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

SCREAMING_SNAKE_CASE__		:					Optional[Any]						=					logging.get_logger(__name__)







def     _a				(      lowercase__  :			bool     ,  lowercase__  :			bool ):







     '''simple docstring'''


     def run_func(lowercase__  :			List[Any] ):
          @wraps(lowercase__ )
          def run_in_eager_mode(*lowercase__  :			Optional[Any]     ,  **lowercase__  :			Dict ):
               return func(*lowercase__     ,  **lowercase__ )

          @wraps(lowercase__ )
          @tf.function(experimental_compile=lowercase__ )
          def run_in_graph_mode(*lowercase__  :			int     ,  **lowercase__  :			str ):
               return func(*lowercase__     ,  **lowercase__ )

          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     _a				(      lowercase__  :			int     ,  lowercase__  :			int     ,  lowercase__  :			int ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		random.Random()
     SCREAMING_SNAKE_CASE__				:				List[str]			=		[rng.randint(0     ,  vocab_size - 1 ) for i in range(batch_size * sequence_length )]
     return tf.constant(lowercase__     ,  shape=(batch_size, sequence_length)     ,  dtype=tf.intaa )







class       snake_case					(      __lowerCAmelCase						):
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     "TensorFlow"




  @property
  def 	__lowercase(      self     :	Union[str, Any]				)->       List[str]:




       """simple docstring"""
       return tf.__version__




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	str     , a_     :	int     , a_     :	int				)->       float:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		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_speed(_inference				)




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	str     , a_     :	int     , a_     :	int				)->       float:




       """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__				:				str			=		self._prepare_train_func(lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__				)
       return self._measure_speed(_train				)




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	str     , a_     :	int     , a_     :	int				)->       [Memory, Optional[MemorySummary]]:




       """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__				:				int			=		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_memory(_inference				)




  def 	__lowercase(      self     :	Tuple     , a_     :	str     , a_     :	int     , a_     :	int				)->       [Memory, Optional[MemorySummary]]:




       """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__				:				Tuple			=		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_memory(_train				)




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	str     , a_     :	int     , a_     :	int				)->       Callable[[], None]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.config_dict[model_name]

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

       SCREAMING_SNAKE_CASE__				:				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__				:				List[Any]			=		'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                 SCREAMING_SNAKE_CASE__				:				int			=		__import__('transformers'     , fromlist=[model_class]				)
                 SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		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__				:				Union[str, Any]			=		TF_MODEL_MAPPING[config.__class__](lowerCamelCase__				)

       # encoder-decoder has vocab size saved differently
       SCREAMING_SNAKE_CASE__				:				int			=		config.vocab_size if hasattr(lowerCamelCase__     , 'vocab_size'				) else config.encoder.vocab_size
       SCREAMING_SNAKE_CASE__				:				str			=		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__				:				List[str]			=		encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

       return _inference




  def 	__lowercase(      self     :	Tuple     , a_     :	str     , a_     :	int     , a_     :	int				)->       Callable[[], None]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		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[Any]			=		(
           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__				:				Union[str, Any]			=		'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                 SCREAMING_SNAKE_CASE__				:				Any			=		__import__('transformers'     , fromlist=[model_class]				)
                 SCREAMING_SNAKE_CASE__				:				Any			=		getattr(lowerCamelCase__     , lowerCamelCase__				)
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		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__				:				List[Any]			=		TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase__				)

       # encoder-decoder has vocab size saved differently
       SCREAMING_SNAKE_CASE__				:				Any			=		config.vocab_size if hasattr(lowerCamelCase__     , 'vocab_size'				) else config.encoder.vocab_size
       SCREAMING_SNAKE_CASE__				:				str			=		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__				:				str			=		model(lowerCamelCase__     , decoder_input_ids=lowerCamelCase__     , labels=lowerCamelCase__     , training=lowerCamelCase__				)[0]
            SCREAMING_SNAKE_CASE__				:				int			=		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__				:				Any			=		tf.gradients(lowerCamelCase__     , model.trainable_variables				)
            return gradients

