





import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
  from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
  _snake_case				= None

if is_ray_available():
  import ray  # noqa: E402 # isort:skip
  from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
  _snake_case				= None
  _snake_case				= None
  _snake_case				= None


def _A   (    __magic_name__       ):
   if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
      lowercase__    =  unittest.skip("test requires Datasets, Faiss, psutil"       )(__magic_name__       )
   return test_case







@require_distributed_retrieval
class 					lowerCAmelCase							(   lowercase_					):







  def   UpperCAmelCase     (			self  :Union[str, Any]					):






     '''simple docstring'''





     lowercase__    =  tempfile.mkdtemp()
     lowercase__    =  8

     # DPR tok
     lowercase__    =  [
         "[UNK]",
         "[CLS]",
         "[SEP]",
         "[PAD]",
         "[MASK]",
         "want",
         "##want",
         "##ed",
         "wa",
         "un",
         "runn",
         "##ing",
         ",",
         "low",
         "lowest",
     ]
     lowercase__    =  os.path.join(self.tmpdirname    ,							"dpr_tokenizer"					)
     os.makedirs(_lowercase    ,							exist_ok=_lowercase					)
     lowercase__    =  os.path.join(_lowercase    ,							DPR_VOCAB_FILES_NAMES["vocab_file"]					)
     with open(self.vocab_file    ,							"w"    ,							encoding="utf-8"					) as vocab_writer:
        vocab_writer.write("".join([x + "\n" for x in vocab_tokens]					)					)

     # BART tok
     lowercase__    =  [
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "\u0120",
         "\u0120l",
         "\u0120n",
         "\u0120lo",
         "\u0120low",
         "er",
         "\u0120lowest",
         "\u0120newer",
         "\u0120wider",
         "<unk>",
     ]
     lowercase__    =  dict(zip(_lowercase    ,							range(len(_lowercase					)					)					)					)
     lowercase__    =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
     lowercase__    =  {"unk_token": "<unk>"}

     lowercase__    =  os.path.join(self.tmpdirname    ,							"bart_tokenizer"					)
     os.makedirs(_lowercase    ,							exist_ok=_lowercase					)
     lowercase__    =  os.path.join(_lowercase    ,							BART_VOCAB_FILES_NAMES["vocab_file"]					)
     lowercase__    =  os.path.join(_lowercase    ,							BART_VOCAB_FILES_NAMES["merges_file"]					)
     with open(self.vocab_file    ,							"w"    ,							encoding="utf-8"					) as fp:
        fp.write(json.dumps(_lowercase					) + "\n"					)
     with open(self.merges_file    ,							"w"    ,							encoding="utf-8"					) as fp:
        fp.write("\n".join(_lowercase					)					)







  def   UpperCAmelCase     (			self  :Optional[Any]					):






     '''simple docstring'''





     return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,							"dpr_tokenizer"					)					)







  def   UpperCAmelCase     (			self  :str					):






     '''simple docstring'''





     return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,							"bart_tokenizer"					)					)







  def   UpperCAmelCase     (			self  :Any					):






     '''simple docstring'''





     shutil.rmtree(self.tmpdirname					)







  def   UpperCAmelCase     (			self  :Dict					):






     '''simple docstring'''





     lowercase__    =  Dataset.from_dict(
         {
             "id": ["0", "1"],
             "text": ["foo", "bar"],
             "title": ["Foo", "Bar"],
             "embeddings": [np.ones(self.retrieval_vector_size					), 2 * np.ones(self.retrieval_vector_size					)],
         }					)
     dataset.add_faiss_index("embeddings"    ,							string_factory="Flat"    ,							metric_type=faiss.METRIC_INNER_PRODUCT					)
     return dataset







  def   UpperCAmelCase     (			self  :Union[str, Any]    ,							_lowercase  :bool    ,							_lowercase  :Optional[Any]=1_23_45					):






     '''simple docstring'''





     lowercase__    =  self.get_dummy_dataset()
     lowercase__    =  RagConfig(
         retrieval_vector_size=self.retrieval_vector_size    ,							question_encoder=DPRConfig().to_dict()    ,							generator=BartConfig().to_dict()    ,							)
     with patch("transformers.models.rag.retrieval_rag.load_dataset"					) as mock_load_dataset:
        lowercase__    =  dataset
        lowercase__    =  RagPyTorchDistributedRetriever(
            _lowercase    ,							question_encoder_tokenizer=self.get_dpr_tokenizer()    ,							generator_tokenizer=self.get_bart_tokenizer()    ,							)
        if init_retrieval:
           retriever.init_retrieval(_lowercase					)
     return retriever







  def   UpperCAmelCase     (			self  :Union[str, Any]    ,							_lowercase  :bool					):






