

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







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase=False   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	[]

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







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Tuple  		=	["head.weight", "head.bias"]
						for k in ignore_keys:
												state_dict.pop(_UpperCAmelCase    ,     _UpperCAmelCase   )







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	dct.pop(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Dict  		=	val







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







@torch.no_grad()
def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=False   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	BitConfig(
						    global_padding="same"    ,     layer_type="bottleneck"    ,     depths=(3, 4, 9)    ,     out_features=["stage3"]    ,     embedding_dynamic_padding=_UpperCAmelCase    ,     )
						SCREAMING_SNAKE_CASE_:			Any  		=	ViTHybridConfig(backbone_config=_UpperCAmelCase    ,     image_size=3_84    ,     num_labels=10_00   )
						SCREAMING_SNAKE_CASE_:			Dict  		=	False

						# load original model from timm
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	timm.create_model(_UpperCAmelCase    ,     pretrained=_UpperCAmelCase   )
						timm_model.eval()

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

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

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

						# create image processor
						SCREAMING_SNAKE_CASE_:			List[str]  		=	create_transform(**resolve_data_config({}    ,     model=_UpperCAmelCase   )   )
						SCREAMING_SNAKE_CASE_:			Dict  		=	transform.transforms

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

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

						SCREAMING_SNAKE_CASE_:			Dict  		=	prepare_img()
						SCREAMING_SNAKE_CASE_:			int  		=	transform(_UpperCAmelCase   ).unsqueeze(0   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	processor(_UpperCAmelCase    ,     return_tensors="pt"   ).pixel_values

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

						# verify logits
						with torch.no_grad():
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	model(_UpperCAmelCase   )
												SCREAMING_SNAKE_CASE_:			List[str]  		=	outputs.logits

						print("Predicted class:"    ,     logits.argmax(-1   ).item()   )
						if base_model:
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	timm_model.forward_features(_UpperCAmelCase   )
												assert timm_pooled_output.shape == outputs.pooler_output.shape
												assert torch.allclose(_UpperCAmelCase    ,     outputs.pooler_output    ,     atol=1e-3   )
						else:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	timm_model(_UpperCAmelCase   )
												assert timm_logits.shape == outputs.logits.shape
												assert torch.allclose(_UpperCAmelCase    ,     outputs.logits    ,     atol=1e-3   )
						print("Looks ok!"   )

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

						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__":
						lowerCAmelCase							:  List[str]            =						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."""
						)

						lowerCAmelCase							:  Union[str, Any]            =						parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase							:  Optional[int]            =						{"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any            =						["""YolosFeatureExtractor"""]
						lowerCAmelCase							:  Tuple            =						["""YolosImageProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any            =						[
						    """YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """YolosForObjectDetection""",
						    """YolosModel""",
						    """YolosPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_yolos import YolosFeatureExtractor
												from .image_processing_yolos import YolosImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_yolos import (
												    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    YolosForObjectDetection,
												    YolosModel,
												    YolosPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase							:  int            =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






