


"""simple docstring"""







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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_       :       Optional[Any]      						=  logging.get_logger(__name__)
def _A						(						lowercase		):







    """simple docstring"""
    a					=YolosConfig()

    # size of the architecture
    if "yolos_ti" in yolos_name:
        a					=1_92
        a					=7_68
        a					=12
        a					=3
        a					=[8_00, 13_33]
        a					=False
    elif yolos_name == "yolos_s_dWr":
        a					=3_30
        a					=14
        a					=6
        a					=13_20
    elif "yolos_s" in yolos_name:
        a					=3_84
        a					=15_36
        a					=12
        a					=6
    elif "yolos_b" in yolos_name:
        a					=[8_00, 13_44]

    a					=91
    a					="huggingface/label-files"
    a					="coco-detection-id2label.json"
    a					=json.load(open(hf_hub_download(__snake_case	,   __snake_case	,   repo_type='''dataset'''		)	,   '''r'''		)		)
    a					={int(__snake_case		): v for k, v in idalabel.items()}
    a					=idalabel
    a					={v: k for k, v in idalabel.items()}

    return config
def _A						(						lowercase	,   lowercase	,   lowercase = False		):







    """simple docstring"""
    for i in range(config.num_hidden_layers		):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        a					=state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''		)
        a					=state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''		)
        # next, add query, keys and values (in that order) to the state dict
        a					=in_proj_weight[: config.hidden_size, :]
        a					=in_proj_bias[: config.hidden_size]
        a					=in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        a					=in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        a					=in_proj_weight[-config.hidden_size :, :]
        a					=in_proj_bias[-config.hidden_size :]
def _A						(						lowercase		):







    """simple docstring"""
    if "backbone" in name:
        a					=name.replace('''backbone'''	,   '''vit'''		)
    if "cls_token" in name:
        a					=name.replace('''cls_token'''	,   '''embeddings.cls_token'''		)
    if "det_token" in name:
        a					=name.replace('''det_token'''	,   '''embeddings.detection_tokens'''		)
    if "mid_pos_embed" in name:
        a					=name.replace('''mid_pos_embed'''	,   '''encoder.mid_position_embeddings'''		)
    if "pos_embed" in name:
        a					=name.replace('''pos_embed'''	,   '''embeddings.position_embeddings'''		)
    if "patch_embed.proj" in name:
        a					=name.replace('''patch_embed.proj'''	,   '''embeddings.patch_embeddings.projection'''		)
    if "blocks" in name:
        a					=name.replace('''blocks'''	,   '''encoder.layer'''		)
    if "attn.proj" in name:
        a					=name.replace('''attn.proj'''	,   '''attention.output.dense'''		)
    if "attn" in name:
        a					=name.replace('''attn'''	,   '''attention.self'''		)
    if "norm1" in name:
        a					=name.replace('''norm1'''	,   '''layernorm_before'''		)
    if "norm2" in name:
        a					=name.replace('''norm2'''	,   '''layernorm_after'''		)
    if "mlp.fc1" in name:
        a					=name.replace('''mlp.fc1'''	,   '''intermediate.dense'''		)
    if "mlp.fc2" in name:
        a					=name.replace('''mlp.fc2'''	,   '''output.dense'''		)
    if "class_embed" in name:
        a					=name.replace('''class_embed'''	,   '''class_labels_classifier'''		)
    if "bbox_embed" in name:
        a					=name.replace('''bbox_embed'''	,   '''bbox_predictor'''		)
    if "vit.norm" in name:
        a					=name.replace('''vit.norm'''	,   '''vit.layernorm'''		)

    return name
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    for key in orig_state_dict.copy().keys():
        a					=orig_state_dict.pop(__snake_case		)

        if "qkv" in key:
            a					=key.split('''.'''		)
            a					=int(key_split[2]		)
            a					=model.vit.encoder.layer[layer_num].attention.attention.all_head_size
            if "weight" in key:
                a					=val[:dim, :]
                a					=val[
                    dim : dim * 2, :
                ]
                a					=val[-dim:, :]
            else:
                a					=val[:dim]
                a					=val[dim : dim * 2]
                a					=val[-dim:]
        else:
            a					=val

    return orig_state_dict
def _A						(						):







    """simple docstring"""
    a					="http://images.cocodataset.org/val2017/000000039769.jpg"
    a					=Image.open(requests.get(__snake_case	,   stream=__snake_case		).raw		)
    return im
@torch.no_grad()
def _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase = False		):







