

import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


__A			:					Any						=			logging.getLogger(__name__)





def 	__a     (      A__			:  str=2			,  A__			:  str=3			,  A__			:  Optional[int]=16			,  A__			:  int = 10			,  A__			:  int = 2       ):

    def get_dataset(A__			:  Tuple       ):
        SCREAMING_SNAKE_CASE										=   torch.randn(batch_size * n_batches			,  1       )
        return TensorDataset(A__			,  a * x + b + 0.1 * torch.randn(batch_size * n_batches			,  1       )       )

    SCREAMING_SNAKE_CASE										=   get_dataset(A__       )
    SCREAMING_SNAKE_CASE										=   get_dataset(A__       )
    SCREAMING_SNAKE_CASE										=   DataLoader(A__			,  shuffle=A__			,  batch_size=A__			,  num_workers=4       )
    SCREAMING_SNAKE_CASE										=   DataLoader(A__			,  shuffle=A__			,  batch_size=A__			,  num_workers=4       )
    return (train_dataloader, valid_dataloader)





def 	__a     (      A__			:  Any			,  A__			:  List[str]			,  A__			:  int			,  A__			:  str			,  A__			:  Optional[Any]			,  A__			:  Union[str, Any]=None       ):
    SCREAMING_SNAKE_CASE										=   []
    for epoch in range(A__       ):
        # Train quickly
        model.train()
        for batch in dataloader:
            SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   batch
            SCREAMING_SNAKE_CASE										=   model(A__       )
            SCREAMING_SNAKE_CASE										=   torch.nn.functional.mse_loss(A__			,  A__       )
            accelerator.backward(A__       )
            optimizer.step()
            optimizer.zero_grad()
        rands.append(random.random()       )  # Introduce some randomness
        if scheduler is not None:
            scheduler.step()
    return rands





class 			_SCREAMING_SNAKE_CASE (   nn.Module		):







   '''simple docstring'''




   def __init__(       self					:			Dict			):
       super().__init__()
       SCREAMING_SNAKE_CASE										=   nn.Parameter(torch.randn(1			)			)
       SCREAMING_SNAKE_CASE										=   nn.Parameter(torch.randn(1			)			)





   def      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			List[Any]			):
       return x * self.a + self.b





class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			List[str]			):
       with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42			)
           SCREAMING_SNAKE_CASE										=   DummyModel()
           SCREAMING_SNAKE_CASE										=   torch.optim.Adam(params=model.parameters()     , lr=1e-3			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   dummy_dataloaders()
           SCREAMING_SNAKE_CASE										=   ProjectConfiguration(total_limit=1     , project_dir=__lowerCamelCase     , automatic_checkpoint_naming=__lowerCamelCase			)
           # Train baseline
           SCREAMING_SNAKE_CASE										=   Accelerator(project_config=__lowerCamelCase			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   accelerator.prepare(
               __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           # Save initial
           accelerator.save_state()

           # Save second state
           accelerator.save_state()
           self.assertEqual(len(os.listdir(accelerator.project_dir			)			)     , 1			)





   def      _snake_case		(       self					:			List[Any]			):
       with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42			)
           SCREAMING_SNAKE_CASE										=   DummyModel()
           SCREAMING_SNAKE_CASE										=   torch.optim.Adam(params=model.parameters()     , lr=1e-3			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   dummy_dataloaders()
           # Train baseline
           SCREAMING_SNAKE_CASE										=   Accelerator()
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   accelerator.prepare(
               __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           # Save initial
           SCREAMING_SNAKE_CASE										=   os.path.join(__lowerCamelCase     , "initial"			)
           accelerator.save_state(__lowerCamelCase			)
           ((SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE))										=   model.a.item(), model.b.item()
           SCREAMING_SNAKE_CASE										=   optimizer.state_dict()
           SCREAMING_SNAKE_CASE										=   train(3     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           ((SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE))										=   model.a.item(), model.b.item()
           SCREAMING_SNAKE_CASE										=   optimizer.state_dict()

