import argparse
import os
import re


lowerCAmelCase__     						=			'src/transformers'

# Pattern that looks at the indentation in a line.
lowerCAmelCase__     						=			re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCAmelCase__     						=			re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowerCAmelCase__     						=			re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
lowerCAmelCase__     						=			re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowerCAmelCase__     						=			re.compile(r'\[([^\]]+)\]')






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
							lowerCAmelCase__					     = _re_indent.search(lowerCAmelCase__     )
							return "" if search is None else search.groups()[0]






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__=""     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ):
							lowerCAmelCase__					     = 0
							lowerCAmelCase__					     = code.split('\n'     )
							if start_prompt is not None:
														while not lines[index].startswith(lowerCAmelCase__     ):
																					index += 1
														lowerCAmelCase__					     = ['\n'.join(lines[:index]     )]
							else:
														lowerCAmelCase__					     = []

							# We split into blocks until we get to the `end_prompt` (or the end of the block).
							lowerCAmelCase__					     = [lines[index]]
							index += 1
							while index < len(lowerCAmelCase__     ) and (end_prompt is None or not lines[index].startswith(lowerCAmelCase__     )):
														if len(lines[index]     ) > 0 and get_indent(lines[index]     ) == indent_level:
																					if len(lowerCAmelCase__     ) > 0 and get_indent(current_block[-1]     ).startswith(indent_level + ' '     ):
																												current_block.append(lines[index]     )
																												blocks.append('\n'.join(lowerCAmelCase__     )     )
																												if index < len(lowerCAmelCase__     ) - 1:
																																			lowerCAmelCase__					     = [lines[index + 1]]
																																			index += 1
																												else:
																																			lowerCAmelCase__					     = []
																					else:
																												blocks.append('\n'.join(lowerCAmelCase__     )     )
																												lowerCAmelCase__					     = [lines[index]]
														else:
																					current_block.append(lines[index]     )
														index += 1

							# Adds current block if it's nonempty.
							if len(lowerCAmelCase__     ) > 0:
														blocks.append('\n'.join(lowerCAmelCase__     )     )

							# Add final block after end_prompt if provided.
							if end_prompt is not None and index < len(lowerCAmelCase__     ):
														blocks.append('\n'.join(lines[index:]     )     )

							return blocks






def 				__lowerCamelCase		(						lowerCAmelCase__     ):

							def _inner(lowerCAmelCase__     ):
														return key(lowerCAmelCase__     ).lower().replace('_'     ,  ''     )

							return _inner






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__=None     ):

							# If no key is provided, we use a noop.
							def noop(lowerCAmelCase__     ):
														return x

							if key is None:
														lowerCAmelCase__					     = noop
							# Constants are all uppercase, they go first.
							lowerCAmelCase__					     = [obj for obj in objects if key(lowerCAmelCase__     ).isupper()]
							# Classes are not all uppercase but start with a capital, they go second.
							lowerCAmelCase__					     = [obj for obj in objects if key(lowerCAmelCase__     )[0].isupper() and not key(lowerCAmelCase__     ).isupper()]
							# Functions begin with a lowercase, they go last.
							lowerCAmelCase__					     = [obj for obj in objects if not key(lowerCAmelCase__     )[0].isupper()]

							lowerCAmelCase__					     = ignore_underscore(lowerCAmelCase__     )
							return sorted(lowerCAmelCase__     ,  key=lowerCAmelCase__     ) + sorted(lowerCAmelCase__     ,  key=lowerCAmelCase__     ) + sorted(lowerCAmelCase__     ,  key=lowerCAmelCase__     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ):

							# This inner function sort imports between [ ].
							def _replace(lowerCAmelCase__     ):
														lowerCAmelCase__					     = match.groups()[0]
														if "," not in imports:
																					return F"""[{imports}]"""
														lowerCAmelCase__					     = [part.strip().replace('"'     ,  ''     ) for part in imports.split(','     )]
														# We will have a final empty element if the line finished with a comma.
														if len(keys[-1]     ) == 0:
																					lowerCAmelCase__					     = keys[:-1]
														return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(lowerCAmelCase__     )]     ) + "]"

							lowerCAmelCase__					     = import_statement.split('\n'     )
							if len(lowerCAmelCase__     ) > 3:
														# Here we have to sort internal imports that are on several lines (one per name):
														# key: [
														#     "object1",
														#     "object2",
														#     ...
														# ]

