

'''simple docstring'''






import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_PRETRAINING_MAPPING,
     NezhaForMaskedLM,
     NezhaForMultipleChoice,
     NezhaForNextSentencePrediction,
     NezhaForPreTraining,
     NezhaForQuestionAnswering,
     NezhaForSequenceClassification,
     NezhaForTokenClassification,
     NezhaModel,
 )
 from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST






class      lowercase_      :




  """simple docstring"""




  def __init__( self  :	Dict   ,lowercase__  :	Dict   ,lowercase__  :	int=1_3   ,lowercase__  :	List[str]=7   ,lowercase__  :	int=True   ,lowercase__  :	int=True   ,lowercase__  :	Union[str, Any]=True   ,lowercase__  :	List[Any]=True   ,lowercase__  :	str=9_9   ,lowercase__  :	Optional[Any]=3_2   ,lowercase__  :	Union[str, Any]=5   ,lowercase__  :	List[Any]=4   ,lowercase__  :	str=3_7   ,lowercase__  :	Tuple="gelu"   ,lowercase__  :	List[Any]=0.1   ,lowercase__  :	Dict=0.1   ,lowercase__  :	int=1_2_8   ,lowercase__  :	Dict=3_2   ,lowercase__  :	Dict=1_6   ,lowercase__  :	Any=2   ,lowercase__  :	int=0.0_2   ,lowercase__  :	List[str]=3   ,lowercase__  :	Dict=4   ,lowercase__  :	Optional[int]=None   ,):
   __lowercase				    =					parent
   __lowercase				    =					batch_size
   __lowercase				    =					seq_length
   __lowercase				    =					is_training
   __lowercase				    =					use_input_mask
   __lowercase				    =					use_token_type_ids
   __lowercase				    =					use_labels
   __lowercase				    =					vocab_size
   __lowercase				    =					hidden_size
   __lowercase				    =					num_hidden_layers
   __lowercase				    =					num_attention_heads
   __lowercase				    =					intermediate_size
   __lowercase				    =					hidden_act
   __lowercase				    =					hidden_dropout_prob
   __lowercase				    =					attention_probs_dropout_prob
   __lowercase				    =					max_position_embeddings
   __lowercase				    =					type_vocab_size
   __lowercase				    =					type_sequence_label_size
   __lowercase				    =					initializer_range
   __lowercase				    =					num_labels
   __lowercase				    =					num_choices
   __lowercase				    =					scope






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   __lowercase				    =					ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size     )

   __lowercase				    =					None
   if self.use_input_mask:
    __lowercase				    =					random_attention_mask([self.batch_size, self.seq_length]     )

   __lowercase				    =					None
   if self.use_token_type_ids:
    __lowercase				    =					ids_tensor([self.batch_size, self.seq_length]   ,self.type_vocab_size     )

   __lowercase				    =					None
   __lowercase				    =					None
   __lowercase				    =					None
   if self.use_labels:
    __lowercase				    =					ids_tensor([self.batch_size]   ,self.type_sequence_label_size     )
    __lowercase				    =					ids_tensor([self.batch_size, self.seq_length]   ,self.num_labels     )
    __lowercase				    =					ids_tensor([self.batch_size]   ,self.num_choices     )

   __lowercase				    =					self.get_config()

   return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   return NezhaConfig(
       vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,max_position_embeddings=self.max_position_embeddings   ,type_vocab_size=self.type_vocab_size   ,is_decoder=lowercase__   ,initializer_range=self.initializer_range   ,)






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   (
       (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      
   )				    =					self.prepare_config_and_inputs()

   __lowercase				    =					True
   __lowercase				    =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
   __lowercase				    =					ids_tensor([self.batch_size, self.seq_length]   ,vocab_size=2     )

