
"""simple docstring"""




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def        _lowerCamelCase ( __a,							__a=0.9_9_9,							__a="cosine",							):
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(__a    ):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2    ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(__a    ):
						return math.exp(t * -1_2.0    )

		else:
				raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )

		SCREAMING_SNAKE_CASE_						 = []
		for i in range(__a    ):
				SCREAMING_SNAKE_CASE_						 = i / num_diffusion_timesteps
				SCREAMING_SNAKE_CASE_						 = (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(__a    ) / alpha_bar_fn(__a    ),							__a    )    )
		return torch.tensor(__a,							dtype=torch.floataa    )





class  snake_case     (							__lowercase       ,			__lowercase   ):
							UpperCAmelCase__						     =      [e.name for e in KarrasDiffusionSchedulers]
							UpperCAmelCase__						     =      2

							@register_to_config
							def __init__(self       ,       SCREAMING_SNAKE_CASE_ = 10_00       ,       SCREAMING_SNAKE_CASE_ = 0.0_00_85       ,       SCREAMING_SNAKE_CASE_ = 0.0_12       ,       SCREAMING_SNAKE_CASE_ = "linear"       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = "epsilon"       ,       SCREAMING_SNAKE_CASE_ = "linspace"       ,       SCREAMING_SNAKE_CASE_ = 0       ,       ):

									"""simple docstring"""







									if trained_betas is not None:
											SCREAMING_SNAKE_CASE_						 = torch.tensor(SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    )
									elif beta_schedule == "linear":
											SCREAMING_SNAKE_CASE_						 = torch.linspace(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    )
									elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											SCREAMING_SNAKE_CASE_						 = (
											    torch.linspace(beta_start**0.5       ,       beta_end**0.5       ,       SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    ) ** 2
											)
									elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											SCREAMING_SNAKE_CASE_						 = betas_for_alpha_bar(SCREAMING_SNAKE_CASE_    )
									else:
											raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'    )

									SCREAMING_SNAKE_CASE_						 = 1.0 - self.betas
									SCREAMING_SNAKE_CASE_						 = torch.cumprod(self.alphas       ,       dim=0    )

									#  set all values
									self.set_timesteps(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=None    ):

									"""simple docstring"""







									if schedule_timesteps is None:
											SCREAMING_SNAKE_CASE_						 = self.timesteps

									SCREAMING_SNAKE_CASE_						 = (schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter    ) == 0:
											SCREAMING_SNAKE_CASE_						 = 1 if len(SCREAMING_SNAKE_CASE_    ) > 1 else 0
									else:
											SCREAMING_SNAKE_CASE_						 = timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_    ) else timestep
											SCREAMING_SNAKE_CASE_						 = self._index_counter[timestep_int]

									return indices[pos].item()

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.index_for_timestep(SCREAMING_SNAKE_CASE_    )

									if self.state_in_first_order:
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index]
									else:
											SCREAMING_SNAKE_CASE_						 = self.sigmas_interpol[step_index]

									SCREAMING_SNAKE_CASE_						 = sample / ((sigma**2 + 1) ** 0.5)
									return sample

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = num_inference_steps

									SCREAMING_SNAKE_CASE_						 = num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
											SCREAMING_SNAKE_CASE_						 = np.linspace(0       ,       num_train_timesteps - 1       ,       SCREAMING_SNAKE_CASE_       ,       dtype=SCREAMING_SNAKE_CASE_    )[::-1].copy()
									elif self.config.timestep_spacing == "leading":
											SCREAMING_SNAKE_CASE_						 = num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											SCREAMING_SNAKE_CASE_						 = (np.arange(0       ,       SCREAMING_SNAKE_CASE_    ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_    )
											timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
											SCREAMING_SNAKE_CASE_						 = num_train_timesteps / self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											SCREAMING_SNAKE_CASE_						 = (np.arange(SCREAMING_SNAKE_CASE_       ,       0       ,       -step_ratio    )).round().copy().astype(SCREAMING_SNAKE_CASE_    )
											timesteps -= 1
									else:
											raise ValueError(
											    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'    )

									SCREAMING_SNAKE_CASE_						 = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
									SCREAMING_SNAKE_CASE_						 = torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_    )    ).to(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = np.interp(SCREAMING_SNAKE_CASE_       ,       np.arange(0       ,       len(SCREAMING_SNAKE_CASE_    )    )       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
									SCREAMING_SNAKE_CASE_						 = torch.from_numpy(SCREAMING_SNAKE_CASE_    ).to(device=SCREAMING_SNAKE_CASE_    )

