
'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowercase			:  str   							=	logging.get_logger(__name__)

_lowercase			:  Optional[int]   							=	"▁"

_lowercase			:  Union[str, Any]   							=	{"vocab_file": "sentencepiece.bpe.model"}

_lowercase			:  Union[str, Any]   							=	{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

_lowercase			:  Any   							=	{
    "facebook/nllb-200-distilled-600M": 1_0_2_4,
}

# fmt: off
_lowercase			:  List[str]   							=	["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					VOCAB_FILES_NAMES
					lowerCAmelCase_					       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_					       =					PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_					       =					['''input_ids''', '''attention_mask''']

					lowerCAmelCase_					       =					[]
					lowerCAmelCase_					       =					[]






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE="<s>"    ,							__SCREAMING_SNAKE_CASE="</s>"    ,							__SCREAMING_SNAKE_CASE="</s>"    ,							__SCREAMING_SNAKE_CASE="<s>"    ,							__SCREAMING_SNAKE_CASE="<unk>"    ,							__SCREAMING_SNAKE_CASE="<pad>"    ,							__SCREAMING_SNAKE_CASE="<mask>"    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=False    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else mask_token

											lowercase_     :					Tuple													=	{} if sp_model_kwargs is None else sp_model_kwargs
											lowercase_     :					Any													=	legacy_behaviour

											super().__init__(
											    bos_token=__SCREAMING_SNAKE_CASE    ,							eos_token=__SCREAMING_SNAKE_CASE    ,							unk_token=__SCREAMING_SNAKE_CASE    ,							sep_token=__SCREAMING_SNAKE_CASE    ,							cls_token=__SCREAMING_SNAKE_CASE    ,							pad_token=__SCREAMING_SNAKE_CASE    ,							mask_token=__SCREAMING_SNAKE_CASE    ,							tokenizer_file=__SCREAMING_SNAKE_CASE    ,							src_lang=__SCREAMING_SNAKE_CASE    ,							tgt_lang=__SCREAMING_SNAKE_CASE    ,							additional_special_tokens=__SCREAMING_SNAKE_CASE    ,							sp_model_kwargs=self.sp_model_kwargs    ,							legacy_behaviour=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE    ,							)

											lowercase_     :					List[Any]													=	spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(str(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					str													=	vocab_file
											# Original fairseq vocab and spm vocab must be "aligned":
											# Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
											# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
											# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
											# spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

											# Mimic fairseq token-to-id alignment for the first 4 token
											lowercase_     :					List[str]													=	{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

											# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
											lowercase_     :					Any													=	1

											lowercase_     :					Any													=	len(self.sp_model		)
											lowercase_     :					Union[str, Any]													=	{
											    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__SCREAMING_SNAKE_CASE		)
											}
											lowercase_     :					str													=	{v: k for k, v in self.lang_code_to_id.items()}
											lowercase_     :					Any													=	len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

											self.fairseq_tokens_to_ids.update(self.lang_code_to_id		)
											lowercase_     :					Union[str, Any]													=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}
											lowercase_     :					List[str]													=	list(self.lang_code_to_id.keys()		)

											if additional_special_tokens is not None:
																	# Only add those special tokens if they are not already there.
																	self._additional_special_tokens.extend(
																	    [t for t in additional_special_tokens if t not in self._additional_special_tokens]		)

											lowercase_     :					Any													=	src_lang if src_lang is not None else '''eng_Latn'''
											lowercase_     :					Any													=	self.lang_code_to_id[self._src_lang]
											lowercase_     :					Optional[int]													=	tgt_lang
											self.set_src_lang_special_tokens(self._src_lang		)






					def __getstate__(       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	self.__dict__.copy()
											lowercase_     :					Union[str, Any]													=	None
											lowercase_     :					Union[str, Any]													=	self.sp_model.serialized_model_proto()
											return state






					def __setstate__(       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					str													=	d

											# for backward compatibility
											if not hasattr(self    ,							'''sp_model_kwargs'''		):
																	lowercase_     :					Union[str, Any]													=	{}

											lowercase_     :					Optional[Any]													=	spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset + 1  # Plus 1 for the mask token






