




'''simple docstring'''


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
						import tensorflow as tf


lowerCAmelCase__										= logging.get_logger(__name__)



@dataclass
class 					lowercase						(  UpperCamelCase__ ):
							_lowerCamelCase		:      int=   [
							    """no_inference""",
							    """no_cuda""",
							    """no_tpu""",
							    """no_speed""",
							    """no_memory""",
							    """no_env_print""",
							    """no_multi_process""",
							]


							def __init__(			self    ,			**_snake_case)							->				Union[str, Any]:
								for deprecated_arg in self.deprecated_args:
									if deprecated_arg in kwargs:
										UpperCAmelCase_		:      Any       	=					deprecated_arg[3:]
										UpperCAmelCase_		:      Optional[Any]       	=					not kwargs.pop(_snake_case)
										logger.warning(
										    F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
										    F""" {positive_arg}={kwargs[positive_arg]}""")
								UpperCAmelCase_		:      Optional[int]       	=					kwargs.pop('tpu_name'    ,			self.tpu_name)
								UpperCAmelCase_		:      Optional[Any]       	=					kwargs.pop('device_idx'    ,			self.device_idx)
								UpperCAmelCase_		:      Dict       	=					kwargs.pop('eager_mode'    ,			self.eager_mode)
								UpperCAmelCase_		:      Union[str, Any]       	=					kwargs.pop('use_xla'    ,			self.use_xla)
								super().__init__(**_snake_case)

							_lowerCamelCase		:      str=   field(
							    default=UpperCamelCase__,					metadata={"help": "Name of TPU"},					)
							_lowerCamelCase		:      int=   field(
							    default=0,					metadata={"help": "CPU / GPU device index. Defaults to 0."},					)
							_lowerCamelCase		:      bool=   field(default=UpperCamelCase__,					metadata={"help": "Benchmark models in eager model."} )
							_lowerCamelCase		:      bool=   field(
							    default=UpperCamelCase__,					metadata={
							        "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
							    },					)


							@cached_property
							def 						_snake_case							(			self)							->				Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
								requires_backends(self    ,			['tf'])
								UpperCAmelCase_		:      Optional[int]       	=					None
								if self.tpu:
									try:
										if self.tpu_name:
											UpperCAmelCase_		:      Optional[int]       	=					tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name)
										else:
											UpperCAmelCase_		:      Optional[int]       	=					tf.distribute.cluster_resolver.TPUClusterResolver()
									except ValueError:
										UpperCAmelCase_		:      List[Any]       	=					None
								return tpu


							@cached_property
							def 						_snake_case							(			self)							->				Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
								requires_backends(self    ,			['tf'])
								if self.is_tpu:
									tf.config.experimental_connect_to_cluster(self._setup_tpu)
									tf.tpu.experimental.initialize_tpu_system(self._setup_tpu)

									UpperCAmelCase_		:      Any       	=					tf.distribute.TPUStrategy(self._setup_tpu)
								else:
									# currently no multi gpu is allowed
									if self.is_gpu:
										# TODO: Currently only single GPU is supported
										tf.config.set_visible_devices(self.gpu_list[self.device_idx]    ,			'GPU')
										UpperCAmelCase_		:      Optional[Any]       	=					tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}""")
									else:
										tf.config.set_visible_devices([]    ,			'GPU')  # disable GPU
										UpperCAmelCase_		:      Union[str, Any]       	=					tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}""")

								return strategy


							@property
							def 						_snake_case							(			self)							->				bool:
								requires_backends(self    ,			['tf'])
								return self._setup_tpu is not None


							@property
							def 						_snake_case							(			self)							->				"tf.distribute.Strategy":
								requires_backends(self    ,			['tf'])
								return self._setup_strategy


							@property
							def 						_snake_case							(			self)							->				Union[str, Any]:
								requires_backends(self    ,			['tf'])
								return tf.config.list_physical_devices('GPU')


							@property
							def 						_snake_case							(			self)							->				int:
								requires_backends(self    ,			['tf'])
								if self.cuda:
									return len(self.gpu_list)
								return 0





							@property
							def 						_snake_case							(			self)							->				bool:
								return self.n_gpu > 0









'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

						parser.add_argument(
						    "--txt2img_unclip",
						    default="kakaobrain/karlo-v1-alpha",
						    type=str,
						    required=False,
						    help="The pretrained txt2img unclip.",
						)

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

						lowerCAmelCase__										= CLIPImageProcessor()
						lowerCAmelCase__										= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

						lowerCAmelCase__										= UnCLIPImageVariationPipeline(
						    decoder=txtaimg.decoder,
						    text_encoder=txtaimg.text_encoder,
						    tokenizer=txtaimg.tokenizer,
						    text_proj=txtaimg.text_proj,
						    feature_extractor=feature_extractor,
						    image_encoder=image_encoder,
						    super_res_first=txtaimg.super_res_first,
						    super_res_last=txtaimg.super_res_last,
						    decoder_scheduler=txtaimg.decoder_scheduler,
						    super_res_scheduler=txtaimg.super_res_scheduler,
						)

						imgaimg.save_pretrained(args.dump_path)






