






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





def 					lowercase_ (    __snake_case     :							Tuple	,	__snake_case     :							Tuple=False      )     -> str:





				'''simple docstring'''


				snake_case__      :Any         =   []
				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__      :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 					lowercase_ (    __snake_case     :							List[Any]	,	__snake_case     :							List[str]	,	__snake_case     :							Optional[Any]=False      )     -> Any:





				'''simple docstring'''


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





def 					lowercase_ (    __snake_case     :							Dict	,	__snake_case     :							int	,	__snake_case     :							Tuple      )     -> Tuple:





				'''simple docstring'''


				snake_case__      :Optional[int]         =   dct.pop(__snake_case      )
				snake_case__      :Dict         =   val





def 					lowercase_ (    )     -> int:





				'''simple docstring'''


				snake_case__      :Tuple         =   "http://images.cocodataset.org/val2017/000000039769.jpg"
				snake_case__      :Optional[int]         =   Image.open(requests.get(__snake_case	,	stream=__snake_case      ).raw      )
				return im





@torch.no_grad()
def 					lowercase_ (    __snake_case     :							Tuple	,	__snake_case     :							List[Any]      )     -> Any:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   DeiTConfig()
				# all deit models have fine-tuned heads
				snake_case__      :Tuple         =   False
				# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
				snake_case__      :Optional[int]         =   10_00
				snake_case__      :Tuple         =   "huggingface/label-files"
				snake_case__      :Union[str, Any]         =   "imagenet-1k-id2label.json"
				snake_case__      :Dict         =   json.load(open(hf_hub_download(__snake_case	,	__snake_case	,	repo_type="dataset"      )	,	"r"      )      )
				snake_case__      :int         =   {int(__snake_case      ): v for k, v in idalabel.items()}
				snake_case__      :List[str]         =   idalabel
				snake_case__      :Union[str, Any]         =   {v: k for k, v in idalabel.items()}
				snake_case__      :int         =   int(deit_name[-6:-4]      )
				snake_case__      :str         =   int(deit_name[-3:]      )
				# size of the architecture
				if deit_name[9:].startswith("tiny"      ):
								snake_case__      :Union[str, Any]         =   1_92
								snake_case__      :Optional[Any]         =   7_68
								snake_case__      :Tuple         =   12
								snake_case__      :int         =   3
				elif deit_name[9:].startswith("small"      ):
								snake_case__      :Any         =   3_84
								snake_case__      :Union[str, Any]         =   15_36
								snake_case__      :List[Any]         =   12
								snake_case__      :Optional[Any]         =   6
				if deit_name[9:].startswith("base"      ):
								pass
				elif deit_name[4:].startswith("large"      ):
								snake_case__      :Tuple         =   10_24
								snake_case__      :int         =   40_96
								snake_case__      :List[Any]         =   24
								snake_case__      :Any         =   16

				# load original model from timm
				snake_case__      :Optional[Any]         =   timm.create_model(__snake_case	,	pretrained=__snake_case      )
				timm_model.eval()

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

				# load HuggingFace model
				snake_case__      :List[Any]         =   DeiTForImageClassificationWithTeacher(__snake_case      ).eval()
				model.load_state_dict(__snake_case      )

				# Check outputs on an image, prepared by DeiTImageProcessor
				snake_case__      :List[str]         =   int(
				    (2_56 / 2_24) * 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__      :str         =   DeiTImageProcessor(size=__snake_case	,	crop_size=config.image_size      )
				snake_case__      :List[Any]         =   image_processor(images=prepare_img()	,	return_tensors="pt"      )
				snake_case__      :str         =   encoding["pixel_values"]
				snake_case__      :List[str]         =   model(__snake_case      )

				snake_case__      :Optional[Any]         =   timm_model(__snake_case      )
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(__snake_case	,	outputs.logits	,	atol=1e-3      )

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


if __name__ == "__main__":
	__UpperCAmelCase							:   Dict							=  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							:   Optional[int]							=  parser.parse_args()
	convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__UpperCAmelCase							:   str							=  logging.get_logger(__name__)

__UpperCAmelCase							:   List[Any]							=  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase							:   List[Any]							=  {
    "vocab_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
    },
    "merges_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
    },
}

__UpperCAmelCase							:   str							=  {"allegro/herbert-base-cased": 5_1_4}
__UpperCAmelCase							:   List[str]							=  {}


class 					_snake_case						(			_A ):
	_A          =   VOCAB_FILES_NAMES
	_A          =   PRETRAINED_VOCAB_FILES_MAP
	_A          =   PRETRAINED_INIT_CONFIGURATION
	_A          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A          =   HerbertTokenizer






	def __init__(		self					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase="<s>"					,UpperCamelCase="<unk>"					,UpperCamelCase="<pad>"					,UpperCamelCase="<mask>"					,UpperCamelCase="</s>"					,**UpperCamelCase					,)						->    Dict:
					super().__init__(
					    UpperCamelCase					,UpperCamelCase					,tokenizer_file=UpperCamelCase					,cls_token=UpperCamelCase					,unk_token=UpperCamelCase					,pad_token=UpperCamelCase					,mask_token=UpperCamelCase					,sep_token=UpperCamelCase					,**UpperCamelCase					,)






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    List[int]:
					snake_case__      :Optional[int]         =   [self.cls_token_id]
					snake_case__      :Any         =   [self.sep_token_id]
					if token_ids_a is None:
									return cls + token_ids_a + sep

					return cls + token_ids_a + sep + token_ids_a + sep






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = False       )						->    List[int]:
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=UpperCamelCase					,token_ids_a=UpperCamelCase					,already_has_special_tokens=UpperCamelCase       )

					if token_ids_a is None:
									return [1] + ([0] * len(UpperCamelCase       )) + [1]
					return [1] + ([0] * len(UpperCamelCase       )) + [1] + ([0] * len(UpperCamelCase       )) + [1]






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    List[int]:
					snake_case__      :Any         =   [self.sep_token_id]
					snake_case__      :Union[str, Any]         =   [self.cls_token_id]

					if token_ids_a is None:
									return len(cls + token_ids_a + sep       ) * [0]
					return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    Tuple[str]:
					snake_case__      :List[str]         =   self._tokenizer.model.save(UpperCamelCase					,name=UpperCamelCase       )
					return tuple(UpperCamelCase       )