
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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase  :					Optional[Any]													=       logging.get_logger(__name__)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Dict						,  _lowerCamelCase :			List[str]=False)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Tuple			   =					[]
							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'deit.encoder.layer.{i}.layernorm_before.weight'))
														rename_keys.append((F'blocks.{i}.norm1.bias', F'deit.encoder.layer.{i}.layernorm_before.bias'))
														rename_keys.append((F'blocks.{i}.attn.proj.weight', F'deit.encoder.layer.{i}.attention.output.dense.weight'))
														rename_keys.append((F'blocks.{i}.attn.proj.bias', F'deit.encoder.layer.{i}.attention.output.dense.bias'))
														rename_keys.append((F'blocks.{i}.norm2.weight', F'deit.encoder.layer.{i}.layernorm_after.weight'))
														rename_keys.append((F'blocks.{i}.norm2.bias', F'deit.encoder.layer.{i}.layernorm_after.bias'))
														rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'deit.encoder.layer.{i}.intermediate.dense.weight'))
														rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'deit.encoder.layer.{i}.intermediate.dense.bias'))
														rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'deit.encoder.layer.{i}.output.dense.weight'))
														rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'deit.encoder.layer.{i}.output.dense.bias'))

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ("cls_token", "deit.embeddings.cls_token"),
							        ("dist_token", "deit.embeddings.distillation_token"),
							        ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
							        ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
							        ("pos_embed", "deit.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 "deit" from all keys that start with "deit"
														__UpperCamelCase	:   List[Any]			   =					[(pair[0], pair[1][4:]) if pair[1].startswith("deit") else pair for pair in rename_keys]
							else:
														# layernorm + classification heads
														rename_keys.extend(
														    [
														        ("norm.weight", "deit.layernorm.weight"),
														        ("norm.bias", "deit.layernorm.bias"),
														        ("head.weight", "cls_classifier.weight"),
														        ("head.bias", "cls_classifier.bias"),
														        ("head_dist.weight", "distillation_classifier.weight"),
														        ("head_dist.bias", "distillation_classifier.bias"),
														    ])

							return rename_keys




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Tuple						,  _lowerCamelCase :			str						,  _lowerCamelCase :			Any=False)					->    Dict:






							'''simple docstring'''





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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			int						,  _lowerCamelCase :			int)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[int]			   =					dct.pop(_lowerCamelCase)
							__UpperCamelCase	:   Optional[int]			   =					val




def 	_SCREAMING_SNAKE_CASE					(		)					->    List[str]:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
							__UpperCamelCase	:   str			   =					Image.open(requests.get(_lowerCamelCase						,  stream=_lowerCamelCase).raw)
							return im




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






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					DeiTConfig()
							# all deit models have fine-tuned heads
							__UpperCamelCase	:   Union[str, Any]			   =					False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							__UpperCamelCase	:   Union[str, Any]			   =					1_000
							__UpperCamelCase	:   int			   =					"huggingface/label-files"
							__UpperCamelCase	:   List[Any]			   =					"imagenet-1k-id2label.json"
							__UpperCamelCase	:   Optional[int]			   =					json.load(open(hf_hub_download(_lowerCamelCase						,  _lowerCamelCase						,  repo_type="dataset")						,  "r"))
							__UpperCamelCase	:   List[str]			   =					{int(_lowerCamelCase): v for k, v in idalabel.items()}
							__UpperCamelCase	:   List[Any]			   =					idalabel
							__UpperCamelCase	:   Optional[Any]			   =					{v: k for k, v in idalabel.items()}
							__UpperCamelCase	:   str			   =					int(deit_name[-6:-4])
							__UpperCamelCase	:   List[Any]			   =					int(deit_name[-3:])
							# size of the architecture
							if deit_name[9:].startswith("tiny"):
														__UpperCamelCase	:   List[str]			   =					192
														__UpperCamelCase	:   Optional[Any]			   =					768
														__UpperCamelCase	:   str			   =					12
														__UpperCamelCase	:   Dict			   =					3
							elif deit_name[9:].startswith("small"):
														__UpperCamelCase	:   Dict			   =					384
														__UpperCamelCase	:   Dict			   =					1_536
														__UpperCamelCase	:   int			   =					12
														__UpperCamelCase	:   List[str]			   =					6
							if deit_name[9:].startswith("base"):
														pass
							elif deit_name[4:].startswith("large"):
														__UpperCamelCase	:   Union[str, Any]			   =					1_024
														__UpperCamelCase	:   List[str]			   =					4_096
														__UpperCamelCase	:   str			   =					24
														__UpperCamelCase	:   Tuple			   =					16

							# load original model from timm
							__UpperCamelCase	:   str			   =					timm.create_model(_lowerCamelCase						,  pretrained=_lowerCamelCase)
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							__UpperCamelCase	:   Dict			   =					timm_model.state_dict()
							__UpperCamelCase	:   int			   =					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
							__UpperCamelCase	:   Optional[int]			   =					DeiTForImageClassificationWithTeacher(_lowerCamelCase).eval()
							model.load_state_dict(_lowerCamelCase)

							# Check outputs on an image, prepared by DeiTImageProcessor
							__UpperCamelCase	:   int			   =					int(
							    (256 / 224) * config.image_size)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
							__UpperCamelCase	:   List[Any]			   =					DeiTImageProcessor(size=_lowerCamelCase						,  crop_size=config.image_size)
							__UpperCamelCase	:   Any			   =					image_processor(images=prepare_img()						,  return_tensors="pt")
							__UpperCamelCase	:   Tuple			   =					encoding["pixel_values"]
							__UpperCamelCase	:   Any			   =					model(_lowerCamelCase)

							__UpperCamelCase	:   Tuple			   =					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 {deit_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__":
		lowercase  :					List[str]													=       argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--deit_name',
		    default='vit_deit_base_distilled_patch16_224',
		    type=str,
		    help='Name of the DeiT 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.'
		)

		lowercase  :					Optional[Any]													=       parser.parse_args()
		convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)

def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			int)					->    float:






							'''simple docstring'''





							return base * power(_lowerCamelCase						,  (exponent - 1)) if exponent else 1


if __name__ == "__main__":
		print('Raise base to the power of exponent using recursion...')
		lowercase  :					Optional[int]													=       int(input('Enter the base: ').strip())
		lowercase  :					Tuple													=       int(input('Enter the exponent: ').strip())
		lowercase  :					str													=       power(base, abs(exponent))
		if exponent < 0:  # power() does not properly deal w/ negative exponents
				lowercase  :					Tuple													=       1 / result
		print(f"{base} to the power of {exponent} is {result}")