






'''simple docstring'''





import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler


def        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Dict=[]   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						size[0] - overlap_pixels * 2
						lowerCAmelCase__       =						size[1] - overlap_pixels * 2
						for letter in ["l", "r"]:
												if letter in remove_borders:
																		size_x += overlap_pixels
						for letter in ["t", "b"]:
												if letter in remove_borders:
																		size_y += overlap_pixels
						lowerCAmelCase__       =						np.ones((size_y, size_x)    ,   dtype=np.uinta   ) * 255
						lowerCAmelCase__       =						np.pad(UpperCamelCase_    ,   mode="linear_ramp"    ,   pad_width=UpperCamelCase_    ,   end_values=0   )

						if "l" in remove_borders:
												lowerCAmelCase__       =						mask[:, overlap_pixels : mask.shape[1]]
						if "r" in remove_borders:
												lowerCAmelCase__       =						mask[:, 0 : mask.shape[1] - overlap_pixels]
						if "t" in remove_borders:
												lowerCAmelCase__       =						mask[overlap_pixels : mask.shape[0], :]
						if "b" in remove_borders:
												lowerCAmelCase__       =						mask[0 : mask.shape[0] - overlap_pixels, :]
						return mask


def        _a  (     UpperCamelCase_					:				Optional[int]    ,   UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				List[str]   ) ->   Union[str, Any]:
						"""simple docstring"""







						return max(UpperCamelCase_    ,   min(UpperCamelCase_    ,   UpperCamelCase_   )   )


def        _a  (     UpperCamelCase_					:				[int]    ,   UpperCamelCase_					:				[int]    ,   UpperCamelCase_					:				[int]   ) ->   List[Any]:
						"""simple docstring"""







						return (
						    clamp(rect[0]    ,   min[0]    ,   max[0]   ),
						    clamp(rect[1]    ,   min[1]    ,   max[1]   ),
						    clamp(rect[2]    ,   min[0]    ,   max[0]   ),
						    clamp(rect[3]    ,   min[1]    ,   max[1]   ),
						)


def        _a  (     UpperCamelCase_					:				[int]    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				[int]   ) ->   Optional[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						list(UpperCamelCase_   )
						rect[0] -= overlap
						rect[1] -= overlap
						rect[2] += overlap
						rect[3] += overlap
						lowerCAmelCase__       =						clamp_rect(UpperCamelCase_    ,   [0, 0]    ,   [image_size[0], image_size[1]]   )
						return rect


def        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				Optional[int]    ,   UpperCamelCase_					:				Optional[Any]   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						Image.new("RGB"    ,   (tile.size[0] + original_slice, tile.size[1])   )
						result.paste(
						    original_image.resize((tile.size[0], tile.size[1])    ,   Image.BICUBIC   ).crop(
						        (slice_x, 0, slice_x + original_slice, tile.size[1])   )    ,   (0, 0)    ,   )
						result.paste(UpperCamelCase_    ,   (original_slice, 0)   )
						return result


def        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				int   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						(original_image_slice * 4, 0, tile.size[0], tile.size[1])
						lowerCAmelCase__       =						tile.crop(UpperCamelCase_   )
						return tile


def        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Dict   ) ->   Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__       =						n % d
						return n - divisor


class      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = 350		,    )->					int:







							'''simple docstring'''





							super().__init__(
							    vae=__UpperCAmelCase		,    text_encoder=__UpperCAmelCase		,    tokenizer=__UpperCAmelCase		,    unet=__UpperCAmelCase		,    low_res_scheduler=__UpperCAmelCase		,    scheduler=__UpperCAmelCase		,    max_noise_level=__UpperCAmelCase		,    )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    **__UpperCAmelCase       )->					Optional[Any]:







							'''simple docstring'''





							torch.manual_seed(0       )
							lowerCAmelCase__       =						(
							    min(image.size[0] - (tile_size + original_image_slice)		,    x * tile_size       ),
							    min(image.size[1] - (tile_size + original_image_slice)		,    y * tile_size       ),
							    min(image.size[0]		,    (x + 1) * tile_size       ),
							    min(image.size[1]		,    (y + 1) * tile_size       ),
							)
							lowerCAmelCase__       =						add_overlap_rect(__UpperCAmelCase		,    __UpperCAmelCase		,    image.size       )
							lowerCAmelCase__       =						image.crop(__UpperCAmelCase       )
							lowerCAmelCase__       =						((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
							lowerCAmelCase__       =						translated_slice_x - (original_image_slice / 2)
							lowerCAmelCase__       =						max(0		,    __UpperCAmelCase       )
							lowerCAmelCase__       =						squeeze_tile(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )
							lowerCAmelCase__       =						to_input.size
							lowerCAmelCase__       =						to_input.resize((tile_size, tile_size)		,    Image.BICUBIC       )
							lowerCAmelCase__       =						super(__UpperCAmelCase		,    self       ).__call__(image=__UpperCAmelCase		,    **__UpperCAmelCase       ).images[0]
							lowerCAmelCase__       =						upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)		,    Image.BICUBIC       )
							lowerCAmelCase__       =						unsqueeze_tile(__UpperCAmelCase		,    __UpperCAmelCase       )
							lowerCAmelCase__       =						upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)		,    Image.BICUBIC       )
							lowerCAmelCase__       =						[]
							if x == 0:
													remove_borders.append("l"       )
							elif crop_rect[2] == image.size[0]:
													remove_borders.append("r"       )
							if y == 0:
													remove_borders.append("t"       )
							elif crop_rect[3] == image.size[1]:
													remove_borders.append("b"       )
							lowerCAmelCase__       =						Image.fromarray(
							    make_transparency_mask(
							        (upscaled_tile.size[0], upscaled_tile.size[1])		,    tile_border * 4		,    remove_borders=__UpperCAmelCase       )		,    mode="L"		,    )
							final_image.paste(
							    __UpperCAmelCase		,    (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)		,    __UpperCAmelCase       )


