




"""simple docstring"""





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A   =       logging.get_logger(__name__)

A   =       """▁"""

A   =       {
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

A   =       {
    """vocab_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
        ),
    },
    """spm_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
        )
    },
}

A   =       {
    """facebook/s2t-small-librispeech-asr""": 1_024,
}

A   =       ["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

A   =       {"""mustc""": MUSTC_LANGS}



class      a__				(	__magic_name__   ):
			lowercase_    =	VOCAB_FILES_NAMES
			lowercase_    =	PRETRAINED_VOCAB_FILES_MAP
			lowercase_    =	MAX_MODEL_INPUT_SIZES
			lowercase_    =	["input_ids", "attention_mask"]

			lowercase_    =	[]


			def __init__(      self      :    int      ,  UpperCamelCase_      :    Union[str, Any]      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    int="<s>"      ,  UpperCamelCase_      :    Any="</s>"      ,  UpperCamelCase_      :    Any="<pad>"      ,  UpperCamelCase_      :    Tuple="<unk>"      ,  UpperCamelCase_      :    Union[str, Any]=False      ,  UpperCamelCase_      :    List[Any]=False      ,  UpperCamelCase_      :    int=None      ,  UpperCamelCase_      :    List[str]=None      ,  UpperCamelCase_      :    Optional[Dict[str, Any]] = None      ,  **UpperCamelCase_      :    Optional[Any]      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=UpperCamelCase_      ,  eos_token=UpperCamelCase_      ,  unk_token=UpperCamelCase_      ,  pad_token=UpperCamelCase_      ,  do_upper_case=UpperCamelCase_      ,  do_lower_case=UpperCamelCase_      ,  tgt_lang=UpperCamelCase_      ,  lang_codes=UpperCamelCase_      ,  sp_model_kwargs=self.sp_model_kwargs      ,  **UpperCamelCase_      ,  )
						__UpperCAmelCase	: List[str]		=					do_upper_case
						__UpperCAmelCase	: Union[str, Any]		=					do_lower_case

						__UpperCAmelCase	: List[str]		=					load_json(UpperCamelCase_)
						__UpperCAmelCase	: str		=					{v: k for k, v in self.encoder.items()}
						__UpperCAmelCase	: int		=					spm_file
						__UpperCAmelCase	: Dict		=					load_spm(UpperCamelCase_      ,  self.sp_model_kwargs)

						if lang_codes is not None:
									__UpperCAmelCase	: List[Any]		=					lang_codes
									__UpperCAmelCase	: Dict		=					LANGUAGES[lang_codes]
									__UpperCAmelCase	: str		=					[F"<lang:{lang}>" for lang in self.langs]
									__UpperCAmelCase	: List[str]		=					{lang: self.sp_model.PieceToId(F"<lang:{lang}>") for lang in self.langs}

									__UpperCAmelCase	: Tuple		=					self.lang_tokens
									__UpperCAmelCase	: Optional[int]		=					tgt_lang if tgt_lang is not None else self.langs[0]

									self.set_tgt_lang_special_tokens(self._tgt_lang)
						else:
									__UpperCAmelCase	: List[str]		=					{}


			@property
			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						return len(self.encoder)


			@property
			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						return self._tgt_lang


			@tgt_lang.setter
			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					new_tgt_lang
						self.set_tgt_lang_special_tokens(UpperCamelCase_)


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    str):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.lang_code_to_id[tgt_lang]
						__UpperCAmelCase	: List[str]		=					[lang_code_id]


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    str):


						"""simple docstring"""




						return self.sp_model.encode(UpperCamelCase_      ,  out_type=UpperCamelCase_)


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    Any):


						"""simple docstring"""




						return self.encoder.get(UpperCamelCase_      ,  self.encoder[self.unk_token])


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    int):


						"""simple docstring"""




						return self.decoder.get(UpperCamelCase_      ,  self.unk_token)


			def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					[]
						__UpperCAmelCase	: Any		=					""
						for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
												__UpperCAmelCase	: Optional[int]		=					self.sp_model.decode(UpperCamelCase_)
												out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
												__UpperCAmelCase	: Any		=					[]
									else:
												current_sub_tokens.append(UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					self.sp_model.decode(UpperCamelCase_)
						out_string += decoded.upper() if self.do_upper_case else decoded
						return out_string.strip()


			def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    List[str]      ,  UpperCamelCase_      :    List[Any]=None):


						"""simple docstring"""




						if token_ids_a is None:
									return self.prefix_tokens + token_ids_a + [self.eos_token_id]
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    List[int]      ,  UpperCamelCase_      :    Optional[List[int]] = None      ,  UpperCamelCase_      :    bool = False):


						"""simple docstring"""




						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=UpperCamelCase_      ,  token_ids_a=UpperCamelCase_      ,  already_has_special_tokens=UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					[1] * len(self.prefix_tokens)
						__UpperCAmelCase	: Dict		=					[1]
						if token_ids_a is None:
									return prefix_ones + ([0] * len(UpperCamelCase_)) + suffix_ones
						return prefix_ones + ([0] * len(UpperCamelCase_)) + ([0] * len(UpperCamelCase_)) + suffix_ones


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					self.encoder.copy()
						vocab.update(self.added_tokens_encoder)
						return vocab


			def __getstate__(      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.__dict__.copy()
						__UpperCAmelCase	: int		=					None
						return state


			def __setstate__(      self      :    List[str]      ,  UpperCamelCase_      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					d

						# for backward compatibility
						if not hasattr(self      ,  "sp_model_kwargs"):
									__UpperCAmelCase	: Tuple		=					{}

						__UpperCAmelCase	: List[str]		=					load_spm(self.spm_file      ,  self.sp_model_kwargs)


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    Optional[str] = None):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					Path(UpperCamelCase_)
						assert save_dir.is_dir(), F"{save_directory} should be a directory"
						__UpperCAmelCase	: Optional[int]		=					save_dir / (
						    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
						)
						__UpperCAmelCase	: List[str]		=					save_dir / (
						    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
						)

						save_json(self.encoder      ,  UpperCamelCase_)

						if os.path.abspath(self.spm_file) != os.path.abspath(UpperCamelCase_) and os.path.isfile(self.spm_file):
									copyfile(self.spm_file      ,  UpperCamelCase_)
						elif not os.path.isfile(self.spm_file):
									with open(UpperCamelCase_      ,  "wb") as fi:
												__UpperCAmelCase	: Any		=					self.sp_model.serialized_model_proto()
												fi.write(UpperCamelCase_)

						return (str(UpperCamelCase_), str(UpperCamelCase_))






def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			sentencepiece.SentencePieceProcessor:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[Any]		=					sentencepiece.SentencePieceProcessor(**UpperCamelCase							)
			spm.Load(str(UpperCamelCase							)							)
			return spm

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


			"""simple docstring"""
			with open(UpperCamelCase				,       "r"							) as f:
						return json.load(UpperCamelCase							)





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			None:


			"""simple docstring"""
			with open(UpperCamelCase				,       "w"							) as f:
						json.dump(UpperCamelCase				,       UpperCamelCase				,       indent=2							)










"""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)


