


'''simple docstring'''
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCAmelCase_ :     int								=      {
    'sample_size': 3_2,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1_0_0_0,
    'block_out_channels': [3_2, 6_4],
    'attention_head_dim': 8,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

UpperCAmelCase_ :     int								=      {
    'sample_size': 6_4,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1_0_0_0,
    'block_out_channels': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    'attention_head_dim': 6_4,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

UpperCAmelCase_ :     Union[str, Any]								=      {
    'sample_size': 2_5_6,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    'attention_head_dim': 6_4,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'default',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

UpperCAmelCase_ :     Any								=      {
    'num_train_timesteps': 4_0,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}

UpperCAmelCase_ :     Union[str, Any]								=      {
    'num_train_timesteps': 2_0_1,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}

UpperCAmelCase_ :     str								=      {
    'num_train_timesteps': 1_5_1,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}


def 			_lowercase       (				UpperCamelCase__       :					Tuple			):
 if isinstance(UpperCamelCase__,				UpperCamelCase__			):
  return v
 if v.lower() in ("yes", "true", "t", "y", "1"):
  return True
 elif v.lower() in ("no", "false", "f", "n", "0"):
  return False
 else:
  raise argparse.ArgumentTypeError('boolean value expected'			)


def 			_lowercase       (				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					int,				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					Any=False			):
 __A  :		Tuple       =			checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
 __A  :		Tuple       =			checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
 __A  :		Union[str, Any]       =			checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
 __A  :		List[Any]       =			checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
 __A  :		Union[str, Any]       =			checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
 __A  :		int       =			checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
 __A  :		Tuple       =			checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
 __A  :		List[Any]       =			checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
 __A  :		List[Any]       =			checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
 __A  :		Dict       =			checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

 if has_skip:
  __A  :		Any       =			checkpoint[f"""{old_prefix}.skip_connection.weight"""]
  __A  :		List[Any]       =			checkpoint[f"""{old_prefix}.skip_connection.bias"""]

 return new_checkpoint


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					str,				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Optional[Any]=None			):
 __A     ,__A     ,__A  :		Tuple       =			checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3,				dim=0			)
 __A     ,__A     ,__A  :		Tuple       =			checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3,				dim=0			)

 __A  :		Optional[int]       =			checkpoint[f"""{old_prefix}.norm.weight"""]
 __A  :		List[Any]       =			checkpoint[f"""{old_prefix}.norm.bias"""]

 __A  :		int       =			weight_q.squeeze(-1			).squeeze(-1			)
 __A  :		Tuple       =			bias_q.squeeze(-1			).squeeze(-1			)
 __A  :		List[str]       =			weight_k.squeeze(-1			).squeeze(-1			)
 __A  :		Any       =			bias_k.squeeze(-1			).squeeze(-1			)
 __A  :		Any       =			weight_v.squeeze(-1			).squeeze(-1			)
 __A  :		str       =			bias_v.squeeze(-1			).squeeze(-1			)

 __A  :		int       =			(
     checkpoint[f"""{old_prefix}.proj_out.weight"""].squeeze(-1			).squeeze(-1			)
 )
 __A  :		Tuple       =			checkpoint[f"""{old_prefix}.proj_out.bias"""].squeeze(-1			).squeeze(-1			)

 return new_checkpoint


def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					Any			):
 __A  :		Tuple       =			torch.load(UpperCamelCase__,				map_location='cpu'			)
 __A  :		Dict       =			{}

 __A  :		List[str]       =			checkpoint['time_embed.0.weight']
 __A  :		List[str]       =			checkpoint['time_embed.0.bias']
 __A  :		List[Any]       =			checkpoint['time_embed.2.weight']
 __A  :		Tuple       =			checkpoint['time_embed.2.bias']

 if unet_config["num_class_embeds"] is not None:
  __A  :		List[Any]       =			checkpoint['label_emb.weight']

 __A  :		List[Any]       =			checkpoint['input_blocks.0.0.weight']
 __A  :		Tuple       =			checkpoint['input_blocks.0.0.bias']

