
import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset

_snake_case      = random.Random()

def  __lowerCamelCase					( _lowercase						,     _lowercase=1.0						,     _lowercase=None						,     _lowercase=None			)       ->      Union[str, Any]:
     if rng is None:
          UpperCamelCase						    =					global_rng

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

     return values
class       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""

 def __init__(						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=7					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=4_00					, SCREAMING_SNAKE_CASE__			:			str=20_00					, SCREAMING_SNAKE_CASE__			:			Dict=20_48					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=1_28					, SCREAMING_SNAKE_CASE__			:			List[str]=1					, SCREAMING_SNAKE_CASE__			:			List[str]=5_12					, SCREAMING_SNAKE_CASE__			:			int=30					, SCREAMING_SNAKE_CASE__			:			Dict=4_41_00					, ):






      """simple docstring"""



      UpperCamelCase						    =					parent
      UpperCamelCase						    =					batch_size
      UpperCamelCase						    =					min_seq_length
      UpperCamelCase						    =					max_seq_length
      UpperCamelCase						    =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      UpperCamelCase						    =					spectrogram_length
      UpperCamelCase						    =					feature_size
      UpperCamelCase						    =					num_audio_channels
      UpperCamelCase						    =					hop_length
      UpperCamelCase						    =					chunk_length
      UpperCamelCase						    =					sampling_rate

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      return {
          "spectrogram_length": self.spectrogram_length,
          "feature_size": self.feature_size,
          "num_audio_channels": self.num_audio_channels,
          "hop_length": self.hop_length,
          "chunk_length": self.chunk_length,
          "sampling_rate": self.sampling_rate,
      }






 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=False					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=False   ):






      """simple docstring"""



      def _flatten(SCREAMING_SNAKE_CASE__			:			Optional[Any]   ):
           return list(itertools.chain(*UpperCAmelCase__   )   )

      if equal_length:
           UpperCamelCase						    =					[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
      else:
           # make sure that inputs increase in size
           UpperCamelCase						    =					[
               floats_list((x, self.feature_size)   )
               for x in range(self.min_seq_length					, self.max_seq_length					, self.seq_length_diff   )
           ]
      if numpify:
           UpperCamelCase						    =					[np.asarray(UpperCAmelCase__   ) for x in speech_inputs]
      return speech_inputs




@require_torch
@require_torchaudio
class       _lowerCAmelCase  (		lowercase__			,	unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Dict       						=TvltFeatureExtractor

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					TvltFeatureExtractionTester(self   )

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class(**self.feat_extract_dict   )
      self.assertTrue(hasattr(UpperCAmelCase__					, 'spectrogram_length'   )   )
      self.assertTrue(hasattr(UpperCAmelCase__					, 'feature_size'   )   )
      self.assertTrue(hasattr(UpperCAmelCase__					, 'num_audio_channels'   )   )
      self.assertTrue(hasattr(UpperCAmelCase__					, 'hop_length'   )   )
      self.assertTrue(hasattr(UpperCAmelCase__					, 'chunk_length'   )   )
      self.assertTrue(hasattr(UpperCAmelCase__					, 'sampling_rate'   )   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class(**self.feat_extract_dict   )

      with tempfile.TemporaryDirectory() as tmpdirname:
           UpperCamelCase						    =					feat_extract_first.save_pretrained(UpperCAmelCase__   )[0]
           check_json_file_has_correct_format(UpperCAmelCase__   )
           UpperCamelCase						    =					self.feature_extraction_class.from_pretrained(UpperCAmelCase__   )

      UpperCamelCase						    =					feat_extract_first.to_dict()
      UpperCamelCase						    =					feat_extract_second.to_dict()
      UpperCamelCase						    =					dict_first.pop('mel_filters'   )
      UpperCamelCase						    =					dict_second.pop('mel_filters'   )
      self.assertTrue(np.allclose(UpperCAmelCase__					, UpperCAmelCase__   )   )
      self.assertEqual(UpperCAmelCase__					, UpperCAmelCase__   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class(**self.feat_extract_dict   )

      with tempfile.TemporaryDirectory() as tmpdirname:
           UpperCamelCase						    =					os.path.join(UpperCAmelCase__					, 'feat_extract.json'   )
           feat_extract_first.to_json_file(UpperCAmelCase__   )
           UpperCamelCase						    =					self.feature_extraction_class.from_json_file(UpperCAmelCase__   )

