


import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BridgeTowerImageProcessor







class       lowerCamelCase__   (       unittest.TestCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase = True		,     UpperCAmelCase = None		,     UpperCAmelCase = 3_2		,     UpperCAmelCase = True		,     UpperCAmelCase = 1 / 2_5_5		,     UpperCAmelCase = True		,     UpperCAmelCase = True		,     UpperCAmelCase = [0.4814_5466, 0.457_8275, 0.4082_1073]		,     UpperCAmelCase = [0.2686_2954, 0.2613_0258, 0.2757_7711]		,     UpperCAmelCase = True		,     UpperCAmelCase=7		,     UpperCAmelCase=3_0		,     UpperCAmelCase=4_0_0		,     UpperCAmelCase=3		,     )     ->   List[str]:
      _lowercase							      =parent
      _lowercase							      =do_resize
      _lowercase							      =size if size is not None else {"""shortest_edge""": 2_8_8}
      _lowercase							      =size_divisor
      _lowercase							      =do_rescale
      _lowercase							      =rescale_factor
      _lowercase							      =do_normalize
      _lowercase							      =do_center_crop
      _lowercase							      =image_mean
      _lowercase							      =image_std
      _lowercase							      =do_pad
      _lowercase							      =batch_size
      _lowercase							      =num_channels
      _lowercase							      =min_resolution
      _lowercase							      =max_resolution





     def      __A					(self       )     ->   Union[str, Any]:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase=False       )     ->   Optional[int]:
      if not batched:
       _lowercase							      =self.size["""shortest_edge"""]
       _lowercase							      =image_inputs[0]
       if isinstance(lowerCamelCase__		,     Image.Image       ):
        _lowercase							      =image.size
       else:
        _lowercase							      =image.shape[1], image.shape[2]
       _lowercase							      =size / min(lowerCamelCase__		,     lowerCamelCase__       )
       if h < w:
        _lowercase							      =size, scale * w
       else:
        _lowercase							      =scale * h, size

       _lowercase							      =int((1_3_3_3 / 8_0_0) * size       )
       if max(lowerCamelCase__		,     lowerCamelCase__       ) > max_size:
        _lowercase							      =max_size / max(lowerCamelCase__		,     lowerCamelCase__       )
        _lowercase							      =newh * scale
        _lowercase							      =neww * scale

       _lowercase							      =int(newh + 0.5       ), int(neww + 0.5       )
       _lowercase							      =(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

      else:
       _lowercase							      =[]
       for image in image_inputs:
        _lowercase							      =self.get_expected_values([image]       )
        expected_values.append((expected_height, expected_width)       )
       _lowercase							      =max(lowerCamelCase__		,     key=lambda UpperCAmelCase       : item[0]       )[0]
       _lowercase							      =max(lowerCamelCase__		,     key=lambda UpperCAmelCase       : item[1]       )[1]

      return expected_height, expected_width







@require_torch
@require_vision
class       lowerCamelCase__   (       SCREAMING_SNAKE_CASE__   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			BridgeTowerImageProcessor if is_vision_available() else None





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =BridgeTowerImageProcessingTester(self       )





     @property
     def      __A					(self       )     ->   List[str]:
      return self.image_processor_tester.prepare_image_processor_dict()





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =self.image_processing_class(**self.image_processor_dict       )
      self.assertTrue(hasattr(lowerCamelCase__		,     '''image_mean'''       )       )
      self.assertTrue(hasattr(lowerCamelCase__		,     '''image_std'''       )       )
      self.assertTrue(hasattr(lowerCamelCase__		,     '''do_normalize'''       )       )
      self.assertTrue(hasattr(lowerCamelCase__		,     '''do_resize'''       )       )
      self.assertTrue(hasattr(lowerCamelCase__		,     '''size'''       )       )
      self.assertTrue(hasattr(lowerCamelCase__		,     '''size_divisor'''       )       )





     def      __A					(self       )     ->   Optional[Any]:
      pass





     def      __A					(self       )     ->   Union[str, Any]:
      # Initialize image processor
      _lowercase							      =self.image_processing_class(**self.image_processor_dict       )
      # create random PIL images
      _lowercase							      =prepare_image_inputs(self.image_processor_tester		,     equal_resolution=lowerCamelCase__       )
      for image in image_inputs:
       self.assertIsInstance(lowerCamelCase__		,     Image.Image       )

      # Test not batched input
      _lowercase							      =image_processing(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values

      _lowercase							      =self.image_processor_tester.get_expected_values(lowerCamelCase__       )
      self.assertEqual(
          encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

      # Test batched
      _lowercase							      =image_processing(lowerCamelCase__		,     return_tensors='''pt'''       ).pixel_values

      _lowercase							      =self.image_processor_tester.get_expected_values(lowerCamelCase__		,     batched=lowerCamelCase__       )
      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )		,     )





     def      __A					(self       )     ->   Tuple:
      # Initialize image processor
      _lowercase							      =self.image_processing_class(**self.image_processor_dict       )
      # create random numpy tensors
      _lowercase							      =prepare_image_inputs(self.image_processor_tester		,     equal_resolution=lowerCamelCase__		,     numpify=lowerCamelCase__       )
      for image in image_inputs:
       self.assertIsInstance(lowerCamelCase__		,     np.ndarray       )

      # Test not batched input
      _lowercase							      =image_processing(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values

      _lowercase							      =self.image_processor_tester.get_expected_values(lowerCamelCase__       )
      self.assertEqual(
          encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

      # Test batched
      _lowercase							      =image_processing(lowerCamelCase__		,     return_tensors='''pt'''       ).pixel_values

      _lowercase							      =self.image_processor_tester.get_expected_values(lowerCamelCase__		,     batched=lowerCamelCase__       )
      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )		,     )





     def      __A					(self       )     ->   int:
      # Initialize image processor
      _lowercase							      =self.image_processing_class(**self.image_processor_dict       )
      # create random PyTorch tensors
      _lowercase							      =prepare_image_inputs(self.image_processor_tester		,     equal_resolution=lowerCamelCase__		,     torchify=lowerCamelCase__       )
      for image in image_inputs:
       self.assertIsInstance(lowerCamelCase__		,     torch.Tensor       )

      # Test not batched input
      _lowercase							      =image_processing(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values

      _lowercase							      =self.image_processor_tester.get_expected_values(lowerCamelCase__       )
      self.assertEqual(
          encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

      # Test batched
      _lowercase							      =image_processing(lowerCamelCase__		,     return_tensors='''pt'''       ).pixel_values

      _lowercase							      =self.image_processor_tester.get_expected_values(lowerCamelCase__		,     batched=lowerCamelCase__       )
      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )		,     )






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


SCREAMING_SNAKE_CASE_:List[Any]             =						version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=False      ,     )       ->  int:



    """simple docstring"""






    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      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = False		)       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        A :       Union[str, Any]          =   """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 :       Any          =   Path(_lowerCAmelCase		)

    # VAE DECODER
    A :       Union[str, Any]          =   AutoencoderKL.from_pretrained(model_path + """/vae"""		)
    A :       Any          =   vae_decoder.config.latent_channels
    # forward only through the decoder part
    A :       Optional[int]          =   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__":
     SCREAMING_SNAKE_CASE_:Tuple             =						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""")

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


