


"""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       =       logging.get_logger(__name__)

__snake_case       =       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

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


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







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Tuple				    =    VOCAB_FILES_NAMES
   __UpperCAmelCase					:			List[str]				    =    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase					:			Union[str, Any]				    =    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase					:			Optional[Any]				    =    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			str				    =    VOCAB_FILES_NAMES
   __UpperCAmelCase					:			List[str]				    =    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase					:			Dict				    =    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase					:			Any				    =    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



__snake_case       =       collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

__snake_case       =       collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


__snake_case       =       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(snake_case_      )
class _lowerCAmelCase							:







   def __call__(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = False		,      UpperCamelCase__ = False		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      **UpperCamelCase__		,      )      ->						BatchEncoding:

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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__ = 16		,      UpperCamelCase__ = 64		,      UpperCamelCase__ = 4		,      )      ->						List[DPRSpanPrediction]:

       '''simple docstring'''




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

           snake_case       : 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=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 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      )      ->						List[DPRSpanPrediction]:

       '''simple docstring'''




       snake_case       : List[str]    =       []
       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)		)
       snake_case       : Optional[int]    =       sorted(UpperCamelCase__		,      key=lambda UpperCamelCase__		: x[1]		,      reverse=UpperCamelCase__		)
       snake_case       : Union[str, Any]    =       []
       for (start_index, end_index), score in scores:
           if start_index > end_index:
               raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]'		)
           snake_case       : Tuple    =       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(UpperCamelCase__		) == top_spans:
               break
       return chosen_span_intervals







@add_end_docstrings(snake_case_      )
class _lowerCAmelCase							( snake_case_       ,				snake_case_      ):
   __UpperCAmelCase					:			List[str]				    =    VOCAB_FILES_NAMES
   __UpperCAmelCase					:			Dict				    =    READER_PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase					:			int				    =    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase					:			Optional[Any]				    =    READER_PRETRAINED_INIT_CONFIGURATION
   __UpperCAmelCase					:			Optional[int]				    =    ['''input_ids''', '''attention_mask''']









"""simple docstring"""



import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







def     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		Union[str, Any]:


    """simple docstring"""





    snake_case   ,snake_case       : List[str]    =       image.size
    snake_case   ,snake_case       : int    =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    snake_case       : Any    =       image.resize((w, h)		,				resample=PIL_INTERPOLATION["lanczos"]					)
    snake_case       : Dict    =       np.array(lowercase					).astype(np.floataa					) / 255.0
    snake_case       : List[str]    =       image[None].transpose(0		,				3		,				1		,				2					)
    snake_case       : List[Any]    =       torch.from_numpy(lowercase					)
    return 2.0 * image - 1.0







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      )      ->						Optional[Any]:

       '''simple docstring'''




       super().__init__()
       self.register_modules(vqvae=UpperCamelCase__		,      unet=UpperCamelCase__		,      scheduler=UpperCamelCase__		)







   @torch.no_grad()
   def __call__(      self		,      UpperCamelCase__ = None		,      UpperCamelCase__ = 1		,      UpperCamelCase__ = 100		,      UpperCamelCase__ = 0.0		,      UpperCamelCase__ = None		,      UpperCamelCase__ = "pil"		,      UpperCamelCase__ = True		,      )      ->						Union[Tuple, ImagePipelineOutput]:

       '''simple docstring'''




       if isinstance(UpperCamelCase__		,      PIL.Image.Image		):
           snake_case       : Tuple    =       1
       elif isinstance(UpperCamelCase__		,      torch.Tensor		):
           snake_case       : str    =       image.shape[0]
       else:
           raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__		)}'		)

       if isinstance(UpperCamelCase__		,      PIL.Image.Image		):
           snake_case       : Optional[int]    =       preprocess(UpperCamelCase__		)

       snake_case   ,snake_case       : List[str]    =       image.shape[-2:]

       # in_channels should be 6: 3 for latents, 3 for low resolution image
       snake_case       : Any    =       (batch_size, self.unet.config.in_channels // 2, height, width)
       snake_case       : List[str]    =       next(self.unet.parameters()		).dtype

       snake_case       : int    =       randn_tensor(UpperCamelCase__		,      generator=UpperCamelCase__		,      device=self.device		,      dtype=UpperCamelCase__		)

       snake_case       : List[Any]    =       image.to(device=self.device		,      dtype=UpperCamelCase__		)

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(UpperCamelCase__		,      device=self.device		)
       snake_case       : Optional[Any]    =       self.scheduler.timesteps

       # scale the initial noise by the standard deviation required by the scheduler
       snake_case       : Optional[int]    =       latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       snake_case       : Optional[int]    =       "eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
       snake_case       : Union[str, Any]    =       {}
       if accepts_eta:
           snake_case       : List[str]    =       eta

       for t in self.progress_bar(UpperCamelCase__		):
           # concat latents and low resolution image in the channel dimension.
           snake_case       : int    =       torch.cat([latents, image]		,      dim=1		)
           snake_case       : List[Any]    =       self.scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)
           # predict the noise residual
           snake_case       : int    =       self.unet(UpperCamelCase__		,      UpperCamelCase__		).sample
           # compute the previous noisy sample x_t -> x_t-1
           snake_case       : List[Any]    =       self.scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      **UpperCamelCase__		).prev_sample

       # decode the image latents with the VQVAE
       snake_case       : Optional[int]    =       self.vqvae.decode(UpperCamelCase__		).sample
       snake_case       : Tuple    =       torch.clamp(UpperCamelCase__		,      -1.0		,      1.0		)
       snake_case       : Union[str, Any]    =       image / 2 + 0.5
       snake_case       : Optional[Any]    =       image.cpu().permute(0		,      2		,      3		,      1		).numpy()

       if output_type == "pil":
           snake_case       : Union[str, Any]    =       self.numpy_to_pil(UpperCamelCase__		)

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=UpperCamelCase__		)





