

import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = {
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class 			a					(		UpperCAmelCase			):
						_lowercase        =     "detr"
						_lowercase        =     ["past_key_values"]
						_lowercase        =     {
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}


						def __init__(    self  ,   A_=True  ,   A_=None  ,   A_=3  ,   A_=100  ,   A_=6  ,   A_=2048  ,   A_=8  ,   A_=6  ,   A_=2048  ,   A_=8  ,   A_=0.0  ,   A_=0.0  ,   A_=True  ,   A_="relu"  ,   A_=256  ,   A_=0.1  ,   A_=0.0  ,   A_=0.0  ,   A_=0.02  ,   A_=1.0  ,   A_=False  ,   A_="sine"  ,   A_="resnet50"  ,   A_=True  ,   A_=False  ,   A_=1  ,   A_=5  ,   A_=2  ,   A_=1  ,   A_=1  ,   A_=5  ,   A_=2  ,   A_=0.1  ,   **A_  ,   ):







										'''simple docstring'''


										if backbone_config is not None and use_timm_backbone:
														raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."		)

										if not use_timm_backbone:
														if backbone_config is None:
																		logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."		)
																		_UpperCAmelCase    :						Dict							=      CONFIG_MAPPING["resnet"](out_features=["stage4"]		)
														elif isinstance(A_  ,   A_		):
																		_UpperCAmelCase    :						List[Any]							=      backbone_config.get("model_type"		)
																		_UpperCAmelCase    :						Optional[int]							=      CONFIG_MAPPING[backbone_model_type]
																		_UpperCAmelCase    :						Optional[Any]							=      config_class.from_dict(A_		)
														# set timm attributes to None
														_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						List[str]							=      None, None, None

										_UpperCAmelCase    :						Any							=      use_timm_backbone
										_UpperCAmelCase    :						Optional[Any]							=      backbone_config
										_UpperCAmelCase    :						List[Any]							=      num_channels
										_UpperCAmelCase    :						str							=      num_queries
										_UpperCAmelCase    :						Optional[Any]							=      d_model
										_UpperCAmelCase    :						Tuple							=      encoder_ffn_dim
										_UpperCAmelCase    :						Tuple							=      encoder_layers
										_UpperCAmelCase    :						List[Any]							=      encoder_attention_heads
										_UpperCAmelCase    :						str							=      decoder_ffn_dim
										_UpperCAmelCase    :						Dict							=      decoder_layers
										_UpperCAmelCase    :						Tuple							=      decoder_attention_heads
										_UpperCAmelCase    :						Dict							=      dropout
										_UpperCAmelCase    :						Dict							=      attention_dropout
										_UpperCAmelCase    :						List[str]							=      activation_dropout
										_UpperCAmelCase    :						int							=      activation_function
										_UpperCAmelCase    :						Dict							=      init_std
										_UpperCAmelCase    :						int							=      init_xavier_std
										_UpperCAmelCase    :						List[str]							=      encoder_layerdrop
										_UpperCAmelCase    :						Optional[int]							=      decoder_layerdrop
										_UpperCAmelCase    :						int							=      encoder_layers
										_UpperCAmelCase    :						Optional[int]							=      auxiliary_loss
										_UpperCAmelCase    :						str							=      position_embedding_type
										_UpperCAmelCase    :						str							=      backbone
										_UpperCAmelCase    :						List[str]							=      use_pretrained_backbone
										_UpperCAmelCase    :						Optional[int]							=      dilation
										# Hungarian matcher
										_UpperCAmelCase    :						List[str]							=      class_cost
										_UpperCAmelCase    :						Union[str, Any]							=      bbox_cost
										_UpperCAmelCase    :						Optional[int]							=      giou_cost
										# Loss coefficients
										_UpperCAmelCase    :						Tuple							=      mask_loss_coefficient
										_UpperCAmelCase    :						List[str]							=      dice_loss_coefficient
										_UpperCAmelCase    :						Any							=      bbox_loss_coefficient
										_UpperCAmelCase    :						Optional[int]							=      giou_loss_coefficient
										_UpperCAmelCase    :						Any							=      eos_coefficient
										super().__init__(is_encoder_decoder=A_  ,   **A_		)


