



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()
UpperCAmelCase__   =       logging.get_logger(__name__)





def 							A	(     _UpperCAmelCase				:   str							,   _UpperCAmelCase				:   Union[str, Any]=False    )				-> Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase		       =   []
			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"
						_UpperCAmelCase		       =   [(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 							A	(     _UpperCAmelCase				:   Union[str, Any]							,   _UpperCAmelCase				:   Tuple							,   _UpperCAmelCase				:   Tuple=False    )				-> str:







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





def 							A	(     _UpperCAmelCase				:   List[str]    )				-> List[str]:







			'''simple docstring'''
			_UpperCAmelCase		       =   ['head.weight', 'head.bias']
			for k in ignore_keys:
						state_dict.pop(__lowerCamelCase							,   __lowerCamelCase    )





def 							A	(     _UpperCAmelCase				:   Tuple							,   _UpperCAmelCase				:   Dict							,   _UpperCAmelCase				:   str    )				-> List[Any]:







			'''simple docstring'''
			_UpperCAmelCase		       =   dct.pop(__lowerCamelCase    )
			_UpperCAmelCase		       =   val





def 							A	(     )				-> Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase		       =   'http://images.cocodataset.org/val2017/000000039769.jpg'
			_UpperCAmelCase		       =   Image.open(requests.get(__lowerCamelCase							,   stream=__lowerCamelCase    ).raw    )
			return im





@torch.no_grad()
def 							A	(     _UpperCAmelCase				:   Optional[Any]							,   _UpperCAmelCase				:   Union[str, Any]    )				-> Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase		       =   ViTConfig()
			_UpperCAmelCase		       =   False
			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			if vit_name[-5:] == "in21k":
						_UpperCAmelCase		       =   True
						_UpperCAmelCase		       =   int(vit_name[-12:-10]    )
						_UpperCAmelCase		       =   int(vit_name[-9:-6]    )
			else:
						_UpperCAmelCase		       =   1_000
						_UpperCAmelCase		       =   'huggingface/label-files'
						_UpperCAmelCase		       =   'imagenet-1k-id2label.json'
						_UpperCAmelCase		       =   json.load(open(hf_hub_download(__lowerCamelCase							,   __lowerCamelCase							,   repo_type='dataset'    )							,   'r'    )    )
						_UpperCAmelCase		       =   {int(__lowerCamelCase    ): v for k, v in idalabel.items()}
						_UpperCAmelCase		       =   idalabel
						_UpperCAmelCase		       =   {v: k for k, v in idalabel.items()}
						_UpperCAmelCase		       =   int(vit_name[-6:-4]    )
						_UpperCAmelCase		       =   int(vit_name[-3:]    )
			# size of the architecture
			if "deit" in vit_name:
						if vit_name[9:].startswith('tiny'    ):
									_UpperCAmelCase		       =   192
									_UpperCAmelCase		       =   768
									_UpperCAmelCase		       =   12
									_UpperCAmelCase		       =   3
						elif vit_name[9:].startswith('small'    ):
									_UpperCAmelCase		       =   384
									_UpperCAmelCase		       =   1_536
									_UpperCAmelCase		       =   12
									_UpperCAmelCase		       =   6
						else:
									pass
			else:
						if vit_name[4:].startswith('small'    ):
									_UpperCAmelCase		       =   768
									_UpperCAmelCase		       =   2_304
									_UpperCAmelCase		       =   8
									_UpperCAmelCase		       =   8
						elif vit_name[4:].startswith('base'    ):
									pass
						elif vit_name[4:].startswith('large'    ):
									_UpperCAmelCase		       =   1_024
									_UpperCAmelCase		       =   4_096
									_UpperCAmelCase		       =   24
									_UpperCAmelCase		       =   16
						elif vit_name[4:].startswith('huge'    ):
									_UpperCAmelCase		       =   1_280
									_UpperCAmelCase		       =   5_120
									_UpperCAmelCase		       =   32
									_UpperCAmelCase		       =   16

    # load original model from timm
			_UpperCAmelCase		       =   timm.create_model(__lowerCamelCase							,   pretrained=__lowerCamelCase    )
			timm_model.eval()

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

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						_UpperCAmelCase		       =   ViTModel(__lowerCamelCase    ).eval()
			else:
						_UpperCAmelCase		       =   ViTForImageClassification(__lowerCamelCase    ).eval()
			model.load_state_dict(__lowerCamelCase    )

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

			if base_model:
						_UpperCAmelCase		       =   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		       =   timm_model(__lowerCamelCase    )
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(__lowerCamelCase							,   outputs.logits							,   atol=1E-3    )

			Path(__lowerCamelCase    ).mkdir(exist_ok=__lowerCamelCase    )
			print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"    )
			model.save_pretrained(__lowerCamelCase    )
			print(F"Saving image processor to {pytorch_dump_folder_path}"    )
			image_processor.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
							UpperCAmelCase__   =       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."
							)

							UpperCAmelCase__   =       parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''data2vec-text'''


    def __init__(		self ,		A=3_0522 ,		A=768 ,		A=12 ,		A=12 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=2 ,		A=0.02 ,		A=1e-12 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		A=None ,		**A ,		)		->   int:
        super().__init__(pad_token_id=A ,		bos_token_id=A ,		eos_token_id=A ,		**A       )

        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			type_vocab_size
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			position_embedding_type
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			classifier_dropout




class 					a_     (				snake_case_       ):

    '''simple docstring'''


    @property
    def 			snake_case_(		self       )		->   Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]       )



