





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()
__a      :       Tuple				  =   logging.get_logger(__name__)



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			Optional[Any]=False    )						->      Union[str, Any]:





    """simple docstring"""

    __A         =	[]
    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"
        __A         =	[(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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			List[Any]			,				__lowercase   :			Any=False    )						->      Dict:





    """simple docstring"""

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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Union[str, Any]    )						->      Union[str, Any]:





    """simple docstring"""

    __A         =	["""head.weight""", """head.bias"""]
    for k in ignore_keys:
        state_dict.pop(_snake_case			,				_snake_case    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			Any			,				__lowercase   :			Dict    )						->      List[Any]:





    """simple docstring"""

    __A         =	dct.pop(_snake_case    )
    __A         =	val



def 			_SCREAMING_SNAKE_CASE						( )						->      List[str]:





    """simple docstring"""

    __A         =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    __A         =	Image.open(requests.get(_snake_case			,				stream=_snake_case    ).raw    )
    return im



@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			Tuple    )						->      List[Any]:





    """simple docstring"""

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

    # load original model from timm
    __A         =	timm.create_model(_snake_case			,				pretrained=_snake_case    )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    __A         =	timm_model.state_dict()
    if base_model:
        remove_classification_head_(_snake_case    )
    __A         =	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":
        __A         =	ViTModel(_snake_case    ).eval()
    else:
        __A         =	ViTForImageClassification(_snake_case    ).eval()
    model.load_state_dict(_snake_case    )

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

    if base_model:
        __A         =	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:
        __A         =	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__":
  __a      :       int				  =   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."
  )

  __a      :       str				  =   parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)












from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def 			_SCREAMING_SNAKE_CASE						( )						->      Tuple:





    """simple docstring"""

    __A         =	HfArgumentParser(__lowercase    )
    __A         =	parser.parse_args_into_dataclasses()[0]
    __A         =	TensorFlowBenchmark(args=__lowercase    )
    try:
        __A         =	parser.parse_args_into_dataclasses()[0]
    except ValueError as e:
        __A         =	"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
        __A         =	""" """.join(str(__lowercase    ).split(""" """    )[:-1]    )
        __A         =	""""""
        __A         =	eval(str(__lowercase    ).split(""" """    )[-1]    )
        __A         =	[]
        for arg in depreciated_args:
            # arg[2:] removes '--'
            if arg[2:] in TensorFlowBenchmark.deprecated_args:
                # arg[5:] removes '--no_'
                full_error_msg += arg_error_msg.format(arg[5:]    )
            else:
                wrong_args.append(__lowercase    )
        if len(__lowercase    ) > 0:
            __A         =	full_error_msg + begin_error_msg + str(__lowercase    )
        raise ValueError(__lowercase    )
    benchmark.run()


if __name__ == "__main__":
  main()