           # Train partially
           set_seed(42			)
           SCREAMING_SNAKE_CASE										=   DummyModel()
           SCREAMING_SNAKE_CASE										=   torch.optim.Adam(params=model.parameters()     , lr=1e-3			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   dummy_dataloaders()
           SCREAMING_SNAKE_CASE										=   Accelerator()
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   accelerator.prepare(
               __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           accelerator.load_state(__lowerCamelCase			)
           ((SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE))										=   model.a.item(), model.b.item()
           SCREAMING_SNAKE_CASE										=   optimizer.state_dict()
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   train(2     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           # Save everything
           SCREAMING_SNAKE_CASE										=   os.path.join(__lowerCamelCase     , "checkpoint"			)
           accelerator.save_state(__lowerCamelCase			)

           # Load everything back in and make sure all states work
           accelerator.load_state(__lowerCamelCase			)
           test_rands += train(1     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           ((SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE))										=   model.a.item(), model.b.item()
           SCREAMING_SNAKE_CASE										=   optimizer.state_dict()
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Dict			):
       with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42			)
           SCREAMING_SNAKE_CASE										=   DummyModel()
           SCREAMING_SNAKE_CASE										=   torch.optim.Adam(params=model.parameters()     , lr=1e-3			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   dummy_dataloaders()
           SCREAMING_SNAKE_CASE										=   ProjectConfiguration(automatic_checkpoint_naming=__lowerCamelCase			)

           # Train baseline
           SCREAMING_SNAKE_CASE										=   Accelerator(project_dir=__lowerCamelCase     , project_config=__lowerCamelCase			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   accelerator.prepare(
               __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           # Save initial
           accelerator.save_state()
           ((SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE))										=   model.a.item(), model.b.item()
           SCREAMING_SNAKE_CASE										=   optimizer.state_dict()
           SCREAMING_SNAKE_CASE										=   train(3     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           ((SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE))										=   model.a.item(), model.b.item()
           SCREAMING_SNAKE_CASE										=   optimizer.state_dict()

           # Train partially
           set_seed(42			)
           SCREAMING_SNAKE_CASE										=   DummyModel()
           SCREAMING_SNAKE_CASE										=   torch.optim.Adam(params=model.parameters()     , lr=1e-3			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   dummy_dataloaders()
           SCREAMING_SNAKE_CASE										=   ProjectConfiguration(iteration=1     , automatic_checkpoint_naming=__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   Accelerator(project_dir=__lowerCamelCase     , project_config=__lowerCamelCase			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   accelerator.prepare(
               __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           accelerator.load_state(os.path.join(__lowerCamelCase     , "checkpoints"     , "checkpoint_0"			)			)
           ((SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE))										=   model.a.item(), model.b.item()
           SCREAMING_SNAKE_CASE										=   optimizer.state_dict()
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   train(2     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           # Save everything
           accelerator.save_state()

           # Load everything back in and make sure all states work
           accelerator.load_state(os.path.join(__lowerCamelCase     , "checkpoints"     , "checkpoint_1"			)			)
           test_rands += train(1     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           ((SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE))										=   model.a.item(), model.b.item()
           SCREAMING_SNAKE_CASE										=   optimizer.state_dict()
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   torch.tensor([1, 2, 3]			)
       SCREAMING_SNAKE_CASE										=   torch.tensor([2, 3, 4]			)
       SCREAMING_SNAKE_CASE										=   DummyModel()
       SCREAMING_SNAKE_CASE										=   torch.optim.Adam(net.parameters()			)
       SCREAMING_SNAKE_CASE										=   Accelerator()
       with self.assertRaises(__lowerCamelCase			) as ve:
           accelerator.register_for_checkpointing(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   str(ve.exception			)
       self.assertTrue("Item at index 0" in message			)
       self.assertTrue("Item at index 1" in message			)
       self.assertFalse("Item at index 2" in message			)
       self.assertFalse("Item at index 3" in message			)





