





'''simple docstring'''

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 : Dict											=			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase : Dict											=			[]
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 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

    """simple docstring"""






    lowercase         = state_dict.pop(_lowerCamelCase							)
    lowercase         = val







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

    """simple docstring"""






    lowercase         = OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            lowercase         = key.replace("backbone.0.body"		,							"backbone.conv_encoder.model"							)
            lowercase         = value
        else:
            lowercase         = value

    return new_state_dict







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_=False							):

    """simple docstring"""






    lowercase         = ''
    if is_panoptic:
        lowercase         = '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)
        lowercase         = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'							)
        lowercase         = 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
        lowercase         = in_proj_weight[:256, :]
        lowercase         = in_proj_bias[:256]
        lowercase         = in_proj_weight[256:512, :]
        lowercase         = in_proj_bias[256:512]
        lowercase         = in_proj_weight[-256:, :]
        lowercase         = in_proj_bias[-256:]







def 							UpperCAmelCase_    (	):

    """simple docstring"""






    lowercase         = 'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowercase         = Image.open(requests.get(_lowerCamelCase		,							stream=_lowerCamelCase							).raw							)

    return im







@torch.no_grad()
def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

    """simple docstring"""






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

    # load image processor
    lowercase         = 'coco_panoptic' if is_panoptic else 'coco_detection'
    lowercase         = ConditionalDetrImageProcessor(format=_lowerCamelCase							)

    # prepare image
    lowercase         = prepare_img()
    lowercase         = image_processor(images=_lowerCamelCase		,							return_tensors="pt"							)
    lowercase         = encoding['pixel_values']

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

    # load original model from torch hub
    lowercase         = torch.hub.load("DeppMeng/ConditionalDETR"		,							_lowerCamelCase		,							pretrained=_lowerCamelCase							).eval()
    lowercase         = conditional_detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            lowercase         = 'conditional_detr.' + src
        rename_key(_lowerCamelCase		,							_lowerCamelCase		,							_lowerCamelCase							)
    lowercase         = 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
    lowercase         = '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"							)
            ):
                lowercase         = state_dict.pop(_lowerCamelCase							)
                lowercase         = val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                lowercase         = state_dict.pop(_lowerCamelCase							)
                lowercase         = val
            elif key.startswith("bbox_attention"							) or key.startswith("mask_head"							):
                continue
            else:
                lowercase         = state_dict.pop(_lowerCamelCase							)
                lowercase         = val
        else:
            if not key.startswith("class_labels_classifier"							) and not key.startswith("bbox_predictor"							):
                lowercase         = state_dict.pop(_lowerCamelCase							)
                lowercase         = val
    # finally, create HuggingFace model and load state dict
    lowercase         = 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
    lowercase         = conditional_detr(_lowerCamelCase							)
    lowercase         = 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 : Tuple											=			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 : List[str]											=			parser.parse_args()
   convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)











'''simple docstring'''

import datasets

from .evaluate import evaluate


__lowerCamelCase : Optional[Any]											=			"\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

__lowerCamelCase : Any											=			"\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n"

__lowerCamelCase : Union[str, Any]											=			"\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the SQuAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0}\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,       _KWARGS_DESCRIPTION  )
class       UpperCAmelCase (						datasets.Metric  ):




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Union[str, Any]:
						return datasets.MetricInfo(
						    description=_DESCRIPTION							,			citation=_CITATION							,			inputs_description=_KWARGS_DESCRIPTION							,			features=datasets.Features(
						        {
						            "predictions": {"id": datasets.Value("string"				), "prediction_text": datasets.Value("string"				)},
						            "references": {
						                "id": datasets.Value("string"				),
						                "answers": datasets.features.Sequence(
						                    {
						                        "text": datasets.Value("string"				),
						                        "answer_start": datasets.Value("int32"				),
						                    }				),
						            },
						        }				)							,			codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]							,			reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]							,			)





		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	List[Any]							,			A__      :	List[str]				)					-> str:
						lowercase         = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
						lowercase         = [
						    {
						        "paragraphs": [
						            {
						                "qas": [
						                    {
						                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
						                        "id": ref["id"],
						                    }
						                    for ref in references
						                ]
						            }
						        ]
						    }
						]
						lowercase         = evaluate(dataset=A__							,			predictions=A__				)
						return score




