






"""simple docstring"""



from ..utils import DummyObject, requires_backends






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Tuple     ,      *snake_case :  List[str]     ,      **snake_case :  Tuple							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Any     ,      *snake_case :  Dict     ,      **snake_case :  Optional[Any]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  int     ,      *snake_case :  Tuple     ,      **snake_case :  Optional[Any]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Tuple     ,      *snake_case :  Any     ,      **snake_case :  Tuple							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  List[Any]     ,      *snake_case :  List[str]     ,      **snake_case :  Dict							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  List[str]     ,      *snake_case :  str     ,      **snake_case :  int							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Tuple     ,      *snake_case :  Any     ,      **snake_case :  Any							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Any     ,      *snake_case :  Tuple     ,      **snake_case :  Optional[Any]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  str     ,      *snake_case :  List[str]     ,      **snake_case :  List[Any]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Dict     ,      *snake_case :  str     ,      **snake_case :  Optional[int]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Optional[int]     ,      *snake_case :  Any     ,      **snake_case :  List[str]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  int     ,      *snake_case :  Optional[int]     ,      **snake_case :  List[str]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Dict     ,      *snake_case :  Optional[int]     ,      **snake_case :  List[Any]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Any     ,      *snake_case :  Optional[int]     ,      **snake_case :  str							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Any     ,      *snake_case :  List[str]     ,      **snake_case :  Union[str, Any]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  int     ,      *snake_case :  str     ,      **snake_case :  Optional[Any]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Dict     ,      *snake_case :  Dict     ,      **snake_case :  List[str]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  List[str]     ,      *snake_case :  Any     ,      **snake_case :  Optional[int]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Optional[Any]     ,      *snake_case :  Union[str, Any]     ,      **snake_case :  Union[str, Any]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  str     ,      *snake_case :  Any     ,      **snake_case :  List[Any]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Dict     ,      *snake_case :  int     ,      **snake_case :  Optional[Any]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Any     ,      *snake_case :  Dict     ,      **snake_case :  List[str]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  int     ,      *snake_case :  List[Any]     ,      **snake_case :  Optional[Any]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Dict     ,      *snake_case :  List[str]     ,      **snake_case :  Optional[Any]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Optional[Any]     ,      *snake_case :  Optional[int]     ,      **snake_case :  Tuple							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  List[Any]     ,      *snake_case :  Dict     ,      **snake_case :  Optional[int]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  Dict     ,      *snake_case :  Optional[Any]     ,      **snake_case :  Dict							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  List[str]     ,      *snake_case :  Tuple     ,      **snake_case :  List[str]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  int     ,      *snake_case :  Tuple     ,      **snake_case :  List[str]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  str     ,      *snake_case :  List[Any]     ,      **snake_case :  Optional[Any]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)






class 							__SCREAMING_SNAKE_CASE  (			metaclass=UpperCamelCase						):
							snake_case_       =							['sentencepiece']


							def __init__(     self :  List[Any]     ,      *snake_case :  Optional[int]     ,      **snake_case :  int							):
													'''simple docstring'''





													requires_backends(self     ,      ["""sentencepiece"""]							)














"""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()
A_      =							logging.get_logger(__name__)




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




def        _lowerCAmelCase						(							UpperCAmelCase__    :   List[Any],  UpperCAmelCase__    :   List[Any],  UpperCAmelCase__    :   Union[str, Any] )						->Any:
						A__  :					int											=					dct.pop(UpperCAmelCase__ )
						A__  :					Tuple											=					val




def        _lowerCAmelCase						(							)						->List[Any]:
						A__  :					Optional[int]											=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						A__  :					int											=					Image.open(requests.get(UpperCAmelCase__,  stream=UpperCAmelCase__ ).raw )
						return im




@torch.no_grad()
def        _lowerCAmelCase						(							UpperCAmelCase__    :   Dict,  UpperCAmelCase__    :   Any )						->Tuple:
						A__  :					List[Any]											=					DeiTConfig()
						# all deit models have fine-tuned heads
						A__  :					Tuple											=					False
						# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
						A__  :					str											=					1_0_0_0
						A__  :					List[str]											=					"""huggingface/label-files"""
						A__  :					Dict											=					"""imagenet-1k-id2label.json"""
						A__  :					List[str]											=					json.load(open(hf_hub_download(UpperCAmelCase__,  UpperCAmelCase__,  repo_type="""dataset""" ),  """r""" ) )
						A__  :					Dict											=					{int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
						A__  :					Optional[int]											=					idalabel
						A__  :					Dict											=					{v: k for k, v in idalabel.items()}
						A__  :					List[str]											=					int(deit_name[-6:-4] )
						A__  :					str											=					int(deit_name[-3:] )
						# size of the architecture
						if deit_name[9:].startswith("""tiny""" ):
												A__  :					List[str]											=					1_9_2
												A__  :					int											=					7_6_8
												A__  :					List[Any]											=					1_2
												A__  :					Dict											=					3
						elif deit_name[9:].startswith("""small""" ):
												A__  :					List[Any]											=					3_8_4
												A__  :					List[str]											=					1_5_3_6
												A__  :					Any											=					1_2
												A__  :					Union[str, Any]											=					6
						if deit_name[9:].startswith("""base""" ):
												pass
						elif deit_name[4:].startswith("""large""" ):
												A__  :					int											=					1_0_2_4
												A__  :					str											=					4_0_9_6
												A__  :					Any											=					2_4
												A__  :					int											=					1_6

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

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

						# load HuggingFace model
						A__  :					str											=					DeiTForImageClassificationWithTeacher(UpperCAmelCase__ ).eval()
						model.load_state_dict(UpperCAmelCase__ )

						# Check outputs on an image, prepared by DeiTImageProcessor
						A__  :					int											=					int(
						    (2_5_6 / 2_2_4) * config.image_size )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
						A__  :					Any											=					DeiTImageProcessor(size=UpperCAmelCase__,  crop_size=config.image_size )
						A__  :					Union[str, Any]											=					image_processor(images=prepare_img(),  return_tensors="""pt""" )
						A__  :					Optional[Any]											=					encoding["""pixel_values"""]
						A__  :					Union[str, Any]											=					model(UpperCAmelCase__ )

						A__  :					Union[str, Any]											=					timm_model(UpperCAmelCase__ )
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(UpperCAmelCase__,  outputs.logits,  atol=1e-3 )

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


if __name__ == "__main__":
						A_      =							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.'''
						)

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