   return (
       config,
       input_ids,
       token_type_ids,
       input_mask,
       sequence_labels,
       token_labels,
       choice_labels,
       encoder_hidden_states,
       encoder_attention_mask,
   )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]   ,lowercase__  :	Union[str, Any]   ,lowercase__  :	List[str]   ,lowercase__  :	List[str]   ,lowercase__  :	List[str]   ,lowercase__  :	Tuple   ,lowercase__  :	Tuple   ,lowercase__  :	str     ):
   __lowercase				    =					NezhaModel(config=lowercase__     )
   model.to(lowercase__     )
   model.eval()
   __lowercase				    =					model(lowercase__   ,attention_mask=lowercase__   ,token_type_ids=lowercase__     )
   __lowercase				    =					model(lowercase__   ,token_type_ids=lowercase__     )
   __lowercase				    =					model(lowercase__     )
   self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)     )
   self.parent.assertEqual(result.pooler_output.shape   ,(self.batch_size, self.hidden_size)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]   ,lowercase__  :	Dict   ,lowercase__  :	str   ,lowercase__  :	Optional[Any]   ,lowercase__  :	Optional[Any]   ,lowercase__  :	List[str]   ,lowercase__  :	Tuple   ,lowercase__  :	Tuple   ,lowercase__  :	Optional[int]   ,lowercase__  :	List[Any]   ,):
   __lowercase				    =					True
   __lowercase				    =					NezhaModel(lowercase__     )
   model.to(lowercase__     )
   model.eval()
   __lowercase				    =					model(
       lowercase__   ,attention_mask=lowercase__   ,token_type_ids=lowercase__   ,encoder_hidden_states=lowercase__   ,encoder_attention_mask=lowercase__   ,)
   __lowercase				    =					model(
       lowercase__   ,attention_mask=lowercase__   ,token_type_ids=lowercase__   ,encoder_hidden_states=lowercase__   ,)
   __lowercase				    =					model(lowercase__   ,attention_mask=lowercase__   ,token_type_ids=lowercase__     )
   self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)     )
   self.parent.assertEqual(result.pooler_output.shape   ,(self.batch_size, self.hidden_size)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	List[str]   ,lowercase__  :	Dict   ,lowercase__  :	Tuple   ,lowercase__  :	Optional[Any]   ,lowercase__  :	List[Any]   ,lowercase__  :	List[Any]   ,lowercase__  :	Optional[Any]     ):
   __lowercase				    =					NezhaForMaskedLM(config=lowercase__     )
   model.to(lowercase__     )
   model.eval()
   __lowercase				    =					model(lowercase__   ,attention_mask=lowercase__   ,token_type_ids=lowercase__   ,labels=lowercase__     )
   self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.vocab_size)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,lowercase__  :	List[str]   ,lowercase__  :	Dict   ,lowercase__  :	Any   ,lowercase__  :	int   ,lowercase__  :	Union[str, Any]   ,lowercase__  :	Optional[int]   ,lowercase__  :	Any     ):
   __lowercase				    =					NezhaForNextSentencePrediction(config=lowercase__     )
   model.to(lowercase__     )
   model.eval()
   __lowercase				    =					model(
       lowercase__   ,attention_mask=lowercase__   ,token_type_ids=lowercase__   ,labels=lowercase__   ,)
   self.parent.assertEqual(result.logits.shape   ,(self.batch_size, 2)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	str   ,lowercase__  :	Dict   ,lowercase__  :	Tuple   ,lowercase__  :	Dict   ,lowercase__  :	Tuple   ,lowercase__  :	int   ,lowercase__  :	int     ):
   __lowercase				    =					NezhaForPreTraining(config=lowercase__     )
   model.to(lowercase__     )
   model.eval()
   __lowercase				    =					model(
       lowercase__   ,attention_mask=lowercase__   ,token_type_ids=lowercase__   ,labels=lowercase__   ,next_sentence_label=lowercase__   ,)
   self.parent.assertEqual(result.prediction_logits.shape   ,(self.batch_size, self.seq_length, self.vocab_size)     )
