






"""simple docstring"""



import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


_lowerCAmelCase	:	Union[str, Any]				=      {
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]				)->		int:






     '''simple docstring'''



     assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
     assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
     if args.mlm:
          assert os.path.isfile(args.token_counts				)
          assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
     else:
          assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

     assert args.teacher_type == args.student_type or (
         args.student_type == "distilbert" and args.teacher_type == "bert"
     )
     assert os.path.isfile(args.student_config				)
     if args.student_pretrained_weights is not None:
          assert os.path.isfile(args.student_pretrained_weights				)

     if args.freeze_token_type_embds:
          assert args.student_type in ["roberta"]

     assert args.alpha_ce >= 0.0
     assert args.alpha_mlm >= 0.0
     assert args.alpha_clm >= 0.0
     assert args.alpha_mse >= 0.0
     assert args.alpha_cos >= 0.0
     assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]			,					snake_case       : Tuple				)->		Optional[int]:






     '''simple docstring'''



     if args.student_type == "roberta":
          UpperCAmelCase__						:    str							=							False
     elif args.student_type == "gpt2":
          UpperCAmelCase__						:    List[Any]							=							False


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]			,					snake_case       : Optional[Any]				)->		Dict:






     '''simple docstring'''



     if args.student_type == "roberta":
          UpperCAmelCase__						:    str							=							False


def 	SCREAMING_SNAKE_CASE__       (  )->		Any:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[Any]							=							argparse.ArgumentParser(description="Training"				)
     parser.add_argument("--force"			,					action="store_true"			,					help="Overwrite dump_path if it already exists."				)

     parser.add_argument(
         "--dump_path"			,					type=snake_case			,					required=snake_case			,					help="The output directory (log, checkpoints, parameters, etc.)"				)
     parser.add_argument(
         "--data_file"			,					type=snake_case			,					required=snake_case			,					help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."			,					)

     parser.add_argument(
         "--student_type"			,					type=snake_case			,					choices=["distilbert", "roberta", "gpt2"]			,					required=snake_case			,					help="The student type (DistilBERT, RoBERTa)."			,					)
     parser.add_argument("--student_config"			,					type=snake_case			,					required=snake_case			,					help="Path to the student configuration."				)
     parser.add_argument(
         "--student_pretrained_weights"			,					default=snake_case			,					type=snake_case			,					help="Load student initialization checkpoint."				)

     parser.add_argument(
         "--teacher_type"			,					choices=["bert", "roberta", "gpt2"]			,					required=snake_case			,					help="Teacher type (BERT, RoBERTa)."				)
     parser.add_argument("--teacher_name"			,					type=snake_case			,					required=snake_case			,					help="The teacher model."				)

     parser.add_argument("--temperature"			,					default=2.0			,					type=snake_case			,					help="Temperature for the softmax temperature."				)
     parser.add_argument(
         "--alpha_ce"			,					default=0.5			,					type=snake_case			,					help="Linear weight for the distillation loss. Must be >=0."				)
     parser.add_argument(
         "--alpha_mlm"			,					default=0.0			,					type=snake_case			,					help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."			,					)
     parser.add_argument("--alpha_clm"			,					default=0.5			,					type=snake_case			,					help="Linear weight for the CLM loss. Must be >=0."				)
     parser.add_argument("--alpha_mse"			,					default=0.0			,					type=snake_case			,					help="Linear weight of the MSE loss. Must be >=0."				)
     parser.add_argument(
         "--alpha_cos"			,					default=0.0			,					type=snake_case			,					help="Linear weight of the cosine embedding loss. Must be >=0."				)

     parser.add_argument(
         "--mlm"			,					action="store_true"			,					help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."				)
     parser.add_argument(
         "--mlm_mask_prop"			,					default=0.15			,					type=snake_case			,					help="Proportion of tokens for which we need to make a prediction."			,					)
     parser.add_argument("--word_mask"			,					default=0.8			,					type=snake_case			,					help="Proportion of tokens to mask out."				)
     parser.add_argument("--word_keep"			,					default=0.1			,					type=snake_case			,					help="Proportion of tokens to keep."				)
     parser.add_argument("--word_rand"			,					default=0.1			,					type=snake_case			,					help="Proportion of tokens to randomly replace."				)
     parser.add_argument(
         "--mlm_smoothing"			,					default=0.7			,					type=snake_case			,					help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."			,					)
     parser.add_argument("--token_counts"			,					type=snake_case			,					help="The token counts in the data_file for MLM."				)

     parser.add_argument(
         "--restrict_ce_to_mask"			,					action="store_true"			,					help="If true, compute the distillation loss only the [MLM] prediction distribution."			,					)
     parser.add_argument(
         "--freeze_pos_embs"			,					action="store_true"			,					help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."			,					)
     parser.add_argument(
         "--freeze_token_type_embds"			,					action="store_true"			,					help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."			,					)

