


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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case					:		str      =    logging.get_logger(__name__)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase=False     ):
							__snake_case		:      List[str]										=       []
							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')     )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ("cls_token", "vit.embeddings.cls_token"),
							        ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
							        ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
							        ("pos_embed", "vit.embeddings.position_embeddings"),
							    ]     )

							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		:      Dict										=       [(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"),
														    ]     )

							return rename_keys







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=False     ):
							for i in range(config.num_hidden_layers     ):
														if base_model:
																					__snake_case		:      str										=       ""
														else:
																					__snake_case		:      List[Any]										=       "vit."
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														__snake_case		:      List[Any]										=       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		:      List[Any]										=       in_proj_weight[
														    : config.hidden_size, :
														]
														__snake_case		:      Any										=       in_proj_bias[: config.hidden_size]
														__snake_case		:      List[str]										=       in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__snake_case		:      List[str]										=       in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														__snake_case		:      Union[str, Any]										=       in_proj_weight[
														    -config.hidden_size :, :
														]
														__snake_case		:      Dict										=       in_proj_bias[-config.hidden_size :]







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







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







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







@torch.no_grad()
def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       ViTConfig()
							__snake_case		:      Optional[Any]										=       False
							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							if vit_name[-5:] == "in21k":
														__snake_case		:      Optional[Any]										=       True
														__snake_case		:      Optional[Any]										=       int(vit_name[-1_2:-1_0]     )
														__snake_case		:      int										=       int(vit_name[-9:-6]     )
							else:
														__snake_case		:      Any										=       1_0_0_0
														__snake_case		:      Union[str, Any]										=       "huggingface/label-files"
														__snake_case		:      Optional[int]										=       "imagenet-1k-id2label.json"
														__snake_case		:      str										=       json.load(open(hf_hub_download(__snake_case							,     __snake_case							,     repo_type="dataset"     )							,     "r"     )     )
														__snake_case		:      Dict										=       {int(__snake_case     ): v for k, v in idalabel.items()}
														__snake_case		:      Dict										=       idalabel
														__snake_case		:      Optional[int]										=       {v: k for k, v in idalabel.items()}
														__snake_case		:      List[Any]										=       int(vit_name[-6:-4]     )
														__snake_case		:      List[str]										=       int(vit_name[-3:]     )
							# size of the architecture
							if "deit" in vit_name:
														if vit_name[9:].startswith("tiny"     ):
																					__snake_case		:      List[Any]										=       1_9_2
																					__snake_case		:      Tuple										=       7_6_8
																					__snake_case		:      Optional[int]										=       1_2
																					__snake_case		:      List[Any]										=       3
														elif vit_name[9:].startswith("small"     ):
																					__snake_case		:      Any										=       3_8_4
																					__snake_case		:      Dict										=       1_5_3_6
																					__snake_case		:      Tuple										=       1_2
																					__snake_case		:      Dict										=       6
														else:
																					pass
							else:
														if vit_name[4:].startswith("small"     ):
																					__snake_case		:      Optional[int]										=       7_6_8
																					__snake_case		:      str										=       2_3_0_4
																					__snake_case		:      Optional[Any]										=       8
																					__snake_case		:      Optional[Any]										=       8
														elif vit_name[4:].startswith("base"     ):
																					pass
														elif vit_name[4:].startswith("large"     ):
																					__snake_case		:      Optional[int]										=       1_0_2_4
																					__snake_case		:      Optional[int]										=       4_0_9_6
																					__snake_case		:      Tuple										=       2_4
																					__snake_case		:      List[Any]										=       1_6
														elif vit_name[4:].startswith("huge"     ):
																					__snake_case		:      Optional[Any]										=       1_2_8_0
																					__snake_case		:      List[Any]										=       5_1_2_0
																					__snake_case		:      Optional[int]										=       3_2
																					__snake_case		:      Dict										=       1_6

    # load original model from timm
							__snake_case		:      List[str]										=       timm.create_model(__snake_case							,     pretrained=__snake_case     )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							__snake_case		:      Optional[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     )

							# load HuggingFace model
							if vit_name[-5:] == "in21k":
														__snake_case		:      Union[str, Any]										=       ViTModel(__snake_case     ).eval()
							else:
														__snake_case		:      Union[str, Any]										=       ViTForImageClassification(__snake_case     ).eval()
							model.load_state_dict(__snake_case     )

							# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
							if "deit" in vit_name:
														__snake_case		:      Optional[int]										=       DeiTImageProcessor(size=config.image_size     )
							else:
														__snake_case		:      List[Any]										=       ViTImageProcessor(size=config.image_size     )
							__snake_case		:      Optional[int]										=       image_processor(images=prepare_img()							,     return_tensors="pt"     )
							__snake_case		:      int										=       encoding["pixel_values"]
							__snake_case		:      str										=       model(__snake_case     )

							if base_model:
														__snake_case		:      Tuple										=       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		:      Any										=       timm_model(__snake_case     )
														assert timm_logits.shape == outputs.logits.shape
														assert torch.allclose(__snake_case							,     outputs.logits							,     atol=1e-3     )

							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 image processor to {pytorch_dump_folder_path}'     )
							image_processor.save_pretrained(__snake_case     )


if __name__ == "__main__":
			_snake_case					:		Dict      =    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--vit_name",
			    default="vit_base_patch16_224",
			    type=str,
			    help="Name of the 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."
			)

			_snake_case					:		Any      =    parser.parse_args()
			convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_snake_case					:		int      =    logging.get_logger(__name__)

_snake_case					:		int      =    {
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  "beit"

							def __init__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  Any=8192   ,     lowerCamelCase	:  Dict=768   ,     lowerCamelCase	:  int=12   ,     lowerCamelCase	:  Optional[Any]=12   ,     lowerCamelCase	:  List[str]=3072   ,     lowerCamelCase	:  Tuple="gelu"   ,     lowerCamelCase	:  Union[str, Any]=0.0   ,     lowerCamelCase	:  int=0.0   ,     lowerCamelCase	:  Dict=0.02   ,     lowerCamelCase	:  List[str]=1E-12   ,     lowerCamelCase	:  Optional[Any]=224   ,     lowerCamelCase	:  Optional[int]=16   ,     lowerCamelCase	:  Any=3   ,     lowerCamelCase	:  Optional[int]=False   ,     lowerCamelCase	:  Any=False   ,     lowerCamelCase	:  Optional[Any]=False   ,     lowerCamelCase	:  int=False   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  Tuple=0.1   ,     lowerCamelCase	:  Optional[int]=True   ,     lowerCamelCase	:  int=[3, 5, 7, 11]   ,     lowerCamelCase	:  str=[1, 2, 3, 6]   ,     lowerCamelCase	:  int=True   ,     lowerCamelCase	:  List[Any]=0.4   ,     lowerCamelCase	:  int=256   ,     lowerCamelCase	:  str=1   ,     lowerCamelCase	:  List[str]=False   ,     lowerCamelCase	:  List[str]=255   ,     **lowerCamelCase	:  Dict   ,     )     ->   int:
														super().__init__(**lowerCamelCase    )

														__snake_case		:      Any										=       vocab_size
														__snake_case		:      List[str]										=       hidden_size
														__snake_case		:      List[Any]										=       num_hidden_layers
														__snake_case		:      Tuple										=       num_attention_heads
														__snake_case		:      Dict										=       intermediate_size
														__snake_case		:      Union[str, Any]										=       hidden_act
														__snake_case		:      Optional[Any]										=       hidden_dropout_prob
														__snake_case		:      Optional[int]										=       attention_probs_dropout_prob
														__snake_case		:      Union[str, Any]										=       initializer_range
														__snake_case		:      str										=       layer_norm_eps

														__snake_case		:      Optional[Any]										=       image_size
														__snake_case		:      List[str]										=       patch_size
														__snake_case		:      Optional[Any]										=       num_channels
														__snake_case		:      Any										=       use_mask_token
														__snake_case		:      List[str]										=       use_absolute_position_embeddings
														__snake_case		:      List[Any]										=       use_relative_position_bias
														__snake_case		:      str										=       use_shared_relative_position_bias
														__snake_case		:      str										=       layer_scale_init_value
														__snake_case		:      Any										=       drop_path_rate
														__snake_case		:      int										=       use_mean_pooling
														# decode head attributes (semantic segmentation)
														__snake_case		:      Optional[Any]										=       out_indices
														__snake_case		:      List[str]										=       pool_scales
														# auxiliary head attributes (semantic segmentation)
														__snake_case		:      int										=       use_auxiliary_head
														__snake_case		:      int										=       auxiliary_loss_weight
														__snake_case		:      Optional[int]										=       auxiliary_channels
														__snake_case		:      int										=       auxiliary_num_convs
														__snake_case		:      str										=       auxiliary_concat_input
														__snake_case		:      List[str]										=       semantic_loss_ignore_index

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  version.parse("1.11"				)

							@property
							def    __snake_case (							self	:  Dict    )     ->   Mapping[str, Mapping[int, str]]:
														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ]    )






							@property
							def    __snake_case (							self	:  str    )     ->   float:
														return 1E-4

