



'''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 import BertTokenizer


snake_case_ :	List[Any]         =  logging.get_logger(__name__)

snake_case_ :	List[Any]         =  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case_ :	List[str]         =  {
    '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'
        ),
    },
}
snake_case_ :	Optional[int]         =  {
    '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'
        ),
    },
}
snake_case_ :	str         =  {
    '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'
        ),
    },
}

snake_case_ :	Optional[int]         =  {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
snake_case_ :	Tuple         =  {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
snake_case_ :	Union[str, Any]         =  {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


snake_case_ :	Union[str, Any]         =  {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
snake_case_ :	Dict         =  {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
snake_case_ :	Dict         =  {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}






class 							lowercase__	(							lowerCAmelCase__   ):
	lowercase__  				=			VOCAB_FILES_NAMES
	lowercase__  				=			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	lowercase__  				=			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__  				=			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class 							lowercase__	(							lowerCAmelCase__   ):
	lowercase__  				=			VOCAB_FILES_NAMES
	lowercase__  				=			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	lowercase__  				=			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__  				=			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
snake_case_ :	int         =  collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

snake_case_ :	Union[str, Any]         =  collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


snake_case_ :	Union[str, Any]         =  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(lowerCAmelCase__   )
class 							lowercase__	:





	def __call__( self					:				List[Any] ,lowerCamelCase__					:				List[str] ,lowerCamelCase__					:				Union[str, Any] = None ,lowerCamelCase__					:				Any = None ,lowerCamelCase__					:				Union[str, Any] = False ,lowerCamelCase__					:				Any = False ,lowerCamelCase__					:				Optional[int] = None ,lowerCamelCase__					:				Dict = None ,lowerCamelCase__					:				str = None ,**lowerCamelCase__					:				str ,):







							'''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[int] 				=   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					:		Union[str, Any] 				=   titles if not isinstance(__lowerCAmelCase ,__lowerCAmelCase ) else [titles]
							_UpperCamelCase					:		Tuple 				=   texts if not isinstance(__lowerCAmelCase ,__lowerCAmelCase ) else [texts]
							_UpperCamelCase					:		Dict 				=   len(__lowerCAmelCase )
							_UpperCamelCase					:		Any 				=   questions if not isinstance(__lowerCAmelCase ,__lowerCAmelCase ) else [questions] * n_passages
							if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
													raise ValueError(
													    F'There should be as many titles than texts but got {len(__lowerCAmelCase )} titles and {len(__lowerCAmelCase )} texts.' )
							_UpperCamelCase					:		Optional[int] 				=   super().__call__(__lowerCAmelCase ,__lowerCAmelCase ,padding=__lowerCAmelCase ,truncation=__lowerCAmelCase )['input_ids']
							_UpperCamelCase					:		Any 				=   super().__call__(__lowerCAmelCase ,add_special_tokens=__lowerCAmelCase ,padding=__lowerCAmelCase ,truncation=__lowerCAmelCase )['input_ids']
							_UpperCamelCase					:		Tuple 				=   {
							    '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					:		int 				=   attention_mask
							return self.pad(__lowerCAmelCase ,padding=__lowerCAmelCase ,max_length=__lowerCAmelCase ,return_tensors=__lowerCAmelCase )





	def 	UpperCamelCase_  ( self					:				List[Any] ,lowerCamelCase__					:				Dict ,lowerCamelCase__					:				str ,lowerCamelCase__					:				str = 16 ,lowerCamelCase__					:				Dict = 64 ,lowerCamelCase__					:				Dict = 4 ,):







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

													_UpperCamelCase					:		str 				=   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 	UpperCamelCase_  ( self					:				Tuple ,lowerCamelCase__					:				Optional[int] ,lowerCamelCase__					:				List[Any] ,lowerCamelCase__					:				Tuple ,lowerCamelCase__					:				List[str] ,):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   []
							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					:		int 				=   sorted(__lowerCAmelCase ,key=lambda lowerCamelCase__ : x[1] ,reverse=__lowerCAmelCase )
							_UpperCamelCase					:		str 				=   []
							for (start_index, end_index), score in scores:
													if start_index > end_index:
																			raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]' )
													_UpperCamelCase					:		Optional[int] 				=   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(__lowerCAmelCase ) == top_spans:
																			break
							return chosen_span_intervals






@add_end_docstrings(lowerCAmelCase__   )
class 							lowercase__	(							lowerCAmelCase__						,					lowerCAmelCase__   ):
	lowercase__  				=			VOCAB_FILES_NAMES
	lowercase__  				=			READER_PRETRAINED_VOCAB_FILES_MAP
	lowercase__  				=			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__  				=			READER_PRETRAINED_INIT_CONFIGURATION
	lowercase__  				=			['''input_ids''', '''attention_mask''']



'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase          =  {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''InstructBlipQFormerModel''',
						    '''InstructBlipPreTrainedModel''',
						    '''InstructBlipForConditionalGeneration''',
						    '''InstructBlipVisionModel''',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




