



import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
		from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
		__UpperCAmelCase    	=  None

if is_ray_available():
		import ray  # noqa: E402 # isort:skip
		from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
		__UpperCAmelCase    	=  None
		__UpperCAmelCase    	=  None
		__UpperCAmelCase    	=  None





def     __lowerCamelCase							(   __magic_name__						:	str    ):

							if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
														a__:							int									=unittest.skip("test requires Datasets, Faiss, psutil"    )(__magic_name__    )
							return test_case







@require_distributed_retrieval
class 						lowerCamelCase__			(		_a ):


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													a__:							str									=tempfile.mkdtemp()
													a__:							List[str]									=8

													# DPR tok
													a__:							Tuple									=[
													    "[UNK]",
													    "[CLS]",
													    "[SEP]",
													    "[PAD]",
													    "[MASK]",
													    "want",
													    "##want",
													    "##ed",
													    "wa",
													    "un",
													    "runn",
													    "##ing",
													    ",",
													    "low",
													    "lowest",
													]
													a__:							Union[str, Any]									=os.path.join(self.tmpdirname   ,							"dpr_tokenizer"      )
													os.makedirs(snake_case_   ,							exist_ok=snake_case_      )
													a__:							List[str]									=os.path.join(snake_case_   ,							DPR_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]      )      )

													# BART tok
													a__:							List[Any]									=[
													    "l",
													    "o",
													    "w",
													    "e",
													    "r",
													    "s",
													    "t",
													    "i",
													    "d",
													    "n",
													    "\u0120",
													    "\u0120l",
													    "\u0120n",
													    "\u0120lo",
													    "\u0120low",
													    "er",
													    "\u0120lowest",
													    "\u0120newer",
													    "\u0120wider",
													    "<unk>",
													]
													a__:							Union[str, Any]									=dict(zip(snake_case_   ,							range(len(snake_case_      )      )      )      )
													a__:							List[str]									=["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
													a__:							str									={"unk_token": "<unk>"}

													a__:							Any									=os.path.join(self.tmpdirname   ,							"bart_tokenizer"      )
													os.makedirs(snake_case_   ,							exist_ok=snake_case_      )
													a__:							List[str]									=os.path.join(snake_case_   ,							BART_VOCAB_FILES_NAMES["vocab_file"]      )
													a__:							Union[str, Any]									=os.path.join(snake_case_   ,							BART_VOCAB_FILES_NAMES["merges_file"]      )
													with open(self.vocab_file   ,							"w"   ,							encoding="utf-8"      ) as fp:
																				fp.write(json.dumps(snake_case_      ) + "\n"      )
													with open(self.merges_file   ,							"w"   ,							encoding="utf-8"      ) as fp:
																				fp.write("\n".join(snake_case_      )      )


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,							"dpr_tokenizer"      )      )


						def 	_lowerCamelCase      (			self    :	Dict      ):
													return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,							"bart_tokenizer"      )      )


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													shutil.rmtree(self.tmpdirname      )


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__:							Any									=Dataset.from_dict(
													    {
													        "id": ["0", "1"],
													        "text": ["foo", "bar"],
													        "title": ["Foo", "Bar"],
													        "embeddings": [np.ones(self.retrieval_vector_size      ), 2 * np.ones(self.retrieval_vector_size      )],
													    }      )
													dataset.add_faiss_index("embeddings"   ,							string_factory="Flat"   ,							metric_type=faiss.METRIC_INNER_PRODUCT      )
													return dataset


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	Union[str, Any]   ,							_a    :	Union[str, Any]=1_2_3_4_5      ):
													a__:							Tuple									=self.get_dummy_dataset()
													a__:							Union[str, Any]									=RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size   ,							question_encoder=DPRConfig().to_dict()   ,							generator=BartConfig().to_dict()   ,							)
													with patch("transformers.models.rag.retrieval_rag.load_dataset"      ) as mock_load_dataset:
																				a__:							Union[str, Any]									=dataset
																				a__:							str									=RagPyTorchDistributedRetriever(
																				    snake_case_   ,							question_encoder_tokenizer=self.get_dpr_tokenizer()   ,							generator_tokenizer=self.get_bart_tokenizer()   ,							)
																				if init_retrieval:
																											retriever.init_retrieval(snake_case_      )
													return retriever