     parser.add_argument("--n_epoch"			,					type=snake_case			,					default=3			,					help="Number of pass on the whole dataset."				)
     parser.add_argument("--batch_size"			,					type=snake_case			,					default=5			,					help="Batch size (for each process)."				)
     parser.add_argument(
         "--group_by_size"			,					action="store_false"			,					help="If true, group sequences that have similar length into the same batch. Default is true."			,					)

     parser.add_argument(
         "--gradient_accumulation_steps"			,					type=snake_case			,					default=50			,					help="Gradient accumulation for larger training batches."			,					)
     parser.add_argument("--warmup_prop"			,					default=0.05			,					type=snake_case			,					help="Linear warmup proportion."				)
     parser.add_argument("--weight_decay"			,					default=0.0			,					type=snake_case			,					help="Weight decay if we apply some."				)
     parser.add_argument("--learning_rate"			,					default=5E-4			,					type=snake_case			,					help="The initial learning rate for Adam."				)
     parser.add_argument("--adam_epsilon"			,					default=1E-6			,					type=snake_case			,					help="Epsilon for Adam optimizer."				)
     parser.add_argument("--max_grad_norm"			,					default=5.0			,					type=snake_case			,					help="Max gradient norm."				)
     parser.add_argument("--initializer_range"			,					default=0.02			,					type=snake_case			,					help="Random initialization range."				)

     parser.add_argument(
         "--fp16"			,					action="store_true"			,					help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"			,					)
     parser.add_argument(
         "--fp16_opt_level"			,					type=snake_case			,					default="O1"			,					help=(
             "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
             "See details at https://nvidia.github.io/apex/amp.html"
         )			,					)
     parser.add_argument("--n_gpu"			,					type=snake_case			,					default=1			,					help="Number of GPUs in the node."				)
     parser.add_argument("--local_rank"			,					type=snake_case			,					default=-1			,					help="Distributed training - Local rank"				)
     parser.add_argument("--seed"			,					type=snake_case			,					default=56			,					help="Random seed"				)

     parser.add_argument("--log_interval"			,					type=snake_case			,					default=500			,					help="Tensorboard logging interval."				)
     parser.add_argument("--checkpoint_interval"			,					type=snake_case			,					default=4000			,					help="Checkpoint interval."				)
     UpperCAmelCase__						:    Optional[int]							=							parser.parse_args()
     sanity_checks(snake_case				)

     # ARGS #
     init_gpu_params(snake_case				)
     set_seed(snake_case				)
     if args.is_master:
          if os.path.exists(args.dump_path				):
               if not args.force:
                    raise ValueError(
                        f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
                        " itUse `--force` if you want to overwrite it"				)
               else:
                    shutil.rmtree(args.dump_path				)

          if not os.path.exists(args.dump_path				):
               os.makedirs(args.dump_path				)
          logger.info(f'Experiment will be dumped and logged in {args.dump_path}'				)

          # SAVE PARAMS #
          logger.info(f'Param: {args}'				)
          with open(os.path.join(args.dump_path			,					"parameters.json"				)			,					"w"				) as f:
               json.dump(vars(snake_case				)			,					snake_case			,					indent=4				)
          git_log(args.dump_path				)

     UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    int							=							MODEL_CLASSES[args.student_type]
     UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    List[str]							=							MODEL_CLASSES[args.teacher_type]

     # TOKENIZER #
     UpperCAmelCase__						:    int							=							teacher_tokenizer_class.from_pretrained(args.teacher_name				)
     UpperCAmelCase__						:    Any							=							{}
     for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
          UpperCAmelCase__						:    Tuple							=							tokenizer.all_special_tokens.index(snake_case				)
          UpperCAmelCase__						:    Union[str, Any]							=							tokenizer.all_special_ids[idx]
     logger.info(f'Special tokens {special_tok_ids}'				)
     UpperCAmelCase__						:    Dict							=							special_tok_ids
     UpperCAmelCase__						:    Any							=							tokenizer.max_model_input_sizes[args.teacher_name]

     # DATA LOADER #
     logger.info(f'Loading data from {args.data_file}'				)
     with open(args.data_file			,					"rb"				) as fp:
          UpperCAmelCase__						:    List[Any]							=							pickle.load(snake_case				)

     if args.mlm:
          logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)'				)
          with open(args.token_counts			,					"rb"				) as fp:
               UpperCAmelCase__						:    Any							=							pickle.load(snake_case				)

