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			:							Union[str, Any]     =logging.get_logger(__name__)







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    def run_func(__A	):
        @wraps(__A	)
        def run_in_eager_mode(*__A   , **__A	):
            return func(*__A   , **__A	)

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

        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__  (					__A   , __A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Union[str, Any] 						=						random.Random()
    _lowerCamelCase     :		Any 						=						[rng.randint(0   , vocab_size - 1	) for i in range(batch_size * sequence_length	)]
    return tf.constant(__A   , shape=(batch_size, sequence_length)   , dtype=tf.intaa	)




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       42
      _snake_case    		=       42
      _snake_case    		=       "TensorFlow"



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[str]:


          """simple docstring"""


          return tf.__version__



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				int)				->float:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				int)				->float:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.args.strategy
          if strategy is None:
              raise ValueError("""A device strategy has to be initialized before using TensorFlow.""")
          _lowerCamelCase     :		str 						=						self._prepare_train_func(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)
          return self._measure_speed(_train)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				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]    ,  _UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						self.args.strategy
          if strategy is None:
              raise ValueError("""A device strategy has to be initialized before using TensorFlow.""")
          _lowerCamelCase     :		List[Any] 						=						self._prepare_inference_func(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)
          return self._measure_memory(_inference)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				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]    ,  _UpperCamelCase)
          _lowerCamelCase     :		Optional[int] 						=						self.args.strategy
          if strategy is None:
              raise ValueError("""A device strategy has to be initialized before using TensorFlow.""")

          _lowerCamelCase     :		Optional[int] 						=						self._prepare_train_func(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)
          return self._measure_memory(_train)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				int)				->Callable[[], None]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						self.config_dict[model_name]

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

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

          # encoder-decoder has vocab size saved differently
          _lowerCamelCase     :		int 						=						config.vocab_size if hasattr(_UpperCamelCase    ,  """vocab_size""") else config.encoder.vocab_size
          _lowerCamelCase     :		List[Any] 						=						random_input_ids(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)

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

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

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

          return _inference



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				int)				->Callable[[], None]:


          """simple docstring"""


          _lowerCamelCase     :		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.""")

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

          # encoder-decoder has vocab size saved differently
          _lowerCamelCase     :		int 						=						config.vocab_size if hasattr(_UpperCamelCase    ,  """vocab_size""") else config.encoder.vocab_size
          _lowerCamelCase     :		Union[str, Any] 						=						random_input_ids(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)

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

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

          _lowerCamelCase     :		List[str] 						=						encoder_decoder_train if config.is_encoder_decoder else encoder_train

          return _train



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				int)				->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(_UpperCamelCase    ,  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
                  _lowerCamelCase     :		Tuple 						=						timeit.repeat(
                      _UpperCamelCase    ,  repeat=self.args.repeat    ,  number=10    ,  )

                  return min(_UpperCamelCase) / 1_0.0
              except ResourceExhaustedError as e:
                  self.print_fn(F"""Doesn't fit on GPU. {e}""")




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

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





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				float)				->float:


          """simple docstring"""


          return 0.0







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		int 						=						min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
    _lowerCamelCase     :		Tuple 						=						max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
    return lowest, highest







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						512
    _lowerCamelCase     :		Tuple 						=						[1] + [0] * (size - 1)
    _lowerCamelCase     :		Optional[Any] 						=						[filter_type.process(__A	) for item in inputs]

    _lowerCamelCase     :		Optional[Any] 						=						[0] * (samplerate - size)  # zero-padding
    outputs += filler
    _lowerCamelCase     :		Tuple 						=						np.abs(np.fft.fft(__A	)	)
    _lowerCamelCase     :		List[Any] 						=						20 * np.logaa(__A	)

    # Frequencies on log scale from 24 to nyquist frequency
    plt.xlim(24   , samplerate / 2 - 1	)
    plt.xlabel("""Frequency (Hz)"""	)
    plt.xscale("""log"""	)

    # Display within reasonable bounds
    _lowerCamelCase     :		Any 						=						get_bounds(__A   , __A	)
    plt.ylim(max([-80, bounds[0]]	)   , min([80, bounds[1]]	)	)
    plt.ylabel("""Gain (dB)"""	)

    plt.plot(__A	)
    plt.show()







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						512
    _lowerCamelCase     :		Union[str, Any] 						=						[1] + [0] * (size - 1)
    _lowerCamelCase     :		int 						=						[filter_type.process(__A	) for item in inputs]

    _lowerCamelCase     :		Optional[Any] 						=						[0] * (samplerate - size)  # zero-padding
    outputs += filler
    _lowerCamelCase     :		Any 						=						np.angle(np.fft.fft(__A	)	)

    # Frequencies on log scale from 24 to nyquist frequency
    plt.xlim(24   , samplerate / 2 - 1	)
    plt.xlabel("""Frequency (Hz)"""	)
    plt.xscale("""log"""	)

    plt.ylim(-2 * pi   , 2 * pi	)
    plt.ylabel("""Phase shift (Radians)"""	)
    plt.plot(np.unwrap(__A   , -2 * pi	)	)
    plt.show()




