


'''simple docstring'''







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     42
     __magic_name__           =     42
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     1







     @register_to_config
     def __init__(			self   ,						snake_case__ = 2000   ,						snake_case__ = 0.15   ,						snake_case__ = 0.01   ,						snake_case__ = 1348.0   ,						snake_case__ = 1E-5   ,						snake_case__ = 1   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							sigma_max

            # setable values
            _lowerCAmelCase     :				Dict       =							None

            self.set_sigmas(snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            return sample







     def a						(			self   ,						snake_case__   ,						snake_case__ = None   ,						snake_case__ = None ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							sampling_eps if sampling_eps is not None else self.config.sampling_eps

            _lowerCAmelCase     :				List[str]       =							torch.linspace(1   ,						snake_case__   ,						snake_case__   ,						device=snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = None   ,						snake_case__ = None   ,						snake_case__ = None ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							sigma_min if sigma_min is not None else self.config.sigma_min
            _lowerCAmelCase     :				Optional[int]       =							sigma_max if sigma_max is not None else self.config.sigma_max
            _lowerCAmelCase     :				Union[str, Any]       =							sampling_eps if sampling_eps is not None else self.config.sampling_eps
            if self.timesteps is None:
                   self.set_timesteps(snake_case__   ,						snake_case__ )

            _lowerCAmelCase     :				str       =							sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
            _lowerCAmelCase     :				int       =							torch.exp(torch.linspace(math.log(snake_case__ )   ,						math.log(snake_case__ )   ,						snake_case__ ) )
            _lowerCAmelCase     :				int       =							torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            return torch.where(
                timesteps == 0   ,						torch.zeros_like(t.to(timesteps.device ) )   ,						self.discrete_sigmas[timesteps - 1].to(timesteps.device )   ,						)







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ = None   ,						snake_case__ = True   ,						):

            '''simple docstring'''

            if self.timesteps is None:
                   raise ValueError(
                       '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler' )

            _lowerCAmelCase     :				Tuple       =							timestep * torch.ones(
                sample.shape[0]   ,						device=sample.device )  # torch.repeat_interleave(timestep, sample.shape[0])
            _lowerCAmelCase     :				str       =							(timestep * (len(self.timesteps ) - 1)).long()

            # mps requires indices to be in the same device, so we use cpu as is the default with cuda
            _lowerCAmelCase     :				List[str]       =							timesteps.to(self.discrete_sigmas.device )

            _lowerCAmelCase     :				Union[str, Any]       =							self.discrete_sigmas[timesteps].to(sample.device )
            _lowerCAmelCase     :				Optional[Any]       =							self.get_adjacent_sigma(snake_case__   ,						snake_case__ ).to(sample.device )
            _lowerCAmelCase     :				List[str]       =							torch.zeros_like(snake_case__ )
            _lowerCAmelCase     :				int       =							(sigma**2 - adjacent_sigma**2) ** 0.5

            # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
            # also equation 47 shows the analog from SDE models to ancestral sampling methods
            _lowerCAmelCase     :				Any       =							diffusion.flatten()
            while len(diffusion.shape ) < len(sample.shape ):
                   _lowerCAmelCase     :				Tuple       =							diffusion.unsqueeze(-1 )
            _lowerCAmelCase     :				Any       =							drift - diffusion**2 * model_output

            #  equation 6: sample noise for the diffusion term of
            _lowerCAmelCase     :				Dict       =							randn_tensor(
                sample.shape   ,						layout=sample.layout   ,						generator=snake_case__   ,						device=sample.device   ,						dtype=sample.dtype )
            _lowerCAmelCase     :				List[Any]       =							sample - drift  # subtract because `dt` is a small negative timestep
            # TODO is the variable diffusion the correct scaling term for the noise?
            _lowerCAmelCase     :				Dict       =							prev_sample_mean + diffusion * noise  # add impact of diffusion field g

            if not return_dict:
                   return (prev_sample, prev_sample_mean)

            return SdeVeOutput(prev_sample=snake_case__   ,						prev_sample_mean=snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__ = None   ,						snake_case__ = True   ,						):

            '''simple docstring'''

            if self.timesteps is None:
                   raise ValueError(
                       '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler' )

            # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
            # sample noise for correction
            _lowerCAmelCase     :				List[str]       =							randn_tensor(sample.shape   ,						layout=sample.layout   ,						generator=snake_case__ ).to(sample.device )

            # compute step size from the model_output, the noise, and the snr
            _lowerCAmelCase     :				List[str]       =							torch.norm(model_output.reshape(model_output.shape[0]   ,						-1 )   ,						dim=-1 ).mean()
            _lowerCAmelCase     :				Optional[Any]       =							torch.norm(noise.reshape(noise.shape[0]   ,						-1 )   ,						dim=-1 ).mean()
            _lowerCAmelCase     :				Optional[int]       =							(self.config.snr * noise_norm / grad_norm) ** 2 * 2
            _lowerCAmelCase     :				Optional[Any]       =							step_size * torch.ones(sample.shape[0] ).to(sample.device )
            # self.repeat_scalar(step_size, sample.shape[0])

            # compute corrected sample: model_output term and noise term
            _lowerCAmelCase     :				List[str]       =							step_size.flatten()
            while len(step_size.shape ) < len(sample.shape ):
                   _lowerCAmelCase     :				Optional[int]       =							step_size.unsqueeze(-1 )
            _lowerCAmelCase     :				Optional[int]       =							sample + step_size * model_output
            _lowerCAmelCase     :				Dict       =							prev_sample_mean + ((step_size * 2) ** 0.5) * noise

            if not return_dict:
                   return (prev_sample,)

            return SchedulerOutput(prev_sample=snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							timesteps.to(original_samples.device )
            _lowerCAmelCase     :				Tuple       =							self.discrete_sigmas.to(original_samples.device )[timesteps]
            _lowerCAmelCase     :				Optional[Any]       =							(
                noise * sigmas[:, None, None, None]
                if noise is not None
                else torch.randn_like(snake_case__ ) * sigmas[:, None, None, None]
            )
            _lowerCAmelCase     :				Any       =							noise + original_samples
            return noisy_samples







     def __len__(			self ):

