






'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_mbart import MBartTokenizer
else:
  a_ 		=       None


a_ 		=       logging.get_logger(__name__)


a_ 		=       {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

a_ 		=       {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

a_ 		=       {
    'facebook/mbart-large-en-ro': 1_0_2_4,
    'facebook/mbart-large-cc25': 1_0_2_4,
}

# fmt: off
a_ 		=       ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']






class    __SCREAMING_SNAKE_CASE					(	snake_case_ ):
 snake_case_     							=							VOCAB_FILES_NAMES
 snake_case_     							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case_     							=							PRETRAINED_VOCAB_FILES_MAP
 snake_case_     							=							["""input_ids""", """attention_mask"""]
 snake_case_     							=							MBartTokenizer

 snake_case_     							=							[]
 snake_case_     							=							[]




 def __init__(					self		:							int    ,					__lowercase		:							int=None    ,					__lowercase		:							Dict=None    ,					__lowercase		:							Dict="<s>"    ,					__lowercase		:							List[str]="</s>"    ,					__lowercase		:							str="</s>"    ,					__lowercase		:							int="<s>"    ,					__lowercase		:							Tuple="<unk>"    ,					__lowercase		:							Dict="<pad>"    ,					__lowercase		:							Tuple="<mask>"    ,					__lowercase		:							int=None    ,					__lowercase		:							int=None    ,					__lowercase		:							Union[str, Any]=None    ,					**__lowercase		:							Dict    ,					)		->  Dict:
       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE__   : List[str]													=AddedToken(__lowercase    ,					lstrip=__lowercase    ,					rstrip=__lowercase					) if isinstance(__lowercase    ,					__lowercase					) else mask_token

       super().__init__(
           vocab_file=__lowercase    ,					tokenizer_file=__lowercase    ,					bos_token=__lowercase    ,					eos_token=__lowercase    ,					sep_token=__lowercase    ,					cls_token=__lowercase    ,					unk_token=__lowercase    ,					pad_token=__lowercase    ,					mask_token=__lowercase    ,					src_lang=__lowercase    ,					tgt_lang=__lowercase    ,					additional_special_tokens=__lowercase    ,					**__lowercase    ,					)

       SCREAMING_SNAKE_CASE__   : Dict													=vocab_file
       SCREAMING_SNAKE_CASE__   : List[str]													=False if not self.vocab_file else True

       SCREAMING_SNAKE_CASE__   : int													=FAIRSEQ_LANGUAGE_CODES.copy()

       if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             _additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in _additional_special_tokens]					)

       self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}					)
       SCREAMING_SNAKE_CASE__   : str													={
           lang_code: self.convert_tokens_to_ids(__lowercase					) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       SCREAMING_SNAKE_CASE__   : int													=src_lang if src_lang is not None else '''en_XX'''
       SCREAMING_SNAKE_CASE__   : List[Any]													=self.convert_tokens_to_ids(self._src_lang					)
       SCREAMING_SNAKE_CASE__   : Tuple													=tgt_lang
       self.set_src_lang_special_tokens(self._src_lang					)




 @property
 def    __magic_name__		(					self		:							int					)		->  str:
       return self._src_lang




 @src_lang.setter
 def    __magic_name__		(					self		:							List[Any]    ,					__lowercase		:							str					)		->  None:
       SCREAMING_SNAKE_CASE__   : Tuple													=new_src_lang
       self.set_src_lang_special_tokens(self._src_lang					)




 def    __magic_name__		(					self		:							Tuple    ,					__lowercase		:							Tuple    ,					__lowercase		:							List[str] = None					)		->  List[int]:
       if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




 def    __magic_name__		(					self		:							List[Any]    ,					__lowercase		:							Tuple    ,					__lowercase		:							Tuple = None					)		->  List[int]:
       SCREAMING_SNAKE_CASE__   : Tuple													=[self.sep_token_id]
       SCREAMING_SNAKE_CASE__   : str													=[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]




 def    __magic_name__		(					self		:							Union[str, Any]    ,					__lowercase		:							Any    ,					__lowercase		:							List[Any]    ,					__lowercase		:							Optional[int]    ,					__lowercase		:							Dict    ,					**__lowercase		:							Optional[Any]					)		->  List[Any]:
       if src_lang is None or tgt_lang is None:
             raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''					)
       SCREAMING_SNAKE_CASE__   : Tuple													=src_lang
       SCREAMING_SNAKE_CASE__   : Dict													=self(__lowercase    ,					add_special_tokens=__lowercase    ,					return_tensors=__lowercase    ,					**__lowercase					)
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=self.convert_tokens_to_ids(__lowercase					)
       SCREAMING_SNAKE_CASE__   : Tuple													=tgt_lang_id
       return inputs




