





'''simple docstring'''


import argparse
import os
import shutil
from pathlib import Path

import onnx
import torch
from packaging import version
from torch.onnx import export

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline


lowerCAmelCase_   :   Union[str, Any]       =					version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     tuple					,  __lowerCamelCase  :     Path					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     Any					,  __lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     str=False					,  )   ->    Dict:
			output_path.parent.mkdir(parents=__lowerCamelCase					,  exist_ok=__lowerCamelCase     )
			# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
			# so we check the torch version for backwards compatibility
			if is_torch_less_than_1_11:
						export(
						    __lowerCamelCase					,  __lowerCamelCase					,  f=output_path.as_posix()					,  input_names=__lowerCamelCase					,  output_names=__lowerCamelCase					,  dynamic_axes=__lowerCamelCase					,  do_constant_folding=__lowerCamelCase					,  use_external_data_format=__lowerCamelCase					,  enable_onnx_checker=__lowerCamelCase					,  opset_version=__lowerCamelCase					,  )
			else:
						export(
						    __lowerCamelCase					,  __lowerCamelCase					,  f=output_path.as_posix()					,  input_names=__lowerCamelCase					,  output_names=__lowerCamelCase					,  dynamic_axes=__lowerCamelCase					,  do_constant_folding=__lowerCamelCase					,  opset_version=__lowerCamelCase					,  )







@torch.no_grad()
def    _lowerCamelCase  (__lowerCamelCase  :     str					,  __lowerCamelCase  :     str					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     bool = False     )   ->    Optional[int]:
			a__     = torch.floataa if fpaa else torch.floataa
			if fpaa and torch.cuda.is_available():
						a__     = "cuda"
			elif fpaa and not torch.cuda.is_available():
						raise ValueError("`float16` model export is only supported on GPUs with CUDA"     )
			else:
						a__     = "cpu"
			a__     = StableDiffusionPipeline.from_pretrained(__lowerCamelCase					,  torch_dtype=__lowerCamelCase     ).to(__lowerCamelCase     )
			a__     = Path(__lowerCamelCase     )

			# TEXT ENCODER
			a__     = pipeline.text_encoder.config.max_position_embeddings
			a__     = pipeline.text_encoder.config.hidden_size
			a__     = pipeline.tokenizer(
			    "A sample prompt"					,  padding="max_length"					,  max_length=pipeline.tokenizer.model_max_length					,  truncation=__lowerCamelCase					,  return_tensors="pt"					,  )
			onnx_export(
			    pipeline.text_encoder					,  model_args=(text_input.input_ids.to(device=__lowerCamelCase					,  dtype=torch.intaa     ))					,  output_path=output_path / "text_encoder" / "model.onnx"					,  ordered_input_names=["input_ids"]					,  output_names=["last_hidden_state", "pooler_output"]					,  dynamic_axes={
			        "input_ids": {0: "batch", 1: "sequence"},
			    }					,  opset=__lowerCamelCase					,  )
			del pipeline.text_encoder

			# UNET
			a__     = pipeline.unet.config.in_channels
			a__     = pipeline.unet.config.sample_size
			a__     = output_path / "unet" / "model.onnx"
			onnx_export(
			    pipeline.unet					,  model_args=(
			        torch.randn(2					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     ).to(device=__lowerCamelCase					,  dtype=__lowerCamelCase     ),
			        torch.randn(2     ).to(device=__lowerCamelCase					,  dtype=__lowerCamelCase     ),
			        torch.randn(2					,  __lowerCamelCase					,  __lowerCamelCase     ).to(device=__lowerCamelCase					,  dtype=__lowerCamelCase     ),
			        False,
			    )					,  output_path=__lowerCamelCase					,  ordered_input_names=["sample", "timestep", "encoder_hidden_states", "return_dict"]					,  output_names=["out_sample"]					,  dynamic_axes={
			        "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
			        "timestep": {0: "batch"},
			        "encoder_hidden_states": {0: "batch", 1: "sequence"},
			    }					,  opset=__lowerCamelCase					,  use_external_data_format=__lowerCamelCase					,  )
			a__     = str(unet_path.absolute().as_posix()     )
			a__     = os.path.dirname(__lowerCamelCase     )
			a__     = onnx.load(__lowerCamelCase     )
			# clean up existing tensor files
			shutil.rmtree(__lowerCamelCase     )
			os.mkdir(__lowerCamelCase     )
			# collate external tensor files into one
			onnx.save_model(
			    __lowerCamelCase					,  __lowerCamelCase					,  save_as_external_data=__lowerCamelCase					,  all_tensors_to_one_file=__lowerCamelCase					,  location="weights.pb"					,  convert_attribute=__lowerCamelCase					,  )
			del pipeline.unet

