


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



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_=False       )->       Dict:
				lowerCAmelCase_       :					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"
								lowerCAmelCase_       :					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       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_=False       )->       Tuple:





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



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       )->       Dict:
				lowerCAmelCase_       :					List[Any]			 = dct.pop(lowerCAmelCase_       )
				lowerCAmelCase_       :					Optional[int]			 = val



def 	lowerCAmelCase       (						)->       Tuple:
				lowerCAmelCase_       :					List[str]			 = "http://images.cocodataset.org/val2017/000000039769.jpg"
				lowerCAmelCase_       :					str			 = Image.open(requests.get(lowerCAmelCase_       ,      stream=lowerCAmelCase_       ).raw       )
				return im



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

				# load original model from timm
				lowerCAmelCase_       :					List[Any]			 = timm.create_model(lowerCAmelCase_       ,      pretrained=lowerCAmelCase_       )
				timm_model.eval()

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

				# Check outputs on an image, prepared by DeiTImageProcessor
				lowerCAmelCase_       :					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
				lowerCAmelCase_       :					List[Any]			 = DeiTImageProcessor(size=lowerCAmelCase_       ,      crop_size=config.image_size       )
				lowerCAmelCase_       :					Dict			 = image_processor(images=prepare_img()       ,      return_tensors='''pt'''       )
				lowerCAmelCase_       :					Any			 = encoding["pixel_values"]
				lowerCAmelCase_       :					Optional[int]			 = model(lowerCAmelCase_       )

				lowerCAmelCase_       :					Optional[Any]			 = 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__":
						_UpperCAmelCase	:     List[Any]   =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	:     Dict   =parser.parse_args()
						convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_rembert import RemBertTokenizer
else:
  __snake_case   			=    None

__snake_case   			=    logging.get_logger(__name__)
__snake_case   			=    {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

__snake_case   			=    {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

__snake_case   			=    {
    """google/rembert""": 256,
}

__snake_case   			=    """▁"""







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       List[str]							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase :       Union[str, Any]							  =  PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase :       str							  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase :       str							  =  RemBertTokenizer







   def __init__(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=False						,	UpperCamelCase__="[CLS]"						,	UpperCamelCase__="[SEP]"						,	UpperCamelCase__="<unk>"						,	UpperCamelCase__="[SEP]"						,	UpperCamelCase__="<pad>"						,	UpperCamelCase__="[CLS]"						,	UpperCamelCase__="[MASK]"						,	**UpperCamelCase__						,	)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Dict      					=    AddedToken(UpperCamelCase__						,	lstrip=UpperCamelCase__						,	rstrip=UpperCamelCase__						) if isinstance(UpperCamelCase__						,	UpperCamelCase__						) else mask_token

       super().__init__(
           UpperCamelCase__						,	tokenizer_file=UpperCamelCase__						,	do_lower_case=UpperCamelCase__						,	remove_space=UpperCamelCase__						,	keep_accents=UpperCamelCase__						,	bos_token=UpperCamelCase__						,	eos_token=UpperCamelCase__						,	unk_token=UpperCamelCase__						,	sep_token=UpperCamelCase__						,	pad_token=UpperCamelCase__						,	cls_token=UpperCamelCase__						,	mask_token=UpperCamelCase__						,	**UpperCamelCase__						,	)

       snake_case    :      int      					=    do_lower_case
       snake_case    :      Union[str, Any]      					=    remove_space
       snake_case    :      Optional[int]      					=    keep_accents
       snake_case    :      Any      					=    vocab_file
       snake_case    :      Any      					=    False if not self.vocab_file else True







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    [self.sep_token_id]
       snake_case    :      Optional[int]      					=    [self.cls_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]:

       '''simple docstring'''




       if already_has_special_tokens:
           if token_ids_a is not None:
               raise ValueError(
                   "You should not supply a second sequence if the provided sequence of "
                   "ids is already formatted with special tokens for the model."						)
           return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
           return [1] + ([0] * len(UpperCamelCase__						)) + [1] + ([0] * len(UpperCamelCase__						)) + [1]
       return [1] + ([0] * len(UpperCamelCase__						)) + [1]







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    [self.sep_token_id]
       snake_case    :      Optional[int]      					=    [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]:

       '''simple docstring'''




       if not os.path.isdir(UpperCamelCase__						):
           logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase__						)						)
           return
       snake_case    :      str      					=    os.path.join(
           UpperCamelCase__						,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

       if os.path.abspath(self.vocab_file						) != os.path.abspath(UpperCamelCase__						):
           copyfile(self.vocab_file						,	UpperCamelCase__						)

       return (out_vocab_file,)





