



"""simple docstring"""

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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


logging.set_verbosity_info()
lowercase__    :  Optional[int]							    =	logging.get_logger(__name__)




def __lowercase			(							_a		,			_a=False    ):
    snake_case_ :     Optional[int]				=	[]
    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'''),
            ]    )

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        snake_case_ :     int				=	[(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 __lowercase			(							_a		,			_a		,			_a=False    ):





    for i in range(config.num_hidden_layers    ):
        if base_model:
            snake_case_ :     List[str]				=	''''''
        else:
            snake_case_ :     Tuple				=	'''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_ :     int				=	state_dict.pop(f"blocks.{i}.attn.qkv.bias"    )
        # next, add query, keys and values (in that order) to the state dict
        snake_case_ :     Any				=	in_proj_weight[
            : config.hidden_size, :
        ]
        snake_case_ :     List[str]				=	in_proj_bias[: config.hidden_size]
        snake_case_ :     str				=	in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        snake_case_ :     int				=	in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        snake_case_ :     List[str]				=	in_proj_weight[
            -config.hidden_size :, :
        ]
        snake_case_ :     List[Any]				=	in_proj_bias[-config.hidden_size :]




def __lowercase			(							_a    ):
    snake_case_ :     Dict				=	['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(_UpperCAmelCase		,			_UpperCAmelCase    )




def __lowercase			(							_a		,			_a		,			_a    ):
    snake_case_ :     Dict				=	dct.pop(_UpperCAmelCase    )
    snake_case_ :     Optional[int]				=	val




def __lowercase			(							):
    snake_case_ :     Dict				=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    snake_case_ :     List[Any]				=	Image.open(requests.get(_UpperCAmelCase		,			stream=_UpperCAmelCase    ).raw    )
    return im




@torch.no_grad()
def __lowercase			(							_a		,			_a		,			_a=True    ):
    snake_case_ :     int				=	ViTConfig()
    # patch_size
    if model_name[-1] == "8":
        snake_case_ :     Optional[Any]				=	8
    # set labels if required
    if not base_model:
        snake_case_ :     Optional[int]				=	1_000
        snake_case_ :     int				=	'''huggingface/label-files'''
        snake_case_ :     str				=	'''imagenet-1k-id2label.json'''
        snake_case_ :     str				=	json.load(open(hf_hub_download(_UpperCAmelCase		,			_UpperCAmelCase		,			repo_type='''dataset'''    )		,			'''r'''    )    )
        snake_case_ :     Dict				=	{int(_UpperCAmelCase    ): v for k, v in idalabel.items()}
        snake_case_ :     str				=	idalabel
        snake_case_ :     Tuple				=	{v: k for k, v in idalabel.items()}
    # size of the architecture
    if model_name in ["dino_vits8", "dino_vits16"]:
        snake_case_ :     List[str]				=	384
        snake_case_ :     str				=	1_536
        snake_case_ :     List[Any]				=	12
        snake_case_ :     List[Any]				=	6

    # load original model from torch hub
    snake_case_ :     str				=	torch.hub.load('''facebookresearch/dino:main'''		,			_UpperCAmelCase    )
    original_model.eval()

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

    # load HuggingFace model
    if base_model:
        snake_case_ :     int				=	ViTModel(_UpperCAmelCase		,			add_pooling_layer=_UpperCAmelCase    ).eval()
    else:
        snake_case_ :     Optional[int]				=	ViTForImageClassification(_UpperCAmelCase    ).eval()
    model.load_state_dict(_UpperCAmelCase    )

    # Check outputs on an image, prepared by ViTImageProcessor
    snake_case_ :     Tuple				=	ViTImageProcessor()
    snake_case_ :     Tuple				=	image_processor(images=prepare_img()		,			return_tensors='''pt'''    )
    snake_case_ :     List[Any]				=	encoding['''pixel_values''']
    snake_case_ :     Optional[Any]				=	model(_UpperCAmelCase    )

    if base_model:
        snake_case_ :     List[str]				=	original_model(_UpperCAmelCase    )
        assert torch.allclose(_UpperCAmelCase		,			outputs.last_hidden_state[:, 0, :]		,			atol=1E-1    )
    else:
        snake_case_ :     Tuple				=	original_model(_UpperCAmelCase    )
        assert logits.shape == outputs.logits.shape
        assert torch.allclose(_UpperCAmelCase		,			outputs.logits		,			atol=1E-3    )

