





import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

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


UpperCAmelCase__						:			int									=logging.get_logger(__name__)


UpperCAmelCase__						:			Optional[int]									={
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

UpperCAmelCase__						:			Tuple									={
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

UpperCAmelCase__						:			Tuple									={'''facebook/blenderbot_small-90M''': 5_12}





def      _lowercase					(						_UpperCAmelCase				)						->  str:
				lowerCamelCase	 =set()
				lowerCamelCase	 =word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)				)
								lowerCamelCase	 =char

				lowerCamelCase	 =set(_UpperCAmelCase				)
				return pairs




class       __A     (						a    ):
				__A      =				VOCAB_FILES_NAMES
				__A      =				PRETRAINED_VOCAB_FILES_MAP
				__A      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A      =				["""input_ids""", """attention_mask"""]


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_="__start__"   , UpperCAmelCase_="__end__"   , UpperCAmelCase_="__unk__"   , UpperCAmelCase_="__null__"   , **UpperCAmelCase_   , ):
								super().__init__(unk_token=UpperCAmelCase_   , bos_token=UpperCAmelCase_   , eos_token=UpperCAmelCase_   , pad_token=UpperCAmelCase_   , **UpperCAmelCase_							)

								with open(UpperCAmelCase_   , encoding="""utf-8"""							) as vocab_handle:
												lowerCamelCase	 =json.load(UpperCAmelCase_							)
								lowerCamelCase	 ={v: k for k, v in self.encoder.items()}
								with open(UpperCAmelCase_   , encoding="""utf-8"""							) as merges_handle:
												lowerCamelCase	 =merges_handle.read().split("""\n"""							)[1:-1]
								lowerCamelCase	 =[tuple(merge.split()							) for merge in merges]
								lowerCamelCase	 =dict(zip(UpperCAmelCase_   , range(len(UpperCAmelCase_							)							)							)							)
								lowerCamelCase	 ={}


				@property
				def _snake_case       (		self							):
								return len(self.encoder							)


				def _snake_case       (		self							):
								return dict(self.encoder   , **self.added_tokens_encoder							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								if token in self.cache:
												return self.cache[token]
								lowerCamelCase	 =re.sub("""([.,!?()])"""   , r""" \1"""   , UpperCAmelCase_							)
								lowerCamelCase	 =re.sub("""(')"""   , r""" \1 """   , UpperCAmelCase_							)
								lowerCamelCase	 =re.sub(r"""\s{2,}"""   , """ """   , UpperCAmelCase_							)
								if "\n" in token:
												lowerCamelCase	 =token.replace("""\n"""   , """ __newln__"""							)

								lowerCamelCase	 =token.split(""" """							)
								lowerCamelCase	 =[]
								for token in tokens:
												if not len(UpperCAmelCase_							):
																continue

												lowerCamelCase	 =token.lower()
												lowerCamelCase	 =tuple(UpperCAmelCase_							)
												lowerCamelCase	 =tuple(list(word[:-1]							) + [word[-1] + """</w>"""]							)
												lowerCamelCase	 =get_pairs(UpperCAmelCase_							)

												if not pairs:
																words.append(UpperCAmelCase_							)
																continue

												while True:
																lowerCamelCase	 =min(UpperCAmelCase_   , key=lambda UpperCAmelCase_							: self.bpe_ranks.get(UpperCAmelCase_   , float("""inf"""							)							)							)
																if bigram not in self.bpe_ranks:
																				break
																lowerCamelCase ,  lowerCamelCase	 =bigram
																lowerCamelCase	 =[]
																lowerCamelCase	 =0

																while i < len(UpperCAmelCase_							):
																				try:
																								lowerCamelCase	 =word.index(UpperCAmelCase_   , UpperCAmelCase_							)
																								new_word.extend(word[i:j]							)
																								lowerCamelCase	 =j
																				except ValueError:
																								new_word.extend(word[i:]							)
																								break