   def      _snake_case		(       self					:			Tuple			):
       with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42			)
           SCREAMING_SNAKE_CASE										=   DummyModel()
           SCREAMING_SNAKE_CASE										=   torch.optim.Adam(params=model.parameters()     , lr=1e-3			)
           SCREAMING_SNAKE_CASE										=   torch.optim.lr_scheduler.StepLR(__lowerCamelCase     , step_size=1     , gamma=0.99			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   dummy_dataloaders()
           SCREAMING_SNAKE_CASE										=   ProjectConfiguration(automatic_checkpoint_naming=__lowerCamelCase			)
           # Train baseline
           SCREAMING_SNAKE_CASE										=   Accelerator(project_dir=__lowerCamelCase     , project_config=__lowerCamelCase			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   accelerator.prepare(
               __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           # Save initial
           accelerator.save_state()
           SCREAMING_SNAKE_CASE										=   scheduler.state_dict()
           train(3     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           self.assertNotEqual(__lowerCamelCase     , scheduler.state_dict()			)

           # Load everything back in and make sure all states work
           accelerator.load_state(os.path.join(__lowerCamelCase     , "checkpoints"     , "checkpoint_0"			)			)
           self.assertEqual(__lowerCamelCase     , scheduler.state_dict()			)





   def      _snake_case		(       self					:			Any			):
       with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42			)
           SCREAMING_SNAKE_CASE										=   DummyModel()
           SCREAMING_SNAKE_CASE										=   ProjectConfiguration(automatic_checkpoint_naming=__lowerCamelCase     , total_limit=2			)
           # Train baseline
           SCREAMING_SNAKE_CASE										=   Accelerator(project_dir=__lowerCamelCase     , project_config=__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   accelerator.prepare(__lowerCamelCase			)
           # Save 3 states:
           for _ in range(11			):
               accelerator.save_state()
           self.assertTrue(not os.path.exists(os.path.join(__lowerCamelCase     , "checkpoints"     , "checkpoint_0"			)			)			)
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase     , "checkpoints"     , "checkpoint_9"			)			)			)
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase     , "checkpoints"     , "checkpoint_10"			)			)			)





   @require_cuda
   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__			)]
       execute_subprocess_async(__lowerCamelCase     , env=os.environ.copy()			)


if __name__ == "__main__":
    __A			:					Union[str, Any]						=			'/tmp/accelerate/state_checkpointing'
    __A			:					Any						=			DummyModel()
    __A			:					Optional[int]						=			torch.optim.Adam(params=model.parameters(), lr=1e-3)
    __A			:					Any						=			torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
    __A			, __A			:					List[Any]						=			dummy_dataloaders()
    __A			:					Union[str, Any]						=			ProjectConfiguration(automatic_checkpoint_naming=True)
    # Train baseline
    __A			:					Optional[Any]						=			Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
    if accelerator.process_index == 0:
        if os.path.exists(savedir):
            shutil.rmtree(savedir)
        os.makedirs(savedir)
    __A			, __A			, __A			, __A			, __A			:					Any						=			accelerator.prepare(
        model, optimizer, train_dataloader, valid_dataloader, scheduler
    )
    __A			, __A			:					Optional[int]						=			accelerator.prepare(model, optimizer)
    train(3, model, train_dataloader, optimizer, accelerator, scheduler)
    # Check that the intial optimizer is loaded on the GPU
    for group in optimizer.param_groups:
        __A			:					Dict						=			group['params'][0].device
        break
    assert param_device.type == accelerator.device.type
    __A			:					int						=			model.cpu()
    accelerator.wait_for_everyone()
    accelerator.save_state()
    accelerator.wait_for_everyone()

    # Check CPU state
    accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
    for group in optimizer.param_groups:
        __A			:					Optional[Any]						=			group['params'][0].device
        break
    assert (
        param_device.type == torch.device('cpu').type
    ), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

    # Check device state
    model.to(accelerator.device)
    accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
    for group in optimizer.param_groups:
        __A			:					Any						=			group['params'][0].device
        break
    assert (
        param_device.type == accelerator.device.type
    ), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