     '''simple docstring'''





     ray.init(local_mode=_lowercase					)
     lowercase__    =  RagConfig(
         retrieval_vector_size=self.retrieval_vector_size    ,							question_encoder=DPRConfig().to_dict()    ,							generator=BartConfig().to_dict()    ,							)
     lowercase__    =  ray.remote(_lowercase					)
     lowercase__    =  [remote_cls.remote() for _ in range(1					)]
     with patch("transformers.models.rag.retrieval_rag.load_dataset"					) as mock_load_dataset:
        lowercase__    =  self.get_dummy_dataset()
        lowercase__    =  RagRayDistributedRetriever(
            _lowercase    ,							question_encoder_tokenizer=self.get_dpr_tokenizer()    ,							generator_tokenizer=self.get_bart_tokenizer()    ,							retrieval_workers=_lowercase    ,							)
        if init_retrieval:
           retriever.init_retrieval()
     return retriever







  def   UpperCAmelCase     (			self  :int    ,							_lowercase  :bool    ,							_lowercase  :bool    ,							_lowercase  :Optional[Any]=1_23_45					):






     '''simple docstring'''





     lowercase__    =  self.get_dummy_dataset()
     lowercase__    =  RagConfig(
         retrieval_vector_size=self.retrieval_vector_size    ,							question_encoder=DPRConfig().to_dict()    ,							generator=BartConfig().to_dict()    ,							index_name="custom"    ,							)
     if from_disk:
        lowercase__    =  os.path.join(self.tmpdirname    ,							"dataset"					)
        lowercase__    =  os.path.join(self.tmpdirname    ,							"index.faiss"					)
        dataset.get_index("embeddings"					).save(os.path.join(self.tmpdirname    ,							"index.faiss"					)					)
        dataset.drop_index("embeddings"					)
        dataset.save_to_disk(os.path.join(self.tmpdirname    ,							"dataset"					)					)
        del dataset
        lowercase__    =  RagPyTorchDistributedRetriever(
            _lowercase    ,							question_encoder_tokenizer=self.get_dpr_tokenizer()    ,							generator_tokenizer=self.get_bart_tokenizer()    ,							)
     else:
        lowercase__    =  RagPyTorchDistributedRetriever(
            _lowercase    ,							question_encoder_tokenizer=self.get_dpr_tokenizer()    ,							generator_tokenizer=self.get_bart_tokenizer()    ,							index=CustomHFIndex(config.retrieval_vector_size    ,							_lowercase					)    ,							)
     if init_retrieval:
        retriever.init_retrieval(_lowercase					)
     return retriever







  def   UpperCAmelCase     (			self  :Optional[Any]    ,							_lowercase  :bool    ,							_lowercase  :bool					):






     '''simple docstring'''





     ray.init(local_mode=_lowercase					)
     lowercase__    =  self.get_dummy_dataset()
     lowercase__    =  RagConfig(
         retrieval_vector_size=self.retrieval_vector_size    ,							question_encoder=DPRConfig().to_dict()    ,							generator=BartConfig().to_dict()    ,							index_name="custom"    ,							)
     lowercase__    =  ray.remote(_lowercase					)
     lowercase__    =  [remote_cls.remote() for _ in range(1					)]
     if from_disk:
        lowercase__    =  os.path.join(self.tmpdirname    ,							"dataset"					)
        lowercase__    =  os.path.join(self.tmpdirname    ,							"index.faiss"					)
        dataset.get_index("embeddings"					).save(os.path.join(self.tmpdirname    ,							"index.faiss"					)					)
        dataset.drop_index("embeddings"					)
        dataset.save_to_disk(os.path.join(self.tmpdirname    ,							"dataset"					)					)
        del dataset
        lowercase__    =  RagRayDistributedRetriever(
            _lowercase    ,							question_encoder_tokenizer=self.get_dpr_tokenizer()    ,							generator_tokenizer=self.get_bart_tokenizer()    ,							retrieval_workers=_lowercase    ,							index=CustomHFIndex.load_from_disk(
                vector_size=config.retrieval_vector_size    ,							dataset_path=config.passages_path    ,							index_path=config.index_path    ,							)    ,							)
     else:
        lowercase__    =  RagRayDistributedRetriever(
            _lowercase    ,							question_encoder_tokenizer=self.get_dpr_tokenizer()    ,							generator_tokenizer=self.get_bart_tokenizer()    ,							retrieval_workers=_lowercase    ,							index=CustomHFIndex(config.retrieval_vector_size    ,							_lowercase					)    ,							)
     if init_retrieval:
        retriever.init_retrieval()
     return retriever







  def   UpperCAmelCase     (			self  :Optional[int]    ,							_lowercase  :RagRetriever    ,							_lowercase  :np.array    ,							_lowercase  :int					):






