




'''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


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': 5_12,
    'facebook/dpr-ctx_encoder-multiset-base': 5_12,
}
lowerCamelCase_			=						{
    'facebook/dpr-question_encoder-single-nq-base': 5_12,
    'facebook/dpr-question_encoder-multiset-base': 5_12,
}
lowerCamelCase_			=						{
    'facebook/dpr-reader-single-nq-base': 5_12,
    'facebook/dpr-reader-multiset-base': 5_12,
}


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 		lowercase_   (			A_       ):


   """simple docstring"""

   lowerCamelCase_      =						VOCAB_FILES_NAMES
   lowerCamelCase_      =						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_      =						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_      =						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class 		lowercase_   (			A_       ):


   """simple docstring"""

   lowerCamelCase_      =						VOCAB_FILES_NAMES
   lowerCamelCase_      =						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_      =						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_      =						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(A_       )
class 		lowercase_   :


   """simple docstring"""

   def __call__(   self :     Tuple  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     Optional[str] = None  ,				__lowerCamelCase :     Optional[str] = None  ,				__lowerCamelCase :     Union[bool, str] = False  ,				__lowerCamelCase :     Union[bool, str] = False  ,				__lowerCamelCase :     Optional[int] = None  ,				__lowerCamelCase :     Optional[Union[str, TensorType]] = None  ,				__lowerCamelCase :     Optional[bool] = None  ,				**__lowerCamelCase :     Optional[int]  ,				):

      """simple docstring"""


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



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     BatchEncoding  ,				__lowerCamelCase :     DPRReaderOutput  ,				__lowerCamelCase :     int = 1_6  ,				__lowerCamelCase :     int = 6_4  ,				__lowerCamelCase :     int = 4  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				reader_input["input_ids"]
      _SCREAMING_SNAKE_CASE	=				reader_output[:3]
      _SCREAMING_SNAKE_CASE	=				len(snake_case__   )
      _SCREAMING_SNAKE_CASE	=				sorted(range(snake_case__   )  ,				reverse=snake_case__  ,				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(snake_case__   )

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



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     List[int]  ,				__lowerCamelCase :     List[int]  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     int  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[]
      for start_index, start_score in enumerate(snake_case__   ):
         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(snake_case__  ,				key=lambda __lowerCamelCase   : x[1]  ,				reverse=snake_case__   )
      _SCREAMING_SNAKE_CASE	=				[]
      for (start_index, end_index), score in scores:
         if start_index > end_index:
            raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""   )
         _SCREAMING_SNAKE_CASE	=				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(snake_case__   ) == top_spans:
            break
      return chosen_span_intervals




@add_end_docstrings(A_       )
class 		lowercase_   (			A_					,  A_       ):


   """simple docstring"""

   lowerCamelCase_      =						VOCAB_FILES_NAMES
   lowerCamelCase_      =						READER_PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_      =						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_      =						READER_PRETRAINED_INIT_CONFIGURATION
   lowerCamelCase_      =						["input_ids", "attention_mask"]







'''simple docstring'''



from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''efficientnet'''



   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     int = 3  ,				__lowerCamelCase :     int = 6_0_0  ,				__lowerCamelCase :     float = 2.0  ,				__lowerCamelCase :     float = 3.1  ,				__lowerCamelCase :     int = 8  ,				__lowerCamelCase :     List[int] = [3, 3, 5, 3, 5, 5, 3]  ,				__lowerCamelCase :     List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]  ,				__lowerCamelCase :     List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]  ,				__lowerCamelCase :     List[int] = []  ,				__lowerCamelCase :     List[int] = [1, 2, 2, 2, 1, 2, 1]  ,				__lowerCamelCase :     List[int] = [1, 2, 2, 3, 3, 4, 1]  ,				__lowerCamelCase :     List[int] = [1, 6, 6, 6, 6, 6, 6]  ,				__lowerCamelCase :     float = 0.2_5  ,				__lowerCamelCase :     str = "swish"  ,				__lowerCamelCase :     int = 2_5_6_0  ,				__lowerCamelCase :     str = "mean"  ,				__lowerCamelCase :     float = 0.0_2  ,				__lowerCamelCase :     float = 0.0_0_1  ,				__lowerCamelCase :     float = 0.9_9  ,				__lowerCamelCase :     float = 0.5  ,				__lowerCamelCase :     float = 0.2  ,				**__lowerCamelCase :     Tuple  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				num_channels
      _SCREAMING_SNAKE_CASE	=				image_size
      _SCREAMING_SNAKE_CASE	=				width_coefficient
      _SCREAMING_SNAKE_CASE	=				depth_coefficient
      _SCREAMING_SNAKE_CASE	=				depth_divisor
      _SCREAMING_SNAKE_CASE	=				kernel_sizes
      _SCREAMING_SNAKE_CASE	=				in_channels
      _SCREAMING_SNAKE_CASE	=				out_channels
      _SCREAMING_SNAKE_CASE	=				depthwise_padding
      _SCREAMING_SNAKE_CASE	=				strides
      _SCREAMING_SNAKE_CASE	=				num_block_repeats
      _SCREAMING_SNAKE_CASE	=				expand_ratios
      _SCREAMING_SNAKE_CASE	=				squeeze_expansion_ratio
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dim
      _SCREAMING_SNAKE_CASE	=				pooling_type
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				batch_norm_eps
      _SCREAMING_SNAKE_CASE	=				batch_norm_momentum
      _SCREAMING_SNAKE_CASE	=				dropout_rate
      _SCREAMING_SNAKE_CASE	=				drop_connect_rate
      _SCREAMING_SNAKE_CASE	=				sum(__lowerCamelCase   ) * 4







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						version.parse('''1.11'''       )



   @property
   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
          ]   )



   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      return 1e-5

