

import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCAmelCase						=		[]
for i in range(6):
 # 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}.cross_attn.out_proj.weight''',
         F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
     )
 )
 rename_keys.append(
     (
         F'''transformer.decoder.layers.{i}.cross_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'''))

 # q, k, v projections in self/cross-attention in decoder for conditional DETR
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
 )

 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
 )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[str]  =    state_dict.pop(_lowerCamelCase   )
    _lowerCAmelCase       :   Tuple  =    val

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _lowerCAmelCase       :   Dict  =    key.replace('backbone.0.body' ,   'backbone.conv_encoder.model'   )
            _lowerCAmelCase       :   Dict  =    value
        else:
            _lowerCAmelCase       :   Any  =    value

    return new_state_dict

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=False   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[Any]  =    ''
    if is_panoptic:
        _lowerCAmelCase       :   Optional[Any]  =    'conditional_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       :   Optional[int]  =    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
        _lowerCAmelCase       :   Tuple  =    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       :   Dict  =    in_proj_weight[:256, :]
        _lowerCAmelCase       :   List[Any]  =    in_proj_bias[:256]
        _lowerCAmelCase       :   Dict  =    in_proj_weight[256:512, :]
        _lowerCAmelCase       :   Union[str, Any]  =    in_proj_bias[256:512]
        _lowerCAmelCase       :   Any  =    in_proj_weight[-256:, :]
        _lowerCAmelCase       :   Any  =    in_proj_bias[-256:]

def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowerCAmelCase       :   Union[str, Any]  =    Image.open(requests.get(_lowerCamelCase ,   stream=_lowerCamelCase   ).raw   )

    return im

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[Any]  =    ConditionalDetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        _lowerCAmelCase       :   Dict  =    'resnet101'
    if "dc5" in model_name:
        _lowerCAmelCase       :   List[Any]  =    True
    _lowerCAmelCase       :   Dict  =    'panoptic' in model_name
    if is_panoptic:
        _lowerCAmelCase       :   List[str]  =    250
    else:
        _lowerCAmelCase       :   str  =    91
        _lowerCAmelCase       :   Any  =    'huggingface/label-files'
        _lowerCAmelCase       :   List[str]  =    'coco-detection-id2label.json'
        _lowerCAmelCase       :   int  =    json.load(open(hf_hub_download(_lowerCamelCase ,   _lowerCamelCase ,   repo_type='dataset'   ) ,   'r'   )   )
        _lowerCAmelCase       :   str  =    {int(_lowerCamelCase   ): v for k, v in idalabel.items()}
        _lowerCAmelCase       :   Union[str, Any]  =    idalabel
        _lowerCAmelCase       :   Any  =    {v: k for k, v in idalabel.items()}

    # load image processor
    _lowerCAmelCase       :   Optional[int]  =    'coco_panoptic' if is_panoptic else 'coco_detection'
    _lowerCAmelCase       :   str  =    ConditionalDetrImageProcessor(format=_lowerCamelCase   )

    # prepare image
    _lowerCAmelCase       :   List[str]  =    prepare_img()
    _lowerCAmelCase       :   int  =    image_processor(images=_lowerCamelCase ,   return_tensors='pt'   )
    _lowerCAmelCase       :   Dict  =    encoding['pixel_values']

    logger.info(f"""Converting model {model_name}..."""   )