 def    __magic_name__		(					self		:							Any    ,					__lowercase		:							int    ,					__lowercase		:							Any = "en_XX"    ,					__lowercase		:							List[Any] = None    ,					__lowercase		:							Dict = "ro_RO"    ,					**__lowercase		:							str    ,					)		->  BatchEncoding:
       SCREAMING_SNAKE_CASE__   : List[str]													=src_lang
       SCREAMING_SNAKE_CASE__   : str													=tgt_lang
       return super().prepare_seqaseq_batch(__lowercase    ,					__lowercase    ,					**__lowercase					)




 def    __magic_name__		(					self		:							Optional[int]					)		->  Dict:
       return self.set_src_lang_special_tokens(self.src_lang					)




 def    __magic_name__		(					self		:							Optional[int]					)		->  Optional[Any]:
       return self.set_tgt_lang_special_tokens(self.tgt_lang					)




 def    __magic_name__		(					self		:							int    ,					__lowercase		:							Union[str, Any]					)		->  None:
       SCREAMING_SNAKE_CASE__   : Optional[int]													=self.convert_tokens_to_ids(__lowercase					)
       SCREAMING_SNAKE_CASE__   : Tuple													=[]
       SCREAMING_SNAKE_CASE__   : Tuple													=[self.eos_token_id, self.cur_lang_code]

       SCREAMING_SNAKE_CASE__   : List[str]													=self.convert_ids_to_tokens(self.prefix_tokens					)
       SCREAMING_SNAKE_CASE__   : str													=self.convert_ids_to_tokens(self.suffix_tokens					)

       SCREAMING_SNAKE_CASE__   : List[Any]													=processors.TemplateProcessing(
           single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str    ,					pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str    ,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,					self.prefix_tokens + self.suffix_tokens					)					)    ,					)




 def    __magic_name__		(					self		:							Any    ,					__lowercase		:							Dict					)		->  None:
       SCREAMING_SNAKE_CASE__   : str													=self.convert_tokens_to_ids(__lowercase					)
       SCREAMING_SNAKE_CASE__   : Optional[int]													=[]
       SCREAMING_SNAKE_CASE__   : List[str]													=[self.eos_token_id, self.cur_lang_code]

       SCREAMING_SNAKE_CASE__   : Dict													=self.convert_ids_to_tokens(self.prefix_tokens					)
       SCREAMING_SNAKE_CASE__   : Optional[int]													=self.convert_ids_to_tokens(self.suffix_tokens					)

       SCREAMING_SNAKE_CASE__   : List[Any]													=processors.TemplateProcessing(
           single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str    ,					pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str    ,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,					self.prefix_tokens + self.suffix_tokens					)					)    ,					)




 def    __magic_name__		(					self		:							Any    ,					__lowercase		:							int    ,					__lowercase		:							List[str] = None					)		->  Tuple[str]:
       if not self.can_save_slow_tokenizer:
             raise ValueError(
                 '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                 '''tokenizer.'''					)

       if not os.path.isdir(__lowercase					):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory."					)
             return
       SCREAMING_SNAKE_CASE__   : List[Any]													=os.path.join(
           __lowercase    ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

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

       return (out_vocab_file,)

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


__A				=pytest.mark.integration




@require_faiss
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			Dataset.from_dict({"filename": ["my_name-train" + "_" + str(lowercase ) for x in np.arange(30 ).tolist()]} )
					return dset



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					import faiss

					lowerCamelCase_						=			self._create_dummy_dataset()
					lowerCamelCase_						=			dset.map(
					    lambda lowercase						,  lowercase : {"vecs": i * np.ones(5						,  dtype=np.floataa )}						,  with_indices=lowercase						,  keep_in_memory=lowercase )
					lowerCamelCase_						=			dset.add_faiss_index("vecs"						,  batch_size=100						,  metric_type=faiss.METRIC_INNER_PRODUCT )
					lowerCamelCase_	,    lowerCamelCase_						=			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 )				->       Dict:
					import faiss

					lowerCamelCase_						=			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						,  )
					lowerCamelCase_	,    lowerCamelCase_						=			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 )				->       Optional[int]:
					import faiss

					lowerCamelCase_						=			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=lowercase ) as tmp_file:
									dset.save_faiss_index("vecs"						,  tmp_file.name )
									dset.load_faiss_index("vecs2"						,  tmp_file.name )
					os.unlink(tmp_file.name )

					lowerCamelCase_	,    lowerCamelCase_						=			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 )				->       Union[str, Any]:
					lowerCamelCase_						=			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(lowercase						,  partial(dset.get_nearest_examples						,  "vecs2"						,  np.ones(5						,  dtype=np.floataa ) ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					from elasticsearch import Elasticsearch

					lowerCamelCase_						=			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:
									lowerCamelCase_						=			{"acknowledged": True}
									mocked_bulk.return_value([(True, None)] * 30 )
									lowerCamelCase_						=			{"hits": {"hits": [{"_score": 1, "_id": 29}]}}
									lowerCamelCase_						=			Elasticsearch()

									dset.add_elasticsearch_index("filename"						,  es_client=lowercase )
									lowerCamelCase_	,    lowerCamelCase_						=			dset.get_nearest_examples("filename"						,  "my_name-train_29" )
									self.assertEqual(examples["filename"][0]						,  "my_name-train_29" )




