
"""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 __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







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







								"""simple docstring"""
								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(_snake_case       ,   exist_ok=_snake_case	)
								A_				 =						os.path.join(_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]	)	)

								A_				 =						os.path.join(self.tmpdirname       ,   "realm_block_records"	)
								os.makedirs(_snake_case       ,   exist_ok=_snake_case	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> RealmTokenizer:







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




			def      lowerCamelCase__     (				self						:					Dict	)      -> Optional[int]:







								"""simple docstring"""
								shutil.rmtree(self.tmpdirname	)




			def      lowerCamelCase__     (				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						RealmConfig(num_block_records=self.num_block_records	)
								return config




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Any:







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




			def      lowerCamelCase__     (				self						:					Dict	)      -> str:







								"""simple docstring"""
								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=_snake_case       ,   )
								return block_records




			def      lowerCamelCase__     (				self						:					Dict	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						RealmRetriever(
								    block_records=self.get_dummy_block_records()       ,   tokenizer=self.get_tokenizer()       ,   )
								return retriever




			def      lowerCamelCase__     (				self						:					List[str]	)      -> Dict:







								"""simple docstring"""
								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=_snake_case       ,   return_token_type_ids=_snake_case       ,   return_attention_mask=_snake_case       ,   ).input_ids
								A_				 =						config.reader_seq_len

								A_ ,    A_ ,    A_ ,    A_				 =						retriever(
								    _snake_case       ,   _snake_case       ,   answer_ids=_snake_case       ,   max_length=_snake_case       ,   return_tensors="np"	)

								self.assertEqual(len(_snake_case	)       ,   2	)
								self.assertEqual(len(_snake_case	)       ,   2	)
								self.assertEqual(len(_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      lowerCamelCase__     (				self						:					Optional[int]	)      -> str:







								"""simple docstring"""
								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=_snake_case       ,   return_token_type_ids=_snake_case       ,   return_attention_mask=_snake_case       ,   ).input_ids
								A_				 =						config.reader_seq_len

								A_ ,    A_ ,    A_ ,    A_				 =						retriever(
								    _snake_case       ,   _snake_case       ,   answer_ids=_snake_case       ,   max_length=_snake_case       ,   return_tensors="np"	)

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






			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[Any]:







								"""simple docstring"""
								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"	)






"""simple docstring"""





from __future__ import annotations

import math

UpperCamelCase_					:						List[str]				= '''2020.9.26'''
UpperCamelCase_					:						List[Any]				= '''xcodz-dot, cclaus, dhruvmanila'''
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not all(isinstance(__a				,   (float, int)				) for val in locals().values()				):
										A_				 =						f'Input values must either be float or int: {list(locals().values()				)}'
										raise TypeError(__a				)
					A_				 =						((x * distance) / (z + distance)) * scale
					A_				 =						((y * distance) / (z + distance)) * scale
					return projected_x, projected_y
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not isinstance(__a				,   __a				):
										raise TypeError("Axis must be a str"				)
					A_				 =						locals()
					del input_variables["axis"]
					if not all(isinstance(__a				,   (float, int)				) for val in input_variables.values()				):
										A_				 =						(
										    "Input values except axis must either be float or int: "
										    f'{list(input_variables.values()				)}'
										)
										raise TypeError(__a				)
					A_				 =						(angle % 360) / 450 * 180 / math.pi
					if axis == "z":
										A_				 =						x * math.cos(__a				) - y * math.sin(__a				)
										A_				 =						y * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						z
					elif axis == "x":
										A_				 =						y * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + y * math.sin(__a				)
										A_				 =						x
					elif axis == "y":
										A_				 =						x * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						y
					else:
										raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'"				)

					return new_x, new_y, new_z


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
		print(F"""{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }""")