														# We may have to ignore one or two lines on each side.
														lowerCAmelCase__					     = 2 if lines[1].strip() == '[' else 1
														lowerCAmelCase__					     = [(i, _re_strip_line.search(lowerCAmelCase__     ).groups()[0]) for i, line in enumerate(lines[idx:-idx]     )]
														lowerCAmelCase__					     = sort_objects(lowerCAmelCase__     ,  key=lambda lowerCAmelCase__     : x[1]     )
														lowerCAmelCase__					     = [lines[x[0] + idx] for x in sorted_indices]
														return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]     )
							elif len(lowerCAmelCase__     ) == 3:
														# Here we have to sort internal imports that are on one separate line:
														# key: [
														#     "object1", "object2", ...
														# ]
														if _re_bracket_content.search(lines[1]     ) is not None:
																					lowerCAmelCase__					     = _re_bracket_content.sub(_replace     ,  lines[1]     )
														else:
																					lowerCAmelCase__					     = [part.strip().replace('"'     ,  ''     ) for part in lines[1].split(','     )]
																					# We will have a final empty element if the line finished with a comma.
																					if len(keys[-1]     ) == 0:
																												lowerCAmelCase__					     = keys[:-1]
																					lowerCAmelCase__					     = get_indent(lines[1]     ) + ', '.join([F"""\"{k}\"""" for k in sort_objects(lowerCAmelCase__     )]     )
														return "\n".join(lowerCAmelCase__     )
							else:
														# Finally we have to deal with imports fitting on one line
														lowerCAmelCase__					     = _re_bracket_content.sub(_replace     ,  lowerCAmelCase__     )
														return import_statement






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__=True     ):
							with open(lowerCAmelCase__     ,  encoding='utf-8'     ) as f:
														lowerCAmelCase__					     = f.read()

							if "_import_structure" not in code:
														return

							# Blocks of indent level 0
							lowerCAmelCase__					     = split_code_in_indented_blocks(
							    lowerCAmelCase__     ,  start_prompt='_import_structure = {'     ,  end_prompt='if TYPE_CHECKING:'     )

							# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
							for block_idx in range(1     ,  len(lowerCAmelCase__     ) - 1     ):
														# Check if the block contains some `_import_structure`s thingy to sort.
														lowerCAmelCase__					     = main_blocks[block_idx]
														lowerCAmelCase__					     = block.split('\n'     )

														# Get to the start of the imports.
														lowerCAmelCase__					     = 0
														while line_idx < len(lowerCAmelCase__     ) and "_import_structure" not in block_lines[line_idx]:
																					# Skip dummy import blocks
																					if "import dummy" in block_lines[line_idx]:
																												lowerCAmelCase__					     = len(lowerCAmelCase__     )
																					else:
																												line_idx += 1
														if line_idx >= len(lowerCAmelCase__     ):
																					continue

														# Ignore beginning and last line: they don't contain anything.
														lowerCAmelCase__					     = '\n'.join(block_lines[line_idx:-1]     )
														lowerCAmelCase__					     = get_indent(block_lines[1]     )
														# Slit the internal block into blocks of indent level 1.
														lowerCAmelCase__					     = split_code_in_indented_blocks(lowerCAmelCase__     ,  indent_level=lowerCAmelCase__     )
														# We have two categories of import key: list or _import_structure[key].append/extend
														lowerCAmelCase__					     = _re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
														# Grab the keys, but there is a trap: some lines are empty or just comments.
														lowerCAmelCase__					     = [(pattern.search(lowerCAmelCase__     ).groups()[0] if pattern.search(lowerCAmelCase__     ) is not None else None) for b in internal_blocks]
														# We only sort the lines with a key.
														lowerCAmelCase__					     = [(i, key) for i, key in enumerate(lowerCAmelCase__     ) if key is not None]
														lowerCAmelCase__					     = [x[0] for x in sorted(lowerCAmelCase__     ,  key=lambda lowerCAmelCase__     : x[1]     )]

														# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
														lowerCAmelCase__					     = 0
														lowerCAmelCase__					     = []
														for i in range(len(lowerCAmelCase__     )     ):
																					if keys[i] is None:
																												reorderded_blocks.append(internal_blocks[i]     )
																					else:
																												lowerCAmelCase__					     = sort_objects_in_import(internal_blocks[sorted_indices[count]]     )
																												reorderded_blocks.append(lowerCAmelCase__     )
																												count += 1

        # And we put our main block back together with its first and last line.
														lowerCAmelCase__					     = '\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]     )

							if code != "\n".join(lowerCAmelCase__     ):
														if check_only:
																					return True
														else:
																					print(F"""Overwriting {file}."""     )
																					with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as f:
																												f.write('\n'.join(lowerCAmelCase__     )     )






def 				__lowerCamelCase		(						lowerCAmelCase__=True     ):
							lowerCAmelCase__					     = []
							for root, _, files in os.walk(lowerCAmelCase__     ):
														if "__init__.py" in files:
																					lowerCAmelCase__					     = sort_imports(os.path.join(lowerCAmelCase__     ,  '__init__.py'     )     ,  check_only=lowerCAmelCase__     )
																					if result:
																												lowerCAmelCase__					     = [os.path.join(lowerCAmelCase__     ,  '__init__.py'     )]
							if len(lowerCAmelCase__     ) > 0:
														raise ValueError(F"""Would overwrite {len(lowerCAmelCase__     )} files, run `make style`."""     )


if __name__ == "__main__":
				lowerCAmelCase__     						=			argparse.ArgumentParser()
				parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
				lowerCAmelCase__     						=			parser.parse_args()

				sort_imports_in_all_inits(check_only=args.check_only)

