
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


snake_case :		int							      =  pytest.mark.integration





@require_faiss
class       _snake_case					(     snake_case				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     Dataset.from_dict({"filename": ["my_name-train" + "_" + str(_a		) for x in np.arange(30		).tolist()]}		)
				return dset







			def 			SCREAMING_SNAKE_CASE    (				self		):
				import faiss

				__magic_name__		:		Dataset	   =     self._create_dummy_dataset()
				__magic_name__		:		Dict	   =     dset.map(
				    lambda _a						,   _a		: {"vecs": i * np.ones(5						,   dtype=np.floataa		)}						,   with_indices=_a						,   keep_in_memory=_a		)
				__magic_name__		:		int	   =     dset.add_faiss_index("vecs"						,   batch_size=100						,   metric_type=faiss.METRIC_INNER_PRODUCT		)
				__magic_name__   ,    __magic_name__		:		List[Any]	   =     dset.get_nearest_examples("vecs"						,   np.ones(5						,   dtype=np.floataa		)		)
				self.assertEqual(examples["filename"][0]						,   "my_name-train_29"		)
				dset.drop_index("vecs"		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				import faiss

				__magic_name__		:		Dataset	   =     self._create_dummy_dataset()
				dset.add_faiss_index_from_external_arrays(
				    external_arrays=np.ones((30, 5)		) * np.arange(30		).reshape(-1						,   1		)						,   index_name="vecs"						,   batch_size=100						,   metric_type=faiss.METRIC_INNER_PRODUCT						,   )
				__magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     dset.get_nearest_examples("vecs"						,   np.ones(5						,   dtype=np.floataa		)		)
				self.assertEqual(examples["filename"][0]						,   "my_name-train_29"		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				import faiss

				__magic_name__		:		Dataset	   =     self._create_dummy_dataset()
				dset.add_faiss_index_from_external_arrays(
				    external_arrays=np.ones((30, 5)		) * np.arange(30		).reshape(-1						,   1		)						,   index_name="vecs"						,   metric_type=faiss.METRIC_INNER_PRODUCT						,   )

				# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
				# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
				# see https://bugs.python.org/issue14243 and
				# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
				with tempfile.NamedTemporaryFile(delete=_a		) as tmp_file:
					dset.save_faiss_index("vecs"						,   tmp_file.name		)
					dset.load_faiss_index("vecs2"						,   tmp_file.name		)
				os.unlink(tmp_file.name		)

				__magic_name__   ,    __magic_name__		:		Optional[int]	   =     dset.get_nearest_examples("vecs2"						,   np.ones(5						,   dtype=np.floataa		)		)
				self.assertEqual(examples["filename"][0]						,   "my_name-train_29"		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dataset	   =     self._create_dummy_dataset()
				dset.add_faiss_index_from_external_arrays(
				    external_arrays=np.ones((30, 5)		) * np.arange(30		).reshape(-1						,   1		)						,   index_name="vecs"		)
				dset.drop_index("vecs"		)
				self.assertRaises(_a						,   partial(dset.get_nearest_examples						,   "vecs2"						,   np.ones(5						,   dtype=np.floataa		)		)		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				from elasticsearch import Elasticsearch

				__magic_name__		:		Dataset	   =     self._create_dummy_dataset()
				with patch("elasticsearch.Elasticsearch.search"		) as mocked_search, patch(
				    "elasticsearch.client.IndicesClient.create"		) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk"		) as mocked_bulk:
					__magic_name__		:		Union[str, Any]	   =     {"acknowledged": True}
					mocked_bulk.return_value([(True, None)] * 30		)
					__magic_name__		:		Any	   =     {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
					__magic_name__		:		Union[str, Any]	   =     Elasticsearch()

					dset.add_elasticsearch_index("filename"						,   es_client=_a		)
					__magic_name__   ,    __magic_name__		:		Tuple	   =     dset.get_nearest_examples("filename"						,   "my_name-train_29"		)
					self.assertEqual(examples["filename"][0]						,   "my_name-train_29"		)







@require_faiss
class       _snake_case					(     snake_case				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				import faiss