			# VAE ENCODER
			a__     = pipeline.vae
			a__     = vae_encoder.config.in_channels
			a__     = vae_encoder.config.sample_size
			# need to get the raw tensor output (sample) from the encoder
			a__     = lambda __lowerCamelCase					,  __lowerCamelCase     : vae_encoder.encode(__lowerCamelCase					,  __lowerCamelCase     )[0].sample()
			onnx_export(
			    __lowerCamelCase					,  model_args=(
			        torch.randn(1					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     ).to(device=__lowerCamelCase					,  dtype=__lowerCamelCase     ),
			        False,
			    )					,  output_path=output_path / "vae_encoder" / "model.onnx"					,  ordered_input_names=["sample", "return_dict"]					,  output_names=["latent_sample"]					,  dynamic_axes={
			        "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
			    }					,  opset=__lowerCamelCase					,  )

			# VAE DECODER
			a__     = pipeline.vae
			a__     = vae_decoder.config.latent_channels
			a__     = vae_decoder.config.out_channels
			# forward only through the decoder part
			a__     = vae_encoder.decode
			onnx_export(
			    __lowerCamelCase					,  model_args=(
			        torch.randn(1					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     ).to(device=__lowerCamelCase					,  dtype=__lowerCamelCase     ),
			        False,
			    )					,  output_path=output_path / "vae_decoder" / "model.onnx"					,  ordered_input_names=["latent_sample", "return_dict"]					,  output_names=["sample"]					,  dynamic_axes={
			        "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
			    }					,  opset=__lowerCamelCase					,  )
			del pipeline.vae

			# SAFETY CHECKER
			if pipeline.safety_checker is not None:
						a__     = pipeline.safety_checker
						a__     = safety_checker.config.vision_config.num_channels
						a__     = safety_checker.config.vision_config.image_size
						a__     = safety_checker.forward_onnx
						onnx_export(
						    pipeline.safety_checker					,  model_args=(
						        torch.randn(
						            1					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  ).to(device=__lowerCamelCase					,  dtype=__lowerCamelCase     ),
						        torch.randn(1					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     ).to(device=__lowerCamelCase					,  dtype=__lowerCamelCase     ),
						    )					,  output_path=output_path / "safety_checker" / "model.onnx"					,  ordered_input_names=["clip_input", "images"]					,  output_names=["out_images", "has_nsfw_concepts"]					,  dynamic_axes={
						        "clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"},
						        "images": {0: "batch", 1: "height", 2: "width", 3: "channels"},
						    }					,  opset=__lowerCamelCase					,  )
						del pipeline.safety_checker
						a__     = OnnxRuntimeModel.from_pretrained(output_path / "safety_checker"     )
						a__     = pipeline.feature_extractor
			else:
						a__     = None
						a__     = None

			a__     = OnnxStableDiffusionPipeline(
			    vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"     )					,  vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder"     )					,  text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder"     )					,  tokenizer=pipeline.tokenizer					,  unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"     )					,  scheduler=pipeline.scheduler					,  safety_checker=__lowerCamelCase					,  feature_extractor=__lowerCamelCase					,  requires_safety_checker=safety_checker is not None					,  )

			onnx_pipeline.save_pretrained(__lowerCamelCase     )
			print("ONNX pipeline saved to"					,  __lowerCamelCase     )

			del pipeline
			del onnx_pipeline
			a__     = OnnxStableDiffusionPipeline.from_pretrained(__lowerCamelCase					,  provider="CPUExecutionProvider"     )
			print("ONNX pipeline is loadable"     )


if __name__ == "__main__":
		lowerCAmelCase_   :   str       =					argparse.ArgumentParser()

		parser.add_argument(
		    "--model_path",
		    type=str,
		    required=True,
		    help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
		)

		parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")

		parser.add_argument(
		    "--opset",
		    default=14,
		    type=int,
		    help="The version of the ONNX operator set to use.",
		)
		parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

		lowerCAmelCase_   :   int       =					parser.parse_args()

		convert_models(args.model_path, args.output_path, args.opset, args.fpaa)









'''simple docstring'''


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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_nllb import NllbTokenizer
else:
		lowerCAmelCase_   :   Optional[Any]       =					None


lowerCAmelCase_   :   List[str]       =					logging.get_logger(__name__)


lowerCAmelCase_   :   Union[str, Any]       =					{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_   :   List[Any]       =					{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
        ),
    },
}

lowerCAmelCase_   :   List[str]       =					{
    "facebook/nllb-large-en-ro": 1024,
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
lowerCAmelCase_   :   int       =					["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       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			int								=						VOCAB_FILES_NAMES
	lowerCAmelCase__					:			str								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__					:			int								=						PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__					:			int								=						["input_ids", "attention_mask"]
	lowerCAmelCase__					:			List[Any]								=						NllbTokenizer

	lowerCAmelCase__					:			List[int]								=						[]
	lowerCAmelCase__					:			List[int]								=						[]