 __A  :		Dict       =			unet_config['down_block_types']
 __A  :		Any       =			unet_config['layers_per_block']
 __A  :		Tuple       =			unet_config['attention_head_dim']
 __A  :		str       =			unet_config['block_out_channels']
 __A  :		Any       =			1
 __A  :		Any       =			channels_list[0]

 for i, layer_type in enumerate(UpperCamelCase__			):
  __A  :		Dict       =			channels_list[i]
  __A  :		Union[str, Any]       =			current_channels != prev_channels
  if layer_type == "ResnetDownsampleBlock2D":
   for j in range(UpperCamelCase__			):
    __A  :		Any       =			f"""down_blocks.{i}.resnets.{j}"""
    __A  :		int       =			f"""input_blocks.{current_layer}.0"""
    __A  :		List[str]       =			True if j == 0 and downsample_block_has_skip else False
    __A  :		Any       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				has_skip=UpperCamelCase__			)
    current_layer += 1

  elif layer_type == "AttnDownBlock2D":
   for j in range(UpperCamelCase__			):
    __A  :		List[str]       =			f"""down_blocks.{i}.resnets.{j}"""
    __A  :		Optional[Any]       =			f"""input_blocks.{current_layer}.0"""
    __A  :		str       =			True if j == 0 and downsample_block_has_skip else False
    __A  :		Tuple       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				has_skip=UpperCamelCase__			)
    __A  :		Dict       =			f"""down_blocks.{i}.attentions.{j}"""
    __A  :		Dict       =			f"""input_blocks.{current_layer}.1"""
    __A  :		Any       =			convert_attention(
        UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
    current_layer += 1

  if i != len(UpperCamelCase__			) - 1:
   __A  :		Dict       =			f"""down_blocks.{i}.downsamplers.0"""
   __A  :		Dict       =			f"""input_blocks.{current_layer}.0"""
   __A  :		int       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
   current_layer += 1

  __A  :		Union[str, Any]       =			current_channels

 # hardcoded the mid-block for now
 __A  :		int       =			'mid_block.resnets.0'
 __A  :		int       =			'middle_block.0'
 __A  :		Optional[int]       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
 __A  :		List[Any]       =			'mid_block.attentions.0'
 __A  :		Union[str, Any]       =			'middle_block.1'
 __A  :		Union[str, Any]       =			convert_attention(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
 __A  :		Union[str, Any]       =			'mid_block.resnets.1'
 __A  :		Dict       =			'middle_block.2'
 __A  :		List[str]       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

 __A  :		Union[str, Any]       =			0
 __A  :		Any       =			unet_config['up_block_types']

 for i, layer_type in enumerate(UpperCamelCase__			):
  if layer_type == "ResnetUpsampleBlock2D":
   for j in range(layers_per_block + 1			):
    __A  :		Any       =			f"""up_blocks.{i}.resnets.{j}"""
    __A  :		Optional[int]       =			f"""output_blocks.{current_layer}.0"""
    __A  :		Optional[int]       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				has_skip=UpperCamelCase__			)
    current_layer += 1

   if i != len(UpperCamelCase__			) - 1:
    __A  :		Optional[int]       =			f"""up_blocks.{i}.upsamplers.0"""
    __A  :		Tuple       =			f"""output_blocks.{current_layer-1}.1"""
    __A  :		List[Any]       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
  elif layer_type == "AttnUpBlock2D":
   for j in range(layers_per_block + 1			):
    __A  :		Optional[int]       =			f"""up_blocks.{i}.resnets.{j}"""
    __A  :		List[str]       =			f"""output_blocks.{current_layer}.0"""
    __A  :		Any       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				has_skip=UpperCamelCase__			)
    __A  :		int       =			f"""up_blocks.{i}.attentions.{j}"""
    __A  :		Optional[Any]       =			f"""output_blocks.{current_layer}.1"""
    __A  :		Tuple       =			convert_attention(
        UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
    current_layer += 1

   if i != len(UpperCamelCase__			) - 1:
    __A  :		Dict       =			f"""up_blocks.{i}.upsamplers.0"""
    __A  :		List[Any]       =			f"""output_blocks.{current_layer-1}.2"""
    __A  :		Union[str, Any]       =			convert_resnet(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

 __A  :		Tuple       =			checkpoint['out.0.weight']
 __A  :		Any       =			checkpoint['out.0.bias']
 __A  :		int       =			checkpoint['out.2.weight']
 __A  :		Tuple       =			checkpoint['out.2.bias']

 return new_checkpoint


if __name__ == "__main__":
 UpperCAmelCase_ :     Optional[int]								=      argparse.ArgumentParser()

 parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
 parser.add_argument(
     '--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
 )
 parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')