    # load original model from torch hub
    _lowerCAmelCase       :   Any  =    torch.hub.load('DeppMeng/ConditionalDETR' ,   _lowerCamelCase ,   pretrained=_lowerCamelCase   ).eval()
    _lowerCAmelCase       :   Union[str, Any]  =    conditional_detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            _lowerCAmelCase       :   Optional[Any]  =    'conditional_detr.' + src
        rename_key(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    rename_backbone_keys(_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       :   Dict  =    'conditional_detr.model.' if is_panoptic else 'model.'
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith('conditional_detr'   )
                and not key.startswith('class_labels_classifier'   )
                and not key.startswith('bbox_predictor'   )
            ):
                _lowerCAmelCase       :   List[Any]  =    state_dict.pop(_lowerCamelCase   )
                _lowerCAmelCase       :   Tuple  =    val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                _lowerCAmelCase       :   Any  =    state_dict.pop(_lowerCamelCase   )
                _lowerCAmelCase       :   Dict  =    val
            elif key.startswith('bbox_attention'   ) or key.startswith('mask_head'   ):
                continue
            else:
                _lowerCAmelCase       :   str  =    state_dict.pop(_lowerCamelCase   )
                _lowerCAmelCase       :   List[str]  =    val
        else:
            if not key.startswith('class_labels_classifier'   ) and not key.startswith('bbox_predictor'   ):
                _lowerCAmelCase       :   Dict  =    state_dict.pop(_lowerCamelCase   )
                _lowerCAmelCase       :   List[Any]  =    val
    # finally, create HuggingFace model and load state dict
    _lowerCAmelCase       :   str  =    ConditionalDetrForSegmentation(_lowerCamelCase   ) if is_panoptic else ConditionalDetrForObjectDetection(_lowerCamelCase   )
    model.load_state_dict(_lowerCamelCase   )
    model.eval()
    model.push_to_hub(repo_id=_lowerCamelCase ,   organization='DepuMeng' ,   commit_message='Add model'   )
    # verify our conversion
    _lowerCAmelCase       :   List[Any]  =    conditional_detr(_lowerCamelCase   )
    _lowerCAmelCase       :   Dict  =    model(_lowerCamelCase   )
    assert torch.allclose(outputs.logits ,   original_outputs['pred_logits'] ,   atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes ,   original_outputs['pred_boxes'] ,   atol=1e-4   )
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks ,   original_outputs['pred_masks'] ,   atol=1e-4   )

    # 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   )
    image_processor.save_pretrained(_lowerCamelCase   )


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

 parser.add_argument(
     """--model_name""",
     default="""conditional_detr_resnet50""",
     type=str,
     help="""Name of the CONDITIONAL_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."""
 )
 _lowerCAmelCase						=		parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








"""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


_lowerCAmelCase						=		logging.get_logger(__name__)

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

_lowerCAmelCase						=		{
    """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						=		{
    """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						=		{
    """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						=		{
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_1_2,
}
_lowerCAmelCase						=		{
    """facebook/dpr-question_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-question_encoder-multiset-base""": 5_1_2,
}
_lowerCAmelCase						=		{
    """facebook/dpr-reader-single-nq-base""": 5_1_2,
    """facebook/dpr-reader-multiset-base""": 5_1_2,
}


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







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





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

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


_lowerCAmelCase						=		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    :

  def __call__(		self    ,_A    ,_A = None    ,_A = None    ,_A = False    ,_A = False    ,_A = None    ,_A = None    ,_A = None    ,**_A    ,):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A = 16    ,_A = 64    ,_A = 4    ,):
      '''simple docstring'''

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

          _lowerCAmelCase       :   Optional[Any]  =    self._get_best_spans(
              start_logits=start_logits[doc_id][passage_offset:sequence_len]    ,end_logits=end_logits[doc_id][passage_offset:sequence_len]    ,max_answer_length=_A    ,top_spans=_A    ,)
          for start_index, end_index in best_spans:
              start_index += passage_offset
              end_index += passage_offset
              nbest_spans_predictions.append(
                  DPRSpanPrediction(
                      span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]    ,relevance_score=relevance_logits[doc_id]    ,doc_id=_A    ,start_index=_A    ,end_index=_A    ,text=self.decode(sequence_ids[start_index : end_index + 1]      )    ,)      )
          if len(_A      ) >= num_spans:
              break
      return nbest_spans_predictions[:num_spans]







  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    []
      for start_index, start_score in enumerate(_A      ):
          for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]      ):
              scores.append(((start_index, start_index + answer_length), start_score + end_score)      )
      _lowerCAmelCase       :   Tuple  =    sorted(_A    ,key=lambda _A      : x[1]    ,reverse=_A      )
      _lowerCAmelCase       :   int  =    []
      for (start_index, end_index), score in scores:
          if start_index > end_index:
              raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""      )
          _lowerCAmelCase       :   List[str]  =    end_index - start_index + 1
          if length > max_answer_length:
              raise ValueError(F"""Span is too long: {length} > {max_answer_length}"""      )
          if any(
              start_index <= prev_start_index <= prev_end_index <= end_index
              or prev_start_index <= start_index <= end_index <= prev_end_index
              for (prev_start_index, prev_end_index) in chosen_span_intervals      ):
              continue
          chosen_span_intervals.append((start_index, end_index)      )

          if len(_A      ) == top_spans:
              break
      return chosen_span_intervals







@add_end_docstrings(a__						)
class 	__UpperCamelCase    (    a__							,							a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		READER_PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		READER_PRETRAINED_INIT_CONFIGURATION
  _UpperCAmelCase          =		["input_ids", "attention_mask"]