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




				'''simple docstring'''
				# Mask token behave like a normal word, i.e. include the space before it
				a__     = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase		) if isinstance(lowerCamelCase , lowerCamelCase		) else mask_token
				a__     = legacy_behaviour
				super().__init__(
				    vocab_file=lowerCamelCase , tokenizer_file=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , sep_token=lowerCamelCase , cls_token=lowerCamelCase , unk_token=lowerCamelCase , pad_token=lowerCamelCase , mask_token=lowerCamelCase , src_lang=lowerCamelCase , tgt_lang=lowerCamelCase , additional_special_tokens=lowerCamelCase , legacy_behaviour=lowerCamelCase , **lowerCamelCase , )

				a__     = vocab_file
				a__     = False if not self.vocab_file else True

				a__     = FAIRSEQ_LANGUAGE_CODES.copy()

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

				self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}		)
				a__     = {
				    lang_code: self.convert_tokens_to_ids(lowerCamelCase		) for lang_code in FAIRSEQ_LANGUAGE_CODES
				}

				a__     = src_lang if src_lang is not None else "eng_Latn"
				a__     = self.convert_tokens_to_ids(self._src_lang		)
				a__     = tgt_lang
				self.set_src_lang_special_tokens(self._src_lang		)







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




				'''simple docstring'''
				return self._src_lang







	@src_lang.setter
	def 						__a   (				self			:   int , lowerCamelCase			:   str		):




				'''simple docstring'''
				a__     = new_src_lang
				self.set_src_lang_special_tokens(self._src_lang		)







	def 						__a   (				self			:   Any , lowerCamelCase			:   List[int] , lowerCamelCase			:   Optional[List[int]] = 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 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   List[int] , lowerCamelCase			:   Optional[List[int]] = None		):




				'''simple docstring'''
				a__     = [self.sep_token_id]
				a__     = [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 						__a   (				self			:   str , lowerCamelCase			:   str , lowerCamelCase			:   str , lowerCamelCase			:   Optional[str] , lowerCamelCase			:   Optional[str] , **lowerCamelCase			:   List[str]		):




				'''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"		)
				a__     = src_lang
				a__     = self(lowerCamelCase , add_special_tokens=lowerCamelCase , return_tensors=lowerCamelCase , **lowerCamelCase		)
				a__     = self.convert_tokens_to_ids(lowerCamelCase		)
				a__     = tgt_lang_id
				return inputs







	def 						__a   (				self			:   str , lowerCamelCase			:   List[str] , lowerCamelCase			:   str = "eng_Latn" , lowerCamelCase			:   Optional[List[str]] = None , lowerCamelCase			:   str = "fra_Latn" , **lowerCamelCase			:   Any , ):




				'''simple docstring'''
				a__     = src_lang
				a__     = tgt_lang
				return super().prepare_seqaseq_batch(lowerCamelCase , lowerCamelCase , **lowerCamelCase		)







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				return self.set_src_lang_special_tokens(self.src_lang		)







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				return self.set_tgt_lang_special_tokens(self.tgt_lang		)







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = self.convert_tokens_to_ids(lowerCamelCase		)

				if self.legacy_behaviour:
							a__     = []
							a__     = [self.eos_token_id, self.cur_lang_code]
				else:
							a__     = [self.cur_lang_code]
							a__     = [self.eos_token_id]

				a__     = self.convert_ids_to_tokens(self.prefix_tokens		)
				a__     = self.convert_ids_to_tokens(self.suffix_tokens		)

				a__     = processors.TemplateProcessing(
				    single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens		)		) , )







	def 						__a   (				self			:   Tuple , lowerCamelCase			:   str		):




				'''simple docstring'''
				a__     = self.convert_tokens_to_ids(lowerCamelCase		)
				if self.legacy_behaviour:
							a__     = []
							a__     = [self.eos_token_id, self.cur_lang_code]
				else:
							a__     = [self.cur_lang_code]
							a__     = [self.eos_token_id]

				a__     = self.convert_ids_to_tokens(self.prefix_tokens		)
				a__     = self.convert_ids_to_tokens(self.suffix_tokens		)

				a__     = processors.TemplateProcessing(
				    single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens		)		) , )







	def 						__a   (				self			:   Tuple , lowerCamelCase			:   str , lowerCamelCase			:   Optional[str] = None		):




				'''simple docstring'''
				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."		)

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

				if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase		):
							copyfile(self.vocab_file , lowerCamelCase		)

				return (out_vocab_file,)