    Path(_UpperCAmelCase    ).mkdir(exist_ok=_UpperCAmelCase    )
    print(f"Saving model {model_name} to {pytorch_dump_folder_path}"    )
    model.save_pretrained(_UpperCAmelCase    )
    print(f"Saving image processor to {pytorch_dump_folder_path}"    )
    image_processor.save_pretrained(_UpperCAmelCase    )


if __name__ == "__main__":
     lowercase__    :  Dict							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--model_name''',
         default='''dino_vitb16''',
         type=str,
         help='''Name of the model trained with DINO 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(
         '''--base_model''',
         action='''store_true''',
         help='''Whether to only convert the base model (no projection head weights).''',
     )

     parser.set_defaults(base_model=True)
     lowercase__    :  Tuple							    =	parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)










from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
				from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__A   			=logging.get_logger(__name__)

__A   			={
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       """deberta-v2"""




			def __init__(   self							: Optional[int]       ,       a_							: List[str]=12_81_00       ,       a_							: Optional[Any]=15_36       ,       a_							: Optional[Any]=24       ,       a_							: List[Any]=24       ,       a_							: Optional[int]=61_44       ,       a_							: List[Any]="gelu"       ,       a_							: Any=0.1       ,       a_							: Tuple=0.1       ,       a_							: Optional[Any]=5_12       ,       a_							: Tuple=0       ,       a_							: Dict=0.0_2       ,       a_							: Optional[Any]=1e-7       ,       a_							: List[str]=False       ,       a_							: List[Any]=-1       ,       a_							: List[str]=0       ,       a_							: Optional[Any]=True       ,       a_							: List[Any]=None       ,       a_							: Optional[int]=0       ,       a_							: Tuple="gelu"       ,       **a_							: List[str]       ,       ):
					'''simple docstring'''





					super().__init__(**a_ )

					__UpperCAmelCase    :     Any													=	hidden_size
					__UpperCAmelCase    :     Any													=	num_hidden_layers
					__UpperCAmelCase    :     List[Any]													=	num_attention_heads
					__UpperCAmelCase    :     Union[str, Any]													=	intermediate_size
					__UpperCAmelCase    :     List[str]													=	hidden_act
					__UpperCAmelCase    :     int													=	hidden_dropout_prob
					__UpperCAmelCase    :     List[str]													=	attention_probs_dropout_prob
					__UpperCAmelCase    :     str													=	max_position_embeddings
					__UpperCAmelCase    :     int													=	type_vocab_size
					__UpperCAmelCase    :     Tuple													=	initializer_range
					__UpperCAmelCase    :     Optional[int]													=	relative_attention
					__UpperCAmelCase    :     int													=	max_relative_positions
					__UpperCAmelCase    :     Any													=	pad_token_id
					__UpperCAmelCase    :     int													=	position_biased_input

					# Backwards compatibility
					if type(a_ ) == str:
							__UpperCAmelCase    :     Optional[Any]													=	[x.strip() for x in pos_att_type.lower().split('''|''' )]

					__UpperCAmelCase    :     Tuple													=	pos_att_type
					__UpperCAmelCase    :     int													=	vocab_size
					__UpperCAmelCase    :     Optional[Any]													=	layer_norm_eps

					__UpperCAmelCase    :     str													=	kwargs.get('''pooler_hidden_size'''       ,       a_ )
					__UpperCAmelCase    :     Union[str, Any]													=	pooler_dropout
					__UpperCAmelCase    :     Tuple													=	pooler_hidden_act

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			@property
			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					if self.task == "multiple-choice":
							__UpperCAmelCase    :     List[Any]													=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
							__UpperCAmelCase    :     int													=	{0: '''batch''', 1: '''sequence'''}
					if self._config.type_vocab_size > 0:
							return OrderedDict(
							    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)] )
					else:
							return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)] )




			@property
			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					return 12




			def  snake_case__     (   self							: Any       ,       a_							: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,       a_							: int = -1       ,       a_							: int = -1       ,       a_							: int = -1       ,       a_							: bool = False       ,       a_							: Optional["TensorType"] = None       ,       a_							: int = 3       ,       a_							: int = 40       ,       a_							: int = 40       ,       a_							: "PreTrainedTokenizerBase" = None       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	super().generate_dummy_inputs(preprocessor=a_       ,       framework=a_ )
					if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
							del dummy_inputs["token_type_ids"]
					return dummy_inputs





