

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE		:Optional[Any]						=     logging.get_logger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=False			)->						Dict:



    """simple docstring"""






    UpperCamelCase_			  =						[]

    # fmt: off
    # stem:
    rename_keys.append(("cls_token", "vit.embeddings.cls_token")			)
    rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")			)

    rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")			)
    rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")			)

    # backbone
    rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")			)
    rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")			)
    rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")			)

    for stage_idx in range(len(config.backbone_config.depths			)			):
        for layer_idx in range(config.backbone_config.depths[stage_idx]			):
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")			)
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")			)
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")			)
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")			)
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")			)
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")			)
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")			)
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")			)
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")			)

        rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")			)
        rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")			)
        rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")			)

    # transformer encoder
    for i in range(config.num_hidden_layers			):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")			)
        rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")			)
        rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")			)
        rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")			)
        rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")			)
        rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")			)
        rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")			)
        rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")			)
        rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")			)
        rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")			)

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ("norm.weight", "layernorm.weight"),
                ("norm.bias", "layernorm.bias"),
                ("pre_logits.fc.weight", "pooler.dense.weight"),
                ("pre_logits.fc.bias", "pooler.dense.bias"),
            ]			)

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        UpperCamelCase_			  =						[(pair[0], pair[1][4:]) if pair[1].startswith("vit"			) else pair for pair in rename_keys]
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("norm.weight", "vit.layernorm.weight"),
                ("norm.bias", "vit.layernorm.bias"),
                ("head.weight", "classifier.weight"),
                ("head.bias", "classifier.bias"),
            ]			)
    # fmt: on

    return rename_keys




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=False			)->						Union[str, Any]:



    """simple docstring"""






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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						dct.pop(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						val




def 			lowerCAmelCase(      )->						int:



    """simple docstring"""






    UpperCamelCase_			  =						"http://images.cocodataset.org/val2017/000000039769.jpg"
    UpperCamelCase_			  =						Image.open(requests.get(SCREAMING_SNAKE_CASE_			,						stream=SCREAMING_SNAKE_CASE_			).raw			)
    return im




@torch.no_grad()
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=False			)->						List[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						BitConfig(
        global_padding="same"			,						layer_type="bottleneck"			,						depths=(3, 4, 9)			,						out_features=["stage3"]			,						embedding_dynamic_padding=SCREAMING_SNAKE_CASE_			,						)
    UpperCamelCase_			  =						ViTHybridConfig(backbone_config=SCREAMING_SNAKE_CASE_			,						image_size=3_8_4			,						num_labels=1_0_0_0			)
    UpperCamelCase_			  =						False

    # load original model from timm
    UpperCamelCase_			  =						timm.create_model(SCREAMING_SNAKE_CASE_			,						pretrained=SCREAMING_SNAKE_CASE_			)
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    UpperCamelCase_			  =						timm_model.state_dict()
    if base_model:
        remove_classification_head_(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						create_rename_keys(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
    read_in_q_k_v(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						"huggingface/label-files"
    UpperCamelCase_			  =						"imagenet-1k-id2label.json"
    UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
    UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
    UpperCamelCase_			  =						idalabel
    UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        UpperCamelCase_			  =						ViTHybridModel(SCREAMING_SNAKE_CASE_			).eval()
    else:
        UpperCamelCase_			  =						ViTHybridForImageClassification(SCREAMING_SNAKE_CASE_			).eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE_			)

    # create image processor
    UpperCamelCase_			  =						create_transform(**resolve_data_config({}			,						model=SCREAMING_SNAKE_CASE_			)			)
    UpperCamelCase_			  =						transform.transforms

    UpperCamelCase_			  =						{
        "bilinear": PILImageResampling.BILINEAR,
        "bicubic": PILImageResampling.BICUBIC,
        "nearest": PILImageResampling.NEAREST,
    }

    UpperCamelCase_			  =						ViTHybridImageProcessor(
        do_resize=SCREAMING_SNAKE_CASE_			,						size={"shortest_edge": timm_transforms[0].size}			,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]			,						do_center_crop=SCREAMING_SNAKE_CASE_			,						crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}			,						do_normalize=SCREAMING_SNAKE_CASE_			,						image_mean=timm_transforms[-1].mean.tolist()			,						image_std=timm_transforms[-1].std.tolist()			,						)

    UpperCamelCase_			  =						prepare_img()
    UpperCamelCase_			  =						transform(SCREAMING_SNAKE_CASE_			).unsqueeze(0			)
    UpperCamelCase_			  =						processor(SCREAMING_SNAKE_CASE_			,						return_tensors="pt"			).pixel_values

