import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


UpperCamelCase      			=     logging.get_logger(__name__)

if is_tf_available():
					import tensorflow as tf


@dataclass
class _a       (	SCREAMING_SNAKE_CASE_ ):

	'''simple docstring'''



	lowerCamelCase_   :      Dict       =	"""tf"""
	lowerCamelCase_   :      List[str]       =	field(
	    default=SCREAMING_SNAKE_CASE_   ,							metadata={"""help""": """Name of TPU"""}   ,							)

	lowerCamelCase_   :      Dict       =	field(
	    default=SCREAMING_SNAKE_CASE_   ,							metadata={"""help""": """Zone of TPU"""}   ,							)

	lowerCamelCase_   :      str       =	field(
	    default=SCREAMING_SNAKE_CASE_   ,							metadata={"""help""": """Name of Cloud TPU-enabled project"""}   ,							)

	lowerCamelCase_   :      Any       =	field(
	    default=1.0   ,							metadata={"""help""": """Power for the Polynomial decay LR scheduler."""}   ,							)

	lowerCamelCase_   :      Union[str, Any]       =	field(default=SCREAMING_SNAKE_CASE_   ,							metadata={"""help""": """Whether to activate the XLA compilation or not"""} )

	@cached_property
	def 					__UpperCAmelCase(       self   ):
			requires_backends(self							,			["tf"]   )
			logger.info("Tensorflow: setting up strategy"   )

			__A							:   List[Any]	  =				tf.config.list_physical_devices("GPU"   )

			# Set to float16 at first
			if self.fpaa:
					tf.keras.mixed_precision.set_global_policy("mixed_float16"   )

			if self.no_cuda:
					__A							:   Any	  =				tf.distribute.OneDeviceStrategy(device="/cpu:0"   )
			else:
					try:
							if self.tpu_name:
									__A							:   Optional[int]	  =				tf.distribute.cluster_resolver.TPUClusterResolver(
									    self.tpu_name							,			zone=self.tpu_zone							,			project=self.gcp_project   )
							else:
									__A							:   Dict	  =				tf.distribute.cluster_resolver.TPUClusterResolver()
					except ValueError:
							if self.tpu_name:
									raise RuntimeError(F"Couldn't connect to TPU {self.tpu_name}!"   )
							else:
									__A							:   Dict	  =				None

					if tpu:
							# Set to bfloat16 in case of TPU
							if self.fpaa:
									tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"   )

							tf.config.experimental_connect_to_cluster(SCREAMING_SNAKE_CASE_   )
							tf.tpu.experimental.initialize_tpu_system(SCREAMING_SNAKE_CASE_   )

							__A							:   Dict	  =				tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE_   )

					elif len(SCREAMING_SNAKE_CASE_   ) == 0:
							__A							:   Union[str, Any]	  =				tf.distribute.OneDeviceStrategy(device="/cpu:0"   )
					elif len(SCREAMING_SNAKE_CASE_   ) == 1:
							__A							:   Any	  =				tf.distribute.OneDeviceStrategy(device="/gpu:0"   )
					elif len(SCREAMING_SNAKE_CASE_   ) > 1:
							# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
							__A							:   Dict	  =				tf.distribute.MirroredStrategy()
					else:
							raise ValueError("Cannot find the proper strategy, please check your environment properties."   )

			return strategy

	@property
	def 					__UpperCAmelCase(       self   ):
			requires_backends(self							,			["tf"]   )
			return self._setup_strategy

	@property
	def 					__UpperCAmelCase(       self   ):
			requires_backends(self							,			["tf"]   )
			return self._setup_strategy.num_replicas_in_sync

	@property
	def 					__UpperCAmelCase(       self   ):
			return False  # TF Logging is handled by Keras not the Trainer

	@property
	def 					__UpperCAmelCase(       self   ):






			if self.per_gpu_train_batch_size:
					logger.warning(
					    "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
					    "version. Using `--per_device_train_batch_size` is preferred."   )
			__A							:   Dict	  =				self.per_gpu_train_batch_size or self.per_device_train_batch_size
			return per_device_batch_size * self.n_replicas

	@property
	def 					__UpperCAmelCase(       self   ):






			if self.per_gpu_eval_batch_size:
					logger.warning(
					    "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
					    "version. Using `--per_device_eval_batch_size` is preferred."   )
			__A							:   List[Any]	  =				self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
			return per_device_batch_size * self.n_replicas







	@property
	def 					__UpperCAmelCase(       self   ):
			requires_backends(self							,			["tf"]   )
			warnings.warn(
			    "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."							,			SCREAMING_SNAKE_CASE_							,			)
			return self._setup_strategy.num_replicas_in_sync






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .image_encoder import PaintByExampleImageEncoder
					from .pipeline_paint_by_example import PaintByExamplePipeline





