





"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor






@require_vision
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				tempfile.mkdtemp()

         lowerCAmelCase__     :Union[str, Any]											=				[
             '[UNK]',
             '[CLS]',
             '[SEP]',
             '[PAD]',
             '[MASK]',
             '的',
             '价',
             '格',
             '是',
             '15',
             '便',
             'alex',
             '##andra',
             '，',
             '。',
             '-',
             't',
             'shirt',
         ]
         lowerCAmelCase__     :List[str]											=				os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES['vocab_file'] )
         with open(self.vocab_file       ,					'w'       ,					encoding='utf-8' ) as vocab_writer:
           vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )

         lowerCAmelCase__     :Tuple											=				{
             'do_resize': True,
             'size': {'height': 2_2_4, 'width': 2_2_4},
             'do_center_crop': True,
             'crop_size': {'height': 1_8, 'width': 1_8},
             'do_normalize': True,
             'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
             'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
             'do_convert_rgb': True,
         }
         lowerCAmelCase__     :List[Any]											=				os.path.join(self.tmpdirname       ,					__UpperCAmelCase )
         with open(self.image_processor_file       ,					'w'       ,					encoding='utf-8' ) as fp:
           json.dump(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         return BertTokenizer.from_pretrained(self.tmpdirname       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         return BertTokenizerFast.from_pretrained(self.tmpdirname       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname       ,					**__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         shutil.rmtree(self.tmpdirname )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				[np.random.randint(2_5_5       ,					size=(3, 3_0, 4_0_0)       ,					dtype=np.uinta )]

         lowerCAmelCase__     :str											=				[Image.fromarray(np.moveaxis(__UpperCAmelCase       ,					0       ,					-1 ) ) for x in image_inputs]

         return image_inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				self.get_tokenizer()
         lowerCAmelCase__     :Any											=				self.get_rust_tokenizer()
         lowerCAmelCase__     :List[Any]											=				self.get_image_processor()

         lowerCAmelCase__     :Any											=				ChineseCLIPProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )
         processor_slow.save_pretrained(self.tmpdirname )
         lowerCAmelCase__     :List[Any]											=				ChineseCLIPProcessor.from_pretrained(self.tmpdirname       ,					use_fast=__UpperCAmelCase )

         lowerCAmelCase__     :str											=				ChineseCLIPProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )
         processor_fast.save_pretrained(self.tmpdirname )
         lowerCAmelCase__     :Tuple											=				ChineseCLIPProcessor.from_pretrained(self.tmpdirname )

         self.assertEqual(processor_slow.tokenizer.get_vocab()       ,					tokenizer_slow.get_vocab() )
         self.assertEqual(processor_fast.tokenizer.get_vocab()       ,					tokenizer_fast.get_vocab() )
         self.assertEqual(tokenizer_slow.get_vocab()       ,					tokenizer_fast.get_vocab() )
         self.assertIsInstance(processor_slow.tokenizer       ,					__UpperCAmelCase )
         self.assertIsInstance(processor_fast.tokenizer       ,					__UpperCAmelCase )

         self.assertEqual(processor_slow.image_processor.to_json_string()       ,					image_processor.to_json_string() )
         self.assertEqual(processor_fast.image_processor.to_json_string()       ,					image_processor.to_json_string() )
         self.assertIsInstance(processor_slow.image_processor       ,					__UpperCAmelCase )
         self.assertIsInstance(processor_fast.image_processor       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				ChineseCLIPProcessor(tokenizer=self.get_tokenizer()       ,					image_processor=self.get_image_processor() )
         processor.save_pretrained(self.tmpdirname )

         lowerCAmelCase__     :Any											=				self.get_tokenizer(cls_token='(CLS)'       ,					sep_token='(SEP)' )
         lowerCAmelCase__     :Union[str, Any]											=				self.get_image_processor(do_normalize=__UpperCAmelCase )