      UpperCamelCase						    =					feat_extract_first.to_dict()
      UpperCamelCase						    =					feat_extract_second.to_dict()
      UpperCamelCase						    =					dict_first.pop('mel_filters'   )
      UpperCamelCase						    =					dict_second.pop('mel_filters'   )
      self.assertTrue(np.allclose(UpperCAmelCase__					, UpperCAmelCase__   )   )
      self.assertEqual(UpperCAmelCase__					, UpperCAmelCase__   )

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class(**self.feat_extract_dict   )

      # create three inputs of length 800, 1000, and 1200
      UpperCamelCase						    =					[floats_list((1, x)   )[0] for x in range(8_00					, 14_00					, 2_00   )]
      UpperCamelCase						    =					[np.asarray(UpperCAmelCase__   ) for speech_input in speech_inputs]

      # Test not batched input
      UpperCamelCase						    =					feature_extractor(np_speech_inputs[0]					, return_tensors='np'					, sampling_rate=4_41_00   ).audio_values

      self.assertTrue(encoded_audios.ndim == 4   )
      self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
      self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
      self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

      # Test batched
      UpperCamelCase						    =					feature_extractor(UpperCAmelCase__					, return_tensors='np'					, sampling_rate=4_41_00   ).audio_values

      self.assertTrue(encoded_audios.ndim == 4   )
      self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
      self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
      self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

      # Test audio masking
      UpperCamelCase						    =					feature_extractor(
          UpperCAmelCase__					, return_tensors='np'					, sampling_rate=4_41_00					, mask_audio=UpperCAmelCase__   ).audio_values

      self.assertTrue(encoded_audios.ndim == 4   )
      self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
      self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
      self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

      # Test 2-D numpy arrays are batched.
      UpperCamelCase						    =					[floats_list((1, x)   )[0] for x in (8_00, 8_00, 8_00)]
      UpperCamelCase						    =					np.asarray(UpperCAmelCase__   )
      UpperCamelCase						    =					feature_extractor(UpperCAmelCase__					, return_tensors='np'					, sampling_rate=4_41_00   ).audio_values
      self.assertTrue(encoded_audios.ndim == 4   )
      self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
      self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
      self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					load_dataset('hf-internal-testing/librispeech_asr_dummy'					, 'clean'					, split='validation'   )
      # automatic decoding with librispeech
      UpperCamelCase						    =					ds.sort('id'   ).select(range(UpperCAmelCase__   )   )[:num_samples]['''audio''']

      return [x["array"] for x in speech_samples]






 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					self._load_datasamples(1   )
      UpperCamelCase						    =					TvltFeatureExtractor()
      UpperCamelCase						    =					feature_extractor(UpperCAmelCase__					, return_tensors='pt'   ).audio_values

      self.assertEquals(audio_values.shape					, (1, 1, 1_92, 1_28)   )

      UpperCamelCase						    =					torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]   )
      self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]					, UpperCAmelCase__					, atol=1e-4   )   )





'''simple docstring'''





import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


UpperCamelCase_    						=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_    						=       """cuda""" if torch.cuda.is_available() else """cpu"""






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     Tuple=100   , __magic_name__						:     Optional[int]=" "						)				->  List[str]:
						lowercase :							List[Any]    		=text.split(__magic_name__						)
						return [character.join(text[i : i + n]						).strip() for i in range(0   , len(__magic_name__						)   , __magic_name__						)]






def 				_lowerCAmelCase			(   __magic_name__						:     dict						)				->  dict:
						lowercase		,	lowercase :							int    		=[], []
						for title, text in zip(documents['''title''']   , documents['''text''']						):
												if text is not None:
																		for passage in split_text(__magic_name__						):
																								titles.append(title if title is not None else ''''''						)
																								texts.append(__magic_name__						)
						return {"title": titles, "text": texts}






def 				_lowerCAmelCase			(   __magic_name__						:     dict   , __magic_name__						:     DPRContextEncoder   , __magic_name__						:     DPRContextEncoderTokenizerFast						)				->  dict:
						lowercase :							Dict    		=ctx_tokenizer(
						    documents['''title''']   , documents['''text''']   , truncation=__magic_name__   , padding='''longest'''   , return_tensors='''pt'''						)['''input_ids''']
						lowercase :							Optional[int]    		=ctx_encoder(input_ids.to(device=__magic_name__						)   , return_dict=__magic_name__						).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}






def 				_lowerCAmelCase			(   __magic_name__						:     "RagExampleArguments"   , __magic_name__						:     "ProcessingArguments"   , __magic_name__						:     "IndexHnswArguments"   , )				->  str:
						######################################
						logger.info('''Step 1 - Create the dataset'''						)
						######################################

