




'''simple docstring'''






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:
					_lowerCAmelCase				     = None

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

def       __lowerCAmelCase (   snake_case__						):
			if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
						__UpperCamelCase				:      List[Any]									=       unittest.skip("test requires Datasets, Faiss, psutil"						)(snake_case__						)
			return test_case




@require_distributed_retrieval
class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				def 	a_	(self      )     ->	Optional[int]:
							__UpperCamelCase				:      Optional[int]									=       tempfile.mkdtemp()
							__UpperCamelCase				:      Optional[int]									=       8

							# DPR tok
							__UpperCamelCase				:      Dict									=       [
							    "[UNK]",
							    "[CLS]",
							    "[SEP]",
							    "[PAD]",
							    "[MASK]",
							    "want",
							    "##want",
							    "##ed",
							    "wa",
							    "un",
							    "runn",
							    "##ing",
							    ",",
							    "low",
							    "lowest",
							]
							__UpperCamelCase				:      List[Any]									=       os.path.join(self.tmpdirname			,  "dpr_tokenizer"      )
							os.makedirs(_UpperCAmelCase			,  exist_ok=_UpperCAmelCase      )
							__UpperCamelCase				:      Dict									=       os.path.join(_UpperCAmelCase			,  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
							__UpperCamelCase				:      List[Any]									=       [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							__UpperCamelCase				:      Union[str, Any]									=       dict(zip(_UpperCAmelCase			,  range(len(_UpperCAmelCase      )      )      )      )
							__UpperCamelCase				:      Optional[Any]									=       ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							__UpperCamelCase				:      int									=       {"unk_token": "<unk>"}

