






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()
__UpperCamelCase					:		Any	  =						logging.get_logger(__name__)
def      a_ (      _A      ,   _A=False  )			->			Union[str, Any]:







				"""simple docstring"""
				snake_case__									=     []
				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"
								snake_case__									=     [(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      a_ (      _A      ,   _A      ,   _A=False  )			->			Dict:







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







				"""simple docstring"""
				snake_case__									=     dct.pop(_A  )
				snake_case__									=     val
def      a_ (      )			->			Tuple:







				"""simple docstring"""
				snake_case__									=     'http://images.cocodataset.org/val2017/000000039769.jpg'
				snake_case__									=     Image.open(requests.get(_A      ,   stream=_A  ).raw  )
				return im
@torch.no_grad()
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				snake_case__									=     DeiTConfig()
				# all deit models have fine-tuned heads
				snake_case__									=     False
				# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
				snake_case__									=     1000
				snake_case__									=     'huggingface/label-files'
				snake_case__									=     'imagenet-1k-id2label.json'
				snake_case__									=     json.load(open(hf_hub_download(_A      ,   _A      ,   repo_type='dataset'  )      ,   'r'  )  )
				snake_case__									=     {int(_A  ): v for k, v in idalabel.items()}
				snake_case__									=     idalabel
				snake_case__									=     {v: k for k, v in idalabel.items()}
				snake_case__									=     int(deit_name[-6:-4]  )
				snake_case__									=     int(deit_name[-3:]  )
				# size of the architecture
				if deit_name[9:].startswith('tiny'  ):
								snake_case__									=     192
								snake_case__									=     768
								snake_case__									=     12
								snake_case__									=     3
				elif deit_name[9:].startswith('small'  ):
								snake_case__									=     384
								snake_case__									=     1536
								snake_case__									=     12
								snake_case__									=     6
				if deit_name[9:].startswith('base'  ):
								pass
				elif deit_name[4:].startswith('large'  ):
								snake_case__									=     1024
								snake_case__									=     4096
								snake_case__									=     24
								snake_case__									=     16

				# load original model from timm
				snake_case__									=     timm.create_model(_A      ,   pretrained=_A  )
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				snake_case__									=     timm_model.state_dict()
				snake_case__									=     create_rename_keys(_A      ,   _A  )
				for src, dest in rename_keys:
								rename_key(_A      ,   _A      ,   _A  )
				read_in_q_k_v(_A      ,   _A      ,   _A  )

				# load HuggingFace model
				snake_case__									=     DeiTForImageClassificationWithTeacher(_A  ).eval()
				model.load_state_dict(_A  )

				# Check outputs on an image, prepared by DeiTImageProcessor
				snake_case__									=     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
				snake_case__									=     DeiTImageProcessor(size=_A      ,   crop_size=config.image_size  )
				snake_case__									=     image_processor(images=prepare_img()      ,   return_tensors='pt'  )
				snake_case__									=     encoding['pixel_values']
				snake_case__									=     model(_A  )

				snake_case__									=     timm_model(_A  )
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(_A      ,   outputs.logits      ,   atol=1e-3  )

				Path(_A  ).mkdir(exist_ok=_A  )
				print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}'''  )
				model.save_pretrained(_A  )
				print(f'''Saving image processor to {pytorch_dump_folder_path}'''  )
				image_processor.save_pretrained(_A  )


if __name__ == "__main__":
							__UpperCamelCase					:		Tuple	  =						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."""
							)

							__UpperCamelCase					:		List[Any]	  =						parser.parse_args()
							convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)












def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				if digit_amount > 0:
								return round(number - int(_A  )      ,   _A  )
				return number - int(_A  )


if __name__ == "__main__":
							print(decimal_isolate(1.5_3, 0))
							print(decimal_isolate(3_5.3_4_5, 1))
							print(decimal_isolate(3_5.3_4_5, 2))
							print(decimal_isolate(3_5.3_4_5, 3))
							print(decimal_isolate(-1_4.7_8_9, 3))
							print(decimal_isolate(0, 2))
							print(decimal_isolate(-1_4.1_2_3, 1))
							print(decimal_isolate(-1_4.1_2_3, 2))
							print(decimal_isolate(-1_4.1_2_3, 3))




