






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  a__ (__lowercase							:List[str]					,  __lowercase							:str=0.999					,  __lowercase							:Tuple="cosine"					,  )		-> Union[str, Any]:
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(__lowercase							:Any   ):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(__lowercase							:int   ):
												return math.exp(t * -12.0   )

				else:
								raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""   )

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






class 						UpperCAmelCase__ (							__snake_case     ,				__snake_case							):
		__snake_case      :       Union[str, Any]     =   [e.name for e in KarrasDiffusionSchedulers]
		__snake_case      :       Optional[Any]     =   2







		@register_to_config
		def __init__(					self       ,A__ = 1000       ,A__ = 0.0_00_85       ,A__ = 0.0_12       ,A__ = "linear"       ,A__ = None       ,A__ = "epsilon"       ,A__ = "linspace"       ,A__ = 0       ,):
						if trained_betas is not None:
										_A	:				Tuple   =      torch.tensor(A__       ,dtype=torch.floataa		)
						elif beta_schedule == "linear":
										_A	:				Optional[int]   =      torch.linspace(A__       ,A__       ,A__       ,dtype=torch.floataa		)
						elif beta_schedule == "scaled_linear":
										# this schedule is very specific to the latent diffusion model.
										_A	:				Tuple   =      (
										    torch.linspace(beta_start**0.5       ,beta_end**0.5       ,A__       ,dtype=torch.floataa		) ** 2
										)
						elif beta_schedule == "squaredcos_cap_v2":
										# Glide cosine schedule
										_A	:				Dict   =      betas_for_alpha_bar(A__		)
						else:
										raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""		)

						_A	:				Dict   =      1.0 - self.betas
						_A	:				Any   =      torch.cumprod(self.alphas       ,dim=0		)

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







		def  A__      (					self       ,A__       ,A__=None		):
						if schedule_timesteps is None:
										_A	:				Union[str, Any]   =      self.timesteps

						_A	:				List[str]   =      (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:
										_A	:				Dict   =      1 if len(A__		) > 1 else 0
						else:
										_A	:				Union[str, Any]   =      timestep.cpu().item() if torch.is_tensor(A__		) else timestep
										_A	:				Optional[Any]   =      self._index_counter[timestep_int]

						return indices[pos].item()







		@property
		def  A__      (					self		):
						# standard deviation of the initial noise distribution
						if self.config.timestep_spacing in ["linspace", "trailing"]:
										return self.sigmas.max()

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







		def  A__      (					self       ,A__       ,A__       ,):
						_A	:				Dict   =      self.index_for_timestep(A__		)

						if self.state_in_first_order:
										_A	:				int   =      self.sigmas[step_index]
						else:
										_A	:				List[Any]   =      self.sigmas_interpol[step_index]

						_A	:				int   =      sample / ((sigma**2 + 1) ** 0.5)
						return sample







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,):
						_A	:				Union[str, Any]   =      num_inference_steps

						_A	:				Union[str, Any]   =      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":
										_A	:				str   =      np.linspace(0       ,num_train_timesteps - 1       ,A__       ,dtype=A__		)[::-1].copy()
						elif self.config.timestep_spacing == "leading":
										_A	:				str   =      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
										_A	:				str   =      (np.arange(0       ,A__		) * step_ratio).round()[::-1].copy().astype(A__		)
										timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
										_A	:				Any   =      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
										_A	:				List[Any]   =      (np.arange(A__       ,0       ,-step_ratio		)).round().copy().astype(A__		)
										timesteps -= 1
						else:
										raise ValueError(
										    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""		)

						_A	:				Dict   =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
						_A	:				Any   =      torch.from_numpy(np.log(A__		)		).to(A__		)

						_A	:				str   =      np.interp(A__       ,np.arange(0       ,len(A__		)		)       ,A__		)
						_A	:				int   =      np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
						_A	:				int   =      torch.from_numpy(A__		).to(device=A__		)

						# interpolate sigmas
						_A	:				List[str]   =      sigmas.log().lerp(sigmas.roll(1		).log()       ,0.5		).exp()

						_A	:				Union[str, Any]   =      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2		), sigmas[-1:]]		)
						_A	:				Any   =      torch.cat(
						    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2		), sigmas_interpol[-1:]]		)

						if str(A__		).startswith('''mps'''		):
										# mps does not support float64
										_A	:				List[Any]   =      torch.from_numpy(A__		).to(A__       ,dtype=torch.floataa		)
						else:
										_A	:				List[Any]   =      torch.from_numpy(A__		).to(A__		)

