





'''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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				Union[str, Any]:



			"""simple docstring"""


			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"]:



			"""simple docstring"""


			__snake_case					:      Dict							      =     random.Random()
			__snake_case					:      str							      =     [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 (					__lowercase		):
		lowercase__:   TensorFlowBenchmarkArguments
		lowercase__:   PretrainedConfig
		lowercase__:   str				     =   "TensorFlow"

		@property
		def 	lowercase__     (   self					:   Tuple			)					->    Dict:


					"""simple docstring"""

					return tf.__version__

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    float:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    float:


					"""simple docstring"""

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

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

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   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]  ,      __magic_name__			)
					__snake_case					:      Tuple							      =     self.args.strategy
					if strategy is None:
								raise ValueError("""A device strategy has to be initialized before using TensorFlow."""			)

					__snake_case					:      List[str]							      =     self._prepare_train_func(__magic_name__  ,      __magic_name__  ,      __magic_name__			)
					return self._measure_memory(_train			)

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    Callable[[], None]:


					"""simple docstring"""

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

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

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

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

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

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

					__snake_case					:      Any							      =     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

					return _inference

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    Callable[[], None]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     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."""			)

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

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

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

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

					__snake_case					:      str							      =     encoder_decoder_train if config.is_encoder_decoder else encoder_train

					return _train

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   Tuple			)					->    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(__magic_name__  ,      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
											__snake_case					:      Optional[int]							      =     timeit.repeat(
											    __magic_name__  ,      repeat=self.args.repeat  ,      number=10  ,      )

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

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

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









'''simple docstring'''






from __future__ import annotations

from typing import Any




class  _A :

		def __init__(   self					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int  ,      __magic_name__					:   float = 0			)					->    None:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Optional[Any]							      =     row, column
					__snake_case					:      Dict							      =     [[default_value for c in range(__magic_name__			)] for r in range(__magic_name__			)]

		def __str__(   self					:   List[Any]			)					->    str:


					"""simple docstring"""

					__snake_case					:      Dict							      =     f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

					# Make string identifier
					__snake_case					:      Optional[int]							      =     0
					for row_vector in self.array:
								for obj in row_vector:
											__snake_case					:      Optional[int]							      =     max(__magic_name__  ,      len(str(__magic_name__			)			)			)
					__snake_case					:      str							      =     f'''%{max_element_length}s'''

					# Make string and return
					def single_line(__magic_name__					:   list[float]			) -> str:
								nonlocal string_format_identifier
								__snake_case					:      Union[str, Any]							      =     """["""
								line += ", ".join(string_format_identifier % (obj,) for obj in row_vector			)
								line += "]"
								return line

					s += "\n".join(single_line(__magic_name__			) for row_vector in self.array			)
					return s

		def __repr__(   self					:   Optional[int]			)					->    str:


					"""simple docstring"""

					return str(self			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   tuple[int, int]			)					->    bool:


					"""simple docstring"""

					if not (isinstance(__magic_name__  ,      (list, tuple)			) and len(__magic_name__			) == 2):
								return False
					elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
								return False
					else:
								return True

		def __getitem__(   self					:   int  ,      __magic_name__					:   tuple[int, int]			)					->    Any:


					"""simple docstring"""

					assert self.validate_indicies(__magic_name__			)
					return self.array[loc[0]][loc[1]]

		def __setitem__(   self					:   List[str]  ,      __magic_name__					:   tuple[int, int]  ,      __magic_name__					:   float			)					->    None:


					"""simple docstring"""

					assert self.validate_indicies(__magic_name__			)
					__snake_case					:      Optional[int]							      =     value

		def __add__(   self					:   Any  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					assert isinstance(__magic_name__  ,      __magic_name__			)
					assert self.row == another.row and self.column == another.column

					# Add
					__snake_case					:      Union[str, Any]							      =     Matrix(self.row  ,      self.column			)
					for r in range(self.row			):
								for c in range(self.column			):
											__snake_case					:      List[Any]							      =     self[r, c] + another[r, c]
					return result

		def __neg__(   self					:   Tuple			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     Matrix(self.row  ,      self.column			)
					for r in range(self.row			):
								for c in range(self.column			):
											__snake_case					:      List[Any]							      =     -self[r, c]
					return result

		def __sub__(   self					:   Optional[int]  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					return self + (-another)

		def __mul__(   self					:   List[Any]  ,      __magic_name__					:   int | float | Matrix			)					->    Matrix:


					"""simple docstring"""

					if isinstance(__magic_name__  ,      (int, float)			):  # Scalar multiplication
								__snake_case					:      Optional[int]							      =     Matrix(self.row  ,      self.column			)
								for r in range(self.row			):
											for c in range(self.column			):
														__snake_case					:      Tuple							      =     self[r, c] * another
								return result
					elif isinstance(__magic_name__  ,      __magic_name__			):  # Matrix multiplication
								assert self.column == another.row
								__snake_case					:      Dict							      =     Matrix(self.row  ,      another.column			)
								for r in range(self.row			):
											for c in range(another.column			):
														for i in range(self.column			):
																	result[r, c] += self[r, i] * another[i, c]
								return result
					else:
								__snake_case					:      Optional[int]							      =     f'''Unsupported type given for another ({type(__magic_name__			)})'''
								raise TypeError(__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      Any							      =     Matrix(self.column  ,      self.row			)
					for r in range(self.row			):
								for c in range(self.column			):
											__snake_case					:      str							      =     self[r, c]
					return result

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   Matrix  ,      __magic_name__					:   Matrix			)					->    Any:


					"""simple docstring"""

					assert isinstance(__magic_name__  ,      __magic_name__			) and isinstance(__magic_name__  ,      __magic_name__			)
					assert self.row == self.column == u.row == v.row  # u, v should be column vector
					assert u.column == v.column == 1  # u, v should be column vector

					# Calculate
					__snake_case					:      List[str]							      =     v.transpose()
					__snake_case					:      Tuple							      =     (v_t * self * u)[0, 0] + 1
					if numerator_factor == 0:
								return None  # It's not invertable
					return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":







	def 					_a						(       ) ->				None:



				"""simple docstring"""


				__snake_case					:      Tuple							      =     Matrix(3    ,	3    ,	0 )
				for i in range(3 ):
							__snake_case					:      Any							      =     1
				print(F'''a^(-1) is {ainv}''' )
				# u, v
				__snake_case					:      Dict							      =     Matrix(3    ,	1    ,	0 )
				__snake_case      ,     __snake_case      ,     __snake_case					:      Union[str, Any]							      =     1, 2, -3
				__snake_case					:      str							      =     Matrix(3    ,	1    ,	0 )
				__snake_case      ,     __snake_case      ,     __snake_case					:      Tuple							      =     4, -2, 5
				print(F'''u is {u}''' )
				print(F'''v is {v}''' )
				print(F'''uv^T is {u * v.transpose()}''' )
				# Sherman Morrison
				print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(_lowerCamelCase    ,	_lowerCamelCase )}''' )







	def 					_a						(       ) ->				None:



				"""simple docstring"""


				import doctest

				doctest.testmod()

	testa()


