






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_							(							__magic_name__					:  Dict					)		->			str:






	"""simple docstring"""



	UpperCamelCase     :Tuple          =	model.config

	UpperCamelCase     :Optional[int]          =	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	,			)
	UpperCamelCase     :str          =	MBartConfig(
	    is_decoder=__magic_name__	,			is_encoder_decoder=__magic_name__	,			add_cross_attention=__magic_name__	,			decoder_layers=original_config.decoder_layer	,			max_position_embeddings=original_config.max_position_embeddings	,			vocab_size=len(
	        model.decoder.tokenizer					)	,			scale_embedding=__magic_name__	,			add_final_layer_norm=__magic_name__	,			)

	return encoder_config, decoder_config

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]					)		->			int:






	"""simple docstring"""



	if "encoder.model" in name:
		UpperCamelCase     :Optional[int]          =	name.replace("""encoder.model"""	,			"""encoder"""					)
	if "decoder.model" in name:
		UpperCamelCase     :int          =	name.replace("""decoder.model"""	,			"""decoder"""					)
	if "patch_embed.proj" in name:
		UpperCamelCase     :Dict          =	name.replace("""patch_embed.proj"""	,			"""embeddings.patch_embeddings.projection"""					)
	if "patch_embed.norm" in name:
		UpperCamelCase     :Tuple          =	name.replace("""patch_embed.norm"""	,			"""embeddings.norm"""					)
	if name.startswith("""encoder"""					):
		if "layers" in name:
			UpperCamelCase     :Optional[Any]          =	"""encoder.""" + name
		if "attn.proj" in name:
			UpperCamelCase     :Union[str, Any]          =	name.replace("""attn.proj"""	,			"""attention.output.dense"""					)
		if "attn" in name and "mask" not in name:
			UpperCamelCase     :Tuple          =	name.replace("""attn"""	,			"""attention.self"""					)
		if "norm1" in name:
			UpperCamelCase     :Union[str, Any]          =	name.replace("""norm1"""	,			"""layernorm_before"""					)
		if "norm2" in name:
			UpperCamelCase     :str          =	name.replace("""norm2"""	,			"""layernorm_after"""					)
		if "mlp.fc1" in name:
			UpperCamelCase     :Optional[Any]          =	name.replace("""mlp.fc1"""	,			"""intermediate.dense"""					)
		if "mlp.fc2" in name:
			UpperCamelCase     :Optional[int]          =	name.replace("""mlp.fc2"""	,			"""output.dense"""					)

		if name == "encoder.norm.weight":
			UpperCamelCase     :List[Any]          =	"""encoder.layernorm.weight"""
		if name == "encoder.norm.bias":
			UpperCamelCase     :Optional[Any]          =	"""encoder.layernorm.bias"""

	return name

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  Tuple					)		->			Optional[int]:






	"""simple docstring"""



	for key in orig_state_dict.copy().keys():
		UpperCamelCase     :str          =	orig_state_dict.pop(__magic_name__					)

		if "qkv" in key:
			UpperCamelCase     :List[Any]          =	key.split("""."""					)
			UpperCamelCase     :Union[str, Any]          =	int(key_split[3]					)
			UpperCamelCase     :Optional[int]          =	int(key_split[5]					)
			UpperCamelCase     :List[Any]          =	model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				UpperCamelCase     :Union[str, Any]          =	val[:dim, :]
				UpperCamelCase     :Any          =	val[dim : dim * 2, :]
				UpperCamelCase     :int          =	val[-dim:, :]
			else:
				UpperCamelCase     :Optional[int]          =	val[:dim]
				UpperCamelCase     :Union[str, Any]          =	val[dim : dim * 2]
				UpperCamelCase     :Any          =	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:
			UpperCamelCase     :Optional[int]          =	val

	return orig_state_dict

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]	,			__magic_name__					:  str=None	,			__magic_name__					:  Union[str, Any]=False					)		->			Optional[int]:






	"""simple docstring"""



	UpperCamelCase     :Optional[int]          =	DonutModel.from_pretrained(__magic_name__					).eval()

	# load HuggingFace model
	UpperCamelCase     ,      UpperCamelCase     :int          =	get_configs(__magic_name__					)
	UpperCamelCase     :Tuple          =	DonutSwinModel(__magic_name__					)
	UpperCamelCase     :List[Any]          =	MBartForCausalLM(__magic_name__					)
	UpperCamelCase     :str          =	VisionEncoderDecoderModel(encoder=__magic_name__	,			decoder=__magic_name__					)
	model.eval()

