


'''simple docstring'''


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 		lowerCAmelCase__	(	SCREAMING_SNAKE_CASE_		):




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



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					tempfile.mkdtemp()
					_UpperCAmelCase						:				Dict	    =					8

					# DPR tok
					_UpperCAmelCase						:				int	    =					[
					    '[UNK]',
					    '[CLS]',
					    '[SEP]',
					    '[PAD]',
					    '[MASK]',
					    'want',
					    '##want',
					    '##ed',
					    'wa',
					    'un',
					    'runn',
					    '##ing',
					    ',',
					    'low',
					    'lowest',
					]
					_UpperCAmelCase						:				int	    =					os.path.join(self.tmpdirname		,     "dpr_tokenizer"       )
					os.makedirs(_a		,     exist_ok=_a       )
					_UpperCAmelCase						:				Union[str, Any]	    =					os.path.join(_a		,     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						:				int	    =					[
					    'l',
					    'o',
					    'w',
					    'e',
					    'r',
					    's',
					    't',
					    'i',
					    'd',
					    'n',
					    '\u0120',
					    '\u0120l',
					    '\u0120n',
					    '\u0120lo',
					    '\u0120low',
					    'er',
					    '\u0120lowest',
					    '\u0120newer',
					    '\u0120wider',
					    '<unk>',
					]
					_UpperCAmelCase						:				str	    =					dict(zip(_a		,     range(len(_a       )       )       )       )
					_UpperCAmelCase						:				List[Any]	    =					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
					_UpperCAmelCase						:				Tuple	    =					{'unk_token': '<unk>'}

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




			def 							lowerCAmelCase__			(						self     :   int       )      ->DPRQuestionEncoderTokenizer:



					'''simple docstring'''







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




			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->DPRContextEncoderTokenizer:



					'''simple docstring'''







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




			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->BartTokenizer:



					'''simple docstring'''







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




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



					'''simple docstring'''







					shutil.rmtree(self.tmpdirname       )




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



					'''simple docstring'''







					_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 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					self.get_dummy_dataset()
					_UpperCAmelCase						:				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						:				Union[str, Any]	    =					dataset
							_UpperCAmelCase						:				List[str]	    =					RagRetriever(
							    _a		,     question_encoder_tokenizer=self.get_dpr_tokenizer()		,     generator_tokenizer=self.get_bart_tokenizer()		,     )
					return retriever




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   bool       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					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"		,     )
					if from_disk:
							_UpperCAmelCase						:				Dict	    =					os.path.join(self.tmpdirname		,     "dataset"       )
							_UpperCAmelCase						:				Optional[int]	    =					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]	    =					RagRetriever(
							    _a		,     question_encoder_tokenizer=self.get_dpr_tokenizer()		,     generator_tokenizer=self.get_bart_tokenizer()		,     )
					else:
							_UpperCAmelCase						:				Tuple	    =					RagRetriever(
							    _a		,     question_encoder_tokenizer=self.get_dpr_tokenizer()		,     generator_tokenizer=self.get_bart_tokenizer()		,     index=CustomHFIndex(config.retrieval_vector_size		,     _a       )		,     )
					return retriever




			def 							lowerCAmelCase__			(						self     :   int       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					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       )

					_UpperCAmelCase						:				Union[str, Any]	    =					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"       )       )

					_UpperCAmelCase						:				List[Any]	    =					os.path.join(self.tmpdirname		,     "psgs_w100.tsv.pkl"       )
					_UpperCAmelCase						:				Dict	    =					{sample['id']: [sample['text'], sample['title']] for sample in dataset}
					pickle.dump(_a		,     open(_a		,     "wb"       )       )

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




			def 							lowerCAmelCase__			(						self     :   Dict       )      ->Any:



					'''simple docstring'''







