

"""simple docstring"""


import collections
import importlib.util
import os
import re
from pathlib import Path


__A						       =      "src/transformers"


# Matches is_xxx_available()
__A						       =      re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
__A						       =      re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
__A						       =      re.compile(r"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
__A						       =      re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
__A						       =      re.compile(r"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
__A						       =      re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma:     "MyModel",
__A						       =      re.compile("^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only:    ["foo", "bar"],
__A						       =      re.compile("^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
__A						       =      re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
__A						       =      re.compile(r"^\s*try:")
# Catches a line with else:
__A						       =      re.compile(r"^\s*else:")






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		if _re_test_backend.search(__SCREAMING_SNAKE_CASE			) is None:
				return None
		__lowerCAmelCase:	Union[str, Any]   				=      [b[0] for b in _re_backend.findall(__SCREAMING_SNAKE_CASE			)]
		backends.sort()
		return "_and_".join(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		with open(__SCREAMING_SNAKE_CASE ,			"r" ,			encoding="utf-8" ,			newline="\n"			) as f:
				__lowerCAmelCase:	Optional[int]   				=      f.readlines()

		__lowerCAmelCase:	Dict   				=      0
		while line_index < len(__SCREAMING_SNAKE_CASE			) and not lines[line_index].startswith("_import_structure = {"			):
				line_index += 1

		# If this is a traditional init, just return.
		if line_index >= len(__SCREAMING_SNAKE_CASE			):
				return None

		# First grab the objects without a specific backend in _import_structure
		__lowerCAmelCase:	Optional[Any]   				=      []
		while not lines[line_index].startswith("if TYPE_CHECKING"			) and find_backend(lines[line_index]			) is None:
				__lowerCAmelCase:	Optional[int]   				=      lines[line_index]
				# If we have everything on a single line, let's deal with it.
				if _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE			):
						__lowerCAmelCase:	List[Any]   				=      _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE			).groups()[0]
						__lowerCAmelCase:	List[Any]   				=      re.findall("\[([^\]]+)\]" ,			__SCREAMING_SNAKE_CASE			)
						for imp in imports:
								objects.extend([obj[1:-1] for obj in imp.split(", "			)]			)
						line_index += 1
						continue
				__lowerCAmelCase:	str   				=      _re_import_struct_key_value.search(__SCREAMING_SNAKE_CASE			)
				if single_line_import_search is not None:
						__lowerCAmelCase:	str   				=      [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", "			) if len(__SCREAMING_SNAKE_CASE			) > 0]
						objects.extend(__SCREAMING_SNAKE_CASE			)
				elif line.startswith(" " * 8 + "\""			):
						objects.append(line[9:-3]			)
				line_index += 1

		__lowerCAmelCase:	Tuple   				=      {"none": objects}
		# Let's continue with backend-specific objects in _import_structure
		while not lines[line_index].startswith("if TYPE_CHECKING"			):
				# If the line is an if not is_backend_available, we grab all objects associated.
				__lowerCAmelCase:	Optional[int]   				=      find_backend(lines[line_index]			)
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1]			) is None:
						__lowerCAmelCase:	Optional[int]   				=      None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index]			) is None:
								line_index += 1

						line_index += 1

						__lowerCAmelCase:	Optional[int]   				=      []
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]			) <= 1 or lines[line_index].startswith(" " * 4			):
								__lowerCAmelCase:	Optional[Any]   				=      lines[line_index]
								if _re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE			) is not None:
										objects.append(_re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE			).groups()[0]			)
								elif _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE			) is not None:
										__lowerCAmelCase:	Union[str, Any]   				=      _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE			).groups()[0].split(", "			)
										__lowerCAmelCase:	int   				=      [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE			) > 0]
										objects.extend(__SCREAMING_SNAKE_CASE			)
								elif _re_between_brackets.search(__SCREAMING_SNAKE_CASE			) is not None:
										__lowerCAmelCase:	Tuple   				=      _re_between_brackets.search(__SCREAMING_SNAKE_CASE			).groups()[0].split(", "			)
										__lowerCAmelCase:	Tuple   				=      [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE			) > 0]
										objects.extend(__SCREAMING_SNAKE_CASE			)
								elif _re_quote_object.search(__SCREAMING_SNAKE_CASE			) is not None:
										objects.append(_re_quote_object.search(__SCREAMING_SNAKE_CASE			).groups()[0]			)
								elif line.startswith(" " * 8 + "\""			):
										objects.append(line[9:-3]			)
								elif line.startswith(" " * 1_2 + "\""			):
										objects.append(line[1_3:-3]			)
								line_index += 1

						__lowerCAmelCase:	Union[str, Any]   				=      objects
				else:
						line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
		__lowerCAmelCase:	str   				=      []
		while (
		    line_index < len(__SCREAMING_SNAKE_CASE			)
		    and find_backend(lines[line_index]			) is None
		    and not lines[line_index].startswith("else"			)
		):
				__lowerCAmelCase:	List[Any]   				=      lines[line_index]
				__lowerCAmelCase:	Tuple   				=      _re_import.search(__SCREAMING_SNAKE_CASE			)
				if single_line_import_search is not None:
						objects.extend(single_line_import_search.groups()[0].split(", "			)			)
				elif line.startswith(" " * 8			):
						objects.append(line[8:-2]			)
				line_index += 1