@require_faiss
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					import faiss

					lowerCamelCase_						=			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
					lowerCamelCase_						=			np.zeros(5						,  dtype=np.floataa )
					lowerCamelCase_						=			1
					lowerCamelCase_	,    lowerCamelCase_						=			index.search(lowercase )
					self.assertRaises(lowercase						,  index.search						,  query.reshape(-1						,  1 ) )
					self.assertGreater(scores[0]						,  0 )
					self.assertEqual(indices[0]						,  1 )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					import faiss

					lowerCamelCase_						=			FaissIndex(string_factory="Flat" )
					index.add_vectors(np.eye(5						,  dtype=np.floataa ) )
					self.assertIsInstance(index.faiss_index						,  faiss.IndexFlat )
					lowerCamelCase_						=			FaissIndex(string_factory="LSH" )
					index.add_vectors(np.eye(5						,  dtype=np.floataa ) )
					self.assertIsInstance(index.faiss_index						,  faiss.IndexLSH )
					with self.assertRaises(lowercase ):
									lowerCamelCase_						=			FaissIndex(string_factory="Flat"						,  custom_index=faiss.IndexFlat(5 ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					import faiss

					lowerCamelCase_						=			faiss.IndexFlat(5 )
					lowerCamelCase_						=			FaissIndex(custom_index=lowercase )
					index.add_vectors(np.eye(5						,  dtype=np.floataa ) )
					self.assertIsInstance(index.faiss_index						,  faiss.IndexFlat )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					import faiss

					lowerCamelCase_						=			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=lowercase ) as tmp_file:
									index.save(tmp_file.name )
									lowerCamelCase_						=			FaissIndex.load(tmp_file.name )
					os.unlink(tmp_file.name )

					lowerCamelCase_						=			np.zeros(5						,  dtype=np.floataa )
					lowerCamelCase_						=			1
					lowerCamelCase_	,    lowerCamelCase_						=			index.search(lowercase )
					self.assertGreater(scores[0]						,  0 )
					self.assertEqual(indices[0]						,  1 )





@require_faiss
def 			lowerCamelCase_ (				lowerCamelCase__    ):
				import faiss

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

				lowerCamelCase_						=			"index.faiss"
				lowerCamelCase_						=			F'mock://{index_name}'
				index.save(lowerCamelCase__	, storage_options=mockfs.storage_options    )
				lowerCamelCase_						=			FaissIndex.load(lowerCamelCase__	, storage_options=mockfs.storage_options    )

				lowerCamelCase_						=			np.zeros(5	, dtype=np.floataa    )
				lowerCamelCase_						=			1
				lowerCamelCase_	,    lowerCamelCase_						=			index.search(lowerCamelCase__    )
				assert scores[0] > 0
				assert indices[0] == 1





@require_elasticsearch
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					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:
									lowerCamelCase_						=			Elasticsearch()
									lowerCamelCase_						=			{"acknowledged": True}
									lowerCamelCase_						=			ElasticSearchIndex(es_client=lowercase )
									mocked_bulk.return_value([(True, None)] * 3 )
									index.add_documents(["foo", "bar", "foobar"] )

									# single query
									lowerCamelCase_						=			"foo"
									lowerCamelCase_						=			{"hits": {"hits": [{"_score": 1, "_id": 0}]}}
									lowerCamelCase_	,    lowerCamelCase_						=			index.search(lowercase )
									self.assertEqual(scores[0]						,  1 )
									self.assertEqual(indices[0]						,  0 )

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

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

									# batched queries with timeout
									lowerCamelCase_						=			["foo", "bar", "foobar"]
									lowerCamelCase_						=			{"hits": {"hits": [{"_score": 1, "_id": 1}]}}
									lowerCamelCase_	,    lowerCamelCase_						=			index.search_batch(lowercase						,  request_timeout=30 )
									lowerCamelCase_						=			[scores[0] for scores in total_scores]
									lowerCamelCase_						=			[indices[0] for indices in total_indices]
									self.assertGreater(np.min(lowercase )						,  0 )
									self.assertListEqual([1, 1, 1]						,  lowercase )


