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  _lowerCamelCase					(   a_				: int						,			a_				: tuple						,			a_				: Path						,			a_				: List[Any]						,			a_				: List[str]						,			a_				: List[str]						,			a_				: Dict						,			a_				: Any=False						,			):
			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):
			lowerCamelCase						:Optional[Any]						  =     torch.floataa if fpaa else torch.floataa
			if fpaa and torch.cuda.is_available():
						lowerCamelCase						: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:
						lowerCamelCase						:int						  =     'cpu'
			lowerCamelCase						:Any						  =     StableDiffusionPipeline.from_pretrained(a_						,			torch_dtype=a_).to(a_)
			lowerCamelCase						:Optional[int]						  =     Path(a_)

			# TEXT ENCODER
			lowerCamelCase						:Any						  =     pipeline.text_encoder.config.max_position_embeddings
			lowerCamelCase						:Tuple						  =     pipeline.text_encoder.config.hidden_size
			lowerCamelCase						:str						  =     pipeline.tokenizer(
			    '''A sample prompt'''						,			padding='''max_length'''						,			max_length=pipeline.tokenizer.model_max_length						,			truncation=a_						,			return_tensors='''pt'''						,			)
			onnx_export(
			    pipeline.text_encoder						,			model_args=(text_input.input_ids.to(device=a_						,			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=a_						,			)
			del pipeline.text_encoder

			# UNET
			lowerCamelCase						:List[str]						  =     pipeline.unet.config.in_channels
			lowerCamelCase						:str						  =     pipeline.unet.config.sample_size
			lowerCamelCase						:List[str]						  =     output_path / 'unet' / 'model.onnx'
			onnx_export(
			    pipeline.unet						,			model_args=(
			        torch.randn(2						,			a_						,			a_						,			a_).to(device=a_						,			dtype=a_),
			        torch.randn(2).to(device=a_						,			dtype=a_),
			        torch.randn(2						,			a_						,			a_).to(device=a_						,			dtype=a_),
			        False,
			    )						,			output_path=a_						,			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=a_						,			use_external_data_format=a_						,			)
			lowerCamelCase						:Optional[int]						  =     str(unet_path.absolute().as_posix())
			lowerCamelCase						:List[Any]						  =     os.path.dirname(a_)
			lowerCamelCase						:List[str]						  =     onnx.load(a_)
			# clean up existing tensor files
			shutil.rmtree(a_)
			os.mkdir(a_)
			# collate external tensor files into one
			onnx.save_model(
			    a_						,			a_						,			save_as_external_data=a_						,			all_tensors_to_one_file=a_						,			location='''weights.pb'''						,			convert_attribute=a_						,			)
			del pipeline.unet

			# VAE ENCODER
			lowerCamelCase						:Any						  =     pipeline.vae
			lowerCamelCase						:Optional[Any]						  =     vae_encoder.config.in_channels
			lowerCamelCase						:List[Any]						  =     vae_encoder.config.sample_size
			# need to get the raw tensor output (sample) from the encoder
			lowerCamelCase						:Dict						  =     lambda a_						,			a_: vae_encoder.encode(a_						,			a_)[0].sample()
			onnx_export(
			    a_						,			model_args=(
			        torch.randn(1						,			a_						,			a_						,			a_).to(device=a_						,			dtype=a_),
			        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=a_						,			)

			# VAE DECODER
			lowerCamelCase						:List[str]						  =     pipeline.vae
			lowerCamelCase						:Any						  =     vae_decoder.config.latent_channels
			lowerCamelCase						:Optional[Any]						  =     vae_decoder.config.out_channels
			# forward only through the decoder part
			lowerCamelCase						:List[str]						  =     vae_encoder.decode
			onnx_export(
			    a_						,			model_args=(
			        torch.randn(1						,			a_						,			a_						,			a_).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 pipeline.vae

