

import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
    import jax.numpy as jnp

if is_tf_available():
    import tensorflow as tf

if is_torch_available():
    import torch

class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							{
                  """task_specific_params""": {
                      """summarization""": {"""length_penalty""": 1.0, """max_length""": 1_28, """min_length""": 12, """num_beams""": 4},
                      """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_42, """min_length""": 56, """num_beams""": 4},
                      """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
                  }
              }
              __snake_case       	=							{
                  """task_specific_params.summarization.length_penalty""": 1.0,
                  """task_specific_params.summarization.max_length""": 1_28,
                  """task_specific_params.summarization.min_length""": 12,
                  """task_specific_params.summarization.num_beams""": 4,
                  """task_specific_params.summarization_cnn.length_penalty""": 2.0,
                  """task_specific_params.summarization_cnn.max_length""": 1_42,
                  """task_specific_params.summarization_cnn.min_length""": 56,
                  """task_specific_params.summarization_cnn.num_beams""": 4,
                  """task_specific_params.summarization_xsum.length_penalty""": 1.0,
                  """task_specific_params.summarization_xsum.max_length""": 62,
                  """task_specific_params.summarization_xsum.min_length""": 11,
                  """task_specific_params.summarization_xsum.num_beams""": 6,
              }

              self.assertEqual(flatten_dict(_UpperCamelCase   )      , _UpperCamelCase   )


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							np.random.randn(3      , 4   )
              self.assertTrue(np.allclose(transpose(_UpperCamelCase   )      , x.transpose()   )   )

              __snake_case       	=							np.random.randn(3      , 4      , 5   )
              self.assertTrue(np.allclose(transpose(_UpperCamelCase      , axes=(1, 2, 0)   )      , x.transpose((1, 2, 0)   )   )   )


       @require_torch
       def 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							np.random.randn(3      , 4   )
              __snake_case       	=							torch.tensor(_UpperCamelCase   )
              self.assertTrue(np.allclose(transpose(_UpperCamelCase   )      , transpose(_UpperCamelCase   ).numpy()   )   )

              __snake_case       	=							np.random.randn(3      , 4      , 5   )
              __snake_case       	=							torch.tensor(_UpperCamelCase   )
              self.assertTrue(np.allclose(transpose(_UpperCamelCase      , axes=(1, 2, 0)   )      , transpose(_UpperCamelCase      , axes=(1, 2, 0)   ).numpy()   )   )


       @require_tf
       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							np.random.randn(3      , 4   )
              __snake_case       	=							tf.constant(_UpperCamelCase   )
              self.assertTrue(np.allclose(transpose(_UpperCamelCase   )      , transpose(_UpperCamelCase   ).numpy()   )   )

              __snake_case       	=							np.random.randn(3      , 4      , 5   )
              __snake_case       	=							tf.constant(_UpperCamelCase   )
              self.assertTrue(np.allclose(transpose(_UpperCamelCase      , axes=(1, 2, 0)   )      , transpose(_UpperCamelCase      , axes=(1, 2, 0)   ).numpy()   )   )


       @require_flax
       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							np.random.randn(3      , 4   )
              __snake_case       	=							jnp.array(_UpperCamelCase   )
              self.assertTrue(np.allclose(transpose(_UpperCamelCase   )      , np.asarray(transpose(_UpperCamelCase   )   )   )   )

              __snake_case       	=							np.random.randn(3      , 4      , 5   )
              __snake_case       	=							jnp.array(_UpperCamelCase   )
              self.assertTrue(np.allclose(transpose(_UpperCamelCase      , axes=(1, 2, 0)   )      , np.asarray(transpose(_UpperCamelCase      , axes=(1, 2, 0)   )   )   )   )


       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							np.random.randn(3      , 4   )
              self.assertTrue(np.allclose(reshape(_UpperCamelCase      , (4, 3)   )      , np.reshape(_UpperCamelCase      , (4, 3)   )   )   )

              __snake_case       	=							np.random.randn(3      , 4      , 5   )
              self.assertTrue(np.allclose(reshape(_UpperCamelCase      , (12, 5)   )      , np.reshape(_UpperCamelCase      , (12, 5)   )   )   )


       @require_torch
       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							np.random.randn(3      , 4   )
              __snake_case       	=							torch.tensor(_UpperCamelCase   )
              self.assertTrue(np.allclose(reshape(_UpperCamelCase      , (4, 3)   )      , reshape(_UpperCamelCase      , (4, 3)   ).numpy()   )   )

              __snake_case       	=							np.random.randn(3      , 4      , 5   )
              __snake_case       	=							torch.tensor(_UpperCamelCase   )
              self.assertTrue(np.allclose(reshape(_UpperCamelCase      , (12, 5)   )      , reshape(_UpperCamelCase      , (12, 5)   ).numpy()   )   )


       @require_tf
       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							np.random.randn(3      , 4   )
              __snake_case       	=							tf.constant(_UpperCamelCase   )
              self.assertTrue(np.allclose(reshape(_UpperCamelCase      , (4, 3)   )      , reshape(_UpperCamelCase      , (4, 3)   ).numpy()   )   )