    # Check error
    with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
        accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
    accelerator.wait_for_everyone()
    if accelerator.process_index == 0:
        shutil.rmtree(savedir)
    accelerator.wait_for_everyone()





import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


a__		     =     logging.get_logger(__name__)

a__		     =     {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a__		     =     {
    '''vocab_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
a__		     =     {
    '''vocab_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
a__		     =     {
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

a__		     =     {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
a__		     =     {
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
a__		     =     {
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


a__		     =     {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
a__		     =     {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
a__		     =     {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Union[str, Any]      	=		VOCAB_FILES_NAMES
	__magic_name__   :       List[Any]      	=		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	__magic_name__   :       Optional[Any]      	=		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__   :       Union[str, Any]      	=		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
	__magic_name__   :       Optional[Any]      	=		DPRContextEncoderTokenizer






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Tuple      	=		VOCAB_FILES_NAMES
	__magic_name__   :       Dict      	=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	__magic_name__   :       Optional[Any]      	=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__   :       Tuple      	=		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
	__magic_name__   :       int      	=		DPRQuestionEncoderTokenizer






a__		     =     collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

a__		     =     collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


a__		     =     R'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''


@add_start_docstrings(_UpperCamelCase   )
class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def __call__(					self		:						Tuple					,     lowerCAmelCase		:						Union[str, Any]					,     lowerCAmelCase		:						Optional[str] = None					,     lowerCAmelCase		:						Optional[str] = None					,     lowerCAmelCase		:						Union[bool, str] = False					,     lowerCAmelCase		:						Union[bool, str] = False					,     lowerCAmelCase		:						Optional[int] = None					,     lowerCAmelCase		:						Optional[Union[str, TensorType]] = None					,     lowerCAmelCase		:						Optional[bool] = None					,     **lowerCAmelCase		:						Tuple					,     )	->       BatchEncoding:







						"""simple docstring"""







						if titles is None and texts is None:
											return super().__call__(
											    lowerCAmelCase					,     padding=lowerCAmelCase					,     truncation=lowerCAmelCase					,     max_length=lowerCAmelCase					,     return_tensors=lowerCAmelCase					,     return_attention_mask=lowerCAmelCase					,     **lowerCAmelCase					,     )
						elif titles is None or texts is None:
											__UpperCamelCase					:	Optional[Any]             =      titles if texts is None else texts
											return super().__call__(
											    lowerCAmelCase					,     lowerCAmelCase					,     padding=lowerCAmelCase					,     truncation=lowerCAmelCase					,     max_length=lowerCAmelCase					,     return_tensors=lowerCAmelCase					,     return_attention_mask=lowerCAmelCase					,     **lowerCAmelCase					,     )
						__UpperCamelCase					:	Optional[Any]             =      titles if not isinstance(lowerCAmelCase					,     lowerCAmelCase	) else [titles]
						__UpperCamelCase					:	Union[str, Any]             =      texts if not isinstance(lowerCAmelCase					,     lowerCAmelCase	) else [texts]
						__UpperCamelCase					:	Optional[Any]             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	str             =      questions if not isinstance(lowerCAmelCase					,     lowerCAmelCase	) else [questions] * n_passages
						assert len(lowerCAmelCase	) == len(
						    lowerCAmelCase	), F'''There should be as many titles than texts but got {len(lowerCAmelCase	)} titles and {len(lowerCAmelCase	)} texts.'''
						__UpperCamelCase					:	List[str]             =      super().__call__(lowerCAmelCase					,     lowerCAmelCase					,     padding=lowerCAmelCase					,     truncation=lowerCAmelCase	)["""input_ids"""]
						__UpperCamelCase					:	Tuple             =      super().__call__(lowerCAmelCase					,     add_special_tokens=lowerCAmelCase					,     padding=lowerCAmelCase					,     truncation=lowerCAmelCase	)["""input_ids"""]
						__UpperCamelCase					:	List[Any]             =      {
						    """input_ids""": [
						        (encoded_question_and_title + encoded_text)[:max_length]
						        if max_length is not None and truncation
						        else encoded_question_and_title + encoded_text
						        for encoded_question_and_title, encoded_text in zip(lowerCAmelCase					,     lowerCAmelCase	)
						    ]
						}
						if return_attention_mask is not False:
											__UpperCamelCase					:	Any             =      []
											for input_ids in encoded_inputs["input_ids"]:
																attention_mask.append([int(input_id != self.pad_token_id	) for input_id in input_ids]	)
											__UpperCamelCase					:	Optional[Any]             =      attention_mask
						return self.pad(lowerCAmelCase					,     padding=lowerCAmelCase					,     max_length=lowerCAmelCase					,     return_tensors=lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						BatchEncoding					,     lowerCAmelCase		:						DPRReaderOutput					,     lowerCAmelCase		:						int = 16					,     lowerCAmelCase		:						int = 64					,     lowerCAmelCase		:						int = 4					,     )	->       List[DPRSpanPrediction]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      reader_input["""input_ids"""]
						__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      reader_output[:3]
						__UpperCamelCase					:	Optional[int]             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[Any]             =      sorted(range(lowerCAmelCase	)					,     reverse=lowerCAmelCase					,     key=relevance_logits.__getitem__	)
						__UpperCamelCase					:	List[DPRReaderOutput]             =      []
						for doc_id in sorted_docs:
											__UpperCamelCase					:	Optional[int]             =      list(input_ids[doc_id]	)
											# assuming question & title information is at the beginning of the sequence
											__UpperCamelCase					:	Optional[Any]             =      sequence_ids.index(self.sep_token_id					,     2	) + 1  # second sep id
											if sequence_ids[-1] == self.pad_token_id:
																__UpperCamelCase					:	Dict             =      sequence_ids.index(self.pad_token_id	)
											else:
																__UpperCamelCase					:	str             =      len(lowerCAmelCase	)

