
'''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


lowercase    :							Optional[Any]      =					8







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=BITS	)       ->	Tuple:
	_snake_case   						=		x.device

	_snake_case   						=		(x * 255).int().clamp(0			,				255	)

	_snake_case   						=		2 ** torch.arange(bits - 1			,				-1			,				-1			,				device=__A	)
	_snake_case   						=		rearrange(__A			,				'd -> d 1 1'	)
	_snake_case   						=		rearrange(__A			,				'b c h w -> b c 1 h w'	)

	_snake_case   						=		((x & mask) != 0).float()
	_snake_case   						=		rearrange(__A			,				'b c d h w -> b (c d) h w'	)
	_snake_case   						=		bits * 2 - 1
	return bits







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=BITS	)       ->	Optional[int]:
	_snake_case   						=		x.device

	_snake_case   						=		(x > 0).int()
	_snake_case   						=		2 ** torch.arange(bits - 1			,				-1			,				-1			,				device=__A			,				dtype=torch.intaa	)

	_snake_case   						=		rearrange(__A			,				'd -> d 1 1'	)
	_snake_case   						=		rearrange(__A			,				'b (c d) h w -> b c d h w'			,				d=8	)
	_snake_case   						=		reduce(x * mask			,				'b c d h w -> b c h w'			,				'sum'	)
	return (dec / 255).clamp(0.0			,				1.0	)







def 				SCREAMING_SNAKE_CASE__	(   self			,				__A			,				__A			,				__A			,				__A = 0.0			,				__A = True			,				__A=None			,				__A = 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)
	_snake_case   						=		timestep - self.config.num_train_timesteps // self.num_inference_steps

	# 2. compute alphas, betas
	_snake_case   						=		self.alphas_cumprod[timestep]
	_snake_case   						=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

	_snake_case   						=		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
	_snake_case   						=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

	# 4. Clip "predicted x_0"
	_snake_case   						=		self.bit_scale
	if self.config.clip_sample:
		_snake_case   						=		torch.clamp(__A			,				-scale			,				__A	)

	# 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)
	_snake_case   						=		self._get_variance(__A			,				__A	)
	_snake_case   						=		eta * variance ** 0.5

	if use_clipped_model_output:
		# the model_output is always re-derived from the clipped x_0 in Glide
		_snake_case   						=		(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
	_snake_case   						=		(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
	_snake_case   						=		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
		_snake_case   						=		model_output.device if torch.is_tensor(__A	) else 'cpu'
		_snake_case   						=		torch.randn(model_output.shape			,				dtype=model_output.dtype			,				generator=__A	).to(__A	)
		_snake_case   						=		self._get_variance(__A			,				__A	) ** 0.5 * eta * noise

		_snake_case   						=		prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=__A			,				pred_original_sample=__A	)







def 				SCREAMING_SNAKE_CASE__	(   self			,				__A			,				__A			,				__A			,				__A="epsilon"			,				__A=None			,				__A = True			,				)       ->	Union[DDPMSchedulerOutput, Tuple]:
	_snake_case   						=		timestep

	if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
		_snake_case						, _snake_case   						=		torch.split(__A			,				sample.shape[1]			,				dim=1	)
	else:
		_snake_case   						=		None

	# 1. compute alphas, betas
	_snake_case   						=		self.alphas_cumprod[t]
	_snake_case   						=		self.alphas_cumprod[t - 1] if t > 0 else self.one
	_snake_case   						=		1 - alpha_prod_t
	_snake_case   						=		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":
		_snake_case   						=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
	elif prediction_type == "sample":
		_snake_case   						=		model_output
	else:
		raise ValueError(F'Unsupported prediction_type {prediction_type}.'	)

	# 3. Clip "predicted x_0"
	_snake_case   						=		self.bit_scale
	if self.config.clip_sample:
		_snake_case   						=		torch.clamp(__A			,				-scale			,				__A	)

	# 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
	_snake_case   						=		(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
	_snake_case   						=		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
	_snake_case   						=		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

	# 6. Add noise
	_snake_case   						=		0
	if t > 0:
		_snake_case   						=		torch.randn(
		    model_output.size()			,				dtype=model_output.dtype			,				layout=model_output.layout			,				generator=__A	).to(model_output.device	)
		_snake_case   						=		(self._get_variance(__A			,				predicted_variance=__A	) ** 0.5) * noise

