





import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.framework.errors_impl import ResourceExhaustedError

							from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
							import pyanvml.pyanvml as nvml

lowerCAmelCase							:       List[str]					 =	logging.get_logger(__name__)






def 		lowerCAmelCase  (	UpperCamelCase__   :   bool       ,			UpperCamelCase__   :   bool					)      ->    List[Any]:

						"""simple docstring"""





						def run_func(UpperCamelCase__   :   Tuple					):
												@wraps(lowerCamelCase__					)
												def run_in_eager_mode(*UpperCamelCase__   :   Any       ,			**UpperCamelCase__   :   Optional[int]					):
																		return func(*lowerCamelCase__       ,			**lowerCamelCase__					)

												@wraps(lowerCamelCase__					)
												@tf.function(experimental_compile=lowerCamelCase__					)
												def run_in_graph_mode(*UpperCamelCase__   :   Tuple       ,			**UpperCamelCase__   :   Any					):
																		return func(*lowerCamelCase__       ,			**lowerCamelCase__					)

												if do_eager_mode is True:
																		if use_xla is not False:
																								raise ValueError(
																								    '''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'''					)
																		return run_in_eager_mode
												else:
																		return run_in_graph_mode

						return run_func






def 		lowerCAmelCase  (	UpperCamelCase__   :   int       ,			UpperCamelCase__   :   int       ,			UpperCamelCase__   :   int					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   random.Random()
						__SCREAMING_SNAKE_CASE:				Tuple           =   [rng.randint(0       ,			vocab_size - 1					) for i in range(batch_size * sequence_length					)]
						return tf.constant(lowerCamelCase__       ,			shape=(batch_size, sequence_length)       ,			dtype=tf.intaa					)





class 				a			(      a_			):
				SCREAMING_SNAKE_CASE__     :   TensorFlowBenchmarkArguments
				SCREAMING_SNAKE_CASE__     :   PretrainedConfig
				SCREAMING_SNAKE_CASE__     :   str		      =     "TensorFlow"






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return tf.__version__






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   self.args.strategy
										if strategy is None:
																raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self._prepare_inference_func(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)
										return self._measure_speed(_inference					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   self.args.strategy
										if strategy is None:
																raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self._prepare_train_func(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)
										return self._measure_speed(_train					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										if self.args.is_gpu:
																tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]			, _lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.args.strategy
										if strategy is None:
																raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   self._prepare_inference_func(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)
										return self._measure_memory(_inference					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										if self.args.is_gpu:
																tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]			, _lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.args.strategy
										if strategy is None:
																raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self._prepare_train_func(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)
										return self._measure_memory(_train					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.config_dict[model_name]

										if self.args.fpaa:
																raise NotImplementedError('''Mixed precision is currently not supported.'''					)

										__SCREAMING_SNAKE_CASE:				Tuple           =   (
										    hasattr(_lowerCAmelCase			, '''architectures'''					)
										    and isinstance(config.architectures			, _lowerCAmelCase					)
										    and len(config.architectures					) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
																try:
																						__SCREAMING_SNAKE_CASE:				Dict           =   '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
																						__SCREAMING_SNAKE_CASE:				Any           =   __import__('''transformers'''			, fromlist=[model_class]					)
																						__SCREAMING_SNAKE_CASE:				Optional[int]           =   getattr(_lowerCAmelCase			, _lowerCAmelCase					)
																						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model_cls(_lowerCAmelCase					)
																except ImportError:
																						raise ImportError(
																						    f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
																						    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''					)
										else:
																__SCREAMING_SNAKE_CASE:				str           =   TF_MODEL_MAPPING[config.__class__](_lowerCAmelCase					)

										# encoder-decoder has vocab size saved differently
										__SCREAMING_SNAKE_CASE:				List[Any]           =   config.vocab_size if hasattr(_lowerCAmelCase			, '''vocab_size'''					) else config.encoder.vocab_size
										__SCREAMING_SNAKE_CASE:				Tuple           =   random_input_ids(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)

