



"""simple docstring"""





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						return ConvertCommand(
						    args.model_type					, args.tf_checkpoint					, args.pytorch_dump_output					, args.config					, args.finetuning_task_name    )


__lowerCAmelCase       :					Optional[int]      					=					'''\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'''






class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		@staticmethod
		def    UpperCAmelCase__							(					_lowercase   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : Tuple						=			parser.add_parser(
								    """convert"""							,    help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."""							,    )
								train_parser.add_argument("""--model_type"""							,    type=_SCREAMING_SNAKE_CASE							,    required=_SCREAMING_SNAKE_CASE							,    help="""Model's type."""   )
								train_parser.add_argument(
								    """--tf_checkpoint"""							,    type=_SCREAMING_SNAKE_CASE							,    required=_SCREAMING_SNAKE_CASE							,    help="""TensorFlow checkpoint path or folder."""   )
								train_parser.add_argument(
								    """--pytorch_dump_output"""							,    type=_SCREAMING_SNAKE_CASE							,    required=_SCREAMING_SNAKE_CASE							,    help="""Path to the PyTorch saved model output."""   )
								train_parser.add_argument("""--config"""							,    type=_SCREAMING_SNAKE_CASE							,    default=""""""							,    help="""Configuration file path or folder."""   )
								train_parser.add_argument(
								    """--finetuning_task_name"""							,    type=_SCREAMING_SNAKE_CASE							,    default=_SCREAMING_SNAKE_CASE							,    help="""Optional fine-tuning task name if the TF model was a finetuned model."""							,    )
								train_parser.set_defaults(func=_SCREAMING_SNAKE_CASE   )





		def __init__(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    *_lowercase							,    ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : Dict						=			logging.get_logger("""transformers-cli/converting"""   )

								self._logger.info(f'Loading model {model_type}'   )
								snake_case_   : Tuple						=			model_type
								snake_case_   : int						=			tf_checkpoint
								snake_case_   : Optional[Any]						=			pytorch_dump_output
								snake_case_   : List[Any]						=			config
								snake_case_   : Optional[Any]						=			finetuning_task_name





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								if self._model_type == "albert":
														try:
																				from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
																				    convert_tf_checkpoint_to_pytorch,
																				)
														except ImportError:
																				raise ImportError(_SCREAMING_SNAKE_CASE   )

														convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,    self._config							,    self._pytorch_dump_output   )
								elif self._model_type == "bert":
														try:
																				from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
																				    convert_tf_checkpoint_to_pytorch,
																				)
														except ImportError:
																				raise ImportError(_SCREAMING_SNAKE_CASE   )

														convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,    self._config							,    self._pytorch_dump_output   )
								elif self._model_type == "funnel":
														try:
																				from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
																				    convert_tf_checkpoint_to_pytorch,
																				)
														except ImportError:
																				raise ImportError(_SCREAMING_SNAKE_CASE   )

														convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,    self._config							,    self._pytorch_dump_output   )
								elif self._model_type == "t5":
														try:
																				from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
														except ImportError:
																				raise ImportError(_SCREAMING_SNAKE_CASE   )

														convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,    self._config							,    self._pytorch_dump_output   )
								elif self._model_type == "gpt":
														from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
														    convert_openai_checkpoint_to_pytorch,
														)

														convert_openai_checkpoint_to_pytorch(self._tf_checkpoint							,    self._config							,    self._pytorch_dump_output   )
								elif self._model_type == "transfo_xl":
														try:
																				from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
																				    convert_transfo_xl_checkpoint_to_pytorch,
																				)
														except ImportError:
																				raise ImportError(_SCREAMING_SNAKE_CASE   )

														if "ckpt" in self._tf_checkpoint.lower():
																				snake_case_   : Any						=			self._tf_checkpoint
																				snake_case_   : Dict						=			""""""
														else:
																				snake_case_   : Optional[int]						=			self._tf_checkpoint
																				snake_case_   : int						=			""""""
														convert_transfo_xl_checkpoint_to_pytorch(
														    _SCREAMING_SNAKE_CASE							,    self._config							,    self._pytorch_dump_output							,    _SCREAMING_SNAKE_CASE   )
								elif self._model_type == "gpt2":
														try:
																				from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
																				    convert_gpta_checkpoint_to_pytorch,
																				)
														except ImportError:
																				raise ImportError(_SCREAMING_SNAKE_CASE   )

														convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint							,    self._config							,    self._pytorch_dump_output   )
								elif self._model_type == "xlnet":
														try:
																				from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
																				    convert_xlnet_checkpoint_to_pytorch,
																				)
														except ImportError:
																				raise ImportError(_SCREAMING_SNAKE_CASE   )

														convert_xlnet_checkpoint_to_pytorch(
														    self._tf_checkpoint							,    self._config							,    self._pytorch_dump_output							,    self._finetuning_task_name   )
								elif self._model_type == "xlm":
														from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
														    convert_xlm_checkpoint_to_pytorch,
														)

														convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint							,    self._pytorch_dump_output   )
								elif self._model_type == "lxmert":
														from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
														    convert_lxmert_checkpoint_to_pytorch,
														)

														convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint							,    self._pytorch_dump_output   )
								elif self._model_type == "rembert":
														from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
														    convert_rembert_tf_checkpoint_to_pytorch,
														)

														convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,    self._config							,    self._pytorch_dump_output   )
								else:
														raise ValueError(
														    """--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"""   )






"""simple docstring"""





import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


__lowerCAmelCase       :					Optional[int]      					=					argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
__lowerCAmelCase       :					Optional[Any]      					=					parser.parse_args()


__lowerCAmelCase       :					Dict      					=					'''cpu'''
__lowerCAmelCase       :					Optional[Any]      					=					'''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''

__lowerCAmelCase       :					Tuple      					=					'''path-to-your-trained-model'''
__lowerCAmelCase       :					List[Any]      					=					StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
					__lowerCAmelCase       :					Optional[int]      					=					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__lowerCAmelCase       :					List[Any]      					=					pipe.to(device)

# to channels last
__lowerCAmelCase       :					Optional[Any]      					=					pipe.unet.to(memory_format=torch.channels_last)
__lowerCAmelCase       :					List[str]      					=					pipe.vae.to(memory_format=torch.channels_last)
__lowerCAmelCase       :					Optional[Any]      					=					pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					__lowerCAmelCase       :					Dict      					=					pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
__lowerCAmelCase       :					Tuple      					=					torch.randn(2, 4, 64, 64)
__lowerCAmelCase       :					Any      					=					torch.rand(1) * 999
__lowerCAmelCase       :					List[str]      					=					torch.randn(2, 77, 768)
__lowerCAmelCase       :					Optional[int]      					=					(sample, timestep, encoder_hidden_status)
try:
					__lowerCAmelCase       :					List[Any]      					=					ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
					__lowerCAmelCase       :					Optional[Any]      					=					ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase       :					Any      					=					ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase       :					int      					=					ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
					__lowerCAmelCase       :					Union[str, Any]      					=					ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
__lowerCAmelCase       :					List[str]      					=					666
__lowerCAmelCase       :					Optional[int]      					=					torch.Generator(device).manual_seed(seed)
__lowerCAmelCase       :					List[Any]      					=					{'''generator''': generator}
if args.steps is not None:
					__lowerCAmelCase       :					Any      					=					args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
					__lowerCAmelCase       :					str      					=					pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('''generated.png''')

