






"""simple docstring"""



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()
A__	:    Dict			 = logging.get_logger(__name__)

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

def 			_snake_case    (					lowerCamelCase__     :   int  )    ->     Dict:
							lowerCamelCase_	:							Optional[int]									=["head.weight", "head.bias"]
							for k in ignore_keys:
														state_dict.pop(lowerCamelCase__    ,						lowerCamelCase__  )

def 			_snake_case    (					lowerCamelCase__     :   Union[str, Any]    ,						lowerCamelCase__     :   str    ,						lowerCamelCase__     :   Union[str, Any]  )    ->     str:
							lowerCamelCase_	:							List[Any]									=dct.pop(lowerCamelCase__  )
							lowerCamelCase_	:							Optional[int]									=val

def 			_snake_case    (					)    ->     str:
							lowerCamelCase_	:							Union[str, Any]									="http://images.cocodataset.org/val2017/000000039769.jpg"
							lowerCamelCase_	:							Any									=Image.open(requests.get(lowerCamelCase__    ,						stream=lowerCamelCase__  ).raw  )
							return im

@torch.no_grad()
def 			_snake_case    (					lowerCamelCase__     :   Any    ,						lowerCamelCase__     :   Union[str, Any]    ,						lowerCamelCase__     :   List[str]=False  )    ->     Any:
							lowerCamelCase_	:							List[Any]									=BitConfig(
							    global_padding="same"    ,						layer_type="bottleneck"    ,						depths=(3, 4, 9)    ,						out_features=["stage3"]    ,						embedding_dynamic_padding=lowerCamelCase__    ,						)
							lowerCamelCase_	:							Optional[Any]									=ViTHybridConfig(backbone_config=lowerCamelCase__    ,						image_size=384    ,						num_labels=1_000  )
							lowerCamelCase_	:							Optional[Any]									=False

							# load original model from timm
							lowerCamelCase_	:							Optional[int]									=timm.create_model(lowerCamelCase__    ,						pretrained=lowerCamelCase__  )
							timm_model.eval()

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

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

							# load HuggingFace model
							if vit_name[-5:] == "in21k":
														lowerCamelCase_	:							Any									=ViTHybridModel(lowerCamelCase__  ).eval()
							else:
														lowerCamelCase_	:							Dict									=ViTHybridForImageClassification(lowerCamelCase__  ).eval()
							model.load_state_dict(lowerCamelCase__  )

							# create image processor
							lowerCamelCase_	:							Tuple									=create_transform(**resolve_data_config({}    ,						model=lowerCamelCase__  )  )
							lowerCamelCase_	:							Dict									=transform.transforms

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

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

							lowerCamelCase_	:							Dict									=prepare_img()
							lowerCamelCase_	:							Optional[Any]									=transform(lowerCamelCase__  ).unsqueeze(0  )
							lowerCamelCase_	:							Dict									=processor(lowerCamelCase__    ,						return_tensors="pt"  ).pixel_values

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

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

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

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

							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__":
						A__	:    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.'
						)

						A__	:    Any			 = parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__	:    List[Any]			 = {
    'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    List[str]			 = [
						    'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'LiltForQuestionAnswering',
						    'LiltForSequenceClassification',
						    'LiltForTokenClassification',
						    'LiltModel',
						    'LiltPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

						A__	:    Optional[Any]			 = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