	UpperCamelCase     :List[Any]          =	original_model.state_dict()
	UpperCamelCase     :Optional[int]          =	convert_state_dict(__magic_name__	,			__magic_name__					)
	model.load_state_dict(__magic_name__					)

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

	UpperCamelCase     :Optional[Any]          =	XLMRobertaTokenizerFast.from_pretrained(__magic_name__	,			from_slow=__magic_name__					)
	UpperCamelCase     :Optional[int]          =	DonutImageProcessor(
	    do_align_long_axis=original_model.config.align_long_axis	,			size=original_model.config.input_size[::-1]					)
	UpperCamelCase     :Dict          =	DonutProcessor(__magic_name__	,			__magic_name__					)
	UpperCamelCase     :List[str]          =	processor(__magic_name__	,			return_tensors="""pt"""					).pixel_values

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

	UpperCamelCase     :Tuple          =	original_model.encoder.model.patch_embed(__magic_name__					)
	UpperCamelCase     ,      UpperCamelCase     :Dict          =	model.encoder.embeddings(__magic_name__					)
	assert torch.allclose(__magic_name__	,			__magic_name__	,			atol=1E-3					)

	# verify encoder hidden states
	UpperCamelCase     :List[str]          =	original_model.encoder(__magic_name__					)
	UpperCamelCase     :str          =	model.encoder(__magic_name__					).last_hidden_state
	assert torch.allclose(__magic_name__	,			__magic_name__	,			atol=1E-2					)

	# verify decoder hidden states
	UpperCamelCase     :str          =	original_model(__magic_name__	,			__magic_name__	,			__magic_name__					).logits
	UpperCamelCase     :List[str]          =	model(__magic_name__	,			decoder_input_ids=__magic_name__					).logits
	assert torch.allclose(__magic_name__	,			__magic_name__	,			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(__magic_name__					)
		processor.save_pretrained(__magic_name__					)

	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__":
	UpperCAmelCase_ :						int      =      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.''',
	)

	UpperCAmelCase_ :						List[Any]      =      parser.parse_args()
	convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCAmelCase_ :						Union[str, Any]      =      16
UpperCAmelCase_ :						int      =      32

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Accelerator	,			__magic_name__					:  int = 16	,			__magic_name__					:  str = "bert-base-cased"					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	AutoTokenizer.from_pretrained(__magic_name__					)
	UpperCamelCase     :Union[str, Any]          =	load_dataset("""glue"""	,			"""mrpc"""					)

	def tokenize_function(__magic_name__					:  Tuple					):
		# max_length=None => use the model max length (it's actually the default)
		UpperCamelCase     :List[Any]          =	tokenizer(examples["""sentence1"""]	,			examples["""sentence2"""]	,			truncation=__magic_name__	,			max_length=__magic_name__					)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	UpperCamelCase     :List[Any]          =	datasets.map(
	    __magic_name__	,			batched=__magic_name__	,			remove_columns=["""idx""", """sentence1""", """sentence2"""]	,			load_from_cache_file=__magic_name__					)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	UpperCamelCase     :Optional[Any]          =	tokenized_datasets.rename_column("""label"""	,			"""labels"""					)

	def collate_fn(__magic_name__					:  Any					):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(__magic_name__	,			padding="""max_length"""	,			max_length=128	,			return_tensors="""pt"""					)
		return tokenizer.pad(__magic_name__	,			padding="""longest"""	,			return_tensors="""pt"""					)

	# Instantiate dataloaders.
	UpperCamelCase     :List[str]          =	DataLoader(
	    tokenized_datasets["""train"""]	,			shuffle=__magic_name__	,			collate_fn=__magic_name__	,			batch_size=__magic_name__					)
	UpperCamelCase     :List[Any]          =	DataLoader(
	    tokenized_datasets["""validation"""]	,			shuffle=__magic_name__	,			collate_fn=__magic_name__	,			batch_size=__magic_name__					)

	return train_dataloader, eval_dataloader

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Optional[Any]					)		->			List[Any]:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	UpperCamelCase     :Union[str, Any]          =	config["""lr"""]
	UpperCamelCase     :List[str]          =	int(config["""num_epochs"""]					)
	UpperCamelCase     :str          =	int(config["""seed"""]					)
	UpperCamelCase     :Dict          =	int(config["""batch_size"""]					)
	UpperCamelCase     :Union[str, Any]          =	args.model_name_or_path

	set_seed(__magic_name__					)
	UpperCamelCase     ,      UpperCamelCase     :Dict          =	get_dataloaders(__magic_name__	,			__magic_name__	,			__magic_name__					)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCamelCase     :List[str]          =	AutoModelForSequenceClassification.from_pretrained(__magic_name__	,			return_dict=__magic_name__					)

