






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        a       (					unittest.TestCase  ):



      """simple docstring"""


      def __init__( self   :     Tuple						, __lowercase   :     Union[str, Any]						, __lowercase   :     Any=13						, __lowercase   :     Optional[int]=7						, __lowercase   :     str=True						, __lowercase   :     Optional[Any]=True						, __lowercase   :     int=True						, __lowercase   :     int=True						, __lowercase   :     List[str]=99						, __lowercase   :     int=32						, __lowercase   :     int=5						, __lowercase   :     Tuple=4						, __lowercase   :     str=37						, __lowercase   :     Optional[int]="gelu"						, __lowercase   :     Tuple=0.1						, __lowercase   :     str=0.1						, __lowercase   :     Dict=512						, __lowercase   :     List[Any]=16						, __lowercase   :     Dict=2						, __lowercase   :     Union[str, Any]=0.02						, __lowercase   :     Dict=4						, )				-> int:
        __UpperCAmelCase				:      Dict	 =    parent
        __UpperCAmelCase				:      Any	 =    batch_size
        __UpperCAmelCase				:      str	 =    seq_length
        __UpperCAmelCase				:      Tuple	 =    is_training
        __UpperCAmelCase				:      List[Any]	 =    use_attention_mask
        __UpperCAmelCase				:      Dict	 =    use_token_type_ids
        __UpperCAmelCase				:      Optional[int]	 =    use_labels
        __UpperCAmelCase				:      Tuple	 =    vocab_size
        __UpperCAmelCase				:      List[Any]	 =    hidden_size
        __UpperCAmelCase				:      Optional[Any]	 =    num_hidden_layers
        __UpperCAmelCase				:      Any	 =    num_attention_heads
        __UpperCAmelCase				:      List[Any]	 =    intermediate_size
        __UpperCAmelCase				:      Any	 =    hidden_act
        __UpperCAmelCase				:      Any	 =    hidden_dropout_prob
        __UpperCAmelCase				:      List[Any]	 =    attention_probs_dropout_prob
        __UpperCAmelCase				:      Dict	 =    max_position_embeddings
        __UpperCAmelCase				:      List[str]	 =    type_vocab_size
        __UpperCAmelCase				:      str	 =    type_sequence_label_size
        __UpperCAmelCase				:      List[str]	 =    initializer_range
        __UpperCAmelCase				:      Tuple	 =    num_choices
      def       UpperCAmelCase ( self   :     Dict   )				-> Tuple:
        __UpperCAmelCase				:      Any	 =    ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size   )

        __UpperCAmelCase				:      Optional[Any]	 =    None
        if self.use_attention_mask:
          __UpperCAmelCase				:      Any	 =    random_attention_mask([self.batch_size, self.seq_length]   )

        __UpperCAmelCase				:      List[Any]	 =    None
        if self.use_token_type_ids:
          __UpperCAmelCase				:      Dict	 =    ids_tensor([self.batch_size, self.seq_length]						, self.type_vocab_size   )

        __UpperCAmelCase				:      Optional[Any]	 =    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=__lowercase						, initializer_range=self.initializer_range						, )

        return config, input_ids, token_type_ids, attention_mask
      def       UpperCAmelCase ( self   :     Tuple   )				-> List[Any]:
        __UpperCAmelCase				:      int	 =    self.prepare_config_and_inputs()
        __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Union[str, Any]	 =    config_and_inputs
        __UpperCAmelCase				:      List[Any]	 =    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
        return config, inputs_dict




      def       UpperCAmelCase ( self   :     Any   )				-> List[str]:
        __UpperCAmelCase				:      List[Any]	 =    self.prepare_config_and_inputs()
        __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Optional[int]	 =    config_and_inputs

        __UpperCAmelCase				:      int	 =    True
        __UpperCAmelCase				:      str	 =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
        __UpperCAmelCase				:      Union[str, Any]	 =    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        a       (					lowercase__				,			unittest.TestCase  ):



      """simple docstring"""


      a    :					Optional[Any]							=      True

      a    :					List[Any]							=      (
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      def       UpperCAmelCase ( self   :     Dict   )				-> Union[str, Any]:
        __UpperCAmelCase				:      List[str]	 =    FlaxRobertaModelTester(self   )




      @slow
      def       UpperCAmelCase ( self   :     str   )				-> List[Any]:
        for model_class_name in self.all_model_classes:
          __UpperCAmelCase				:      Union[str, Any]	 =    model_class_name.from_pretrained("""roberta-base"""						, from_pt=__lowercase   )
          __UpperCAmelCase				:      int	 =    model(np.ones((1, 1)   )   )
          self.assertIsNotNone(__lowercase   )




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""")





