

"""simple docstring"""


import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=False			):
		try:
				__lowerCAmelCase:	Optional[int]   				=      os.environ[key]
		except KeyError:
				# KEY isn't set, default to `default`.
				__lowerCAmelCase:	Optional[int]   				=      default
		else:
				# KEY is set, convert it to True or False.
				try:
						__lowerCAmelCase:	str   				=      strtobool(__SCREAMING_SNAKE_CASE			)
				except ValueError:
						# More values are supported, but let's keep the message simple.
						raise ValueError(F"If set, {key} must be yes or no."			)
		return _value


__A						       =      parse_flag_from_env("RUN_SLOW", default=False)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skip("Test was skipped"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(_run_slow_tests ,			"test is slow"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(not torch.cuda.is_available() ,			"test requires only a CPU"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(torch.cuda.is_available() ,			"test requires a GPU"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(is_xpu_available() ,			"test requires a XPU"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(is_mps_available() ,			"test requires a `mps` backend support in `torch`"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(
		    is_transformers_available() and is_datasets_available() ,			"test requires the Hugging Face suite"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(is_bnb_available() ,			"test requires the bitsandbytes library"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(is_tpu_available() ,			"test requires TPU"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(torch.cuda.device_count() == 1 ,			"test requires a GPU"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(torch.xpu.device_count() == 1 ,			"test requires a XPU"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(torch.cuda.device_count() > 1 ,			"test requires multiple GPUs"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(torch.xpu.device_count() > 1 ,			"test requires multiple XPUs"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(is_safetensors_available() ,			"test requires safetensors"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(is_deepspeed_available() ,			"test requires DeepSpeed"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(is_torch_version(">=" ,			"1.12.0"			) ,			"test requires torch version >= 1.12.0"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None			):
		if test_case is None:
				return partial(__SCREAMING_SNAKE_CASE ,			version=__SCREAMING_SNAKE_CASE			)
		return unittest.skipUnless(is_torch_version(">=" ,			__SCREAMING_SNAKE_CASE			) ,			F"test requires torch version >= {version}"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(is_tensorboard_available() ,			"test requires Tensorboard"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(is_wandb_available() ,			"test requires wandb"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(is_comet_ml_available() ,			"test requires comet_ml"			)(__SCREAMING_SNAKE_CASE			)


__A						       =      (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		return unittest.skipUnless(
		    _atleast_one_tracker_available ,			"test requires at least one tracker to be available and for `comet_ml` to not be installed" ,			)(__SCREAMING_SNAKE_CASE			)





class    snake_case     (    unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  True

				@classmethod
				def 							lowercase_					(     cls							:    Tuple)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      tempfile.mkdtemp()

				@classmethod
				def 							lowercase_					(     cls							:    List[Any])->       List[str]:



						'''simple docstring'''


						if os.path.exists(cls.tmpdir):
								shutil.rmtree(cls.tmpdir)




				def 							lowercase_					(     self							:    int)->       Dict:



						'''simple docstring'''


						if self.clear_on_setup:
								for path in Path(self.tmpdir).glob("**/*"):
										if path.is_file():
												path.unlink()
										elif path.is_dir():
												shutil.rmtree(UpperCamelCase__)





class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    Dict)->       List[str]:



						'''simple docstring'''


						super().tearDown()
						# Reset the state of the AcceleratorState singleton.
						AcceleratorState._reset_state()
						PartialState._reset_state()





class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    Union[mock.Mock, List[mock.Mock]])->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      mocks if isinstance(UpperCamelCase__					,  (tuple, list)) else [mocks]
						for m in self.mocks:
								m.start()
								self.addCleanup(m.stop)






def    a__   (   __SCREAMING_SNAKE_CASE			):
		__lowerCAmelCase:	Any   				=      AcceleratorState()
		__lowerCAmelCase:	Optional[int]   				=      tensor[None].clone().to(state.device			)
		__lowerCAmelCase:	int   				=      gather(__SCREAMING_SNAKE_CASE			).cpu()
		__lowerCAmelCase:	Union[str, Any]   				=      tensor[0].cpu()
		for i in range(tensors.shape[0]			):
				if not torch.equal(tensors[i] ,			__SCREAMING_SNAKE_CASE			):
						return False
		return True





class    snake_case     :

				def __init__(     self							:    Any					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Union[str, Any]					,  UpperCamelCase__							:    List[str])->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      returncode
						__lowerCAmelCase:	List[str]   				=      stdout
						__lowerCAmelCase:	Tuple   				=      stderr






async def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			):
		while True:
				__lowerCAmelCase:	Dict   				=      await stream.readline()
				if line:
						callback(__SCREAMING_SNAKE_CASE			)
				else:
						break






async def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=False ,			__SCREAMING_SNAKE_CASE=False			):
		if echo:
				print("\nRunning: " ,			" ".join(__SCREAMING_SNAKE_CASE			)			)

