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	:					List[Any]			  =					logging.get_logger(__name__)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Union[str, Any]:
 def run_func(_lowerCAmelCase					):
  @wraps(_lowerCAmelCase					)
  def run_in_eager_mode(*_lowerCAmelCase							,       **_lowerCAmelCase					):
   return func(*_lowerCAmelCase							,       **_lowerCAmelCase					)

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

  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_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			["tf.Tensor"]:
 UpperCamelCase     : Optional[int]	     =       random.Random()
 UpperCamelCase     : int	     =       [rng.randint(0							,       vocab_size - 1					) for i in range(batch_size * sequence_length					)]
 return tf.constant(_lowerCAmelCase							,       shape=(batch_size, sequence_length)							,       dtype=tf.intaa					)



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :TensorFlowBenchmarkArguments
      _UpperCAmelCase     :PretrainedConfig
      _UpperCAmelCase     :str						   =       "TensorFlow"





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return tf.__version__





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       self.args.strategy
       if strategy is None:
        raise ValueError("A device strategy has to be initialized before using TensorFlow."       )
       UpperCamelCase     : str	     =       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_       )
       UpperCamelCase     : Optional[Any]	     =       self.args.strategy
       if strategy is None:
        raise ValueError("A device strategy has to be initialized before using TensorFlow."       )
       UpperCamelCase     : str	     =       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_       )
       UpperCamelCase     : int	     =       self.args.strategy
       if strategy is None:
        raise ValueError("A device strategy has to be initialized before using TensorFlow."       )

       UpperCamelCase     : Any	     =       self._prepare_train_func(A_	,							A_	,							A_       )
       return self._measure_memory(_train       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




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

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

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

       # encoder-decoder has vocab size saved differently
       UpperCamelCase     : Union[str, Any]	     =       config.vocab_size if hasattr(A_	,							"vocab_size"       ) else config.encoder.vocab_size
       UpperCamelCase     : Optional[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_       )

       UpperCamelCase     : Tuple	     =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

       return _inference





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : 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."       )

       UpperCamelCase     : str	     =       (
           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:
         UpperCamelCase     : Optional[Any]	     =       "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
         UpperCamelCase     : int	     =       __import__("transformers"	,							fromlist=[model_class]       )
         UpperCamelCase     : Optional[Any]	     =       getattr(A_	,							A_       )
         UpperCamelCase     : int	     =       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:
        UpperCamelCase     : Union[str, Any]	     =       TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A_       )

       # encoder-decoder has vocab size saved differently
       UpperCamelCase     : Optional[Any]	     =       config.vocab_size if hasattr(A_	,							"vocab_size"       ) else config.encoder.vocab_size
       UpperCamelCase     : str	     =       random_input_ids(A_	,							A_	,							A_       )

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

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

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

import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       )						->			list[float]:
 UpperCamelCase     ,   UpperCamelCase     : List[Any]	     =       coefficient_matrix.shape
 UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       constant_matrix.shape

 if rowsa != colsa:
  UpperCamelCase     : List[Any]	     =       F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
  raise ValueError(_lowerCAmelCase					)

 if colsa != 1:
  UpperCamelCase     : Any	     =       F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
  raise ValueError(_lowerCAmelCase					)

 if rowsa != rowsa:
  UpperCamelCase     : Tuple	     =       (
      "Coefficient and constant matrices dimensions must be nxn and nx1 but "
      F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
  )
  raise ValueError(_lowerCAmelCase					)

 if len(_lowerCAmelCase					) != rowsa:
  UpperCamelCase     : Any	     =       (
      "Number of initial values must be equal to number of rows in coefficient "
      F"""matrix but received {len(_lowerCAmelCase					)} and {rowsa}"""
  )
  raise ValueError(_lowerCAmelCase					)

 if iterations <= 0:
  raise ValueError("Iterations must be at least 1"					)

 UpperCamelCase     : NDArray[floataa]	     =       np.concatenate(
     (coefficient_matrix, constant_matrix)							,       axis=1					)

 UpperCamelCase     ,   UpperCamelCase     : str	     =       table.shape

 strictly_diagonally_dominant(_lowerCAmelCase					)

 # Iterates the whole matrix for given number of times
 for _ in range(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       []
  for row in range(_lowerCAmelCase					):
   UpperCamelCase     : Optional[int]	     =       0
   for col in range(_lowerCAmelCase					):
    if col == row:
     UpperCamelCase     : Union[str, Any]	     =       table[row][col]
    elif col == cols - 1:
     UpperCamelCase     : List[Any]	     =       table[row][col]
    else:
     temp += (-1) * table[row][col] * init_val[col]
   UpperCamelCase     : Dict	     =       (temp + val) / denom
   new_val.append(_lowerCAmelCase					)
  UpperCamelCase     : List[str]	     =       new_val

 return [float(_lowerCAmelCase					) for i in new_val]



def 		A_    (					_lowerCAmelCase					)						->			bool:
 UpperCamelCase     ,   UpperCamelCase     : Dict	     =       table.shape

 UpperCamelCase     : List[Any]	     =       True

 for i in range(0							,       _lowerCAmelCase					):
  UpperCamelCase     : Union[str, Any]	     =       0
  for j in range(0							,       cols - 1					):
   if i == j:
    continue
   else:
    total += table[i][j]

  if table[i][i] <= total:
   raise ValueError("Coefficient matrix is not strictly diagonally dominant"					)

 return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
    import doctest

    doctest.testmod()
