



'''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


UpperCamelCase__  :			List[str]          =					logging.get_logger(__name__)

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

UpperCamelCase__  :			List[Any]          =					{
    '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__  :			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'
        ),
    },
}
UpperCamelCase__  :			int          =					{
    '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__  :			Any          =					{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
UpperCamelCase__  :			List[str]          =					{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
UpperCamelCase__  :			Union[str, Any]          =					{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


UpperCamelCase__  :			Optional[Any]          =					{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
UpperCamelCase__  :			Dict          =					{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
UpperCamelCase__  :			Optional[int]          =					{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}


class 				_lowerCAmelCase     (   __UpperCamelCase				):


 """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 				_lowerCAmelCase     (   __UpperCamelCase				):


 """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

UpperCamelCase__  :			Union[str, Any]          =					collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

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


UpperCamelCase__  :			Optional[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(__UpperCamelCase				)
class 				_lowerCAmelCase     :


 """simple docstring"""

 def __call__(	self ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = False ,  _lowerCamelCase = False ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  )  ->  str:





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







 def 	UpperCAmelCase_    (	self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = 16 ,  _lowerCamelCase = 64 ,  _lowerCamelCase = 4 ,  )  ->  Tuple:
    A_   :						List[Any]		= reader_input['''input_ids''']
    A_   :						List[Any]		= reader_output[:3]
    A_   :						List[Any]		= len(a_   )
    A_   :						List[Any]		= sorted(range(a_   ) ,  reverse=a_ ,  key=relevance_logits.__getitem__   )
    A_   :						List[DPRReaderOutput]		= []
    for doc_id in sorted_docs:
       A_   :						List[Any]		= list(input_ids[doc_id]   )
       # assuming question & title information is at the beginning of the sequence
       A_   :						str		= sequence_ids.index(self.sep_token_id ,  2   ) + 1  # second sep id
       if sequence_ids[-1] == self.pad_token_id:
          A_   :						Optional[Any]		= sequence_ids.index(self.pad_token_id   )
       else:
          A_   :						int		= len(a_   )

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







 def 	UpperCAmelCase_    (	self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  )  ->  Dict:
    A_   :						List[str]		= []
    for start_index, start_score in enumerate(a_   ):
       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)   )
    A_   :						int		= sorted(a_ ,  key=lambda _lowerCamelCase   : x[1] ,  reverse=a_   )
    A_   :						int		= []
    for (start_index, end_index), score in scores:
       if start_index > end_index:
          raise ValueError(F"Wrong span indices: [{start_index}:{end_index}]"   )
       A_   :						str		= 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(a_   ) == top_spans:
          break
    return chosen_span_intervals


@add_end_docstrings(__UpperCamelCase				)
class 				_lowerCAmelCase     (   __UpperCamelCase,				__UpperCamelCase				):


 """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''']





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
				from .pipeline_kandinsky import KandinskyPipeline
				from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
				from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
				from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
				from .text_encoder import MultilingualCLIP





