



"""simple docstring"""







import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
			import PIL.Image

			from .features import FeatureType


UpperCamelCase     =							None
UpperCamelCase     =							"""<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
UpperCamelCase     =							[
    np.dtype("""|b1"""),
    np.dtype("""|u1"""),
    np.dtype("""<u2"""),
    np.dtype(""">u2"""),
    np.dtype("""<i2"""),
    np.dtype(""">i2"""),
    np.dtype("""<u4"""),
    np.dtype(""">u4"""),
    np.dtype("""<i4"""),
    np.dtype(""">i4"""),
    np.dtype("""<f4"""),
    np.dtype(""">f4"""),
    np.dtype("""<f8"""),
    np.dtype(""">f8"""),
]






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


			A__	:					bool							  =     True
			A__	:					Optional[str]							  =     None
			# Automatically constructed
			A__	:					ClassVar[str]							  =     "PIL.Image.Image"
			A__	:					ClassVar[Any]							  =     pa.struct({"bytes": pa.binary(), "path": pa.string()}      )
			A__	:					str							  =     field(default="Image"       ,		init=_lowerCAmelCase       ,		repr=_lowerCAmelCase      )


			def __call__(      self		)      ->	str:
				return self.pa_type


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	dict:
				if config.PIL_AVAILABLE:
					import PIL.Image
				else:
					raise ImportError("To support encoding images, please install 'Pillow'."		)

				if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					A__											=					np.array(SCREAMING_SNAKE_CASE__		)

				if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					return {"path": value, "bytes": None}
				elif isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					return {"path": None, "bytes": value}
				elif isinstance(SCREAMING_SNAKE_CASE__						,	np.ndarray		):
					# convert the image array to PNG/TIFF bytes
					return encode_np_array(SCREAMING_SNAKE_CASE__		)
				elif isinstance(SCREAMING_SNAKE_CASE__						,	PIL.Image.Image		):
					# convert the PIL image to bytes (default format is PNG/TIFF)
					return encode_pil_image(SCREAMING_SNAKE_CASE__		)
				elif value.get("path"		) is not None and os.path.isfile(value["path"]		):
					# we set "bytes": None to not duplicate the data if they're already available locally
					return {"bytes": None, "path": value.get("path"		)}
				elif value.get("bytes"		) is not None or value.get("path"		) is not None:
					# store the image bytes, and path is used to infer the image format using the file extension
					return {"bytes": value.get("bytes"		), "path": value.get("path"		)}
				else:
					raise ValueError(
					    f"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None		)      ->	"PIL.Image.Image":
				if not self.decode:
					raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead."		)

				if config.PIL_AVAILABLE:
					import PIL.Image
				else:
					raise ImportError("To support decoding images, please install 'Pillow'."		)

				if token_per_repo_id is None:
					A__											=					{}

				A__							,							A__											=					value["path"], value["bytes"]
				if bytes_ is None:
					if path is None:
						raise ValueError(f"""An image should have one of 'path' or 'bytes' but both are None in {value}."""		)
					else:
						if is_local_path(SCREAMING_SNAKE_CASE__		):
							A__											=					PIL.Image.open(SCREAMING_SNAKE_CASE__		)
						else:
							A__											=					path.split("::"		)[-1]
							try:
								A__											=					string_to_dict(SCREAMING_SNAKE_CASE__						,	config.HUB_DATASETS_URL		)["repo_id"]
								A__											=					token_per_repo_id.get(SCREAMING_SNAKE_CASE__		)
							except ValueError:
								A__											=					None
							with xopen(SCREAMING_SNAKE_CASE__						,	"rb"						,	use_auth_token=SCREAMING_SNAKE_CASE__		) as f:
								A__											=					BytesIO(f.read()		)
							A__											=					PIL.Image.open(bytes_		)
				else:
					A__											=					PIL.Image.open(BytesIO(bytes_		)		)
				image.load()  # to avoid "Too many open files" errors
				return image


			def   snake_case__	(      self		)      ->	Union["FeatureType", Dict[str, "FeatureType"]]:
				from .features import Value

				return (
				    self
				    if self.decode
				    else {
				        "bytes": Value("binary"		),
				        "path": Value("string"		),
				    }
				)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	pa.StructArray:
				if pa.types.is_string(storage.type		):
					A__											=					pa.array([None] * len(SCREAMING_SNAKE_CASE__		)						,	type=pa.binary()		)
					A__											=					pa.StructArray.from_arrays([bytes_array, storage]						,	["bytes", "path"]						,	mask=storage.is_null()		)
				elif pa.types.is_binary(storage.type		):
					A__											=					pa.array([None] * len(SCREAMING_SNAKE_CASE__		)						,	type=pa.string()		)
					A__											=					pa.StructArray.from_arrays([storage, path_array]						,	["bytes", "path"]						,	mask=storage.is_null()		)
				elif pa.types.is_struct(storage.type		):
					if storage.type.get_field_index("bytes"		) >= 0:
						A__											=					storage.field("bytes"		)
					else:
						A__											=					pa.array([None] * len(SCREAMING_SNAKE_CASE__		)						,	type=pa.binary()		)
					if storage.type.get_field_index("path"		) >= 0:
						A__											=					storage.field("path"		)
					else:
						A__											=					pa.array([None] * len(SCREAMING_SNAKE_CASE__		)						,	type=pa.string()		)
					A__											=					pa.StructArray.from_arrays([bytes_array, path_array]						,	["bytes", "path"]						,	mask=storage.is_null()		)
				elif pa.types.is_list(storage.type		):
					A__											=					pa.array(
					    [encode_np_array(np.array(SCREAMING_SNAKE_CASE__		)		)["bytes"] if arr is not None else None for arr in storage.to_pylist()]						,	type=pa.binary()						,	)
					A__											=					pa.array([None] * len(SCREAMING_SNAKE_CASE__		)						,	type=pa.string()		)
					A__											=					pa.StructArray.from_arrays(
					    [bytes_array, path_array]						,	["bytes", "path"]						,	mask=bytes_array.is_null()		)
				return array_cast(SCREAMING_SNAKE_CASE__						,	self.pa_type		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	pa.StructArray:

				@no_op_if_value_is_null
				def path_to_bytes(SCREAMING_SNAKE_CASE__		):
					with xopen(SCREAMING_SNAKE_CASE__						,	"rb"		) as f:
						A__											=					f.read()
					return bytes_

				A__											=					pa.array(
				    [
				        (path_to_bytes(x["path"]		) if x["bytes"] is None else x["bytes"]) if x is not None else None
				        for x in storage.to_pylist()
				    ]						,	type=pa.binary()						,	)
				A__											=					pa.array(
				    [os.path.basename(SCREAMING_SNAKE_CASE__		) if path is not None else None for path in storage.field("path"		).to_pylist()]						,	type=pa.string()						,	)
				A__											=					pa.StructArray.from_arrays([bytes_array, path_array]						,	["bytes", "path"]						,	mask=bytes_array.is_null()		)
				return array_cast(SCREAMING_SNAKE_CASE__						,	self.pa_type		)



def       _lowerCamelCase				(							)	-> List[str]:






	"""simple docstring"""





	if config.PIL_AVAILABLE:
		import PIL.Image
	else:
		raise ImportError("To support encoding images, please install 'Pillow'."     )

	global _IMAGE_COMPRESSION_FORMATS
	if _IMAGE_COMPRESSION_FORMATS is None:
		PIL.Image.init()
		A__											=					list(set(PIL.Image.OPEN.keys()     ) & set(PIL.Image.SAVE.keys()     )     )
	return _IMAGE_COMPRESSION_FORMATS

def       _lowerCamelCase				(							UpperCAmelCase_ :       "PIL.Image.Image"     )	-> bytes:






	"""simple docstring"""





	A__											=					BytesIO()
	if image.format in list_image_compression_formats():
		A__											=					image.format
	else:
		A__											=					"PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
	image.save(UpperCAmelCase_,	format=UpperCAmelCase_     )
	return buffer.getvalue()

def       _lowerCamelCase				(							UpperCAmelCase_ :       "PIL.Image.Image"     )	-> dict:






	"""simple docstring"""





	if hasattr(UpperCAmelCase_,	"filename"     ) and image.filename != "":
		return {"path": image.filename, "bytes": None}
	else:
		return {"path": None, "bytes": image_to_bytes(UpperCAmelCase_     )}

def       _lowerCamelCase				(							UpperCAmelCase_ :       np.ndarray     )	-> dict:






	"""simple docstring"""





	if config.PIL_AVAILABLE:
		import PIL.Image
	else:
		raise ImportError("To support encoding images, please install 'Pillow'."     )

	A__											=					array.dtype
	A__											=					dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
	A__											=					dtype.kind
	A__											=					dtype.itemsize

	A__											=					None

	# Multi-channel array case (only np.dtype("|u1") is allowed)
	if array.shape[2:]:
		A__											=					np.dtype("|u1"     )
		if dtype_kind not in ["u", "i"]:
			raise TypeError(
			    F"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."""     )
		if dtype is not dest_dtype:
			warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""     )
    # Exact match
	elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
		A__											=					dtype
	else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
		while dtype_itemsize >= 1:
			A__											=					dtype_byteorder + dtype_kind + str(UpperCAmelCase_     )
			A__											=					np.dtype(UpperCAmelCase_     )
			if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
				warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""     )
				break
			else:
				dtype_itemsize //= 2

	if dest_dtype is None:
		raise TypeError(
		    F"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"""     )
	A__											=					PIL.Image.fromarray(array.astype(UpperCAmelCase_     )     )
	return {"path": None, "bytes": image_to_bytes(UpperCAmelCase_     )}




def       _lowerCamelCase				(							UpperCAmelCase_ :       Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]     )	-> List[dict]:






	"""simple docstring"""





	if config.PIL_AVAILABLE:
		import PIL.Image
	else:
		raise ImportError("To support encoding images, please install 'Pillow'."     )

	if objs:
		A__							,							A__											=					first_non_null_value(UpperCAmelCase_     )
		if isinstance(UpperCAmelCase_,	UpperCAmelCase_     ):
			return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
		if isinstance(UpperCAmelCase_,	np.ndarray     ):
			A__											=					no_op_if_value_is_null(UpperCAmelCase_     )
			return [obj_to_image_dict_func(UpperCAmelCase_     ) for obj in objs]
		elif isinstance(UpperCAmelCase_,	PIL.Image.Image     ):
			A__											=					no_op_if_value_is_null(UpperCAmelCase_     )
			return [obj_to_image_dict_func(UpperCAmelCase_     ) for obj in objs]
		else:
			return objs
	else:
		return objs










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