            '''simple docstring'''

            return self.config.num_train_timesteps





'''simple docstring'''







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

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							tempfile.mkdtemp()
            _lowerCAmelCase     :				List[str]       =							5

            # Realm tok
            _lowerCAmelCase     :				str       =							[
                '[UNK]',
                '[CLS]',
                '[SEP]',
                '[PAD]',
                '[MASK]',
                'test',
                'question',
                'this',
                'is',
                'the',
                'first',
                'second',
                'third',
                'fourth',
                'fifth',
                'record',
                'want',
                '##want',
                '##ed',
                'wa',
                'un',
                'runn',
                '##ing',
                ',',
                'low',
                'lowest',
            ]
            _lowerCAmelCase     :				Union[str, Any]       =							os.path.join(self.tmpdirname   ,						'realm_tokenizer' )
            os.makedirs(snake_case__   ,						exist_ok=snake_case__ )
            _lowerCAmelCase     :				Dict       =							os.path.join(snake_case__   ,						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] ) )

            _lowerCAmelCase     :				List[str]       =							os.path.join(self.tmpdirname   ,						'realm_block_records' )
            os.makedirs(snake_case__   ,						exist_ok=snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,						'realm_tokenizer' ) )







     def a						(			self ):

            '''simple docstring'''

            shutil.rmtree(self.tmpdirname )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							RealmConfig(num_block_records=self.num_block_records )
            return config







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							Dataset.from_dict(
                {
                    'id': ['0', '1'],
                    'question': ['foo', 'bar'],
                    'answers': [['Foo', 'Bar'], ['Bar']],
                } )
            return dataset







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							np.array(
                [
                    b'This is the first record',
                    b'This is the second record',
                    b'This is the third record',
                    b'This is the fourth record',
                    b'This is the fifth record',
                    b'This is a longer longer longer record',
                ]   ,						dtype=snake_case__   ,						)
            return block_records







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							RealmRetriever(
                block_records=self.get_dummy_block_records()   ,						tokenizer=self.get_tokenizer()   ,						)
            return retriever







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.get_config()
            _lowerCAmelCase     :				List[Any]       =							self.get_dummy_retriever()
            _lowerCAmelCase     :				Any       =							retriever.tokenizer

            _lowerCAmelCase     :				Tuple       =							np.array([0, 3]   ,						dtype='long' )
            _lowerCAmelCase     :				Dict       =							tokenizer(['Test question'] ).input_ids
            _lowerCAmelCase     :				int       =							tokenizer(
                ['the fourth']   ,						add_special_tokens=snake_case__   ,						return_token_type_ids=snake_case__   ,						return_attention_mask=snake_case__   ,						).input_ids
            _lowerCAmelCase     :				Tuple       =							config.reader_seq_len

            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							retriever(
                snake_case__   ,						snake_case__   ,						answer_ids=snake_case__   ,						max_length=snake_case__   ,						return_tensors='np' )

            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(concat_inputs.input_ids.shape   ,						(2, 10) )
            self.assertEqual(concat_inputs.attention_mask.shape   ,						(2, 10) )
            self.assertEqual(concat_inputs.token_type_ids.shape   ,						(2, 10) )
            self.assertEqual(concat_inputs.special_tokens_mask.shape   ,						(2, 10) )
            self.assertEqual(
                tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] )   ,						['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]']   ,						)
            self.assertEqual(
                tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] )   ,						['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]']   ,						)







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.get_config()
            _lowerCAmelCase     :				int       =							self.get_dummy_retriever()
            _lowerCAmelCase     :				List[str]       =							retriever.tokenizer

            _lowerCAmelCase     :				List[Any]       =							np.array([0, 3, 5]   ,						dtype='long' )
            _lowerCAmelCase     :				str       =							tokenizer(['Test question'] ).input_ids
            _lowerCAmelCase     :				Union[str, Any]       =							tokenizer(
                ['the fourth', 'longer longer']   ,						add_special_tokens=snake_case__   ,						return_token_type_ids=snake_case__   ,						return_attention_mask=snake_case__   ,						).input_ids
            _lowerCAmelCase     :				Optional[int]       =							config.reader_seq_len

            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							retriever(
                snake_case__   ,						snake_case__   ,						answer_ids=snake_case__   ,						max_length=snake_case__   ,						return_tensors='np' )

            self.assertEqual([False, True, True]   ,						snake_case__ )
            self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]   ,						snake_case__ )
            self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self.get_dummy_retriever()
            retriever.save_pretrained(os.path.join(self.tmpdirname   ,						'realm_block_records' ) )

            # Test local path
            _lowerCAmelCase     :				Union[str, Any]       =							retriever.from_pretrained(os.path.join(self.tmpdirname   ,						'realm_block_records' ) )
            self.assertEqual(retriever.block_records[0]   ,						b'This is the first record' )

            # Test mocked remote path
            with patch('transformers.models.realm.retrieval_realm.hf_hub_download' ) as mock_hf_hub_download:
                   _lowerCAmelCase     :				Any       =							os.path.join(
                       os.path.join(self.tmpdirname   ,						'realm_block_records' )   ,						_REALM_BLOCK_RECORDS_FILENAME )
                   _lowerCAmelCase     :				str       =							RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa' )

            self.assertEqual(retriever.block_records[0]   ,						b'This is the first record' )