				__magic_name__		:		List[str]	   =     FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT		)

				# add vectors
				index.add_vectors(np.eye(5						,   dtype=np.floataa		)		)
				self.assertIsNotNone(index.faiss_index		)
				self.assertEqual(index.faiss_index.ntotal						,   5		)
				index.add_vectors(np.zeros((5, 5)						,   dtype=np.floataa		)		)
				self.assertEqual(index.faiss_index.ntotal						,   10		)

				# single query
				__magic_name__		:		List[str]	   =     np.zeros(5						,   dtype=np.floataa		)
				__magic_name__		:		Tuple	   =     1
				__magic_name__   ,    __magic_name__		:		str	   =     index.search(_a		)
				self.assertRaises(_a						,   index.search						,   query.reshape(-1						,   1		)		)
				self.assertGreater(scores[0]						,   0		)
				self.assertEqual(indices[0]						,   1		)

				# batched queries
				__magic_name__		:		List[Any]	   =     np.eye(5						,   dtype=np.floataa		)[::-1]
				__magic_name__   ,    __magic_name__		:		List[Any]	   =     index.search_batch(_a		)
				self.assertRaises(_a						,   index.search_batch						,   queries[0]		)
				__magic_name__		:		Optional[Any]	   =     [scores[0] for scores in total_scores]
				__magic_name__		:		Optional[Any]	   =     [indices[0] for indices in total_indices]
				self.assertGreater(np.min(_a		)						,   0		)
				self.assertListEqual([4, 3, 2, 1, 0]						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				import faiss

				__magic_name__		:		Dict	   =     FaissIndex(string_factory="Flat"		)
				index.add_vectors(np.eye(5						,   dtype=np.floataa		)		)
				self.assertIsInstance(index.faiss_index						,   faiss.IndexFlat		)
				__magic_name__		:		int	   =     FaissIndex(string_factory="LSH"		)
				index.add_vectors(np.eye(5						,   dtype=np.floataa		)		)
				self.assertIsInstance(index.faiss_index						,   faiss.IndexLSH		)
				with self.assertRaises(_a		):
					__magic_name__		:		str	   =     FaissIndex(string_factory="Flat"						,   custom_index=faiss.IndexFlat(5		)		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				import faiss

				__magic_name__		:		int	   =     faiss.IndexFlat(5		)
				__magic_name__		:		Union[str, Any]	   =     FaissIndex(custom_index=_a		)
				index.add_vectors(np.eye(5						,   dtype=np.floataa		)		)
				self.assertIsInstance(index.faiss_index						,   faiss.IndexFlat		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				import faiss

				__magic_name__		:		Tuple	   =     FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT		)
				index.add_vectors(np.eye(5						,   dtype=np.floataa		)		)

				# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
				# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
				# see https://bugs.python.org/issue14243 and
				# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
				with tempfile.NamedTemporaryFile(delete=_a		) as tmp_file:
					index.save(tmp_file.name		)
					__magic_name__		:		List[str]	   =     FaissIndex.load(tmp_file.name		)
				os.unlink(tmp_file.name		)

				__magic_name__		:		Optional[Any]	   =     np.zeros(5						,   dtype=np.floataa		)
				__magic_name__		:		Dict	   =     1
				__magic_name__   ,    __magic_name__		:		str	   =     index.search(_a		)
				self.assertGreater(scores[0]						,   0		)
				self.assertEqual(indices[0]						,   1		)







@require_faiss
def   lowerCAmelCase_						(					_snake_case      :  int   )				->		List[Any]:







	'''simple docstring'''


	import faiss

	__magic_name__		:		List[str]	   =     FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT   )
	index.add_vectors(np.eye(5      ,   dtype=np.floataa   )   )

	__magic_name__		:		List[str]	   =     "index.faiss"
	__magic_name__		:		Optional[int]	   =     F'''mock://{index_name}'''
	index.save(_snake_case      ,   storage_options=mockfs.storage_options   )
	__magic_name__		:		List[str]	   =     FaissIndex.load(_snake_case      ,   storage_options=mockfs.storage_options   )

	__magic_name__		:		int	   =     np.zeros(5      ,   dtype=np.floataa   )
	__magic_name__		:		Union[str, Any]	   =     1
	__magic_name__   ,    __magic_name__		:		int	   =     index.search(_snake_case   )
	assert scores[0] > 0
	assert indices[0] == 1