						def 	_lowerCamelCase      (			self    :	Tuple   ,							_a    :	List[Any]      ):
													# Have to run in local mode because sys.path modifications at top of
													# file are not propogated to remote workers.
													# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
													ray.init(local_mode=snake_case_      )
													a__:							Union[str, Any]									=RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size   ,							question_encoder=DPRConfig().to_dict()   ,							generator=BartConfig().to_dict()   ,							)
													a__:							Dict									=ray.remote(snake_case_      )
													a__:							List[str]									=[remote_cls.remote() for _ in range(1      )]
													with patch("transformers.models.rag.retrieval_rag.load_dataset"      ) as mock_load_dataset:
																				a__:							Any									=self.get_dummy_dataset()
																				a__:							Dict									=RagRayDistributedRetriever(
																				    snake_case_   ,							question_encoder_tokenizer=self.get_dpr_tokenizer()   ,							generator_tokenizer=self.get_bart_tokenizer()   ,							retrieval_workers=snake_case_   ,							)
																				if init_retrieval:
																											retriever.init_retrieval()
													return retriever


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	int   ,							_a    :	int   ,							_a    :	List[str]=1_2_3_4_5      ):
													a__:							Optional[int]									=self.get_dummy_dataset()
													a__:							List[Any]									=RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size   ,							question_encoder=DPRConfig().to_dict()   ,							generator=BartConfig().to_dict()   ,							index_name="custom"   ,							)
													if from_disk:
																				a__:							Optional[Any]									=os.path.join(self.tmpdirname   ,							"dataset"      )
																				a__:							Optional[Any]									=os.path.join(self.tmpdirname   ,							"index.faiss"      )
																				dataset.get_index("embeddings"      ).save(os.path.join(self.tmpdirname   ,							"index.faiss"      )      )
																				dataset.drop_index("embeddings"      )
																				dataset.save_to_disk(os.path.join(self.tmpdirname   ,							"dataset"      )      )
																				del dataset
																				a__:							Dict									=RagPyTorchDistributedRetriever(
																				    snake_case_   ,							question_encoder_tokenizer=self.get_dpr_tokenizer()   ,							generator_tokenizer=self.get_bart_tokenizer()   ,							)
													else:
																				a__:							List[Any]									=RagPyTorchDistributedRetriever(
																				    snake_case_   ,							question_encoder_tokenizer=self.get_dpr_tokenizer()   ,							generator_tokenizer=self.get_bart_tokenizer()   ,							index=CustomHFIndex(config.retrieval_vector_size   ,							snake_case_      )   ,							)
													if init_retrieval:
																				retriever.init_retrieval(snake_case_      )
													return retriever


						def 	_lowerCamelCase      (			self    :	Optional[int]   ,							_a    :	Union[str, Any]   ,							_a    :	Union[str, Any]      ):
													# Have to run in local mode because sys.path modifications at top of
													# file are not propogated to remote workers.
													# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
													ray.init(local_mode=snake_case_      )
													a__:							Optional[Any]									=self.get_dummy_dataset()
													a__:							List[str]									=RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size   ,							question_encoder=DPRConfig().to_dict()   ,							generator=BartConfig().to_dict()   ,							index_name="custom"   ,							)
													a__:							Optional[Any]									=ray.remote(snake_case_      )
													a__:							Union[str, Any]									=[remote_cls.remote() for _ in range(1      )]
													if from_disk:
																				a__:							List[str]									=os.path.join(self.tmpdirname   ,							"dataset"      )
																				a__:							str									=os.path.join(self.tmpdirname   ,							"index.faiss"      )
																				dataset.get_index("embeddings"      ).save(os.path.join(self.tmpdirname   ,							"index.faiss"      )      )
																				dataset.drop_index("embeddings"      )
																				dataset.save_to_disk(os.path.join(self.tmpdirname   ,							"dataset"      )      )
																				del dataset
																				a__:							Tuple									=RagRayDistributedRetriever(
																				    snake_case_   ,							question_encoder_tokenizer=self.get_dpr_tokenizer()   ,							generator_tokenizer=self.get_bart_tokenizer()   ,							retrieval_workers=snake_case_   ,							index=CustomHFIndex.load_from_disk(
																				        vector_size=config.retrieval_vector_size   ,							dataset_path=config.passages_path   ,							index_path=config.index_path   ,							)   ,							)
													else:
																				a__:							Tuple									=RagRayDistributedRetriever(
																				    snake_case_   ,							question_encoder_tokenizer=self.get_dpr_tokenizer()   ,							generator_tokenizer=self.get_bart_tokenizer()   ,							retrieval_workers=snake_case_   ,							index=CustomHFIndex(config.retrieval_vector_size   ,							snake_case_      )   ,							)
													if init_retrieval:
																				retriever.init_retrieval()
													return retriever


