






import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
     from transformers.models.roberta.modeling_flax_roberta import (
         FlaxRobertaForCausalLM,
         FlaxRobertaForMaskedLM,
         FlaxRobertaForMultipleChoice,
         FlaxRobertaForQuestionAnswering,
         FlaxRobertaForSequenceClassification,
         FlaxRobertaForTokenClassification,
         FlaxRobertaModel,
     )






class 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      def __init__( self :		Optional[int]					,   __UpperCAmelCase :		Union[str, Any]					,   __UpperCAmelCase :		Dict=13					,   __UpperCAmelCase :		List[str]=7					,   __UpperCAmelCase :		int=True					,   __UpperCAmelCase :		Union[str, Any]=True					,   __UpperCAmelCase :		Optional[Any]=True					,   __UpperCAmelCase :		Tuple=True					,   __UpperCAmelCase :		Any=99					,   __UpperCAmelCase :		Tuple=32					,   __UpperCAmelCase :		int=5					,   __UpperCAmelCase :		str=4					,   __UpperCAmelCase :		str=37					,   __UpperCAmelCase :		str="gelu"					,   __UpperCAmelCase :		Any=0.1					,   __UpperCAmelCase :		int=0.1					,   __UpperCAmelCase :		List[Any]=512					,   __UpperCAmelCase :		Tuple=16					,   __UpperCAmelCase :		Tuple=2					,   __UpperCAmelCase :		Dict=0.02					,   __UpperCAmelCase :		str=4					,   )       ->Union[str, Any]:




       """simple docstring"""


       a						    = parent
       a						    = batch_size
       a						    = seq_length
       a						    = is_training
       a						    = use_attention_mask
       a						    = use_token_type_ids
       a						    = use_labels
       a						    = vocab_size
       a						    = hidden_size
       a						    = num_hidden_layers
       a						    = num_attention_heads
       a						    = intermediate_size
       a						    = hidden_act
       a						    = hidden_dropout_prob
       a						    = attention_probs_dropout_prob
       a						    = max_position_embeddings
       a						    = type_vocab_size
       a						    = type_sequence_label_size
       a						    = initializer_range
       a						    = num_choices







      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->Optional[int]:




       """simple docstring"""


       a						    = ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size    )

       a						    = None
       if self.use_attention_mask:
        a						    = random_attention_mask([self.batch_size, self.seq_length]    )

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

       a						    = RobertaConfig(
           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=__UpperCAmelCase					,   initializer_range=self.initializer_range					,   )

       return config, input_ids, token_type_ids, attention_mask







      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->int:




       """simple docstring"""


       a						    = self.prepare_config_and_inputs()
       a    ,	a    ,	a    ,	a						    = config_and_inputs
       a						    = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict







      def  __lowerCAmelCase   ( self :		int    )       ->Optional[int]:




       """simple docstring"""


       a						    = self.prepare_config_and_inputs()
       a    ,	a    ,	a    ,	a						    = config_and_inputs

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

       return (
           config,
           input_ids,
           token_type_ids,
           encoder_hidden_states,
           encoder_attention_mask,
       )






@require_flax
class 						lowercase_   (      lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




      __snake_case	    =    True

      __snake_case	    =    (
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )







      def  __lowerCAmelCase   ( self :		List[Any]    )       ->Optional[Any]:




       """simple docstring"""


       a						    = FlaxRobertaModelTester(self    )







      @slow
      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->Tuple:




       """simple docstring"""


       for model_class_name in self.all_model_classes:
        a						    = model_class_name.from_pretrained('''roberta-base'''					,   from_pt=__UpperCAmelCase    )
        a						    = model(np.ones((1, 1)    )    )
        self.assertIsNotNone(__UpperCAmelCase    )










import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCAmelCase__							  =						version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 		_a       ( a					:Union[str, Any]      ,				a					:tuple      ,				a					:Path      ,				a					:List[Any]      ,				a					:Union[str, Any]      ,				a					:Optional[int]      ,				a					:List[Any]      ,				a					:Union[str, Any]=False      ,				)				->     Any:
 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					:str      ,				a					:str      ,				a					:int      ,				a					:bool = False					)				->     Union[str, Any]:
 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(a					)

 # 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(
     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__":
     UpperCAmelCase__							  =						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")

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