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return self._src_lang






					@src_lang.setter
					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	new_src_lang
											self.set_src_lang_special_tokens(self._src_lang		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = False		):


											"""simple docstring"""





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

											lowercase_     :					List[str]													=	[1] * len(self.prefix_tokens		)
											lowercase_     :					str													=	[1] * len(self.suffix_tokens		)
											if token_ids_a is None:
																	return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE		)) + suffix_ones
											return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE		)) + ([0] * len(__SCREAMING_SNAKE_CASE		)) + suffix_ones






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if token_ids_a is None:
																	return self.prefix_tokens + token_ids_a + self.suffix_tokens
											# 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.suffix_tokens






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											lowercase_     :					str													=	[self.sep_token_id]
											lowercase_     :					str													=	[self.cls_token_id]

											if token_ids_a is None:
																	return len(cls + token_ids_a + sep		) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if src_lang is None or tgt_lang is None:
																	raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''		)
											lowercase_     :					Optional[int]													=	src_lang
											lowercase_     :					Dict													=	self(__SCREAMING_SNAKE_CASE    ,							add_special_tokens=__SCREAMING_SNAKE_CASE    ,							return_tensors=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					int													=	self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					List[str]													=	tgt_lang_id
											return inputs






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											lowercase_     :					Union[str, Any]													=	self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE		)

											# Need to return unknown token if the SP model returned 0
											return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	''''''.join(__SCREAMING_SNAKE_CASE		).replace(__SCREAMING_SNAKE_CASE    ,							''' '''		).strip()
											return out_string






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if not os.path.isdir(__SCREAMING_SNAKE_CASE		):
																	logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
																	return
											lowercase_     :					Any													=	os.path.join(
											    __SCREAMING_SNAKE_CASE    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(__SCREAMING_SNAKE_CASE		) and os.path.isfile(self.vocab_file		):
																	copyfile(self.vocab_file    ,							__SCREAMING_SNAKE_CASE		)
											elif not os.path.isfile(self.vocab_file		):
																	with open(__SCREAMING_SNAKE_CASE    ,							'''wb'''		) as fi:
																							lowercase_     :					List[str]													=	self.sp_model.serialized_model_proto()
																							fi.write(__SCREAMING_SNAKE_CASE		)

											return (out_vocab_file,)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = "eng_Latn"    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = "fra_Latn"    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											lowercase_     :					Any													=	src_lang
											lowercase_     :					List[Any]													=	tgt_lang
											return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											return self.set_src_lang_special_tokens(self.src_lang		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											return self.set_tgt_lang_special_tokens(self.tgt_lang		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	self.lang_code_to_id[src_lang]
											if self.legacy_behaviour:
																	lowercase_     :					Any													=	[]
																	lowercase_     :					Tuple													=	[self.eos_token_id, self.cur_lang_code]
											else:
																	lowercase_     :					str													=	[self.cur_lang_code]
																	lowercase_     :					Optional[Any]													=	[self.eos_token_id]






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	self.lang_code_to_id[lang]
											if self.legacy_behaviour:
																	lowercase_     :					Tuple													=	[]
																	lowercase_     :					int													=	[self.eos_token_id, self.cur_lang_code]
											else:
																	lowercase_     :					Any													=	[self.cur_lang_code]
																	lowercase_     :					str													=	[self.eos_token_id]





'''simple docstring'''







import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Dict													=	flatten_dict(__SCREAMING_SNAKE_CASE							)
						return flax_params
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					int													=	{}

						lowercase_     :					Any													=	{
						    '''token_embedder''': '''embeddings''',
						    '''encoder_norm''': '''layernorm''',
						    '''kernel''': '''weight''',
						    '''.out''': '''.output''',
						    '''scale''': '''weight''',
						    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
						    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
						}

						lowercase_     :					Tuple													=	{
						    '''query''': '''attention.query''',
						    '''key''': '''attention.key''',
						    '''value''': '''attention.value''',
						    '''output.dense''': '''output''',
						    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
						    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
						    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
						    '''mlp.''': '''mlp.DenseReluDense.''',
						    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
						    '''self_attention.o''': '''self_attention.attention.o''',
						    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
						    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
						    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
						    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
						}

						for key in flax_dict.keys():
												if "target" in key:
																		# remove the first prefix from the key
																		lowercase_     :					Tuple													=	'''.'''.join(key[1:]							)

