






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  UpperCAmelCase__						(    lowerCamelCase	:			str			,lowerCamelCase	:			List[str]=1_00			,lowerCamelCase	:			Optional[Any]=" "    ):
   lowerCamelCase									=text.split(lowerCamelCase    )
   return [character.join(text[i : i + n]    ).strip() for i in range(0			,len(lowerCamelCase    )			,lowerCamelCase    )]






def  UpperCAmelCase__						(    lowerCamelCase	:			List[Any]    ):
   lowerCamelCase				,       lowerCamelCase									=[], []
   for title, text in zip(documents["""title"""]			,documents["""text"""]    ):
      if text is not None:
         for passage in split_text(lowerCamelCase    ):
            titles.append(title if title is not None else """"""    )
            texts.append(lowerCamelCase    )
   return {"title": titles, "text": texts}






def  UpperCAmelCase__						(    lowerCamelCase	:			Dict			,lowerCamelCase	:			Optional[Any]			,lowerCamelCase	:			List[str]			,lowerCamelCase	:			List[Any]			,lowerCamelCase	:			int			,lowerCamelCase	:			Any    ):
   lowerCamelCase									=load_dataset(
       """csv"""			,data_files=[csv_path]			,split="""train"""			,delimiter="""\t"""			,column_names=["""title""", """text"""]    )
   lowerCamelCase									=kb_dataset.map(
       lowerCamelCase			,batched=lowerCamelCase			,num_proc=1    )  # if you want you can load already splitted csv.
   lowerCamelCase									=[kb_dataset.shard(lowerCamelCase			,lowerCamelCase			,contiguous=lowerCamelCase    ) for i in range(lowerCamelCase    )]
   lowerCamelCase									=kb_list[process_num]

   lowerCamelCase									="""data_""" + str(lowerCamelCase    )
   lowerCamelCase									=os.path.join(lowerCamelCase			,lowerCamelCase    )

   lowerCamelCase									=DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""    )
   lowerCamelCase									=ctx_encoder.to(device=lowerCamelCase    )

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

   lowerCamelCase									=Features(
       {"""text""": Value("""string"""    ), """title""": Value("""string"""    ), """embeddings""": Sequence(Value("""float32"""    )    )}    )  # optional, save as float32 instead of float64 to save space

   lowerCamelCase									=data_shrad.map(
       partial(lowerCamelCase			,ctx_encoder=lowerCamelCase			,ctx_tokenizer=lowerCamelCase			,device=lowerCamelCase    )			,batched=lowerCamelCase			,batch_size=16			,features=lowerCamelCase			,)
   dataset.save_to_disk(lowerCamelCase    )







def  UpperCAmelCase__						(    lowerCamelCase	:			List[str]			,lowerCamelCase	:			str    ):
   lowerCamelCase									=[]

   for shard_address in glob(str(lowerCamelCase    ) + """/*/"""    ):
      data_shard_list.append(load_from_disk(lowerCamelCase    )    )

   lowerCamelCase									=concatenate_datasets(lowerCamelCase    )
   faiss.omp_set_num_threads(96    )

   lowerCamelCase									=faiss.IndexHNSWFlat(7_68			,1_28			,faiss.METRIC_INNER_PRODUCT    )
   concat.add_faiss_index("""embeddings"""			,custom_index=lowerCamelCase    )
   concat.get_index("""embeddings"""    ).save(
       lowerCamelCase    )  # since we load the index in to memory,we can directly update the index in the disk

"""simple docstring"""




import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
					pass

if is_speech_available():
					from transformers import EncodecFeatureExtractor, MusicgenProcessor


__lowerCAmelCase							:  Union[str, Any] 				=		random.Random()

def __snake_case (      UpperCamelCase						,					UpperCamelCase=1.0						,					UpperCamelCase=None						,					UpperCamelCase=None    )       ->    Tuple:


		"""simple docstring"""





		if rng is None:
				a__   		=     global_rng

		a__   		=     []
		for batch_idx in range(shape[0]    ):
				values.append([]    )
				for _ in range(shape[1]    ):
						values[-1].append(rng.random() * scale    )

		return values




@require_torch
@require_sentencepiece
class SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







	def    _UpperCamelCase							(  self :Optional[int]			)			->   List[str]:

