstring'''

								warnings.warn(
								    """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
								    """ use BeitImageProcessor instead."""       ,							a       ,							)
								super().__init__(*a       ,							**a				)



