



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							__SCREAMING_SNAKE_CASE     =	"""platform"""

							import jax
							import jax.numpy as jnp

							from transformers import AutoTokenizer
							from transformers.models.mbart.modeling_flax_mbart import (
							    FlaxMBartForConditionalGeneration,
							    FlaxMBartForQuestionAnswering,
							    FlaxMBartForSequenceClassification,
							    FlaxMBartModel,
							    shift_tokens_right,
							)




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase=None			,     _lowerCamelCase=None			,     _lowerCamelCase=None			,     _lowerCamelCase=None			,     _lowerCamelCase=None			,     _lowerCamelCase=None			,     ):
			if attention_mask is None:
						A				:       List[Any]														=    np.where(input_ids != config.pad_token_id			,     1			,     0     )
			if decoder_attention_mask is None:
						A				:       Tuple														=    np.where(decoder_input_ids != config.pad_token_id			,     1			,     0     )
			if head_mask is None:
						A				:       str														=    np.ones((config.encoder_layers, config.encoder_attention_heads)     )
			if decoder_head_mask is None:
						A				:       int														=    np.ones((config.decoder_layers, config.decoder_attention_heads)     )
			if cross_attn_head_mask is None:
						A				:       Dict														=    np.ones((config.decoder_layers, config.decoder_attention_heads)     )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			}

class 						lowerCamelCase_     (			unittest.TestCase    ):






						'''simple docstring'''







						def __init__(						self   :      List[Any]						,						__lowerCamelCase   :      int						,						__lowerCamelCase   :      Optional[Any]=13						,						__lowerCamelCase   :      int=7						,						__lowerCamelCase   :      Optional[int]=True						,						__lowerCamelCase   :      str=False						,						__lowerCamelCase   :      Optional[int]=99						,						__lowerCamelCase   :      Tuple=16						,						__lowerCamelCase   :      List[str]=2						,						__lowerCamelCase   :      Optional[Any]=4						,						__lowerCamelCase   :      List[str]=4						,						__lowerCamelCase   :      List[Any]="gelu"						,						__lowerCamelCase   :      Union[str, Any]=0.1						,						__lowerCamelCase   :      Dict=0.1						,						__lowerCamelCase   :      int=32						,						__lowerCamelCase   :      List[Any]=2						,						__lowerCamelCase   :      str=1						,						__lowerCamelCase   :      Union[str, Any]=0						,						__lowerCamelCase   :      Union[str, Any]=2						,						__lowerCamelCase   :      int=0.02						,						)   ->					Dict:
									A				:       Any														=    parent
									A				:       Any														=    batch_size
									A				:       int														=    seq_length
									A				:       int														=    is_training
									A				:       Optional[Any]														=    use_labels
									A				:       Dict														=    vocab_size
									A				:       str														=    hidden_size
									A				:       Tuple														=    num_hidden_layers
									A				:       List[str]														=    num_attention_heads
									A				:       List[str]														=    intermediate_size
									A				:       Any														=    hidden_act
									A				:       str														=    hidden_dropout_prob
									A				:       Tuple														=    attention_probs_dropout_prob
									A				:       int														=    max_position_embeddings
									A				:       Optional[Any]														=    eos_token_id
									A				:       Optional[Any]														=    pad_token_id
									A				:       int														=    bos_token_id
									A				:       Union[str, Any]														=    decoder_start_token_id
									A				:       List[str]														=    initializer_range







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Optional[int]:
									A				:       Optional[Any]														=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						,						self.vocab_size			)						,						3						,						self.vocab_size			)
									A				:       Any														=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						,						dtype=np.intaa			))						,						-1			)

									A				:       Dict														=    shift_tokens_right(UpperCAmelCase_						,						1			)

									A				:       Any														=    MBartConfig(
									    vocab_size=self.vocab_size						,						d_model=self.hidden_size						,						encoder_layers=self.num_hidden_layers						,						decoder_layers=self.num_hidden_layers						,						encoder_attention_heads=self.num_attention_heads						,						decoder_attention_heads=self.num_attention_heads						,						encoder_ffn_dim=self.intermediate_size						,						decoder_ffn_dim=self.intermediate_size						,						dropout=self.hidden_dropout_prob						,						attention_dropout=self.attention_probs_dropout_prob						,						max_position_embeddings=self.max_position_embeddings						,						eos_token_id=self.eos_token_id						,						bos_token_id=self.bos_token_id						,						pad_token_id=self.pad_token_id						,						decoder_start_token_id=self.decoder_start_token_id						,						initializer_range=self.initializer_range						,						use_cache=UpperCAmelCase_						,						)
									A				:       List[str]														=    prepare_mbart_inputs_dict(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_			)
									return config, inputs_dict