							__UpperCamelCase				:      Union[str, Any]									=       os.path.join(self.tmpdirname			,  "bart_tokenizer"      )
							os.makedirs(_UpperCAmelCase			,  exist_ok=_UpperCAmelCase      )
							__UpperCamelCase				:      Optional[int]									=       os.path.join(_UpperCAmelCase			,  BART_VOCAB_FILES_NAMES["vocab_file"]      )
							__UpperCamelCase				:      Dict									=       os.path.join(_UpperCAmelCase			,  BART_VOCAB_FILES_NAMES["merges_file"]      )
							with open(self.vocab_file			,  "w"			,  encoding="utf-8"      ) as fp:
										fp.write(json.dumps(_UpperCAmelCase      ) + "\n"      )
							with open(self.merges_file			,  "w"			,  encoding="utf-8"      ) as fp:
										fp.write("\n".join(_UpperCAmelCase      )      )
				def 	a_	(self      )     ->	DPRQuestionEncoderTokenizer:
							return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,  "dpr_tokenizer"      )      )
				def 	a_	(self      )     ->	BartTokenizer:
							return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,  "bart_tokenizer"      )      )
				def 	a_	(self      )     ->	Dict:
							shutil.rmtree(self.tmpdirname      )
				def 	a_	(self      )     ->	Union[str, Any]:
							__UpperCamelCase				:      List[str]									=       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 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase=1_2_3_4_5      )     ->	RagPyTorchDistributedRetriever:
							__UpperCamelCase				:      Union[str, Any]									=       self.get_dummy_dataset()
							__UpperCamelCase				:      Optional[int]									=       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:
										__UpperCamelCase				:      Optional[Any]									=       dataset
										__UpperCamelCase				:      List[str]									=       RagPyTorchDistributedRetriever(
										    _UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  )
										if init_retrieval:
													retriever.init_retrieval(_UpperCAmelCase      )
							return retriever
				def 	a_	(self			,  _UpperCAmelCase      )     ->	RagRayDistributedRetriever:
							# Have to run in local mode because sys.path modifications at top of
							# file are not propogated to remote workers.
							# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
							ray.init(local_mode=_UpperCAmelCase      )
							__UpperCamelCase				:      str									=       RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size			,  question_encoder=DPRConfig().to_dict()			,  generator=BartConfig().to_dict()			,  )
							__UpperCamelCase				:      str									=       ray.remote(_UpperCAmelCase      )
							__UpperCamelCase				:      str									=       [remote_cls.remote() for _ in range(1      )]
							with patch("transformers.models.rag.retrieval_rag.load_dataset"      ) as mock_load_dataset:
										__UpperCamelCase				:      str									=       self.get_dummy_dataset()
										__UpperCamelCase				:      Union[str, Any]									=       RagRayDistributedRetriever(
										    _UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  retrieval_workers=_UpperCAmelCase			,  )
										if init_retrieval:
													retriever.init_retrieval()
							return retriever
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase=1_2_3_4_5      )     ->	List[str]:
							__UpperCamelCase				:      Union[str, Any]									=       self.get_dummy_dataset()
							__UpperCamelCase				:      Optional[int]									=       RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size			,  question_encoder=DPRConfig().to_dict()			,  generator=BartConfig().to_dict()			,  index_name="custom"			,  )
							if from_disk:
										__UpperCamelCase				:      Optional[int]									=       os.path.join(self.tmpdirname			,  "dataset"      )
										__UpperCamelCase				:      str									=       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
										__UpperCamelCase				:      List[str]									=       RagPyTorchDistributedRetriever(
										    _UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  )
							else:
										__UpperCamelCase				:      Any									=       RagPyTorchDistributedRetriever(
										    _UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  index=CustomHFIndex(config.retrieval_vector_size			,  _UpperCAmelCase      )			,  )
							if init_retrieval:
										retriever.init_retrieval(_UpperCAmelCase      )
							return retriever
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	Optional[int]:
							# Have to run in local mode because sys.path modifications at top of
							# file are not propogated to remote workers.
							# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
							ray.init(local_mode=_UpperCAmelCase      )
							__UpperCamelCase				:      Optional[int]									=       self.get_dummy_dataset()
							__UpperCamelCase				:      Dict									=       RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size			,  question_encoder=DPRConfig().to_dict()			,  generator=BartConfig().to_dict()			,  index_name="custom"			,  )
							__UpperCamelCase				:      Union[str, Any]									=       ray.remote(_UpperCAmelCase      )
							__UpperCamelCase				:      str									=       [remote_cls.remote() for _ in range(1      )]
							if from_disk:
										__UpperCamelCase				:      Tuple									=       os.path.join(self.tmpdirname			,  "dataset"      )
										__UpperCamelCase				:      List[Any]									=       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
										__UpperCamelCase				:      Tuple									=       RagRayDistributedRetriever(
										    _UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  retrieval_workers=_UpperCAmelCase			,  index=CustomHFIndex.load_from_disk(
										        vector_size=config.retrieval_vector_size			,  dataset_path=config.passages_path			,  index_path=config.index_path			,  )			,  )
							else:
										__UpperCamelCase				:      List[Any]									=       RagRayDistributedRetriever(
										    _UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  retrieval_workers=_UpperCAmelCase			,  index=CustomHFIndex(config.retrieval_vector_size			,  _UpperCAmelCase      )			,  )
							if init_retrieval:
										retriever.init_retrieval()
							return retriever
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      Union[str, Any]									=       retriever.retrieve(_UpperCAmelCase			,  n_docs=_UpperCAmelCase      )
							self.assertEqual(retrieved_doc_embeds.shape			,  (2, n_docs, self.retrieval_vector_size)      )
							self.assertEqual(len(_UpperCAmelCase      )			,  2      )
							self.assertEqual(sorted(doc_dicts[0]      )			,  ["embeddings", "id", "text", "title"]      )
							self.assertEqual(len(doc_dicts[0]["id"]      )			,  _UpperCAmelCase      )
							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 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      Optional[Any]									=       1
							__UpperCamelCase				:      Optional[Any]									=       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=_UpperCAmelCase      )			,  _UpperCAmelCase			,  _UpperCAmelCase      )
				def 	a_	(self      )     ->	str:
							__UpperCamelCase				:      Optional[int]									=       1
							__UpperCamelCase				:      List[str]									=       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=_UpperCAmelCase			,  from_disk=_UpperCAmelCase      )			,  _UpperCAmelCase			,  _UpperCAmelCase			,  )
				def 	a_	(self      )     ->	List[Any]:
							__UpperCamelCase				:      Optional[int]									=       1
							__UpperCamelCase				:      Any									=       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=_UpperCAmelCase			,  from_disk=_UpperCAmelCase      )			,  _UpperCAmelCase			,  _UpperCAmelCase			,  )
				@require_ray
				def 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      List[str]									=       1
							__UpperCamelCase				:      Tuple									=       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=_UpperCAmelCase      )			,  _UpperCAmelCase			,  _UpperCAmelCase      )
							ray.shutdown()
				@require_ray
				def 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      Optional[int]									=       1
							__UpperCamelCase				:      Any									=       np.array(
							    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]			,  dtype=np.floataa      )
							with self.assertRaises(_UpperCAmelCase      ):
										self.distributed_retriever_check(
										    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_UpperCAmelCase			,  from_disk=_UpperCAmelCase      )			,  _UpperCAmelCase			,  _UpperCAmelCase			,  )
							ray.shutdown()





				@require_ray
				def 	a_	(self      )     ->	Optional[int]:
							__UpperCamelCase				:      str									=       1
							__UpperCamelCase				:      Any									=       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=_UpperCAmelCase			,  from_disk=_UpperCAmelCase      )			,  _UpperCAmelCase			,  _UpperCAmelCase      )
							ray.shutdown()









