






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

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
		import faiss







@require_faiss
class    A_	(a_							):
							"""simple docstring"""




							def 							_A	(		self			:Dict				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					int   =       tempfile.mkdtemp()
										snake_case_				:					Union[str, Any]   =       8

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

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

							def 							_A	(		self			:Union[str, Any]				)				->  DPRQuestionEncoderTokenizer:


										'''simple docstring'''




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

							def 							_A	(		self			:List[str]				)				->  DPRContextEncoderTokenizer:


										'''simple docstring'''




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

							def 							_A	(		self			:Union[str, Any]				)				->  BartTokenizer:


										'''simple docstring'''




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

							def 							_A	(		self			:int				)				->  Dict:


										'''simple docstring'''




										shutil.rmtree(self.tmpdirname				)

							def 							_A	(		self			:Union[str, Any]				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Dict   =       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			:int				)				->  int:


										'''simple docstring'''




										snake_case_				:					str   =       self.get_dummy_dataset()
										snake_case_				:					str   =       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:
													snake_case_				:					List[Any]   =       dataset
													snake_case_				:					Optional[Any]   =       RagRetriever(
													    lowerCAmelCase__       ,							question_encoder_tokenizer=self.get_dpr_tokenizer()       ,							generator_tokenizer=self.get_bart_tokenizer()       ,							)
										return retriever

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:bool				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       self.get_dummy_dataset()
										snake_case_				:					str   =       RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size       ,							question_encoder=DPRConfig().to_dict()       ,							generator=BartConfig().to_dict()       ,							index_name="custom"       ,							)
										if from_disk:
													snake_case_				:					Any   =       os.path.join(self.tmpdirname       ,							"dataset"				)
													snake_case_				:					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
													snake_case_				:					int   =       RagRetriever(
													    lowerCAmelCase__       ,							question_encoder_tokenizer=self.get_dpr_tokenizer()       ,							generator_tokenizer=self.get_bart_tokenizer()       ,							)
										else:
													snake_case_				:					str   =       RagRetriever(
													    lowerCAmelCase__       ,							question_encoder_tokenizer=self.get_dpr_tokenizer()       ,							generator_tokenizer=self.get_bart_tokenizer()       ,							index=CustomHFIndex(config.retrieval_vector_size       ,							lowerCAmelCase__				)       ,							)
										return retriever

							def 							_A	(		self			:Optional[Any]				)				->  Optional[int]:


										'''simple docstring'''




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

										snake_case_				:					str   =       os.path.join(self.tmpdirname       ,							"hf_bert_base.hnswSQ8_correct_phi_128.c_index"				)
										dataset.save_faiss_index("embeddings"       ,							index_file_name + ".index.dpr"				)
										pickle.dump(dataset["id"]       ,							open(index_file_name + ".index_meta.dpr"       ,							"wb"				)				)

										snake_case_				:					List[str]   =       os.path.join(self.tmpdirname       ,							"psgs_w100.tsv.pkl"				)
										snake_case_				:					int   =       {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
										pickle.dump(lowerCAmelCase__       ,							open(lowerCAmelCase__       ,							"wb"				)				)

										snake_case_				:					str   =       RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size       ,							question_encoder=DPRConfig().to_dict()       ,							generator=BartConfig().to_dict()       ,							index_name="legacy"       ,							index_path=self.tmpdirname       ,							)
										snake_case_				:					Union[str, Any]   =       RagRetriever(
										    lowerCAmelCase__       ,							question_encoder_tokenizer=self.get_dpr_tokenizer()       ,							generator_tokenizer=self.get_bart_tokenizer()				)
										return retriever

