


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()
lowercase__				     =		logging.get_logger(__name__)






def __UpperCamelCase   (			__lowerCamelCase		: List[str]      ,  __lowerCamelCase		: List[str]=False				)      ->     int:







     '''simple docstring'''






     _a						=		[]
     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						=		[(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 __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: Tuple=False				)      ->     str:







     '''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: Union[str, Any]				)      ->     List[Any]:







     '''simple docstring'''






     _a						=		dct.pop(__lowerCamelCase				)
     _a						=		val






def __UpperCamelCase   (			)      ->     Tuple:







     '''simple docstring'''






     _a						=		"http://images.cocodataset.org/val2017/000000039769.jpg"
     _a						=		Image.open(requests.get(__lowerCamelCase      ,  stream=__lowerCamelCase				).raw				)
     return im






@torch.no_grad()
def __UpperCamelCase   (			__lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: Union[str, Any]				)      ->     Optional[int]:







     '''simple docstring'''






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

     # load original model from timm
     _a						=		timm.create_model(__lowerCamelCase      ,  pretrained=__lowerCamelCase				)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     _a						=		timm_model.state_dict()
     _a						=		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
     _a						=		DeiTForImageClassificationWithTeacher(__lowerCamelCase				).eval()
     model.load_state_dict(__lowerCamelCase				)

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

     _a						=		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__":
   lowercase__				     =		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."
   )

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





'''simple docstring'''

import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase__				     =		data_utils.TransfoXLTokenizer
lowercase__				     =		data_utils.TransfoXLCorpus
lowercase__				     =		data_utils
lowercase__				     =		data_utils






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: Tuple      ,  __lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: Tuple				)      ->     Dict:







     '''simple docstring'''






     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(__lowerCamelCase      ,  "rb"				) as fp:
               _a						=		pickle.load(__lowerCamelCase      ,  encoding="latin1"				)
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          _a						=		pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
          print(F"Save vocabulary to {pytorch_vocab_dump_path}"				)
          _a						=		corpus.vocab.__dict__
          torch.save(__lowerCamelCase      ,  __lowerCamelCase				)

          _a						=		corpus.__dict__
          corpus_dict_no_vocab.pop("vocab"      ,  __lowerCamelCase				)
          _a						=		pytorch_dump_folder_path + "/" + CORPUS_NAME
          print(F"Save dataset to {pytorch_dataset_dump_path}"				)
          torch.save(__lowerCamelCase      ,  __lowerCamelCase				)

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          _a						=		os.path.abspath(__lowerCamelCase				)
          _a						=		os.path.abspath(__lowerCamelCase				)

          print(F"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."				)
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               _a						=		TransfoXLConfig()
          else:
               _a						=		TransfoXLConfig.from_json_file(__lowerCamelCase				)
          print(F"Building PyTorch model from configuration: {config}"				)
          _a						=		TransfoXLLMHeadModel(__lowerCamelCase				)

          _a						=		load_tf_weights_in_transfo_xl(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)
          # Save pytorch-model
          _a						=		os.path.join(__lowerCamelCase      ,  __lowerCamelCase				)
          _a						=		os.path.join(__lowerCamelCase      ,  __lowerCamelCase				)
          print(F"Save PyTorch model to {os.path.abspath(__lowerCamelCase				)}"				)
          torch.save(model.state_dict()      ,  __lowerCamelCase				)
          print(F"Save configuration file to {os.path.abspath(__lowerCamelCase				)}"				)
          with open(__lowerCamelCase      ,  "w"      ,  encoding="utf-8"				) as f:
               f.write(config.to_json_string()				)


if __name__ == "__main__":
   lowercase__				     =		argparse.ArgumentParser()
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default=None,
       type=str,
       required=True,
       help="Path to the folder to store the PyTorch model or dataset/vocab.",
   )
   parser.add_argument(
       "--tf_checkpoint_path",
       default="",
       type=str,
       help="An optional path to a TensorFlow checkpoint path to be converted.",
   )
   parser.add_argument(
       "--transfo_xl_config_file",
       default="",
       type=str,
       help=(
           "An optional config json file corresponding to the pre-trained BERT model. \n"
           "This specifies the model architecture."
       ),
   )
   parser.add_argument(
       "--transfo_xl_dataset_file",
       default="",
       type=str,
       help="An optional dataset file to be converted in a vocabulary.",
   )
   lowercase__				     =		parser.parse_args()
   convert_transfo_xl_checkpoint_to_pytorch(
       args.tf_checkpoint_path,
       args.transfo_xl_config_file,
       args.pytorch_dump_folder_path,
       args.transfo_xl_dataset_file,
   )

