



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


__snake_case     :List[str]			       =version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	tuple			,	lowerCAmelCase__	:	Path			,	lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	str=False			,	)       ->							Dict:

							'''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  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	bool = False				)       ->							int:

							'''simple docstring'''






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

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

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

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

							# VAE DECODER
							A      						=    pipeline.vae
							A      						=    vae_decoder.config.latent_channels
							A      						=    vae_decoder.config.out_channels
							# forward only through the decoder part
							A      						=    vae_encoder.decode
							onnx_export(
							    lowerCAmelCase__			,	model_args=(
							        torch.randn(1			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				).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 pipeline.vae

							# SAFETY CHECKER
							if pipeline.safety_checker is not None:
														A      						=    pipeline.safety_checker
														A      						=    safety_checker.config.vision_config.num_channels
														A      						=    safety_checker.config.vision_config.image_size
														A      						=    safety_checker.forward_onnx
														onnx_export(
														    pipeline.safety_checker			,	model_args=(
														        torch.randn(
														            1			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	).to(device=lowerCAmelCase__			,	dtype=lowerCAmelCase__				),
														        torch.randn(1			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				).to(device=lowerCAmelCase__			,	dtype=lowerCAmelCase__				),
														    )			,	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=lowerCAmelCase__			,	)
														del pipeline.safety_checker
														A      						=    OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'				)
														A      						=    pipeline.feature_extractor
							else:
														A      						=    None
														A      						=    None

							A      						=    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=lowerCAmelCase__			,	feature_extractor=lowerCAmelCase__			,	requires_safety_checker=safety_checker is not None			,	)

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

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


if __name__ == "__main__":
				__snake_case     :Union[str, Any]			       =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')

				__snake_case     :str			       =parser.parse_args()

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




import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
				from test_module.custom_tokenization_fast import CustomTokenizerFast



class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  Any       )		->     Tuple:
											# A mock response for an HTTP head request to emulate server down
											A      						=    mock.Mock()
											A      						=    500
											A      						=    {}
											A      						=    HTTPError
											A      						=    {}

											# Download this model to make sure it's in the cache.
											A      						=    BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'       )

											# Under the mock environment we get a 500 error when trying to reach the tokenizer.
											with mock.patch('requests.Session.request'						, return_value=__UpperCamelCase       ) as mock_head:
																		A      						=    BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'       )
																		# This check we did call the fake head request
																		mock_head.assert_called()


				@require_tokenizers
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Union[str, Any]:
											# A mock response for an HTTP head request to emulate server down
											A      						=    mock.Mock()
											A      						=    500
											A      						=    {}
											A      						=    HTTPError
											A      						=    {}

											# Download this model to make sure it's in the cache.
											A      						=    GPTaTokenizerFast.from_pretrained('gpt2'       )

											# Under the mock environment we get a 500 error when trying to reach the tokenizer.
											with mock.patch('requests.Session.request'						, return_value=__UpperCamelCase       ) as mock_head:
																		A      						=    GPTaTokenizerFast.from_pretrained('gpt2'       )
																		# This check we did call the fake head request
																		mock_head.assert_called()


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     List[str]:
											# This test is for deprecated behavior and can be removed in v5
											try:
																		A      						=    tempfile.mktemp()
																		with open(__UpperCamelCase						, 'wb'       ) as f:
																									http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'						, __UpperCamelCase       )

																		A      						=    AlbertTokenizer.from_pretrained(__UpperCamelCase       )
											finally:
																		os.remove(__UpperCamelCase       )

											# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
											# the current folder and have the right name.
											if os.path.isfile('tokenizer.json'       ):
																		# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
																		return
											try:
																		with open('tokenizer.json'						, 'wb'       ) as f:
																									http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json'						, __UpperCamelCase       )
																		A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
																		# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
																		self.assertEqual(tokenizer.vocab_size						, 1_000       )
																		# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

											finally:
																		os.remove('tokenizer.json'       )


				def __UpperCamelCase						(							self      :  str       )		->     int:
											# This test is for deprecated behavior and can be removed in v5
											A      						=    AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'       )





