
'''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()
_lowercase :  int    							=       logging.get_logger(__name__)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: Tuple=False							):



						"""simple docstring"""





						lowercase_	:     Tuple													=       []

						# 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"
												lowercase_	:     Union[str, Any]													=       [(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_		(				__SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: Any=False							):



						"""simple docstring"""





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



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       ['''head.weight''', '''head.bias''']
						for k in ignore_keys:
												state_dict.pop(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[Any]      ,      __SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: Tuple							):



						"""simple docstring"""





						lowercase_	:     Union[str, Any]													=       dct.pop(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     str													=       val
def 					snake_case_		(				):



						"""simple docstring"""





						lowercase_	:     Tuple													=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowercase_	:     Optional[Any]													=       Image.open(requests.get(__SCREAMING_SNAKE_CASE      ,      stream=__SCREAMING_SNAKE_CASE							).raw							)
						return im
@torch.no_grad()
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]      ,      __SCREAMING_SNAKE_CASE					: int=False							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       BitConfig(
						    global_padding='''same'''      ,      layer_type='''bottleneck'''      ,      depths=(3, 4, 9)      ,      out_features=['''stage3''']      ,      embedding_dynamic_padding=__SCREAMING_SNAKE_CASE      ,      )
						lowercase_	:     Optional[Any]													=       ViTHybridConfig(backbone_config=__SCREAMING_SNAKE_CASE      ,      image_size=384      ,      num_labels=1000							)
						lowercase_	:     Optional[int]													=       False

						# load original model from timm
						lowercase_	:     Tuple													=       timm.create_model(__SCREAMING_SNAKE_CASE      ,      pretrained=__SCREAMING_SNAKE_CASE							)
						timm_model.eval()

						# load state_dict of original model, remove and rename some keys
						lowercase_	:     str													=       timm_model.state_dict()
						if base_model:
												remove_classification_head_(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     List[Any]													=       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							)

						lowercase_	:     Tuple													=       '''huggingface/label-files'''
						lowercase_	:     Union[str, Any]													=       '''imagenet-1k-id2label.json'''
						lowercase_	:     Union[str, Any]													=       json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      repo_type='''dataset'''							)      ,      '''r'''							)							)
						lowercase_	:     Dict													=       {int(__SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
						lowercase_	:     Dict													=       idalabel
						lowercase_	:     Any													=       {v: k for k, v in idalabel.items()}

						# load HuggingFace model
						if vit_name[-5:] == "in21k":
												lowercase_	:     Optional[int]													=       ViTHybridModel(__SCREAMING_SNAKE_CASE							).eval()
						else:
												lowercase_	:     int													=       ViTHybridForImageClassification(__SCREAMING_SNAKE_CASE							).eval()
						model.load_state_dict(__SCREAMING_SNAKE_CASE							)

						# create image processor
						lowercase_	:     Any													=       create_transform(**resolve_data_config({}      ,      model=__SCREAMING_SNAKE_CASE							)							)
						lowercase_	:     Optional[Any]													=       transform.transforms

						lowercase_	:     int													=       {
						    '''bilinear''': PILImageResampling.BILINEAR,
						    '''bicubic''': PILImageResampling.BICUBIC,
						    '''nearest''': PILImageResampling.NEAREST,
						}

						lowercase_	:     int													=       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()      ,      )

						lowercase_	:     List[str]													=       prepare_img()
						lowercase_	:     Any													=       transform(__SCREAMING_SNAKE_CASE							).unsqueeze(0							)
						lowercase_	:     Optional[int]													=       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():
												lowercase_	:     Union[str, Any]													=       model(__SCREAMING_SNAKE_CASE							)
												lowercase_	:     Optional[int]													=       outputs.logits

						print('''Predicted class:'''      ,      logits.argmax(-1							).item()							)
						if base_model:
												lowercase_	:     Dict													=       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:
												lowercase_	:     Union[str, Any]													=       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__":
							_lowercase :  int    							=       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."
							)

							_lowercase :  Tuple    							=       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, is_vision_available


_lowercase :  Union[str, Any]    							=       {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  Optional[int]    							=       ["Mask2FormerImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  str    							=       [
							    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Mask2FormerForUniversalSegmentation",
							    "Mask2FormerModel",
							    "Mask2FormerPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_maskaformer import MaskaFormerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_maskaformer import (
														    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaskaFormerForUniversalSegmentation,
														    MaskaFormerModel,
														    MaskaFormerPreTrainedModel,
														)


else:
							import sys

							_lowercase :  Dict    							=       _LazyModule(__name__, globals()["__file__"], _import_structure)