																				if word[i] == first and i < len(UpperCAmelCase_							) - 1 and word[i + 1] == second:
																								new_word.append(first + second							)
																								i += 2
																				else:
																								new_word.append(word[i]							)
																								i += 1
																lowerCamelCase	 =tuple(UpperCAmelCase_							)
																lowerCamelCase	 =new_word
																if len(UpperCAmelCase_							) == 1:
																				break
																else:
																				lowerCamelCase	 =get_pairs(UpperCAmelCase_							)
												lowerCamelCase	 ="""@@ """.join(UpperCAmelCase_							)
												lowerCamelCase	 =word[:-4]

												lowerCamelCase	 =word
												words.append(UpperCAmelCase_							)
								return " ".join(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =[]

								lowerCamelCase	 =re.findall(r"""\S+\n?"""   , UpperCAmelCase_							)

								for token in words:
												split_tokens.extend(list(self.bpe(UpperCAmelCase_							).split(""" """							)							)							)
								return split_tokens


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =token.lower()
								return self.encoder.get(UpperCAmelCase_   , self.encoder.get(self.unk_token							)							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								return self.decoder.get(UpperCAmelCase_   , self.unk_token							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =""" """.join(UpperCAmelCase_							).replace("""@@ """   , """"""							).strip()
								return out_string


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_ = None							):
								if not os.path.isdir(UpperCAmelCase_							):
												logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
												return
								lowerCamelCase	 =os.path.join(
								    UpperCAmelCase_   , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
								lowerCamelCase	 =os.path.join(
								    UpperCAmelCase_   , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

								with open(UpperCAmelCase_   , """w"""   , encoding="""utf-8"""							) as f:
												f.write(json.dumps(self.encoder   , indent=2   , sort_keys=UpperCAmelCase_   , ensure_ascii=UpperCAmelCase_							) + """\n"""							)

								lowerCamelCase	 =0
								with open(UpperCAmelCase_   , """w"""   , encoding="""utf-8"""							) as writer:
												writer.write("""#version: 0.2\n"""							)
												for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   , key=lambda UpperCAmelCase_							: kv[1]							):
																if index != token_index:
																				logger.warning(
																				    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																				    """ Please check that the tokenizer is not corrupted!"""							)
																				lowerCamelCase	 =token_index
																writer.write(""" """.join(UpperCAmelCase_							) + """\n"""							)
																index += 1

								return vocab_file, merge_file












import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCAmelCase__						:			Tuple									=version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=False			,      )						->  int:
				output_path.parent.mkdir(parents=_UpperCAmelCase			,      exist_ok=_UpperCAmelCase				)
				# 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(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      use_external_data_format=_UpperCAmelCase			,      enable_onnx_checker=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )
				else:
								export(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase = False				)						->  Tuple:
				lowerCamelCase	 =torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								lowerCamelCase	 ="""cuda"""
				elif fpaa and not torch.cuda.is_available():
								raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""				)
				else:
								lowerCamelCase	 ="""cpu"""
				lowerCamelCase	 =Path(_UpperCAmelCase				)

				# VAE DECODER
				lowerCamelCase	 =AutoencoderKL.from_pretrained(model_path + """/vae"""				)
				lowerCamelCase	 =vae_decoder.config.latent_channels
				# forward only through the decoder part
				lowerCamelCase	 =vae_decoder.decode
				onnx_export(
				    _UpperCAmelCase			,      model_args=(
				        torch.randn(1			,      _UpperCAmelCase			,      25			,      25				).to(device=_UpperCAmelCase			,      dtype=_UpperCAmelCase				),
				        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=_UpperCAmelCase			,      )
				del vae_decoder


if __name__ == "__main__":
							UpperCAmelCase__						:			Union[str, Any]									=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''')

							UpperCAmelCase__						:			Union[str, Any]									=parser.parse_args()
							print(args.output_path)
							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
							print('''SD: Done: ONNX''')