@is_staging_test
class        lowerCAmelCase__					(  unittest.TestCase       ):
				A_   :     str								=     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']


				@classmethod
				def __UpperCamelCase						(							cls      :  Tuple       )		->     Dict:
											A      						=    TOKEN
											HfFolder.save_token(__UpperCamelCase       )


				@classmethod
				def __UpperCamelCase						(							cls      :  Any       )		->     Dict:
											try:
																		delete_repo(token=cls._token						, repo_id='test-tokenizer'       )
											except HTTPError:
																		pass

											try:
																		delete_repo(token=cls._token						, repo_id='valid_org/test-tokenizer-org'       )
											except HTTPError:
																		pass

											try:
																		delete_repo(token=cls._token						, repo_id='test-dynamic-tokenizer'       )
											except HTTPError:
																		pass


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     List[str]:
											with tempfile.TemporaryDirectory() as tmp_dir:
																		A      						=    os.path.join(__UpperCamelCase						, 'vocab.txt'       )
																		with open(__UpperCamelCase						, 'w'						, encoding='utf-8'       ) as vocab_writer:
																									vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]       )       )
																		A      						=    BertTokenizer(__UpperCamelCase       )

											tokenizer.push_to_hub('test-tokenizer'						, use_auth_token=self._token       )
											A      						=    BertTokenizer.from_pretrained(f'''{USER}/test-tokenizer'''       )
											self.assertDictEqual(new_tokenizer.vocab						, tokenizer.vocab       )

											# Reset repo
											delete_repo(token=self._token						, repo_id='test-tokenizer'       )

											# Push to hub via save_pretrained
											with tempfile.TemporaryDirectory() as tmp_dir:
																		tokenizer.save_pretrained(__UpperCamelCase						, repo_id='test-tokenizer'						, push_to_hub=__UpperCamelCase						, use_auth_token=self._token       )

											A      						=    BertTokenizer.from_pretrained(f'''{USER}/test-tokenizer'''       )
											self.assertDictEqual(new_tokenizer.vocab						, tokenizer.vocab       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     Tuple:
											with tempfile.TemporaryDirectory() as tmp_dir:
																		A      						=    os.path.join(__UpperCamelCase						, 'vocab.txt'       )
																		with open(__UpperCamelCase						, 'w'						, encoding='utf-8'       ) as vocab_writer:
																									vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]       )       )
																		A      						=    BertTokenizer(__UpperCamelCase       )

											tokenizer.push_to_hub('valid_org/test-tokenizer-org'						, use_auth_token=self._token       )
											A      						=    BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'       )
											self.assertDictEqual(new_tokenizer.vocab						, tokenizer.vocab       )

											# Reset repo
											delete_repo(token=self._token						, repo_id='valid_org/test-tokenizer-org'       )

											# Push to hub via save_pretrained
											with tempfile.TemporaryDirectory() as tmp_dir:
																		tokenizer.save_pretrained(
																		    __UpperCamelCase						, repo_id='valid_org/test-tokenizer-org'						, push_to_hub=__UpperCamelCase						, use_auth_token=self._token       )

											A      						=    BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'       )
											self.assertDictEqual(new_tokenizer.vocab						, tokenizer.vocab       )


				@require_tokenizers
				def __UpperCamelCase						(							self      :  Any       )		->     Union[str, Any]:
											CustomTokenizer.register_for_auto_class()
											with tempfile.TemporaryDirectory() as tmp_dir:
																		A      						=    os.path.join(__UpperCamelCase						, 'vocab.txt'       )
																		with open(__UpperCamelCase						, 'w'						, encoding='utf-8'       ) as vocab_writer:
																									vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]       )       )
																		A      						=    CustomTokenizer(__UpperCamelCase       )