   self.parent.assertEqual(result.seq_relationship_logits.shape   ,(self.batch_size, 2)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]   ,lowercase__  :	Union[str, Any]   ,lowercase__  :	Optional[Any]   ,lowercase__  :	Tuple   ,lowercase__  :	List[str]   ,lowercase__  :	Dict   ,lowercase__  :	Optional[int]   ,lowercase__  :	Union[str, Any]     ):
   __lowercase				    =					NezhaForQuestionAnswering(config=lowercase__     )
   model.to(lowercase__     )
   model.eval()
   __lowercase				    =					model(
       lowercase__   ,attention_mask=lowercase__   ,token_type_ids=lowercase__   ,start_positions=lowercase__   ,end_positions=lowercase__   ,)
   self.parent.assertEqual(result.start_logits.shape   ,(self.batch_size, self.seq_length)     )
   self.parent.assertEqual(result.end_logits.shape   ,(self.batch_size, self.seq_length)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	Tuple   ,lowercase__  :	str   ,lowercase__  :	List[str]   ,lowercase__  :	Dict   ,lowercase__  :	Any   ,lowercase__  :	Optional[int]   ,lowercase__  :	int     ):
   __lowercase				    =					self.num_labels
   __lowercase				    =					NezhaForSequenceClassification(lowercase__     )
   model.to(lowercase__     )
   model.eval()
   __lowercase				    =					model(lowercase__   ,attention_mask=lowercase__   ,token_type_ids=lowercase__   ,labels=lowercase__     )
   self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,lowercase__  :	Union[str, Any]   ,lowercase__  :	List[str]   ,lowercase__  :	int   ,lowercase__  :	List[Any]   ,lowercase__  :	List[Any]   ,lowercase__  :	Any   ,lowercase__  :	Optional[Any]     ):
   __lowercase				    =					self.num_labels
   __lowercase				    =					NezhaForTokenClassification(config=lowercase__     )
   model.to(lowercase__     )
   model.eval()
   __lowercase				    =					model(lowercase__   ,attention_mask=lowercase__   ,token_type_ids=lowercase__   ,labels=lowercase__     )
   self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.num_labels)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,lowercase__  :	List[Any]   ,lowercase__  :	List[Any]   ,lowercase__  :	Optional[Any]   ,lowercase__  :	List[str]   ,lowercase__  :	Dict   ,lowercase__  :	List[Any]   ,lowercase__  :	str     ):
   __lowercase				    =					self.num_choices
   __lowercase				    =					NezhaForMultipleChoice(config=lowercase__     )
   model.to(lowercase__     )
   model.eval()
   __lowercase				    =					input_ids.unsqueeze(1     ).expand(-1   ,self.num_choices   ,-1     ).contiguous()
   __lowercase				    =					token_type_ids.unsqueeze(1     ).expand(-1   ,self.num_choices   ,-1     ).contiguous()
   __lowercase				    =					input_mask.unsqueeze(1     ).expand(-1   ,self.num_choices   ,-1     ).contiguous()
   __lowercase				    =					model(
       lowercase__   ,attention_mask=lowercase__   ,token_type_ids=lowercase__   ,labels=lowercase__   ,)
   self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_choices)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					self.prepare_config_and_inputs()
   (
       (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      
   )				    =					config_and_inputs
   __lowercase				    =					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_torch
class      lowercase_      (lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Dict							     =     (
      (
          NezhaModel,
          NezhaForMaskedLM,
          NezhaForMultipleChoice,
          NezhaForNextSentencePrediction,
          NezhaForPreTraining,
          NezhaForQuestionAnswering,
          NezhaForSequenceClassification,
          NezhaForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE       :    Tuple							     =     (
      {
          'feature-extraction': NezhaModel,
          'fill-mask': NezhaForMaskedLM,
          'question-answering': NezhaForQuestionAnswering,
          'text-classification': NezhaForSequenceClassification,
          'token-classification': NezhaForTokenClassification,
          'zero-shot': NezhaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE       :    List[str]							     =     True