    """simple docstring"""
    a					=get_yolos_config(__snake_case		)

    # load original state_dict
    a					=torch.load(__snake_case	,   map_location='''cpu'''		)["model"]

    # load 🤗 model
    a					=YolosForObjectDetection(__snake_case		)
    model.eval()
    a					=convert_state_dict(__snake_case	,   __snake_case		)
    model.load_state_dict(__snake_case		)

    # Check outputs on an image, prepared by YolosImageProcessor
    a					=8_00 if yolos_name != "yolos_ti" else 5_12
    a					=YolosImageProcessor(format='''coco_detection'''	,   size=__snake_case		)
    a					=image_processor(images=prepare_img()	,   return_tensors='''pt'''		)
    a					=model(**__snake_case		)
    a					=outputs.logits, outputs.pred_boxes

    a					=None, None
    if yolos_name == "yolos_ti":
        a					=torch.tensor(
            [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]		)
        a					=torch.tensor(
            [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]		)
    elif yolos_name == "yolos_s_200_pre":
        a					=torch.tensor(
            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]		)
        a					=torch.tensor(
            [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]		)
    elif yolos_name == "yolos_s_300_pre":
        a					=torch.tensor(
            [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]		)
        a					=torch.tensor(
            [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]		)
    elif yolos_name == "yolos_s_dWr":
        a					=torch.tensor(
            [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]		)
        a					=torch.tensor(
            [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]		)
    elif yolos_name == "yolos_base":
        a					=torch.tensor(
            [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]		)
        a					=torch.tensor(
            [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]		)
    else:
        raise ValueError(f'''Unknown yolos_name: {yolos_name}'''		)

    assert torch.allclose(logits[0, :3, :3]	,   __snake_case	,   atol=1E-4		)
    assert torch.allclose(pred_boxes[0, :3, :3]	,   __snake_case	,   atol=1E-4		)

    Path(__snake_case		).mkdir(exist_ok=__snake_case		)
    print(f'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''		)
    model.save_pretrained(__snake_case		)
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''		)
    image_processor.save_pretrained(__snake_case		)

    if push_to_hub:
        a					={
            "yolos_ti": "yolos-tiny",
            "yolos_s_200_pre": "yolos-small",
            "yolos_s_300_pre": "yolos-small-300",
            "yolos_s_dWr": "yolos-small-dwr",
            "yolos_base": "yolos-base",
        }

        print('''Pushing to the hub...'''		)
        a					=model_mapping[yolos_name]
        image_processor.push_to_hub(__snake_case	,   organization='''hustvl'''		)
        model.push_to_hub(__snake_case	,   organization='''hustvl'''		)


if __name__ == "__main__":
       lowerCamelCase_       :       List[Any]      						=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--yolos_name""",
           default="""yolos_s_200_pre""",
           type=str,
           help=(
               """Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\',"""
               """ \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'."""
           ),
       )
       parser.add_argument(
           """--checkpoint_path""", default=None, type=str, help="""Path to the original state dict (.pth file)."""
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
       )
       parser.add_argument(
           """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
       )

       lowerCamelCase_       :       Dict      						=  parser.parse_args()
       convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_       :       int      						=  {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Dict      						=  ["""MBartTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  ["""MBartTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[str]      						=  [
           """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """MBartForCausalLM""",
           """MBartForConditionalGeneration""",
           """MBartForQuestionAnswering""",
           """MBartForSequenceClassification""",
           """MBartModel""",
           """MBartPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[Any]      						=  [
           """TFMBartForConditionalGeneration""",
           """TFMBartModel""",
           """TFMBartPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  [
           """FlaxMBartForConditionalGeneration""",
           """FlaxMBartForQuestionAnswering""",
           """FlaxMBartForSequenceClassification""",
           """FlaxMBartModel""",
           """FlaxMBartPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart import MBartTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart_fast import MBartTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mbart import (
                  MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MBartForCausalLM,
                  MBartForConditionalGeneration,
                  MBartForQuestionAnswering,
                  MBartForSequenceClassification,
                  MBartModel,
                  MBartPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_mbart import (
                  FlaxMBartForConditionalGeneration,
                  FlaxMBartForQuestionAnswering,
                  FlaxMBartForSequenceClassification,
                  FlaxMBartModel,
                  FlaxMBartPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase_       :       List[Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)