	_snake_case   						=		pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=__A			,				pred_original_sample=__A	)





class 						__UpperCAmelCase      (					_lowerCamelCase						):


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = 1.0    ,   ):





								"""simple docstring"""






								super().__init__()
								_snake_case   						=		bit_scale
								_snake_case   						=		(
								    ddim_bit_scheduler_step if isinstance(lowerCAmelCase_    ,   lowerCAmelCase_  ) else ddpm_bit_scheduler_step
								)

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




							@torch.no_grad()
							def __call__(       self    ,   lowerCAmelCase_ = 2_56    ,   lowerCAmelCase_ = 2_56    ,   lowerCAmelCase_ = 50    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = 1    ,   lowerCAmelCase_ = "pil"    ,   lowerCAmelCase_ = True    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		torch.randn(
								    (batch_size, self.unet.config.in_channels, height, width)    ,   generator=lowerCAmelCase_    ,   )
								_snake_case   						=		decimal_to_bits(lowerCAmelCase_  ) * self.bit_scale
								_snake_case   						=		latents.to(self.device  )

								self.scheduler.set_timesteps(lowerCAmelCase_  )

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

									# compute the previous noisy sample x_t -> x_t-1
									_snake_case   						=		self.scheduler.step(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ).prev_sample

								_snake_case   						=		bits_to_decimal(lowerCAmelCase_  )

								if output_type == "pil":
									_snake_case   						=		self.numpy_to_pil(lowerCAmelCase_  )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase_  )









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__				   =		pytest.mark.integration






@require_faiss
class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Any:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Any:




       """simple docstring"""


       import faiss

       a		  =      self._create_dummy_dataset()
       a		  =      dset.map(
           lambda __UpperCAmelCase ,					__UpperCAmelCase  : {"vecs": i * np.ones(5 ,					dtype=np.floataa  )} ,					with_indices=__UpperCAmelCase ,					keep_in_memory=__UpperCAmelCase  )
       a		  =      dset.add_faiss_index('''vecs''' ,					batch_size=100 ,					metric_type=faiss.METRIC_INNER_PRODUCT  )
       a  ,     a		  =      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       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Optional[Any]:




       """simple docstring"""


       import faiss

       a		  =      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		  =      dset.get_nearest_examples('''vecs''' ,					np.ones(5 ,					dtype=np.floataa  )  )
       self.assertEqual(examples['''filename'''][0] ,					'''my_name-train_29'''  )







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




       """simple docstring"""


       import faiss

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







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Any:




       """simple docstring"""


       a		  =      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(__UpperCAmelCase ,					partial(dset.get_nearest_examples ,					'''vecs2''' ,					np.ones(5 ,					dtype=np.floataa  )  )  )







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




       """simple docstring"""


       from elasticsearch import Elasticsearch

       a		  =      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		  =      {'''acknowledged''': True}
        mocked_bulk.return_value([(True, None)] * 30  )
        a		  =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
        a		  =      Elasticsearch()

        dset.add_elasticsearch_index('''filename''' ,					es_client=__UpperCAmelCase  )
        a  ,     a		  =      dset.get_nearest_examples('''filename''' ,					'''my_name-train_29'''  )
        self.assertEqual(examples['''filename'''][0] ,					'''my_name-train_29'''  )






@require_faiss
class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						Any  )    ->Any:




       """simple docstring"""


       import faiss

       a		  =      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		  =      np.zeros(5 ,					dtype=np.floataa  )
       a		  =      1
       a  ,     a		  =      index.search(__UpperCAmelCase  )
       self.assertRaises(__UpperCAmelCase ,					index.search ,					query.reshape(-1 ,					1  )  )
       self.assertGreater(scores[0] ,					0  )
       self.assertEqual(indices[0] ,					1  )

       # batched queries
       a		  =      np.eye(5 ,					dtype=np.floataa  )[::-1]
       a  ,     a		  =      index.search_batch(__UpperCAmelCase  )
       self.assertRaises(__UpperCAmelCase ,					index.search_batch ,					queries[0]  )
       a		  =      [scores[0] for scores in total_scores]
       a		  =      [indices[0] for indices in total_indices]
       self.assertGreater(np.min(__UpperCAmelCase  ) ,					0  )
       self.assertListEqual([4, 3, 2, 1, 0] ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Any  )    ->List[Any]:




