




'''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


_snake_case				:				str       							=		logging.getLogger(__name__)
torch.set_grad_enabled(False)
_snake_case				:				Dict       							=		'cuda' if torch.cuda.is_available() else 'cpu'
def 		snake_case_   (UpperCamelCase	:  str			,						UpperCamelCase	:  str=100			,						UpperCamelCase	:  Union[str, Any]=" "				):





       '''simple docstring'''


       _a     					=    text.split(lowerCAmelCase__				)
       return [character.join(text[i : i + n]				).strip() for i in range(0			,						len(lowerCAmelCase__				)			,						lowerCAmelCase__				)]
def 		snake_case_   (UpperCamelCase	:  dict				):





       '''simple docstring'''


       _a     					=    [], []
       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 		snake_case_   (UpperCamelCase	:  dict			,						UpperCamelCase	:  DPRContextEncoder			,						UpperCamelCase	:  DPRContextEncoderTokenizerFast				):





       '''simple docstring'''


       _a     					=    ctx_tokenizer(
           documents['''title''']			,						documents['''text''']			,						truncation=lowerCAmelCase__			,						padding='''longest'''			,						return_tensors='''pt'''				)['''input_ids''']
       _a     					=    ctx_encoder(input_ids.to(device=lowerCAmelCase__				)			,						return_dict=lowerCAmelCase__				).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}
def 		snake_case_   (UpperCamelCase	:  "RagExampleArguments"			,						UpperCamelCase	:  "ProcessingArguments"			,						UpperCamelCase	:  "IndexHnswArguments"			,						):





       '''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     					=    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     					=    dataset.map(lowerCAmelCase__			,						batched=lowerCAmelCase__			,						num_proc=processing_args.num_proc				)

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

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

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

@dataclass
class       A   :
       lowercase_          =    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\''}					,)
       lowercase_          =    field(
           default=__lowercase					,metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}					,)
       lowercase_          =    field(
           default='facebook/rag-sequence-nq'					,metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}					,)
       lowercase_          =    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\''
               )
           }					,)
       lowercase_          =    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       A   :
       lowercase_          =    field(
           default=__lowercase					,metadata={
               'help': 'The number of processes to use to split the documents into passages. Default is single process.'
           }					,)
       lowercase_          =    field(
           default=16					,metadata={
               'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
           }					,)



@dataclass
class       A   :
       lowercase_          =    field(
           default=768					,metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}					,)
       lowercase_          =    field(
           default=128					,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)

    _snake_case				:				Optional[int]       							=		HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    _snake_case							,    _snake_case							,    _snake_case				:				int       							=		parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        _snake_case				:				str       							=		rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_args)







'''simple docstring'''


import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM

@require_torch
class       A   :







							def __init__(  self	:  Union[str, Any]							,      lowerCAmelCase_	:  List[Any]							,      lowerCAmelCase_	:  Union[str, Any]=99							,      lowerCAmelCase_	:  Optional[int]=13							,      lowerCAmelCase_	:  Tuple=16							,      lowerCAmelCase_	:  Optional[int]=7							,      lowerCAmelCase_	:  Optional[Any]=True							,      lowerCAmelCase_	:  Union[str, Any]=True							,      lowerCAmelCase_	:  Any=True							,      lowerCAmelCase_	:  Union[str, Any]=False							,      lowerCAmelCase_	:  int=True							,      lowerCAmelCase_	:  Tuple=2							,      lowerCAmelCase_	:  Any=32							,      lowerCAmelCase_	:  str=4							,      lowerCAmelCase_	:  Optional[Any]=4							,      lowerCAmelCase_	:  Optional[Any]=30							,      lowerCAmelCase_	:  int=0							,      lowerCAmelCase_	:  Any=1							,      lowerCAmelCase_	:  str=2							,      lowerCAmelCase_	:  Union[str, Any]=None							,      )      ->							Any:


														"""simple docstring"""


														_a     					=    parent
														_a     					=    batch_size
														_a     					=    decoder_seq_length
														# For common tests
														_a     					=    self.decoder_seq_length
														_a     					=    is_training
														_a     					=    use_attention_mask
														_a     					=    use_labels

														_a     					=    vocab_size
														_a     					=    d_model
														_a     					=    d_model
														_a     					=    decoder_layers
														_a     					=    decoder_layers
														_a     					=    decoder_ffn_dim
														_a     					=    decoder_attention_heads
														_a     					=    decoder_attention_heads
														_a     					=    eos_token_id
														_a     					=    bos_token_id
														_a     					=    pad_token_id
														_a     					=    decoder_start_token_id
														_a     					=    use_cache
														_a     					=    max_position_embeddings

