

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

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


lowercase_       		=  logging.get_logger(__name__)


lowercase_       		=  {
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
    "merges_file": "merges.txt",
}

lowercase_       		=  {
    "vocab_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"
        ),
    },
    "tokenizer_config_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"
        ),
    },
    "merges_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"
        ),
    },
}

lowercase_       		=  "</w>"
lowercase_       		=  "@@ "
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]      )							->      Tuple:





    '''simple docstring'''






    A__          =   set()
    A__          =   word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)      )
        A__          =   char
    return pairs


# Speech2Text2 has no max input length
lowercase_       		=  {"facebook/s2t-wav2vec2-large-en-de": 1024}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							VOCAB_FILES_NAMES
    lowerCamelCase			=							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase			=							['input_ids', 'attention_mask']


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





        '''simple docstring'''







        super().__init__(
            unk_token=lowercase_,bos_token=lowercase_,eos_token=lowercase_,pad_token=lowercase_,do_lower_case=lowercase_,**lowercase_,)

        A__          =   do_lower_case

        with open(lowercase_,encoding='utf-8'		) as vocab_handle:
            A__          =   json.load(lowercase_		)
        A__          =   {v: k for k, v in self.encoder.items()}

        if merges_file is None:
            logger.info(F'No merges files provided. {self.__class__.__name__} can only be used for decoding.'		)

            A__          =   None
            A__          =   None
        else:
            with open(lowercase_,encoding='utf-8'		) as merges_handle:
                A__          =   merges_handle.read().split('\n'		)[:-1]

            A__          =   [tuple(merge.split()[:2]		) for merge in merges]
            A__          =   dict(zip(lowercase_,range(len(lowercase_		)		)		)		)
            A__          =   {}


    @property
    def 						snake_case__     (      self :       str		)->    int:





        '''simple docstring'''







        return len(self.decoder		)


    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        return dict(self.encoder,**self.added_tokens_encoder		)


    def 						snake_case__     (      self :       Optional[int],lowercase_ :       int		)->    str:





        '''simple docstring'''







        A__          =   tuple(token[:-1]		) + (token[-1] + BPE_TOKEN_MERGES,)
        if token in self.cache:
            return self.cache[token]
        A__          =   get_pairs(lowercase_		)

        if not pairs:
            return token

        while True:
            A__          =   min(lowercase_,key=lambda lowercase_		: self.bpe_ranks.get(lowercase_,float('inf'		)		)		)
            if bigram not in self.bpe_ranks:
                break
            A__      ,						A__          =   bigram
            A__          =   []
            A__          =   0
            while i < len(lowercase_		):
                try:
                    A__          =   word.index(lowercase_,lowercase_		)
                except ValueError:
                    new_word.extend(word[i:]		)
                    break
                else:
                    new_word.extend(word[i:j]		)
                    A__          =   j

                if word[i] == first and i < len(lowercase_		) - 1 and word[i + 1] == second:
                    new_word.append(first + second		)
                    i += 2
                else:
                    new_word.append(word[i]		)
                    i += 1
            A__          =   tuple(lowercase_		)
            A__          =   new_word
            if len(lowercase_		) == 1:
                break
            else:
                A__          =   get_pairs(lowercase_		)
        A__          =   ' '.join(lowercase_		)
        if word == "\n  " + BPE_TOKEN_MERGES:
            A__          =   '\n' + BPE_TOKEN_MERGES

        if word.endswith(lowercase_		):
            A__          =   word.replace(lowercase_,''		)

        A__          =   word.replace(' ',lowercase_		)
        A__          =   word
        return word


    def 						snake_case__     (      self :       int,lowercase_ :       Optional[Any]		)->    Optional[int]:





        '''simple docstring'''







        if self.bpe_ranks is None:
            raise ValueError(
                'This tokenizer was instantiated without a `merges.txt` file, so'
                ' that it can only be used for decoding, not for encoding.'
                'Make sure to provide `merges.txt` file at instantiation to enable '
                'encoding.'		)

        if self.do_lower_case:
            A__          =   text.lower()

        A__          =   text.split()

        A__          =   []
        for token in text:
            if token:
                split_tokens.extend(list(self.bpe(lowercase_		).split(' '		)		)		)

        return split_tokens


    def 						snake_case__     (      self :       Any,lowercase_ :       str		)->    int:





        '''simple docstring'''







        return self.encoder.get(lowercase_,self.encoder.get(self.unk_token		)		)


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       int		)->    str:





        '''simple docstring'''







        A__          =   self.decoder.get(lowercase_,self.unk_token		)
        return result


    def 						snake_case__     (      self :       Dict,lowercase_ :       List[str]		)->    str:





        '''simple docstring'''







        A__          =   ' '.join(lowercase_		)

        # make sure @@ tokens are concatenated
        A__          =   ''.join(string.split(lowercase_		)		)

        return string


    def 						snake_case__     (      self :       int,lowercase_ :       str,lowercase_ :       Optional[str] = None		)->    Tuple[str]:





        '''simple docstring'''







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

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

        A__          =   0
        if self.bpe_ranks is None:
            return (vocab_file,)

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

        return (vocab_file, merges_file)




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowercase_       		=  version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	tuple    ,			SCREAMING_SNAKE_CASE__			:	Path    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]=False    ,			)							->      Union[str, Any]:





    '''simple docstring'''






    output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__    ,			exist_ok=SCREAMING_SNAKE_CASE__      )
    # 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(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			use_external_data_format=SCREAMING_SNAKE_CASE__    ,			enable_onnx_checker=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
    else:
        export(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      Tuple:





    '''simple docstring'''






    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__          =   Path(SCREAMING_SNAKE_CASE__      )

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


if __name__ == "__main__":
     lowercase_       		=  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")

     lowercase_       		=  parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("SD: Done: ONNX")