              __snake_case       	=							np.random.randn(3      , 4      , 5   )
              __snake_case       	=							tf.constant(_UpperCamelCase   )
              self.assertTrue(np.allclose(reshape(_UpperCamelCase      , (12, 5)   )      , reshape(_UpperCamelCase      , (12, 5)   ).numpy()   )   )


       @require_flax
       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							np.random.randn(3      , 4   )
              __snake_case       	=							jnp.array(_UpperCamelCase   )
              self.assertTrue(np.allclose(reshape(_UpperCamelCase      , (4, 3)   )      , np.asarray(reshape(_UpperCamelCase      , (4, 3)   )   )   )   )

              __snake_case       	=							np.random.randn(3      , 4      , 5   )
              __snake_case       	=							jnp.array(_UpperCamelCase   )
              self.assertTrue(np.allclose(reshape(_UpperCamelCase      , (12, 5)   )      , np.asarray(reshape(_UpperCamelCase      , (12, 5)   )   )   )   )


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							np.random.randn(1      , 3      , 4   )
              self.assertTrue(np.allclose(squeeze(_UpperCamelCase   )      , np.squeeze(_UpperCamelCase   )   )   )

              __snake_case       	=							np.random.randn(1      , 4      , 1      , 5   )
              self.assertTrue(np.allclose(squeeze(_UpperCamelCase      , axis=2   )      , np.squeeze(_UpperCamelCase      , axis=2   )   )   )


       @require_torch
       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							np.random.randn(1      , 3      , 4   )
              __snake_case       	=							torch.tensor(_UpperCamelCase   )
              self.assertTrue(np.allclose(squeeze(_UpperCamelCase   )      , squeeze(_UpperCamelCase   ).numpy()   )   )

              __snake_case       	=							np.random.randn(1      , 4      , 1      , 5   )
              __snake_case       	=							torch.tensor(_UpperCamelCase   )
              self.assertTrue(np.allclose(squeeze(_UpperCamelCase      , axis=2   )      , squeeze(_UpperCamelCase      , axis=2   ).numpy()   )   )


       @require_tf
       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							np.random.randn(1      , 3      , 4   )
              __snake_case       	=							tf.constant(_UpperCamelCase   )
              self.assertTrue(np.allclose(squeeze(_UpperCamelCase   )      , squeeze(_UpperCamelCase   ).numpy()   )   )

              __snake_case       	=							np.random.randn(1      , 4      , 1      , 5   )
              __snake_case       	=							tf.constant(_UpperCamelCase   )
              self.assertTrue(np.allclose(squeeze(_UpperCamelCase      , axis=2   )      , squeeze(_UpperCamelCase      , axis=2   ).numpy()   )   )


       @require_flax
       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							np.random.randn(1      , 3      , 4   )
              __snake_case       	=							jnp.array(_UpperCamelCase   )
              self.assertTrue(np.allclose(squeeze(_UpperCamelCase   )      , np.asarray(squeeze(_UpperCamelCase   )   )   )   )

              __snake_case       	=							np.random.randn(1      , 4      , 1      , 5   )
              __snake_case       	=							jnp.array(_UpperCamelCase   )
              self.assertTrue(np.allclose(squeeze(_UpperCamelCase      , axis=2   )      , np.asarray(squeeze(_UpperCamelCase      , axis=2   )   )   )   )


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






              """simple docstring"""


              __snake_case       	=							np.random.randn(3      , 4   )
              self.assertTrue(np.allclose(expand_dims(_UpperCamelCase      , axis=1   )      , np.expand_dims(_UpperCamelCase      , axis=1   )   )   )


       @require_torch
       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							np.random.randn(3      , 4   )
              __snake_case       	=							torch.tensor(_UpperCamelCase   )
              self.assertTrue(np.allclose(expand_dims(_UpperCamelCase      , axis=1   )      , expand_dims(_UpperCamelCase      , axis=1   ).numpy()   )   )


       @require_tf
       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							np.random.randn(3      , 4   )
              __snake_case       	=							tf.constant(_UpperCamelCase   )
              self.assertTrue(np.allclose(expand_dims(_UpperCamelCase      , axis=1   )      , expand_dims(_UpperCamelCase      , axis=1   ).numpy()   )   )






       @require_flax
       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							np.random.randn(3      , 4   )
              __snake_case       	=							jnp.array(_UpperCamelCase   )
              self.assertTrue(np.allclose(expand_dims(_UpperCamelCase      , axis=1   )      , np.asarray(expand_dims(_UpperCamelCase      , axis=1   )   )   )   )




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 						lowerCamelCase__      ( __A    :Any	,__A    :tuple	,__A    :Path	,__A    :int	,__A    :Union[str, Any]	,__A    :Optional[Any]	,__A    :Optional[Any]	,__A    :List[Any]=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 						lowerCamelCase__      ( __A    :str	,__A    :str	,__A    :int	,__A    :bool = False		):


       """simple docstring"""




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

       # VAE DECODER
       __snake_case       	=							AutoencoderKL.from_pretrained(model_path + """/vae"""		)
       __snake_case       	=							vae_decoder.config.latent_channels
       # forward only through the decoder part
       __snake_case       	=							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__":
    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''')