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Tuple:
									A				:       Optional[Any]														=    self.prepare_config_and_inputs()
									return config, inputs_dict







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						__lowerCamelCase   :      Tuple						,						__lowerCamelCase   :      Tuple						,						__lowerCamelCase   :      Optional[int]			)   ->					Dict:
									A				:       Tuple														=    20
									A				:       Optional[Any]														=    model_class_name(UpperCAmelCase_			)

									A				:       Dict														=    model.encode(inputs_dict["input_ids"]			)

									A				:       int														=    (
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

									A				:       Optional[Any]														=    model.init_cache(decoder_input_ids.shape[0]						,						UpperCAmelCase_						,						UpperCAmelCase_			)
									A				:       Any														=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,						dtype="i4"			)

									A				:       List[str]														=    jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]						,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,						)
									A				:       Optional[Any]														=    model.decode(
									    decoder_input_ids[:, :-1]						,						UpperCAmelCase_						,						decoder_attention_mask=UpperCAmelCase_						,						past_key_values=UpperCAmelCase_						,						decoder_position_ids=UpperCAmelCase_						,						)

									A				:       Any														=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,						dtype="i4"			)
									A				:       List[str]														=    model.decode(
									    decoder_input_ids[:, -1:]						,						UpperCAmelCase_						,						decoder_attention_mask=UpperCAmelCase_						,						past_key_values=outputs_cache.past_key_values						,						decoder_position_ids=UpperCAmelCase_						,						)

									A				:       str														=    model.decode(UpperCAmelCase_						,						UpperCAmelCase_			)

									A				:       Union[str, Any]														=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
									self.parent.assertTrue(diff < 1e-3						,						msg=F"""Max diff is {diff}"""			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						__lowerCamelCase   :      List[str]						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Dict			)   ->					Union[str, Any]:
									A				:       int														=    20
									A				:       int														=    model_class_name(UpperCAmelCase_			)

									A				:       List[Any]														=    model.encode(inputs_dict["input_ids"]			)

									A				:       Dict														=    (
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

									A				:       List[str]														=    jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
									    ]						,						axis=-1						,						)

									A				:       Dict														=    model.init_cache(decoder_input_ids.shape[0]						,						UpperCAmelCase_						,						UpperCAmelCase_			)
									A				:       List[Any]														=    jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]						,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,						)

									A				:       Any														=    model.decode(
									    decoder_input_ids[:, :-1]						,						UpperCAmelCase_						,						decoder_attention_mask=UpperCAmelCase_						,						past_key_values=UpperCAmelCase_						,						decoder_position_ids=UpperCAmelCase_						,						)
									A				:       Union[str, Any]														=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,						dtype="i4"			)
									A				:       List[str]														=    model.decode(
									    decoder_input_ids[:, -1:]						,						UpperCAmelCase_						,						past_key_values=outputs_cache.past_key_values						,						decoder_attention_mask=UpperCAmelCase_						,						decoder_position_ids=UpperCAmelCase_						,						)

									A				:       List[Any]														=    model.decode(UpperCAmelCase_						,						UpperCAmelCase_						,						decoder_attention_mask=UpperCAmelCase_			)

									A				:       int														=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
									self.parent.assertTrue(diff < 1e-3						,						msg=F"""Max diff is {diff}"""			)







@require_flax
class 						lowerCamelCase_     (			unittest.TestCase    ):






						'''simple docstring'''
						a__ =	99







						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					int:
									A				:       Dict														=    np.array(
									    [
									        [71, 82, 18, 33, 46, 91, 2],
									        [68, 34, 26, 58, 30, 82, 2],
									        [5, 97, 17, 39, 94, 40, 2],
									        [76, 83, 94, 25, 70, 78, 2],
									        [87, 59, 41, 35, 48, 66, 2],
									        [55, 13, 16, 58, 5, 2, 1],  # note padding
									        [64, 27, 31, 51, 12, 75, 2],
									        [52, 64, 86, 17, 83, 39, 2],
									        [48, 61, 9, 24, 71, 82, 2],
									        [26, 1, 60, 48, 22, 13, 2],
									        [21, 5, 62, 28, 14, 76, 2],
									        [45, 98, 37, 86, 59, 48, 2],
									        [70, 70, 50, 9, 28, 0, 2],
									    ]						,						dtype=np.intaa						,						)

