


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

snake_case				:    List[str]       				=     logging.get_logger(__name__)
def 				__lowerCamelCase						( UpperCAmelCase_						:		bool ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





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

            @wraps(UpperCAmelCase_						)
            @tf.function(experimental_compile=UpperCAmelCase_						)
            def run_in_graph_mode(*UpperCAmelCase_						:		Optional[Any] ,  **UpperCAmelCase_						:		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 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      a						:int 							=       random.Random()
      a						:Dict 							=       [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 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       42
 SCREAMING_SNAKE_CASE__      =       42
 SCREAMING_SNAKE_CASE__      =       "TensorFlow"




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return tf.__version__




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       # initialize GPU on separate process
       a						:int 							=       self.args.strategy
       if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''    )
       a						:List[str] 							=       self._prepare_inference_func(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       return self._measure_speed(_inference    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:str 							=       self.args.strategy
       if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''    )
       a						:int 							=       self._prepare_train_func(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       return self._measure_speed(_train    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       # initialize GPU on separate process
       if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]					,  _lowerCamelCase    )
       a						:int 							=       self.args.strategy
       if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''    )
       a						:Optional[int] 							=       self._prepare_inference_func(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       return self._measure_memory(_inference    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]					,  _lowerCamelCase    )
       a						:Optional[Any] 							=       self.args.strategy
       if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''    )

       a						:Any 							=       self._prepare_train_func(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       return self._measure_memory(_train    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Any 							=       self.config_dict[model_name]

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

       a						: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:
                   a						:List[Any] 							=       '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   a						:int 							=       __import__('''transformers'''					,  fromlist=[model_class]    )
                   a						:str 							=       getattr(_lowerCamelCase					,  _lowerCamelCase    )
                   a						: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:
             a						:Dict 							=       TF_MODEL_MAPPING[config.__class__](_lowerCamelCase    )

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

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

       return _inference




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Any 							=       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.'''    )

       a						: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:
                   a						:int 							=       '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   a						:Optional[Any] 							=       __import__('''transformers'''					,  fromlist=[model_class]    )
                   a						:int 							=       getattr(_lowerCamelCase					,  _lowerCamelCase    )
                   a						:List[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:
             a						:int 							=       TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_lowerCamelCase    )

       # encoder-decoder has vocab size saved differently
       a						:str 							=       config.vocab_size if hasattr(_lowerCamelCase					,  '''vocab_size'''    ) else config.encoder.vocab_size
       a						:Dict 							=       random_input_ids(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )

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

       @run_with_tf_optimizations(self.args.eager_mode					,  self.args.use_xla    )
       def encoder_train():
             a						:int 							=       model(_lowerCamelCase					,  labels=_lowerCamelCase					,  training=_lowerCamelCase    )[0]
             a						:Dict 							=       tf.gradients(_lowerCamelCase					,  model.trainable_variables    )
             return gradients

       a						:Optional[Any] 							=       encoder_decoder_train if config.is_encoder_decoder else encoder_train

       return _train




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

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









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'ClapFeatureExtractor'
 SCREAMING_SNAKE_CASE__      =       ('RobertaTokenizer', 'RobertaTokenizerFast')




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       super().__init__(_lowerCamelCase					,  _lowerCamelCase    )




 def __call__(		self					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  **_lowerCamelCase    ):
       a						:Dict 							=       kwargs.pop('''sampling_rate'''					,  _lowerCamelCase    )

       if text is None and audios is None:
             raise ValueError('''You have to specify either text or audios. Both cannot be none.'''    )

       if text is not None:
             a						:Optional[int] 							=       self.tokenizer(_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if audios is not None:
             a						:Tuple 							=       self.feature_extractor(
                 _lowerCamelCase					,  sampling_rate=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if text is not None and audios is not None:
             a						:Union[str, Any] 							=       audio_features.input_features
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**_lowerCamelCase    )					,  tensor_type=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.batch_decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.tokenizer.model_input_names
       a						:str 							=       self.feature_extractor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names    )    )





