
"""simple docstring"""


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def  lowercase						(	lowerCAmelCase__						):
			return ConvertCommand(
			    args.model_type	,args.tf_checkpoint	,args.pytorch_dump_output	,args.config	,args.finetuning_task_name						)


A_    							=					"""
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""




class  __lowerCamelCase  ( lowerCAmelCase			):




				@staticmethod
				def        UpperCAmelCase__						(				UpperCAmelCase   ):
							lowerCamelCase_					=  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=UpperCAmelCase							,						required=UpperCAmelCase							,						help='''Model\'s type.'''   )
							train_parser.add_argument(
							    '''--tf_checkpoint'''							,						type=UpperCAmelCase							,						required=UpperCAmelCase							,						help='''TensorFlow checkpoint path or folder.'''   )
							train_parser.add_argument(
							    '''--pytorch_dump_output'''							,						type=UpperCAmelCase							,						required=UpperCAmelCase							,						help='''Path to the PyTorch saved model output.'''   )
							train_parser.add_argument('''--config'''							,						type=UpperCAmelCase							,						default=''''''							,						help='''Configuration file path or folder.'''   )
							train_parser.add_argument(
							    '''--finetuning_task_name'''							,						type=UpperCAmelCase							,						default=UpperCAmelCase							,						help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							,						)
							train_parser.set_defaults(func=UpperCAmelCase   )




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						*UpperCAmelCase							,						):
							lowerCamelCase_					=  logging.get_logger('''transformers-cli/converting'''   )

							self._logger.info(f"Loading model {model_type}"   )
							lowerCamelCase_					=  model_type
							lowerCamelCase_					=  tf_checkpoint
							lowerCamelCase_					=  pytorch_dump_output
							lowerCamelCase_					=  config
							lowerCamelCase_					=  finetuning_task_name





				def        UpperCAmelCase__						(				self   ):
							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(UpperCAmelCase   )

										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(UpperCAmelCase   )

										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(UpperCAmelCase   )

										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(UpperCAmelCase   )

										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(UpperCAmelCase   )

										if "ckpt" in self._tf_checkpoint.lower():
													lowerCamelCase_					=  self._tf_checkpoint
													lowerCamelCase_					=  ''''''
										else:
													lowerCamelCase_					=  self._tf_checkpoint
													lowerCamelCase_					=  ''''''
										convert_transfo_xl_checkpoint_to_pytorch(
										    UpperCAmelCase							,						self._config							,						self._pytorch_dump_output							,						UpperCAmelCase   )
							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(UpperCAmelCase   )

										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(UpperCAmelCase   )

										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


lowercase__		:     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')
lowercase__		:     Union[str, Any]			=							parser.parse_args()


lowercase__		:     Union[str, Any]			=							'cpu'
lowercase__		:     Tuple			=							'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

lowercase__		:     List[Any]			=							'path-to-your-trained-model'
lowercase__		:     Optional[Any]			=							StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
   lowercase__		:     Union[str, Any]			=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase__		:     Dict			=							pipe.to(device)

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

# optimize with ipex
lowercase__		:     List[str]			=							torch.randn(2, 4, 64, 64)
lowercase__		:     int			=							torch.rand(1) * 9_99
lowercase__		:     Optional[int]			=							torch.randn(2, 77, 7_68)
lowercase__		:     List[str]			=							(sample, timestep, encoder_hidden_status)
try:
   lowercase__		:     Optional[Any]			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   lowercase__		:     Any			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Tuple			=							ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Any			=							ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   lowercase__		:     Union[str, Any]			=							ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase__		:     Optional[Any]			=							6_66
lowercase__		:     Tuple			=							torch.Generator(device).manual_seed(seed)
lowercase__		:     Dict			=							{'generator': generator}
if args.steps is not None:
   lowercase__		:     int			=							args.steps

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

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