         lowerCAmelCase__     :Dict											=				ChineseCLIPProcessor.from_pretrained(
             self.tmpdirname       ,					cls_token='(CLS)'       ,					sep_token='(SEP)'       ,					do_normalize=__UpperCAmelCase )

         self.assertEqual(processor.tokenizer.get_vocab()       ,					tokenizer_add_kwargs.get_vocab() )
         self.assertIsInstance(processor.tokenizer       ,					__UpperCAmelCase )

         self.assertEqual(processor.image_processor.to_json_string()       ,					image_processor_add_kwargs.to_json_string() )
         self.assertIsInstance(processor.image_processor       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.get_image_processor()
         lowerCAmelCase__     :int											=				self.get_tokenizer()

         lowerCAmelCase__     :Optional[int]											=				ChineseCLIPProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )

         lowerCAmelCase__     :Any											=				self.prepare_image_inputs()

         lowerCAmelCase__     :int											=				image_processor(__UpperCAmelCase       ,					return_tensors='np' )
         lowerCAmelCase__     :List[Any]											=				processor(images=__UpperCAmelCase       ,					return_tensors='np' )

         for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()       ,					input_processor[key].sum()       ,					delta=1E-2 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				self.get_image_processor()
         lowerCAmelCase__     :str											=				self.get_tokenizer()

         lowerCAmelCase__     :Any											=				ChineseCLIPProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )

         lowerCAmelCase__     :Any											=				'Alexandra，T-shirt的价格是15便士。'

         lowerCAmelCase__     :str											=				processor(text=__UpperCAmelCase )

         lowerCAmelCase__     :List[str]											=				tokenizer(__UpperCAmelCase )

         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]       ,					encoded_processor[key] )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				self.get_image_processor()
         lowerCAmelCase__     :Optional[Any]											=				self.get_tokenizer()

         lowerCAmelCase__     :Optional[int]											=				ChineseCLIPProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )

         lowerCAmelCase__     :int											=				'Alexandra，T-shirt的价格是15便士。'
         lowerCAmelCase__     :int											=				self.prepare_image_inputs()

         lowerCAmelCase__     :List[str]											=				processor(text=__UpperCAmelCase       ,					images=__UpperCAmelCase )

         self.assertListEqual(list(inputs.keys() )       ,					['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )

         # test if it raises when no input is passed
         with pytest.raises(__UpperCAmelCase ):
           processor()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.get_image_processor()
         lowerCAmelCase__     :Optional[Any]											=				self.get_tokenizer()

         lowerCAmelCase__     :Union[str, Any]											=				ChineseCLIPProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )

         lowerCAmelCase__     :Any											=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowerCAmelCase__     :str											=				processor.batch_decode(__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				tokenizer.batch_decode(__UpperCAmelCase )

         self.assertListEqual(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				self.get_image_processor()
         lowerCAmelCase__     :Optional[int]											=				self.get_tokenizer()

         lowerCAmelCase__     :List[str]											=				ChineseCLIPProcessor(tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase )

         lowerCAmelCase__     :str											=				'Alexandra，T-shirt的价格是15便士。'
         lowerCAmelCase__     :Optional[Any]											=				self.prepare_image_inputs()

         lowerCAmelCase__     :Dict											=				processor(text=__UpperCAmelCase       ,					images=__UpperCAmelCase )

         self.assertListEqual(list(inputs.keys() )       ,					processor.model_input_names )