		__lowerCAmelCase:	Union[str, Any]   				=      await asyncio.create_subprocess_exec(
		    cmd[0] ,			*cmd[1:] ,			stdin=__SCREAMING_SNAKE_CASE ,			stdout=asyncio.subprocess.PIPE ,			stderr=asyncio.subprocess.PIPE ,			env=__SCREAMING_SNAKE_CASE ,			)

		# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
		# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
		#
		# If it starts hanging, will need to switch to the following code. The problem is that no data
		# will be seen until it's done and if it hangs for example there will be no debug info.
		# out, err = await p.communicate()
		# return _RunOutput(p.returncode, out, err)

		__lowerCAmelCase:	int   				=      []
		__lowerCAmelCase:	List[str]   				=      []

		def tee(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=""			):
				__lowerCAmelCase:	int   				=      line.decode("utf-8"			).rstrip()
				sink.append(__SCREAMING_SNAKE_CASE			)
				if not quiet:
						print(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			file=__SCREAMING_SNAKE_CASE			)

    # XXX: the timeout doesn't seem to make any difference here
		await asyncio.wait(
		    [
		        asyncio.create_task(_read_stream(p.stdout ,			lambda __SCREAMING_SNAKE_CASE			: tee(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			sys.stdout ,			label="stdout:"			)			)			),
		        asyncio.create_task(_read_stream(p.stderr ,			lambda __SCREAMING_SNAKE_CASE			: tee(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			sys.stderr ,			label="stderr:"			)			)			),
		    ] ,			timeout=__SCREAMING_SNAKE_CASE ,			)
		return _RunOutput(await p.wait() ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=1_8_0 ,			__SCREAMING_SNAKE_CASE=False ,			__SCREAMING_SNAKE_CASE=True			):
		__lowerCAmelCase:	Optional[int]   				=      asyncio.get_event_loop()
		__lowerCAmelCase:	int   				=      loop.run_until_complete(
		    _stream_subprocess(__SCREAMING_SNAKE_CASE ,			env=__SCREAMING_SNAKE_CASE ,			stdin=__SCREAMING_SNAKE_CASE ,			timeout=__SCREAMING_SNAKE_CASE ,			quiet=__SCREAMING_SNAKE_CASE ,			echo=__SCREAMING_SNAKE_CASE			)			)

		__lowerCAmelCase:	Union[str, Any]   				=      " ".join(__SCREAMING_SNAKE_CASE			)
		if result.returncode > 0:
				__lowerCAmelCase:	Dict   				=      "\n".join(result.stderr			)
				raise RuntimeError(
				    F"'{cmd_str}' failed with returncode {result.returncode}\n\n"
				    F"The combined stderr from workers follows:\n{stderr}"			)

		return result





class    snake_case     (    __snake_case  ):
				pass






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=False			):
		try:
				__lowerCAmelCase:	Union[str, Any]   				=      subprocess.check_output(__SCREAMING_SNAKE_CASE ,			stderr=subprocess.STDOUT			)
				if return_stdout:
						if hasattr(__SCREAMING_SNAKE_CASE ,			"decode"			):
								__lowerCAmelCase:	List[str]   				=      output.decode("utf-8"			)
						return output
		except subprocess.CalledProcessError as e:
				raise SubprocessCallException(
				    F"Command `{' '.join(__SCREAMING_SNAKE_CASE			)}` failed with the following error:\n\n{e.output.decode()}"			) from e





"""simple docstring"""


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    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :				Any       				=  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :				int       				=  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  DPRContextEncoderTokenizer





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  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"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "





@add_start_docstrings(__snake_case  )
class    snake_case     :

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



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    BatchEncoding					,  UpperCamelCase__							:    DPRReaderOutput					,  UpperCamelCase__							:    int = 1_6					,  UpperCamelCase__							:    int = 6_4					,  UpperCamelCase__							:    int = 4					,  )->       List[DPRSpanPrediction]:



						'''simple docstring'''


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

								__lowerCAmelCase:	Tuple   				=      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=UpperCamelCase__					,  top_spans=UpperCamelCase__					,  )
								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=UpperCamelCase__					,  start_index=UpperCamelCase__					,  end_index=UpperCamelCase__					,  text=self.decode(sequence_ids[start_index : end_index + 1])					,  ))
								if len(UpperCamelCase__) >= num_spans:
										break
						return nbest_spans_predictions[:num_spans]




				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    List[int]					,  UpperCamelCase__							:    List[int]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  )->       List[DPRSpanPrediction]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      []
						for start_index, start_score in enumerate(UpperCamelCase__):
								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))
						__lowerCAmelCase:	Tuple   				=      sorted(UpperCamelCase__					,  key=lambda UpperCamelCase__: x[1]					,  reverse=UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      []
						for (start_index, end_index), score in scores:
								assert start_index <= end_index, f"Wrong span indices: [{start_index}:{end_index}]"
								__lowerCAmelCase:	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(UpperCamelCase__) == top_spans:
										break
						return chosen_span_intervals





@add_end_docstrings(__snake_case  )
class    snake_case     (    __snake_case,							__snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :				str       				=  READER_PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :				int       				=  READER_PRETRAINED_INIT_CONFIGURATION
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  DPRReaderTokenizer


