





'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import (
			    AutoProcessor,
			    BertTokenizerFast,
			    BlipImageProcessor,
			    GPTaTokenizer,
			    InstructBlipProcessor,
			    PreTrainedTokenizerFast,
			)






@require_vision
class 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		Optional[Any]:
		snake_case__	       =   tempfile.mkdtemp()

		snake_case__	       =   BlipImageProcessor()
		snake_case__	       =   GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'		)
		snake_case__	       =   BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert'		)

		snake_case__	       =   InstructBlipProcessor(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)

		processor.save_pretrained(self.tmpdirname		)





	def   _snake_case   (		self      ,       **UpperCamelCase_		)      ->		Optional[int]:
		return AutoProcessor.from_pretrained(self.tmpdirname      ,       **UpperCamelCase_		).tokenizer





	def   _snake_case   (		self      ,       **UpperCamelCase_		)      ->		Tuple:
		return AutoProcessor.from_pretrained(self.tmpdirname      ,       **UpperCamelCase_		).image_processor





	def   _snake_case   (		self      ,       **UpperCamelCase_		)      ->		Optional[Any]:
		return AutoProcessor.from_pretrained(self.tmpdirname      ,       **UpperCamelCase_		).qformer_tokenizer





	def   _snake_case   (		self		)      ->		Tuple:
		shutil.rmtree(self.tmpdirname		)





	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   [np.random.randint(255      ,       size=(3, 30, 400)      ,       dtype=np.uinta		)]

		snake_case__	       =   [Image.fromarray(np.moveaxis(UpperCamelCase_      ,       0      ,       -1		)		) for x in image_inputs]

		return image_inputs





	def   _snake_case   (		self		)      ->		Optional[Any]:
		snake_case__	       =   InstructBlipProcessor(
		    tokenizer=self.get_tokenizer()      ,       image_processor=self.get_image_processor()      ,       qformer_tokenizer=self.get_qformer_tokenizer()      ,       )
		processor.save_pretrained(self.tmpdirname		)

		snake_case__	       =   self.get_tokenizer(bos_token='(BOS)'      ,       eos_token='(EOS)'		)
		snake_case__	       =   self.get_image_processor(do_normalize=UpperCamelCase_      ,       padding_value=1.0		)

		snake_case__	       =   InstructBlipProcessor.from_pretrained(
		    self.tmpdirname      ,       bos_token='(BOS)'      ,       eos_token='(EOS)'      ,       do_normalize=UpperCamelCase_      ,       padding_value=1.0		)

		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_		)
		self.assertIsInstance(processor.qformer_tokenizer      ,       UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.get_image_processor()
		snake_case__	       =   self.get_tokenizer()
		snake_case__	       =   self.get_qformer_tokenizer()

		snake_case__	       =   InstructBlipProcessor(
		    tokenizer=UpperCamelCase_      ,       image_processor=UpperCamelCase_      ,       qformer_tokenizer=UpperCamelCase_		)

		snake_case__	       =   self.prepare_image_inputs()

		snake_case__	       =   image_processor(UpperCamelCase_      ,       return_tensors='np'		)
		snake_case__	       =   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		)      ->		List[Any]:
		snake_case__	       =   self.get_image_processor()
		snake_case__	       =   self.get_tokenizer()
		snake_case__	       =   self.get_qformer_tokenizer()

		snake_case__	       =   InstructBlipProcessor(
		    tokenizer=UpperCamelCase_      ,       image_processor=UpperCamelCase_      ,       qformer_tokenizer=UpperCamelCase_		)

		snake_case__	       =   'lower newer'

		snake_case__	       =   processor(text=UpperCamelCase_		)

		snake_case__	       =   tokenizer(UpperCamelCase_      ,       return_token_type_ids=UpperCamelCase_		)
		snake_case__	       =   qformer_tokenizer(UpperCamelCase_      ,       return_token_type_ids=UpperCamelCase_		)

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

		for key in encoded_tokens_qformer.keys():
			self.assertListEqual(encoded_tokens_qformer[key]      ,       encoded_processor['qformer_' + key]		)





	def   _snake_case   (		self		)      ->		Dict:
		snake_case__	       =   self.get_image_processor()
		snake_case__	       =   self.get_tokenizer()
		snake_case__	       =   self.get_qformer_tokenizer()

		snake_case__	       =   InstructBlipProcessor(
		    tokenizer=UpperCamelCase_      ,       image_processor=UpperCamelCase_      ,       qformer_tokenizer=UpperCamelCase_		)

		snake_case__	       =   'lower newer'
		snake_case__	       =   self.prepare_image_inputs()

		snake_case__	       =   processor(text=UpperCamelCase_      ,       images=UpperCamelCase_		)

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

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





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   self.get_image_processor()
		snake_case__	       =   self.get_tokenizer()
		snake_case__	       =   self.get_qformer_tokenizer()

		snake_case__	       =   InstructBlipProcessor(
		    tokenizer=UpperCamelCase_      ,       image_processor=UpperCamelCase_      ,       qformer_tokenizer=UpperCamelCase_		)

		snake_case__	       =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		snake_case__	       =   processor.batch_decode(UpperCamelCase_		)
		snake_case__	       =   tokenizer.batch_decode(UpperCamelCase_		)

		self.assertListEqual(UpperCamelCase_      ,       UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   self.get_image_processor()
		snake_case__	       =   self.get_tokenizer()
		snake_case__	       =   self.get_qformer_tokenizer()

		snake_case__	       =   InstructBlipProcessor(
		    tokenizer=UpperCamelCase_      ,       image_processor=UpperCamelCase_      ,       qformer_tokenizer=UpperCamelCase_		)

		snake_case__	       =   'lower newer'
		snake_case__	       =   self.prepare_image_inputs()

		snake_case__	       =   processor(text=UpperCamelCase_      ,       images=UpperCamelCase_		)

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










'''simple docstring'''




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


a__					:			Optional[int]          =     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=False					,				)   ->Optional[int]:
	output_path.parent.mkdir(parents=UpperCAmelCase_					,				exist_ok=UpperCAmelCase_							)
	# 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(
		    UpperCAmelCase_					,				UpperCAmelCase_					,				f=output_path.as_posix()					,				input_names=UpperCAmelCase_					,				output_names=UpperCAmelCase_					,				dynamic_axes=UpperCAmelCase_					,				do_constant_folding=UpperCAmelCase_					,				use_external_data_format=UpperCAmelCase_					,				enable_onnx_checker=UpperCAmelCase_					,				opset_version=UpperCAmelCase_					,				)
	else:
		export(
		    UpperCAmelCase_					,				UpperCAmelCase_					,				f=output_path.as_posix()					,				input_names=UpperCAmelCase_					,				output_names=UpperCAmelCase_					,				dynamic_axes=UpperCAmelCase_					,				do_constant_folding=UpperCAmelCase_					,				opset_version=UpperCAmelCase_					,				)




@torch.no_grad()
def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_ = False							)   ->Union[str, Any]:
	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(UpperCAmelCase_							)

	# 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(
	    UpperCAmelCase_					,				model_args=(
	        torch.randn(1					,				UpperCAmelCase_					,				25					,				25							).to(device=UpperCAmelCase_					,				dtype=UpperCAmelCase_							),
	        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=UpperCAmelCase_					,				)
	del vae_decoder


if __name__ == "__main__":
			a__					:			Optional[int]          =     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__					:			List[str]          =     parser.parse_args()
			print(args.output_path)
			convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
			print('''SD: Done: ONNX''')