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



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					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:
									_UpperCAmelCase						:				Any	    =					self.get_dummy_dataset()
									retriever.save_pretrained(_a       )
									_UpperCAmelCase						:				str	    =					RagRetriever.from_pretrained(_a       )
									self.assertIsInstance(_a		,     _a       )
									_UpperCAmelCase						:				Any	    =					np.array(
									    [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]		,     dtype=np.floataa       )
									_UpperCAmelCase						:				Optional[int]	    =					retriever.retrieve(_a		,     n_docs=1       )
									self.assertTrue(out is not None       )




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



					'''simple docstring'''







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



					'''simple docstring'''







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




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



					'''simple docstring'''







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



					'''simple docstring'''







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




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



					'''simple docstring'''







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



					'''simple docstring'''







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




			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def 							lowerCAmelCase__			(						self     :   Dict       )      ->str:



					'''simple docstring'''







					import torch

					_UpperCAmelCase						:				List[str]	    =					1
					_UpperCAmelCase						:				Optional[Any]	    =					self.get_dummy_canonical_hf_index_retriever()
					_UpperCAmelCase						:				Optional[int]	    =					[[5, 7], [10, 11]]
					_UpperCAmelCase						:				str	    =					np.array(
					    [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]		,     dtype=np.floataa       )
					_UpperCAmelCase						:				Optional[int]	    =					retriever(_a		,     _a		,     prefix=retriever.config.generator.prefix		,     n_docs=_a       )
					_UpperCAmelCase						:				Tuple	    =					(
					    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(_a		,     _a       )
					self.assertIsInstance(_a		,     _a       )
					self.assertIsInstance(_a		,     np.ndarray       )

					_UpperCAmelCase						:				Union[str, Any]	    =					retriever(
					    _a		,     _a		,     prefix=retriever.config.generator.prefix		,     n_docs=_a		,     return_tensors="pt"		,     )
					_UpperCAmelCase						:				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(_a		,     torch.Tensor       )
					self.assertIsInstance(_a		,     torch.Tensor       )
					self.assertIsInstance(_a		,     torch.Tensor       )






			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def 							lowerCAmelCase__			(						self     :   Any       )      ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					self.get_dpr_ctx_encoder_tokenizer()
					_UpperCAmelCase						:				Dict	    =					1
					_UpperCAmelCase						:				int	    =					self.get_dummy_custom_hf_index_retriever(from_disk=_a       )
					retriever.set_ctx_encoder_tokenizer(_a       )

					_UpperCAmelCase						:				Union[str, Any]	    =					[[5, 7], [10, 11]]
					_UpperCAmelCase						:				Any	    =					np.array(
					    [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]		,     dtype=np.floataa       )
					_UpperCAmelCase						:				Any	    =					retriever(_a		,     _a		,     prefix=retriever.config.generator.prefix		,     n_docs=_a       )

					self.assertEqual(
					    len(_a       )		,     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")       )		,     _a       )  # check for doc token related keys in dictionary.








'''simple docstring'''





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_UpperCamelCase											=						8

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=BITS       )      ->      Any:
  __lowerCamelCase       :       List[str]           = x.device

  __lowerCamelCase       :       int           = (x * 255).int().clamp(0       ,255       )

  __lowerCamelCase       :       Any           = 2 ** torch.arange(bits - 1       ,-1       ,-1       ,device=_lowerCAmelCase       )
  __lowerCamelCase       :       List[str]           = rearrange(_lowerCAmelCase       ,'d -> d 1 1'       )
  __lowerCamelCase       :       Optional[Any]           = rearrange(_lowerCAmelCase       ,'b c h w -> b c 1 h w'       )

  __lowerCamelCase       :       Optional[Any]           = ((x & mask) != 0).float()
  __lowerCamelCase       :       Tuple           = rearrange(_lowerCAmelCase       ,'b c d h w -> b (c d) h w'       )
  __lowerCamelCase       :       Union[str, Any]           = bits * 2 - 1
  return bits

