

"""simple docstring"""




import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=True				,     __UpperCamelCase="pt"   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Optional[int]        =       {"""add_prefix_space""": True} if isinstance(__UpperCamelCase				,     __UpperCamelCase   ) and not line.startswith(""" """   ) else {}
 UpperCAmelCase__       :	List[str]        =       padding_side
 return tokenizer(
     [line]				,     max_length=__UpperCamelCase				,     padding="""max_length""" if pad_to_max_length else None				,     truncation=__UpperCamelCase				,     return_tensors=__UpperCamelCase				,     add_special_tokens=__UpperCamelCase				,     **__UpperCamelCase				,     )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=None				,     ):
 '''simple docstring'''





 UpperCAmelCase__       :	str        =       input_ids.ne(__UpperCamelCase   ).any(dim=0   )
 if attention_mask is None:
  return input_ids[:, keep_column_mask]
 else:
  return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])







class   __lowercase      (     __lowerCamelCase       ):

  def __init__(	self		:      Tuple       ,A		:      List[Any]       ,A		:      Union[str, Any]       ,A		:      Any       ,A		:      Optional[int]       ,A		:      Union[str, Any]="train"       ,A		:      Tuple=None       ,A		:      Union[str, Any]=None       ,A		:      Tuple=None       ,A		:      int=""       ,):






   '''simple docstring'''
   super().__init__()
   UpperCAmelCase__       :	Optional[Any]        =       Path(A     ).joinpath(type_path + """.source"""     )
   UpperCAmelCase__       :	List[str]        =       Path(A     ).joinpath(type_path + """.target"""     )
   UpperCAmelCase__       :	Dict        =       self.get_char_lens(self.src_file     )
   UpperCAmelCase__       :	int        =       max_source_length
   UpperCAmelCase__       :	List[str]        =       max_target_length
   assert min(self.src_lens     ) > 0, f"found empty line in {self.src_file}"
   UpperCAmelCase__       :	Dict        =       tokenizer
   UpperCAmelCase__       :	str        =       prefix
   if n_obs is not None:
    UpperCAmelCase__       :	int        =       self.src_lens[:n_obs]
   UpperCAmelCase__       :	Any        =       src_lang
   UpperCAmelCase__       :	Any        =       tgt_lang

  def __len__(	self		:      Optional[Any]     ):






   '''simple docstring'''
   return len(self.src_lens     )

  def __getitem__(	self		:      Union[str, Any]       ,A		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       index + 1  # linecache starts at 1
   UpperCAmelCase__       :	Tuple        =       self.prefix + linecache.getline(str(self.src_file     )       ,A     ).rstrip("""\n"""     )
   UpperCAmelCase__       :	Dict        =       linecache.getline(str(self.tgt_file     )       ,A     ).rstrip("""\n"""     )
   assert source_line, f"empty source line for index {index}"
   assert tgt_line, f"empty tgt line for index {index}"

   # Need to add eos token manually for T5
   if isinstance(self.tokenizer       ,A     ):
    source_line += self.tokenizer.eos_token
    tgt_line += self.tokenizer.eos_token

   # Pad source and target to the right
   UpperCAmelCase__       :	str        =       (
       self.tokenizer.question_encoder if isinstance(self.tokenizer       ,A     ) else self.tokenizer
   )
   UpperCAmelCase__       :	Tuple        =       self.tokenizer.generator if isinstance(self.tokenizer       ,A     ) else self.tokenizer

   UpperCAmelCase__       :	Tuple        =       encode_line(A       ,A       ,self.max_source_length       ,"""right"""     )
   UpperCAmelCase__       :	Dict        =       encode_line(A       ,A       ,self.max_target_length       ,"""right"""     )

   UpperCAmelCase__       :	Optional[Any]        =       source_inputs["""input_ids"""].squeeze()
   UpperCAmelCase__       :	List[str]        =       target_inputs["""input_ids"""].squeeze()
   UpperCAmelCase__       :	Union[str, Any]        =       source_inputs["""attention_mask"""].squeeze()
   return {
       "input_ids": source_ids,
       "attention_mask": src_mask,
       "decoder_input_ids": target_ids,
   }

  @staticmethod
  def  __lowercase							(	A		:      int     ):






   '''simple docstring'''
   return [len(A     ) for x in Path(A     ).open().readlines()]




  def  __lowercase							(	self		:      List[Any]       ,A		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	int        =       torch.stack([x["""input_ids"""] for x in batch]     )
   UpperCAmelCase__       :	Union[str, Any]        =       torch.stack([x["""attention_mask"""] for x in batch]     )
   UpperCAmelCase__       :	Any        =       torch.stack([x["""decoder_input_ids"""] for x in batch]     )
   UpperCAmelCase__       :	List[Any]        =       (
       self.tokenizer.generator.pad_token_id
       if isinstance(self.tokenizer       ,A     )
       else self.tokenizer.pad_token_id
   )
   UpperCAmelCase__       :	Any        =       (
       self.tokenizer.question_encoder.pad_token_id
       if isinstance(self.tokenizer       ,A     )
       else self.tokenizer.pad_token_id
   )
   UpperCAmelCase__       :	str        =       trim_batch(A       ,A     )
   UpperCAmelCase__ ,   UpperCAmelCase__       :	Union[str, Any]        =       trim_batch(A       ,A       ,attention_mask=A     )
   UpperCAmelCase__       :	List[str]        =       {
       """input_ids""": source_ids,
       """attention_mask""": source_mask,
       """decoder_input_ids""": y,
   }
   return batch