									# interpolate sigmas
									SCREAMING_SNAKE_CASE_						 = sigmas.log().lerp(sigmas.roll(1    ).log()       ,       0.5    ).exp()

									SCREAMING_SNAKE_CASE_						 = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
									SCREAMING_SNAKE_CASE_						 = torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2    ), sigmas_interpol[-1:]]    )

									if str(SCREAMING_SNAKE_CASE_    ).startswith('''mps'''    ):
											# mps does not support float64
											SCREAMING_SNAKE_CASE_						 = torch.from_numpy(SCREAMING_SNAKE_CASE_    ).to(SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    )
									else:
											SCREAMING_SNAKE_CASE_						 = torch.from_numpy(SCREAMING_SNAKE_CASE_    ).to(SCREAMING_SNAKE_CASE_    )

									# interpolate timesteps
									SCREAMING_SNAKE_CASE_						 = self.sigma_to_t(SCREAMING_SNAKE_CASE_    ).to(SCREAMING_SNAKE_CASE_       ,       dtype=timesteps.dtype    )
									SCREAMING_SNAKE_CASE_						 = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])       ,       dim=-1    ).flatten()

									SCREAMING_SNAKE_CASE_						 = torch.cat([timesteps[:1], interleaved_timesteps]    )

									SCREAMING_SNAKE_CASE_						 = None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									SCREAMING_SNAKE_CASE_						 = defaultdict(SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = sigma.log()

									# get distribution
									SCREAMING_SNAKE_CASE_						 = log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									SCREAMING_SNAKE_CASE_						 = dists.ge(0    ).cumsum(dim=0    ).argmax(dim=0    ).clamp(max=self.log_sigmas.shape[0] - 2    )
									SCREAMING_SNAKE_CASE_						 = low_idx + 1

									SCREAMING_SNAKE_CASE_						 = self.log_sigmas[low_idx]
									SCREAMING_SNAKE_CASE_						 = self.log_sigmas[high_idx]

									# interpolate sigmas
									SCREAMING_SNAKE_CASE_						 = (low - log_sigma) / (low - high)
									SCREAMING_SNAKE_CASE_						 = w.clamp(0       ,       1    )

									# transform interpolation to time range
									SCREAMING_SNAKE_CASE_						 = (1 - w) * low_idx + w * high_idx
									SCREAMING_SNAKE_CASE_						 = t.view(sigma.shape    )
									return t

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self.sample is None

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = True       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.index_for_timestep(SCREAMING_SNAKE_CASE_    )

									# advance index counter by 1
									SCREAMING_SNAKE_CASE_						 = timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_    ) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index]
											SCREAMING_SNAKE_CASE_						 = self.sigmas_interpol[step_index + 1]
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index + 1]
									else:
											# 2nd order / KDPM2's method
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index - 1]
											SCREAMING_SNAKE_CASE_						 = self.sigmas_interpol[step_index]
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									SCREAMING_SNAKE_CASE_						 = 0
									SCREAMING_SNAKE_CASE_						 = sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
											SCREAMING_SNAKE_CASE_						 = sigma_hat if self.state_in_first_order else sigma_interpol
											SCREAMING_SNAKE_CASE_						 = sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
											SCREAMING_SNAKE_CASE_						 = sigma_hat if self.state_in_first_order else sigma_interpol
											SCREAMING_SNAKE_CASE_						 = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
											    sample / (sigma_input**2 + 1)
											)
									elif self.config.prediction_type == "sample":
											raise NotImplementedError('''prediction_type not implemented yet: sample'''    )
									else:
											raise ValueError(
											    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'    )

									if self.state_in_first_order:
											# 2. Convert to an ODE derivative for 1st order
											SCREAMING_SNAKE_CASE_						 = (sample - pred_original_sample) / sigma_hat
											# 3. delta timestep
											SCREAMING_SNAKE_CASE_						 = sigma_interpol - sigma_hat

											# store for 2nd order step
											SCREAMING_SNAKE_CASE_						 = sample
									else:
											# DPM-Solver-2
											# 2. Convert to an ODE derivative for 2nd order
											SCREAMING_SNAKE_CASE_						 = (sample - pred_original_sample) / sigma_interpol

											# 3. delta timestep
											SCREAMING_SNAKE_CASE_						 = sigma_next - sigma_hat

											SCREAMING_SNAKE_CASE_						 = self.sample
											SCREAMING_SNAKE_CASE_						 = None