							def 							_A	(		self			:int				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       1
										snake_case_				:					List[str]   =       self.get_dummy_canonical_hf_index_retriever()
										snake_case_				:					Optional[int]   =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
										snake_case_				:					Union[str, Any]   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=lowerCAmelCase__				)
										self.assertEqual(retrieved_doc_embeds.shape       ,							(2, n_docs, self.retrieval_vector_size)				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										self.assertEqual(sorted(doc_dicts[0]				)       ,							["embeddings", "id", "text", "title"]				)
										self.assertEqual(len(doc_dicts[0]["id"]				)       ,							lowerCAmelCase__				)
										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			:List[Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       self.get_dummy_canonical_hf_index_retriever()
										with tempfile.TemporaryDirectory() as tmp_dirname:
													with patch("transformers.models.rag.retrieval_rag.load_dataset"				) as mock_load_dataset:
																snake_case_				:					Optional[int]   =       self.get_dummy_dataset()
																retriever.save_pretrained(lowerCAmelCase__				)
																snake_case_				:					Optional[Any]   =       RagRetriever.from_pretrained(lowerCAmelCase__				)
																self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
																snake_case_				:					Tuple   =       np.array(
																    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
																snake_case_				:					str   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=1				)
																self.assertTrue(out is not None				)

							def 							_A	(		self			:int				)				->  Any:


										'''simple docstring'''




										snake_case_				:					str   =       1
										snake_case_				:					Union[str, Any]   =       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase__				)
										snake_case_				:					Optional[Any]   =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
										snake_case_				:					Dict   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=lowerCAmelCase__				)
										self.assertEqual(retrieved_doc_embeds.shape       ,							(2, n_docs, self.retrieval_vector_size)				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										self.assertEqual(sorted(doc_dicts[0]				)       ,							["embeddings", "id", "text", "title"]				)
										self.assertEqual(len(doc_dicts[0]["id"]				)       ,							lowerCAmelCase__				)
										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			:Optional[int]				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase__				)
										with tempfile.TemporaryDirectory() as tmp_dirname:
													retriever.save_pretrained(lowerCAmelCase__				)
													snake_case_				:					Union[str, Any]   =       RagRetriever.from_pretrained(lowerCAmelCase__				)
													self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
													snake_case_				:					int   =       np.array(
													    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
													snake_case_				:					Any   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=1				)
													self.assertTrue(out is not None				)

							def 							_A	(		self			:str				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       1
										snake_case_				:					Any   =       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase__				)
										snake_case_				:					int   =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
										snake_case_				:					int   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=lowerCAmelCase__				)
										self.assertEqual(retrieved_doc_embeds.shape       ,							(2, n_docs, self.retrieval_vector_size)				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										self.assertEqual(sorted(doc_dicts[0]				)       ,							["embeddings", "id", "text", "title"]				)
										self.assertEqual(len(doc_dicts[0]["id"]				)       ,							lowerCAmelCase__				)
										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			:List[Any]				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Any   =       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase__				)
										with tempfile.TemporaryDirectory() as tmp_dirname:
													retriever.save_pretrained(lowerCAmelCase__				)
													snake_case_				:					Dict   =       RagRetriever.from_pretrained(lowerCAmelCase__				)
													self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
													snake_case_				:					Any   =       np.array(
													    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
													snake_case_				:					int   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=1				)
													self.assertTrue(out is not None				)

							def 							_A	(		self			:int				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       1
										snake_case_				:					Dict   =       self.get_dummy_legacy_index_retriever()
										snake_case_				:					Optional[Any]   =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
										snake_case_				:					Optional[Any]   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=lowerCAmelCase__				)
										self.assertEqual(retrieved_doc_embeds.shape       ,							(2, n_docs, self.retrieval_vector_size)				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										self.assertEqual(sorted(doc_dicts[0]				)       ,							["text", "title"]				)
										self.assertEqual(len(doc_dicts[0]["text"]				)       ,							lowerCAmelCase__				)
										self.assertEqual(doc_dicts[0]["text"][0]       ,							"bar"				)  # max inner product is reached with second doc
										self.assertEqual(doc_dicts[1]["text"][0]       ,							"foo"				)  # max inner product is reached with first doc
										self.assertListEqual(doc_ids.tolist()       ,							[[1], [0]]				)

							def 							_A	(		self			:List[Any]				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       self.get_dummy_legacy_index_retriever()
										with tempfile.TemporaryDirectory() as tmp_dirname:
													retriever.save_pretrained(lowerCAmelCase__				)
													snake_case_				:					Optional[Any]   =       RagRetriever.from_pretrained(lowerCAmelCase__				)
													self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
													snake_case_				:					List[Any]   =       np.array(
													    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
													snake_case_				:					List[Any]   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=1				)
													self.assertTrue(out is not None				)