											# No fast custom tokenizer
											tokenizer.push_to_hub('test-dynamic-tokenizer'						, use_auth_token=self._token       )

											A      						=    AutoTokenizer.from_pretrained(f'''{USER}/test-dynamic-tokenizer'''						, trust_remote_code=__UpperCamelCase       )
											# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
											self.assertEqual(tokenizer.__class__.__name__						, 'CustomTokenizer'       )

											# Fast and slow custom tokenizer
											CustomTokenizerFast.register_for_auto_class()
											with tempfile.TemporaryDirectory() as tmp_dir:
																		A      						=    os.path.join(__UpperCamelCase						, 'vocab.txt'       )
																		with open(__UpperCamelCase						, 'w'						, encoding='utf-8'       ) as vocab_writer:
																									vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]       )       )

																		A      						=    BertTokenizerFast.from_pretrained(__UpperCamelCase       )
																		bert_tokenizer.save_pretrained(__UpperCamelCase       )
																		A      						=    CustomTokenizerFast.from_pretrained(__UpperCamelCase       )

											tokenizer.push_to_hub('test-dynamic-tokenizer'						, use_auth_token=self._token       )

											A      						=    AutoTokenizer.from_pretrained(f'''{USER}/test-dynamic-tokenizer'''						, trust_remote_code=__UpperCamelCase       )
											# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
											self.assertEqual(tokenizer.__class__.__name__						, 'CustomTokenizerFast'       )
											A      						=    AutoTokenizer.from_pretrained(
											    f'''{USER}/test-dynamic-tokenizer'''						, use_fast=__UpperCamelCase						, trust_remote_code=__UpperCamelCase       )
											# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
											self.assertEqual(tokenizer.__class__.__name__						, 'CustomTokenizer'       )





class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Any:
											A      						=    Trie()
											trie.add('Hello 友達'       )
											self.assertEqual(trie.data						, {'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}}       )
											trie.add('Hello'       )
											trie.data
											self.assertEqual(trie.data						, {'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}}       )


				def __UpperCamelCase						(							self      :  Dict       )		->     Tuple:
											A      						=    Trie()
											self.assertEqual(trie.split('[CLS] This is a extra_id_100'       )						, ['[CLS] This is a extra_id_100']       )
											trie.add('[CLS]'       )
											trie.add('extra_id_1'       )
											trie.add('extra_id_100'       )
											self.assertEqual(trie.split('[CLS] This is a extra_id_100'       )						, ['[CLS]', ' This is a ', 'extra_id_100']       )


				def __UpperCamelCase						(							self      :  Any       )		->     Optional[int]:
											A      						=    Trie()
											trie.add('A'       )
											self.assertEqual(trie.split('ABC'       )						, ['A', 'BC']       )
											self.assertEqual(trie.split('BCA'       )						, ['BC', 'A']       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     str:
											A      						=    Trie()
											trie.add('TOKEN]'       )
											trie.add('[SPECIAL_TOKEN]'       )
											self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'       )						, ['This is something ', '[SPECIAL_TOKEN]']       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											A      						=    Trie()
											trie.add('A'       )
											trie.add('P'       )
											trie.add('[SPECIAL_TOKEN]'       )
											self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'       )						, ['This is something ', '[SPECIAL_TOKEN]']       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     Tuple:
											A      						=    Trie()
											trie.add('AB'       )
											trie.add('B'       )
											trie.add('C'       )
											self.assertEqual(trie.split('ABC'       )						, ['AB', 'C']       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[Any]:
											A      						=    Trie()
											trie.add('ABC'       )
											trie.add('B'       )
											trie.add('CD'       )
											self.assertEqual(trie.split('ABCD'       )						, ['ABC', 'D']       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[Any]:
											# Even if the offsets are wrong, we necessarily output correct string
											# parts.
											A      						=    Trie()
											A      						=    trie.cut_text('ABC'						, [0, 0, 2, 1, 2, 3]       )
											self.assertEqual(__UpperCamelCase						, ['AB', 'C']       )