"""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

SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)



def 					_lowerCamelCase(					a ,  a							):
							def run_func(a							):
														@wraps(_UpperCamelCase							)
														def run_in_eager_mode(*a ,  **a							):
																					return func(*_UpperCamelCase ,  **_UpperCamelCase							)

														@wraps(_UpperCamelCase							)
														@tf.function(experimental_compile=_UpperCamelCase							)
														def run_in_graph_mode(*a ,  **a							):
																					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(					a ,  a ,  a							):
							__a     				=  random.Random()
							__a     				=  [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__     (   _UpperCamelCase   ):
	_snake_case   :			TensorFlowBenchmarkArguments
	_snake_case   :			PretrainedConfig
	_snake_case   :			str           =		"TensorFlow"


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


	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
								# initialize GPU on separate process
								__a     				=  self.args.strategy
								if strategy is None:
															raise ValueError("A device strategy has to be initialized before using TensorFlow."		)
								__a     				=  self._prepare_inference_func(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE		)
								return self._measure_speed(_inference		)


	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
								__a     				=  self.args.strategy
								if strategy is None:
															raise ValueError("A device strategy has to be initialized before using TensorFlow."		)
								__a     				=  self._prepare_train_func(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE		)
								return self._measure_speed(_train		)


	def 		a__ (				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]  ,						_SCREAMING_SNAKE_CASE		)
								__a     				=  self.args.strategy
								if strategy is None:
															raise ValueError("A device strategy has to be initialized before using TensorFlow."		)
								__a     				=  self._prepare_inference_func(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE		)
								return self._measure_memory(_inference		)


	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
								if self.args.is_gpu:
															tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]  ,						_SCREAMING_SNAKE_CASE		)
								__a     				=  self.args.strategy
								if strategy is None:
															raise ValueError("A device strategy has to be initialized before using TensorFlow."		)

								__a     				=  self._prepare_train_func(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE		)
								return self._measure_memory(_train		)


	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
								__a     				=  self.config_dict[model_name]

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

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

								# encoder-decoder has vocab size saved differently
								__a     				=  config.vocab_size if hasattr(_SCREAMING_SNAKE_CASE  ,						"vocab_size"		) else config.encoder.vocab_size
								__a     				=  random_input_ids(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE		)

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

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

								__a     				=  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

								return _inference


	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
								__a     				=  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     				=  (
								    hasattr(_SCREAMING_SNAKE_CASE  ,						"architectures"		)
								    and isinstance(config.architectures  ,						_SCREAMING_SNAKE_CASE		)
								    and len(config.architectures		) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																						__a     				=  "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
																						__a     				=  __import__("transformers"  ,						fromlist=[model_class]		)
																						__a     				=  getattr(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE		)
																						__a     				=  model_cls(_SCREAMING_SNAKE_CASE		)
															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     				=  TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_SCREAMING_SNAKE_CASE		)

								# encoder-decoder has vocab size saved differently
								__a     				=  config.vocab_size if hasattr(_SCREAMING_SNAKE_CASE  ,						"vocab_size"		) else config.encoder.vocab_size
								__a     				=  random_input_ids(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE		)

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

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

								__a     				=  encoder_decoder_train if config.is_encoder_decoder else encoder_train

								return _train


	def 		a__ (				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(_SCREAMING_SNAKE_CASE  ,						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     				=  timeit.repeat(
																						    _SCREAMING_SNAKE_CASE  ,						repeat=self.args.repeat  ,						number=10  ,						)

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






	def 		a__ (				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     				=  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     				=  "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     				=  nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx		)
																																				__a     				=  nvml.nvmlDeviceGetMemoryInfo(_SCREAMING_SNAKE_CASE		)
																																				__a     				=  meminfo.used
																																				__a     				=  Memory(_SCREAMING_SNAKE_CASE		)
																																				# 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     				=  None
																													else:
																																				__a     				=  measure_peak_memory_cpu(_SCREAMING_SNAKE_CASE		)
																																				__a     				=  Memory(_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE		) else memory_bytes
																						if self.args.trace_memory_line_by_line:
																													__a     				=  stop_memory_tracing(_SCREAMING_SNAKE_CASE		)
																													if memory is None:
																																				__a     				=  summary.total
																						else:
																													__a     				=  None

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

"""simple docstring"""



SCREAMING_SNAKE_CASE__:Any			=	{
    """A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
    """H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": """--""", """N""": """-.""",
    """O""": """---""", """P""": """.--.""", """Q""": """--.-""", """R""": """.-.""", """S""": """...""", """T""": """-""", """U""": """..-""",
    """V""": """...-""", """W""": """.--""", """X""": """-..-""", """Y""": """-.--""", """Z""": """--..""", """1""": """.----""",
    """2""": """..---""", """3""": """...--""", """4""": """....-""", """5""": """.....""", """6""": """-....""", """7""": """--...""",
    """8""": """---..""", """9""": """----.""", """0""": """-----""", """&""": """.-...""", """@""": """.--.-.""",
    """:""": """---...""", """,""": """--..--""", """.""": """.-.-.-""", """'""": """.----.""", """\"""": """.-..-.""",
    """?""": """..--..""", """/""": """-..-.""", """=""": """-...-""", """+""": """.-.-.""", """-""": """-....-""",
    """(""": """-.--.""", """)""": """-.--.-""", """!""": """-.-.--""", """ """: """/"""
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
SCREAMING_SNAKE_CASE__:Any			=	{value: key for key, value in MORSE_CODE_DICT.items()}



def 					_lowerCamelCase(					a							):
       return " ".join(MORSE_CODE_DICT[char] for char in message.upper()							)



def 					_lowerCamelCase(					a							):
       return "".join(REVERSE_DICT[char] for char in message.split()							)



def 					_lowerCamelCase(					):
       __a     				=  "Morse code here!"
       print(a							)
       __a     				=  encrypt(a							)
       print(a							)
       __a     				=  decrypt(a							)
       print(a							)


if __name__ == "__main__":
   main()