						def 	_lowerCamelCase      (			self    :	Tuple   ,							_a    :	List[str]   ,							_a    :	Any   ,							_a    :	Optional[int]      ):
													a__				,      a__				,      a__:							List[str]									=retriever.retrieve(snake_case_   ,							n_docs=snake_case_      )
													self.assertEqual(retrieved_doc_embeds.shape   ,							(2, n_docs, self.retrieval_vector_size)      )
													self.assertEqual(len(snake_case_      )   ,							2      )
													self.assertEqual(sorted(doc_dicts[0]      )   ,							["embeddings", "id", "text", "title"]      )
													self.assertEqual(len(doc_dicts[0]["id"]      )   ,							snake_case_      )
													self.assertEqual(doc_dicts[0]["id"][0]   ,							"1"      )  # max inner product is reached with second doc
													self.assertEqual(doc_dicts[1]["id"][0]   ,							"0"      )  # max inner product is reached with first doc
													self.assertListEqual(doc_ids.tolist()   ,							[[1], [0]]      )


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							List[str]									=1
													a__:							str									=np.array(
													    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,							dtype=np.floataa      )

													self.distributed_retriever_check(
													    self.get_dummy_pytorch_distributed_retriever(init_retrieval=snake_case_      )   ,							snake_case_   ,							snake_case_      )


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							Union[str, Any]									=1
													a__:							int									=np.array(
													    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,							dtype=np.floataa      )

													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=snake_case_   ,							from_disk=snake_case_      )   ,							snake_case_   ,							snake_case_   ,							)


						def 	_lowerCamelCase      (			self    :	Tuple      ):
													a__:							Optional[Any]									=1
													a__:							Any									=np.array(
													    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,							dtype=np.floataa      )

													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=snake_case_   ,							from_disk=snake_case_      )   ,							snake_case_   ,							snake_case_   ,							)


						@require_ray
						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													a__:							Optional[Any]									=1
													a__:							Any									=np.array(
													    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,							dtype=np.floataa      )

													self.distributed_retriever_check(
													    self.get_dummy_ray_distributed_retriever(init_retrieval=snake_case_      )   ,							snake_case_   ,							snake_case_      )
													ray.shutdown()


						@require_ray
						def 	_lowerCamelCase      (			self    :	Any      ):
													a__:							Tuple									=1
													a__:							int									=np.array(
													    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,							dtype=np.floataa      )
													with self.assertRaises(snake_case_      ):
																				self.distributed_retriever_check(
																				    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case_   ,							from_disk=snake_case_      )   ,							snake_case_   ,							snake_case_   ,							)
													ray.shutdown()







						@require_ray
						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							Tuple									=1
													a__:							Any									=np.array(
													    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,							dtype=np.floataa      )

													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case_   ,							from_disk=snake_case_      )   ,							snake_case_   ,							snake_case_      )
													ray.shutdown()













"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A__       :			str=						[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

    return [int(SCREAMING_SNAKE_CASE       ) for c in F'{ruleset:08}'[:8]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

    UpperCamelCase__								=     len(cells[0]       )  # 31
    UpperCamelCase__								=     []
    for i in range(SCREAMING_SNAKE_CASE       ):
        # Get the neighbors of each cell
        # Handle neighbours outside bounds by using 0 as their value
        UpperCamelCase__								=     0 if i == 0 else cells[time][i - 1]
        UpperCamelCase__								=     0 if i == population - 1 else cells[time][i + 1]
        # Define a new cell and add it to the new generation
        UpperCamelCase__								=     7 - int(F'{left_neighbor}{cells[time][i]}{right_neighbor}'			,						2       )
        next_generation.append(rule[situation]       )
    return next_generation







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						Image.Image:
    """simple docstring"""

    UpperCamelCase__								=     Image.new('RGB'			,						(len(cells[0]       ), len(SCREAMING_SNAKE_CASE       ))       )
    UpperCamelCase__								=     img.load()
    # Generates image
    for w in range(img.width       ):
        for h in range(img.height       ):
            UpperCamelCase__								=     2_55 - int(2_55 * cells[h][w]       )
            UpperCamelCase__								=     (color, color, color)
    return img


if __name__ == "__main__":
  A__       :			Optional[int]=						bin(int(input("""Rule:\n""").strip()))[2:]
  A__       :			str=						format_ruleset(int(rule_num))
  for time in range(16):
    CELLS.append(new_generation(CELLS, rule, time))
  A__       :			Tuple=						generate_image(CELLS)
  # Uncomment to save the image
  # img.save(f"rule_{rule_num}.png")
  img.show()