										@run_with_tf_optimizations(self.args.eager_mode			, self.args.use_xla					)
										def encoder_decoder_forward():
																return model(_lowerCAmelCase			, decoder_input_ids=_lowerCAmelCase			, training=_lowerCAmelCase					)

										@run_with_tf_optimizations(self.args.eager_mode			, self.args.use_xla					)
										def encoder_forward():
																return model(_lowerCAmelCase			, training=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

										return _inference






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   self.config_dict[model_name]

										if self.args.eager_mode is not False:
																raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'''					)

										if self.args.fpaa:
																raise NotImplementedError('''Mixed precision is currently not supported.'''					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   (
										    hasattr(_lowerCAmelCase			, '''architectures'''					)
										    and isinstance(config.architectures			, _lowerCAmelCase					)
										    and len(config.architectures					) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
																try:
																						__SCREAMING_SNAKE_CASE:				int           =   '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
																						__SCREAMING_SNAKE_CASE:				Tuple           =   __import__('''transformers'''			, fromlist=[model_class]					)
																						__SCREAMING_SNAKE_CASE:				Tuple           =   getattr(_lowerCAmelCase			, _lowerCAmelCase					)
																						__SCREAMING_SNAKE_CASE:				Optional[int]           =   model_cls(_lowerCAmelCase					)
																except ImportError:
																						raise ImportError(
																						    f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
																						    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''					)
										else:
																__SCREAMING_SNAKE_CASE:				str           =   TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_lowerCAmelCase					)

										# encoder-decoder has vocab size saved differently
										__SCREAMING_SNAKE_CASE:				List[Any]           =   config.vocab_size if hasattr(_lowerCAmelCase			, '''vocab_size'''					) else config.encoder.vocab_size
										__SCREAMING_SNAKE_CASE:				Any           =   random_input_ids(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)

										@run_with_tf_optimizations(self.args.eager_mode			, self.args.use_xla					)
										def encoder_decoder_train():
																__SCREAMING_SNAKE_CASE:				Tuple           =   model(_lowerCAmelCase			, decoder_input_ids=_lowerCAmelCase			, labels=_lowerCAmelCase			, training=_lowerCAmelCase					)[0]
																__SCREAMING_SNAKE_CASE:				List[str]           =   tf.gradients(_lowerCAmelCase			, model.trainable_variables					)
																return gradients

										@run_with_tf_optimizations(self.args.eager_mode			, self.args.use_xla					)
										def encoder_train():
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model(_lowerCAmelCase			, labels=_lowerCAmelCase			, training=_lowerCAmelCase					)[0]
																__SCREAMING_SNAKE_CASE:				Tuple           =   tf.gradients(_lowerCAmelCase			, model.trainable_variables					)
																return gradients

										__SCREAMING_SNAKE_CASE:				List[Any]           =   encoder_decoder_train if config.is_encoder_decoder else encoder_train

										return _train






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										with self.args.strategy.scope():
																try:
																						if self.args.is_tpu or self.args.use_xla:
																												# run additional 10 times to stabilize compilation for tpu
																												logger.info('''Do inference on TPU. Running model 5 times to stabilize compilation'''					)
																												timeit.repeat(_lowerCAmelCase			, repeat=1			, number=5					)

