





class 	UpperCamelCase_							:


							def __init__(				self     :Optional[int]				,					__A     :str = ""				,					__A     :bool = False		) -> None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   {}

									# A node will be a leaf if the tree contains its word
									SCREAMING_SNAKE_CASE__								=   is_leaf

									SCREAMING_SNAKE_CASE__								=   prefix


							def   _snake_case	(				self     :Optional[int]				,					__A     :str		) -> tuple[str, str, str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   0
									for q, w in zip(self.prefix				,					__A		):
											if q != w:
													break

											x += 1

									return self.prefix[:x], self.prefix[x:], word[x:]


							def   _snake_case	(				self     :Any				,					__A     :list[str]		) -> None:



									"""simple docstring"""



									for word in words:
											self.insert(__A		)


							def   _snake_case	(				self     :Tuple				,					__A     :str		) -> None:



									"""simple docstring"""



									if self.prefix == word:
											SCREAMING_SNAKE_CASE__								=   True

									# Case 2: The node has no edges that have a prefix to the word
									# Solution: We create an edge from the current node to a new one
									# containing the word
									elif word[0] not in self.nodes:
											SCREAMING_SNAKE_CASE__								=   RadixNode(prefix=__A				,					is_leaf=__A		)

									else:
											SCREAMING_SNAKE_CASE__								=   self.nodes[word[0]]
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   incoming_node.match(
											    __A		)

											# Case 3: The node prefix is equal to the matching
											# Solution: We insert remaining word on the next node
											if remaining_prefix == "":
													self.nodes[matching_string[0]].insert(__A		)

											# Case 4: The word is greater equal to the matching
											# Solution: Create a node in between both nodes, change
											# prefixes and add the new node for the remaining word
											else:
													SCREAMING_SNAKE_CASE__								=   remaining_prefix

													SCREAMING_SNAKE_CASE__								=   self.nodes[matching_string[0]]
													SCREAMING_SNAKE_CASE__								=   RadixNode(__A				,					__A		)
													SCREAMING_SNAKE_CASE__								=   aux_node

													if remaining_word == "":
															SCREAMING_SNAKE_CASE__								=   True
													else:
															self.nodes[matching_string[0]].insert(__A		)


							def   _snake_case	(				self     :Optional[int]				,					__A     :str		) -> bool:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.nodes.get(word[0]				,					__A		)
									if not incoming_node:
											return False
									else:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   incoming_node.match(
											    __A		)
											# If there is remaining prefix, the word can't be on the tree
											if remaining_prefix != "":
													return False
											# This applies when the word and the prefix are equal
											elif remaining_word == "":
													return incoming_node.is_leaf
											# We have word remaining so we check the next node
											else:
													return incoming_node.find(__A		)


							def   _snake_case	(				self     :Optional[Any]				,					__A     :str		) -> bool:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.nodes.get(word[0]				,					__A		)
									if not incoming_node:
											return False
									else:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   incoming_node.match(
											    __A		)
											# If there is remaining prefix, the word can't be on the tree
											if remaining_prefix != "":
													return False
											# We have word remaining so we check the next node
											elif remaining_word != "":
													return incoming_node.delete(__A		)
											else:
													# If it is not a leaf, we don't have to delete
													if not incoming_node.is_leaf:
															return False
													else:
															# We delete the nodes if no edges go from it
															if len(incoming_node.nodes		) == 0:
																	del self.nodes[word[0]]
																	# We merge the current node with its only child
																	if len(self.nodes		) == 1 and not self.is_leaf:
																			SCREAMING_SNAKE_CASE__								=   list(self.nodes.values()		)[0]
																			SCREAMING_SNAKE_CASE__								=   merging_node.is_leaf
																			self.prefix += merging_node.prefix
																			SCREAMING_SNAKE_CASE__								=   merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
															elif len(incoming_node.nodes		) > 1:
																	SCREAMING_SNAKE_CASE__								=   False
															# If there is 1 edge, we merge it with its child
															else:
																	SCREAMING_SNAKE_CASE__								=   list(incoming_node.nodes.values()		)[0]
																	SCREAMING_SNAKE_CASE__								=   merging_node.is_leaf
																	incoming_node.prefix += merging_node.prefix
																	SCREAMING_SNAKE_CASE__								=   merging_node.nodes

															return True


							def   _snake_case	(				self     :Optional[Any]				,					__A     :int = 0		) -> None:



									"""simple docstring"""



									if self.prefix != "":
											print("""-""" * height				,					self.prefix				,					"""  (leaf)""" if self.is_leaf else """"""		)

									for value in self.nodes.values():
											value.print_tree(height + 1		)






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   """banana bananas bandana band apple all beast""".split()
		SCREAMING_SNAKE_CASE__								=   RadixNode()
		root.insert_many(UpperCamelCase__     )

		assert all(root.find(UpperCamelCase__     ) for word in words     )
		assert not root.find("""bandanas"""     )
		assert not root.find("""apps"""     )
		root.delete("""all"""     )
		assert not root.find("""all"""     )
		root.delete("""banana"""     )
		assert not root.find("""banana"""     )
		assert root.find("""bananas"""     )

		return True






def 					SCREAMING_SNAKE_CASE__				(							):
		assert test_trie()






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   RadixNode()
		SCREAMING_SNAKE_CASE__								=   """banana bananas bandanas bandana band apple all beast""".split()
		root.insert_many(UpperCamelCase__     )

		print("""Words:"""       , UpperCamelCase__     )
		print("""Tree:"""     )
		root.print_tree()


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)