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	List[str]   ,lowercase__  :	str   ,lowercase__  :	Any=False     ):
   __lowercase				    =					super()._prepare_for_class(lowercase__   ,lowercase__   ,return_labels=lowercase__     )

   if return_labels:
    if model_class in get_values(lowercase__     ):
     __lowercase				    =					torch.zeros(
         (self.model_tester.batch_size, self.model_tester.seq_length)   ,dtype=torch.long   ,device=lowercase__     )
     __lowercase				    =					torch.zeros(
         self.model_tester.batch_size   ,dtype=torch.long   ,device=lowercase__     )
   return inputs_dict






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   __lowercase				    =					NezhaModelTester(self     )
   __lowercase				    =					ConfigTester(self   ,config_class=lowercase__   ,hidden_size=3_7     )






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   self.config_tester.run_common_tests()






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(*lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   # This regression test was failing with PyTorch < 1.3
   (
       (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      (
       __lowercase
   )    ,      
   )				    =					self.model_tester.prepare_config_and_inputs_for_decoder()

   __lowercase				    =					None

   self.model_tester.create_and_check_model_as_decoder(
       lowercase__   ,lowercase__   ,lowercase__   ,lowercase__   ,lowercase__   ,lowercase__   ,lowercase__   ,lowercase__   ,lowercase__   ,)






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_multiple_choice(*lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_next_sequence_prediction(*lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*lowercase__     )






  @slow
  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase				    =					NezhaModel.from_pretrained(lowercase__     )
    self.assertIsNotNone(lowercase__     )






  @slow
  @require_torch_gpu
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   __lowercase    ,      __lowercase				    =					self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes:
    # NezhaForMultipleChoice behaves incorrectly in JIT environments.
    if model_class == NezhaForMultipleChoice:
     return

    __lowercase				    =					True
    __lowercase				    =					model_class(config=lowercase__     )

    __lowercase				    =					self._prepare_for_class(lowercase__   ,lowercase__     )
    __lowercase				    =					torch.jit.trace(
        lowercase__   ,(inputs_dict['''input_ids'''].to('''cpu'''     ), inputs_dict['''attention_mask'''].to('''cpu'''     ))     )

    with tempfile.TemporaryDirectory() as tmp:
     torch.jit.save(lowercase__   ,os.path.join(lowercase__   ,'''bert.pt'''     )     )
     __lowercase				    =					torch.jit.load(os.path.join(lowercase__   ,'''bert.pt'''     )   ,map_location=lowercase__     )
     loaded(inputs_dict['''input_ids'''].to(lowercase__     )   ,inputs_dict['''attention_mask'''].to(lowercase__     )     )






@require_torch
class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  @slow
  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					NezhaModel.from_pretrained('''sijunhe/nezha-cn-base'''     )
   __lowercase				    =					torch.tensor([[0, 1, 2, 3, 4, 5]]     )
   __lowercase				    =					torch.tensor([[0, 1, 1, 1, 1, 1]]     )
   with torch.no_grad():
    __lowercase				    =					model(lowercase__   ,attention_mask=lowercase__     )[0]
   __lowercase				    =					torch.Size((1, 6, 7_6_8)     )
   self.assertEqual(output.shape   ,lowercase__     )
   __lowercase				    =					torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]]     )

   self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   ,lowercase__   ,atol=1e-4     )     )






  @slow
  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base'''     )
   __lowercase				    =					torch.tensor([[0, 1, 2, 3, 4, 5]]     )
   __lowercase				    =					torch.tensor([[1, 1, 1, 1, 1, 1]]     )
   with torch.no_grad():
    __lowercase				    =					model(lowercase__   ,attention_mask=lowercase__     )[0]
   __lowercase				    =					torch.Size((1, 6, 2_1_1_2_8)     )
   self.assertEqual(output.shape   ,lowercase__     )
   __lowercase				    =					torch.tensor(
       [[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]]     )

   self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   ,lowercase__   ,atol=1e-4     )     )



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      UpperCamelCase   (       __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					tuple							,   __lowerCamelCase	:					Path							,   __lowerCamelCase	:					Tuple							,   __lowerCamelCase	:					Any							,   __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					Optional[Any]=False							,   ):
       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      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					bool = False       ):
       snake_case			:  Optional[int]     							= torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              snake_case			:  str     							= "cuda"
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("`float16` model export is only supported on GPUs with CUDA"       )
       else:
              snake_case			:  Tuple     							= "cpu"
       snake_case			:  Union[str, Any]     							= Path(__lowerCamelCase       )

       # VAE DECODER
       snake_case			:  Any     							= AutoencoderKL.from_pretrained(model_path + "/vae"       )
       snake_case			:  Tuple     							= vae_decoder.config.latent_channels
       # forward only through the decoder part
       snake_case			:  Tuple     							= vae_decoder.decode
       onnx_export(
           __lowerCamelCase							,   model_args=(
               torch.randn(1							,   __lowerCamelCase							,   25							,   25       ).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 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""")





