

'''simple docstring'''






import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowerCAmelCase__											=	logging.getLogger(__name__)






@dataclass
class 			lowercase_	:




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  str
  SCREAMING_SNAKE_CASE    :  List[str]
  SCREAMING_SNAKE_CASE    :  Optional[List[str]]






@dataclass
class 			lowercase_	:




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[int]
  SCREAMING_SNAKE_CASE    :  List[int]
  SCREAMING_SNAKE_CASE    :  Optional[List[int]]							=			None
  SCREAMING_SNAKE_CASE    :  Optional[List[int]]							=			None






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[str]							=			'train'
  SCREAMING_SNAKE_CASE    :  int							=			'dev'
  SCREAMING_SNAKE_CASE    :  List[str]							=			'test'






class 			lowercase_	:




  """simple docstring"""




  @staticmethod
  def 					SCREAMING_SNAKE_CASE      (      lowercase__   :     Optional[Any]					,lowercase__   :     Union[Split, str]			):
   raise NotImplementedError






  @staticmethod
  def 					SCREAMING_SNAKE_CASE      (      lowercase__   :     str			):
   raise NotImplementedError






  @staticmethod
  def 					SCREAMING_SNAKE_CASE      (      lowercase__   :     List[InputExample]					,lowercase__   :     List[str]					,lowercase__   :     int					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     int=False					,lowercase__   :     List[Any]="[CLS]"					,lowercase__   :     int=1					,lowercase__   :     Optional[int]="[SEP]"					,lowercase__   :     int=False					,lowercase__   :     Any=False					,lowercase__   :     Optional[Any]=0					,lowercase__   :     Any=0					,lowercase__   :     Optional[int]=-1_0_0					,lowercase__   :     str=0					,lowercase__   :     Optional[int]=True					,):
   __lowercase							  = {label: i for i, label in enumerate(lowercase__			)}

   __lowercase							  = []
   for ex_index, example in enumerate(lowercase__			):
    if ex_index % 1_0_0_0_0 == 0:
     logger.info('''Writing example %d of %d'''					,lowercase__					,len(lowercase__			)			)

    __lowercase							  = []
    __lowercase							  = []
    for word, label in zip(example.words					,example.labels			):
     __lowercase							  = tokenizer.tokenize(lowercase__			)

     # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
     if len(lowercase__			) > 0:
      tokens.extend(lowercase__			)
      # Use the real label id for the first token of the word, and padding ids for the remaining tokens
      label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowercase__			) - 1)			)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
    __lowercase							  = tokenizer.num_special_tokens_to_add()
    if len(lowercase__			) > max_seq_length - special_tokens_count:
     __lowercase							  = tokens[: (max_seq_length - special_tokens_count)]
     __lowercase							  = label_ids[: (max_seq_length - special_tokens_count)]

    # The convention in BERT is:
    # (a) For sequence pairs:
    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
    #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
    # (b) For single sequences:
    #  tokens:   [CLS] the dog is hairy . [SEP]
    #  type_ids:   0   0   0   0  0     0   0
    #
    # Where "type_ids" are used to indicate whether this is the first
    # sequence or the second sequence. The embedding vectors for `type=0` and
    # `type=1` were learned during pre-training and are added to the wordpiece
    # embedding vector (and position vector). This is not *strictly* necessary
    # since the [SEP] token unambiguously separates the sequences, but it makes
    # it easier for the model to learn the concept of sequences.
    #
    # For classification tasks, the first vector (corresponding to [CLS]) is
    # used as the "sentence vector". Note that this only makes sense because
    # the entire model is fine-tuned.
    tokens += [sep_token]
    label_ids += [pad_token_label_id]
    if sep_token_extra:
     # roberta uses an extra separator b/w pairs of sentences
     tokens += [sep_token]
     label_ids += [pad_token_label_id]
    __lowercase							  = [sequence_a_segment_id] * len(lowercase__			)

    if cls_token_at_end:
     tokens += [cls_token]
     label_ids += [pad_token_label_id]
     segment_ids += [cls_token_segment_id]
    else:
     __lowercase							  = [cls_token] + tokens
     __lowercase							  = [pad_token_label_id] + label_ids
     __lowercase							  = [cls_token_segment_id] + segment_ids

