






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


UpperCamelCase			    =     logging.get_logger(__name__)

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

UpperCamelCase			    =     {
    '''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'''
        ),
    },
}
UpperCamelCase			    =     {
    '''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'''
        ),
    },
}
UpperCamelCase			    =     {
    '''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'''
        ),
    },
}

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


UpperCamelCase			    =     {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
UpperCamelCase			    =     {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
UpperCamelCase			    =     {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				str									=							VOCAB_FILES_NAMES
			__snake_case    :				Dict									=							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			__snake_case    :				Dict									=							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case    :				Optional[int]									=							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
			__snake_case    :				Union[str, Any]									=							DPRContextEncoderTokenizer



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Tuple									=							VOCAB_FILES_NAMES
			__snake_case    :				Any									=							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			__snake_case    :				int									=							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case    :				Optional[int]									=							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
			__snake_case    :				int									=							DPRQuestionEncoderTokenizer
UpperCamelCase			    =     collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

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


UpperCamelCase			    =     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 				__UpperCAmelCase		:







			def __call__(     self:  Union[str, Any]				,     UpperCAmelCase_:  Union[str, 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_:  Any				,     ):







									'''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:
															_SCREAMING_SNAKE_CASE     				=  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_				,     )
									_SCREAMING_SNAKE_CASE     				=  titles if not isinstance(UpperCAmelCase_				,     UpperCAmelCase_					) else [titles]
									_SCREAMING_SNAKE_CASE     				=  texts if not isinstance(UpperCAmelCase_				,     UpperCAmelCase_					) else [texts]
									_SCREAMING_SNAKE_CASE     				=  len(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  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.'
									_SCREAMING_SNAKE_CASE     				=  super().__call__(UpperCAmelCase_				,     UpperCAmelCase_				,     padding=UpperCAmelCase_				,     truncation=UpperCAmelCase_					)["""input_ids"""]
									_SCREAMING_SNAKE_CASE     				=  super().__call__(UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_				,     padding=UpperCAmelCase_				,     truncation=UpperCAmelCase_					)["""input_ids"""]
									_SCREAMING_SNAKE_CASE     				=  {
									    """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:
															_SCREAMING_SNAKE_CASE     				=  []
															for input_ids in encoded_inputs["input_ids"]:
																					attention_mask.append([int(input_id != self.pad_token_id					) for input_id in input_ids]					)
															_SCREAMING_SNAKE_CASE     				=  attention_mask
									return self.pad(UpperCAmelCase_				,     padding=UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     return_tensors=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  BatchEncoding				,     UpperCAmelCase_:  DPRReaderOutput				,     UpperCAmelCase_:  int = 16				,     UpperCAmelCase_:  int = 64				,     UpperCAmelCase_:  int = 4				,     ):







									'''simple docstring'''




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

															_SCREAMING_SNAKE_CASE     				=  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 							UpperCamelCase				(     self:  str				,     UpperCAmelCase_:  List[int]				,     UpperCAmelCase_:  List[int]				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  int				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  []
									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)					)
									_SCREAMING_SNAKE_CASE     				=  sorted(UpperCAmelCase_				,     key=lambda UpperCAmelCase_					: x[1]				,     reverse=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  []
									for (start_index, end_index), score in scores:
															assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
															_SCREAMING_SNAKE_CASE     				=  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(_UpperCAmelCase   )
class 				__UpperCAmelCase		(_UpperCAmelCase	,_UpperCAmelCase   ):
			__snake_case    :				Any									=							VOCAB_FILES_NAMES
			__snake_case    :				str									=							READER_PRETRAINED_VOCAB_FILES_MAP
			__snake_case    :				Dict									=							READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case    :				Tuple									=							READER_PRETRAINED_INIT_CONFIGURATION
			__snake_case    :				str									=							["input_ids", "attention_mask"]
			__snake_case    :				Optional[int]									=							DPRReaderTokenizer









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Any									=							"mobilenet_v1"







			def __init__(     self:  List[str]				,     UpperCAmelCase_:  List[Any]=3				,     UpperCAmelCase_:  List[str]=224				,     UpperCAmelCase_:  Optional[Any]=1.0				,     UpperCAmelCase_:  Dict=8				,     UpperCAmelCase_:  List[str]="relu6"				,     UpperCAmelCase_:  List[Any]=True				,     UpperCAmelCase_:  Dict=0.9_99				,     UpperCAmelCase_:  Union[str, Any]=0.02				,     UpperCAmelCase_:  Tuple=0.0_01				,     **UpperCAmelCase_:  List[str]				,     ):







									'''simple docstring'''




									super().__init__(**UpperCAmelCase_					)

									if depth_multiplier <= 0:
															raise ValueError("""depth_multiplier must be greater than zero."""					)

									_SCREAMING_SNAKE_CASE     				=  num_channels
									_SCREAMING_SNAKE_CASE     				=  image_size
									_SCREAMING_SNAKE_CASE     				=  depth_multiplier
									_SCREAMING_SNAKE_CASE     				=  min_depth
									_SCREAMING_SNAKE_CASE     				=  hidden_act
									_SCREAMING_SNAKE_CASE     				=  tf_padding
									_SCREAMING_SNAKE_CASE     				=  classifier_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  initializer_range
									_SCREAMING_SNAKE_CASE     				=  layer_norm_eps



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				List[Any]									=							version.parse("1.11"   )







			@property
			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									return OrderedDict([("""pixel_values""", {0: """batch"""})]					)







			@property
			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									if self.task == "image-classification":
															return OrderedDict([("""logits""", {0: """batch"""})]					)
									else:
															return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]					)







			@property
			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									return 1E-4

