




'''simple docstring'''



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

UpperCamelCase_    	=				logging.get_logger(__name__)




def   lowercase__(			__UpperCamelCase:					bool      ,__UpperCamelCase:					bool    ):

      """simple docstring"""






      def run_func(__UpperCamelCase:					Dict    ):
            @wraps(__UpperCamelCase    )
            def run_in_eager_mode(*__UpperCamelCase:					List[str]      ,**__UpperCamelCase:					Union[str, Any]    ):
                  return func(*__UpperCamelCase      ,**__UpperCamelCase    )

            @wraps(__UpperCamelCase    )
            @tf.function(experimental_compile=__UpperCamelCase    )
            def run_in_graph_mode(*__UpperCamelCase:					str      ,**__UpperCamelCase:					Union[str, Any]    ):
                  return func(*__UpperCamelCase      ,**__UpperCamelCase    )

            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   lowercase__(			__UpperCamelCase:					int      ,__UpperCamelCase:					int      ,__UpperCamelCase:					int    ):

      """simple docstring"""






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






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				TensorFlowBenchmarkArguments
       A						:				PretrainedConfig
       A						:				str        =						"TensorFlow"







       @property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return tf.__version__







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             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   :      List[Any]    					=						self._prepare_inference_func(A,							A,							A  )
             return self._measure_speed(_inference  )







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''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   :      Union[str, Any]    					=						self._prepare_train_func(A,							A,							A  )
             return self._measure_speed(_train  )







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             if self.args.is_gpu:
                   tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx],							A  )
             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   :      Union[str, Any]    					=						self._prepare_train_func(A,							A,							A  )
             return self._measure_memory(_train  )







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.config_dict[model_name]

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

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						(
                 hasattr(A,							'architectures'  )
                 and isinstance(config.architectures,							A  )
                 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   :      int    					=						__import__('transformers',							fromlist=[model_class]  )
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						getattr(A,							A  )
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						model_cls(A  )
                   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[str]    					=						TF_MODEL_MAPPING[config.__class__](A  )

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

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

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

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

             return _inference







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						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   :      List[Any]    					=						(
                 hasattr(A,							'architectures'  )
                 and isinstance(config.architectures,							A  )
                 and len(config.architectures  ) > 0
             )
             if not self.args.only_pretrain_model and has_model_class_in_config:
                   try:
                         SCREAMING_SNAKE_CASE   :      str    					=						'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						__import__('transformers',							fromlist=[model_class]  )
                         SCREAMING_SNAKE_CASE   :      Dict    					=						getattr(A,							A  )
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						model_cls(A  )
                   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   :      int    					=						TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A  )

             # encoder-decoder has vocab size saved differently
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						config.vocab_size if hasattr(A,							'vocab_size'  ) else config.encoder.vocab_size
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						random_input_ids(A,							A,							A  )

             @run_with_tf_optimizations(self.args.eager_mode,							self.args.use_xla  )
             def encoder_decoder_train():
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						model(A,							decoder_input_ids=A,							labels=A,							training=A  )[0]
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tf.gradients(A,							model.trainable_variables  )
                   return gradients

             @run_with_tf_optimizations(self.args.eager_mode,							self.args.use_xla  )
             def encoder_train():
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						model(A,							labels=A,							training=A  )[0]
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tf.gradients(A,							model.trainable_variables  )
                   return gradients

             SCREAMING_SNAKE_CASE   :      int    					=						encoder_decoder_train if config.is_encoder_decoder else encoder_train

             return _train







       def        UpperCamelCase_							(      self,							A  ):







             '''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(A,							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   :      Tuple    					=						timeit.repeat(
                             A,							repeat=self.args.repeat,							number=10,							)

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







       def        UpperCamelCase_							(      self,							A  ):







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

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





import re

from filelock import FileLock


try:
      import nltk

      a_     :    Optional[Any]     		=   True
except (ImportError, ModuleNotFoundError):
      a_     :    Union[str, Any]     		=   False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 re.sub('<n>'		,				''		,				_UpperCamelCase						)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(_UpperCamelCase						)						)