          UpperCAmelCase__						:    Union[str, Any]							=							np.maximum(snake_case			,					1				) ** -args.mlm_smoothing
          for idx in special_tok_ids.values():
               UpperCAmelCase__						:    str							=							0.0  # do not predict special tokens
          UpperCAmelCase__						:    Optional[Any]							=							torch.from_numpy(snake_case				)
     else:
          UpperCAmelCase__						:    Optional[int]							=							None

     UpperCAmelCase__						:    int							=							LmSeqsDataset(params=snake_case			,					data=snake_case				)
     logger.info("Data loader created."				)

     # STUDENT #
     logger.info(f'Loading student config from {args.student_config}'				)
     UpperCAmelCase__						:    Optional[int]							=							student_config_class.from_pretrained(args.student_config				)
     UpperCAmelCase__						:    List[Any]							=							True

     if args.student_pretrained_weights is not None:
          logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}'				)
          UpperCAmelCase__						:    str							=							student_model_class.from_pretrained(args.student_pretrained_weights			,					config=snake_case				)
     else:
          UpperCAmelCase__						:    int							=							student_model_class(snake_case				)

     if args.n_gpu > 0:
          student.to(f'cuda:{args.local_rank}'				)
     logger.info("Student loaded."				)

     # TEACHER #
     UpperCAmelCase__						:    Tuple							=							teacher_model_class.from_pretrained(args.teacher_name			,					output_hidden_states=snake_case				)
     if args.n_gpu > 0:
          teacher.to(f'cuda:{args.local_rank}'				)
     logger.info(f'Teacher loaded from {args.teacher_name}.'				)

     # FREEZING #
     if args.freeze_pos_embs:
          freeze_pos_embeddings(snake_case			,					snake_case				)
     if args.freeze_token_type_embds:
          freeze_token_type_embeddings(snake_case			,					snake_case				)

     # SANITY CHECKS #
     assert student.config.vocab_size == teacher.config.vocab_size
     assert student.config.hidden_size == teacher.config.hidden_size
     assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
     if args.mlm:
          assert token_probs.size(0				) == stu_architecture_config.vocab_size

     # DISTILLER #
     torch.cuda.empty_cache()
     UpperCAmelCase__						:    List[str]							=							Distiller(
         params=snake_case			,					dataset=snake_case			,					token_probs=snake_case			,					student=snake_case			,					teacher=snake_case				)
     distiller.train()
     logger.info("Let's go get some drinks."				)


if __name__ == "__main__":
 main()










"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : Any=False			,					snake_case       : Any=False				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							"backbone." if is_semantic else ""

     UpperCAmelCase__						:    Any							=							[]
     for i in range(config.num_hidden_layers				):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'{prefix}blocks.{i}.norm1.weight', f'beit.encoder.layer.{i}.layernorm_before.weight')				)
          rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias')				)
          rename_keys.append(
              (f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight')				)
          rename_keys.append(
              (f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias')				)
          rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight')				)
          rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias')				)
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight')				)
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias')				)
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight')				)
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias')				)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (f'{prefix}cls_token', "beit.embeddings.cls_token"),
             (f'{prefix}patch_embed.proj.weight', "beit.embeddings.patch_embeddings.projection.weight"),
             (f'{prefix}patch_embed.proj.bias', "beit.embeddings.patch_embeddings.projection.bias"),
             (f'{prefix}pos_embed', "beit.embeddings.position_embeddings"),
         ]				)

     if has_lm_head:
          # mask token + layernorm
          rename_keys.extend(
              [
                  ("mask_token", "beit.embeddings.mask_token"),
                  ("norm.weight", "layernorm.weight"),
                  ("norm.bias", "layernorm.bias"),
              ]				)
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ("fc_norm.weight", "beit.pooler.layernorm.weight"),
                  ("fc_norm.bias", "beit.pooler.layernorm.bias"),
                  ("head.weight", "classifier.weight"),
                  ("head.bias", "classifier.bias"),
              ]				)

     return rename_keys


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : List[str]			,					snake_case       : List[Any]=False			,					snake_case       : int=False				)->		Any:






     '''simple docstring'''



     for i in range(config.num_hidden_layers				):
          UpperCAmelCase__						:    Dict							=							"backbone." if is_semantic else ""
          # queries, keys and values
          UpperCAmelCase__						:    List[str]							=							state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'				)
          UpperCAmelCase__						:    int							=							state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'				)
          UpperCAmelCase__						:    Optional[int]							=							state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'				)

          UpperCAmelCase__						:    List[Any]							=							in_proj_weight[
              : config.hidden_size, :
          ]
          UpperCAmelCase__						:    Dict							=							q_bias
          UpperCAmelCase__						:    Optional[int]							=							in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          UpperCAmelCase__						:    Any							=							in_proj_weight[
              -config.hidden_size :, :
          ]
          UpperCAmelCase__						:    int							=							v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          UpperCAmelCase__						:    int							=							state_dict.pop(f'{prefix}blocks.{i}.gamma_1'				)
          UpperCAmelCase__						:    Tuple							=							state_dict.pop(f'{prefix}blocks.{i}.gamma_2'				)