			'''simple docstring'''






			a__   		=     '''facebook/musicgen-small'''
			a__   		=     tempfile.mkdtemp()


	def    _UpperCamelCase							(  self :Union[str, Any]       ,    **__magic_name__ :Union[str, Any]			)			->   str:

			'''simple docstring'''






			return TaTokenizer.from_pretrained(self.checkpoint       ,    **__magic_name__			)


	def    _UpperCamelCase							(  self :Optional[int]       ,    **__magic_name__ :Any			)			->   Optional[int]:

			'''simple docstring'''






			return EncodecFeatureExtractor.from_pretrained(self.checkpoint       ,    **__magic_name__			)


	def    _UpperCamelCase							(  self :Optional[int]			)			->   Optional[int]:

			'''simple docstring'''






			shutil.rmtree(self.tmpdirname			)


	def    _UpperCamelCase							(  self :Tuple			)			->   Any:

			'''simple docstring'''






			a__   		=     self.get_tokenizer()
			a__   		=     self.get_feature_extractor()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			processor.save_pretrained(self.tmpdirname			)
			a__   		=     MusicgenProcessor.from_pretrained(self.tmpdirname			)

			self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer.get_vocab()			)
			self.assertIsInstance(processor.tokenizer       ,    __magic_name__			)

			self.assertEqual(processor.feature_extractor.to_json_string()       ,    feature_extractor.to_json_string()			)
			self.assertIsInstance(processor.feature_extractor       ,    __magic_name__			)


	def    _UpperCamelCase							(  self :Dict			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     MusicgenProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()			)
			processor.save_pretrained(self.tmpdirname			)

			a__   		=     self.get_tokenizer(bos_token='''(BOS)'''       ,    eos_token='''(EOS)'''			)
			a__   		=     self.get_feature_extractor(do_normalize=__magic_name__       ,    padding_value=1.0			)

			a__   		=     MusicgenProcessor.from_pretrained(
			    self.tmpdirname       ,    bos_token='''(BOS)'''       ,    eos_token='''(EOS)'''       ,    do_normalize=__magic_name__       ,    padding_value=1.0			)

			self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab()			)
			self.assertIsInstance(processor.tokenizer       ,    __magic_name__			)

			self.assertEqual(processor.feature_extractor.to_json_string()       ,    feature_extractor_add_kwargs.to_json_string()			)
			self.assertIsInstance(processor.feature_extractor       ,    __magic_name__			)


	def    _UpperCamelCase							(  self :Any			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     floats_list((3, 1000)			)

			a__   		=     feature_extractor(__magic_name__       ,    return_tensors='''np'''			)
			a__   		=     processor(__magic_name__       ,    return_tensors='''np'''			)

			for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()       ,    input_processor[key].sum()       ,    delta=1e-2			)


	def    _UpperCamelCase							(  self :Tuple			)			->   Dict:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     '''This is a test string'''

			a__   		=     processor(text=__magic_name__			)

			a__   		=     tokenizer(__magic_name__			)

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key]			)


	def    _UpperCamelCase							(  self :Tuple			)			->   Optional[Any]:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			a__   		=     processor.batch_decode(sequences=__magic_name__			)
			a__   		=     tokenizer.batch_decode(__magic_name__			)

			self.assertListEqual(__magic_name__       ,    __magic_name__			)


	def    _UpperCamelCase							(  self :Optional[Any]			)			->   int:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			self.assertListEqual(
			    processor.model_input_names       ,    feature_extractor.model_input_names       ,    msg='''`processor` and `feature_extractor` model input names do not match'''       ,    )






	def    _UpperCamelCase							(  self :Any			)			->   List[str]:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor(padding_side='''left'''			)
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     [floats_list((1, x)			)[0] for x in range(5       ,    20       ,    5			)]
			a__   		=     processor(__magic_name__			).padding_mask

			a__   		=     np.asarray(floats_list((3, 20)			)			)[:, None, :]
			a__   		=     processor.batch_decode(__magic_name__       ,    padding_mask=__magic_name__			)

			self.assertIsInstance(__magic_name__       ,    __magic_name__			)

			for audio in decoded_audios:
					self.assertIsInstance(__magic_name__       ,    np.ndarray			)

			self.assertTrue(decoded_audios[0].shape == (1, 10)			)
			self.assertTrue(decoded_audios[1].shape == (1, 15)			)
			self.assertTrue(decoded_audios[2].shape == (1, 20)			)




