





'''simple docstring'''

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
			import tensorflow as tf

			from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments

@require_tf
class       UpperCAmelCase (						unittest.TestCase  ):




		def 				UpperCAmelCase__   (self      :	Any							,			A__      :	Optional[Any]				)					-> Dict:
						for model_result in results.values():
										for batch_size, sequence_length in zip(model_result["bs"]							,			model_result["ss"]				):
														lowercase         = model_result["result"][batch_size][sequence_length]
														self.assertIsNotNone(lowerCAmelCase__				)




		def 				UpperCAmelCase__   (self      :	int				)					-> List[Any]:
						lowercase         = "sshleifer/tiny-gpt2"
						lowercase         = TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]							,			training=lowerCAmelCase__							,			inference=lowerCAmelCase__							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			eager_mode=lowerCAmelCase__							,			multi_process=lowerCAmelCase__							,			)
						lowercase         = TensorFlowBenchmark(lowerCAmelCase__				)
						lowercase         = benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result				)
						self.check_results_dict_not_empty(results.memory_inference_result				)




		def 				UpperCAmelCase__   (self      :	int				)					-> str:
						lowercase         = "sgugger/tiny-distilbert-classification"
						lowercase         = TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]							,			training=lowerCAmelCase__							,			inference=lowerCAmelCase__							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=lowerCAmelCase__							,			only_pretrain_model=lowerCAmelCase__							,			)
						lowercase         = TensorFlowBenchmark(lowerCAmelCase__				)
						lowercase         = benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result				)
						self.check_results_dict_not_empty(results.memory_inference_result				)




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> str:
						lowercase         = "sshleifer/tiny-gpt2"
						lowercase         = TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]							,			training=lowerCAmelCase__							,			inference=lowerCAmelCase__							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=lowerCAmelCase__							,			)
						lowercase         = TensorFlowBenchmark(lowerCAmelCase__				)
						lowercase         = benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result				)
						self.check_results_dict_not_empty(results.memory_inference_result				)




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Union[str, Any]:
						lowercase         = "sshleifer/tiny-gpt2"
						lowercase         = AutoConfig.from_pretrained(lowerCAmelCase__				)
						lowercase         = TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]							,			training=lowerCAmelCase__							,			inference=lowerCAmelCase__							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			eager_mode=lowerCAmelCase__							,			multi_process=lowerCAmelCase__							,			)
						lowercase         = TensorFlowBenchmark(lowerCAmelCase__							,			[config]				)
						lowercase         = benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result				)
						self.check_results_dict_not_empty(results.memory_inference_result				)




		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[Any]:
						lowercase         = "sshleifer/tiny-gpt2"
						lowercase         = AutoConfig.from_pretrained(lowerCAmelCase__				)
						lowercase         = TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]							,			training=lowerCAmelCase__							,			inference=lowerCAmelCase__							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=lowerCAmelCase__							,			)
						lowercase         = TensorFlowBenchmark(lowerCAmelCase__							,			[config]				)
						lowercase         = benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result				)
						self.check_results_dict_not_empty(results.memory_inference_result				)




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Tuple:
						lowercase         = "sshleifer/tiny-gpt2"
						lowercase         = TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]							,			training=lowerCAmelCase__							,			inference=lowerCAmelCase__							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=lowerCAmelCase__							,			)
						lowercase         = TensorFlowBenchmark(lowerCAmelCase__				)
						lowercase         = benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result				)
						self.check_results_dict_not_empty(results.memory_train_result				)




		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[Any]:
						lowercase         = "sshleifer/tiny-gpt2"
						lowercase         = AutoConfig.from_pretrained(lowerCAmelCase__				)
						lowercase         = TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]							,			training=lowerCAmelCase__							,			inference=lowerCAmelCase__							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=lowerCAmelCase__							,			)
						lowercase         = TensorFlowBenchmark(lowerCAmelCase__							,			[config]				)
						lowercase         = benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result				)
						self.check_results_dict_not_empty(results.memory_train_result				)




		def 				UpperCAmelCase__   (self      :	int				)					-> int:
						lowercase         = "patrickvonplaten/t5-tiny-random"
						lowercase         = AutoConfig.from_pretrained(lowerCAmelCase__				)
						lowercase         = TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]							,			training=lowerCAmelCase__							,			inference=lowerCAmelCase__							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=lowerCAmelCase__							,			)
						lowercase         = TensorFlowBenchmark(lowerCAmelCase__							,			configs=[config]				)
						lowercase         = benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result				)
						self.check_results_dict_not_empty(results.memory_inference_result				)




