


'''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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case											=					logging.get_logger(__name__)


def       a				(      __a			, __a=False       )  ->     Dict:



					'''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[str]							 =			[(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       )  ->     List[str]:



					'''simple docstring'''






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


def       a				(      __a			, __a			, __a       )  ->     Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			dct.pop(lowerCamelCase__       )
					UpperCamelCase__       :Tuple							 =			val


def       a				(      )  ->     Optional[int]:



					'''simple docstring'''






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


@torch.no_grad()
def       a				(      __a			, __a       )  ->     Union[str, Any]:



					'''simple docstring'''






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

					# load original model from timm
					UpperCamelCase__       :Dict							 =			timm.create_model(lowerCamelCase__			, pretrained=lowerCamelCase__       )
					timm_model.eval()

					# load state_dict of original model, remove and rename some keys
					UpperCamelCase__       :List[str]							 =			timm_model.state_dict()
					UpperCamelCase__       :Any							 =			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__       :int							 =			DeiTForImageClassificationWithTeacher(lowerCamelCase__       ).eval()
					model.load_state_dict(lowerCamelCase__       )

					# Check outputs on an image, prepared by DeiTImageProcessor
					UpperCamelCase__       :Any							 =			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__       :Optional[int]							 =			DeiTImageProcessor(size=lowerCamelCase__			, crop_size=config.image_size       )
					UpperCamelCase__       :Union[str, Any]							 =			image_processor(images=prepare_img()			, return_tensors='''pt'''       )
					UpperCamelCase__       :Optional[int]							 =			encoding['''pixel_values''']
					UpperCamelCase__       :str							 =			model(lowerCamelCase__       )

					UpperCamelCase__       :List[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 {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__":
		__snake_case											=					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.'''
		)

		__snake_case											=					parser.parse_args()
		convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






'''simple docstring'''
from math import ceil


def       a				(      __a			, __a       )  ->     Any:



					'''simple docstring'''






					UpperCamelCase__       :str							 =			list(range(0			, __a       )       )

					UpperCamelCase__       :Optional[int]							 =			[item for sublist in list(device_map.values()       ) for item in sublist]

					# Duplicate check
					UpperCamelCase__       :Optional[int]							 =			[]
					for i in device_map_blocks:
										if device_map_blocks.count(__a       ) > 1 and i not in duplicate_blocks:
															duplicate_blocks.append(__a       )
    # Missing blocks
					UpperCamelCase__       :List[str]							 =			[i for i in blocks if i not in device_map_blocks]
					UpperCamelCase__       :Optional[Any]							 =			[i for i in device_map_blocks if i not in blocks]

					if len(__a       ) != 0:
										raise ValueError(
										    '''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'''
										    ''' These attention blocks were specified more than once: ''' + str(__a       )       )
					if len(__a       ) != 0:
										raise ValueError(
										    '''There are attention blocks for this model that are not specified in the device_map. Add these attention '''
										    '''blocks to a device on the device_map: ''' + str(__a       )       )
					if len(__a       ) != 0:
										raise ValueError(
										    '''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
										    + str(__a       )       )







def       a				(      __a			, __a       )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			list(range(__a       )       )
					UpperCamelCase__       :Any							 =			int(ceil(n_layers / len(__a       )       )       )
					UpperCamelCase__       :List[Any]							 =			[layers[i : i + n_blocks] for i in range(0			, __a			, __a       )]

					return dict(zip(__a			, __a       )       )