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 import BertTokenizer


lowerCAmelCase__     						=			logging.get_logger(__name__)

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

lowerCAmelCase__     						=			{
    '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'
        ),
    },
}
lowerCAmelCase__     						=			{
    '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'
        ),
    },
}
lowerCAmelCase__     						=			{
    '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'
        ),
    },
}

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


lowerCAmelCase__     						=			{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__     						=			{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__     						=			{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






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

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


lowerCAmelCase__     						=			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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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(SCREAMING_SNAKE_CASE						)
class     a_			:






 '''simple docstring'''






 def __call__(      self :  Optional[int]      ,   lowercase__ :  List[str]      ,   lowercase__ :  Optional[str] = None      ,   lowercase__ :  Optional[str] = None      ,   lowercase__ :  Union[bool, str] = False      ,   lowercase__ :  Union[bool, str] = False      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   lowercase__ :  Optional[bool] = None      ,   **lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



        if titles is None and texts is None:
               return super().__call__(
                   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__      ,   return_attention_mask=lowercase__      ,   **lowercase__      ,   )
        elif titles is None or texts is None:
               lowerCAmelCase__					     = titles if texts is None else texts
               return super().__call__(
                   lowercase__      ,   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__      ,   return_attention_mask=lowercase__      ,   **lowercase__      ,   )
        lowerCAmelCase__					     = titles if not isinstance(lowercase__      ,   lowercase__) else [titles]
        lowerCAmelCase__					     = texts if not isinstance(lowercase__      ,   lowercase__) else [texts]
        lowerCAmelCase__					     = len(lowercase__)
        lowerCAmelCase__					     = questions if not isinstance(lowercase__      ,   lowercase__) else [questions] * n_passages
        if len(lowercase__) != len(lowercase__):
               raise ValueError(
                   F"""There should be as many titles than texts but got {len(lowercase__)} titles and {len(lowercase__)} texts.""")
        lowerCAmelCase__					     = super().__call__(lowercase__      ,   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__)['input_ids']
        lowerCAmelCase__					     = super().__call__(lowercase__      ,   add_special_tokens=lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__)['input_ids']
        lowerCAmelCase__					     = {
            '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(lowercase__      ,   lowercase__)
            ]
        }
        if return_attention_mask is not False:
               lowerCAmelCase__					     = []
               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__					     = attention_mask
        return self.pad(lowercase__      ,   padding=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__)




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  BatchEncoding      ,   lowercase__ :  DPRReaderOutput      ,   lowercase__ :  int = 16      ,   lowercase__ :  int = 64      ,   lowercase__ :  int = 4      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = reader_input['input_ids']
        lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = reader_output[:3]
        lowerCAmelCase__					     = len(lowercase__)
        lowerCAmelCase__					     = sorted(range(lowercase__)      ,   reverse=lowercase__      ,   key=relevance_logits.__getitem__)
        lowerCAmelCase__					     = []
        for doc_id in sorted_docs:
               lowerCAmelCase__					     = list(input_ids[doc_id])
               # assuming question & title information is at the beginning of the sequence
               lowerCAmelCase__					     = sequence_ids.index(self.sep_token_id      ,   2) + 1  # second sep id
               if sequence_ids[-1] == self.pad_token_id:
                      lowerCAmelCase__					     = sequence_ids.index(self.pad_token_id)
               else:
                      lowerCAmelCase__					     = len(lowercase__)

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




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  List[int]      ,   lowercase__ :  List[int]      ,   lowercase__ :  int      ,   lowercase__ :  int      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = []
        for start_index, start_score in enumerate(lowercase__):
               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__					     = sorted(lowercase__      ,   key=lambda lowercase__: x[1]      ,   reverse=lowercase__)
        lowerCAmelCase__					     = []
        for (start_index, end_index), score in scores:
               if start_index > end_index:
                      raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""")
               lowerCAmelCase__					     = end_index - start_index + 1
               if length > max_answer_length:
                      raise ValueError(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(lowercase__) == top_spans:
                      break
        return chosen_span_intervals






@add_end_docstrings(SCREAMING_SNAKE_CASE						)
class     a_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= READER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= READER_PRETRAINED_INIT_CONFIGURATION
 UpperCAmelCase_     			= ['input_ids', 'attention_mask']
