
import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


_UpperCAmelCase					: str      =       version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")


def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase=False    ,       )   ->  Dict:
		'''simple docstring'''

		output_path.parent.mkdir(parents=lowercase    ,       exist_ok=lowercase )
		# 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(
				    lowercase    ,       lowercase    ,       f=output_path.as_posix()    ,       input_names=lowercase    ,       output_names=lowercase    ,       dynamic_axes=lowercase    ,       do_constant_folding=lowercase    ,       use_external_data_format=lowercase    ,       enable_onnx_checker=lowercase    ,       opset_version=lowercase    ,       )
		else:
				export(
				    lowercase    ,       lowercase    ,       f=output_path.as_posix()    ,       input_names=lowercase    ,       output_names=lowercase    ,       dynamic_axes=lowercase    ,       do_constant_folding=lowercase    ,       opset_version=lowercase    ,       )


@torch.no_grad()
def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase = False )   ->  Tuple:
		'''simple docstring'''

		UpperCamelCase		=     torch.floataa if fpaa else torch.floataa
		if fpaa and torch.cuda.is_available():
				UpperCamelCase		=     'cuda'
		elif fpaa and not torch.cuda.is_available():
				raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
		else:
				UpperCamelCase		=     'cpu'
		UpperCamelCase		=     Path(lowercase )

		# VAE DECODER
		UpperCamelCase		=     AutoencoderKL.from_pretrained(model_path + '/vae' )
		UpperCamelCase		=     vae_decoder.config.latent_channels
		# forward only through the decoder part
		UpperCamelCase		=     vae_decoder.decode
		onnx_export(
		    lowercase    ,       model_args=(
		        torch.randn(1    ,       lowercase    ,       25    ,       25 ).to(device=lowercase    ,       dtype=lowercase ),
		        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=lowercase    ,       )
		del vae_decoder


if __name__ == "__main__":
					_UpperCAmelCase					: Tuple      =       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")

					_UpperCAmelCase					: Optional[int]      =       parser.parse_args()
					print(args.output_path)
					convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
					print("SD: Done: ONNX")


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
					from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self				,      A_       )  ->					List[str]:
				"""simple docstring"""
				for model_result in results.values():
						for batch_size, sequence_length in zip(model_result['bs']				,      model_result['ss']       ):
								UpperCamelCase		=     model_result['result'][batch_size][sequence_length]
								self.assertIsNotNone(A_       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     'sgugger/tiny-distilbert-classification'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      only_pretrain_model=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      torchscript=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		@unittest.skipIf(torch_device == 'cpu'				,      'Cant do half precision'       )
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      fpaa=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				# set architectures equal to `None`
				UpperCamelCase		=     None
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result       )
				self.check_results_dict_not_empty(results.memory_train_result       )



		@unittest.skipIf(torch_device == 'cpu'				,      'Can\'t do half precision'       )
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      fpaa=A_				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result       )
				self.check_results_dict_not_empty(results.memory_train_result       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tinier_bart'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result       )
				self.check_results_dict_not_empty(results.memory_train_result       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tinier_bart'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result       )
				self.check_results_dict_not_empty(results.memory_train_result       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				with tempfile.TemporaryDirectory() as tmp_dir:
						UpperCamelCase		=     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      save_to_csv=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      inference_time_csv_file=os.path.join(A_				,      'inf_time.csv'       )				,      train_memory_csv_file=os.path.join(A_				,      'train_mem.csv'       )				,      inference_memory_csv_file=os.path.join(A_				,      'inf_mem.csv'       )				,      train_time_csv_file=os.path.join(A_				,      'train_time.csv'       )				,      env_info_csv_file=os.path.join(A_				,      'env.csv'       )				,      multi_process=A_				,      )
						UpperCamelCase		=     PyTorchBenchmark(A_       )
						benchmark.run()
						self.assertTrue(Path(os.path.join(A_				,      'inf_time.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'train_time.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'inf_mem.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'train_mem.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'env.csv'       )       ).exists()       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'

				def _check_summary_is_not_empty(A_       ):
						self.assertTrue(hasattr(A_				,      'sequential'       )       )
						self.assertTrue(hasattr(A_				,      'cumulative'       )       )
						self.assertTrue(hasattr(A_				,      'current'       )       )
						self.assertTrue(hasattr(A_				,      'total'       )       )

				with tempfile.TemporaryDirectory() as tmp_dir:
						UpperCamelCase		=     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      log_filename=os.path.join(A_				,      'log.txt'       )				,      log_print=A_				,      trace_memory_line_by_line=A_				,      multi_process=A_				,      )
						UpperCamelCase		=     PyTorchBenchmark(A_       )
						UpperCamelCase		=     benchmark.run()
						_check_summary_is_not_empty(result.inference_summary       )
						_check_summary_is_not_empty(result.train_summary       )
						self.assertTrue(Path(os.path.join(A_				,      'log.txt'       )       ).exists()       )