"""simple docstring"""





import argparse
import os
import shutil
from pathlib import Path

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

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline


__A											=						version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=False	,  )   ->Tuple:





  """simple docstring"""


  output_path.parent.mkdir(parents=_SCREAMING_SNAKE_CASE	,  exist_ok=_SCREAMING_SNAKE_CASE     )
  # 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(
        _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  f=output_path.as_posix()	,  input_names=_SCREAMING_SNAKE_CASE	,  output_names=_SCREAMING_SNAKE_CASE	,  dynamic_axes=_SCREAMING_SNAKE_CASE	,  do_constant_folding=_SCREAMING_SNAKE_CASE	,  use_external_data_format=_SCREAMING_SNAKE_CASE	,  enable_onnx_checker=_SCREAMING_SNAKE_CASE	,  opset_version=_SCREAMING_SNAKE_CASE	,  )
  else:
    export(
        _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  f=output_path.as_posix()	,  input_names=_SCREAMING_SNAKE_CASE	,  output_names=_SCREAMING_SNAKE_CASE	,  dynamic_axes=_SCREAMING_SNAKE_CASE	,  do_constant_folding=_SCREAMING_SNAKE_CASE	,  opset_version=_SCREAMING_SNAKE_CASE	,  )
@torch.no_grad()
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE = False     )   ->List[Any]:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				torch.floataa if fpaa else torch.floataa
  if fpaa and torch.cuda.is_available():
    lowerCAmelCase__     :Tuple											=				'cuda'
  elif fpaa and not torch.cuda.is_available():
    raise ValueError('`float16` model export is only supported on GPUs with CUDA'     )
  else:
    lowerCAmelCase__     :List[Any]											=				'cpu'
  lowerCAmelCase__     :List[str]											=				StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE	,  torch_dtype=_SCREAMING_SNAKE_CASE     ).to(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Dict											=				Path(_SCREAMING_SNAKE_CASE     )

  # TEXT ENCODER
  lowerCAmelCase__     :str											=				pipeline.text_encoder.config.max_position_embeddings
  lowerCAmelCase__     :Dict											=				pipeline.text_encoder.config.hidden_size
  lowerCAmelCase__     :List[Any]											=				pipeline.tokenizer(
      'A sample prompt'	,  padding='max_length'	,  max_length=pipeline.tokenizer.model_max_length	,  truncation=_SCREAMING_SNAKE_CASE	,  return_tensors='pt'	,  )
  onnx_export(
      pipeline.text_encoder	,  model_args=(text_input.input_ids.to(device=_SCREAMING_SNAKE_CASE	,  dtype=torch.intaa     ))	,  output_path=output_path / 'text_encoder' / 'model.onnx'	,  ordered_input_names=['input_ids']	,  output_names=['last_hidden_state', 'pooler_output']	,  dynamic_axes={
          'input_ids': {0: 'batch', 1: 'sequence'},
      }	,  opset=_SCREAMING_SNAKE_CASE	,  )
  del pipeline.text_encoder

  # UNET
  lowerCAmelCase__     :int											=				pipeline.unet.config.in_channels
  lowerCAmelCase__     :Optional[Any]											=				pipeline.unet.config.sample_size
  lowerCAmelCase__     :Dict											=				output_path / 'unet' / 'model.onnx'
  onnx_export(
      pipeline.unet	,  model_args=(
          torch.randn(2	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ).to(device=_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     ),
          torch.randn(2     ).to(device=_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     ),
          torch.randn(2	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ).to(device=_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     ),
          False,
      )	,  output_path=_SCREAMING_SNAKE_CASE	,  ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict']	,  output_names=['out_sample']	,  dynamic_axes={
          'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
          'timestep': {0: 'batch'},
          'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
      }	,  opset=_SCREAMING_SNAKE_CASE	,  use_external_data_format=_SCREAMING_SNAKE_CASE	,  )
  lowerCAmelCase__     :List[Any]											=				str(unet_path.absolute().as_posix()     )
  lowerCAmelCase__     :int											=				os.path.dirname(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :str											=				onnx.load(_SCREAMING_SNAKE_CASE     )
  # clean up existing tensor files
  shutil.rmtree(_SCREAMING_SNAKE_CASE     )
  os.mkdir(_SCREAMING_SNAKE_CASE     )
  # collate external tensor files into one
  onnx.save_model(
      _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  save_as_external_data=_SCREAMING_SNAKE_CASE	,  all_tensors_to_one_file=_SCREAMING_SNAKE_CASE	,  location='weights.pb'	,  convert_attribute=_SCREAMING_SNAKE_CASE	,  )
  del pipeline.unet