							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def 							_A	(		self			:List[str]				)				->  Dict:


										'''simple docstring'''




										import torch

										snake_case_				:					Optional[Any]   =       1
										snake_case_				:					Dict   =       self.get_dummy_canonical_hf_index_retriever()
										snake_case_				:					str   =       [[5, 7], [10, 11]]
										snake_case_				:					List[Any]   =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
										snake_case_				:					List[Any]   =       retriever(lowerCAmelCase__       ,							lowerCAmelCase__       ,							prefix=retriever.config.generator.prefix       ,							n_docs=lowerCAmelCase__				)
										snake_case_				:					Union[str, Any]   =       (
										    out["context_input_ids"],
										    out["context_attention_mask"],
										    out["retrieved_doc_embeds"],
										)
										self.assertEqual(retrieved_doc_embeds.shape       ,							(2, n_docs, self.retrieval_vector_size)				)
										self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
										self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
										self.assertIsInstance(lowerCAmelCase__       ,							np.ndarray				)

										snake_case_				:					Any   =       retriever(
										    lowerCAmelCase__       ,							lowerCAmelCase__       ,							prefix=retriever.config.generator.prefix       ,							n_docs=lowerCAmelCase__       ,							return_tensors="pt"       ,							)
										snake_case_				:					Union[str, Any]   =       (  # noqa: F841
										    out["context_input_ids"],
										    out["context_attention_mask"],
										    out["retrieved_doc_embeds"],
										    out["doc_ids"],
										)
										self.assertEqual(retrieved_doc_embeds.shape       ,							(2, n_docs, self.retrieval_vector_size)				)
										self.assertIsInstance(lowerCAmelCase__       ,							torch.Tensor				)
										self.assertIsInstance(lowerCAmelCase__       ,							torch.Tensor				)
										self.assertIsInstance(lowerCAmelCase__       ,							torch.Tensor				)






							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def 							_A	(		self			:List[str]				)				->  str:


										'''simple docstring'''




										snake_case_				:					str   =       self.get_dpr_ctx_encoder_tokenizer()
										snake_case_				:					int   =       1
										snake_case_				:					Any   =       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase__				)
										retriever.set_ctx_encoder_tokenizer(lowerCAmelCase__				)

										snake_case_				:					Optional[Any]   =       [[5, 7], [10, 11]]
										snake_case_				:					Optional[int]   =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
										snake_case_				:					str   =       retriever(lowerCAmelCase__       ,							lowerCAmelCase__       ,							prefix=retriever.config.generator.prefix       ,							n_docs=lowerCAmelCase__				)

										self.assertEqual(
										    len(lowerCAmelCase__				)       ,							6				)  # check whether the retriever output consist of 6 attributes including tokenized docs
										self.assertEqual(
										    all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask")				)       ,							lowerCAmelCase__				)  # check for doc token related keys in dictionary.












'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:Any				)				->  str:


										'''simple docstring'''




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

							@property
							def 							_A	(		self			:List[Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Any   =       1
										snake_case_				:					Dict   =       3
										snake_case_				:					Union[str, Any]   =       (32, 32)

										snake_case_				:					Optional[int]   =       floats_tensor((batch_size, num_channels) + sizes       ,							rng=random.Random(0				)				).to(lowerCAmelCase__				)
										return image

							@property
							def 							_A	(		self			:Optional[int]				)				->  Any:


										'''simple docstring'''




										torch.manual_seed(0				)
										snake_case_				:					List[str]   =       UNetaDConditionModel(
										    block_out_channels=(32, 64)       ,							layers_per_block=2       ,							sample_size=32       ,							in_channels=4       ,							out_channels=4       ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,							cross_attention_dim=32       ,							)
										return model

							@property
							def 							_A	(		self			:Dict				)				->  Any:


										'''simple docstring'''




										torch.manual_seed(0				)
										snake_case_				:					Optional[Any]   =       AutoencoderKL(
										    block_out_channels=[32, 64]       ,							in_channels=3       ,							out_channels=3       ,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       ,							latent_channels=4       ,							)
										return model

