


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        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						List[Any]					,			lowerCamelCase :						int       )			->					int:
														for model_result in results.values():
																					for batch_size, sequence_length in zip(model_result["bs"]					,			model_result["ss"]       ):
																												__snake_case	:    Optional[int]											=     model_result["result"][batch_size][sequence_length]
																												self.assertIsNotNone(lowerCamelCase       )

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

							def 					__snake_case      (							self :						List[Any]       )			->					List[Any]:
														__snake_case	:    Dict											=     "sgugger/tiny-distilbert-classification"
														__snake_case	:    Optional[Any]											=     TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]					,			training=lowerCamelCase					,			inference=lowerCamelCase					,			sequence_lengths=[8]					,			batch_sizes=[1]					,			multi_process=lowerCamelCase					,			only_pretrain_model=lowerCamelCase					,			)
														__snake_case	:    int											=     TensorFlowBenchmark(lowerCamelCase       )
														__snake_case	:    Any											=     benchmark.run()
														self.check_results_dict_not_empty(results.time_inference_result       )
														self.check_results_dict_not_empty(results.memory_inference_result       )

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

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

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

							def 					__snake_case      (							self :						Optional[int]       )			->					Dict:
														__snake_case	:    List[Any]											=     "sshleifer/tiny-gpt2"
														__snake_case	:    Dict											=     TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]					,			training=lowerCamelCase					,			inference=lowerCamelCase					,			sequence_lengths=[8]					,			batch_sizes=[1]					,			multi_process=lowerCamelCase					,			)
														__snake_case	:    Optional[int]											=     TensorFlowBenchmark(lowerCamelCase       )
														__snake_case	:    Union[str, Any]											=     benchmark.run()
														self.check_results_dict_not_empty(results.time_train_result       )
														self.check_results_dict_not_empty(results.memory_train_result       )

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

							def 					__snake_case      (							self :						Union[str, Any]       )			->					Optional[Any]:
														__snake_case	:    str											=     "patrickvonplaten/t5-tiny-random"
														__snake_case	:    Any											=     AutoConfig.from_pretrained(lowerCamelCase       )
														__snake_case	:    List[Any]											=     TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]					,			training=lowerCamelCase					,			inference=lowerCamelCase					,			sequence_lengths=[8]					,			batch_sizes=[1]					,			multi_process=lowerCamelCase					,			)
														__snake_case	:    Union[str, Any]											=     TensorFlowBenchmark(lowerCamelCase					,			configs=[config]       )
														__snake_case	:    List[Any]											=     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 					__snake_case      (							self :						Union[str, Any]       )			->					List[Any]:
														__snake_case	:    Union[str, Any]											=     "sshleifer/tiny-gpt2"
														__snake_case	:    Any											=     TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]					,			training=lowerCamelCase					,			inference=lowerCamelCase					,			sequence_lengths=[8]					,			batch_sizes=[1]					,			use_xla=lowerCamelCase					,			multi_process=lowerCamelCase					,			)
														__snake_case	:    str											=     TensorFlowBenchmark(lowerCamelCase       )
														__snake_case	:    Dict											=     benchmark.run()
														self.check_results_dict_not_empty(results.time_inference_result       )
														self.check_results_dict_not_empty(results.memory_inference_result       )

							def 					__snake_case      (							self :						Optional[Any]       )			->					Union[str, Any]:
														__snake_case	:    Tuple											=     "sshleifer/tiny-gpt2"
														with tempfile.TemporaryDirectory() as tmp_dir:
																					__snake_case	:    str											=     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					,			)
																					__snake_case	:    int											=     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 					__snake_case      (							self :						Optional[Any]       )			->					Any:
														__snake_case	:    Optional[int]											=     "sshleifer/tiny-gpt2"

														def _check_summary_is_not_empty(lowerCamelCase :						Optional[int]       ):
																					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:
																					__snake_case	:    int											=     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					,			)
																					__snake_case	:    Dict											=     TensorFlowBenchmark(lowerCamelCase       )
																					__snake_case	:    int											=     benchmark.run()
																					_check_summary_is_not_empty(result.inference_summary       )
																					self.assertTrue(Path(os.path.join(lowerCamelCase					,			"log.txt"       )       ).exists()       )





def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if number < 0 or shift_amount < 0:
														raise ValueError("both inputs must be positive integers"			)

							__snake_case	:    Dict											=     str(bin(__lowerCamelCase			)			)
							binary_number += "0" * shift_amount
							return binary_number







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if number < 0 or shift_amount < 0:
														raise ValueError("both inputs must be positive integers"			)

							__snake_case	:    List[str]											=     str(bin(__lowerCamelCase			)			)[2:]
							if shift_amount >= len(__lowerCamelCase			):
														return "0b0"
							__snake_case	:    Union[str, Any]											=     binary_number[: len(__lowerCamelCase			) - shift_amount]
							return "0b" + shifted_binary_number







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if number >= 0:  # Get binary representation of positive number
														__snake_case	:    Optional[int]											=     "0" + str(bin(__lowerCamelCase			)			).strip("-"			)[2:]
							else:  # Get binary (2's complement) representation of negative number
														__snake_case	:    Tuple											=     len(bin(__lowerCamelCase			)[3:]			)  # Find 2's complement of number
														__snake_case	:    Any											=     bin(abs(__lowerCamelCase			) - (1 << binary_number_length)			)[3:]
														__snake_case	:    Optional[Any]											=     (
														    "1" + "0" * (binary_number_length - len(__lowerCamelCase			)) + binary_number
														)

							if shift_amount >= len(__lowerCamelCase			):
														return "0b" + binary_number[0] * len(__lowerCamelCase			)
							return (
							    "0b"
							    + binary_number[0] * shift_amount
							    + binary_number[: len(__lowerCamelCase			) - shift_amount]
							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()