									SCREAMING_SNAKE_CASE_						 = sample + derivative * dt

									if not return_dict:
											return (prev_sample,)

									return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.sigmas.to(device=original_samples.device       ,       dtype=original_samples.dtype    )
									if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_    ):
											# mps does not support float64
											SCREAMING_SNAKE_CASE_						 = self.timesteps.to(original_samples.device       ,       dtype=torch.floataa    )
											SCREAMING_SNAKE_CASE_						 = timesteps.to(original_samples.device       ,       dtype=torch.floataa    )
									else:
											SCREAMING_SNAKE_CASE_						 = self.timesteps.to(original_samples.device    )
											SCREAMING_SNAKE_CASE_						 = timesteps.to(original_samples.device    )

									SCREAMING_SNAKE_CASE_						 = [self.index_for_timestep(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) for t in timesteps]

									SCREAMING_SNAKE_CASE_						 = sigmas[step_indices].flatten()
									while len(sigma.shape    ) < len(original_samples.shape    ):
											SCREAMING_SNAKE_CASE_						 = sigma.unsqueeze(-1    )

									SCREAMING_SNAKE_CASE_						 = original_samples + noise * sigma
									return noisy_samples







							def __len__(self    ):

									"""simple docstring"""







									return self.config.num_train_timesteps

"""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  snake_case     (							__lowercase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE_						 = 8

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

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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									shutil.rmtree(self.tmpdirname    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE_						 = 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:
											SCREAMING_SNAKE_CASE_						 = dataset
											SCREAMING_SNAKE_CASE_						 = RagRetriever(
											    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       )
									return retriever

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE_						 = RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,       question_encoder=DPRConfig().to_dict()       ,       generator=BartConfig().to_dict()       ,       index_name='''custom'''       ,       )
									if from_disk:
											SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       '''dataset'''    )
											SCREAMING_SNAKE_CASE_						 = 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
											SCREAMING_SNAKE_CASE_						 = RagRetriever(
											    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       )
									else:
											SCREAMING_SNAKE_CASE_						 = RagRetriever(
											    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       index=CustomHFIndex(config.retrieval_vector_size       ,       SCREAMING_SNAKE_CASE_    )       ,       )
									return retriever

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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    )

									SCREAMING_SNAKE_CASE_						 = 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'''    )    )

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

									SCREAMING_SNAKE_CASE_						 = RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,       question_encoder=DPRConfig().to_dict()       ,       generator=BartConfig().to_dict()       ,       index_name='''legacy'''       ,       index_path=self.tmpdirname       ,       )
									SCREAMING_SNAKE_CASE_						 = RagRetriever(
									    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()    )
									return retriever

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_canonical_hf_index_retriever()
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       2    )
									self.assertEqual(sorted(doc_dicts[0]    )       ,       ['''embeddings''', '''id''', '''text''', '''title''']    )
									self.assertEqual(len(doc_dicts[0]['''id''']    )       ,       SCREAMING_SNAKE_CASE_    )
									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 _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       2    )
									self.assertEqual(sorted(doc_dicts[0]    )       ,       ['''embeddings''', '''id''', '''text''', '''title''']    )
									self.assertEqual(len(doc_dicts[0]['''id''']    )       ,       SCREAMING_SNAKE_CASE_    )
									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 _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       2    )
									self.assertEqual(sorted(doc_dicts[0]    )       ,       ['''embeddings''', '''id''', '''text''', '''title''']    )
									self.assertEqual(len(doc_dicts[0]['''id''']    )       ,       SCREAMING_SNAKE_CASE_    )
									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 _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_legacy_index_retriever()
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       2    )
									self.assertEqual(sorted(doc_dicts[0]    )       ,       ['''text''', '''title''']    )
									self.assertEqual(len(doc_dicts[0]['''text''']    )       ,       SCREAMING_SNAKE_CASE_    )
									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 _lowercase					(self    ):

									"""simple docstring"""







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

							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def _lowercase					(self    ):

									"""simple docstring"""







									import torch

									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_canonical_hf_index_retriever()
									SCREAMING_SNAKE_CASE_						 = [[5, 7], [10, 11]]
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_						 = retriever(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       prefix=retriever.config.generator.prefix       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = (
									    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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       np.ndarray    )

									SCREAMING_SNAKE_CASE_						 = retriever(
									    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       prefix=retriever.config.generator.prefix       ,       n_docs=SCREAMING_SNAKE_CASE_       ,       return_tensors='''pt'''       ,       )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = (  # 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(SCREAMING_SNAKE_CASE_       ,       torch.Tensor    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       torch.Tensor    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       torch.Tensor    )







							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dpr_ctx_encoder_tokenizer()
									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = [[5, 7], [10, 11]]
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_						 = retriever(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       prefix=retriever.config.generator.prefix       ,       n_docs=SCREAMING_SNAKE_CASE_    )

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