    # verify pixel values
    assert torch.allclose(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

    # verify logits
    with torch.no_grad():
        UpperCamelCase_			  =						model(SCREAMING_SNAKE_CASE_			)
        UpperCamelCase_			  =						outputs.logits

    print("Predicted class:"			,						logits.argmax(-1			).item()			)
    if base_model:
        UpperCamelCase_			  =						timm_model.forward_features(SCREAMING_SNAKE_CASE_			)
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(SCREAMING_SNAKE_CASE_			,						outputs.pooler_output			,						atol=1E-3			)
    else:
        UpperCamelCase_			  =						timm_model(SCREAMING_SNAKE_CASE_			)
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(SCREAMING_SNAKE_CASE_			,						outputs.logits			,						atol=1E-3			)
    print("Looks ok!"			)

    if pytorch_dump_folder_path is not None:
        Path(SCREAMING_SNAKE_CASE_			).mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)
        print(f"Saving model {vit_name} to {pytorch_dump_folder_path}"			)
        model.save_pretrained(SCREAMING_SNAKE_CASE_			)
        print(f"Saving processor to {pytorch_dump_folder_path}"			)
        processor.save_pretrained(SCREAMING_SNAKE_CASE_			)

    if push_to_hub:
        print(f"Pushing model and processor to the hub {vit_name}"			)
        model.push_to_hub(f"ybelkada/{vit_name}"			)
        processor.push_to_hub(f"ybelkada/{vit_name}"			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Any						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--vit_name""",
     default="""vit_base_r50_s16_384""",
     type=str,
     help="""Name of the hybrid ViT timm model you'd like to convert.""",
 )
 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 to upload the model to the HuggingFace hub."""
 )

 SCREAMING_SNAKE_CASE		:str						=     parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE		:Optional[Any]						=     {
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

SCREAMING_SNAKE_CASE		:Union[str, Any]						=     logging.get_logger(__name__)






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Optional[int]										=      """mask2former"""
     UpperCamelCase_	:Dict										=      ["""swin"""]
     UpperCamelCase_	:List[Any]										=      {"""hidden_size""": """hidden_dim"""}






     def __init__(				self		,						_lowercase = None		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 1_024		,						_lowercase = "relu"		,						_lowercase = 6		,						_lowercase = 10		,						_lowercase = 8		,						_lowercase = 0.0		,						_lowercase = 2_048		,						_lowercase = False		,						_lowercase = False		,						_lowercase = 4		,						_lowercase = 255		,						_lowercase = 100		,						_lowercase = 0.1		,						_lowercase = 2.0		,						_lowercase = 5.0		,						_lowercase = 5.0		,						_lowercase = 12_544		,						_lowercase = 3.0		,						_lowercase = 0.75		,						_lowercase = 0.02		,						_lowercase = 1.0		,						_lowercase = True		,						_lowercase = [4, 8, 16, 32]		,						_lowercase = None		,						**_lowercase		,						)->     Union[str, Any]:
         if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."	)
             UpperCamelCase_			  =						CONFIG_MAPPING["swin"](
                 image_size=224		,						in_channels=3		,						patch_size=4		,						embed_dim=96		,						depths=[2, 2, 18, 2]		,						num_heads=[3, 6, 12, 24]		,						window_size=7		,						drop_path_rate=0.3		,						use_absolute_embeddings=_lowercase		,						out_features=["stage1", "stage2", "stage3", "stage4"]		,						)

         if isinstance(_lowercase		,						_lowercase	):
             UpperCamelCase_			  =						backbone_config.pop("model_type"	)
             UpperCamelCase_			  =						CONFIG_MAPPING[backbone_model_type]
             UpperCamelCase_			  =						config_class.from_dict(_lowercase	)

         # verify that the backbone is supported
         if backbone_config.model_type not in self.backbones_supported:
             logger.warning_once(
                 F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
                 F"Supported model types: {','.join(self.backbones_supported	)}"	)

         UpperCamelCase_			  =						backbone_config
         UpperCamelCase_			  =						feature_size
         UpperCamelCase_			  =						mask_feature_size
         UpperCamelCase_			  =						hidden_dim
         UpperCamelCase_			  =						encoder_feedforward_dim
         UpperCamelCase_			  =						activation_function
         UpperCamelCase_			  =						encoder_layers
         UpperCamelCase_			  =						decoder_layers
         UpperCamelCase_			  =						num_attention_heads
         UpperCamelCase_			  =						dropout
         UpperCamelCase_			  =						dim_feedforward
         UpperCamelCase_			  =						pre_norm
         UpperCamelCase_			  =						enforce_input_projection
         UpperCamelCase_			  =						common_stride
         UpperCamelCase_			  =						ignore_value
         UpperCamelCase_			  =						num_queries
         UpperCamelCase_			  =						no_object_weight
         UpperCamelCase_			  =						class_weight
         UpperCamelCase_			  =						mask_weight
         UpperCamelCase_			  =						dice_weight
         UpperCamelCase_			  =						train_num_points
         UpperCamelCase_			  =						oversample_ratio
         UpperCamelCase_			  =						importance_sample_ratio
         UpperCamelCase_			  =						init_std
         UpperCamelCase_			  =						init_xavier_std
         UpperCamelCase_			  =						use_auxiliary_loss
         UpperCamelCase_			  =						feature_strides
         UpperCamelCase_			  =						output_auxiliary_logits
         UpperCamelCase_			  =						decoder_layers

         super().__init__(**_lowercase	)






     @classmethod
     def 	UpperCAmelCase_				(				cls		,						_lowercase		,						**_lowercase	)->     Optional[int]:
         return cls(
             backbone_config=_lowercase		,						**_lowercase		,						)






     def 	UpperCAmelCase_				(				self	)->     Dict[str, any]:
         UpperCamelCase_			  =						copy.deepcopy(self.__dict__	)
         UpperCamelCase_			  =						self.backbone_config.to_dict()
         UpperCamelCase_			  =						self.__class__.model_type
         return output