    __lowercase							  = tokenizer.convert_tokens_to_ids(lowercase__			)

    # The mask has 1 for real tokens and 0 for padding tokens. Only real
    # tokens are attended to.
    __lowercase							  = [1 if mask_padding_with_zero else 0] * len(lowercase__			)

    # Zero-pad up to the sequence length.
    __lowercase							  = max_seq_length - len(lowercase__			)
    if pad_on_left:
     __lowercase							  = ([pad_token] * padding_length) + input_ids
     __lowercase							  = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
     __lowercase							  = ([pad_token_segment_id] * padding_length) + segment_ids
     __lowercase							  = ([pad_token_label_id] * padding_length) + label_ids
    else:
     input_ids += [pad_token] * padding_length
     input_mask += [0 if mask_padding_with_zero else 1] * padding_length
     segment_ids += [pad_token_segment_id] * padding_length
     label_ids += [pad_token_label_id] * padding_length

    assert len(lowercase__			) == max_seq_length
    assert len(lowercase__			) == max_seq_length
    assert len(lowercase__			) == max_seq_length
    assert len(lowercase__			) == max_seq_length

    if ex_index < 5:
     logger.info('''*** Example ***'''			)
     logger.info('''guid: %s'''					,example.guid			)
     logger.info('''tokens: %s'''					,''' '''.join([str(lowercase__			) for x in tokens]			)			)
     logger.info('''input_ids: %s'''					,''' '''.join([str(lowercase__			) for x in input_ids]			)			)
     logger.info('''input_mask: %s'''					,''' '''.join([str(lowercase__			) for x in input_mask]			)			)
     logger.info('''segment_ids: %s'''					,''' '''.join([str(lowercase__			) for x in segment_ids]			)			)
     logger.info('''label_ids: %s'''					,''' '''.join([str(lowercase__			) for x in label_ids]			)			)

    if "token_type_ids" not in tokenizer.model_input_names:
     __lowercase							  = None

    features.append(
        InputFeatures(
            input_ids=lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,label_ids=lowercase__			)			)
   return features


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset






 class 			lowercase_	(lowerCamelCase__      ):




   """simple docstring"""




   SCREAMING_SNAKE_CASE    :  List[InputFeatures]
   SCREAMING_SNAKE_CASE    :  int							=			nn.CrossEntropyLoss().ignore_index






   def __init__(      self   :     List[str]					,lowercase__   :     TokenClassificationTask					,lowercase__   :     str					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     List[str]					,lowercase__   :     str					,lowercase__   :     Optional[int] = None					,lowercase__   :     Any=False					,lowercase__   :     Split = Split.train					,):
    # Load data features from cache or dataset file
    __lowercase							  = os.path.join(
        lowercase__					,'''cached_{}_{}_{}'''.format(mode.value					,tokenizer.__class__.__name__					,str(lowercase__			)			)					,)

    # Make sure only the first process in distributed training processes the dataset,
    # and the others will use the cache.
    __lowercase							  = cached_features_file + '''.lock'''
    with FileLock(lowercase__			):
     if os.path.exists(lowercase__			) and not overwrite_cache:
      logger.info(F"Loading features from cached file {cached_features_file}"			)
      __lowercase							  = torch.load(lowercase__			)
     else:
      logger.info(F"Creating features from dataset file at {data_dir}"			)
      __lowercase							  = token_classification_task.read_examples_from_file(lowercase__					,lowercase__			)
      # TODO clean up all this to leverage built-in features of tokenizers
      __lowercase							  = token_classification_task.convert_examples_to_features(
          lowercase__					,lowercase__					,lowercase__					,lowercase__					,cls_token_at_end=bool(model_type in ['''xlnet''']			)					,cls_token=tokenizer.cls_token					,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,sep_token=tokenizer.sep_token					,sep_token_extra=lowercase__					,pad_on_left=bool(tokenizer.padding_side == '''left'''			)					,pad_token=tokenizer.pad_token_id					,pad_token_segment_id=tokenizer.pad_token_type_id					,pad_token_label_id=self.pad_token_label_id					,)
      logger.info(F"Saving features into cached file {cached_features_file}"			)
      torch.save(self.features					,lowercase__			)






   def __len__(      self   :     List[str]			):
    return len(self.features			)






   def __getitem__(      self   :     List[Any]					,lowercase__   :     Optional[Any]			):
    return self.features[i]


if is_tf_available():
 import tensorflow as tf






 class 			lowercase_	:




