




"""simple docstring"""





import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

A   =       list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

A   =       [file for file in filepaths if file != file.lower()]
if upper_files:
					print(f'''{len(upper_files)} files contain uppercase characters:''')
					print("""\n""".join(upper_files) + """\n""")

A   =       [file for file in filepaths if """ """ in file]
if space_files:
					print(f'''{len(space_files)} files contain space characters:''')
					print("""\n""".join(space_files) + """\n""")

A   =       [file for file in filepaths if """-""" in file]
if hyphen_files:
					print(f'''{len(hyphen_files)} files contain hyphen characters:''')
					print("""\n""".join(hyphen_files) + """\n""")

A   =       [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(f'''{len(nodir_files)} files are not in a directory:''')
					print("""\n""".join(nodir_files) + """\n""")

A   =       len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)








"""simple docstring"""





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 					_UpperCamelCase    (			UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			__UpperCAmelCase	: str		=					model.config

			__UpperCAmelCase	: 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	: Union[str, Any]		=					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 					_UpperCamelCase    (			UpperCamelCase							)			->			Union[str, Any]:


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

						if name == "encoder.norm.weight":
									__UpperCAmelCase	: List[Any]		=					"encoder.layernorm.weight"
						if name == "encoder.norm.bias":
									__UpperCAmelCase	: Tuple		=					"encoder.layernorm.bias"

			return name

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						__UpperCAmelCase	: Any		=					orig_state_dict.pop(UpperCamelCase							)

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

									if "weight" in key:
												__UpperCAmelCase	: List[Any]		=					val[:dim, :]
												__UpperCAmelCase	: Union[str, Any]		=					val[dim : dim * 2, :]
												__UpperCAmelCase	: Any		=					val[-dim:, :]
									else:
												__UpperCAmelCase	: List[str]		=					val[:dim]
												__UpperCAmelCase	: List[Any]		=					val[dim : dim * 2]
												__UpperCAmelCase	: Tuple		=					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	: Dict		=					val

			return orig_state_dict

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase=None				,       UpperCamelCase=False							)			->			List[str]:


			"""simple docstring"""
			# load original model
			__UpperCAmelCase	: Dict		=					DonutModel.from_pretrained(UpperCamelCase							).eval()

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

			__UpperCAmelCase	: Union[str, Any]		=					original_model.state_dict()
			__UpperCAmelCase	: List[str]		=					convert_state_dict(UpperCamelCase				,       UpperCamelCase							)
			model.load_state_dict(UpperCamelCase							)

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

			__UpperCAmelCase	: Union[str, Any]		=					XLMRobertaTokenizerFast.from_pretrained(UpperCamelCase				,       from_slow=UpperCamelCase							)
			__UpperCAmelCase	: str		=					DonutImageProcessor(
			    do_align_long_axis=original_model.config.align_long_axis				,       size=original_model.config.input_size[::-1]							)
			__UpperCAmelCase	: Union[str, Any]		=					DonutProcessor(UpperCamelCase				,       UpperCamelCase							)
			__UpperCAmelCase	: Union[str, Any]		=					processor(UpperCamelCase				,       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	: Any		=					"When is the coffee break?"
						__UpperCAmelCase	: Dict		=					task_prompt.replace("{user_input}"				,       UpperCamelCase							)
			elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
						__UpperCAmelCase	: int		=					"<s_rvlcdip>"
			elif model_name in [
			    "naver-clova-ix/donut-base-finetuned-cord-v1",
			    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
			]:
						__UpperCAmelCase	: Union[str, Any]		=					"<s_cord>"
			elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
						__UpperCAmelCase	: Dict		=					"s_cord-v2>"
			elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
						__UpperCAmelCase	: str		=					"<s_zhtrainticket>"
			elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
						# use a random prompt
						__UpperCAmelCase	: Tuple		=					"hello world"
			else:
						raise ValueError("Model name not supported"							)
			__UpperCAmelCase	: Dict		=					original_model.decoder.tokenizer(UpperCamelCase				,       add_special_tokens=UpperCamelCase				,       return_tensors="pt"							)[
			    "input_ids"
			]

			__UpperCAmelCase	: int		=					original_model.encoder.model.patch_embed(UpperCamelCase							)
			__UpperCAmelCase	,     __UpperCAmelCase	: Tuple		=					model.encoder.embeddings(UpperCamelCase							)
			assert torch.allclose(UpperCamelCase				,       UpperCamelCase				,       atol=1e-3							)

			# verify encoder hidden states
			__UpperCAmelCase	: int		=					original_model.encoder(UpperCamelCase							)
			__UpperCAmelCase	: Union[str, Any]		=					model.encoder(UpperCamelCase							).last_hidden_state
			assert torch.allclose(UpperCamelCase				,       UpperCamelCase				,       atol=1e-2							)

			# verify decoder hidden states
			__UpperCAmelCase	: int		=					original_model(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							).logits
			__UpperCAmelCase	: List[str]		=					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__":
					A   =       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.""",
					)

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