'''simple docstring'''






from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


_lowerCAmelCase				     = logging.get_logger(__name__)  # pylint: disable=invalid-name



class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    ["safety_checker", "feature_extractor"]
				def __init__(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = True			,  )     ->	List[str]:
							super().__init__()
							if hasattr(scheduler.config			,  "steps_offset"      ) and scheduler.config.steps_offset != 1:
										__UpperCamelCase				:      List[str]									=       (
										    f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
										    f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
										    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
										    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
										    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
										    " file"
										)
										deprecate("steps_offset!=1"			,  "1.0.0"			,  _UpperCAmelCase			,  standard_warn=_UpperCAmelCase      )
										__UpperCamelCase				:      str									=       dict(scheduler.config      )
										__UpperCamelCase				:      Optional[Any]									=       1
										__UpperCamelCase				:      Dict									=       FrozenDict(_UpperCAmelCase      )

							self.register_modules(
							    vae=_UpperCAmelCase			,  text_encoder=_UpperCAmelCase			,  tokenizer=_UpperCAmelCase			,  unet=_UpperCAmelCase			,  scheduler=_UpperCAmelCase			,  safety_checker=_UpperCAmelCase			,  feature_extractor=_UpperCAmelCase			,  )
							self.register_to_config(requires_safety_checker=_UpperCAmelCase      )
				@property
				def 	a_	(self      )     ->	Dict[str, Any]:
							return {k: getattr(self			,  _UpperCAmelCase      ) for k in self.config.keys() if not k.startswith("_"      )}
				def 	a_	(self			,  _UpperCAmelCase = "auto"      )     ->	List[str]:
							if slice_size == "auto":
										# half the attention head size is usually a good trade-off between
										# speed and memory
										__UpperCamelCase				:      Dict									=       self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(_UpperCAmelCase      )
				def 	a_	(self      )     ->	Dict:
							self.enable_attention_slicing(_UpperCAmelCase      )
				@torch.no_grad()
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = 0.8			,  _UpperCAmelCase = 5_0			,  _UpperCAmelCase = 7.5			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  _UpperCAmelCase = 0.0			,  _UpperCAmelCase = None			,  _UpperCAmelCase = "pil"			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  )     ->	List[str]:
							# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
							return StableDiffusionInpaintPipelineLegacy(**self.components      )(
							    prompt=_UpperCAmelCase			,  image=_UpperCAmelCase			,  mask_image=_UpperCAmelCase			,  strength=_UpperCAmelCase			,  num_inference_steps=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  negative_prompt=_UpperCAmelCase			,  num_images_per_prompt=_UpperCAmelCase			,  eta=_UpperCAmelCase			,  generator=_UpperCAmelCase			,  output_type=_UpperCAmelCase			,  return_dict=_UpperCAmelCase			,  callback=_UpperCAmelCase			,  )
				@torch.no_grad()
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = 0.8			,  _UpperCAmelCase = 5_0			,  _UpperCAmelCase = 7.5			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  _UpperCAmelCase = 0.0			,  _UpperCAmelCase = None			,  _UpperCAmelCase = "pil"			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  **_UpperCAmelCase			,  )     ->	Optional[Any]:
							# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
							return StableDiffusionImgaImgPipeline(**self.components      )(
							    prompt=_UpperCAmelCase			,  image=_UpperCAmelCase			,  strength=_UpperCAmelCase			,  num_inference_steps=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  negative_prompt=_UpperCAmelCase			,  num_images_per_prompt=_UpperCAmelCase			,  eta=_UpperCAmelCase			,  generator=_UpperCAmelCase			,  output_type=_UpperCAmelCase			,  return_dict=_UpperCAmelCase			,  callback=_UpperCAmelCase			,  callback_steps=_UpperCAmelCase			,  )





				@torch.no_grad()
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase = 5_1_2			,  _UpperCAmelCase = 5_1_2			,  _UpperCAmelCase = 5_0			,  _UpperCAmelCase = 7.5			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  _UpperCAmelCase = 0.0			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  _UpperCAmelCase = "pil"			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  )     ->	Union[str, Any]:
							# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
							return StableDiffusionPipeline(**self.components      )(
							    prompt=_UpperCAmelCase			,  height=_UpperCAmelCase			,  width=_UpperCAmelCase			,  num_inference_steps=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  negative_prompt=_UpperCAmelCase			,  num_images_per_prompt=_UpperCAmelCase			,  eta=_UpperCAmelCase			,  generator=_UpperCAmelCase			,  latents=_UpperCAmelCase			,  output_type=_UpperCAmelCase			,  return_dict=_UpperCAmelCase			,  callback=_UpperCAmelCase			,  callback_steps=_UpperCAmelCase			,  )



