






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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Dict       =  tempfile.mkdtemp()
						snake_case__			:				List[Any]       =  5

						# Realm tok
						snake_case__			:				Tuple       =  [
						    "[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",
						]
						snake_case__			:				List[Any]       =  os.path.join(self.tmpdirname							,		"realm_tokenizer"	)
						os.makedirs(__A							,		exist_ok=__A	)
						snake_case__			:				Dict       =  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]	)	)

						snake_case__			:				Optional[int]       =  os.path.join(self.tmpdirname							,		"realm_block_records"	)
						os.makedirs(__A							,		exist_ok=__A	)



					def 	_lowercase					( self					:  Optional[Any]	):
						return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname							,		"realm_tokenizer"	)	)



					def 	_lowercase					( self					:  Optional[Any]	):
						shutil.rmtree(self.tmpdirname	)



					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				Dict       =  RealmConfig(num_block_records=self.num_block_records	)
						return config



					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				List[Any]       =  Dataset.from_dict(
						    {
						        "id": ["0", "1"],
						        "question": ["foo", "bar"],
						        "answers": [["Foo", "Bar"], ["Bar"]],
						    }	)
						return dataset



					def 	_lowercase					( self					:  Any	):
						snake_case__			:				Dict       =  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 	_lowercase					( self					:  Any	):
						snake_case__			:				Union[str, Any]       =  RealmRetriever(
						    block_records=self.get_dummy_block_records()							,		tokenizer=self.get_tokenizer()							,		)
						return retriever



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				Tuple       =  self.get_config()
						snake_case__			:				Optional[Any]       =  self.get_dummy_retriever()
						snake_case__			:				List[str]       =  retriever.tokenizer

						snake_case__			:				Optional[Any]       =  np.array([0, 3]							,		dtype="long"	)
						snake_case__			:				Dict       =  tokenizer(["Test question"]	).input_ids
						snake_case__			:				List[str]       =  tokenizer(
						    ["the fourth"]							,		add_special_tokens=__A							,		return_token_type_ids=__A							,		return_attention_mask=__A							,		).input_ids
						snake_case__			:				Any       =  config.reader_seq_len

						snake_case__			:				List[Any]       =  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, 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 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Any       =  self.get_config()
						snake_case__			:				str       =  self.get_dummy_retriever()
						snake_case__			:				Union[str, Any]       =  retriever.tokenizer

						snake_case__			:				Union[str, Any]       =  np.array([0, 3, 5]							,		dtype="long"	)
						snake_case__			:				Union[str, Any]       =  tokenizer(["Test question"]	).input_ids
						snake_case__			:				Any       =  tokenizer(
						    ["the fourth", "longer longer"]							,		add_special_tokens=__A							,		return_token_type_ids=__A							,		return_attention_mask=__A							,		).input_ids
						snake_case__			:				str       =  config.reader_seq_len

						snake_case__			:				Union[str, Any]       =  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 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Dict       =  self.get_dummy_retriever()
						retriever.save_pretrained(os.path.join(self.tmpdirname							,		"realm_block_records"	)	)

						# Test local path
						snake_case__			:				Union[str, Any]       =  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:
							snake_case__			:				Any       =  os.path.join(
							    os.path.join(self.tmpdirname							,		"realm_block_records"	)							,		_REALM_BLOCK_RECORDS_FILENAME	)
							snake_case__			:				str       =  RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa"	)

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











from datetime import datetime

import matplotlib.pyplot as plt
import torch







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	for param in module.parameters():
		snake_case__			:				Tuple       =  False







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				Any       =  "cuda" if torch.cuda.is_available() else "cpu"
	if torch.backends.mps.is_available() and torch.backends.mps.is_built():
		snake_case__			:				List[Any]       =  "mps"
	if device == "mps":
		print(
		    "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
		    " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
		    " with generations."       )
	return device







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				List[str]       =  plt.imshow(snake_case_       )
	fig.axes.get_xaxis().set_visible(snake_case_       )
	fig.axes.get_yaxis().set_visible(snake_case_       )
	plt.show()







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				str       =  datetime.now()
	snake_case__			:				Optional[Any]       =  current_time.strftime("%H:%M:%S"       )
	return timestamp