	# Instantiate optimizer
	UpperCamelCase     :Union[str, Any]          =	(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	UpperCamelCase     :Optional[Any]          =	optimizer_cls(params=model.parameters()	,			lr=__magic_name__					)

	if accelerator.state.deepspeed_plugin is not None:
		UpperCamelCase     :Any          =	accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		UpperCamelCase     :Any          =	1
	UpperCamelCase     :Dict          =	(len(__magic_name__					) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		UpperCamelCase     :List[Any]          =	get_linear_schedule_with_warmup(
		    optimizer=__magic_name__	,			num_warmup_steps=0	,			num_training_steps=__magic_name__	,			)
	else:
		UpperCamelCase     :Any          =	DummyScheduler(__magic_name__	,			total_num_steps=__magic_name__	,			warmup_num_steps=0					)

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :str          =	accelerator.prepare(
	    __magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					)

	# We need to keep track of how many total steps we have iterated over
	UpperCamelCase     :int          =	0
	# We also need to keep track of the stating epoch so files are named properly
	UpperCamelCase     :Tuple          =	0

	# Now we train the model
	UpperCamelCase     :Any          =	evaluate.load("""glue"""	,			"""mrpc"""					)
	UpperCamelCase     :Tuple          =	0
	UpperCamelCase     :List[Any]          =	{}
	for epoch in range(__magic_name__	,			__magic_name__					):
		model.train()
		for step, batch in enumerate(__magic_name__					):
			UpperCamelCase     :List[str]          =	model(**__magic_name__					)
			UpperCamelCase     :Dict          =	outputs.loss
			UpperCamelCase     :Optional[int]          =	loss / gradient_accumulation_steps
			accelerator.backward(__magic_name__					)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

		model.eval()
		UpperCamelCase     :str          =	0
		for step, batch in enumerate(__magic_name__					):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device					)
			with torch.no_grad():
				UpperCamelCase     :Optional[int]          =	model(**__magic_name__					)
			UpperCamelCase     :List[Any]          =	outputs.logits.argmax(dim=-1					)
			# It is slightly faster to call this once, than multiple times
			UpperCamelCase     ,      UpperCamelCase     :Optional[int]          =	accelerator.gather(
			    (predictions, batch["""labels"""])					)  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(__magic_name__					) - 1:
					UpperCamelCase     :Dict          =	predictions[: len(eval_dataloader.dataset					) - samples_seen]
					UpperCamelCase     :List[str]          =	references[: len(eval_dataloader.dataset					) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=__magic_name__	,			references=__magic_name__	,			)

		UpperCamelCase     :List[str]          =	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""	,			__magic_name__					)
		UpperCamelCase     :Dict          =	eval_metric["""accuracy"""]

		if best_performance < eval_metric["accuracy"]:
			UpperCamelCase     :str          =	eval_metric["""accuracy"""]

	if args.performance_lower_bound is not None:
		assert (
		    args.performance_lower_bound <= best_performance
		), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir	,			"""all_results.json"""					)	,			"""w"""					) as f:
			json.dump(__magic_name__	,			__magic_name__					)

def    SCREAMING_SNAKE_CASE_							(							)		->			Tuple:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""					)
	parser.add_argument(
	    """--model_name_or_path"""	,			type=__magic_name__	,			default="""bert-base-cased"""	,			help="""Path to pretrained model or model identifier from huggingface.co/models."""	,			required=__magic_name__	,			)
	parser.add_argument(
	    """--output_dir"""	,			type=__magic_name__	,			default="""."""	,			help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""	,			)
	parser.add_argument(
	    """--performance_lower_bound"""	,			type=__magic_name__	,			default=__magic_name__	,			help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""	,			)
	parser.add_argument(
	    """--num_epochs"""	,			type=__magic_name__	,			default=3	,			help="""Number of train epochs."""	,			)
	UpperCamelCase     :str          =	parser.parse_args()
	UpperCamelCase     :Any          =	{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
	training_function(__magic_name__	,			__magic_name__					)


if __name__ == "__main__":
	main()