									A				:       Dict														=    input_ids.shape[0]
									A				:       Optional[int]														=    MBartConfig(
									    vocab_size=self.vocab_size						,						d_model=24						,						encoder_layers=2						,						decoder_layers=2						,						encoder_attention_heads=2						,						decoder_attention_heads=2						,						encoder_ffn_dim=32						,						decoder_ffn_dim=32						,						max_position_embeddings=48						,						eos_token_id=2						,						pad_token_id=1						,						bos_token_id=0						,						)
									return config, input_ids, batch_size







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Tuple:
									A				:       Tuple														=    self._get_config_and_data()
									A				:       Union[str, Any]														=    FlaxMBartForSequenceClassification(UpperCAmelCase_			)
									A				:       Tuple														=    model(input_ids=UpperCAmelCase_						,						decoder_input_ids=UpperCAmelCase_			)
									A				:       Tuple														=    (batch_size, config.num_labels)
									self.assertEqual(outputs["logits"].shape						,						UpperCAmelCase_			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Optional[Any]:
									A				:       Union[str, Any]														=    self._get_config_and_data()
									A				:       List[Any]														=    FlaxMBartForQuestionAnswering(UpperCAmelCase_			)
									A				:       Optional[Any]														=    model(input_ids=UpperCAmelCase_			)

									self.assertEqual(outputs["start_logits"].shape						,						input_ids.shape			)
									self.assertEqual(outputs["end_logits"].shape						,						input_ids.shape			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					List[Any]:
									A				:       List[Any]														=    self._get_config_and_data()
									A				:       Optional[Any]														=    FlaxMBartForConditionalGeneration(UpperCAmelCase_			)
									A				:       Dict														=    lm_model(input_ids=UpperCAmelCase_			)
									A				:       List[str]														=    (batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs["logits"].shape						,						UpperCAmelCase_			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Dict:
									A				:       List[str]														=    MBartConfig(
									    vocab_size=self.vocab_size						,						d_model=14						,						encoder_layers=2						,						decoder_layers=2						,						encoder_attention_heads=2						,						decoder_attention_heads=2						,						encoder_ffn_dim=8						,						decoder_ffn_dim=8						,						max_position_embeddings=48						,						)
									A				:       Tuple														=    FlaxMBartForConditionalGeneration(UpperCAmelCase_			)
									A				:       Optional[Any]														=    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,						dtype=np.intaa			)
									A				:       Tuple														=    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,						dtype=np.intaa			)
									A				:       Union[str, Any]														=    lm_model(input_ids=UpperCAmelCase_						,						decoder_input_ids=UpperCAmelCase_			)
									A				:       Optional[Any]														=    (*summary.shape, config.vocab_size)
									self.assertEqual(outputs["logits"].shape						,						UpperCAmelCase_			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Dict:
									A				:       Any														=    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,						dtype=np.intaa			)
									A				:       Optional[Any]														=    shift_tokens_right(UpperCAmelCase_						,						1			)
									A				:       List[str]														=    np.equal(UpperCAmelCase_						,						1			).astype(np.floataa			).sum()
									A				:       Dict														=    np.equal(UpperCAmelCase_						,						1			).astype(np.floataa			).sum()
									self.assertEqual(shifted.shape						,						input_ids.shape			)
									self.assertEqual(UpperCAmelCase_						,						n_pad_before - 1			)
									self.assertTrue(np.equal(shifted[:, 0]						,						2			).all()			)







@require_flax
class 						lowerCamelCase_     (			a_    ,unittest.TestCase    ,a_    ):






						'''simple docstring'''
						a__ =	True
						a__ =	(
						    (
						        FlaxMBartModel,
						        FlaxMBartForConditionalGeneration,
						        FlaxMBartForSequenceClassification,
						        FlaxMBartForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)
						a__ =	(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()







						def       SCREAMING_SNAKE_CASE__		(						self   :      Dict			)   ->					int:
									A				:       Dict														=    FlaxMBartModelTester(self			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					int:
									A				:       Optional[Any]														=    self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					str:
									A				:       List[Any]														=    self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward_with_attn_mask(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Any:
									A				:       Any														=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__			):
															A				:       Optional[Any]														=    self._prepare_for_class(UpperCAmelCase_						,						UpperCAmelCase_			)
															A				:       Dict														=    model_class(UpperCAmelCase_			)

