
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:
     __magic_name__           =					None

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





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
        snake_case__  =			unittest.skip("test requires Datasets, Faiss, psutil"							)(__lowerCAmelCase							)
    return test_case





@require_distributed_retrieval
class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      def A_     (     self	):
          snake_case__  =			tempfile.mkdtemp()
          snake_case__  =			8

          # DPR tok
          snake_case__  =			[
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          snake_case__  =			os.path.join(self.tmpdirname						,		"dpr_tokenizer"	)
          os.makedirs(lowerCamelCase						,		exist_ok=lowerCamelCase	)
          snake_case__  =			os.path.join(lowerCamelCase						,		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
          snake_case__  =			[
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "\u0120",
              "\u0120l",
              "\u0120n",
              "\u0120lo",
              "\u0120low",
              "er",
              "\u0120lowest",
              "\u0120newer",
              "\u0120wider",
              "<unk>",
          ]
          snake_case__  =			dict(zip(lowerCamelCase						,		range(len(lowerCamelCase	)	)	)	)
          snake_case__  =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          snake_case__  =			{"unk_token": "<unk>"}

          snake_case__  =			os.path.join(self.tmpdirname						,		"bart_tokenizer"	)
          os.makedirs(lowerCamelCase						,		exist_ok=lowerCamelCase	)
          snake_case__  =			os.path.join(lowerCamelCase						,		BART_VOCAB_FILES_NAMES["vocab_file"]	)
          snake_case__  =			os.path.join(lowerCamelCase						,		BART_VOCAB_FILES_NAMES["merges_file"]	)
          with open(self.vocab_file						,		"w"						,		encoding="utf-8"	) as fp:
              fp.write(json.dumps(lowerCamelCase	) + "\n"	)
          with open(self.merges_file						,		"w"						,		encoding="utf-8"	) as fp:
              fp.write("\n".join(lowerCamelCase	)	)
      def A_     (     self	):
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,		"dpr_tokenizer"	)	)
      def A_     (     self	):
          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname						,		"bart_tokenizer"	)	)
      def A_     (     self	):
          shutil.rmtree(self.tmpdirname	)
      def A_     (     self	):
          snake_case__  =			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 A_     (     self						,		lowerCamelCase						,		lowerCamelCase=1_23_45	):
          snake_case__  =			self.get_dummy_dataset()
          snake_case__  =			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:
              snake_case__  =			dataset
              snake_case__  =			RagPyTorchDistributedRetriever(
                  lowerCamelCase						,		question_encoder_tokenizer=self.get_dpr_tokenizer()						,		generator_tokenizer=self.get_bart_tokenizer()						,		)
              if init_retrieval:
                  retriever.init_retrieval(lowerCamelCase	)
          return retriever
      def A_     (     self						,		lowerCamelCase	):
          # 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=lowerCamelCase	)
          snake_case__  =			RagConfig(
              retrieval_vector_size=self.retrieval_vector_size						,		question_encoder=DPRConfig().to_dict()						,		generator=BartConfig().to_dict()						,		)
          snake_case__  =			ray.remote(lowerCamelCase	)
          snake_case__  =			[remote_cls.remote() for _ in range(1	)]
          with patch("transformers.models.rag.retrieval_rag.load_dataset"	) as mock_load_dataset:
              snake_case__  =			self.get_dummy_dataset()
              snake_case__  =			RagRayDistributedRetriever(
                  lowerCamelCase						,		question_encoder_tokenizer=self.get_dpr_tokenizer()						,		generator_tokenizer=self.get_bart_tokenizer()						,		retrieval_workers=lowerCamelCase						,		)
              if init_retrieval:
                  retriever.init_retrieval()
          return retriever
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase=1_23_45	):
          snake_case__  =			self.get_dummy_dataset()
          snake_case__  =			RagConfig(
              retrieval_vector_size=self.retrieval_vector_size						,		question_encoder=DPRConfig().to_dict()						,		generator=BartConfig().to_dict()						,		index_name="custom"						,		)
          if from_disk:
              snake_case__  =			os.path.join(self.tmpdirname						,		"dataset"	)
              snake_case__  =			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
              snake_case__  =			RagPyTorchDistributedRetriever(
                  lowerCamelCase						,		question_encoder_tokenizer=self.get_dpr_tokenizer()						,		generator_tokenizer=self.get_bart_tokenizer()						,		)
          else:
              snake_case__  =			RagPyTorchDistributedRetriever(
                  lowerCamelCase						,		question_encoder_tokenizer=self.get_dpr_tokenizer()						,		generator_tokenizer=self.get_bart_tokenizer()						,		index=CustomHFIndex(config.retrieval_vector_size						,		lowerCamelCase	)						,		)
          if init_retrieval:
              retriever.init_retrieval(lowerCamelCase	)
          return retriever
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase	):
          # 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=lowerCamelCase	)
          snake_case__  =			self.get_dummy_dataset()
          snake_case__  =			RagConfig(
              retrieval_vector_size=self.retrieval_vector_size						,		question_encoder=DPRConfig().to_dict()						,		generator=BartConfig().to_dict()						,		index_name="custom"						,		)
          snake_case__  =			ray.remote(lowerCamelCase	)
          snake_case__  =			[remote_cls.remote() for _ in range(1	)]
          if from_disk:
              snake_case__  =			os.path.join(self.tmpdirname						,		"dataset"	)
              snake_case__  =			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
              snake_case__  =			RagRayDistributedRetriever(
                  lowerCamelCase						,		question_encoder_tokenizer=self.get_dpr_tokenizer()						,		generator_tokenizer=self.get_bart_tokenizer()						,		retrieval_workers=lowerCamelCase						,		index=CustomHFIndex.load_from_disk(
                      vector_size=config.retrieval_vector_size						,		dataset_path=config.passages_path						,		index_path=config.index_path						,		)						,		)
          else:
              snake_case__  =			RagRayDistributedRetriever(
                  lowerCamelCase						,		question_encoder_tokenizer=self.get_dpr_tokenizer()						,		generator_tokenizer=self.get_bart_tokenizer()						,		retrieval_workers=lowerCamelCase						,		index=CustomHFIndex(config.retrieval_vector_size						,		lowerCamelCase	)						,		)
          if init_retrieval:
              retriever.init_retrieval()
          return retriever
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  ,   snake_case__  ,   snake_case__  =			retriever.retrieve(lowerCamelCase						,		n_docs=lowerCamelCase	)
          self.assertEqual(retrieved_doc_embeds.shape						,		(2, n_docs, self.retrieval_vector_size)	)
          self.assertEqual(len(lowerCamelCase	)						,		2	)
          self.assertEqual(sorted(doc_dicts[0]	)						,		["embeddings", "id", "text", "title"]	)
          self.assertEqual(len(doc_dicts[0]["id"]	)						,		lowerCamelCase	)
          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 A_     (     self	):
          snake_case__  =			1
          snake_case__  =			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=lowerCamelCase	)						,		lowerCamelCase						,		lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			1
          snake_case__  =			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=lowerCamelCase						,		from_disk=lowerCamelCase	)						,		lowerCamelCase						,		lowerCamelCase						,		)
      def A_     (     self	):
          snake_case__  =			1
          snake_case__  =			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=lowerCamelCase						,		from_disk=lowerCamelCase	)						,		lowerCamelCase						,		lowerCamelCase						,		)
      @require_ray
      def A_     (     self	):
          snake_case__  =			1
          snake_case__  =			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=lowerCamelCase	)						,		lowerCamelCase						,		lowerCamelCase	)
          ray.shutdown()
      @require_ray
      def A_     (     self	):
          snake_case__  =			1
          snake_case__  =			np.array(
              [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]						,		dtype=np.floataa	)
          with self.assertRaises(lowerCamelCase	):
              self.distributed_retriever_check(
                  self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase						,		from_disk=lowerCamelCase	)						,		lowerCamelCase						,		lowerCamelCase						,		)
          ray.shutdown()



