



'''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()
snake_case_					:      List[Any]    			=    logging.get_logger(__name__)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_=False				):
						_UpperCamelCase   :						Dict												=  []

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






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						str												=  ['head.weight', 'head.bias']
						for k in ignore_keys:
												state_dict.pop(UpperCAmelCase_    ,    UpperCAmelCase_				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  dct.pop(UpperCAmelCase_				)
						_UpperCamelCase   :						Dict												=  val






def A__       (	):
						_UpperCamelCase   :						int												=  'http://images.cocodataset.org/val2017/000000039769.jpg'
						_UpperCamelCase   :						List[Any]												=  Image.open(requests.get(UpperCAmelCase_    ,    stream=UpperCAmelCase_				).raw				)
						return im






@torch.no_grad()
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_=False				):
						_UpperCamelCase   :						Tuple												=  BitConfig(
						    global_padding='same'    ,    layer_type='bottleneck'    ,    depths=(3, 4, 9)    ,    out_features=['stage3']    ,    embedding_dynamic_padding=UpperCAmelCase_    ,    )
						_UpperCamelCase   :						List[Any]												=  ViTHybridConfig(backbone_config=UpperCAmelCase_    ,    image_size=3_8_4    ,    num_labels=1_0_0_0				)
						_UpperCamelCase   :						int												=  False

						# load original model from timm
						_UpperCamelCase   :						List[Any]												=  timm.create_model(UpperCAmelCase_    ,    pretrained=UpperCAmelCase_				)
						timm_model.eval()

						# load state_dict of original model, remove and rename some keys
						_UpperCamelCase   :						Tuple												=  timm_model.state_dict()
						if base_model:
												remove_classification_head_(UpperCAmelCase_				)
						_UpperCamelCase   :						List[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_				)

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

						# load HuggingFace model
						if vit_name[-5:] == "in21k":
												_UpperCamelCase   :						List[str]												=  ViTHybridModel(UpperCAmelCase_				).eval()
						else:
												_UpperCamelCase   :						Tuple												=  ViTHybridForImageClassification(UpperCAmelCase_				).eval()
						model.load_state_dict(UpperCAmelCase_				)

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

						_UpperCamelCase   :						Union[str, Any]												=  {
						    'bilinear': PILImageResampling.BILINEAR,
						    'bicubic': PILImageResampling.BICUBIC,
						    'nearest': PILImageResampling.NEAREST,
						}

						_UpperCamelCase   :						List[str]												=  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()    ,    )

						_UpperCamelCase   :						Optional[int]												=  prepare_img()
						_UpperCamelCase   :						int												=  transform(UpperCAmelCase_				).unsqueeze(0				)
						_UpperCamelCase   :						Any												=  processor(UpperCAmelCase_    ,    return_tensors='pt'				).pixel_values

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

						# verify logits
						with torch.no_grad():
												_UpperCamelCase   :						Optional[Any]												=  model(UpperCAmelCase_				)
												_UpperCamelCase   :						int												=  outputs.logits

						print('Predicted class:'    ,    logits.argmax(-1				).item()				)
						if base_model:
												_UpperCamelCase   :						int												=  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:
												_UpperCamelCase   :						List[str]												=  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__":
					snake_case_					:      Optional[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.'
					)

					snake_case_					:      Tuple    			=    parser.parse_args()
					convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def A__       (	UpperCAmelCase_				):
						if "model" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('model.'    ,    ''				)
						if "norm1" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('norm1'    ,    'attention.output.LayerNorm'				)
						if "norm2" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('norm2'    ,    'output.LayerNorm'				)
						if "norm" in orig_key:
												_UpperCamelCase   :						str												=  orig_key.replace('norm'    ,    'LayerNorm'				)
						if "transformer" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.split('.'				)[0].split('_'				)[-1]
												_UpperCamelCase   :						Tuple												=  orig_key.replace(f'transformer_{layer_num}'    ,    f'encoder.layer.{layer_num}'				)
						if "mha.attn" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('mha.attn'    ,    'attention.self'				)
						if "mha" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('mha'    ,    'attention'				)
						if "W_q" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('W_q'    ,    'self.query'				)
						if "W_k" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_k'    ,    'self.key'				)
						if "W_v" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_v'    ,    'self.value'				)
						if "ff1" in orig_key:
												_UpperCamelCase   :						Optional[Any]												=  orig_key.replace('ff1'    ,    'intermediate.dense'				)
						if "ff2" in orig_key:
												_UpperCamelCase   :						List[str]												=  orig_key.replace('ff2'    ,    'output.dense'				)
						if "ff" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('ff'    ,    'output.dense'				)
						if "mlm_class" in orig_key:
												_UpperCamelCase   :						Tuple												=  orig_key.replace('mlm.mlm_class'    ,    'cls.predictions.decoder'				)
						if "mlm" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('mlm'    ,    'cls.predictions.transform'				)
						if "cls" not in orig_key:
												_UpperCamelCase   :						Optional[int]												=  'yoso.' + orig_key

						return orig_key






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase   :						Any												=  orig_state_dict.pop(UpperCAmelCase_				)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		_UpperCamelCase   :						Union[str, Any]												=  val

						_UpperCamelCase   :						Any												=  orig_state_dict['cls.predictions.decoder.bias']
						_UpperCamelCase   :						Union[str, Any]												=  torch.arange(UpperCAmelCase_				).expand((1, -1)				) + 2

						return orig_state_dict






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[Any]												=  torch.load(UpperCAmelCase_    ,    map_location='cpu'				)['model_state_dict']
						_UpperCamelCase   :						List[str]												=  YosoConfig.from_json_file(UpperCAmelCase_				)
						_UpperCamelCase   :						Union[str, Any]												=  YosoForMaskedLM(UpperCAmelCase_				)

						_UpperCamelCase   :						Tuple												=  convert_checkpoint_helper(config.max_position_embeddings    ,    UpperCAmelCase_				)

						print(model.load_state_dict(UpperCAmelCase_				)				)
						model.eval()
						model.save_pretrained(UpperCAmelCase_				)

						print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'				)


if __name__ == "__main__":
					snake_case_					:      int    			=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					snake_case_					:      List[Any]    			=    parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