 UpperCAmelCase_ :     List[Any]								=      parser.parse_args()
 UpperCAmelCase_ :     int								=      strabool(args.class_cond)

 UpperCAmelCase_ :     str								=      os.path.basename(args.unet_path)
 print(f'''Checkpoint: {ckpt_name}''')

 # Get U-Net config
 if "imagenet64" in ckpt_name:
  UpperCAmelCase_ :     Any								=      IMAGENET_64_UNET_CONFIG
 elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  UpperCAmelCase_ :     Any								=      LSUN_256_UNET_CONFIG
 elif "test" in ckpt_name:
  UpperCAmelCase_ :     List[str]								=      TEST_UNET_CONFIG
 else:
  raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

 if not args.class_cond:
  UpperCAmelCase_ :     int								=      None

 UpperCAmelCase_ :     int								=      con_pt_to_diffuser(args.unet_path, unet_config)

 UpperCAmelCase_ :     Dict								=      UNetaDModel(**unet_config)
 image_unet.load_state_dict(converted_unet_ckpt)

 # Get scheduler config
 if "cd" in ckpt_name or "test" in ckpt_name:
  UpperCAmelCase_ :     Dict								=      CD_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
  UpperCAmelCase_ :     int								=      CT_IMAGENET_64_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  UpperCAmelCase_ :     Dict								=      CT_LSUN_256_SCHEDULER_CONFIG
 else:
  raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

 UpperCAmelCase_ :     List[str]								=      CMStochasticIterativeScheduler(**scheduler_config)

 UpperCAmelCase_ :     Tuple								=      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
 consistency_model.save_pretrained(args.dump_path)










'''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()
UpperCAmelCase_ :     Tuple								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Tuple=False			):
 __A  :		List[str]       =			[]

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


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any]			):
 __A  :		Any       =			['head.weight', 'head.bias']
 for k in ignore_keys:
  state_dict.pop(UpperCamelCase__,				UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Union[str, Any]			):
 __A  :		Dict       =			dct.pop(UpperCamelCase__			)
 __A  :		Optional[Any]       =			val


def 			_lowercase       (				):
 __A  :		Tuple       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A  :		Optional[Any]       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)
 return im


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					int,				UpperCamelCase__       :					Any=False			):
 __A  :		Optional[Any]       =			BitConfig(
     global_padding='same',				layer_type='bottleneck',				depths=(3, 4, 9),				out_features=['stage3'],				embedding_dynamic_padding=UpperCamelCase__,				)
 __A  :		str       =			ViTHybridConfig(backbone_config=UpperCamelCase__,				image_size=384,				num_labels=1000			)
 __A  :		Union[str, Any]       =			False

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

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

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

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  __A  :		List[Any]       =			ViTHybridModel(UpperCamelCase__			).eval()
 else:
  __A  :		int       =			ViTHybridForImageClassification(UpperCamelCase__			).eval()
 model.load_state_dict(UpperCamelCase__			)

 # create image processor
 __A  :		Tuple       =			create_transform(**resolve_data_config({},				model=UpperCamelCase__			)			)
 __A  :		Union[str, Any]       =			transform.transforms

 __A  :		str       =			{
     'bilinear': PILImageResampling.BILINEAR,
     'bicubic': PILImageResampling.BICUBIC,
     'nearest': PILImageResampling.NEAREST,
 }

 __A  :		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(),				)

 __A  :		Optional[Any]       =			prepare_img()
 __A  :		Any       =			transform(UpperCamelCase__			).unsqueeze(0			)
 __A  :		Dict       =			processor(UpperCamelCase__,				return_tensors='pt'			).pixel_values

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

 # verify logits
 with torch.no_grad():
  __A  :		Union[str, Any]       =			model(UpperCamelCase__			)
  __A  :		Dict       =			outputs.logits

 print('Predicted class:',				logits.argmax(-1			).item()			)
 if base_model:
  __A  :		Tuple       =			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:
  __A  :		Any       =			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__":
 UpperCAmelCase_ :     Optional[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.'
 )

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






