




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase								=					logging.get_logger(__name__)



def 							_A       (						lowerCAmelCase_					: Any      ):
     """simple docstring"""

     if "resnet-50" in model_name:
          lowerCAmelCase__          =			ResNetConfig.from_pretrained("microsoft/resnet-50"      )
     elif "resnet-101" in model_name:
          lowerCAmelCase__          =			ResNetConfig.from_pretrained("microsoft/resnet-101"      )
     else:
          raise ValueError("Model name should include either resnet50 or resnet101"      )

     lowerCAmelCase__          =			DetrConfig(use_timm_backbone=lowerCAmelCase_       ,    backbone_config=lowerCAmelCase_      )

     # set label attributes
     lowerCAmelCase__          =			"panoptic" in model_name
     if is_panoptic:
          lowerCAmelCase__          =			250
     else:
          lowerCAmelCase__          =			91
          lowerCAmelCase__          =			"huggingface/label-files"
          lowerCAmelCase__          =			"coco-detection-id2label.json"
          lowerCAmelCase__          =			json.load(open(hf_hub_download(lowerCAmelCase_       ,    lowerCAmelCase_       ,    repo_type="dataset"      )       ,    "r"      )      )
          lowerCAmelCase__          =			{int(lowerCAmelCase_      ): v for k, v in idalabel.items()}
          lowerCAmelCase__          =			idalabel
          lowerCAmelCase__          =			{v: k for k, v in idalabel.items()}

     return config, is_panoptic



def 							_A       (						lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			[]

     # stem
     # fmt: off
     rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")      )
     rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")      )
     rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")      )
     rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")      )
     rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var")      )
     # stages
     for stage_idx in range(len(config.backbone_config.depths      )      ):
          for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
               # shortcut
               if layer_idx == 0:
                    rename_keys.append(
                        (
                            F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
                            F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
                        )      )
                    rename_keys.append(
                        (
                            F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
                            F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
                        )      )
                    rename_keys.append(
                        (
                            F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
                            F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
                        )      )
                    rename_keys.append(
                        (
                            F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
                            F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
                        )      )
                    rename_keys.append(
                        (
                            F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
                            F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
                        )      )
               # 3 convs
               for i in range(3      ):
                    rename_keys.append(
                        (
                            F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
                            F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
                        )      )
                    rename_keys.append(
                        (
                            F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
                            F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
                        )      )
                    rename_keys.append(
                        (
                            F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
                            F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
                        )      )
                    rename_keys.append(
                        (
                            F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
                            F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
                        )      )
                    rename_keys.append(
                        (
                            F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
                            F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
                        )      )
    # fmt: on

     for i in range(config.encoder_layers      ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append(
              (
                  F'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
                  F'encoder.layers.{i}.self_attn.out_proj.weight',
              )      )
          rename_keys.append(
              (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')      )
          rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight')      )
          rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias')      )
          rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight')      )
          rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias')      )
          rename_keys.append(
              (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')      )
          rename_keys.append(
              (F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias')      )
          rename_keys.append(
              (F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight')      )
          rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias')      )
          # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
          rename_keys.append(
              (
                  F'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
                  F'decoder.layers.{i}.self_attn.out_proj.weight',
              )      )
          rename_keys.append(
              (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')      )
          rename_keys.append(
              (
                  F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
                  F'decoder.layers.{i}.encoder_attn.out_proj.weight',
              )      )
          rename_keys.append(
              (
                  F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
                  F'decoder.layers.{i}.encoder_attn.out_proj.bias',
              )      )
          rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight')      )
          rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias')      )
          rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight')      )
          rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias')      )
          rename_keys.append(
              (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')      )
          rename_keys.append(
              (F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias')      )
          rename_keys.append(
              (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')      )
          rename_keys.append(
              (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')      )
          rename_keys.append(
              (F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight')      )
          rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias')      )

     # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
     rename_keys.extend(
         [
             ("input_proj.weight", "input_projection.weight"),
             ("input_proj.bias", "input_projection.bias"),
             ("query_embed.weight", "query_position_embeddings.weight"),
             ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
             ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
             ("class_embed.weight", "class_labels_classifier.weight"),
             ("class_embed.bias", "class_labels_classifier.bias"),
             ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
             ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
             ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
             ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
             ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
             ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
         ]      )

     return rename_keys



def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			state_dict.pop(lowerCAmelCase_      )
     lowerCAmelCase__          =			val



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Optional[int]=False      ):
     """simple docstring"""

     lowerCAmelCase__          =			""
     if is_panoptic:
          lowerCAmelCase__          =			"detr."

