




"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel






@require_tf
class 					lowercase:







 '''simple docstring'''





 lowercase__ 					=					PegasusConfig
 lowercase__ 					=					{}
 lowercase__ 					=					"gelu"


 def __init__(				self:		List[str],      a_:		Any,      a_:		Union[str, Any]=13,      a_:		Tuple=7,      a_:		int=True,      a_:		List[Any]=False,      a_:		int=99,      a_:		str=32,      a_:		str=2,      a_:		List[str]=4,      a_:		Union[str, Any]=37,      a_:		List[Any]=0.1,      a_:		List[str]=0.1,      a_:		List[Any]=40,      a_:		Optional[int]=2,      a_:		Dict=1,      a_:		Dict=0,      ):




   '''simple docstring'''



   _snake_case							:		Dict								=  parent
   _snake_case							:		List[str]								=  batch_size
   _snake_case							:		Optional[int]								=  seq_length
   _snake_case							:		List[str]								=  is_training
   _snake_case							:		Any								=  use_labels
   _snake_case							:		List[str]								=  vocab_size
   _snake_case							:		Optional[int]								=  hidden_size
   _snake_case							:		Tuple								=  num_hidden_layers
   _snake_case							:		Optional[Any]								=  num_attention_heads
   _snake_case							:		str								=  intermediate_size

   _snake_case							:		Optional[Any]								=  hidden_dropout_prob
   _snake_case							:		Optional[Any]								=  attention_probs_dropout_prob
   _snake_case							:		List[Any]								=  max_position_embeddings
   _snake_case							:		Optional[Any]								=  eos_token_id
   _snake_case							:		Dict								=  pad_token_id
   _snake_case							:		List[Any]								=  bos_token_id


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  ids_tensor([self.batch_size, self.seq_length - 1],      self.vocab_size		)
   _snake_case							:		Optional[Any]								=  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		),      1		)
   _snake_case							:		Optional[int]								=  tf.concat([input_ids, eos_tensor],      axis=1		)

   _snake_case							:		int								=  ids_tensor([self.batch_size, self.seq_length],      self.vocab_size		)

   _snake_case							:		Any								=  self.config_cls(
       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_ids=[2],      bos_token_id=self.bos_token_id,      pad_token_id=self.pad_token_id,      decoder_start_token_id=self.pad_token_id,      **self.config_updates,      )
   _snake_case							:		Optional[int]								=  prepare_pegasus_inputs_dict(a_,      a_,      a_		)
   return config, inputs_dict


 def  UpperCamelCase_   (				self:		List[str],      a_:		Union[str, Any],      a_:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  TFPegasusModel(config=a_		).get_decoder()
   _snake_case							:		Dict								=  inputs_dict["""input_ids"""]

   _snake_case							:		int								=  input_ids[:1, :]
   _snake_case							:		Optional[Any]								=  inputs_dict["""attention_mask"""][:1, :]
   _snake_case							:		List[Any]								=  inputs_dict["""head_mask"""]
   _snake_case							:		Any								=  1

   # first forward pass
   _snake_case							:		Dict								=  model(a_,      attention_mask=a_,      head_mask=a_,      use_cache=a_		)

   _snake_case		,					_snake_case							:		int								=  outputs.to_tuple()

   # create hypothetical next token and extent to next_input_ids
   _snake_case							:		Optional[Any]								=  ids_tensor((self.batch_size, 3),      config.vocab_size		)
   _snake_case							:		int								=  tf.cast(ids_tensor((self.batch_size, 3),      2		),      tf.inta		)

   # append to next input_ids and
   _snake_case							:		Dict								=  tf.concat([input_ids, next_tokens],      axis=-1		)
   _snake_case							:		Dict								=  tf.concat([attention_mask, next_attn_mask],      axis=-1		)

   _snake_case							:		Optional[int]								=  model(a_,      attention_mask=a_		)[0]
   _snake_case							:		Optional[int]								=  model(a_,      attention_mask=a_,      past_key_values=a_		)[0]

   self.parent.assertEqual(next_tokens.shape[1],      output_from_past.shape[1]		)

   # select random slice
   _snake_case							:		Any								=  int(ids_tensor((1,),      output_from_past.shape[-1]		)		)
   _snake_case							:		Optional[int]								=  output_from_no_past[:, -3:, random_slice_idx]
   _snake_case							:		int								=  output_from_past[:, :, random_slice_idx]

   # test that outputs are equal for slice
   tf.debugging.assert_near(a_,      a_,      rtol=1E-3		)







def 					UpperCAmelCase__       (snake_case__							:					List[str]		,   snake_case__							:					Optional[Any]		,   snake_case__							:					Dict		,   snake_case__							:					List[Any]=None		,   snake_case__							:					Optional[int]=None		,   snake_case__							:					Dict=None		,   snake_case__							:					Tuple=None		,   snake_case__							:					Tuple=None		,   ):
  """simple docstring"""

  if attention_mask is None:
    _snake_case							:		str								=  tf.cast(tf.math.not_equal(snake_case__		,   config.pad_token_id    )		,   tf.inta    )
  if decoder_attention_mask is None:
    _snake_case							:		int								=  tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape		,   dtype=tf.inta    ),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,   config.pad_token_id    )		,   tf.inta    ),
        ]		,   axis=-1		,   )
  if head_mask is None:
    _snake_case							:		List[Any]								=  tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
  if decoder_head_mask is None:
    _snake_case							:		int								=  tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
  if cross_attn_head_mask is None:
    _snake_case							:		Union[str, Any]								=  tf.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,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }






@require_tf
class 					lowercase(     __a       ,			__a       ,			unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
 lowercase__ 					=					(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
 lowercase__ 					=					(
     {
         "conversational": TFPegasusForConditionalGeneration,
         "feature-extraction": TFPegasusModel,
         "summarization": TFPegasusForConditionalGeneration,
         "text2text-generation": TFPegasusForConditionalGeneration,
         "translation": TFPegasusForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 lowercase__ 					=					True
 lowercase__ 					=					False
 lowercase__ 					=					False


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		str								=  TFPegasusModelTester(self		)
   _snake_case							:		Any								=  ConfigTester(self,      config_class=a_		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   self.config_tester.run_common_tests()


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_decoder_model_past_large_inputs(*a_		)







@require_sentencepiece
@require_tokenizers
@require_tf
class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					[
     " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
     " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
 ]
 lowercase__ 					=					[
     "California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
     " reduce the risk of wildfires.",
     "N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.",
 ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
 lowercase__ 					=					"google/pegasus-xsum"


 @cached_property
 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   return AutoTokenizer.from_pretrained(self.model_name		)


 @cached_property
 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
   return model


 def  UpperCamelCase_   (				self:		Optional[Any],      **a_:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  self.translate_src_text(**a_		)
   assert self.expected_text == generated_words


 def  UpperCamelCase_   (				self:		Tuple,      **a_:		int		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  self.tokenizer(self.src_text,      **a_,      padding=a_,      return_tensors="""tf"""		)
   _snake_case							:		Optional[int]								=  self.model.generate(
       model_inputs.input_ids,      attention_mask=model_inputs.attention_mask,      num_beams=2,      use_cache=a_,      )
   _snake_case							:		List[Any]								=  self.tokenizer.batch_decode(generated_ids.numpy(),      skip_special_tokens=a_		)
   return generated_words


 @slow
 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   self._assert_generated_batch_equal_expected()









"""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


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







def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  Tuple=100				,snake_case_  :  int=" "						)   ->List[str]:



    '''simple docstring'''







    __A    :						Dict						 =     text.split(snake_case_						)
    return [character.join(text[i : i + n]						).strip() for i in range(0				,len(snake_case_						)				,snake_case_						)]







def 		__lowercase     (   snake_case_  :  dict						)   ->dict:



    '''simple docstring'''







    __A ,       __A    :						Optional[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 		__lowercase     (   snake_case_  :  dict				,snake_case_  :  DPRContextEncoder				,snake_case_  :  DPRContextEncoderTokenizerFast						)   ->dict:



    '''simple docstring'''







    __A    :						Tuple						 =     ctx_tokenizer(
        documents['''title''']				,documents['''text''']				,truncation=snake_case_				,padding='''longest'''				,return_tensors='''pt'''						)['''input_ids''']
    __A    :						int						 =     ctx_encoder(input_ids.to(device=snake_case_						)				,return_dict=snake_case_						).pooler_output
    return {"embeddings": embeddings.detach().cpu().numpy()}







def 		__lowercase     (   snake_case_  :  "RagExampleArguments"				,snake_case_  :  "ProcessingArguments"				,snake_case_  :  "IndexHnswArguments"				,)   ->Tuple:



    '''simple docstring'''







    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
    __A    :						Optional[Any]						 =     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
    __A    :						str						 =     dataset.map(snake_case_				,batched=snake_case_				,num_proc=processing_args.num_proc						)

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

    # And finally save your dataset
    __A    :						int						 =     os.path.join(rag_example_args.output_dir				,'''my_knowledge_dataset'''						)
    dataset.save_to_disk(snake_case_						)
    # 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
    __A    :						Tuple						 =     faiss.IndexHNSWFlat(index_hnsw_args.d				,index_hnsw_args.m				,faiss.METRIC_INNER_PRODUCT						)
    dataset.add_faiss_index('''embeddings'''				,custom_index=snake_case_						)

    # And save the index
    __A    :						List[str]						 =     os.path.join(rag_example_args.output_dir				,'''my_knowledge_dataset_hnsw_index.faiss'''						)
    dataset.get_index('''embeddings'''						).save(snake_case_						)
    # dataset.load_faiss_index("embeddings", index_path)  # to reload the index






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     field(
     default=str(Path(SCREAMING_SNAKE_CASE__     ).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=SCREAMING_SNAKE_CASE__							,							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(SCREAMING_SNAKE_CASE__     ).parent / """test_run""" / """dummy-kb"""     )							,							metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}							,							)






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     field(
     default=SCREAMING_SNAKE_CASE__							,							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 		__snake_case					:





 """simple docstring"""


 _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)

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




