
'''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


_lowercase			:  Tuple       							=	version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			Tuple				,				__SCREAMING_SNAKE_CASE :			List[str]=False				,				):



						"""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  snake_case_		(    __SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			Any = False							):



						"""simple docstring"""





						lowercase_     :					List[Any]													=	torch.floataa if fpaa else torch.floataa
						if fpaa and torch.cuda.is_available():
												lowercase_     :					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:
												lowercase_     :					str													=	"""cpu"""
						lowercase_     :					str													=	StableDiffusionPipeline.from_pretrained(_lowerCAmelCase				,				torch_dtype=_lowerCAmelCase							).to(_lowerCAmelCase							)
						lowercase_     :					Union[str, Any]													=	Path(_lowerCAmelCase							)

						# TEXT ENCODER
						lowercase_     :					Dict													=	pipeline.text_encoder.config.max_position_embeddings
						lowercase_     :					Tuple													=	pipeline.text_encoder.config.hidden_size
						lowercase_     :					Union[str, Any]													=	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
						lowercase_     :					str													=	pipeline.unet.config.in_channels
						lowercase_     :					Optional[int]													=	pipeline.unet.config.sample_size
						lowercase_     :					Tuple													=	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				,				)
						lowercase_     :					str													=	str(unet_path.absolute().as_posix()							)
						lowercase_     :					Any													=	os.path.dirname(_lowerCAmelCase							)
						lowercase_     :					List[Any]													=	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
						lowercase_     :					Any													=	pipeline.vae
						lowercase_     :					Union[str, Any]													=	vae_encoder.config.in_channels
						lowercase_     :					Tuple													=	vae_encoder.config.sample_size
						# need to get the raw tensor output (sample) from the encoder
						lowercase_     :					Optional[int]													=	lambda __SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							: 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
						lowercase_     :					Optional[Any]													=	pipeline.vae
						lowercase_     :					Dict													=	vae_decoder.config.latent_channels
						lowercase_     :					str													=	vae_decoder.config.out_channels
						# forward only through the decoder part
						lowercase_     :					Union[str, Any]													=	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:
												lowercase_     :					Optional[Any]													=	pipeline.safety_checker
												lowercase_     :					List[Any]													=	safety_checker.config.vision_config.num_channels
												lowercase_     :					Tuple													=	safety_checker.config.vision_config.image_size
												lowercase_     :					Dict													=	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
												lowercase_     :					Dict													=	OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker'''							)
												lowercase_     :					Any													=	pipeline.feature_extractor
						else:
												lowercase_     :					Any													=	None
												lowercase_     :					Dict													=	None

						lowercase_     :					Tuple													=	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
						lowercase_     :					List[str]													=	OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase				,				provider='''CPUExecutionProvider'''							)
						print('''ONNX pipeline is loadable'''							)


if __name__ == "__main__":
							_lowercase			:  Dict       							=	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=1_4,
							    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")

							_lowercase			:  str       							=	parser.parse_args()

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





'''simple docstring'''







import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Dict													=	flatten_dict(__SCREAMING_SNAKE_CASE							)
						return flax_params
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					int													=	{}

						lowercase_     :					Any													=	{
						    '''token_embedder''': '''embeddings''',
						    '''encoder_norm''': '''layernorm''',
						    '''kernel''': '''weight''',
						    '''.out''': '''.output''',
						    '''scale''': '''weight''',
						    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
						    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
						}

						lowercase_     :					Tuple													=	{
						    '''query''': '''attention.query''',
						    '''key''': '''attention.key''',
						    '''value''': '''attention.value''',
						    '''output.dense''': '''output''',
						    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
						    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
						    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
						    '''mlp.''': '''mlp.DenseReluDense.''',
						    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
						    '''self_attention.o''': '''self_attention.attention.o''',
						    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
						    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
						    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
						    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
						}

						for key in flax_dict.keys():
												if "target" in key:
																		# remove the first prefix from the key
																		lowercase_     :					Tuple													=	'''.'''.join(key[1:]							)

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								lowercase_     :					Optional[Any]													=	new_key.replace(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														lowercase_     :					Optional[Any]													=	new_key.replace(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								lowercase_     :					List[Any]													=	re.sub(R'''layers_(\d+)'''				,				R'''layer.\1'''				,				__SCREAMING_SNAKE_CASE							)
																								lowercase_     :					Union[str, Any]													=	new_key.replace('''encoder'''				,				'''encoder.encoder'''							)

																		elif "layers" in new_key and "decoder" in new_key:
																								# use regex to replace the layer number
																								lowercase_     :					str													=	re.sub(R'''layers_(\d+)'''				,				R'''layer.\1'''				,				__SCREAMING_SNAKE_CASE							)

																		lowercase_     :					Dict													=	flax_dict[key]

						lowercase_     :					Any													=	{}
						# convert converted_dict into torch format
						for key in converted_dict.keys():
												if ("embed_tokens" not in key) and ("embedder" not in key):
																		lowercase_     :					str													=	torch.from_numpy(converted_dict[key].T							)
												else:
																		lowercase_     :					str													=	torch.from_numpy(converted_dict[key]							)

						return converted_torch_dict
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Tuple=False				,				__SCREAMING_SNAKE_CASE :			List[Any]=False							):



						"""simple docstring"""





						lowercase_     :					List[str]													=	get_flax_param(__SCREAMING_SNAKE_CASE							)

						if not use_large:
												lowercase_     :					List[str]													=	PixaStructVisionConfig()
												lowercase_     :					Optional[Any]													=	PixaStructTextConfig()
						else:
												lowercase_     :					Optional[int]													=	PixaStructVisionConfig(
												    hidden_size=1536				,				d_ff=3968				,				num_attention_heads=24				,				num_hidden_layers=18							)
												lowercase_     :					Dict													=	PixaStructTextConfig(hidden_size=1536				,				d_ff=3968				,				num_heads=24				,				num_layers=18							)
						lowercase_     :					str													=	PixaStructConfig(
						    vision_config=encoder_config.to_dict()				,				text_config=decoder_config.to_dict()				,				is_vqa=__SCREAMING_SNAKE_CASE							)

						lowercase_     :					List[str]													=	PixaStructForConditionalGeneration(__SCREAMING_SNAKE_CASE							)

						lowercase_     :					int													=	rename_and_convert_flax_params(__SCREAMING_SNAKE_CASE							)
						model.load_state_dict(__SCREAMING_SNAKE_CASE							)

						lowercase_     :					str													=	AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''							)
						lowercase_     :					List[Any]													=	PixaStructImageProcessor()
						lowercase_     :					int													=	PixaStructProcessor(image_processor=__SCREAMING_SNAKE_CASE				,				tokenizer=__SCREAMING_SNAKE_CASE							)

						if use_large:
												lowercase_     :					Tuple													=	4096

						lowercase_     :					Optional[int]													=	True

						# mkdir if needed
						os.makedirs(__SCREAMING_SNAKE_CASE				,				exist_ok=__SCREAMING_SNAKE_CASE							)

						model.save_pretrained(__SCREAMING_SNAKE_CASE							)
						processor.save_pretrained(__SCREAMING_SNAKE_CASE							)

						print('''Model saved in {}'''.format(__SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
							_lowercase			:  str   							=	argparse.ArgumentParser()
							parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
							parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							parser.add_argument("--use_large", action="store_true", help="Use large model.")
							parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
							_lowercase			:  Tuple   							=	parser.parse_args()

							convert_pixastruct_original_pytorch_checkpoint_to_hf(
							    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
							)



