




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

 # VAE DECODER
 __a			:  List[str]		    =  AutoencoderKL.from_pretrained(model_path + '''/vae'''  )
 __a			:  Optional[int]		    =  vae_decoder.config.latent_channels
 # forward only through the decoder part
 __a			:  int		    =  vae_decoder.decode
 onnx_export(
     _a  ,       model_args=(
         torch.randn(1  ,       _a  ,       2_5  ,       2_5  ).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__":
      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')


import os
import sys
import unittest


lowercase__          =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowercase__          =os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
lowercase__          =os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')



class 		UpperCamelCase__  (	unittest.TestCase   ):
   def 				lowerCAmelCase      (self			: List[Any]       ):
    __a			:  str		    =  get_test_to_tester_mapping(snake_case_       )
    __a			:  Tuple		    =  get_test_to_tester_mapping(snake_case_       )

    __a			:  Union[str, Any]		    =  {'''BertModelTest''': '''BertModelTester'''}

    __a			:  Tuple		    =  {
        '''BlipModelTest''': '''BlipModelTester''',
        '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
        '''BlipTextModelTest''': '''BlipTextModelTester''',
        '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
        '''BlipVQAModelTest''': '''BlipVQAModelTester''',
        '''BlipVisionModelTest''': '''BlipVisionModelTester''',
    }

    self.assertEqual(get_test_info.to_json(snake_case_       ) ,    snake_case_       )
    self.assertEqual(get_test_info.to_json(snake_case_       ) ,    snake_case_       )
   def 				lowerCAmelCase      (self			: str       ):
    __a			:  Optional[int]		    =  get_model_to_test_mapping(snake_case_       )
    __a			:  Any		    =  get_model_to_test_mapping(snake_case_       )

    __a			:  List[Any]		    =  {
        '''BertForMaskedLM''': ['''BertModelTest'''],
        '''BertForMultipleChoice''': ['''BertModelTest'''],
        '''BertForNextSentencePrediction''': ['''BertModelTest'''],
        '''BertForPreTraining''': ['''BertModelTest'''],
        '''BertForQuestionAnswering''': ['''BertModelTest'''],
        '''BertForSequenceClassification''': ['''BertModelTest'''],
        '''BertForTokenClassification''': ['''BertModelTest'''],
        '''BertLMHeadModel''': ['''BertModelTest'''],
        '''BertModel''': ['''BertModelTest'''],
    }

    __a			:  Dict		    =  {
        '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
        '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
        '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
        '''BlipModel''': ['''BlipModelTest'''],
        '''BlipTextModel''': ['''BlipTextModelTest'''],
        '''BlipVisionModel''': ['''BlipVisionModelTest'''],
    }

    self.assertEqual(get_test_info.to_json(snake_case_       ) ,    snake_case_       )
    self.assertEqual(get_test_info.to_json(snake_case_       ) ,    snake_case_       )







   def 				lowerCAmelCase      (self			: int       ):
    __a			:  Any		    =  get_model_to_tester_mapping(snake_case_       )
    __a			:  List[str]		    =  get_model_to_tester_mapping(snake_case_       )

    __a			:  Any		    =  {
        '''BertForMaskedLM''': ['''BertModelTester'''],
        '''BertForMultipleChoice''': ['''BertModelTester'''],
        '''BertForNextSentencePrediction''': ['''BertModelTester'''],
        '''BertForPreTraining''': ['''BertModelTester'''],
        '''BertForQuestionAnswering''': ['''BertModelTester'''],
        '''BertForSequenceClassification''': ['''BertModelTester'''],
        '''BertForTokenClassification''': ['''BertModelTester'''],
        '''BertLMHeadModel''': ['''BertModelTester'''],
        '''BertModel''': ['''BertModelTester'''],
    }

    __a			:  int		    =  {
        '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
        '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
        '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
        '''BlipModel''': ['''BlipModelTester'''],
        '''BlipTextModel''': ['''BlipTextModelTester'''],
        '''BlipVisionModel''': ['''BlipVisionModelTester'''],
    }

    self.assertEqual(get_test_info.to_json(snake_case_       ) ,    snake_case_       )
    self.assertEqual(get_test_info.to_json(snake_case_       ) ,    snake_case_       )