							@property
							def 							_A	(		self			:Dict				)				->  Optional[int]:


										'''simple docstring'''




										torch.manual_seed(0				)
										snake_case_				:					str   =       RobertaSeriesConfig(
										    hidden_size=32       ,							project_dim=32       ,							intermediate_size=37       ,							layer_norm_eps=1E-0_5       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							pad_token_id=1       ,							vocab_size=5_006       ,							)
										return RobertaSeriesModelWithTransformation(lowerCAmelCase__				)

							@property
							def 							_A	(		self			:Any				)				->  str:


										'''simple docstring'''




										def extract(*lowerCAmelCase__			:Any       ,							**lowerCAmelCase__			:List[str]				):







													class    A_	:
																				"""simple docstring"""




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


																							'''simple docstring'''




																							snake_case_				:					str   =       torch.ones([0]				)






																				def 							_A	(		self			:int       ,							lowerCAmelCase__			:List[Any]				)				->  Tuple:


																							'''simple docstring'''




																							self.pixel_values.to(lowerCAmelCase__				)
																							return self
													return Out()

										return extract

							def 							_A	(		self			:int				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					str   =       "cpu"  # ensure determinism for the device-dependent torch.Generator
										snake_case_				:					str   =       self.dummy_cond_unet
										snake_case_				:					Optional[int]   =       PNDMScheduler(skip_prk_steps=lowerCAmelCase__				)
										snake_case_				:					Dict   =       self.dummy_vae
										snake_case_				:					Dict   =       self.dummy_text_encoder
										snake_case_				:					Optional[int]   =       XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"				)
										snake_case_				:					str   =       77

										snake_case_				:					Any   =       self.dummy_image.to(lowerCAmelCase__				)
										snake_case_				:					Tuple   =       init_image / 2 + 0.5

										# make sure here that pndm scheduler skips prk
										snake_case_				:					Optional[Any]   =       AltDiffusionImgaImgPipeline(
										    unet=lowerCAmelCase__       ,							scheduler=lowerCAmelCase__       ,							vae=lowerCAmelCase__       ,							text_encoder=lowerCAmelCase__       ,							tokenizer=lowerCAmelCase__       ,							safety_checker=lowerCAmelCase__       ,							feature_extractor=self.dummy_extractor       ,							)
										snake_case_				:					Union[str, Any]   =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,							do_normalize=lowerCAmelCase__				)
										snake_case_				:					Optional[Any]   =       alt_pipe.to(lowerCAmelCase__				)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

										snake_case_				:					Dict   =       "A painting of a squirrel eating a burger"
										snake_case_				:					List[str]   =       torch.Generator(device=lowerCAmelCase__				).manual_seed(0				)
										snake_case_				:					Dict   =       alt_pipe(
										    [prompt]       ,							generator=lowerCAmelCase__       ,							guidance_scale=6.0       ,							num_inference_steps=2       ,							output_type="np"       ,							image=lowerCAmelCase__       ,							)

										snake_case_				:					Any   =       output.images

										snake_case_				:					List[str]   =       torch.Generator(device=lowerCAmelCase__				).manual_seed(0				)
										snake_case_				:					Optional[Any]   =       alt_pipe(
										    [prompt]       ,							generator=lowerCAmelCase__       ,							guidance_scale=6.0       ,							num_inference_steps=2       ,							output_type="np"       ,							image=lowerCAmelCase__       ,							return_dict=lowerCAmelCase__       ,							)[0]

										snake_case_				:					Tuple   =       image[0, -3:, -3:, -1]
										snake_case_				:					Dict   =       image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										snake_case_				:					int   =       np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-3
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 5E-3

							@unittest.skipIf(torch_device != "cuda"       ,							"This test requires a GPU"				)
							def 							_A	(		self			:int				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       self.dummy_cond_unet
										snake_case_				:					Union[str, Any]   =       PNDMScheduler(skip_prk_steps=lowerCAmelCase__				)
										snake_case_				:					int   =       self.dummy_vae
										snake_case_				:					List[Any]   =       self.dummy_text_encoder
										snake_case_				:					int   =       XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"				)
										snake_case_				:					int   =       77

										snake_case_				:					Dict   =       self.dummy_image.to(lowerCAmelCase__				)

