



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

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       List[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    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       ["tf.Tensor"]:
						UpperCAmelCase         =     random.Random()
						UpperCAmelCase         =     [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  __magic_name__   (							A__						):
		lowercase      :       TensorFlowBenchmarkArguments
		lowercase      :       PretrainedConfig
		lowercase      :       str          ="TensorFlow"






		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					List[str]:
								'''simple docstring'''







								return tf.__version__






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     int      )				->					float:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     int      )				->					float:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		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__      )
								UpperCAmelCase         =     self.args.strategy
								if strategy is None:
														raise ValueError("A device strategy has to be initialized before using TensorFlow."      )
								UpperCAmelCase         =     self._prepare_inference_func(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__      )
								return self._measure_memory(_inference      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		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__      )
								UpperCAmelCase         =     self.args.strategy
								if strategy is None:
														raise ValueError("A device strategy has to be initialized before using TensorFlow."      )

								UpperCAmelCase         =     self._prepare_train_func(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__      )
								return self._measure_memory(_train      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     int      )				->					Callable[[], None]:
								'''simple docstring'''







								UpperCAmelCase         =     self.config_dict[model_name]

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

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

								# encoder-decoder has vocab size saved differently
								UpperCAmelCase         =     config.vocab_size if hasattr(UpperCamelCase__				,		"vocab_size"      ) else config.encoder.vocab_size
								UpperCAmelCase         =     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__      )

								UpperCAmelCase         =     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

								return _inference






		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     int      )				->					Callable[[], None]:
								'''simple docstring'''







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

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

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

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

								UpperCAmelCase         =     encoder_decoder_train if config.is_encoder_decoder else encoder_train

								return _train






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     str      )				->					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
																				UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		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."      )
																										UpperCAmelCase         =     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         =     "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         =     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
																																UpperCAmelCase         =     nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase__      )
																																UpperCAmelCase         =     meminfo.used
																																UpperCAmelCase         =     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."      )
																																UpperCAmelCase         =     None
																										else:
																																UpperCAmelCase         =     measure_peak_memory_cpu(UpperCamelCase__      )
																																UpperCAmelCase         =     Memory(UpperCamelCase__      ) if isinstance(UpperCamelCase__				,		UpperCamelCase__      ) else memory_bytes
																				if self.args.trace_memory_line_by_line:
																										UpperCAmelCase         =     stop_memory_tracing(UpperCamelCase__      )
																										if memory is None:
																																UpperCAmelCase         =     summary.total
																				else:
																										UpperCAmelCase         =     None

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








def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       list[int]:
						UpperCAmelCase         =     int(lowerCamelCase_    )

						# Initialize Result
						UpperCAmelCase         =     []

						# Traverse through all denomination
						for denomination in reversed(lowerCamelCase_    ):
												# Find denominations
												while int(lowerCamelCase_    ) >= int(lowerCamelCase_    ):
																		total_value -= int(lowerCamelCase_    )
																		answer.append(lowerCamelCase_    )  # Append the "answers" array

						return answer


# Driver Code
if __name__ == "__main__":
		__lowerCamelCase     :							Optional[int]  							=				[]
		__lowerCamelCase     :							Dict  							=				"0"

		if (
		    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
		    == "y"
		):
				__lowerCamelCase     :							List[Any]  							=				int(input("Enter the number of denominations you want to add: ").strip())

				for i in range(0, n):
						denominations.append(int(input(F'''Denomination {i}: ''').strip()))
				__lowerCamelCase     :							str  							=				input("Enter the change you want to make in Indian Currency: ").strip()
		else:
				# All denominations of Indian Currency if user does not enter
				__lowerCamelCase     :							Optional[int]  							=				[1, 2, 5, 10, 20, 50, 100, 500, 2_000]
				__lowerCamelCase     :							Optional[int]  							=				input("Enter the change you want to make: ").strip()

		if int(value) == 0 or int(value) < 0:
				print("The total value cannot be zero or negative.")

		else:
				print(F'''Following is minimal change for {value}: ''')
				__lowerCamelCase     :							Union[str, Any]  							=				find_minimum_change(denominations, value)
				# Print result
				for i in range(len(answer)):
						print(answer[i], end=" ")