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

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

						_A	:				List[str]   =      None

						# for exp beta schedules, such as the one for `pipeline_shap_e.py`
						# we need an index counter
						_A	:				List[str]   =      defaultdict(A__		)







		def  A__      (					self       ,A__		):
						# get log sigma
						_A	:				int   =      sigma.log()

						# get distribution
						_A	:				List[Any]   =      log_sigma - self.log_sigmas[:, None]

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

						_A	:				List[str]   =      self.log_sigmas[low_idx]
						_A	:				Optional[int]   =      self.log_sigmas[high_idx]

						# interpolate sigmas
						_A	:				str   =      (low - log_sigma) / (low - high)
						_A	:				Any   =      w.clamp(0       ,1		)

						# transform interpolation to time range
						_A	:				Dict   =      (1 - w) * low_idx + w * high_idx
						_A	:				Optional[int]   =      t.view(sigma.shape		)
						return t







		@property
		def  A__      (					self		):
						return self.sample is None







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__ = True       ,):
						_A	:				Any   =      self.index_for_timestep(A__		)

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

						if self.state_in_first_order:
										_A	:				List[str]   =      self.sigmas[step_index]
										_A	:				Optional[int]   =      self.sigmas_interpol[step_index + 1]
										_A	:				Tuple   =      self.sigmas[step_index + 1]
						else:
										# 2nd order / KDPM2's method
										_A	:				int   =      self.sigmas[step_index - 1]
										_A	:				int   =      self.sigmas_interpol[step_index]
										_A	:				Optional[Any]   =      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
						_A	:				Dict   =      0
						_A	:				Any   =      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":
										_A	:				List[Any]   =      sigma_hat if self.state_in_first_order else sigma_interpol
										_A	:				List[str]   =      sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
										_A	:				Optional[Any]   =      sigma_hat if self.state_in_first_order else sigma_interpol
										_A	:				Union[str, Any]   =      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
										_A	:				Union[str, Any]   =      (sample - pred_original_sample) / sigma_hat
										# 3. delta timestep
										_A	:				List[Any]   =      sigma_interpol - sigma_hat

										# store for 2nd order step
										_A	:				Optional[int]   =      sample
						else:
										# DPM-Solver-2
										# 2. Convert to an ODE derivative for 2nd order
										_A	:				Union[str, Any]   =      (sample - pred_original_sample) / sigma_interpol

										# 3. delta timestep
										_A	:				Any   =      sigma_next - sigma_hat

										_A	:				Union[str, Any]   =      self.sample
										_A	:				Any   =      None

						_A	:				Any   =      sample + derivative * dt

						if not return_dict:
										return (prev_sample,)

						return SchedulerOutput(prev_sample=A__		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,):
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						_A	:				int   =      self.sigmas.to(device=original_samples.device       ,dtype=original_samples.dtype		)
						if original_samples.device.type == "mps" and torch.is_floating_point(A__		):
										# mps does not support float64
										_A	:				List[Any]   =      self.timesteps.to(original_samples.device       ,dtype=torch.floataa		)
										_A	:				Any   =      timesteps.to(original_samples.device       ,dtype=torch.floataa		)
						else:
										_A	:				Optional[int]   =      self.timesteps.to(original_samples.device		)
										_A	:				Optional[int]   =      timesteps.to(original_samples.device		)

						_A	:				List[Any]   =      [self.index_for_timestep(A__       ,A__		) for t in timesteps]

						_A	:				Union[str, Any]   =      sigmas[step_indices].flatten()
						while len(sigma.shape		) < len(original_samples.shape		):
										_A	:				Any   =      sigma.unsqueeze(-1		)

						_A	:				Dict   =      original_samples + noise * sigma
						return noisy_samples







		def __len__(					self		):
						return self.config.num_train_timesteps











import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


_UpperCamelCase			:	Optional[Any]								=pytest.mark.integration






@require_faiss
class 						UpperCAmelCase__ (							__snake_case							):







		def  A__      (					self		):
						_A	:				Any   =      Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(A__		) for x in np.arange(30		).tolist()]}		)
						return dset







		def  A__      (					self		):
						import faiss

						_A	:				Dataset   =      self._create_dummy_dataset()
						_A	:				List[str]   =      dset.map(
						    lambda A__       ,A__		: {"vecs": i * np.ones(5       ,dtype=np.floataa		)}       ,with_indices=A__       ,keep_in_memory=A__		)
						_A	:				Tuple   =      dset.add_faiss_index('''vecs'''       ,batch_size=100       ,metric_type=faiss.METRIC_INNER_PRODUCT		)
						_A  ,				_A	:				List[Any]   =      dset.get_nearest_examples('''vecs'''       ,np.ones(5       ,dtype=np.floataa		)		)
						self.assertEqual(examples['''filename'''][0]       ,'''my_name-train_29'''		)
						dset.drop_index('''vecs'''		)







