






'''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				={
    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
    "gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}

def 				a						( _UpperCAmelCase			)					->					Optional[Any]:






					"""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 				a						( _UpperCAmelCase       ,	_UpperCAmelCase			)					->					str:






					"""simple docstring"""







					if args.student_type == "roberta":
										a_						= False
					elif args.student_type == "gpt2":
										a_						= False

def 				a						( _UpperCAmelCase       ,	_UpperCAmelCase			)					->					Any:






					"""simple docstring"""







					if args.student_type == "roberta":
										a_						= False

def 				a						( )					->					Tuple:






					"""simple docstring"""







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

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

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

					parser.add_argument('--temperature'       ,	default=2.0       ,	type=UpperCamelCase__       ,	help='Temperature for the softmax temperature.'			)
					parser.add_argument(
					    '--alpha_ce'       ,	default=0.5       ,	type=UpperCamelCase__       ,	help='Linear weight for the distillation loss. Must be >=0.'			)
					parser.add_argument(
					    '--alpha_mlm'       ,	default=0.0       ,	type=UpperCamelCase__       ,	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=UpperCamelCase__       ,	help='Linear weight for the CLM loss. Must be >=0.'			)
					parser.add_argument('--alpha_mse'       ,	default=0.0       ,	type=UpperCamelCase__       ,	help='Linear weight of the MSE loss. Must be >=0.'			)
					parser.add_argument(
					    '--alpha_cos'       ,	default=0.0       ,	type=UpperCamelCase__       ,	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=0.1_5       ,	type=UpperCamelCase__       ,	help='Proportion of tokens for which we need to make a prediction.'       ,	)
					parser.add_argument('--word_mask'       ,	default=0.8       ,	type=UpperCamelCase__       ,	help='Proportion of tokens to mask out.'			)
					parser.add_argument('--word_keep'       ,	default=0.1       ,	type=UpperCamelCase__       ,	help='Proportion of tokens to keep.'			)
					parser.add_argument('--word_rand'       ,	default=0.1       ,	type=UpperCamelCase__       ,	help='Proportion of tokens to randomly replace.'			)
					parser.add_argument(
					    '--mlm_smoothing'       ,	default=0.7       ,	type=UpperCamelCase__       ,	help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'       ,	)
					parser.add_argument('--token_counts'       ,	type=UpperCamelCase__       ,	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=UpperCamelCase__       ,	default=3       ,	help='Number of pass on the whole dataset.'			)
					parser.add_argument('--batch_size'       ,	type=UpperCamelCase__       ,	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=UpperCamelCase__       ,	default=5_0       ,	help='Gradient accumulation for larger training batches.'       ,	)
					parser.add_argument('--warmup_prop'       ,	default=0.0_5       ,	type=UpperCamelCase__       ,	help='Linear warmup proportion.'			)
					parser.add_argument('--weight_decay'       ,	default=0.0       ,	type=UpperCamelCase__       ,	help='Weight decay if we apply some.'			)
					parser.add_argument('--learning_rate'       ,	default=5E-4       ,	type=UpperCamelCase__       ,	help='The initial learning rate for Adam.'			)
					parser.add_argument('--adam_epsilon'       ,	default=1E-6       ,	type=UpperCamelCase__       ,	help='Epsilon for Adam optimizer.'			)
					parser.add_argument('--max_grad_norm'       ,	default=5.0       ,	type=UpperCamelCase__       ,	help='Max gradient norm.'			)
					parser.add_argument('--initializer_range'       ,	default=0.0_2       ,	type=UpperCamelCase__       ,	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=UpperCamelCase__       ,	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=UpperCamelCase__       ,	default=1       ,	help='Number of GPUs in the node.'			)
					parser.add_argument('--local_rank'       ,	type=UpperCamelCase__       ,	default=-1       ,	help='Distributed training - Local rank'			)
					parser.add_argument('--seed'       ,	type=UpperCamelCase__       ,	default=5_6       ,	help='Random seed'			)

					parser.add_argument('--log_interval'       ,	type=UpperCamelCase__       ,	default=5_0_0       ,	help='Tensorboard logging interval.'			)
					parser.add_argument('--checkpoint_interval'       ,	type=UpperCamelCase__       ,	default=4_0_0_0       ,	help='Checkpoint interval.'			)
					a_						= parser.parse_args()
					sanity_checks(UpperCamelCase__			)

					# ARGS #
					init_gpu_params(UpperCamelCase__			)
					set_seed(UpperCamelCase__			)
					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(UpperCamelCase__			)       ,	UpperCamelCase__       ,	indent=4			)
										git_log(args.dump_path			)

					a_				,       a_				,       a_						= MODEL_CLASSES[args.student_type]
					a_				,       a_				,       a_						= MODEL_CLASSES[args.teacher_type]