@require_elasticsearch
class       _snake_case					(     snake_case				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				from elasticsearch import Elasticsearch

				with patch("elasticsearch.Elasticsearch.search"		) as mocked_search, patch(
				    "elasticsearch.client.IndicesClient.create"		) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk"		) as mocked_bulk:
					__magic_name__		:		Tuple	   =     Elasticsearch()
					__magic_name__		:		str	   =     {"acknowledged": True}
					__magic_name__		:		List[Any]	   =     ElasticSearchIndex(es_client=_a		)
					mocked_bulk.return_value([(True, None)] * 3		)
					index.add_documents(["foo", "bar", "foobar"]		)

					# single query
					__magic_name__		:		Optional[int]	   =     "foo"
					__magic_name__		:		List[Any]	   =     {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
					__magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     index.search(_a		)
					self.assertEqual(scores[0]						,   1		)
					self.assertEqual(indices[0]						,   0		)

					# single query with timeout
					__magic_name__		:		Dict	   =     "foo"
					__magic_name__		:		int	   =     {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
					__magic_name__   ,    __magic_name__		:		int	   =     index.search(_a						,   request_timeout=30		)
					self.assertEqual(scores[0]						,   1		)
					self.assertEqual(indices[0]						,   0		)

					# batched queries
					__magic_name__		:		Any	   =     ["foo", "bar", "foobar"]
					__magic_name__		:		int	   =     {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
					__magic_name__   ,    __magic_name__		:		List[str]	   =     index.search_batch(_a		)
					__magic_name__		:		Any	   =     [scores[0] for scores in total_scores]
					__magic_name__		:		List[Any]	   =     [indices[0] for indices in total_indices]
					self.assertGreater(np.min(_a		)						,   0		)
					self.assertListEqual([1, 1, 1]						,   _a		)

					# batched queries with timeout
					__magic_name__		:		List[Any]	   =     ["foo", "bar", "foobar"]
					__magic_name__		:		Union[str, Any]	   =     {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
					__magic_name__   ,    __magic_name__		:		Optional[Any]	   =     index.search_batch(_a						,   request_timeout=30		)
					__magic_name__		:		Optional[int]	   =     [scores[0] for scores in total_scores]
					__magic_name__		:		Optional[int]	   =     [indices[0] for indices in total_indices]
					self.assertGreater(np.min(_a		)						,   0		)
					self.assertListEqual([1, 1, 1]						,   _a		)


import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case :		Dict							      =  logging.get_logger(__name__)

snake_case :		Union[str, Any]							      =  {
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
}

snake_case :		Dict							      =  {
    "vocab_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
    },
    "merges_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
    },
}

snake_case :		Union[str, Any]							      =  {
    "vinai/phobert-base": 256,
    "vinai/phobert-large": 256,
}






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     set()
	__magic_name__		:		Any	   =     word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)   )
		__magic_name__		:		int	   =     char

	__magic_name__		:		List[str]	   =     set(_snake_case   )
	return pairs





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    VOCAB_FILES_NAMES
			UpperCamelCase__          =    PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







			def __init__(				self						,   _a						,   _a						,   _a="<s>"						,   _a="</s>"						,   _a="</s>"						,   _a="<s>"						,   _a="<unk>"						,   _a="<pad>"						,   _a="<mask>"						,   **_a						,   ):
				super().__init__(
				    bos_token=_a						,   eos_token=_a						,   unk_token=_a						,   sep_token=_a						,   cls_token=_a						,   pad_token=_a						,   mask_token=_a						,   **_a						,   )

				__magic_name__		:		Dict	   =     vocab_file
				__magic_name__		:		Tuple	   =     merges_file

				__magic_name__		:		List[Any]	   =     {}
				__magic_name__		:		List[Any]	   =     0
				__magic_name__		:		Tuple	   =     1
				__magic_name__		:		int	   =     2
				__magic_name__		:		Union[str, Any]	   =     3

				self.add_from_file(_a		)

				__magic_name__		:		Optional[int]	   =     {v: k for k, v in self.encoder.items()}

				with open(_a						,   encoding="utf-8"		) as merges_handle:
					__magic_name__		:		List[str]	   =     merges_handle.read().split("\n"		)[:-1]
				__magic_name__		:		Union[str, Any]	   =     [tuple(merge.split()[:-1]		) for merge in merges]
				__magic_name__		:		Union[str, Any]	   =     dict(zip(_a						,   range(len(_a		)		)		)		)
				__magic_name__		:		Optional[int]	   =     {}