		__lowerCAmelCase:	Any   				=      {"none": objects}
		# Let's continue with backend-specific objects
		while line_index < len(__SCREAMING_SNAKE_CASE			):
				# If the line is an if is_backend_available, we grab all objects associated.
				__lowerCAmelCase:	Optional[Any]   				=      find_backend(lines[line_index]			)
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1]			) is None:
						__lowerCAmelCase:	Any   				=      None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index]			) is None:
								line_index += 1

						line_index += 1

						__lowerCAmelCase:	List[Any]   				=      []
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]			) <= 1 or lines[line_index].startswith(" " * 8			):
								__lowerCAmelCase:	Optional[int]   				=      lines[line_index]
								__lowerCAmelCase:	Any   				=      _re_import.search(__SCREAMING_SNAKE_CASE			)
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(", "			)			)
								elif line.startswith(" " * 1_2			):
										objects.append(line[1_2:-2]			)
								line_index += 1

						__lowerCAmelCase:	Union[str, Any]   				=      objects
				else:
						line_index += 1

		return import_dict_objects, type_hint_objects






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Tuple:

		def find_duplicates(__SCREAMING_SNAKE_CASE			):
				return [k for k, v in collections.Counter(__SCREAMING_SNAKE_CASE			).items() if v > 1]

		if list(import_dict_objects.keys()			) != list(type_hint_objects.keys()			):
				return ["Both sides of the init do not have the same backends!"]

		__lowerCAmelCase:	Optional[int]   				=      []
		for key in import_dict_objects.keys():
				__lowerCAmelCase:	Union[str, Any]   				=      find_duplicates(import_dict_objects[key]			)
				if duplicate_imports:
						errors.append(F"Duplicate _import_structure definitions for: {duplicate_imports}"			)
				__lowerCAmelCase:	Optional[Any]   				=      find_duplicates(type_hint_objects[key]			)
				if duplicate_type_hints:
						errors.append(F"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"			)

				if sorted(set(import_dict_objects[key]			)			) != sorted(set(type_hint_objects[key]			)			):
						__lowerCAmelCase:	Union[str, Any]   				=      "base imports" if key == "none" else F"{key} backend"
						errors.append(F"Differences for {name}:"			)
						for a in type_hint_objects[key]:
								if a not in import_dict_objects[key]:
										errors.append(F"  {a} in TYPE_HINT but not in _import_structure."			)
						for a in import_dict_objects[key]:
								if a not in type_hint_objects[key]:
										errors.append(F"  {a} in _import_structure but not in TYPE_HINT."			)
		return errors






def    a__   (   )     ->     Tuple:
		__lowerCAmelCase:	Optional[Any]   				=      []
		for root, _, files in os.walk(__SCREAMING_SNAKE_CASE			):
				if "__init__.py" in files:
						__lowerCAmelCase:	List[str]   				=      os.path.join(__SCREAMING_SNAKE_CASE ,			"__init__.py"			)
						__lowerCAmelCase:	int   				=      parse_init(__SCREAMING_SNAKE_CASE			)
						if objects is not None:
								__lowerCAmelCase:	Optional[Any]   				=      analyze_results(*__SCREAMING_SNAKE_CASE			)
								if len(__SCREAMING_SNAKE_CASE			) > 0:
										__lowerCAmelCase:	Union[str, Any]   				=      F"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
										failures.append("\n".join(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) > 0:
				raise ValueError("\n\n".join(__SCREAMING_SNAKE_CASE			)			)






def    a__   (   )     ->     Any:
		__lowerCAmelCase:	Optional[int]   				=      []
		for path, directories, files in os.walk(__SCREAMING_SNAKE_CASE			):
				for folder in directories:
						# Ignore private modules
						if folder.startswith("_"			):
								directories.remove(__SCREAMING_SNAKE_CASE			)
								continue
						# Ignore leftovers from branches (empty folders apart from pycache)
						if len(list((Path(__SCREAMING_SNAKE_CASE			) / folder).glob("*.py"			)			)			) == 0:
								continue
						__lowerCAmelCase:	Optional[int]   				=      str((Path(__SCREAMING_SNAKE_CASE			) / folder).relative_to(__SCREAMING_SNAKE_CASE			)			)
						__lowerCAmelCase:	Tuple   				=      short_path.replace(os.path.sep ,			"."			)
						submodules.append(__SCREAMING_SNAKE_CASE			)
				for fname in files:
						if fname == "__init__.py":
								continue
						__lowerCAmelCase:	Dict   				=      str((Path(__SCREAMING_SNAKE_CASE			) / fname).relative_to(__SCREAMING_SNAKE_CASE			)			)
						__lowerCAmelCase:	Dict   				=      short_path.replace(".py" ,			""			).replace(os.path.sep ,			"."			)
						if len(submodule.split("."			)			) == 1:
								submodules.append(__SCREAMING_SNAKE_CASE			)
		return submodules


__A						       =      [
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
]






def    a__   (   )     ->     Optional[int]:
		# This is to make sure the transformers module imported is the one in the repo.
		__lowerCAmelCase:	Optional[Any]   				=      importlib.util.spec_from_file_location(
		    "transformers" ,			os.path.join(__SCREAMING_SNAKE_CASE ,			"__init__.py"			) ,			submodule_search_locations=[PATH_TO_TRANSFORMERS] ,			)
		__lowerCAmelCase:	Optional[int]   				=      spec.loader.load_module()

		__lowerCAmelCase:	str   				=      [
		    module
		    for module in get_transformers_submodules()
		    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
		]
		if len(__SCREAMING_SNAKE_CASE			) > 0:
				__lowerCAmelCase:	Optional[int]   				=      "\n".join(F"- {module}" for module in module_not_registered			)
				raise ValueError(
				    "The following submodules are not properly registered in the main init of Transformers:\n"
				    F"{list_of_modules}\n"
				    "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."			)


if __name__ == "__main__":
			check_all_inits()
			check_submodules()





"""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