      @require_ray
      def A_     (     self	):
          snake_case__  =			1
          snake_case__  =			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=lowerCamelCase						,		from_disk=lowerCamelCase	)						,		lowerCamelCase						,		lowerCamelCase	)
          ray.shutdown()





import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




@require_tokenizers
class    _SCREAMING_SNAKE_CASE					( unittest.TestCase	):
      _A  :						Optional[Any]      		=       EsmTokenizer
      def A_     (     self	):
          super().setUp()
          snake_case__  =			tempfile.mkdtemp()
          # fmt: off
          snake_case__  =			["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
          # fmt: on
          snake_case__  =			os.path.join(self.tmpdirname						,		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]	)	)
      def A_     (     self						,		**lowerCamelCase	):
          return [self.get_tokenizer(**lowerCamelCase	)]
      def A_     (     self						,		**lowerCamelCase	):
          return self.tokenizer_class.from_pretrained(self.tmpdirname						,		**lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			self.tokenizer_class(self.vocab_file	)

          snake_case__  =			tokenizer.tokenize("LAGVS"	)
          self.assertListEqual(lowerCamelCase						,		["L", "A", "G", "V", "S"]	)
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase	)						,		[4, 5, 6, 7, 8]	)
      def A_     (     self	):
          snake_case__  =			self.tokenizer_class(self.vocab_file	)

          snake_case__  =			"LAGVS"
          self.assertListEqual(tokenizer.encode(lowerCamelCase	)						,		[0, 4, 5, 6, 7, 8, 2]	)
      def A_     (     self	):
          snake_case__  =			self.tokenizer_class(self.vocab_file	)

          snake_case__  =			["LAGVS", "WCB"]
          snake_case__  =			tokenizer(lowerCamelCase						,		padding=lowerCamelCase	)["input_ids"]

          self.assertListEqual(lowerCamelCase						,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]	)
      def A_     (     self	):
          snake_case__  =			self.tokenizer_class(self.vocab_file	)

          snake_case__  =			["LAGVS", "WCB"]
          snake_case__  =			tokenizer(lowerCamelCase						,		padding=lowerCamelCase	)["input_ids"]

          self.assertListEqual(lowerCamelCase						,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]	)



      def A_     (     self	):
          snake_case__  =			self.get_tokenizers(fast=lowerCamelCase	)
          for tokenizer in tokenizers:
              with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
                  snake_case__  =			"<unk>"
                  snake_case__  =			"<mask>"

                  snake_case__  =			tokenizer.tokenize(lowerCamelCase	)
                  snake_case__  =			tokenizer.tokenize(lowerCamelCase	)

                  self.assertEqual(len(lowerCamelCase	)						,		1	)
                  self.assertEqual(len(lowerCamelCase	)						,		1	)
                  self.assertEqual(token_a[0]						,		lowerCamelCase	)
                  self.assertEqual(token_a[0]						,		lowerCamelCase	)