       """simple docstring"""


       import faiss

       a		  =      FaissIndex(string_factory='''Flat'''  )
       index.add_vectors(np.eye(5 ,					dtype=np.floataa  )  )
       self.assertIsInstance(index.faiss_index ,					faiss.IndexFlat  )
       a		  =      FaissIndex(string_factory='''LSH'''  )
       index.add_vectors(np.eye(5 ,					dtype=np.floataa  )  )
       self.assertIsInstance(index.faiss_index ,					faiss.IndexLSH  )
       with self.assertRaises(__UpperCAmelCase  ):
        a		  =      FaissIndex(string_factory='''Flat''' ,					custom_index=faiss.IndexFlat(5  )  )







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




       """simple docstring"""


       import faiss

       a		  =      faiss.IndexFlat(5  )
       a		  =      FaissIndex(custom_index=__UpperCAmelCase  )
       index.add_vectors(np.eye(5 ,					dtype=np.floataa  )  )
       self.assertIsInstance(index.faiss_index ,					faiss.IndexFlat  )







      def       __lowerCAmelCase					(      self      :						int  )    ->Dict:




       """simple docstring"""


       import faiss

       a		  =      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=__UpperCAmelCase  ) as tmp_file:
        index.save(tmp_file.name  )
        a		  =      FaissIndex.load(tmp_file.name  )
       os.unlink(tmp_file.name  )

       a		  =      np.zeros(5 ,					dtype=np.floataa  )
       a		  =      1
       a  ,     a		  =      index.search(__UpperCAmelCase  )
       self.assertGreater(scores[0] ,					0  )
       self.assertEqual(indices[0] ,					1  )
@require_faiss
def 						_a    (      a :Dict )							->			Any:
 import faiss

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

 a		  =      '''index.faiss'''
 a		  =      F"""mock://{index_name}"""
 index.save(a	,							storage_options=mockfs.storage_options )
 a		  =      FaissIndex.load(a	,							storage_options=mockfs.storage_options )

 a		  =      np.zeros(5	,							dtype=np.floataa )
 a		  =      1
 a  ,     a		  =      index.search(a )
 assert scores[0] > 0
 assert indices[0] == 1






@require_elasticsearch
class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




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




       """simple docstring"""


       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		  =      Elasticsearch()
        a		  =      {'''acknowledged''': True}
        a		  =      ElasticSearchIndex(es_client=__UpperCAmelCase  )
        mocked_bulk.return_value([(True, None)] * 3  )
        index.add_documents(['''foo''', '''bar''', '''foobar''']  )

        # single query
        a		  =      '''foo'''
        a		  =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
        a  ,     a		  =      index.search(__UpperCAmelCase  )
        self.assertEqual(scores[0] ,					1  )
        self.assertEqual(indices[0] ,					0  )

        # single query with timeout
        a		  =      '''foo'''
        a		  =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
        a  ,     a		  =      index.search(__UpperCAmelCase ,					request_timeout=30  )
        self.assertEqual(scores[0] ,					1  )
        self.assertEqual(indices[0] ,					0  )

        # batched queries
        a		  =      ['''foo''', '''bar''', '''foobar''']
        a		  =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
        a  ,     a		  =      index.search_batch(__UpperCAmelCase  )
        a		  =      [scores[0] for scores in total_scores]
        a		  =      [indices[0] for indices in total_indices]
        self.assertGreater(np.min(__UpperCAmelCase  ) ,					0  )
        self.assertListEqual([1, 1, 1] ,					__UpperCAmelCase  )

        # batched queries with timeout
        a		  =      ['''foo''', '''bar''', '''foobar''']
        a		  =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
        a  ,     a		  =      index.search_batch(__UpperCAmelCase ,					request_timeout=30  )
        a		  =      [scores[0] for scores in total_scores]
        a		  =      [indices[0] for indices in total_indices]
        self.assertGreater(np.min(__UpperCAmelCase  ) ,					0  )
        self.assertListEqual([1, 1, 1] ,					__UpperCAmelCase  )