	@torch.no_grad()
	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = 75		,    __UpperCAmelCase = 9.0		,    __UpperCAmelCase = 50		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 1		,    __UpperCAmelCase = 0.0		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 1		,    __UpperCAmelCase = 128		,    __UpperCAmelCase = 32		,    __UpperCAmelCase = 32		,    )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						Image.new("RGB"		,    (image.size[0] * 4, image.size[1] * 4)       )
							lowerCAmelCase__       =						math.ceil(image.size[0] / tile_size       )
							lowerCAmelCase__       =						math.ceil(image.size[1] / tile_size       )
							lowerCAmelCase__       =						tcx * tcy
							lowerCAmelCase__       =						0
							for y in range(__UpperCAmelCase       ):
													for x in range(__UpperCAmelCase       ):
																			self._process_tile(
																			    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    prompt=__UpperCAmelCase		,    num_inference_steps=__UpperCAmelCase		,    guidance_scale=__UpperCAmelCase		,    noise_level=__UpperCAmelCase		,    negative_prompt=__UpperCAmelCase		,    num_images_per_prompt=__UpperCAmelCase		,    eta=__UpperCAmelCase		,    generator=__UpperCAmelCase		,    latents=__UpperCAmelCase		,    )
																			current_count += 1
																			if callback is not None:
																									callback({"progress": current_count / total_tile_count, "image": final_image}       )
							return final_image





def        _a  (     ) ->   Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__       =						"stabilityai/stable-diffusion-x4-upscaler"
						lowerCAmelCase__       =						StableDiffusionTiledUpscalePipeline.from_pretrained(UpperCamelCase_    ,   revision="fp16"    ,   torch_dtype=torch.floataa   )
						lowerCAmelCase__       =						pipe.to("cuda"   )
						lowerCAmelCase__       =						Image.open("../../docs/source/imgs/diffusers_library.jpg"   )

						def callback(UpperCamelCase_					:				Dict   ):
												print(F"progress: {obj['progress']:.4f}"   )
												obj["image"].save("diffusers_library_progress.jpg"   )

						lowerCAmelCase__       =						pipe(image=UpperCamelCase_    ,   prompt="Black font, white background, vector"    ,   noise_level=40    ,   callback=UpperCamelCase_   )
						final_image.save("diffusers_library.jpg"   )


if __name__ == "__main__":
	main()














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


a_							=    logging.get_logger(__name__)

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

a_							=    {
    '''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'''
        ),
    },
}
a_							=    {
    '''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'''
        ),
    },
}
a_							=    {
    '''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'''
        ),
    },
}

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


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


class      lowercase__     (			_UpperCAmelCase	):
	a_         =VOCAB_FILES_NAMES
	a_         =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	a_         =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a_         =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class      lowercase__     (			_UpperCAmelCase	):
	a_         =VOCAB_FILES_NAMES
	a_         =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	a_         =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a_         =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





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

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


a_							=    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(_UpperCAmelCase	)
class      lowercase__     :

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

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







							'''simple docstring'''





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

													lowerCAmelCase__       =						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		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					List[DPRSpanPrediction]:







							'''simple docstring'''





							lowerCAmelCase__       =						[]
							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)       )
							lowerCAmelCase__       =						sorted(__UpperCAmelCase		,    key=lambda __UpperCAmelCase       : x[1]		,    reverse=__UpperCAmelCase       )
							lowerCAmelCase__       =						[]
							for (start_index, end_index), score in scores:
													if start_index > end_index:
																			raise ValueError(F"Wrong span indices: [{start_index}:{end_index}]"       )
													lowerCAmelCase__       =						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(_UpperCAmelCase	)
class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase	):
	a_         =VOCAB_FILES_NAMES
	a_         =READER_PRETRAINED_VOCAB_FILES_MAP
	a_         =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a_         =READER_PRETRAINED_INIT_CONFIGURATION
	a_         =["""input_ids""", """attention_mask"""]






