

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  :      Tuple			 =logging.get_logger(__name__)

_lowerCAmelCase  :      Union[str, Any]			 ={"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase  :      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"""
        ),
    },
}
_lowerCAmelCase  :      List[Any]			 ={
    """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  :      Optional[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"""
        ),
    },
}

_lowerCAmelCase  :      List[str]			 ={
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_12,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
_lowerCAmelCase  :      Tuple			 ={
    """facebook/dpr-question_encoder-single-nq-base""": 5_12,
    """facebook/dpr-question_encoder-multiset-base""": 5_12,
}
_lowerCAmelCase  :      Optional[int]			 ={
    """facebook/dpr-reader-single-nq-base""": 5_12,
    """facebook/dpr-reader-multiset-base""": 5_12,
}


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

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   VOCAB_FILES_NAMES
					__magic_name__        =   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					__magic_name__        =   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__magic_name__        =   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   VOCAB_FILES_NAMES
					__magic_name__        =   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					__magic_name__        =   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__magic_name__        =   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
_lowerCAmelCase  :      Optional[Any]			 =collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

_lowerCAmelCase  :      int			 =collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_lowerCAmelCase  :      str			 =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)

    Returns:
        `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(_a		)
class __UpperCamelCase     :






					'''simple docstring'''



					def __call__(				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = False				,      lowerCamelCase__ = False				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      **lowerCamelCase__				,      ):
						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__:	Any									= 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__:	Tuple									= titles if not isinstance(lowerCamelCase__				,      lowerCamelCase__						) else [titles]
						UpperCAmelCase__:	Union[str, Any]									= texts if not isinstance(lowerCamelCase__				,      lowerCamelCase__						) else [texts]
						UpperCAmelCase__:	Tuple									= 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[Any]									= super().__call__(lowerCamelCase__				,      lowerCamelCase__				,      padding=lowerCamelCase__				,      truncation=lowerCamelCase__						)["input_ids"]
						UpperCAmelCase__:	str									= super().__call__(lowerCamelCase__				,      add_special_tokens=lowerCamelCase__				,      padding=lowerCamelCase__				,      truncation=lowerCamelCase__						)["input_ids"]
						UpperCAmelCase__:	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(lowerCamelCase__				,      lowerCamelCase__						)
						    ]
						}
						if return_attention_mask is not False:
							UpperCAmelCase__:	Dict									= []
							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__:	List[Any]									= attention_mask
						return self.pad(lowerCamelCase__				,      padding=lowerCamelCase__				,      max_length=lowerCamelCase__				,      return_tensors=lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = 1_6				,      lowerCamelCase__ = 6_4				,      lowerCamelCase__ = 4				,      ):
						UpperCAmelCase__:	Optional[int]									= reader_input["input_ids"]
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Any									= reader_output[:3]
						UpperCAmelCase__:	int									= len(lowerCamelCase__						)
						UpperCAmelCase__:	Dict									= sorted(range(lowerCamelCase__						)				,      reverse=lowerCamelCase__				,      key=relevance_logits.__getitem__						)
						UpperCAmelCase__:	List[DPRReaderOutput]									= []
						for doc_id in sorted_docs:
							UpperCAmelCase__:	str									= list(input_ids[doc_id]						)
							# assuming question & title information is at the beginning of the sequence
							UpperCAmelCase__:	Dict									= sequence_ids.index(self.sep_token_id				,      2						) + 1  # second sep id
							if sequence_ids[-1] == self.pad_token_id:
								UpperCAmelCase__:	Tuple									= sequence_ids.index(self.pad_token_id						)
							else:
								UpperCAmelCase__:	Union[str, Any]									= len(lowerCamelCase__						)

							UpperCAmelCase__:	Optional[int]									= 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				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      ):
						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__:	str									= sorted(lowerCamelCase__				,      key=lambda lowerCamelCase__						: x[1]				,      reverse=lowerCamelCase__						)
						UpperCAmelCase__:	Any									= []
						for (start_index, end_index), score in scores:
							if start_index > end_index:
								raise ValueError(F"Wrong span indices: [{start_index}:{end_index}]"						)
							UpperCAmelCase__:	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(lowerCamelCase__						) == top_spans:
								break
						return chosen_span_intervals

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






					'''simple docstring'''



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


import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class __UpperCamelCase     (				_a					,_a		):






					'''simple docstring'''



					@register_to_config
					def __init__(				self				,      *,
					    lowerCamelCase__ = 4				,      lowerCamelCase__ = 7_6_8				,      lowerCamelCase__				,      lowerCamelCase__				,      ):
						super().__init__()

						UpperCAmelCase__:	int									= nn.Parameter(torch.zeros(lowerCamelCase__						)						)

						# parameters for additional clip time embeddings
						UpperCAmelCase__:	Optional[Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)

						# parameters for encoder hidden states
						UpperCAmelCase__:	Tuple									= clip_extra_context_tokens
						UpperCAmelCase__:	List[str]									= nn.Linear(
						    lowerCamelCase__				,      self.clip_extra_context_tokens * cross_attention_dim						)
						UpperCAmelCase__:	Union[str, Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.LayerNorm(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      *, lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if do_classifier_free_guidance:
							# Add the classifier free guidance embeddings to the image embeddings
							UpperCAmelCase__:	Any									= image_embeddings.shape[0]
							UpperCAmelCase__:	Optional[Any]									= self.learned_classifier_free_guidance_embeddings.unsqueeze(0						)
							UpperCAmelCase__:	Union[str, Any]									= classifier_free_guidance_embeddings.expand(
							    lowerCamelCase__				,      -1						)
							UpperCAmelCase__:	Tuple									= torch.cat([classifier_free_guidance_embeddings, image_embeddings]				,      dim=0						)

						# The image embeddings batch size and the text embeddings batch size are equal
						assert image_embeddings.shape[0] == prompt_embeds.shape[0]

						UpperCAmelCase__:	str									= prompt_embeds.shape[0]

						# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
						# adding CLIP embeddings to the existing timestep embedding, ...
						UpperCAmelCase__:	List[str]									= self.embedding_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase__						)
						UpperCAmelCase__:	Any									= time_projected_image_embeddings + time_projected_prompt_embeds

						# ... and by projecting CLIP embeddings into four
						# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
						UpperCAmelCase__:	Dict									= self.clip_extra_context_tokens_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= clip_extra_context_tokens.reshape(lowerCamelCase__				,      -1				,      self.clip_extra_context_tokens						)
						UpperCAmelCase__:	Dict									= clip_extra_context_tokens.permute(0				,      2				,      1						)

						UpperCAmelCase__:	Any									= self.encoder_hidden_states_proj(lowerCamelCase__						)
						UpperCAmelCase__:	List[str]									= self.text_encoder_hidden_states_norm(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]				,      dim=1						)

						return text_encoder_hidden_states, additive_clip_time_embeddings