     '''simple docstring'''





     lowercase__   ,							lowercase__   ,							lowercase__    =  retriever.retrieve(_lowercase    ,							n_docs=_lowercase					)
     self.assertEqual(retrieved_doc_embeds.shape    ,							(2, n_docs, self.retrieval_vector_size)					)
     self.assertEqual(len(_lowercase					)    ,							2					)
     self.assertEqual(sorted(doc_dicts[0]					)    ,							["embeddings", "id", "text", "title"]					)
     self.assertEqual(len(doc_dicts[0]["id"]					)    ,							_lowercase					)
     self.assertEqual(doc_dicts[0]["id"][0]    ,							"1"					)  # max inner product is reached with second doc
     self.assertEqual(doc_dicts[1]["id"][0]    ,							"0"					)  # max inner product is reached with first doc
     self.assertListEqual(doc_ids.tolist()    ,							[[1], [0]]					)







  def   UpperCAmelCase     (			self  :int					):






     '''simple docstring'''





     lowercase__    =  1
     lowercase__    =  np.array(
         [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,							dtype=np.floataa					)

     self.distributed_retriever_check(
         self.get_dummy_pytorch_distributed_retriever(init_retrieval=_lowercase					)    ,							_lowercase    ,							_lowercase					)







  def   UpperCAmelCase     (			self  :Dict					):






     '''simple docstring'''





     lowercase__    =  1
     lowercase__    =  np.array(
         [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,							dtype=np.floataa					)

     self.distributed_retriever_check(
         self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowercase    ,							from_disk=_lowercase					)    ,							_lowercase    ,							_lowercase    ,							)







  def   UpperCAmelCase     (			self  :int					):






     '''simple docstring'''





     lowercase__    =  1
     lowercase__    =  np.array(
         [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,							dtype=np.floataa					)

     self.distributed_retriever_check(
         self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowercase    ,							from_disk=_lowercase					)    ,							_lowercase    ,							_lowercase    ,							)







  @require_ray
  def   UpperCAmelCase     (			self  :str					):






     '''simple docstring'''





     lowercase__    =  1
     lowercase__    =  np.array(
         [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,							dtype=np.floataa					)

     self.distributed_retriever_check(
         self.get_dummy_ray_distributed_retriever(init_retrieval=_lowercase					)    ,							_lowercase    ,							_lowercase					)
     ray.shutdown()







  @require_ray
  def   UpperCAmelCase     (			self  :Union[str, Any]					):






     '''simple docstring'''





     lowercase__    =  1
     lowercase__    =  np.array(
         [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,							dtype=np.floataa					)
     with self.assertRaises(_lowercase					):
        self.distributed_retriever_check(
            self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowercase    ,							from_disk=_lowercase					)    ,							_lowercase    ,							_lowercase    ,							)
     ray.shutdown()







  @require_ray
  def   UpperCAmelCase     (			self  :Tuple					):






     '''simple docstring'''





     lowercase__    =  1
     lowercase__    =  np.array(
         [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,							dtype=np.floataa					)

     self.distributed_retriever_check(
         self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowercase    ,							from_disk=_lowercase					)    ,							_lowercase    ,							_lowercase					)
     ray.shutdown()













from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			42
  __lowerCamelCase		=			42







  def __init__(			self  :List[Any]    ,							_lowercase  :UNetaDModel    ,							_lowercase  :PNDMScheduler					):






     '''simple docstring'''





     super().__init__()

     lowercase__    =  PNDMScheduler.from_config(scheduler.config					)

     self.register_modules(unet=_lowercase    ,							scheduler=_lowercase					)







  @torch.no_grad()
  def __call__(			self  :Union[str, Any]    ,							_lowercase  :int = 1    ,							_lowercase  :int = 50    ,							_lowercase  :Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,							_lowercase  :Optional[str] = "pil"    ,							_lowercase  :bool = True    ,							**_lowercase  :List[Any]    ,							):






     '''simple docstring'''





     lowercase__    =  randn_tensor(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,							generator=_lowercase    ,							device=self.device    ,							)

     self.scheduler.set_timesteps(_lowercase					)
     for t in self.progress_bar(self.scheduler.timesteps					):
        lowercase__    =  self.unet(_lowercase    ,							_lowercase					).sample

        lowercase__    =  self.scheduler.step(_lowercase    ,							_lowercase    ,							_lowercase					).prev_sample

     lowercase__    =  (image / 2 + 0.5).clamp(0    ,							1					)
     lowercase__    =  image.cpu().permute(0    ,							2    ,							3    ,							1					).numpy()
     if output_type == "pil":
        lowercase__    =  self.numpy_to_pil(_lowercase					)

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowercase					)






