






"""simple docstring"""




import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import SegformerImageProcessor






class  UpperCamelCase    (			unittest.TestCase   ):


 def __init__(  self	:		List[Any]							,		UpperCAmelCase__	:		int							,		UpperCAmelCase__	:		Optional[Any]=7							,		UpperCAmelCase__	:		Union[str, Any]=3							,		UpperCAmelCase__	:		Optional[Any]=30							,		UpperCAmelCase__	:		Union[str, Any]=400							,		UpperCAmelCase__	:		Dict=True							,		UpperCAmelCase__	:		int=None							,		UpperCAmelCase__	:		int=True							,		UpperCAmelCase__	:		Any=[0.5, 0.5, 0.5]							,		UpperCAmelCase__	:		List[Any]=[0.5, 0.5, 0.5]							,		UpperCAmelCase__	:		Tuple=False							,		)      -> Union[str, Any]:
   _a						: List[Any]    =			size if size is not None else {"""height""": 30, """width""": 30}
   _a						: Tuple    =			parent
   _a						: Dict    =			batch_size
   _a						: int    =			num_channels
   _a						: Union[str, Any]    =			min_resolution
   _a						: Union[str, Any]    =			max_resolution
   _a						: List[Any]    =			do_resize
   _a						: Optional[Any]    =			size
   _a						: Optional[Any]    =			do_normalize
   _a						: Any    =			image_mean
   _a						: List[str]    =			image_std
   _a						: List[Any]    =			do_reduce_labels







 def _lowercase			(  self	:		Tuple							)      -> Optional[int]:
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_reduce_labels": self.do_reduce_labels,
   }







def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: Optional[int]    =			load_dataset("""hf-internal-testing/fixtures_ade20k"""  ,     split="""test"""       )

  _a						: Dict    =			Image.open(dataset[0]["""file"""]       )
  _a						: int    =			Image.open(dataset[1]["""file"""]       )

  return image, map

def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: Tuple    =			load_dataset("""hf-internal-testing/fixtures_ade20k"""  ,     split="""test"""       )

  _a						: int    =			Image.open(dataset[0]["""file"""]       )
  _a						: int    =			Image.open(dataset[1]["""file"""]       )
  _a						: List[Any]    =			Image.open(dataset[2]["""file"""]       )
  _a						: Optional[int]    =			Image.open(dataset[3]["""file"""]       )

  return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class  UpperCamelCase    (			snake_case_	,  unittest.TestCase   ):
 UpperCamelCase      : List[Any]    =      SegformerImageProcessor if is_vision_available() else None


 def _lowercase			(  self	:		str							)      -> Any:
   _a						: int    =			SegformerImageProcessingTester(self							)


 @property
 def _lowercase			(  self	:		List[str]							)      -> Any:
   return self.image_processor_tester.prepare_image_processor_dict()


 def _lowercase			(  self	:		List[str]							)      -> str:
   _a						: Optional[int]    =			self.image_processing_class(**self.image_processor_dict							)
   self.assertTrue(hasattr(UpperCAmelCase__							,		"""do_resize"""							)							)
   self.assertTrue(hasattr(UpperCAmelCase__							,		"""size"""							)							)
   self.assertTrue(hasattr(UpperCAmelCase__							,		"""do_normalize"""							)							)
   self.assertTrue(hasattr(UpperCAmelCase__							,		"""image_mean"""							)							)
   self.assertTrue(hasattr(UpperCAmelCase__							,		"""image_std"""							)							)
   self.assertTrue(hasattr(UpperCAmelCase__							,		"""do_reduce_labels"""							)							)


 def _lowercase			(  self	:		str							)      -> Optional[Any]:
   _a						: Optional[int]    =			self.image_processing_class.from_dict(self.image_processor_dict							)
   self.assertEqual(image_processor.size							,		{"""height""": 30, """width""": 30}							)
   self.assertEqual(image_processor.do_reduce_labels							,		UpperCAmelCase__							)

   _a						: str    =			self.image_processing_class.from_dict(self.image_processor_dict							,		size=42							,		reduce_labels=UpperCAmelCase__							)
   self.assertEqual(image_processor.size							,		{"""height""": 42, """width""": 42}							)
   self.assertEqual(image_processor.do_reduce_labels							,		UpperCAmelCase__							)


 def _lowercase			(  self	:		List[str]							)      -> Dict:
   pass


 def _lowercase			(  self	:		Tuple							)      -> Union[str, Any]:
   # Initialize image_processing
   _a						: Union[str, Any]    =			self.image_processing_class(**self.image_processor_dict							)
   # create random PIL images
   _a						: Optional[int]    =			prepare_image_inputs(self.image_processor_tester							,		equal_resolution=UpperCAmelCase__							)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase__							,		Image.Image							)

   # Test not batched input
   _a						: List[str]    =			image_processing(image_inputs[0]							,		return_tensors="""pt"""							).pixel_values
   self.assertEqual(
       encoded_images.shape							,		(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)