						# The dataset needed for RAG must have three columns:
						# - title (string): title of the document
						# - text (string): text of a passage of the document
						# - embeddings (array of dimension d): DPR representation of the passage
						# Let's say you have documents in tab-separated csv files with columns "title" and "text"
						assert os.path.isfile(rag_example_args.csv_path						), "Please provide a valid path to a csv file"

						# You can load a Dataset object this way
						lowercase :							Tuple    		=load_dataset(
						    '''csv'''   , data_files=[rag_example_args.csv_path]   , split='''train'''   , delimiter='''\t'''   , column_names=['''title''', '''text''']						)

						# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

						# Then split the documents into passages of 100 words
						lowercase :							Optional[int]    		=dataset.map(__magic_name__   , batched=__magic_name__   , num_proc=processing_args.num_proc						)

						# And compute the embeddings
						lowercase :							Any    		=DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__magic_name__						)
						lowercase :							Any    		=DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
						lowercase :							Optional[int]    		=Features(
						    {'''text''': Value('''string'''						), '''title''': Value('''string'''						), '''embeddings''': Sequence(Value('''float32'''						)						)}						)  # optional, save as float32 instead of float64 to save space
						lowercase :							Optional[Any]    		=dataset.map(
						    partial(__magic_name__   , ctx_encoder=__magic_name__   , ctx_tokenizer=__magic_name__						)   , batched=__magic_name__   , batch_size=processing_args.batch_size   , features=__magic_name__   , )

						# And finally save your dataset
						lowercase :							Optional[Any]    		=os.path.join(rag_example_args.output_dir   , '''my_knowledge_dataset'''						)
						dataset.save_to_disk(__magic_name__						)
						# from datasets import load_from_disk
						# dataset = load_from_disk(passages_path)  # to reload the dataset

						######################################
						logger.info('''Step 2 - Index the dataset'''						)
						######################################

						# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
						lowercase :							Union[str, Any]    		=faiss.IndexHNSWFlat(index_hnsw_args.d   , index_hnsw_args.m   , faiss.METRIC_INNER_PRODUCT						)
						dataset.add_faiss_index('''embeddings'''   , custom_index=__magic_name__						)

						# And save the index
						lowercase :							Dict    		=os.path.join(rag_example_args.output_dir   , '''my_knowledge_dataset_hnsw_index.faiss'''						)
						dataset.get_index('''embeddings'''						).save(__magic_name__						)
						# dataset.load_faiss_index("embeddings", index_path)  # to reload the index





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=str(Path(lowercase__      ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'      )						,							metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}						,							)
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}						,							)
				lowerCamelCase_		       =    field(
				    default='facebook/rag-sequence-nq'						,							metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}						,							)
				lowerCamelCase_		       =    field(
				    default='facebook/dpr-ctx_encoder-multiset-base'						,							metadata={
				        'help': (
				            'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
				            ' \'facebook/dpr-ctx_encoder-multiset-base\''
				        )
				    }						,							)
				lowerCamelCase_		       =    field(
				    default=str(Path(lowercase__      ).parent / 'test_run' / 'dummy-kb'      )						,							metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}						,							)





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={
				        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
				    }						,							)
				lowerCamelCase_		       =    field(
				    default=16						,							metadata={
				        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
				    }						,							)





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=7_68						,							metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}						,							)
				lowerCamelCase_		       =    field(
				    default=1_28						,							metadata={
				        'help': (
				            'The number of bi-directional links created for every new element during the HNSW index construction.'
				        )
				    }						,							)


if __name__ == "__main__":
							logging.basicConfig(level=logging.WARNING)
							logger.setLevel(logging.INFO)

							UpperCamelCase_    						=       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							UpperCamelCase_						,   UpperCamelCase_						,   UpperCamelCase_    						=       parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														UpperCamelCase_    						=       rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)