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=BITS       )      ->      Optional[Any]:
  __lowerCamelCase       :       int           = x.device

  __lowerCamelCase       :       int           = (x > 0).int()
  __lowerCamelCase       :       List[str]           = 2 ** torch.arange(bits - 1       ,-1       ,-1       ,device=_lowerCAmelCase       ,dtype=torch.intaa       )

  __lowerCamelCase       :       Any           = rearrange(_lowerCAmelCase       ,'d -> d 1 1'       )
  __lowerCamelCase       :       Any           = rearrange(_lowerCAmelCase       ,'b (c d) h w -> b c d h w'       ,d=8       )
  __lowerCamelCase       :       List[str]           = reduce(x * mask       ,'b c d h w -> b c h w'       ,'sum'       )
  return (dec / 255).clamp(0.0       ,1.0       )

def        a_     (							self       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 0.0       ,_lowerCAmelCase = True       ,_lowerCAmelCase=None       ,_lowerCAmelCase = True       ,)      ->      Union[DDIMSchedulerOutput, Tuple]:
  if self.num_inference_steps is None:
    raise ValueError(
        'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'       )

  # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  # Ideally, read DDIM paper in-detail understanding

  # Notation (<variable name> -> <name in paper>
  # - pred_noise_t -> e_theta(x_t, t)
  # - pred_original_sample -> f_theta(x_t, t) or x_0
  # - std_dev_t -> sigma_t
  # - eta -> η
  # - pred_sample_direction -> "direction pointing to x_t"
  # - pred_prev_sample -> "x_t-1"

  # 1. get previous step value (=t-1)
  __lowerCamelCase       :       Optional[Any]           = timestep - self.config.num_train_timesteps // self.num_inference_steps

  # 2. compute alphas, betas
  __lowerCamelCase       :       str           = self.alphas_cumprod[timestep]
  __lowerCamelCase       :       Optional[Any]           = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

  __lowerCamelCase       :       Union[str, Any]           = 1 - alpha_prod_t

  # 3. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  __lowerCamelCase       :       Optional[int]           = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  __lowerCamelCase       :       Dict           = self.bit_scale
  if self.config.clip_sample:
    __lowerCamelCase       :       Optional[Any]           = torch.clamp(_lowerCAmelCase       ,-scale       ,_lowerCAmelCase       )

  # 5. compute variance: "sigma_t(η)" -> see formula (16)
  # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  __lowerCamelCase       :       int           = self._get_variance(_lowerCAmelCase       ,_lowerCAmelCase       )
  __lowerCamelCase       :       Tuple           = eta * variance ** 0.5

  if use_clipped_model_output:
    # the model_output is always re-derived from the clipped x_0 in Glide
    __lowerCamelCase       :       List[str]           = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

  # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  __lowerCamelCase       :       Union[str, Any]           = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

  # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  __lowerCamelCase       :       Any           = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

  if eta > 0:
    # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
    __lowerCamelCase       :       Dict           = model_output.device if torch.is_tensor(_lowerCAmelCase       ) else 'cpu'
    __lowerCamelCase       :       Dict           = torch.randn(model_output.shape       ,dtype=model_output.dtype       ,generator=_lowerCAmelCase       ).to(_lowerCAmelCase       )
    __lowerCamelCase       :       Optional[Any]           = self._get_variance(_lowerCAmelCase       ,_lowerCAmelCase       ) ** 0.5 * eta * noise

    __lowerCamelCase       :       int           = prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase       ,pred_original_sample=_lowerCAmelCase       )

def        a_     (							self       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase="epsilon"       ,_lowerCAmelCase=None       ,_lowerCAmelCase = True       ,)      ->      Union[DDPMSchedulerOutput, Tuple]:
  __lowerCamelCase       :       Dict           = timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
    __lowerCamelCase       ,__lowerCamelCase       :       int           = torch.split(_lowerCAmelCase       ,sample.shape[1]       ,dim=1       )
  else:
    __lowerCamelCase       :       Optional[Any]           = None