					# TOKENIZER #
					a_						= teacher_tokenizer_class.from_pretrained(args.teacher_name			)
					a_						= {}
					for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
										a_						= tokenizer.all_special_tokens.index(UpperCamelCase__			)
										a_						= tokenizer.all_special_ids[idx]
					logger.info(F'''Special tokens {special_tok_ids}'''			)
					a_						= special_tok_ids
					a_						= 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:
										a_						= pickle.load(UpperCamelCase__			)

					if args.mlm:
										logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)'''			)
										with open(args.token_counts       ,	'rb'			) as fp:
															a_						= pickle.load(UpperCamelCase__			)

										a_						= np.maximum(UpperCamelCase__       ,	1			) ** -args.mlm_smoothing
										for idx in special_tok_ids.values():
															a_						= 0.0  # do not predict special tokens
										a_						= torch.from_numpy(UpperCamelCase__			)
					else:
										a_						= None

					a_						= LmSeqsDataset(params=UpperCamelCase__       ,	data=UpperCamelCase__			)
					logger.info('Data loader created.'			)

					# STUDENT #
					logger.info(F'''Loading student config from {args.student_config}'''			)
					a_						= student_config_class.from_pretrained(args.student_config			)
					a_						= True

					if args.student_pretrained_weights is not None:
										logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}'''			)
										a_						= student_model_class.from_pretrained(args.student_pretrained_weights       ,	config=UpperCamelCase__			)
					else:
										a_						= student_model_class(UpperCamelCase__			)

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

					# TEACHER #
					a_						= teacher_model_class.from_pretrained(args.teacher_name       ,	output_hidden_states=UpperCamelCase__			)
					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(UpperCamelCase__       ,	UpperCamelCase__			)
					if args.freeze_token_type_embds:
										freeze_token_type_embeddings(UpperCamelCase__       ,	UpperCamelCase__			)

					# 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()
					a_						= Distiller(
					    params=UpperCamelCase__       ,	dataset=UpperCamelCase__       ,	token_probs=UpperCamelCase__       ,	student=UpperCamelCase__       ,	teacher=UpperCamelCase__			)
					distiller.train()
					logger.info('Let\'s go get some drinks.'			)


if __name__ == "__main__":
						main()








import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   model.config

		SCREAMING_SNAKE_CASE__								=   DonutSwinConfig(
		    image_size=original_config.input_size       , patch_size=4       , depths=original_config.encoder_layer       , num_heads=[4, 8, 16, 32]       , window_size=original_config.window_size       , embed_dim=128       , )
		SCREAMING_SNAKE_CASE__								=   MBartConfig(
		    is_decoder=UpperCamelCase__       , is_encoder_decoder=UpperCamelCase__       , add_cross_attention=UpperCamelCase__       , decoder_layers=original_config.decoder_layer       , max_position_embeddings=original_config.max_position_embeddings       , vocab_size=len(
		        model.decoder.tokenizer     )       , scale_embedding=UpperCamelCase__       , add_final_layer_norm=UpperCamelCase__       , )

		return encoder_config, decoder_config






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]     ):
		if "encoder.model" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""encoder.model"""       , """encoder"""     )
		if "decoder.model" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""decoder.model"""       , """decoder"""     )
		if "patch_embed.proj" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""patch_embed.proj"""       , """embeddings.patch_embeddings.projection"""     )
		if "patch_embed.norm" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""patch_embed.norm"""       , """embeddings.norm"""     )
		if name.startswith("""encoder"""     ):
				if "layers" in name:
						SCREAMING_SNAKE_CASE__								=   """encoder.""" + name
				if "attn.proj" in name:
						SCREAMING_SNAKE_CASE__								=   name.replace("""attn.proj"""       , """attention.output.dense"""     )
				if "attn" in name and "mask" not in name:
						SCREAMING_SNAKE_CASE__								=   name.replace("""attn"""       , """attention.self"""     )
				if "norm1" in name:
						SCREAMING_SNAKE_CASE__								=   name.replace("""norm1"""       , """layernorm_before"""     )
				if "norm2" in name:
						SCREAMING_SNAKE_CASE__								=   name.replace("""norm2"""       , """layernorm_after"""     )
				if "mlp.fc1" in name:
						SCREAMING_SNAKE_CASE__								=   name.replace("""mlp.fc1"""       , """intermediate.dense"""     )
				if "mlp.fc2" in name:
						SCREAMING_SNAKE_CASE__								=   name.replace("""mlp.fc2"""       , """output.dense"""     )

				if name == "encoder.norm.weight":
						SCREAMING_SNAKE_CASE__								=   """encoder.layernorm.weight"""
				if name == "encoder.norm.bias":
						SCREAMING_SNAKE_CASE__								=   """encoder.layernorm.bias"""

		return name






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				Optional[int]     ):
		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE__								=   orig_state_dict.pop(UpperCamelCase__     )

