
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


UpperCAmelCase       =					logging.get_logger(__name__)







@dataclass
class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	Dict									=			[
	    """no_inference""",
	    """no_cuda""",
	    """no_tpu""",
	    """no_speed""",
	    """no_memory""",
	    """no_env_print""",
	    """no_multi_process""",
	]





	def __init__(			self						,       **snake_case			):
		for deprecated_arg in self.deprecated_args:
			if deprecated_arg in kwargs:
				lowercase	    =       deprecated_arg[3:]
				lowercase	    =       not kwargs.pop(snake_case			)
				logger.warning(
				    F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
				    F''' {positive_arg}={kwargs[positive_arg]}'''			)
		lowercase	    =       kwargs.pop('tpu_name'						,       self.tpu_name			)
		lowercase	    =       kwargs.pop('device_idx'						,       self.device_idx			)
		lowercase	    =       kwargs.pop('eager_mode'						,       self.eager_mode			)
		lowercase	    =       kwargs.pop('use_xla'						,       self.use_xla			)
		super().__init__(**snake_case			)

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





	@cached_property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		requires_backends(self						,       ['tf']			)
		lowercase	    =       None
		if self.tpu:
			try:
				if self.tpu_name:
					lowercase	    =       tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name			)
				else:
					lowercase	    =       tf.distribute.cluster_resolver.TPUClusterResolver()
			except ValueError:
				lowercase	    =       None
		return tpu





	@cached_property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		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			)

			lowercase	    =       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'			)
				lowercase	    =       tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''			)
			else:
				tf.config.set_visible_devices([]						,       'GPU'			)  # disable GPU
				lowercase	    =       tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''			)

		return strategy





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		requires_backends(self						,       ['tf']			)
		return self._setup_tpu is not None





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		requires_backends(self						,       ['tf']			)
		return self._setup_strategy





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		requires_backends(self						,       ['tf']			)
		return tf.config.list_physical_devices('GPU'			)





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





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return self.n_gpu > 0


'''simple docstring'''


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import (
					    ImageTextPipelineOutput,
					    UniDiffuserPipeline,
					)
else:
					from .modeling_text_decoder import UniDiffuserTextDecoder
					from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
					from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