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		encoder_decoder_train if config.is_encoder_decoder else encoder_train

       return _train




  def 	__lowercase(      self     :	Tuple     , a_     :	Union[str, Any]				)->       float:




       """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__				:				Optional[Any]			=		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 	__lowercase(      self     :	Union[str, Any]     , a_     :	Callable[[], None]				)->       [Memory, MemorySummary]:




       """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__				:				List[str]			=		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__				:				Any			=		'''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__				:				List[str]			=		nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx				)
                           SCREAMING_SNAKE_CASE__				:				Tuple			=		nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase__				)
                           SCREAMING_SNAKE_CASE__				:				Dict			=		meminfo.used
                           SCREAMING_SNAKE_CASE__				:				int			=		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__				:				Optional[Any]			=		None
                      else:
                           SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		measure_peak_memory_cpu(lowerCamelCase__				)
                           SCREAMING_SNAKE_CASE__				:				Dict			=		Memory(lowerCamelCase__				) if isinstance(lowerCamelCase__     , lowerCamelCase__				) else memory_bytes
                 if self.args.trace_memory_line_by_line:
                      SCREAMING_SNAKE_CASE__				:				Tuple			=		stop_memory_tracing(lowerCamelCase__				)
                      if memory is None:
                           SCREAMING_SNAKE_CASE__				:				Dict			=		summary.total
                 else:
                      SCREAMING_SNAKE_CASE__				:				Tuple			=		None

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




class       snake_case					(      UpperCamelCase_						):
  pass




class       snake_case					(      UpperCamelCase_						):
  pass




class       snake_case					:




  def __init__(      self     :	Union[str, Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		[
           [],
           [],
           [],
       ]




  def 	__lowercase(      self     :	int     , a_     :	int     , a_     :	int				)->       None:




       """simple docstring"""
       try:
            if len(self.queues[priority]				) >= 100:
                 raise OverflowError('Maximum queue size is 100'				)
            self.queues[priority].append(a_				)
       except IndexError:
            raise ValueError('Valid priorities are 0, 1, and 2'				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       for queue in self.queues:
            if queue:
                 return queue.pop(0				)
       raise UnderFlowError('All queues are empty'				)




  def __str__(      self     :	Any				)->       str:




       """simple docstring"""
       return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues				)				)




class       snake_case					:




  def __init__(      self     :	Union[str, Any]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]




  def 	__lowercase(      self     :	List[str]     , a_     :	int				)->       None:




       """simple docstring"""
       if len(self.queue				) == 100:
            raise OverFlowError('Maximum queue size is 100'				)
       self.queue.append(a_				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       if not self.queue:
            raise UnderFlowError('The queue is empty'				)
       else:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		min(self.queue				)
            self.queue.remove(a_				)
            return data




  def __str__(      self     :	List[str]				)->       str:




       """simple docstring"""
       return str(self.queue				)







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		FixedPriorityQueue()
     fpq.enqueue(0     ,  10 )
     fpq.enqueue(1     ,  70 )
     fpq.enqueue(0     ,  1_00 )
     fpq.enqueue(2     ,  1 )
     fpq.enqueue(2     ,  5 )
     fpq.enqueue(1     ,  7 )
     fpq.enqueue(2     ,  4 )
     fpq.enqueue(1     ,  64 )
     fpq.enqueue(0     ,  1_28 )
     print(lowercase__ )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(lowercase__ )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		ElementPriorityQueue()
     epq.enqueue(10 )
     epq.enqueue(70 )
     epq.enqueue(1_00 )
     epq.enqueue(1 )
     epq.enqueue(5 )
     epq.enqueue(7 )
     epq.enqueue(4 )
     epq.enqueue(64 )
     epq.enqueue(1_28 )
     print(lowercase__ )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(lowercase__ )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )


if __name__ == "__main__":
      fixed_priority_queue()
      element_priority_queue()