														_a     					=    None
														_a     					=    decoder_seq_length
														_a     					=    2
														_a     					=    1







							def        __lowerCAmelCase      (  self	:  str )      ->							str:


														"""simple docstring"""


														_a     					=    ids_tensor([self.batch_size, self.decoder_seq_length]							,      self.vocab_size )

														_a     					=    None
														if self.use_attention_mask:
																					_a     					=    ids_tensor([self.batch_size, self.decoder_seq_length]							,      vocab_size=2 )

														_a     					=    None
														if self.use_labels:
																					_a     					=    ids_tensor([self.batch_size, self.decoder_seq_length]							,      self.vocab_size )

														_a     					=    TrOCRConfig(
														    vocab_size=self.vocab_size							,      d_model=self.d_model							,      decoder_layers=self.decoder_layers							,      decoder_ffn_dim=self.decoder_ffn_dim							,      decoder_attention_heads=self.decoder_attention_heads							,      eos_token_id=self.eos_token_id							,      bos_token_id=self.bos_token_id							,      use_cache=self.use_cache							,      pad_token_id=self.pad_token_id							,      decoder_start_token_id=self.decoder_start_token_id							,      max_position_embeddings=self.max_position_embeddings							,      )

														return (config, input_ids, attention_mask, lm_labels)







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  Any							,      lowerCAmelCase_	:  Dict							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  int							,      )      ->							int:


														"""simple docstring"""


														_a     					=    True
														_a     					=    TrOCRDecoder(config=lowerCAmelCase_ ).to(lowerCAmelCase_ ).eval()
														_a     					=    input_ids[:2]

														input_ids[input_ids == 0] += 1
														# first forward pass
														_a     					=    model(lowerCAmelCase_							,      use_cache=lowerCAmelCase_ )
														_a     					=    model(lowerCAmelCase_ )
														_a     					=    model(lowerCAmelCase_							,      use_cache=lowerCAmelCase_ )

														self.parent.assertTrue(len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ) )
														self.parent.assertTrue(len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ) + 1 )

														_a     					=    outputs['''past_key_values''']

														# create hypothetical next token and extent to next_input_ids
														_a     					=    ids_tensor((2, 1)							,      config.vocab_size - 1 ) + 1

														# append to next input_ids and
														_a     					=    torch.cat([input_ids, next_tokens]							,      dim=-1 )

														_a     					=    model(lowerCAmelCase_ )['''last_hidden_state''']
														_a     					=    model(lowerCAmelCase_							,      past_key_values=lowerCAmelCase_ )['''last_hidden_state''']

														# select random slice
														_a     					=    ids_tensor((1,)							,      output_from_past.shape[-1] ).item()
														_a     					=    output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
														_a     					=    output_from_past[:, 0, random_slice_idx].detach()

														# test that outputs are equal for slice
														assert torch.allclose(lowerCAmelCase_							,      lowerCAmelCase_							,      atol=1e-3 )







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


														"""simple docstring"""


														_a     					=    self.prepare_config_and_inputs()
														_a					,							_a					,							_a					,							_a     					=    config_and_inputs

														_a     					=    {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
														return config, inputs_dict



@require_torch
class       A   (		_a					,_a					,_a					,unittest.TestCase   ):
							lowercase_          =    (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
							lowercase_          =    (TrOCRForCausalLM,) if is_torch_available() else ()
							lowercase_          =    {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
							lowercase_          =    True
							lowercase_          =    False







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							str:


														"""simple docstring"""


														_a     					=    TrOCRStandaloneDecoderModelTester(self							,      is_training=lowerCAmelCase_ )
														_a     					=    ConfigTester(self							,      config_class=lowerCAmelCase_ )







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


														"""simple docstring"""


														pass







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							Dict:


														"""simple docstring"""


														pass







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							Optional[int]:


														"""simple docstring"""


														pass







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							Optional[int]:


														"""simple docstring"""


														self.config_tester.run_common_tests()







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_decoder_model_past(*lowerCAmelCase_ )







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


														"""simple docstring"""


														return







							@unittest.skip('''The model doesn\'t support left padding''' )  # and it's not used enough to be worth fixing :)
							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							Optional[int]:


														"""simple docstring"""


														pass