			# SAFETY CHECKER
			if pipeline.safety_checker is not None:
						lowerCamelCase						:int						  =     pipeline.safety_checker
						lowerCamelCase						:List[str]						  =     safety_checker.config.vision_config.num_channels
						lowerCamelCase						:str						  =     safety_checker.config.vision_config.image_size
						lowerCamelCase						:Tuple						  =     safety_checker.forward_onnx
						onnx_export(
						    pipeline.safety_checker						,			model_args=(
						        torch.randn(
						            1						,			a_						,			a_						,			a_						,			).to(device=a_						,			dtype=a_),
						        torch.randn(1						,			a_						,			a_						,			a_).to(device=a_						,			dtype=a_),
						    )						,			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=a_						,			)
						del pipeline.safety_checker
						lowerCamelCase						:Any						  =     OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker''')
						lowerCamelCase						:Optional[Any]						  =     pipeline.feature_extractor
			else:
						lowerCamelCase						:List[str]						  =     None
						lowerCamelCase						:List[str]						  =     None

			lowerCamelCase						:Dict						  =     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=a_						,			feature_extractor=a_						,			requires_safety_checker=safety_checker is not None						,			)

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

			del pipeline
			del onnx_pipeline
			lowerCamelCase						:Any						  =     OnnxStableDiffusionPipeline.from_pretrained(a_						,			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)




import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     LEDTokenizer
							_UpperCAmelCase						    =     LEDTokenizerFast
							_UpperCAmelCase						    =     True







							def 				snake_case  (				self     :    Any		):
										super().setUp()
										lowerCamelCase						:Optional[int]						  =     [
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''<unk>''',
										]
										lowerCamelCase						:Any						  =     dict(zip(__snake_case	,					range(len(__snake_case		)		)		)		)
										lowerCamelCase						:List[str]						  =     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										lowerCamelCase						:int						  =     {'''unk_token''': '''<unk>'''}

										lowerCamelCase						:Optional[int]						  =     os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES['''vocab_file''']		)
										lowerCamelCase						:Any						  =     os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES['''merges_file''']		)
										with open(self.vocab_file	,					'''w'''	,					encoding='''utf-8'''		) as fp:
													fp.write(json.dumps(__snake_case		) + '''\n'''		)
										with open(self.merges_file	,					'''w'''	,					encoding='''utf-8'''		) as fp:
													fp.write('''\n'''.join(__snake_case		)		)







							def 				snake_case  (				self     :    int	,					**__snake_case     :    int		):
										kwargs.update(self.special_tokens_map		)
										return self.tokenizer_class.from_pretrained(self.tmpdirname	,					**__snake_case		)







							def 				snake_case  (				self     :    Dict	,					**__snake_case     :    Any		):
										kwargs.update(self.special_tokens_map		)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,					**__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    Union[str, Any]		):
										return "lower newer", "lower newer"







							@cached_property
							def 				snake_case  (				self     :    Any		):
										return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''		)







							@cached_property
							def 				snake_case  (				self     :    int		):
										return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''		)







							@require_torch
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Any						  =     ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										lowerCamelCase						:List[Any]						  =     [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Optional[Any]						  =     tokenizer(__snake_case	,					max_length=len(__snake_case		)	,					padding=__snake_case	,					return_tensors='''pt'''		)
													self.assertIsInstance(__snake_case	,					__snake_case		)

													self.assertEqual((2, 9)	,					batch.input_ids.shape		)
													self.assertEqual((2, 9)	,					batch.attention_mask.shape		)
													lowerCamelCase						:List[Any]						  =     batch.input_ids.tolist()[0]
													self.assertListEqual(__snake_case	,					__snake_case		)







							@require_torch
							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Dict						  =     ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Union[str, Any]						  =     tokenizer(__snake_case	,					padding=__snake_case	,					return_tensors='''pt'''		)
													self.assertIn('''input_ids'''	,					__snake_case		)
													self.assertIn('''attention_mask'''	,					__snake_case		)
													self.assertNotIn('''labels'''	,					__snake_case		)
													self.assertNotIn('''decoder_attention_mask'''	,					__snake_case		)