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								lowercase_     :					Optional[Any]													=	new_key.replace(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														lowercase_     :					Optional[Any]													=	new_key.replace(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								lowercase_     :					List[Any]													=	re.sub(R'''layers_(\d+)'''				,				R'''layer.\1'''				,				__SCREAMING_SNAKE_CASE							)
																								lowercase_     :					Union[str, Any]													=	new_key.replace('''encoder'''				,				'''encoder.encoder'''							)

																		elif "layers" in new_key and "decoder" in new_key:
																								# use regex to replace the layer number
																								lowercase_     :					str													=	re.sub(R'''layers_(\d+)'''				,				R'''layer.\1'''				,				__SCREAMING_SNAKE_CASE							)

																		lowercase_     :					Dict													=	flax_dict[key]

						lowercase_     :					Any													=	{}
						# convert converted_dict into torch format
						for key in converted_dict.keys():
												if ("embed_tokens" not in key) and ("embedder" not in key):
																		lowercase_     :					str													=	torch.from_numpy(converted_dict[key].T							)
												else:
																		lowercase_     :					str													=	torch.from_numpy(converted_dict[key]							)

						return converted_torch_dict
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Tuple=False				,				__SCREAMING_SNAKE_CASE :			List[Any]=False							):



						"""simple docstring"""





						lowercase_     :					List[str]													=	get_flax_param(__SCREAMING_SNAKE_CASE							)

						if not use_large:
												lowercase_     :					List[str]													=	PixaStructVisionConfig()
												lowercase_     :					Optional[Any]													=	PixaStructTextConfig()
						else:
												lowercase_     :					Optional[int]													=	PixaStructVisionConfig(
												    hidden_size=1536				,				d_ff=3968				,				num_attention_heads=24				,				num_hidden_layers=18							)
												lowercase_     :					Dict													=	PixaStructTextConfig(hidden_size=1536				,				d_ff=3968				,				num_heads=24				,				num_layers=18							)
						lowercase_     :					str													=	PixaStructConfig(
						    vision_config=encoder_config.to_dict()				,				text_config=decoder_config.to_dict()				,				is_vqa=__SCREAMING_SNAKE_CASE							)

						lowercase_     :					List[str]													=	PixaStructForConditionalGeneration(__SCREAMING_SNAKE_CASE							)

						lowercase_     :					int													=	rename_and_convert_flax_params(__SCREAMING_SNAKE_CASE							)
						model.load_state_dict(__SCREAMING_SNAKE_CASE							)

						lowercase_     :					str													=	AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''							)
						lowercase_     :					List[Any]													=	PixaStructImageProcessor()
						lowercase_     :					int													=	PixaStructProcessor(image_processor=__SCREAMING_SNAKE_CASE				,				tokenizer=__SCREAMING_SNAKE_CASE							)

						if use_large:
												lowercase_     :					Tuple													=	4096

						lowercase_     :					Optional[int]													=	True

						# mkdir if needed
						os.makedirs(__SCREAMING_SNAKE_CASE				,				exist_ok=__SCREAMING_SNAKE_CASE							)

						model.save_pretrained(__SCREAMING_SNAKE_CASE							)
						processor.save_pretrained(__SCREAMING_SNAKE_CASE							)

						print('''Model saved in {}'''.format(__SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
							_lowercase			:  str   							=	argparse.ArgumentParser()
							parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
							parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							parser.add_argument("--use_large", action="store_true", help="Use large model.")
							parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
							_lowercase			:  Tuple   							=	parser.parse_args()

							convert_pixastruct_original_pytorch_checkpoint_to_hf(
							    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
							)