     # first: transformer encoder
     for i in range(6      ):
          # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
          lowerCAmelCase__          =			state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'      )
          lowerCAmelCase__          =			state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'      )
          # next, add query, keys and values (in that order) to the state dict
          lowerCAmelCase__          =			in_proj_weight[:256, :]
          lowerCAmelCase__          =			in_proj_bias[:256]
          lowerCAmelCase__          =			in_proj_weight[256:512, :]
          lowerCAmelCase__          =			in_proj_bias[256:512]
          lowerCAmelCase__          =			in_proj_weight[-256:, :]
          lowerCAmelCase__          =			in_proj_bias[-256:]
     # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
     for i in range(6      ):
          # read in weights + bias of input projection layer of self-attention
          lowerCAmelCase__          =			state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'      )
          lowerCAmelCase__          =			state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'      )
          # next, add query, keys and values (in that order) to the state dict
          lowerCAmelCase__          =			in_proj_weight[:256, :]
          lowerCAmelCase__          =			in_proj_bias[:256]
          lowerCAmelCase__          =			in_proj_weight[256:512, :]
          lowerCAmelCase__          =			in_proj_bias[256:512]
          lowerCAmelCase__          =			in_proj_weight[-256:, :]
          lowerCAmelCase__          =			in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          lowerCAmelCase__          =			state_dict.pop(
              F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'      )
          lowerCAmelCase__          =			state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'      )
          # next, add query, keys and values (in that order) of cross-attention to the state dict
          lowerCAmelCase__          =			in_proj_weight_cross_attn[:256, :]
          lowerCAmelCase__          =			in_proj_bias_cross_attn[:256]
          lowerCAmelCase__          =			in_proj_weight_cross_attn[256:512, :]
          lowerCAmelCase__          =			in_proj_bias_cross_attn[256:512]
          lowerCAmelCase__          =			in_proj_weight_cross_attn[-256:, :]
          lowerCAmelCase__          =			in_proj_bias_cross_attn[-256:]



def 							_A       (						):
     """simple docstring"""

     lowerCAmelCase__          =			"http://images.cocodataset.org/val2017/000000039769.jpg"
     lowerCAmelCase__          =			Image.open(requests.get(lowerCAmelCase_       ,    stream=lowerCAmelCase_      ).raw      )

     return im



@torch.no_grad()
def 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Any=None       ,    lowerCAmelCase_					: int=False      ):
     """simple docstring"""

     lowerCAmelCase__     ,		lowerCAmelCase__          =			get_detr_config(lowerCAmelCase_      )

     # load original model from torch hub
     lowerCAmelCase__          =			{
         "detr-resnet-50": "detr_resnet50",
         "detr-resnet-101": "detr_resnet101",
     }
     logger.info(F'Converting model {model_name}...'      )
     lowerCAmelCase__          =			torch.hub.load("facebookresearch/detr"       ,    model_name_to_original_name[model_name]       ,    pretrained=lowerCAmelCase_      ).eval()
     lowerCAmelCase__          =			detr.state_dict()
     # rename keys
     for src, dest in create_rename_keys(lowerCAmelCase_      ):
          if is_panoptic:
               lowerCAmelCase__          =			"detr." + src
          rename_key(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
     # query, key and value matrices need special treatment
     read_in_q_k_v(lowerCAmelCase_       ,    is_panoptic=lowerCAmelCase_      )
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     lowerCAmelCase__          =			"detr.model." if is_panoptic else "model."
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith("detr"      )
                   and not key.startswith("class_labels_classifier"      )
                   and not key.startswith("bbox_predictor"      )
               ):
                    lowerCAmelCase__          =			state_dict.pop(lowerCAmelCase_      )
                    lowerCAmelCase__          =			val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    lowerCAmelCase__          =			state_dict.pop(lowerCAmelCase_      )
                    lowerCAmelCase__          =			val
               elif key.startswith("bbox_attention"      ) or key.startswith("mask_head"      ):
                    continue
               else:
                    lowerCAmelCase__          =			state_dict.pop(lowerCAmelCase_      )
                    lowerCAmelCase__          =			val
          else:
               if not key.startswith("class_labels_classifier"      ) and not key.startswith("bbox_predictor"      ):
                    lowerCAmelCase__          =			state_dict.pop(lowerCAmelCase_      )
                    lowerCAmelCase__          =			val

    # finally, create HuggingFace model and load state dict
     lowerCAmelCase__          =			DetrForSegmentation(lowerCAmelCase_      ) if is_panoptic else DetrForObjectDetection(lowerCAmelCase_      )
     model.load_state_dict(lowerCAmelCase_      )
     model.eval()