							@require_torch
							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Union[str, Any]						  =     [
										    '''Summary of the text.''',
										    '''Another summary.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:List[Any]						  =     tokenizer(text_target=__snake_case	,					max_length=32	,					padding='''max_length'''	,					return_tensors='''pt'''		)
													self.assertEqual(32	,					targets['''input_ids'''].shape[1]		)







							@require_torch
							def 				snake_case  (				self     :    List[Any]		):
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Optional[Any]						  =     tokenizer(
													    ['''I am a small frog''' * 1024, '''I am a small frog''']	,					padding=__snake_case	,					truncation=__snake_case	,					return_tensors='''pt'''		)
													self.assertIsInstance(__snake_case	,					__snake_case		)
													self.assertEqual(batch.input_ids.shape	,					(2, 5122)		)







							@require_torch
							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:Union[str, Any]						  =     ['''A long paragraph for summarization.''']
										lowerCamelCase						:Any						  =     [
										    '''Summary of the text.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Union[str, Any]						  =     tokenizer(__snake_case	,					return_tensors='''pt'''		)
													lowerCamelCase						:Any						  =     tokenizer(text_target=__snake_case	,					return_tensors='''pt'''		)
													lowerCamelCase						:Optional[int]						  =     inputs['''input_ids''']
													lowerCamelCase						:Any						  =     targets['''input_ids''']
													self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
													self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
													self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
													self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)







							@require_torch
							def 				snake_case  (				self     :    Dict		):
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Optional[int]						  =     ['''Summary of the text.''', '''Another summary.''']
													lowerCamelCase						:List[Any]						  =     [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

													lowerCamelCase						:Optional[int]						  =     tokenizer(__snake_case	,					padding=__snake_case		)
													lowerCamelCase						:Union[str, Any]						  =     [[0] * len(__snake_case		) for x in encoded_output['''input_ids''']]
													lowerCamelCase						:str						  =     tokenizer.pad(__snake_case		)
													self.assertSequenceEqual(outputs['''global_attention_mask''']	,					__snake_case		)







							def 				snake_case  (				self     :    Tuple		):
										pass







							def 				snake_case  (				self     :    Optional[int]		):
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"		):
																lowerCamelCase						:Optional[Any]						  =     self.rust_tokenizer_class.from_pretrained(__snake_case	,					**__snake_case		)
																lowerCamelCase						:Tuple						  =     self.tokenizer_class.from_pretrained(__snake_case	,					**__snake_case		)
																lowerCamelCase						:int						  =     '''A, <mask> AllenNLP sentence.'''
																lowerCamelCase						:str						  =     tokenizer_r.encode_plus(__snake_case	,					add_special_tokens=__snake_case	,					return_token_type_ids=__snake_case		)
																lowerCamelCase						:str						  =     tokenizer_p.encode_plus(__snake_case	,					add_special_tokens=__snake_case	,					return_token_type_ids=__snake_case		)
																self.assertEqual(sum(tokens_r['''token_type_ids''']		)	,					sum(tokens_p['''token_type_ids''']		)		)
																self.assertEqual(
																    sum(tokens_r['''attention_mask''']		) / len(tokens_r['''attention_mask''']		)	,					sum(tokens_p['''attention_mask''']		) / len(tokens_p['''attention_mask''']		)	,					)

																lowerCamelCase						:Optional[int]						  =     tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
																lowerCamelCase						:Tuple						  =     tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']		)
																self.assertSequenceEqual(tokens_p['''input_ids''']	,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)
																self.assertSequenceEqual(tokens_r['''input_ids''']	,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)

																self.assertSequenceEqual(
																    __snake_case	,					['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)
																self.assertSequenceEqual(
																    __snake_case	,					['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)