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.encoder_attention_heads


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.d_model


						@classmethod
						def     _UpperCAmelCase			(    cls  ,   A_  ,   **A_		):







										'''simple docstring'''


										return cls(backbone_config=A_  ,   **A_		)





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      copy.deepcopy(self.__dict__		)
										if output["backbone_config"] is not None:
														_UpperCAmelCase    :						int							=      self.backbone_config.to_dict()
										_UpperCAmelCase    :						Optional[int]							=      self.__class__.model_type
										return output







class 			a					(		UpperCAmelCase			):
						_lowercase        =     version.parse("1.11"			)


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return OrderedDict(
										    [
										        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
										        ("pixel_mask", {0: "batch"}),
										    ]		)


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return 1e-5





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return 12





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()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

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

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

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any					, lowerCAmelCase:					Optional[int]					, lowerCAmelCase:					Dict	)				->    Tuple:
				_UpperCAmelCase    :						str							=      dct.pop(lowerCAmelCase	)
				_UpperCAmelCase    :						Any							=      val

def 							__SCREAMING_SNAKE_CASE							(			)				->    List[Any]:
				_UpperCAmelCase    :						Tuple							=      "http://images.cocodataset.org/val2017/000000039769.jpg"
				_UpperCAmelCase    :						Tuple							=      Image.open(requests.get(lowerCAmelCase					, stream=lowerCAmelCase	).raw	)
				return im

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Tuple					, lowerCAmelCase:					int					, lowerCAmelCase:					List[Any]=False	)				->    Any:
				_UpperCAmelCase    :						List[Any]							=      BitConfig(
				    global_padding="same"					, layer_type="bottleneck"					, depths=(3, 4, 9)					, out_features=["stage3"]					, embedding_dynamic_padding=lowerCAmelCase					, )
				_UpperCAmelCase    :						Optional[Any]							=      ViTHybridConfig(backbone_config=lowerCAmelCase					, image_size=384					, num_labels=1000	)
				_UpperCAmelCase    :						str							=      False

				# load original model from timm
				_UpperCAmelCase    :						Optional[Any]							=      timm.create_model(lowerCAmelCase					, pretrained=lowerCAmelCase	)
				timm_model.eval()

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

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

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								_UpperCAmelCase    :						Union[str, Any]							=      ViTHybridModel(lowerCAmelCase	).eval()
				else:
								_UpperCAmelCase    :						Optional[Any]							=      ViTHybridForImageClassification(lowerCAmelCase	).eval()
				model.load_state_dict(lowerCAmelCase	)

				# create image processor
				_UpperCAmelCase    :						Any							=      create_transform(**resolve_data_config({}					, model=lowerCAmelCase	)	)
				_UpperCAmelCase    :						Tuple							=      transform.transforms

				_UpperCAmelCase    :						Tuple							=      {
				    "bilinear": PILImageResampling.BILINEAR,
				    "bicubic": PILImageResampling.BICUBIC,
				    "nearest": PILImageResampling.NEAREST,
				}

				_UpperCAmelCase    :						Any							=      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()					, )

				_UpperCAmelCase    :						List[str]							=      prepare_img()
				_UpperCAmelCase    :						List[Any]							=      transform(lowerCAmelCase	).unsqueeze(0	)
				_UpperCAmelCase    :						Optional[Any]							=      processor(lowerCAmelCase					, return_tensors="pt"	).pixel_values

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

				# verify logits
				with torch.no_grad():
								_UpperCAmelCase    :						Union[str, Any]							=      model(lowerCAmelCase	)
								_UpperCAmelCase    :						Optional[Any]							=      outputs.logits

				print("Predicted class:"					, logits.argmax(-1	).item()	)
				if base_model:
								_UpperCAmelCase    :						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:
								_UpperCAmelCase    :						Any							=      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__":
					SCREAMING_SNAKE_CASE_            = 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.'
					)

					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)


