


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







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Optional[Any]										=       ["head.weight", "head.bias"]
							for k in ignore_keys:
														state_dict.pop(__lowerCamelCase							,     __lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      List[str]										=       dct.pop(__lowerCamelCase     )
							__snake_case		:      List[Any]										=       val







def     lowerCAmelCase_					( ):
							__snake_case		:      Dict										=       "http://images.cocodataset.org/val2017/000000039769.jpg"
							__snake_case		:      Optional[Any]										=       Image.open(requests.get(__lowerCamelCase							,     stream=__lowerCamelCase     ).raw     )
							return im







@torch.no_grad()
def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=False     ):
							__snake_case		:      str										=       BitConfig(
							    global_padding="same"							,     layer_type="bottleneck"							,     depths=(3, 4, 9)							,     out_features=["stage3"]							,     embedding_dynamic_padding=__lowerCamelCase							,     )
							__snake_case		:      Union[str, Any]										=       ViTHybridConfig(backbone_config=__lowerCamelCase							,     image_size=3_8_4							,     num_labels=1_0_0_0     )
							__snake_case		:      str										=       False

							# load original model from timm
							__snake_case		:      Optional[int]										=       timm.create_model(__lowerCamelCase							,     pretrained=__lowerCamelCase     )
							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_(__lowerCamelCase     )
							__snake_case		:      Dict										=       create_rename_keys(__lowerCamelCase							,     __lowerCamelCase     )
							for src, dest in rename_keys:
														rename_key(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )
							read_in_q_k_v(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )

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

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

							# create image processor
							__snake_case		:      Optional[int]										=       create_transform(**resolve_data_config({}							,     model=__lowerCamelCase     )     )
							__snake_case		:      Tuple										=       transform.transforms

							__snake_case		:      Optional[Any]										=       {
							    "bilinear": PILImageResampling.BILINEAR,
							    "bicubic": PILImageResampling.BICUBIC,
							    "nearest": PILImageResampling.NEAREST,
							}

							__snake_case		:      Union[str, 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()							,     )

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

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

							# verify logits
							with torch.no_grad():
														__snake_case		:      int										=       model(__lowerCamelCase     )
														__snake_case		:      List[Any]										=       outputs.logits

							print("Predicted class:"							,     logits.argmax(-1     ).item()     )
							if base_model:
														__snake_case		:      Union[str, 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:
														__snake_case		:      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__":
			_snake_case					:		List[str]      =    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					:		List[Any]      =    parser.parse_args()
			convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_snake_case					:		List[str]      =    namedtuple(
    "_TestCommandArgs",
    [
        "dataset",
        "name",
        "cache_dir",
        "data_dir",
        "all_configs",
        "save_infos",
        "ignore_verifications",
        "force_redownload",
        "clear_cache",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return (abs(source - target     ) / target) < 0.0_1







@pytest.mark.integration
def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Tuple										=       _TestCommandArgs(dataset=__lowerCamelCase							,     all_configs=__lowerCamelCase							,     save_infos=__lowerCamelCase     )
							__snake_case		:      List[Any]										=       TestCommand(*__lowerCamelCase     )
							test_command.run()
							__snake_case		:      List[Any]										=       os.path.join(__lowerCamelCase							,     "README.md"     )
							assert os.path.exists(__lowerCamelCase     )
							__snake_case		:      Optional[Any]										=       DatasetInfosDict.from_directory(__lowerCamelCase     )
							__snake_case		:      List[str]										=       DatasetInfosDict(
							    {
							        "default": DatasetInfo(
							            features=Features(
							                {
							                    "tokens": Sequence(Value("string"     )     ),
							                    "ner_tags": Sequence(
							                        ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]     )     ),
							                    "langs": Sequence(Value("string"     )     ),
							                    "spans": Sequence(Value("string"     )     ),
							                }     )							,     splits=[
							                {
							                    "name": "train",
							                    "num_bytes": 2_3_5_1_5_6_3,
							                    "num_examples": 1_0_0_0_0,
							                },
							                {
							                    "name": "validation",
							                    "num_bytes": 2_3_8_4_1_8,
							                    "num_examples": 1_0_0_0,
							                },
							            ]							,     download_size=3_9_4_0_6_8_0							,     dataset_size=2_5_8_9_9_8_1							,     )
							    }     )
							assert dataset_infos.keys() == expected_dataset_infos.keys()
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														__snake_case			,      __snake_case		:      Tuple										=       getattr(dataset_infos["default"]							,     __lowerCamelCase     ), getattr(expected_dataset_infos["default"]							,     __lowerCamelCase     )
														if key == "num_bytes":
																					assert is_apercent_close(__lowerCamelCase							,     __lowerCamelCase     )
														elif key == "splits":
																					assert list(__lowerCamelCase     ) == list(__lowerCamelCase     )
																					for split in result:
																												assert result[split].name == expected[split].name
																												assert result[split].num_examples == expected[split].num_examples
																												assert is_apercent_close(result[split].num_bytes							,     expected[split].num_bytes     )
														else:
																					result == expected