															@jax.jit
															def encode_jitted(__lowerCamelCase   :      List[str]						,						__lowerCamelCase   :      List[Any]=None						,						**__lowerCamelCase   :      str			):
																		return model.encode(input_ids=UpperCAmelCase_						,						attention_mask=UpperCAmelCase_			)

															with self.subTest("JIT Enabled"			):
																		A				:       List[Any]														=    encode_jitted(**UpperCAmelCase_			).to_tuple()

															with self.subTest("JIT Disabled"			):
																		with jax.disable_jit():
																					A				:       List[Any]														=    encode_jitted(**UpperCAmelCase_			).to_tuple()

															self.assertEqual(len(UpperCAmelCase_			)						,						len(UpperCAmelCase_			)			)
															for jitted_output, output in zip(UpperCAmelCase_						,						UpperCAmelCase_			):
																		self.assertEqual(jitted_output.shape						,						output.shape			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					int:
									A				:       Union[str, Any]														=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__			):
															A				:       int														=    model_class(UpperCAmelCase_			)
															A				:       Tuple														=    model.encode(inputs_dict["input_ids"]						,						inputs_dict["attention_mask"]			)

															A				:       Optional[int]														=    {
															    'decoder_input_ids': inputs_dict['decoder_input_ids'],
															    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
															    'encoder_outputs': encoder_outputs,
															}

															@jax.jit
															def decode_jitted(__lowerCamelCase   :      Optional[Any]						,						__lowerCamelCase   :      Union[str, Any]						,						__lowerCamelCase   :      str			):
																		return model.decode(
																		    decoder_input_ids=UpperCAmelCase_						,						decoder_attention_mask=UpperCAmelCase_						,						encoder_outputs=UpperCAmelCase_						,						)

															with self.subTest("JIT Enabled"			):
																		A				:       Any														=    decode_jitted(**UpperCAmelCase_			).to_tuple()

															with self.subTest("JIT Disabled"			):
																		with jax.disable_jit():
																					A				:       List[Any]														=    decode_jitted(**UpperCAmelCase_			).to_tuple()

															self.assertEqual(len(UpperCAmelCase_			)						,						len(UpperCAmelCase_			)			)
															for jitted_output, output in zip(UpperCAmelCase_						,						UpperCAmelCase_			):
																		self.assertEqual(jitted_output.shape						,						output.shape			)







						@slow
						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Any:







									for model_class_name in self.all_model_classes:
												A				:       Optional[int]														=    model_class_name.from_pretrained("facebook/mbart-large-cc25"						,						from_pt=UpperCAmelCase_			)
												# FlaxMBartForSequenceClassification expects eos token in input_ids
												A				:       List[str]														=    np.ones((1, 1)			) * model.config.eos_token_id
												A				:       str														=    model(UpperCAmelCase_			)
												self.assertIsNotNone(UpperCAmelCase_			)







@require_flax
@require_sentencepiece
@require_tokenizers
class 						lowerCamelCase_     (			unittest.TestCase    ):






						'''simple docstring'''
						a__ =	[
						    " UN Chief Says There Is No Military Solution in Syria",
						]
						a__ =	[
						    "Şeful ONU declară că nu există o soluţie militară în Siria",
						]
						a__ =	"facebook/mbart-large-en-ro"







						@cached_property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					List[str]:
									return AutoTokenizer.from_pretrained(self.model_name			)







						@cached_property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					List[str]:
									A				:       Dict														=    FlaxMBartForConditionalGeneration.from_pretrained(self.model_name						,						from_pt=UpperCAmelCase_			)
									return model







