



'''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							:int       = logging.get_logger(__name__)



def      a      (			lowerCamelCase__						,				lowerCamelCase__=False   ):
			'''simple docstring'''





			A_     :						Dict     =					[]
			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_     :						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      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=False   ):
			'''simple docstring'''





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



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Tuple     =					["""head.weight""", """head.bias"""]
			for k in ignore_keys:
						state_dict.pop(lowerCamelCase__						,				lowerCamelCase__   )



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						List[Any]     =					dct.pop(lowerCamelCase__   )
			A_     :						Dict     =					val



def      a      (			):
			'''simple docstring'''





			A_     :						Union[str, Any]     =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			A_     :						Tuple     =					Image.open(requests.get(lowerCamelCase__						,				stream=lowerCamelCase__   ).raw   )
			return im



@torch.no_grad()
def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Dict     =					ViTConfig()
			A_     :						Tuple     =					False
			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			if vit_name[-5:] == "in21k":
						A_     :						str     =					True
						A_     :						Optional[int]     =					int(vit_name[-12:-10]   )
						A_     :						Union[str, Any]     =					int(vit_name[-9:-6]   )
			else:
						A_     :						str     =					10_00
						A_     :						Union[str, Any]     =					"""huggingface/label-files"""
						A_     :						List[str]     =					"""imagenet-1k-id2label.json"""
						A_     :						List[str]     =					json.load(open(hf_hub_download(lowerCamelCase__						,				lowerCamelCase__						,				repo_type="""dataset"""   )						,				"""r"""   )   )
						A_     :						str     =					{int(lowerCamelCase__   ): v for k, v in idalabel.items()}
						A_     :						Any     =					idalabel
						A_     :						Optional[int]     =					{v: k for k, v in idalabel.items()}
						A_     :						Optional[int]     =					int(vit_name[-6:-4]   )
						A_     :						Union[str, Any]     =					int(vit_name[-3:]   )
			# size of the architecture
			if "deit" in vit_name:
						if vit_name[9:].startswith("""tiny"""   ):
									A_     :						Dict     =					1_92
									A_     :						int     =					7_68
									A_     :						str     =					12
									A_     :						Any     =					3
						elif vit_name[9:].startswith("""small"""   ):
									A_     :						List[Any]     =					3_84
									A_     :						Optional[Any]     =					15_36
									A_     :						Tuple     =					12
									A_     :						Dict     =					6
						else:
									pass
			else:
						if vit_name[4:].startswith("""small"""   ):
									A_     :						int     =					7_68
									A_     :						int     =					23_04
									A_     :						str     =					8
									A_     :						Tuple     =					8
						elif vit_name[4:].startswith("""base"""   ):
									pass
						elif vit_name[4:].startswith("""large"""   ):
									A_     :						List[str]     =					10_24
									A_     :						List[str]     =					40_96
									A_     :						Optional[Any]     =					24
									A_     :						Optional[int]     =					16
						elif vit_name[4:].startswith("""huge"""   ):
									A_     :						Dict     =					12_80
									A_     :						Union[str, Any]     =					51_20
									A_     :						Dict     =					32
									A_     :						Union[str, Any]     =					16

    # load original model from timm
			A_     :						Optional[int]     =					timm.create_model(lowerCamelCase__						,				pretrained=lowerCamelCase__   )
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			A_     :						Dict     =					timm_model.state_dict()
			if base_model:
						remove_classification_head_(lowerCamelCase__   )
			A_     :						List[str]     =					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":
						A_     :						Union[str, Any]     =					ViTModel(lowerCamelCase__   ).eval()
			else:
						A_     :						str     =					ViTForImageClassification(lowerCamelCase__   ).eval()
			model.load_state_dict(lowerCamelCase__   )

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

			if base_model:
						A_     :						List[Any]     =					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:
						A_     :						str     =					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							:Any       = 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							:Optional[Any]       = parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




'''simple docstring'''




from typing import Dict

from .base import GenericTensor, Pipeline







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				if tokenize_kwargs is None:
							A_     :						Optional[Any]     =					{}

				if truncation is not None:
							if "truncation" in tokenize_kwargs:
										raise ValueError(
										    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""							)
							A_     :						str     =					truncation

				A_     :						List[str]     =					tokenize_kwargs

				A_     :						Dict     =					{}
				if return_tensors is not None:
							A_     :						List[Any]     =					return_tensors

				return preprocess_params, {}, postprocess_params


	def    _a     (self	,				lowercase	,				**lowercase							):
				A_     :						Optional[int]     =					self.framework
				A_     :						str     =					self.tokenizer(lowercase	,				return_tensors=lowercase	,				**lowercase							)
				return model_inputs


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.model(**lowercase							)
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=False							):
				# [0] is the first available tensor, logits or last_hidden_state.
				if return_tensors:
							return model_outputs[0]
				if self.framework == "pt":
							return model_outputs[0].tolist()
				elif self.framework == "tf":
							return model_outputs[0].numpy().tolist()


	def __call__(self	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)