__UpperCAmelCase			=					getLogger(__name__)
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 return list(itertools.chain.from_iterable(__UpperCamelCase   )   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Dict        =       get_git_info()
 save_json(__UpperCamelCase				,     os.path.join(__UpperCamelCase				,     """git_log.json"""   )   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=4				,     **__UpperCamelCase   ):
 '''simple docstring'''





 with open(__UpperCamelCase				,     """w"""   ) as f:
  json.dump(__UpperCamelCase				,     __UpperCamelCase				,     indent=__UpperCamelCase				,     **__UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 with open(__UpperCamelCase   ) as f:
  return json.load(__UpperCamelCase   )
def 	lowerCAmelCase    (       ):
 '''simple docstring'''





 UpperCAmelCase__       :	Union[str, Any]        =       git.Repo(search_parent_directories=__UpperCamelCase   )
 UpperCAmelCase__       :	List[str]        =       {
     """repo_id""": str(__UpperCamelCase   ),
     """repo_sha""": str(repo.head.object.hexsha   ),
     """repo_branch""": str(repo.active_branch   ),
     """hostname""": str(socket.gethostname()   ),
 }
 return repo_infos
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 return list(map(__UpperCamelCase				,     __UpperCamelCase   )   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 with open(__UpperCamelCase				,     """wb"""   ) as f:
  return pickle.dump(__UpperCamelCase				,     __UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 def remove_articles(__UpperCamelCase   ):
  return re.sub(r"""\b(a|an|the)\b"""				,     """ """				,     __UpperCamelCase   )

 def white_space_fix(__UpperCamelCase   ):
  return " ".join(text.split()   )

 def remove_punc(__UpperCamelCase   ):
  UpperCAmelCase__       :	List[Any]        =       set(string.punctuation   )
  return "".join(ch for ch in text if ch not in exclude   )

 def lower(__UpperCamelCase   ):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase   )   )   )   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Union[str, Any]        =       normalize_answer(__UpperCamelCase   ).split()
 UpperCAmelCase__       :	Dict        =       normalize_answer(__UpperCamelCase   ).split()
 UpperCAmelCase__       :	int        =       Counter(__UpperCamelCase   ) & Counter(__UpperCamelCase   )
 UpperCAmelCase__       :	List[str]        =       sum(common.values()   )
 if num_same == 0:
  return 0
 UpperCAmelCase__       :	str        =       1.0 * num_same / len(__UpperCamelCase   )
 UpperCAmelCase__       :	Optional[int]        =       1.0 * num_same / len(__UpperCamelCase   )
 UpperCAmelCase__       :	Tuple        =       (2 * precision * recall) / (precision + recall)
 return fa
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 return normalize_answer(__UpperCamelCase   ) == normalize_answer(__UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 assert len(__UpperCamelCase   ) == len(__UpperCamelCase   )
 UpperCAmelCase__       :	Union[str, Any]        =       0
 for hypo, pred in zip(__UpperCamelCase				,     __UpperCamelCase   ):
  em += exact_match_score(__UpperCamelCase				,     __UpperCamelCase   )
 if len(__UpperCamelCase   ) > 0:
  em /= len(__UpperCamelCase   )
 return {"em": em}
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 return model_prefix.startswith("""rag"""   )




def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Optional[int]        =       {p: p for p in extra_params}
 # T5 models don't have `dropout` param, they have `dropout_rate` instead
 UpperCAmelCase__       :	str        =       """dropout_rate"""
 for p in extra_params:
  if getattr(__UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   ):
   if not hasattr(__UpperCamelCase				,     __UpperCamelCase   ) and not hasattr(__UpperCamelCase				,     equivalent_param[p]   ):
    logger.info("""config doesn't have a `{}` attribute""".format(__UpperCamelCase   )   )
    delattr(__UpperCamelCase				,     __UpperCamelCase   )
    continue
   UpperCAmelCase__       :	Tuple        =       p if hasattr(__UpperCamelCase				,     __UpperCamelCase   ) else equivalent_param[p]
   setattr(__UpperCamelCase				,     __UpperCamelCase				,     getattr(__UpperCamelCase				,     __UpperCamelCase   )   )
   delattr(__UpperCamelCase				,     __UpperCamelCase   )
 return hparams, config












'''simple docstring'''




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


a__					:			Optional[int]          =     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=False					,				)   ->Optional[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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_ = False							)   ->Union[str, Any]:
	snake_case__	       =   torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		snake_case__	       =   'cuda'
	elif fpaa and not torch.cuda.is_available():
		raise ValueError('`float16` model export is only supported on GPUs with CUDA'							)
	else:
		snake_case__	       =   'cpu'
	snake_case__	       =   Path(UpperCAmelCase_							)

	# VAE DECODER
	snake_case__	       =   AutoencoderKL.from_pretrained(model_path + '/vae'							)
	snake_case__	       =   vae_decoder.config.latent_channels
	# forward only through the decoder part
	snake_case__	       =   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__":
			a__					:			Optional[int]          =     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''')

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