				if "qkv" in key:
						SCREAMING_SNAKE_CASE__								=   key.split("""."""     )
						SCREAMING_SNAKE_CASE__								=   int(key_split[3]     )
						SCREAMING_SNAKE_CASE__								=   int(key_split[5]     )
						SCREAMING_SNAKE_CASE__								=   model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								SCREAMING_SNAKE_CASE__								=   val[:dim, :]
								SCREAMING_SNAKE_CASE__								=   val[dim : dim * 2, :]
								SCREAMING_SNAKE_CASE__								=   val[-dim:, :]
						else:
								SCREAMING_SNAKE_CASE__								=   val[:dim]
								SCREAMING_SNAKE_CASE__								=   val[dim : dim * 2]
								SCREAMING_SNAKE_CASE__								=   val[-dim:]
				elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
						# HuggingFace implementation doesn't use attn_mask buffer
						# and model doesn't use final LayerNorms for the encoder
						pass
				else:
						SCREAMING_SNAKE_CASE__								=   val

		return orig_state_dict






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				int=None       , UpperCamelCase__:				str=False     ):
		# load original model
		SCREAMING_SNAKE_CASE__								=   DonutModel.from_pretrained(UpperCamelCase__     ).eval()

		# load HuggingFace model
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_configs(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   DonutSwinModel(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   MBartForCausalLM(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   VisionEncoderDecoderModel(encoder=UpperCamelCase__       , decoder=UpperCamelCase__     )
		model.eval()

		SCREAMING_SNAKE_CASE__								=   original_model.state_dict()
		SCREAMING_SNAKE_CASE__								=   convert_state_dict(UpperCamelCase__       , UpperCamelCase__     )
		model.load_state_dict(UpperCamelCase__     )

		# verify results on scanned document
		SCREAMING_SNAKE_CASE__								=   load_dataset("""hf-internal-testing/example-documents"""     )
		SCREAMING_SNAKE_CASE__								=   dataset["""test"""][0]["""image"""].convert("""RGB"""     )

		SCREAMING_SNAKE_CASE__								=   XLMRobertaTokenizerFast.from_pretrained(UpperCamelCase__       , from_slow=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   DonutImageProcessor(
		    do_align_long_axis=original_model.config.align_long_axis       , size=original_model.config.input_size[::-1]     )
		SCREAMING_SNAKE_CASE__								=   DonutProcessor(UpperCamelCase__       , UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   processor(UpperCamelCase__       , return_tensors="""pt"""     ).pixel_values

		if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
				SCREAMING_SNAKE_CASE__								=   """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
				SCREAMING_SNAKE_CASE__								=   """When is the coffee break?"""
				SCREAMING_SNAKE_CASE__								=   task_prompt.replace("""{user_input}"""       , UpperCamelCase__     )
		elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
				SCREAMING_SNAKE_CASE__								=   """<s_rvlcdip>"""
		elif model_name in [
		    "naver-clova-ix/donut-base-finetuned-cord-v1",
		    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
		]:
				SCREAMING_SNAKE_CASE__								=   """<s_cord>"""
		elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
				SCREAMING_SNAKE_CASE__								=   """s_cord-v2>"""
		elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
				SCREAMING_SNAKE_CASE__								=   """<s_zhtrainticket>"""
		elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
				# use a random prompt
				SCREAMING_SNAKE_CASE__								=   """hello world"""
		else:
				raise ValueError("""Model name not supported"""     )
		SCREAMING_SNAKE_CASE__								=   original_model.decoder.tokenizer(UpperCamelCase__       , add_special_tokens=UpperCamelCase__       , return_tensors="""pt"""     )[
		    """input_ids"""
		]

		SCREAMING_SNAKE_CASE__								=   original_model.encoder.model.patch_embed(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   model.encoder.embeddings(UpperCamelCase__     )
		assert torch.allclose(UpperCamelCase__       , UpperCamelCase__       , atol=1e-3     )

		# verify encoder hidden states
		SCREAMING_SNAKE_CASE__								=   original_model.encoder(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   model.encoder(UpperCamelCase__     ).last_hidden_state
		assert torch.allclose(UpperCamelCase__       , UpperCamelCase__       , atol=1e-2     )

		# verify decoder hidden states
		SCREAMING_SNAKE_CASE__								=   original_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     ).logits
		SCREAMING_SNAKE_CASE__								=   model(UpperCamelCase__       , decoder_input_ids=UpperCamelCase__     ).logits
		assert torch.allclose(UpperCamelCase__       , UpperCamelCase__       , atol=1e-3     )
		print("""Looks ok!"""     )

		if pytorch_dump_folder_path is not None:
				print(f'''Saving model and processor to {pytorch_dump_folder_path}'''     )
				model.save_pretrained(UpperCamelCase__     )
				processor.save_pretrained(UpperCamelCase__     )

		if push_to_hub:
				model.push_to_hub("""nielsr/""" + model_name.split("""/"""     )[-1]       , commit_message="""Update model"""     )
				processor.push_to_hub("""nielsr/""" + model_name.split("""/"""     )[-1]       , commit_message="""Update model"""     )


if __name__ == "__main__":
	_lowerCamelCase 					=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='naver-clova-ix/donut-base-finetuned-docvqa',
	    required=False,
	    type=str,
	    help='Name of the original model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default=None,
	    required=False,
	    type=str,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	    help='Whether or not to push the converted model and processor to the 🤗 hub.',
	)

	_lowerCamelCase 					=      parser.parse_args()
	convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)