          UpperCAmelCase__						:    Dict							=							gamma_a
          UpperCAmelCase__						:    Tuple							=							gamma_a


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : Dict			,					snake_case       : Tuple				)->		Any:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							dct.pop(snake_case				)
     UpperCAmelCase__						:    Tuple							=							val


def 	SCREAMING_SNAKE_CASE__       (  )->		Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[int]							=							"http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase__						:    List[str]							=							Image.open(requests.get(snake_case			,					stream=snake_case				).raw				)
     return im


@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : str			,					snake_case       : Optional[Any]=False				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[Any]							=							False if "rvlcdip" in checkpoint_url else True
     UpperCAmelCase__						:    int							=							BeitConfig(use_absolute_position_embeddings=snake_case			,					use_mask_token=snake_case				)

     # size of the architecture
     if "large" in checkpoint_url or "dit-l" in checkpoint_url:
          UpperCAmelCase__						:    Optional[int]							=							1024
          UpperCAmelCase__						:    Optional[Any]							=							4096
          UpperCAmelCase__						:    List[str]							=							24
          UpperCAmelCase__						:    List[str]							=							16

     # labels
     if "rvlcdip" in checkpoint_url:
          UpperCAmelCase__						:    List[Any]							=							16
          UpperCAmelCase__						:    Optional[int]							=							"huggingface/label-files"
          UpperCAmelCase__						:    List[Any]							=							"rvlcdip-id2label.json"
          UpperCAmelCase__						:    List[str]							=							json.load(open(hf_hub_download(snake_case			,					snake_case			,					repo_type="dataset"				)			,					"r"				)				)
          UpperCAmelCase__						:    Dict							=							{int(snake_case				): v for k, v in idalabel.items()}
          UpperCAmelCase__						:    List[Any]							=							idalabel
          UpperCAmelCase__						:    Optional[Any]							=							{v: k for k, v in idalabel.items()}

     # load state_dict of original model, remove and rename some keys
     UpperCAmelCase__						:    List[Any]							=							torch.hub.load_state_dict_from_url(snake_case			,					map_location="cpu"				)["model"]

     UpperCAmelCase__						:    str							=							create_rename_keys(snake_case			,					has_lm_head=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			,					has_lm_head=snake_case				)

     # load HuggingFace model
     UpperCAmelCase__						:    int							=							BeitForMaskedImageModeling(snake_case				) if has_lm_head else BeitForImageClassification(snake_case				)
     model.eval()
     model.load_state_dict(snake_case				)

     # Check outputs on an image
     UpperCAmelCase__						:    List[Any]							=							BeitImageProcessor(
         size=config.image_size			,					resample=PILImageResampling.BILINEAR			,					do_center_crop=snake_case				)
     UpperCAmelCase__						:    Any							=							prepare_img()

     UpperCAmelCase__						:    Any							=							image_processor(images=snake_case			,					return_tensors="pt"				)
     UpperCAmelCase__						:    List[str]							=							encoding["pixel_values"]

     UpperCAmelCase__						:    Optional[Any]							=							model(snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							outputs.logits

     # verify logits
     UpperCAmelCase__						:    Union[str, Any]							=							[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
     assert logits.shape == torch.Size(snake_case				), "Shape of logits not as expected"

     Path(snake_case				).mkdir(exist_ok=snake_case				)
     print(f'Saving model 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 push_to_hub:
          if has_lm_head:
               UpperCAmelCase__						:    Any							=							"dit-base" if "base" in checkpoint_url else "dit-large"
          else:
               UpperCAmelCase__						:    int							=							"dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
          image_processor.push_to_hub(
              repo_path_or_name=Path(snake_case			,					snake_case				)			,					organization="nielsr"			,					commit_message="Add image processor"			,					use_temp_dir=snake_case			,					)
          model.push_to_hub(
              repo_path_or_name=Path(snake_case			,					snake_case				)			,					organization="nielsr"			,					commit_message="Add model"			,					use_temp_dir=snake_case			,					)


if __name__ == "__main__":
 _lowerCAmelCase	:	Tuple				=      argparse.ArgumentParser()

 parser.add_argument(
     """--checkpoint_url""",
     default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
     type=str,
     help="""URL to the original PyTorch checkpoint (.pth file).""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
 )
 parser.add_argument(
     """--push_to_hub""",
     action="""store_true""",
 )
 _lowerCAmelCase	:	str				=      parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