										# put models in fp16
										snake_case_				:					Optional[Any]   =       unet.half()
										snake_case_				:					Tuple   =       vae.half()
										snake_case_				:					List[str]   =       bert.half()

										# make sure here that pndm scheduler skips prk
										snake_case_				:					Optional[int]   =       AltDiffusionImgaImgPipeline(
										    unet=lowerCAmelCase__       ,							scheduler=lowerCAmelCase__       ,							vae=lowerCAmelCase__       ,							text_encoder=lowerCAmelCase__       ,							tokenizer=lowerCAmelCase__       ,							safety_checker=lowerCAmelCase__       ,							feature_extractor=self.dummy_extractor       ,							)
										snake_case_				:					List[str]   =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,							do_normalize=lowerCAmelCase__				)
										snake_case_				:					Optional[Any]   =       alt_pipe.to(lowerCAmelCase__				)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

										snake_case_				:					List[Any]   =       "A painting of a squirrel eating a burger"
										snake_case_				:					str   =       torch.manual_seed(0				)
										snake_case_				:					Any   =       alt_pipe(
										    [prompt]       ,							generator=lowerCAmelCase__       ,							num_inference_steps=2       ,							output_type="np"       ,							image=lowerCAmelCase__       ,							).images

										assert image.shape == (1, 32, 32, 3)






							@unittest.skipIf(torch_device != "cuda"       ,							"This test requires a GPU"				)
							def 							_A	(		self			:Optional[int]				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"				)
										# resize to resolution that is divisible by 8 but not 16 or 32
										snake_case_				:					str   =       init_image.resize((760, 504)				)

										snake_case_				:					Optional[Any]   =       "BAAI/AltDiffusion"
										snake_case_				:					int   =       AltDiffusionImgaImgPipeline.from_pretrained(
										    lowerCAmelCase__       ,							safety_checker=lowerCAmelCase__       ,							)
										pipe.to(lowerCAmelCase__				)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)
										pipe.enable_attention_slicing()

										snake_case_				:					Tuple   =       "A fantasy landscape, trending on artstation"

										snake_case_				:					int   =       torch.manual_seed(0				)
										snake_case_				:					List[str]   =       pipe(
										    prompt=lowerCAmelCase__       ,							image=lowerCAmelCase__       ,							strength=0.7_5       ,							guidance_scale=7.5       ,							generator=lowerCAmelCase__       ,							output_type="np"       ,							)
										snake_case_				:					str   =       output.images[0]

										snake_case_				:					List[Any]   =       image[255:258, 383:386, -1]

										assert image.shape == (504, 760, 3)
										snake_case_				:					Tuple   =       np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2







@slow
@require_torch_gpu
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:Optional[Any]				)				->  Optional[int]:


										'''simple docstring'''




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






							def 							_A	(		self			:str				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"				)
										snake_case_				:					List[Any]   =       init_image.resize((768, 512)				)
										snake_case_				:					Tuple   =       load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"				)

										snake_case_				:					Any   =       "BAAI/AltDiffusion"
										snake_case_				:					List[str]   =       AltDiffusionImgaImgPipeline.from_pretrained(
										    lowerCAmelCase__       ,							safety_checker=lowerCAmelCase__       ,							)
										pipe.to(lowerCAmelCase__				)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)
										pipe.enable_attention_slicing()

										snake_case_				:					Tuple   =       "A fantasy landscape, trending on artstation"

										snake_case_				:					Tuple   =       torch.manual_seed(0				)
										snake_case_				:					List[Any]   =       pipe(
										    prompt=lowerCAmelCase__       ,							image=lowerCAmelCase__       ,							strength=0.7_5       ,							guidance_scale=7.5       ,							generator=lowerCAmelCase__       ,							output_type="np"       ,							)
										snake_case_				:					Optional[int]   =       output.images[0]

										assert image.shape == (512, 768, 3)
										# img2img is flaky across GPUs even in fp32, so using MAE here
										assert np.abs(expected_image - image				).max() < 1E-2