   # Test batched
   _a						: Tuple    =			image_processing(UpperCAmelCase__							,		return_tensors="""pt"""							).pixel_values
   self.assertEqual(
       encoded_images.shape							,		(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)


 def _lowercase			(  self	:		Tuple							)      -> Optional[int]:
   # Initialize image_processing
   _a						: List[str]    =			self.image_processing_class(**self.image_processor_dict							)
   # create random numpy tensors
   _a						: Tuple    =			prepare_image_inputs(self.image_processor_tester							,		equal_resolution=UpperCAmelCase__							,		numpify=UpperCAmelCase__							)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase__							,		np.ndarray							)

   # Test not batched input
   _a						: Any    =			image_processing(image_inputs[0]							,		return_tensors="""pt"""							).pixel_values
   self.assertEqual(
       encoded_images.shape							,		(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)

   # Test batched
   _a						: List[Any]    =			image_processing(UpperCAmelCase__							,		return_tensors="""pt"""							).pixel_values
   self.assertEqual(
       encoded_images.shape							,		(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)


 def _lowercase			(  self	:		List[Any]							)      -> List[str]:
   # Initialize image_processing
   _a						: List[Any]    =			self.image_processing_class(**self.image_processor_dict							)
   # create random PyTorch tensors
   _a						: Optional[int]    =			prepare_image_inputs(self.image_processor_tester							,		equal_resolution=UpperCAmelCase__							,		torchify=UpperCAmelCase__							)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase__							,		torch.Tensor							)

   # Test not batched input
   _a						: int    =			image_processing(image_inputs[0]							,		return_tensors="""pt"""							).pixel_values
   self.assertEqual(
       encoded_images.shape							,		(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)

   # Test batched
   _a						: List[str]    =			image_processing(UpperCAmelCase__							,		return_tensors="""pt"""							).pixel_values
   self.assertEqual(
       encoded_images.shape							,		(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)


 def _lowercase			(  self	:		Dict							)      -> Optional[int]:
   # Initialize image_processing
   _a						: Dict    =			self.image_processing_class(**self.image_processor_dict							)
   # create random PyTorch tensors
   _a						: Any    =			prepare_image_inputs(self.image_processor_tester							,		equal_resolution=UpperCAmelCase__							,		torchify=UpperCAmelCase__							)
   _a						: Dict    =			[]
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase__							,		torch.Tensor							)
     maps.append(torch.zeros(image.shape[-2:]							).long()							)

   # Test not batched input
   _a						: Tuple    =			image_processing(image_inputs[0]							,		maps[0]							,		return_tensors="""pt"""							)
   self.assertEqual(
       encoding["""pixel_values"""].shape							,		(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)
   self.assertEqual(
       encoding["""labels"""].shape							,		(
           1,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)
   self.assertEqual(encoding["""labels"""].dtype							,		torch.long							)
   self.assertTrue(encoding["""labels"""].min().item() >= 0							)
   self.assertTrue(encoding["""labels"""].max().item() <= 255							)

   # Test batched
   _a						: str    =			image_processing(UpperCAmelCase__							,		UpperCAmelCase__							,		return_tensors="""pt"""							)
   self.assertEqual(
       encoding["""pixel_values"""].shape							,		(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)
   self.assertEqual(
       encoding["""labels"""].shape							,		(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)
   self.assertEqual(encoding["""labels"""].dtype							,		torch.long							)
   self.assertTrue(encoding["""labels"""].min().item() >= 0							)
   self.assertTrue(encoding["""labels"""].max().item() <= 255							)

   # Test not batched input (PIL images)
   _a   ,						_a						: Optional[int]    =			prepare_semantic_single_inputs()

   _a						: int    =			image_processing(UpperCAmelCase__							,		UpperCAmelCase__							,		return_tensors="""pt"""							)
   self.assertEqual(
       encoding["""pixel_values"""].shape							,		(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)
   self.assertEqual(
       encoding["""labels"""].shape							,		(
           1,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)
   self.assertEqual(encoding["""labels"""].dtype							,		torch.long							)
   self.assertTrue(encoding["""labels"""].min().item() >= 0							)
   self.assertTrue(encoding["""labels"""].max().item() <= 255							)

   # Test batched input (PIL images)
   _a   ,						_a						: List[str]    =			prepare_semantic_batch_inputs()

   _a						: str    =			image_processing(UpperCAmelCase__							,		UpperCAmelCase__							,		return_tensors="""pt"""							)
   self.assertEqual(
       encoding["""pixel_values"""].shape							,		(
           2,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)
   self.assertEqual(
       encoding["""labels"""].shape							,		(
           2,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )							,		)
   self.assertEqual(encoding["""labels"""].dtype							,		torch.long							)
   self.assertTrue(encoding["""labels"""].min().item() >= 0							)
   self.assertTrue(encoding["""labels"""].max().item() <= 255							)







 def _lowercase			(  self	:		List[str]							)      -> str:
   # Initialize image_processing
   _a						: List[Any]    =			self.image_processing_class(**self.image_processor_dict							)

   # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
   _a   ,						_a						: Dict    =			prepare_semantic_single_inputs()
   _a						: Optional[int]    =			image_processing(UpperCAmelCase__							,		UpperCAmelCase__							,		return_tensors="""pt"""							)
   self.assertTrue(encoding["""labels"""].min().item() >= 0							)
   self.assertTrue(encoding["""labels"""].max().item() <= 150							)

   _a						: Tuple    =			True
   _a						: Optional[int]    =			image_processing(UpperCAmelCase__							,		UpperCAmelCase__							,		return_tensors="""pt"""							)
   self.assertTrue(encoding["""labels"""].min().item() >= 0							)
   self.assertTrue(encoding["""labels"""].max().item() <= 255							)









"""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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


_snake_case					   =     logging.getLogger(__name__)
torch.set_grad_enabled(False)
_snake_case					   =     'cuda' if torch.cuda.is_available() else 'cpu'

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__=1_0_0  ,     UpperCamelCase__=" "       ):



  '''simple docstring'''
  _a						: str    =			text.split(UpperCamelCase__       )
  return [character.join(text[i : i + n]       ).strip() for i in range(0  ,     len(UpperCamelCase__       )  ,     UpperCamelCase__       )]

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''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(UpperCamelCase__       ):
        titles.append(title if title is not None else """"""       )
        texts.append(UpperCamelCase__       )
  return {"title": titles, "text": texts}

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: List[str]    =			ctx_tokenizer(
      documents["""title"""]  ,     documents["""text"""]  ,     truncation=UpperCamelCase__  ,     padding="""longest"""  ,     return_tensors="""pt"""       )["""input_ids"""]
  _a						: Optional[int]    =			ctx_encoder(input_ids.to(device=UpperCamelCase__       )  ,     return_dict=UpperCamelCase__       ).pooler_output
  return {"embeddings": embeddings.detach().cpu().numpy()}

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     ):



  '''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						: Dict    =			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						: Any    =			dataset.map(UpperCamelCase__  ,     batched=UpperCamelCase__  ,     num_proc=processing_args.num_proc       )

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

  # And finally save your dataset
  _a						: List[Any]    =			os.path.join(rag_example_args.output_dir  ,     """my_knowledge_dataset"""       )
  dataset.save_to_disk(UpperCamelCase__       )
  # 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						: List[str]    =			faiss.IndexHNSWFlat(index_hnsw_args.d  ,     index_hnsw_args.m  ,     faiss.METRIC_INNER_PRODUCT       )
  dataset.add_faiss_index("""embeddings"""  ,     custom_index=UpperCamelCase__       )

  # 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(UpperCamelCase__       )
  # dataset.load_faiss_index("embeddings", index_path)  # to reload the index

  ######################################
  logger.info("""Step 3 - Load RAG"""       )
  ######################################

  # Easy way to load the model
  _a						: str    =			RagRetriever.from_pretrained(
      rag_example_args.rag_model_name  ,     index_name="""custom"""  ,     indexed_dataset=UpperCamelCase__       )
  _a						: Any    =			RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name  ,     retriever=UpperCamelCase__       )
  _a						: List[Any]    =			RagTokenizer.from_pretrained(rag_example_args.rag_model_name       )

  # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
  # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

  ######################################
  logger.info("""Step 4 - Have fun"""       )
  ######################################

  _a						: Optional[Any]    =			rag_example_args.question or """What does Moses' rod turn into ?"""
  _a						: int    =			tokenizer.question_encoder(UpperCamelCase__  ,     return_tensors="""pt"""       )["""input_ids"""]
  _a						: Union[str, Any]    =			model.generate(UpperCamelCase__       )
  _a						: Any    =			tokenizer.batch_decode(UpperCamelCase__  ,     skip_special_tokens=UpperCamelCase__       )[0]
  logger.info("""Q: """ + question       )
  logger.info("""A: """ + generated_string       )






@dataclass
class  UpperCamelCase    :
 UpperCamelCase      : str    =      field(
     default=str(Path(snake_case_   ).parent / '''test_data''' / '''my_knowledge_dataset.csv'''   )	,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}	,  )
 UpperCamelCase      : Optional[str]    =      field(
     default=snake_case_	,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}	,  )
 UpperCamelCase      : str    =      field(
     default='''facebook/rag-sequence-nq'''	,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}	,  )
 UpperCamelCase      : str    =      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\''''
         )
     }	,  )
 UpperCamelCase      : Optional[str]    =      field(
     default=snake_case_	,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}	,  )







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







@dataclass
class  UpperCamelCase    :
 UpperCamelCase      : int    =      field(
     default=768	,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}	,  )
 UpperCamelCase      : int    =      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					   =     HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 _snake_case   ,				_snake_case   ,				_snake_case					   =     parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  _snake_case					   =     rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)