											__UpperCamelCase					:	Union[str, Any]             =      self._get_best_spans(
											    start_logits=start_logits[doc_id][passage_offset:sequence_len]					,     end_logits=end_logits[doc_id][passage_offset:sequence_len]					,     max_answer_length=lowerCAmelCase					,     top_spans=lowerCAmelCase					,     )
											for start_index, end_index in best_spans:
																start_index += passage_offset
																end_index += passage_offset
																nbest_spans_predictions.append(
																    DPRSpanPrediction(
																        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]					,     relevance_score=relevance_logits[doc_id]					,     doc_id=lowerCAmelCase					,     start_index=lowerCAmelCase					,     end_index=lowerCAmelCase					,     text=self.decode(sequence_ids[start_index : end_index + 1]	)					,     )	)
											if len(lowerCAmelCase	) >= num_spans:
																break
						return nbest_spans_predictions[:num_spans]






	def 					lowerCamelCase__     (					self		:						Optional[Any]					,     lowerCAmelCase		:						List[int]					,     lowerCAmelCase		:						List[int]					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						int					,     )	->       List[DPRSpanPrediction]:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      []
						for start_index, start_score in enumerate(lowerCAmelCase	):
											for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]	):
																scores.append(((start_index, start_index + answer_length), start_score + end_score)	)
						__UpperCamelCase					:	Optional[Any]             =      sorted(lowerCAmelCase					,     key=lambda lowerCAmelCase	: x[1]					,     reverse=lowerCAmelCase	)
						__UpperCamelCase					:	str             =      []
						for (start_index, end_index), score in scores:
											assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
											__UpperCamelCase					:	Union[str, Any]             =      end_index - start_index + 1
											assert length <= max_answer_length, F'''Span is too long: {length} > {max_answer_length}'''
											if any(
											    start_index <= prev_start_index <= prev_end_index <= end_index
											    or prev_start_index <= start_index <= end_index <= prev_end_index
											    for (prev_start_index, prev_end_index) in chosen_span_intervals	):
																continue
											chosen_span_intervals.append((start_index, end_index)	)

											if len(lowerCAmelCase	) == top_spans:
																break
						return chosen_span_intervals






@add_end_docstrings(_UpperCamelCase   )
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase	,  _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Any      	=		VOCAB_FILES_NAMES
	__magic_name__   :       str      	=		READER_PRETRAINED_VOCAB_FILES_MAP
	__magic_name__   :       str      	=		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__   :       Tuple      	=		READER_PRETRAINED_INIT_CONFIGURATION
	__magic_name__   :       Dict      	=		['input_ids', 'attention_mask']
	__magic_name__   :       Union[str, Any]      	=		DPRReaderTokenizer