																						# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
																						__SCREAMING_SNAKE_CASE:				Dict           =   timeit.repeat(
																						    _lowerCAmelCase			, repeat=self.args.repeat			, number=10			, )

																						return min(_lowerCAmelCase					) / 10.0
																except ResourceExhaustedError as e:
																						self.print_fn(f"""Doesn\'t fit on GPU. {e}"""					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										logger.info(
										    '''Note that TensorFlow allocates more memory than '''
										    '''it might need to speed up computation. '''
										    '''The memory reported here corresponds to the memory '''
										    '''reported by `nvidia-smi`, which can vary depending '''
										    '''on total available memory on the GPU that is used.'''					)
										with self.args.strategy.scope():
																try:
																						if self.args.trace_memory_line_by_line:
																												if not self.args.eager_mode:
																																		raise ValueError(
																																		    '''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
																																		    ''' consumption line by line.'''					)
																												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   start_memory_tracing('''transformers'''					)

																						if self.args.is_tpu:
																												# tpu
																												raise NotImplementedError(
																												    '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'''
																												    ''' with `args.memory=False`'''					)
																						elif self.args.is_gpu:
																												# gpu
																												if not is_pyanvml_available():
																																		logger.warning(
																																		    '''py3nvml not installed, we won\'t log GPU memory usage. '''
																																		    '''Install py3nvml (pip install py3nvml) to log information about GPU.'''					)
																																		__SCREAMING_SNAKE_CASE:				List[str]           =   '''N/A'''
																												else:
																																		logger.info(
																																		    '''Measuring total GPU usage on GPU device. Make sure to not have additional processes'''
																																		    ''' running on the same GPU.'''					)
																																		# init nvml
																																		nvml.nvmlInit()
																																		func()
																																		__SCREAMING_SNAKE_CASE:				Dict           =   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx					)
																																		__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   nvml.nvmlDeviceGetMemoryInfo(_lowerCAmelCase					)
																																		__SCREAMING_SNAKE_CASE:				Tuple           =   meminfo.used
																																		__SCREAMING_SNAKE_CASE:				Any           =   Memory(_lowerCAmelCase					)
																																		# shutdown nvml
																																		nvml.nvmlShutdown()
																						else:
																												# cpu
																												if self.args.trace_memory_line_by_line:
																																		logger.info(
																																		    '''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
																																		    ''' TensorFlow.'''					)
																																		__SCREAMING_SNAKE_CASE:				Dict           =   None
																												else:
																																		__SCREAMING_SNAKE_CASE:				Any           =   measure_peak_memory_cpu(_lowerCAmelCase					)
																																		__SCREAMING_SNAKE_CASE:				int           =   Memory(_lowerCAmelCase					) if isinstance(_lowerCAmelCase			, _lowerCAmelCase					) else memory_bytes
																						if self.args.trace_memory_line_by_line:
																												__SCREAMING_SNAKE_CASE:				List[str]           =   stop_memory_tracing(_lowerCAmelCase					)
																												if memory is None:
																																		__SCREAMING_SNAKE_CASE:				Any           =   summary.total
																						else:
																												__SCREAMING_SNAKE_CASE:				str           =   None

																						return memory, summary
																except ResourceExhaustedError as e:
																						self.print_fn(f"""Doesn\'t fit on GPU. {e}"""					)
																						return "N/A", None







# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







					super().__init__()
					self.register_modules(unet=A		,      scheduler=A	)







		@torch.no_grad()
		def __call__( self		,      A = 1		,      A = None		,      A = 50		,      A = "pil"		,      A = True		,      **A		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					lowerCamelCase    		=  torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=A		,      )
					lowerCamelCase    		=  image.to(self.device	)

					# set step values
					self.scheduler.set_timesteps(A	)

					for t in self.progress_bar(self.scheduler.timesteps	):
								# 1. predict noise model_output
								lowerCamelCase    		=  self.unet(A		,      A	).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								lowerCamelCase    		=  self.scheduler.step(A		,      A		,      A	).prev_sample

					lowerCamelCase    		=  (image / 2 + 0.5).clamp(0		,      1	)
					lowerCamelCase    		=  image.cpu().permute(0		,      2		,      3		,      1	).numpy()
					if output_type == "pil":
								lowerCamelCase    		=  self.numpy_to_pil(A	)

					if not return_dict:
								return (image,), "This is a local test"

					return ImagePipelineOutput(images=A	), "This is a local test"

