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()
lowerCAmelCase			:							int     =logging.get_logger(__name__)







def 						A__  (					__A	):







    '''simple docstring'''
    # initialize config
    if "resnet-50" in model_name:
        _lowerCamelCase     :		List[Any] 						=						ResNetConfig.from_pretrained("""microsoft/resnet-50"""	)
    elif "resnet-101" in model_name:
        _lowerCamelCase     :		str 						=						ResNetConfig.from_pretrained("""microsoft/resnet-101"""	)
    else:
        raise ValueError("""Model name should include either resnet50 or resnet101"""	)

    _lowerCamelCase     :		Optional[Any] 						=						DetrConfig(use_timm_backbone=__A   , backbone_config=__A	)

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

    return config, is_panoptic







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						[]

    # 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__  (					__A   , __A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Optional[int] 						=						state_dict.pop(__A	)
    _lowerCamelCase     :		Dict 						=						val







def 						A__  (					__A   , __A=False	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						""""""
    if is_panoptic:
        _lowerCamelCase     :		Tuple 						=						"""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     :		List[str] 						=						state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
        _lowerCamelCase     :		Union[str, Any] 						=						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     :		str 						=						in_proj_weight[:256, :]
        _lowerCamelCase     :		int 						=						in_proj_bias[:256]
        _lowerCamelCase     :		Optional[Any] 						=						in_proj_weight[256:512, :]
        _lowerCamelCase     :		Tuple 						=						in_proj_bias[256:512]
        _lowerCamelCase     :		List[Any] 						=						in_proj_weight[-256:, :]
        _lowerCamelCase     :		Dict 						=						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     :		Tuple 						=						state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""	)
        _lowerCamelCase     :		Tuple 						=						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     :		str 						=						in_proj_weight[:256, :]
        _lowerCamelCase     :		List[str] 						=						in_proj_bias[:256]
        _lowerCamelCase     :		Dict 						=						in_proj_weight[256:512, :]
        _lowerCamelCase     :		Union[str, Any] 						=						in_proj_bias[256:512]
        _lowerCamelCase     :		Optional[Any] 						=						in_proj_weight[-256:, :]
        _lowerCamelCase     :		Dict 						=						in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        _lowerCamelCase     :		Union[str, Any] 						=						state_dict.pop(
            F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""	)
        _lowerCamelCase     :		Any 						=						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     :		int 						=						in_proj_weight_cross_attn[:256, :]
        _lowerCamelCase     :		Any 						=						in_proj_bias_cross_attn[:256]
        _lowerCamelCase     :		List[str] 						=						in_proj_weight_cross_attn[256:512, :]
        _lowerCamelCase     :		Any 						=						in_proj_bias_cross_attn[256:512]
        _lowerCamelCase     :		Any 						=						in_proj_weight_cross_attn[-256:, :]
        _lowerCamelCase     :		Optional[int] 						=						in_proj_bias_cross_attn[-256:]







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		List[Any] 						=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    _lowerCamelCase     :		str 						=						Image.open(requests.get(__A   , stream=__A	).raw	)

    return im







@torch.no_grad()
def 						A__  (					__A   , __A=None   , __A=False	):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						get_detr_config(__A	)

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

    # finally, create HuggingFace model and load state dict
    _lowerCamelCase     :		List[str] 						=						DetrForSegmentation(__A	) if is_panoptic else DetrForObjectDetection(__A	)
    model.load_state_dict(__A	)
    model.eval()

    # verify our conversion on an image
    _lowerCamelCase     :		List[Any] 						=						"""coco_panoptic""" if is_panoptic else """coco_detection"""
    _lowerCamelCase     :		List[Any] 						=						DetrImageProcessor(format=__A	)

    _lowerCamelCase     :		Optional[int] 						=						processor(images=prepare_img()   , return_tensors="""pt"""	)
    _lowerCamelCase     :		Optional[int] 						=						encoding["""pixel_values"""]

    _lowerCamelCase     :		List[Any] 						=						detr(__A	)
    _lowerCamelCase     :		Tuple 						=						model(__A	)

    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(__A	).mkdir(exist_ok=__A	)
        model.save_pretrained(__A	)
        processor.save_pretrained(__A	)

    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__":
    lowerCAmelCase			:							List[Any]     =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.")
    lowerCAmelCase			:							int     =parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import math







def 						A__  (					__A	):







    '''simple docstring'''
    assert isinstance(__A   , __A	) and (
        number >= 0
    ), "'number' must been an int and positive"

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or not number % 2:
        # Negatives, 0, 1 and all even numbers are not primes
        return False

    _lowerCamelCase     :		List[Any] 						=						range(3   , int(math.sqrt(__A	) + 1	)   , 2	)
    return not any(not number % i for i in odd_numbers	)







def 						A__  (					__A   , __A=1   , **__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Dict 						=						factor * value
    _lowerCamelCase     :		str 						=						value

    while not is_prime(__A	):
        value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

    if value == first_value_val:
        return next_prime(value + 1   , **__A	)
    return value