     # verify our conversion on an image
     lowerCAmelCase__          =			"coco_panoptic" if is_panoptic else "coco_detection"
     lowerCAmelCase__          =			DetrImageProcessor(format=lowerCAmelCase_      )

     lowerCAmelCase__          =			processor(images=prepare_img()       ,    return_tensors="pt"      )
     lowerCAmelCase__          =			encoding["pixel_values"]

     lowerCAmelCase__          =			detr(lowerCAmelCase_      )
     lowerCAmelCase__          =			model(lowerCAmelCase_      )

     assert torch.allclose(outputs.logits       ,    original_outputs["pred_logits"]       ,    atol=1E-3      )
     assert torch.allclose(outputs.pred_boxes       ,    original_outputs["pred_boxes"]       ,    atol=1E-3      )
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks       ,    original_outputs["pred_masks"]       ,    atol=1E-4      )
     print("Looks ok!"      )

     if pytorch_dump_folder_path is not None:
          # Save model and image processor
          logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'      )
          Path(lowerCAmelCase_      ).mkdir(exist_ok=lowerCAmelCase_      )
          model.save_pretrained(lowerCAmelCase_      )
          processor.save_pretrained(lowerCAmelCase_      )

     if push_to_hub:
          # Upload model and image processor to the hub
          logger.info("Uploading PyTorch model and image processor to the hub..."      )
          model.push_to_hub(F'nielsr/{model_name}'      )
          processor.push_to_hub(F'nielsr/{model_name}'      )


if __name__ == "__main__":
     UpperCamelCase								=					argparse.ArgumentParser()

     parser.add_argument(
         '--model_name',
         default='detr-resnet-50',
         type=str,
         choices=['detr-resnet-50', 'detr-resnet-101'],
         help='Name of the DETR model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
     )
     parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
     UpperCamelCase								=					parser.parse_args()
     convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


UpperCamelCase								=					logging.get_logger(__name__)

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

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


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





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				DPRContextEncoderTokenizer






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				DPRQuestionEncoderTokenizer






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

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


UpperCamelCase								=					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    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\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    Return:\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					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[str] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[str] = None       ,     SCREAMING_SNAKE_CASE__					:					Union[bool, str] = False       ,     SCREAMING_SNAKE_CASE__					:					Union[bool, str] = False       ,     SCREAMING_SNAKE_CASE__					:					Optional[int] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, TensorType]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     **SCREAMING_SNAKE_CASE__					:					List[Any]       ,     )				->	BatchEncoding:
         if titles is None and texts is None:
              return super().__call__(
                  SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       ,     return_attention_mask=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )
         elif titles is None or texts is None:
              lowerCAmelCase__          =			titles if texts is None else texts
              return super().__call__(
                  SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       ,     return_attention_mask=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			titles if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else [titles]
         lowerCAmelCase__          =			texts if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else [texts]
         lowerCAmelCase__          =			len(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			questions if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else [questions] * n_passages
         assert len(SCREAMING_SNAKE_CASE__       ) == len(
             SCREAMING_SNAKE_CASE__       ), f'There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE__       )} titles and {len(SCREAMING_SNAKE_CASE__       )} texts.'
         lowerCAmelCase__          =			super().__call__(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       )["input_ids"]
         lowerCAmelCase__          =			super().__call__(SCREAMING_SNAKE_CASE__       ,     add_special_tokens=SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       )["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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
             ]
         }
         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(SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					BatchEncoding       ,     SCREAMING_SNAKE_CASE__					:					DPRReaderOutput       ,     SCREAMING_SNAKE_CASE__					:					int = 16       ,     SCREAMING_SNAKE_CASE__					:					int = 64       ,     SCREAMING_SNAKE_CASE__					:					int = 4       ,     )				->	List[DPRSpanPrediction]:
         lowerCAmelCase__          =			reader_input["input_ids"]
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			reader_output[:3]
         lowerCAmelCase__          =			len(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			sorted(range(SCREAMING_SNAKE_CASE__       )       ,     reverse=SCREAMING_SNAKE_CASE__       ,     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(SCREAMING_SNAKE_CASE__       )

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


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     )				->	List[DPRSpanPrediction]:
         lowerCAmelCase__          =			[]
         for start_index, start_score in enumerate(SCREAMING_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)       )
         lowerCAmelCase__          =			sorted(SCREAMING_SNAKE_CASE__       ,     key=lambda SCREAMING_SNAKE_CASE__       : x[1]       ,     reverse=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[]
         for (start_index, end_index), score in scores:
              assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
              lowerCAmelCase__          =			end_index - start_index + 1
              assert length <= max_answer_length, 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(SCREAMING_SNAKE_CASE__       ) == top_spans:
                   break
         return chosen_span_intervals






@add_end_docstrings(UpperCamelCase__   )
class 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				READER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				READER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				["input_ids", "attention_mask"]
    snake_case__ =				DPRReaderTokenizer