  # VAE ENCODER
  lowerCAmelCase__     :int											=				pipeline.vae
  lowerCAmelCase__     :Optional[Any]											=				vae_encoder.config.in_channels
  lowerCAmelCase__     :int											=				vae_encoder.config.sample_size
  # need to get the raw tensor output (sample) from the encoder
  lowerCAmelCase__     :str											=				lambda _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     : vae_encoder.encode(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )[0].sample()
  onnx_export(
      _SCREAMING_SNAKE_CASE	,  model_args=(
          torch.randn(1	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ).to(device=_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     ),
          False,
      )	,  output_path=output_path / 'vae_encoder' / 'model.onnx'	,  ordered_input_names=['sample', 'return_dict']	,  output_names=['latent_sample']	,  dynamic_axes={
          'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
      }	,  opset=_SCREAMING_SNAKE_CASE	,  )

  # VAE DECODER
  lowerCAmelCase__     :int											=				pipeline.vae
  lowerCAmelCase__     :List[Any]											=				vae_decoder.config.latent_channels
  lowerCAmelCase__     :Optional[int]											=				vae_decoder.config.out_channels
  # forward only through the decoder part
  lowerCAmelCase__     :Any											=				vae_encoder.decode
  onnx_export(
      _SCREAMING_SNAKE_CASE	,  model_args=(
          torch.randn(1	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ).to(device=_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     ),
          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=_SCREAMING_SNAKE_CASE	,  )
  del pipeline.vae

  # SAFETY CHECKER
  if pipeline.safety_checker is not None:
    lowerCAmelCase__     :Optional[int]											=				pipeline.safety_checker
    lowerCAmelCase__     :Optional[int]											=				safety_checker.config.vision_config.num_channels
    lowerCAmelCase__     :Any											=				safety_checker.config.vision_config.image_size
    lowerCAmelCase__     :List[str]											=				safety_checker.forward_onnx
    onnx_export(
        pipeline.safety_checker	,  model_args=(
            torch.randn(
                1	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  ).to(device=_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     ),
            torch.randn(1	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ).to(device=_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     ),
        )	,  output_path=output_path / 'safety_checker' / 'model.onnx'	,  ordered_input_names=['clip_input', 'images']	,  output_names=['out_images', 'has_nsfw_concepts']	,  dynamic_axes={
            'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
            'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
        }	,  opset=_SCREAMING_SNAKE_CASE	,  )
    del pipeline.safety_checker
    lowerCAmelCase__     :Union[str, Any]											=				OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'     )
    lowerCAmelCase__     :Dict											=				pipeline.feature_extractor
  else:
    lowerCAmelCase__     :Tuple											=				None
    lowerCAmelCase__     :Optional[int]											=				None

  lowerCAmelCase__     :List[str]											=				OnnxStableDiffusionPipeline(
      vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder'     )	,  vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder'     )	,  text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder'     )	,  tokenizer=pipeline.tokenizer	,  unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet'     )	,  scheduler=pipeline.scheduler	,  safety_checker=_SCREAMING_SNAKE_CASE	,  feature_extractor=_SCREAMING_SNAKE_CASE	,  requires_safety_checker=safety_checker is not None	,  )

  onnx_pipeline.save_pretrained(_SCREAMING_SNAKE_CASE     )
  print('ONNX pipeline saved to'	,  _SCREAMING_SNAKE_CASE     )

  del pipeline
  del onnx_pipeline
  lowerCAmelCase__     :Dict											=				OnnxStableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE	,  provider='CPUExecutionProvider'     )
  print('ONNX pipeline is loadable'     )


if __name__ == "__main__":
  __A											=						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""")

  __A											=						parser.parse_args()

  convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