  # 1. compute alphas, betas
  __lowerCamelCase       :       Optional[int]           = self.alphas_cumprod[t]
  __lowerCamelCase       :       Optional[int]           = self.alphas_cumprod[t - 1] if t > 0 else self.one
  __lowerCamelCase       :       Any           = 1 - alpha_prod_t
  __lowerCamelCase       :       Any           = 1 - alpha_prod_t_prev

  # 2. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  if prediction_type == "epsilon":
    __lowerCamelCase       :       List[Any]           = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif prediction_type == "sample":
    __lowerCamelCase       :       Any           = model_output
  else:
    raise ValueError(F'Unsupported prediction_type {prediction_type}.'       )

  # 3. Clip "predicted x_0"
  __lowerCamelCase       :       Optional[Any]           = self.bit_scale
  if self.config.clip_sample:
    __lowerCamelCase       :       Union[str, Any]           = torch.clamp(_lowerCAmelCase       ,-scale       ,_lowerCAmelCase       )

  # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  __lowerCamelCase       :       Union[str, Any]           = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  __lowerCamelCase       :       Dict           = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

  # 5. Compute predicted previous sample µ_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  __lowerCamelCase       :       Dict           = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

  # 6. Add noise
  __lowerCamelCase       :       Optional[Any]           = 0
  if t > 0:
    __lowerCamelCase       :       Any           = torch.randn(
        model_output.size()       ,dtype=model_output.dtype       ,layout=model_output.layout       ,generator=_lowerCAmelCase       ).to(model_output.device       )
    __lowerCamelCase       :       Optional[Any]           = (self._get_variance(_lowerCAmelCase       ,predicted_variance=_lowerCAmelCase       ) ** 0.5) * noise

  __lowerCamelCase       :       str           = pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=_lowerCAmelCase       ,pred_original_sample=_lowerCAmelCase       )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def __init__( self      :	List[str]			,	_a      :	UNetaDConditionModel			,	_a      :	Union[DDIMScheduler, DDPMScheduler]			,	_a      :	Optional[float] = 1.0			,	)					->					Union[str, Any]:
    super().__init__()
    __lowerCamelCase       :       Dict           = bit_scale
    __lowerCamelCase       :       List[str]           = (
        ddim_bit_scheduler_step if isinstance(_a			,	_a   ) else ddpm_bit_scheduler_step
    )

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




  @torch.no_grad()
  def __call__( self      :	Tuple			,	_a      :	Optional[int] = 256			,	_a      :	Optional[int] = 256			,	_a      :	Optional[int] = 50			,	_a      :	Optional[torch.Generator] = None			,	_a      :	Optional[int] = 1			,	_a      :	Optional[str] = "pil"			,	_a      :	bool = True			,	**_a      :	Dict			,	)					->					Union[Tuple, ImagePipelineOutput]:
    __lowerCamelCase       :       str           = torch.randn(
        (batch_size, self.unet.config.in_channels, height, width)			,	generator=_a			,	)
    __lowerCamelCase       :       int           = decimal_to_bits(_a   ) * self.bit_scale
    __lowerCamelCase       :       Dict           = latents.to(self.device   )

    self.scheduler.set_timesteps(_a   )

    for t in self.progress_bar(self.scheduler.timesteps   ):
      # predict the noise residual
      __lowerCamelCase       :       int           = self.unet(_a			,	_a   ).sample

      # compute the previous noisy sample x_t -> x_t-1
      __lowerCamelCase       :       List[Any]           = self.scheduler.step(_a			,	_a			,	_a   ).prev_sample

    __lowerCamelCase       :       Dict           = bits_to_decimal(_a   )

    if output_type == "pil":
      __lowerCamelCase       :       int           = self.numpy_to_pil(_a   )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_a   )