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]						,						**__lowerCamelCase   :      List[str]			)   ->					Dict:
									A				:       Union[str, Any]														=    self.translate_src_text(**UpperCAmelCase_			)
									self.assertListEqual(self.expected_text						,						UpperCAmelCase_			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple						,						**__lowerCamelCase   :      Tuple			)   ->					Optional[Any]:
									A				:       Union[str, Any]														=    self.tokenizer(self.src_text						,						**UpperCAmelCase_						,						return_tensors="np"			)
									A				:       Tuple														=    self.model.generate(
									    model_inputs.input_ids						,						attention_mask=model_inputs.attention_mask						,						decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"]						,						early_stopping=UpperCAmelCase_						,						num_beams=2						,						).sequences
									A				:       Any														=    self.tokenizer.batch_decode(UpperCAmelCase_						,						skip_special_tokens=UpperCAmelCase_			)
									return generated_words







						@slow
						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					List[str]:
									self._assert_generated_batch_equal_expected()
'''simple docstring'''





import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast






def SCREAMING_SNAKE_CASE__  (				snake_case       :				List[Any]				,	snake_case       :				List[str]=100				,	snake_case       :				Any=" "  )   ->				Any:







     """simple docstring"""
     a					:							Union[str, Any]							= text.split(snake_case  )
     return [character.join(text[i : i + n]  ).strip() for i in range(0				,	len(snake_case  )				,	snake_case  )]






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Optional[int]  )   ->				Union[str, Any]:







     """simple docstring"""
     a							,			a					:							int							= [], []
     for title, text in zip(documents['title']				,	documents['text']  ):
          if text is not None:
               for passage in split_text(snake_case  ):
                    titles.append(title if title is not None else ''  )
                    texts.append(snake_case  )
     return {"title": titles, "text": texts}






def SCREAMING_SNAKE_CASE__  (				snake_case       :				int				,	snake_case       :				str				,	snake_case       :				List[Any]				,	snake_case       :				Dict				,	snake_case       :				Union[str, Any]				,	snake_case       :				int  )   ->				Dict:







     """simple docstring"""
     a					:							Tuple							= load_dataset(
         'csv'				,	data_files=[csv_path]				,	split='train'				,	delimiter='\t'				,	column_names=['title', 'text']  )
     a					:							Union[str, Any]							= kb_dataset.map(
         snake_case				,	batched=snake_case				,	num_proc=1  )  # if you want you can load already splitted csv.
     a					:							Optional[Any]							= [kb_dataset.shard(snake_case				,	snake_case				,	contiguous=snake_case  ) for i in range(snake_case  )]
     a					:							List[Any]							= kb_list[process_num]

     a					:							Dict							= 'data_' + str(snake_case  )
     a					:							int							= os.path.join(snake_case				,	snake_case  )

     a					:							Any							= DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'  )
     a					:							List[str]							= ctx_encoder.to(device=snake_case  )

     def embed(
         snake_case       :				dict				,	snake_case       :				DPRContextEncoder				,	snake_case       :				DPRContextEncoderTokenizerFast				,	snake_case       :				Any  ) -> dict:
          a					:							Tuple							= ctx_tokenizer(
              documents['title']				,	documents['text']				,	truncation=snake_case				,	padding='longest'				,	return_tensors='pt'  )['input_ids']
          a					:							Any							= ctx_encoder(input_ids.to(device=snake_case  )				,	return_dict=snake_case  ).pooler_output
          return {"embeddings": embeddings.detach().cpu().numpy()}

     a					:							Union[str, Any]							= Features(
         {'text': Value('string'  ), 'title': Value('string'  ), 'embeddings': Sequence(Value('float32'  )  )}  )  # optional, save as float32 instead of float64 to save space

     a					:							List[str]							= data_shrad.map(
         partial(snake_case				,	ctx_encoder=snake_case				,	ctx_tokenizer=snake_case				,	device=snake_case  )				,	batched=snake_case				,	batch_size=16				,	features=snake_case				,	)
     dataset.save_to_disk(snake_case  )






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Any				,	snake_case       :				Any  )   ->				List[Any]:







     """simple docstring"""
     a					:							int							= []

     for shard_address in glob(str(snake_case  ) + '/*/'  ):
          data_shard_list.append(load_from_disk(snake_case  )  )

     a					:							Tuple							= concatenate_datasets(snake_case  )
     faiss.omp_set_num_threads(96  )

     a					:							List[str]							= faiss.IndexHNSWFlat(768				,	128				,	faiss.METRIC_INNER_PRODUCT  )
     concat.add_faiss_index('embeddings'				,	custom_index=snake_case  )
     concat.get_index('embeddings'  ).save(
         snake_case  )  # since we load the index in to memory,we can directly update the index in the disk

