





from __future__ import annotations

class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     Dict					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     data
			__lowercase											=     None
			__lowercase											=     None





def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    None:  # In Order traversal of the tree
		if tree:
				display(tree.left							)
				print(tree.data							)
				display(tree.right							)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return 1 + max(depth_of_tree(tree.left							)						,   depth_of_tree(tree.right							)							) if tree else 0



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    bool:
		if not tree:
				return True
		if tree.left and tree.right:
				return is_full_binary_tree(tree.left							) and is_full_binary_tree(tree.right							)
		else:
				return not tree.left and not tree.right



def  SCREAMING_SNAKE_CASE				(      )		->    None:  # Main function for testing.
		__lowercase											=     Node(1							)
		__lowercase											=     Node(2							)
		__lowercase											=     Node(3							)
		__lowercase											=     Node(4							)
		__lowercase											=     Node(5							)
		__lowercase											=     Node(6							)
		__lowercase											=     Node(7							)
		__lowercase											=     Node(8							)
		__lowercase											=     Node(9							)

		print(is_full_binary_tree(snake_case							)							)
		print(depth_of_tree(snake_case							)							)
		print('Tree is: '							)
		display(snake_case							)


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				(      snake_case							)		->    Optional[int]:
		__lowercase											=     model.config

		__lowercase											=     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						,   )
		__lowercase											=     MBartConfig(
		    is_decoder=snake_case						,   is_encoder_decoder=snake_case						,   add_cross_attention=snake_case						,   decoder_layers=original_config.decoder_layer						,   max_position_embeddings=original_config.max_position_embeddings						,   vocab_size=len(
		        model.decoder.tokenizer							)						,   scale_embedding=snake_case						,   add_final_layer_norm=snake_case						,   )

		return encoder_config, decoder_config



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		if "encoder.model" in name:
				__lowercase											=     name.replace('encoder.model'						,   'encoder'							)
		if "decoder.model" in name:
				__lowercase											=     name.replace('decoder.model'						,   'decoder'							)
		if "patch_embed.proj" in name:
				__lowercase											=     name.replace('patch_embed.proj'						,   'embeddings.patch_embeddings.projection'							)
		if "patch_embed.norm" in name:
				__lowercase											=     name.replace('patch_embed.norm'						,   'embeddings.norm'							)
		if name.startswith('encoder'							):
				if "layers" in name:
						__lowercase											=     'encoder.' + name
				if "attn.proj" in name:
						__lowercase											=     name.replace('attn.proj'						,   'attention.output.dense'							)
				if "attn" in name and "mask" not in name:
						__lowercase											=     name.replace('attn'						,   'attention.self'							)
				if "norm1" in name:
						__lowercase											=     name.replace('norm1'						,   'layernorm_before'							)
				if "norm2" in name:
						__lowercase											=     name.replace('norm2'						,   'layernorm_after'							)
				if "mlp.fc1" in name:
						__lowercase											=     name.replace('mlp.fc1'						,   'intermediate.dense'							)
				if "mlp.fc2" in name:
						__lowercase											=     name.replace('mlp.fc2'						,   'output.dense'							)

				if name == "encoder.norm.weight":
						__lowercase											=     'encoder.layernorm.weight'
				if name == "encoder.norm.bias":
						__lowercase											=     'encoder.layernorm.bias'

		return name



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    List[Any]:
		for key in orig_state_dict.copy().keys():
				__lowercase											=     orig_state_dict.pop(snake_case							)

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

						if "weight" in key:
								__lowercase											=     val[:dim, :]
								__lowercase											=     val[dim : dim * 2, :]
								__lowercase											=     val[-dim:, :]
						else:
								__lowercase											=     val[:dim]
								__lowercase											=     val[dim : dim * 2]
								__lowercase											=     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:
						__lowercase											=     val

		return orig_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=None						,   snake_case=False							)		->    Any:
		# load original model
		__lowercase											=     DonutModel.from_pretrained(snake_case							).eval()

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

		__lowercase											=     original_model.state_dict()
		__lowercase											=     convert_state_dict(snake_case						,   snake_case							)
		model.load_state_dict(snake_case							)

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

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

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

		__lowercase											=     original_model.encoder.model.patch_embed(snake_case							)
		__lowercase						,      __lowercase											=     model.encoder.embeddings(snake_case							)
		assert torch.allclose(snake_case						,   snake_case						,   atol=1E-3							)

		# verify encoder hidden states
		__lowercase											=     original_model.encoder(snake_case							)
		__lowercase											=     model.encoder(snake_case							).last_hidden_state
		assert torch.allclose(snake_case						,   snake_case						,   atol=1E-2							)

		# verify decoder hidden states
		__lowercase											=     original_model(snake_case						,   snake_case						,   snake_case							).logits
		__lowercase											=     model(snake_case						,   decoder_input_ids=snake_case							).logits
		assert torch.allclose(snake_case						,   snake_case						,   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(snake_case							)
				processor.save_pretrained(snake_case							)

		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__":
		SCREAMING_SNAKE_CASE_   :      Dict			=   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.''',
		)

		SCREAMING_SNAKE_CASE_   :      Tuple			=   parser.parse_args()
		convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