		def  A__      (					self		):
						import faiss

						_A	:				Dataset   =      self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)		) * np.arange(30		).reshape(-1       ,1		)       ,index_name='''vecs'''       ,batch_size=100       ,metric_type=faiss.METRIC_INNER_PRODUCT       ,)
						_A  ,				_A	:				Tuple   =      dset.get_nearest_examples('''vecs'''       ,np.ones(5       ,dtype=np.floataa		)		)
						self.assertEqual(examples['''filename'''][0]       ,'''my_name-train_29'''		)







		def  A__      (					self		):
						import faiss

						_A	:				Dataset   =      self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)		) * np.arange(30		).reshape(-1       ,1		)       ,index_name='''vecs'''       ,metric_type=faiss.METRIC_INNER_PRODUCT       ,)

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=A__		) as tmp_file:
										dset.save_faiss_index('''vecs'''       ,tmp_file.name		)
										dset.load_faiss_index('''vecs2'''       ,tmp_file.name		)
						os.unlink(tmp_file.name		)

						_A  ,				_A	:				Optional[int]   =      dset.get_nearest_examples('''vecs2'''       ,np.ones(5       ,dtype=np.floataa		)		)
						self.assertEqual(examples['''filename'''][0]       ,'''my_name-train_29'''		)







		def  A__      (					self		):
						_A	:				Dataset   =      self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)		) * np.arange(30		).reshape(-1       ,1		)       ,index_name='''vecs'''		)
						dset.drop_index('''vecs'''		)
						self.assertRaises(A__       ,partial(dset.get_nearest_examples       ,'''vecs2'''       ,np.ones(5       ,dtype=np.floataa		)		)		)







		def  A__      (					self		):
						from elasticsearch import Elasticsearch

						_A	:				Dataset   =      self._create_dummy_dataset()
						with patch('''elasticsearch.Elasticsearch.search'''		) as mocked_search, patch(
						    '''elasticsearch.client.IndicesClient.create'''		) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk'''		) as mocked_bulk:
										_A	:				str   =      {'''acknowledged''': True}
										mocked_bulk.return_value([(True, None)] * 30		)
										_A	:				Optional[Any]   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
										_A	:				Optional[Any]   =      Elasticsearch()

										dset.add_elasticsearch_index('''filename'''       ,es_client=A__		)
										_A  ,				_A	:				Tuple   =      dset.get_nearest_examples('''filename'''       ,'''my_name-train_29'''		)
										self.assertEqual(examples['''filename'''][0]       ,'''my_name-train_29'''		)






@require_faiss
class 						UpperCAmelCase__ (							__snake_case							):







		def  A__      (					self		):
						import faiss

						_A	:				Union[str, Any]   =      FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT		)

						# add vectors
						index.add_vectors(np.eye(5       ,dtype=np.floataa		)		)
						self.assertIsNotNone(index.faiss_index		)
						self.assertEqual(index.faiss_index.ntotal       ,5		)
						index.add_vectors(np.zeros((5, 5)       ,dtype=np.floataa		)		)
						self.assertEqual(index.faiss_index.ntotal       ,10		)

						# single query
						_A	:				str   =      np.zeros(5       ,dtype=np.floataa		)
						_A	:				Dict   =      1
						_A  ,				_A	:				Optional[int]   =      index.search(A__		)
						self.assertRaises(A__       ,index.search       ,query.reshape(-1       ,1		)		)
						self.assertGreater(scores[0]       ,0		)
						self.assertEqual(indices[0]       ,1		)

						# batched queries
						_A	:				Optional[Any]   =      np.eye(5       ,dtype=np.floataa		)[::-1]
						_A  ,				_A	:				Dict   =      index.search_batch(A__		)
						self.assertRaises(A__       ,index.search_batch       ,queries[0]		)
						_A	:				Tuple   =      [scores[0] for scores in total_scores]
						_A	:				List[str]   =      [indices[0] for indices in total_indices]
						self.assertGreater(np.min(A__		)       ,0		)
						self.assertListEqual([4, 3, 2, 1, 0]       ,A__		)