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__magic_name__		:		Optional[Any]	   =     [self.cls_token_id]
				__magic_name__		:		Union[str, Any]	   =     [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None						,   _a = False		):

				if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=_a						,   token_ids_a=_a						,   already_has_special_tokens=_a		)

				if token_ids_a is None:
					return [1] + ([0] * len(_a		)) + [1]
				return [1] + ([0] * len(_a		)) + [1, 1] + ([0] * len(_a		)) + [1]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		Optional[Any]	   =     [self.sep_token_id]
				__magic_name__		:		Tuple	   =     [self.cls_token_id]

				if token_ids_a is None:
					return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return len(self.encoder		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return dict(self.encoder						,   **self.added_tokens_encoder		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if token in self.cache:
					return self.cache[token]
				__magic_name__		:		List[Any]	   =     tuple(_a		)
				__magic_name__		:		List[Any]	   =     tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
				__magic_name__		:		Any	   =     get_pairs(_a		)

				if not pairs:
					return token

				while True:
					__magic_name__		:		str	   =     min(_a						,   key=lambda _a		: self.bpe_ranks.get(_a						,   float("inf"		)		)		)
					if bigram not in self.bpe_ranks:
						break
					__magic_name__   ,    __magic_name__		:		List[str]	   =     bigram
					__magic_name__		:		List[str]	   =     []
					__magic_name__		:		List[str]	   =     0
					while i < len(_a		):
						try:
							__magic_name__		:		Any	   =     word.index(_a						,   _a		)
						except ValueError:
							new_word.extend(word[i:]		)
							break
						else:
							new_word.extend(word[i:j]		)
							__magic_name__		:		Tuple	   =     j

						if word[i] == first and i < len(_a		) - 1 and word[i + 1] == second:
							new_word.append(first + second		)
							i += 2
						else:
							new_word.append(word[i]		)
							i += 1
					__magic_name__		:		Union[str, Any]	   =     tuple(_a		)
					__magic_name__		:		Optional[int]	   =     new_word
					if len(_a		) == 1:
						break
					else:
						__magic_name__		:		List[Any]	   =     get_pairs(_a		)
				__magic_name__		:		Optional[int]	   =     "@@ ".join(_a		)
				__magic_name__		:		Tuple	   =     word[:-4]
				__magic_name__		:		str	   =     word
				return word







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     []

				__magic_name__		:		Dict	   =     re.findall(r"\S+\n?"						,   _a		)

				for token in words:
					split_tokens.extend(list(self.bpe(_a		).split(" "		)		)		)
				return split_tokens







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.encoder.get(_a						,   self.encoder.get(self.unk_token		)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.decoder.get(_a						,   self.unk_token		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Tuple	   =     " ".join(_a		).replace("@@ "						,   ""		).strip()
				return out_string







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				if not os.path.isdir(_a		):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
					return
				__magic_name__		:		Optional[int]	   =     os.path.join(
				    _a						,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
				__magic_name__		:		Union[str, Any]	   =     os.path.join(
				    _a						,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(_a		):
					copyfile(self.vocab_file						,   _a		)

				if os.path.abspath(self.merges_file		) != os.path.abspath(_a		):
					copyfile(self.merges_file						,   _a		)

				return out_vocab_file, out_merge_file







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if isinstance(_a						,   _a		):
					try:
						with open(_a						,   "r"						,   encoding="utf-8"		) as fd:
							self.add_from_file(_a		)
					except FileNotFoundError as fnfe:
						raise fnfe
					except UnicodeError:
						raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset'''		)
					return

				__magic_name__		:		List[Any]	   =     f.readlines()
				for lineTmp in lines:
					__magic_name__		:		Optional[Any]	   =     lineTmp.strip()
					__magic_name__		:		Union[str, Any]	   =     line.rfind(" "		)
					if idx == -1:
						raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'"		)
					__magic_name__		:		Optional[int]	   =     line[:idx]
					__magic_name__		:		Dict	   =     len(self.encoder		)