   """simple docstring"""




   SCREAMING_SNAKE_CASE    :  List[InputFeatures]
   SCREAMING_SNAKE_CASE    :  int							=			-1_0_0






   def __init__(      self   :     str					,lowercase__   :     TokenClassificationTask					,lowercase__   :     str					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     List[str]					,lowercase__   :     str					,lowercase__   :     Optional[int] = None					,lowercase__   :     int=False					,lowercase__   :     Split = Split.train					,):
    __lowercase							  = token_classification_task.read_examples_from_file(lowercase__					,lowercase__			)
    # TODO clean up all this to leverage built-in features of tokenizers
    __lowercase							  = token_classification_task.convert_examples_to_features(
        lowercase__					,lowercase__					,lowercase__					,lowercase__					,cls_token_at_end=bool(model_type in ['''xlnet''']			)					,cls_token=tokenizer.cls_token					,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,sep_token=tokenizer.sep_token					,sep_token_extra=lowercase__					,pad_on_left=bool(tokenizer.padding_side == '''left'''			)					,pad_token=tokenizer.pad_token_id					,pad_token_segment_id=tokenizer.pad_token_type_id					,pad_token_label_id=self.pad_token_label_id					,)

    def gen():
     for ex in self.features:
      if ex.token_type_ids is None:
       yield (
           {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
           ex.label_ids,
       )
      else:
       yield (
           {
               "input_ids": ex.input_ids,
               "attention_mask": ex.attention_mask,
               "token_type_ids": ex.token_type_ids,
           },
           ex.label_ids,
       )

    if "token_type_ids" not in tokenizer.model_input_names:
     __lowercase							  = tf.data.Dataset.from_generator(
         lowercase__					,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)					,(
             {'''input_ids''': tf.TensorShape([None]			), '''attention_mask''': tf.TensorShape([None]			)},
             tf.TensorShape([None]			),
         )					,)
    else:
     __lowercase							  = tf.data.Dataset.from_generator(
         lowercase__					,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)					,(
             {
                 '''input_ids''': tf.TensorShape([None]			),
                 '''attention_mask''': tf.TensorShape([None]			),
                 '''token_type_ids''': tf.TensorShape([None]			),
             },
             tf.TensorShape([None]			),
         )					,)






   def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
    __lowercase							  = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features			)			)			)

    return self.dataset






   def __len__(      self   :     Any			):
    return len(self.features			)






   def __getitem__(      self   :     Union[str, Any]					,lowercase__   :     str			):
    return self.features[i]




'''simple docstring'''






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCAmelCase__											=	version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__=False     ,				):






 """simple docstring"""



 output_path.parent.mkdir(parents=A__     ,				exist_ok=A__      )
 # 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(
      A__     ,				A__     ,				f=output_path.as_posix()     ,				input_names=A__     ,				output_names=A__     ,				dynamic_axes=A__     ,				do_constant_folding=A__     ,				use_external_data_format=A__     ,				enable_onnx_checker=A__     ,				opset_version=A__     ,				)
 else:
  export(
      A__     ,				A__     ,				f=output_path.as_posix()     ,				input_names=A__     ,				output_names=A__     ,				dynamic_axes=A__     ,				do_constant_folding=A__     ,				opset_version=A__     ,				)


@torch.no_grad()
def 		_A     (    A__     ,				A__     ,				A__     ,				A__ = False      ):






 """simple docstring"""



 __lowercase							  = torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  __lowercase							  = '''cuda'''
 elif fpaa and not torch.cuda.is_available():
  raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''      )
 else:
  __lowercase							  = '''cpu'''
 __lowercase							  = Path(A__      )

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


if __name__ == "__main__":
 lowerCAmelCase__											=	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__											=	parser.parse_args()
 print(args.output_path)
 convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
 print('''SD: Done: ONNX''')