		@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"				)				) == 0							,			"Cannot do xla on CPU."				)
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> str:
						lowercase         = "sshleifer/tiny-gpt2"
						lowercase         = TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]							,			training=lowerCAmelCase__							,			inference=lowerCAmelCase__							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			use_xla=lowerCAmelCase__							,			multi_process=lowerCAmelCase__							,			)
						lowercase         = TensorFlowBenchmark(lowerCAmelCase__				)
						lowercase         = benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result				)
						self.check_results_dict_not_empty(results.memory_inference_result				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Optional[int]:
						lowercase         = "sshleifer/tiny-gpt2"
						with tempfile.TemporaryDirectory() as tmp_dir:
										lowercase         = TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]							,			inference=lowerCAmelCase__							,			save_to_csv=lowerCAmelCase__							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			inference_time_csv_file=os.path.join(lowerCAmelCase__							,			"inf_time.csv"				)							,			inference_memory_csv_file=os.path.join(lowerCAmelCase__							,			"inf_mem.csv"				)							,			env_info_csv_file=os.path.join(lowerCAmelCase__							,			"env.csv"				)							,			multi_process=lowerCAmelCase__							,			)
										lowercase         = TensorFlowBenchmark(lowerCAmelCase__				)
										benchmark.run()
										self.assertTrue(Path(os.path.join(lowerCAmelCase__							,			"inf_time.csv"				)				).exists()				)
										self.assertTrue(Path(os.path.join(lowerCAmelCase__							,			"inf_mem.csv"				)				).exists()				)
										self.assertTrue(Path(os.path.join(lowerCAmelCase__							,			"env.csv"				)				).exists()				)





		def 				UpperCAmelCase__   (self      :	List[str]				)					-> int:
						lowercase         = "sshleifer/tiny-gpt2"

						def _check_summary_is_not_empty(A__      :	List[str]				):
										self.assertTrue(hasattr(lowerCAmelCase__							,			"sequential"				)				)
										self.assertTrue(hasattr(lowerCAmelCase__							,			"cumulative"				)				)
										self.assertTrue(hasattr(lowerCAmelCase__							,			"current"				)				)
										self.assertTrue(hasattr(lowerCAmelCase__							,			"total"				)				)

						with tempfile.TemporaryDirectory() as tmp_dir:
										lowercase         = TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]							,			inference=lowerCAmelCase__							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			log_filename=os.path.join(lowerCAmelCase__							,			"log.txt"				)							,			log_print=lowerCAmelCase__							,			trace_memory_line_by_line=lowerCAmelCase__							,			eager_mode=lowerCAmelCase__							,			multi_process=lowerCAmelCase__							,			)
										lowercase         = TensorFlowBenchmark(lowerCAmelCase__				)
										lowercase         = benchmark.run()
										_check_summary_is_not_empty(result.inference_summary				)
										self.assertTrue(Path(os.path.join(lowerCAmelCase__							,			"log.txt"				)				).exists()				)











'''simple docstring'''

import functools







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				if not isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) or not all(isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) for day in days							):
								raise ValueError("The parameter days should be a list of integers"							)

				if len(lowerCAmelCase_							) != 3 or not all(isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) for cost in costs							):
								raise ValueError("The parameter costs should be a list of three integers"							)

				if len(lowerCAmelCase_							) == 0:
								return 0

				if min(lowerCAmelCase_							) <= 0:
								raise ValueError("All days elements should be greater than 0"							)

				if max(lowerCAmelCase_							) >= 366:
								raise ValueError("All days elements should be less than 366"							)

				lowercase         = set(lowerCAmelCase_							)

				@functools.cache
				def dynamic_programming(lowerCAmelCase_							) -> int:
								if index > 365:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1							)

								return min(
								    costs[0] + dynamic_programming(index + 1							)		,							costs[1] + dynamic_programming(index + 7							)		,							costs[2] + dynamic_programming(index + 30							)		,							)

				return dynamic_programming(1							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




