"""simple docstring"""






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()
lowerCAmelCase__						:						str		  =			logging.get_logger(__name__)



def 						a_		(      lowerCamelCase      ,    lowerCamelCase=False ):
				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_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase=False ):
				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_		(      lowerCamelCase ):
				UpperCAmelCase__    =							['head.weight', 'head.bias']
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase      ,    lowerCamelCase )



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							dct.pop(lowerCamelCase )
				UpperCAmelCase__    =							val



def 						a_		(      ):
				UpperCAmelCase__    =							'http://images.cocodataset.org/val2017/000000039769.jpg'
				UpperCAmelCase__    =							Image.open(requests.get(lowerCamelCase      ,    stream=lowerCamelCase ).raw )
				return im



@torch.no_grad()
def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				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[-1_2:-1_0] )
								UpperCAmelCase__    =							int(vit_name[-9:-6] )
				else:
								UpperCAmelCase__    =							1_0_0_0
								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__    =							1_9_2
												UpperCAmelCase__    =							7_6_8
												UpperCAmelCase__    =							1_2
												UpperCAmelCase__    =							3
								elif vit_name[9:].startswith('small' ):
												UpperCAmelCase__    =							3_8_4
												UpperCAmelCase__    =							1_5_3_6
												UpperCAmelCase__    =							1_2
												UpperCAmelCase__    =							6
								else:
												pass
				else:
								if vit_name[4:].startswith('small' ):
												UpperCAmelCase__    =							7_6_8
												UpperCAmelCase__    =							2_3_0_4
												UpperCAmelCase__    =							8
												UpperCAmelCase__    =							8
								elif vit_name[4:].startswith('base' ):
												pass
								elif vit_name[4:].startswith('large' ):
												UpperCAmelCase__    =							1_0_2_4
												UpperCAmelCase__    =							4_0_9_6
												UpperCAmelCase__    =							2_4
												UpperCAmelCase__    =							1_6
								elif vit_name[4:].startswith('huge' ):
												UpperCAmelCase__    =							1_2_8_0
												UpperCAmelCase__    =							5_1_2_0
												UpperCAmelCase__    =							3_2
												UpperCAmelCase__    =							1_6

    # 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__":
						lowerCAmelCase__						:						List[str]		  =			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.'
						)

						lowerCAmelCase__						:						List[Any]		  =			parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase__						:						int		  =			{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__						:						Union[str, Any]		  =			[
						    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TapasForMaskedLM',
						    'TapasForQuestionAnswering',
						    'TapasForSequenceClassification',
						    'TapasModel',
						    'TapasPreTrainedModel',
						    'load_tf_weights_in_tapas',
						]
try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__						:						Optional[Any]		  =			[
						    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFTapasForMaskedLM',
						    'TFTapasForQuestionAnswering',
						    'TFTapasForSequenceClassification',
						    'TFTapasModel',
						    'TFTapasPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
						from .tokenization_tapas import TapasTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tapas import (
												    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TapasForMaskedLM,
												    TapasForQuestionAnswering,
												    TapasForSequenceClassification,
												    TapasModel,
												    TapasPreTrainedModel,
												    load_tf_weights_in_tapas,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_tapas import (
												    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFTapasForMaskedLM,
												    TFTapasForQuestionAnswering,
												    TFTapasForSequenceClassification,
												    TFTapasModel,
												    TFTapasPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase__						:						Dict		  =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



