


'''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

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

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			Dict:






       """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 _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			["tf.Tensor"]:






       """simple docstring"""







       lowercase__      =					random.Random()
       lowercase__      =					[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     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	TensorFlowBenchmarkArguments
      lowerCAmelCase__   :	PretrainedConfig
      lowerCAmelCase__   :	str       =				"TensorFlow"



      @property
      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             return tf.__version__



      def  UpperCamelCase__						(self       :       Union[str, Any]  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







             lowercase__      =					self.args.strategy
             if strategy is None:
                    raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''					)
             lowercase__      =					self._prepare_inference_func(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)
             return self._measure_speed(_inference					)



      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







             lowercase__      =					self.args.strategy
             if strategy is None:
                    raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''					)
             lowercase__      =					self._prepare_train_func(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)
             return self._measure_speed(_train					)



      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







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

             lowercase__      =					self._prepare_train_func(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)
             return self._measure_memory(_train					)



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







             lowercase__      =					self.config_dict[model_name]

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

             lowercase__      =					(
                 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:
                           lowercase__      =					'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                           lowercase__      =					__import__('''transformers'''  ,		fromlist=[model_class]					)
                           lowercase__      =					getattr(UpperCamelCase  ,		UpperCamelCase					)
                           lowercase__      =					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:
                    lowercase__      =					TF_MODEL_MAPPING[config.__class__](UpperCamelCase					)

             # encoder-decoder has vocab size saved differently
             lowercase__      =					config.vocab_size if hasattr(UpperCamelCase  ,		'''vocab_size'''					) else config.encoder.vocab_size
             lowercase__      =					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					)

             lowercase__      =					encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

             return _inference



      def  UpperCamelCase__						(self       :       Union[str, Any]  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







             lowercase__      =					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.'''					)

             lowercase__      =					(
                 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:
                           lowercase__      =					'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                           lowercase__      =					__import__('''transformers'''  ,		fromlist=[model_class]					)
                           lowercase__      =					getattr(UpperCamelCase  ,		UpperCamelCase					)
                           lowercase__      =					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:
                    lowercase__      =					TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase					)

             # encoder-decoder has vocab size saved differently
             lowercase__      =					config.vocab_size if hasattr(UpperCamelCase  ,		'''vocab_size'''					) else config.encoder.vocab_size
             lowercase__      =					random_input_ids(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)

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

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

             lowercase__      =					encoder_decoder_train if config.is_encoder_decoder else encoder_train

             return _train



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       Optional[int]					):

             '''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
                           lowercase__      =					timeit.repeat(
                               UpperCamelCase  ,		repeat=self.args.repeat  ,		number=10  ,		)

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






      def  UpperCamelCase__						(self       :       Optional[Any]  ,		UpperCamelCase       :       Callable[[], None]					):

             '''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.'''					)
                                  lowercase__      =					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.'''					)
                                         lowercase__      =					'''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()
                                         lowercase__      =					nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx					)
                                         lowercase__      =					nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase					)
                                         lowercase__      =					meminfo.used
                                         lowercase__      =					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.'''					)
                                         lowercase__      =					None
                                  else:
                                         lowercase__      =					measure_peak_memory_cpu(UpperCamelCase					)
                                         lowercase__      =					Memory(UpperCamelCase					) if isinstance(UpperCamelCase  ,		UpperCamelCase					) else memory_bytes
                           if self.args.trace_memory_line_by_line:
                                  lowercase__      =					stop_memory_tracing(UpperCamelCase					)
                                  if memory is None:
                                         lowercase__      =					summary.total
                           else:
                                  lowercase__      =					None

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








'''simple docstring'''

import unittest

import numpy as np

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A = None    ,					)			->			np.ndarray:






       """simple docstring"""







       lowercase__      =					np.shape(A			)
       lowercase__      =					np.shape(A			)
       lowercase__      =					np.shape(A			)

       if shape_a[0] != shape_b[0]:
              lowercase__      =					(
                  '''Expected the same number of rows for A and B. '''
                  f"Instead found A of size {shape_a} and B of size {shape_b}"
              )
              raise ValueError(A			)

       if shape_b[1] != shape_c[1]:
              lowercase__      =					(
                  '''Expected the same number of columns for B and C. '''
                  f"Instead found B of size {shape_b} and C of size {shape_c}"
              )
              raise ValueError(A			)

       lowercase__      =					pseudo_inv
       if a_inv is None:
              try:
                     lowercase__      =					np.linalg.inv(A			)
              except np.linalg.LinAlgError:
                     raise ValueError(
                         '''Input matrix A is not invertible. Cannot compute Schur complement.'''			)

       return mat_c - mat_b.T @ a_inv @ mat_b
class     __lowerCAmelCase		(unittest.TestCase	):





      '''simple docstring'''






      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             lowercase__      =					np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
             lowercase__      =					np.array([[0, 3], [3, 0], [2, 3]]					)
             lowercase__      =					np.array([[2, 1], [6, 3]]					)

             lowercase__      =					schur_complement(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)

             lowercase__      =					np.block([[a, b], [b.T, c]]					)

             lowercase__      =					np.linalg.det(UpperCamelCase					)
             lowercase__      =					np.linalg.det(UpperCamelCase					)
             lowercase__      =					np.linalg.det(UpperCamelCase					)

             self.assertAlmostEqual(UpperCamelCase  ,		det_a * det_s					)



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
             lowercase__      =					np.array([[0, 3], [3, 0], [2, 3]]					)
             lowercase__      =					np.array([[2, 1], [6, 3]]					)

             with self.assertRaises(UpperCamelCase					):
                    schur_complement(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)






      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
             lowercase__      =					np.array([[0, 3], [3, 0], [2, 3]]					)
             lowercase__      =					np.array([[2, 1, 3], [6, 3, 5]]					)

             with self.assertRaises(UpperCamelCase					):
                    schur_complement(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()