		def  A__      (					self		):
						import faiss

						_A	:				List[Any]   =      FaissIndex(string_factory='''Flat'''		)
						index.add_vectors(np.eye(5       ,dtype=np.floataa		)		)
						self.assertIsInstance(index.faiss_index       ,faiss.IndexFlat		)
						_A	:				str   =      FaissIndex(string_factory='''LSH'''		)
						index.add_vectors(np.eye(5       ,dtype=np.floataa		)		)
						self.assertIsInstance(index.faiss_index       ,faiss.IndexLSH		)
						with self.assertRaises(A__		):
										_A	:				List[str]   =      FaissIndex(string_factory='''Flat'''       ,custom_index=faiss.IndexFlat(5		)		)







		def  A__      (					self		):
						import faiss

						_A	:				Any   =      faiss.IndexFlat(5		)
						_A	:				Any   =      FaissIndex(custom_index=A__		)
						index.add_vectors(np.eye(5       ,dtype=np.floataa		)		)
						self.assertIsInstance(index.faiss_index       ,faiss.IndexFlat		)







		def  A__      (					self		):
						import faiss

						_A	:				Optional[int]   =      FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT		)
						index.add_vectors(np.eye(5       ,dtype=np.floataa		)		)

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=A__		) as tmp_file:
										index.save(tmp_file.name		)
										_A	:				str   =      FaissIndex.load(tmp_file.name		)
						os.unlink(tmp_file.name		)

						_A	:				Tuple   =      np.zeros(5       ,dtype=np.floataa		)
						_A	:				Tuple   =      1
						_A  ,				_A	:				Union[str, Any]   =      index.search(A__		)
						self.assertGreater(scores[0]       ,0		)
						self.assertEqual(indices[0]       ,1		)



@require_faiss
def  a__ (__lowercase							:List[Any]   )		-> int:
				import faiss

				_A	:				Any   =      FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT   )
				index.add_vectors(np.eye(5					,  dtype=np.floataa   )   )

				_A	:				List[Any]   =      '''index.faiss'''
				_A	:				Any   =      f"""mock://{index_name}"""
				index.save(__lowercase					,  storage_options=mockfs.storage_options   )
				_A	:				Dict   =      FaissIndex.load(__lowercase					,  storage_options=mockfs.storage_options   )

				_A	:				Tuple   =      np.zeros(5					,  dtype=np.floataa   )
				_A	:				Union[str, Any]   =      1
				_A  ,				_A	:				Optional[int]   =      index.search(__lowercase   )
				assert scores[0] > 0
				assert indices[0] == 1






@require_elasticsearch
class 						UpperCAmelCase__ (							__snake_case							):







		def  A__      (					self		):
						from elasticsearch import Elasticsearch

						with patch('''elasticsearch.Elasticsearch.search'''		) as mocked_search, patch(
						    '''elasticsearch.client.IndicesClient.create'''		) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk'''		) as mocked_bulk:
										_A	:				Tuple   =      Elasticsearch()
										_A	:				Tuple   =      {'''acknowledged''': True}
										_A	:				int   =      ElasticSearchIndex(es_client=A__		)
										mocked_bulk.return_value([(True, None)] * 3		)
										index.add_documents(['''foo''', '''bar''', '''foobar''']		)

										# single query
										_A	:				Dict   =      '''foo'''
										_A	:				Optional[Any]   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
										_A  ,				_A	:				Optional[Any]   =      index.search(A__		)
										self.assertEqual(scores[0]       ,1		)
										self.assertEqual(indices[0]       ,0		)

										# single query with timeout
										_A	:				Union[str, Any]   =      '''foo'''
										_A	:				Optional[int]   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
										_A  ,				_A	:				Optional[int]   =      index.search(A__       ,request_timeout=30		)
										self.assertEqual(scores[0]       ,1		)
										self.assertEqual(indices[0]       ,0		)

										# batched queries
										_A	:				Dict   =      ['''foo''', '''bar''', '''foobar''']
										_A	:				Dict   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
										_A  ,				_A	:				Dict   =      index.search_batch(A__		)
										_A	:				Union[str, Any]   =      [scores[0] for scores in total_scores]
										_A	:				Tuple   =      [indices[0] for indices in total_indices]
										self.assertGreater(np.min(A__		)       ,0		)
										self.assertListEqual([1, 1, 1]       ,A__		)

										# batched queries with timeout
										_A	:				Union[str, Any]   =      ['''foo''', '''bar''', '''foobar''']
										_A	:				int   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
										_A  ,				_A	:				str   =      index.search_batch(A__       ,request_timeout=30		)
										_A	:				Dict   =      [scores[0] for scores in total_scores]
										_A	:				List[str]   =      [indices[0] for indices in total_indices]
										self.assertGreater(np.min(A__		)       ,0		)
										self.assertListEqual([1, 1, 1]       ,A__		)



