

import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCAmelCase							:  Tuple           =						version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=False    ,     ):
						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       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = False   ):
						SCREAMING_SNAKE_CASE_:			str  		=	torch.floataa if fpaa else torch.floataa
						if fpaa and torch.cuda.is_available():
												SCREAMING_SNAKE_CASE_:			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:
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"cpu"
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Path(_UpperCAmelCase   )

						# VAE DECODER
						SCREAMING_SNAKE_CASE_:			int  		=	AutoencoderKL.from_pretrained(model_path + "/vae"   )
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	vae_decoder.config.latent_channels
						# forward only through the decoder part
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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__":
						lowerCAmelCase							:  Optional[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""")

						lowerCAmelCase							:  List[Any]           =						parser.parse_args()
						print(args.output_path)
						convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
						print("""SD: Done: ONNX""")









import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
						import torch

						from transformers import AutoModelForCausalLM



@require_torch
class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			str  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			str  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	-1

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer.decode(greedy_ids[0])

									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			int  		=	TextStreamer(lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	cs.out[:-1]

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			int  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			int  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.decode(greedy_ids[0])

									SCREAMING_SNAKE_CASE_:			int  		=	TextIteratorStreamer(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
									SCREAMING_SNAKE_CASE_:			Tuple  		=	Thread(target=model.generate       ,						kwargs=lowerCAmelCase__)
									thread.start()
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	""
									for new_text in streamer:
															streamer_text += new_text

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			int  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			int  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	greedy_ids[:, input_ids.shape[1] :]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer.decode(new_greedy_ids[0])

									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			Dict  		=	TextStreamer(lowerCAmelCase__       ,						skip_prompt=lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									SCREAMING_SNAKE_CASE_:			Any  		=	cs.out[:-1]

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
									# with actual models -- the dummy models' tokenizers are not aligned with their models, and
									# `skip_special_tokens=True` has no effect on them
									SCREAMING_SNAKE_CASE_:			Tuple  		=	AutoTokenizer.from_pretrained("distilgpt2")
									SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoModelForCausalLM.from_pretrained("distilgpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.ones((1, 5)       ,						device=lowerCAmelCase__).long() * model.config.bos_token_id
									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	TextStreamer(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=1       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)

									# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
									# re-tokenized, must only contain one token
									SCREAMING_SNAKE_CASE_:			str  		=	cs.out[:-1]  # Remove the final "\n"
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer(lowerCAmelCase__       ,						return_tensors="pt")
									self.assertEqual(streamer_text_tokenized.input_ids.shape       ,						(1, 1))




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	-1

									SCREAMING_SNAKE_CASE_:			List[str]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	TextIteratorStreamer(lowerCAmelCase__       ,						timeout=0.001)
									SCREAMING_SNAKE_CASE_:			Any  		=	{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Thread(target=model.generate       ,						kwargs=lowerCAmelCase__)
									thread.start()

									# The streamer will timeout after 0.001 seconds, so an exception will be raised
									with self.assertRaises(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	""
															for new_text in streamer:
																					streamer_text += new_text






