






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 :      Optional[Any]										=				logging.get_logger(__name__)







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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple       ):
	snake_case__			:				Any       =  ["head.weight", "head.bias"]
	for k in ignore_keys:
		state_dict.pop(snake_case_					,  snake_case_       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]					,  snake_case_ :							Dict					,  snake_case_ :							Tuple       ):
	snake_case__			:				int       =  dct.pop(snake_case_       )
	snake_case__			:				List[str]       =  val







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				Optional[Any]       =  "http://images.cocodataset.org/val2017/000000039769.jpg"
	snake_case__			:				Tuple       =  Image.open(requests.get(snake_case_					,  stream=snake_case_       ).raw       )
	return im







@torch.no_grad()
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							str					,  snake_case_ :							Any=False       ):
	snake_case__			:				List[str]       =  BitConfig(
	    global_padding="same"					,  layer_type="bottleneck"					,  depths=(3, 4, 9)					,  out_features=["stage3"]					,  embedding_dynamic_padding=snake_case_					,  )
	snake_case__			:				str       =  ViTHybridConfig(backbone_config=snake_case_					,  image_size=384					,  num_labels=1000       )
	snake_case__			:				Tuple       =  False

	# load original model from timm
	snake_case__			:				Any       =  timm.create_model(snake_case_					,  pretrained=snake_case_       )
	timm_model.eval()

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

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		snake_case__			:				Optional[Any]       =  ViTHybridModel(snake_case_       ).eval()
	else:
		snake_case__			:				Optional[Any]       =  ViTHybridForImageClassification(snake_case_       ).eval()
	model.load_state_dict(snake_case_       )

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

	snake_case__			:				str       =  {
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

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

	snake_case__			:				List[Any]       =  prepare_img()
	snake_case__			:				Union[str, Any]       =  transform(snake_case_       ).unsqueeze(0       )
	snake_case__			:				str       =  processor(snake_case_					,  return_tensors="pt"       ).pixel_values

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

	# verify logits
	with torch.no_grad():
		snake_case__			:				Tuple       =  model(snake_case_       )
		snake_case__			:				List[Any]       =  outputs.logits

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

	if pytorch_dump_folder_path is not None:
		Path(snake_case_       ).mkdir(exist_ok=snake_case_       )
		print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''       )
		model.save_pretrained(snake_case_       )
		print(F'''Saving processor to {pytorch_dump_folder_path}'''       )
		processor.save_pretrained(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__":
	__lowerCamelCase :      Tuple										=				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 :      Optional[int]										=				parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase :      List[str]										=				logging.get_logger(__name__)

__lowerCamelCase :      List[Any]										=				{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"xmod"



					def __init__( self					:  List[str]							,		__A					:  List[str]=3_0_5_2_2							,		__A					:  Tuple=7_6_8							,		__A					:  str=1_2							,		__A					:  List[Any]=1_2							,		__A					:  List[str]=3_0_7_2							,		__A					:  List[str]="gelu"							,		__A					:  List[Any]=0.1							,		__A					:  Tuple=0.1							,		__A					:  str=5_1_2							,		__A					:  Union[str, Any]=2							,		__A					:  List[Any]=0.0_2							,		__A					:  List[str]=1e-1_2							,		__A					:  Tuple=1							,		__A					:  List[Any]=0							,		__A					:  Optional[Any]=2							,		__A					:  Optional[int]="absolute"							,		__A					:  Optional[int]=True							,		__A					:  Dict=None							,		__A					:  Optional[int]=False							,		__A					:  Dict=2							,		__A					:  List[str]=False							,		__A					:  Dict=True							,		__A					:  Union[str, Any]=True							,		__A					:  Tuple=("en_XX",)							,		__A					:  Optional[Any]=None							,		**__A					:  Tuple							,		):
						super().__init__(pad_token_id=__A							,		bos_token_id=__A							,		eos_token_id=__A							,		**__A	)

						snake_case__			:				Tuple       =  vocab_size
						snake_case__			:				Union[str, Any]       =  hidden_size
						snake_case__			:				int       =  num_hidden_layers
						snake_case__			:				str       =  num_attention_heads
						snake_case__			:				Tuple       =  hidden_act
						snake_case__			:				Optional[Any]       =  intermediate_size
						snake_case__			:				List[str]       =  hidden_dropout_prob
						snake_case__			:				Optional[int]       =  attention_probs_dropout_prob
						snake_case__			:				Union[str, Any]       =  max_position_embeddings
						snake_case__			:				str       =  type_vocab_size
						snake_case__			:				List[str]       =  initializer_range
						snake_case__			:				Dict       =  layer_norm_eps
						snake_case__			:				str       =  position_embedding_type
						snake_case__			:				List[str]       =  use_cache
						snake_case__			:				Tuple       =  classifier_dropout
						snake_case__			:				Any       =  pre_norm
						snake_case__			:				List[str]       =  adapter_reduction_factor
						snake_case__			:				List[Any]       =  adapter_layer_norm
						snake_case__			:				str       =  adapter_reuse_layer_norm
						snake_case__			:				Union[str, Any]       =  ln_before_adapter
						snake_case__			:				Tuple       =  list(__A	)
						snake_case__			:				int       =  default_language



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					@property
					def 	_lowercase					( self					:  int	):
						if self.task == "multiple-choice":
							snake_case__			:				Any       =  {0: "batch", 1: "choice", 2: "sequence"}
						else:
							snake_case__			:				Any       =  {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]	)



