"""simple docstring"""






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
       from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments





@require_torch
class 					_A		(			unittest.TestCase      ):

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """sshleifer/tiny-gpt2"""
            lowercase								=      PyTorchBenchmarkArguments(
                models=[MODEL_ID]						,			training=__lowerCAmelCase						,			inference=__lowerCAmelCase						,			sequence_lengths=[8]						,			batch_sizes=[1]						,			multi_process=__lowerCAmelCase						,			)
            lowercase								=      PyTorchBenchmark(__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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """sgugger/tiny-distilbert-classification"""
            lowercase								=      PyTorchBenchmarkArguments(
                models=[MODEL_ID]						,			training=__lowerCAmelCase						,			inference=__lowerCAmelCase						,			sequence_lengths=[8]						,			batch_sizes=[1]						,			multi_process=__lowerCAmelCase						,			only_pretrain_model=__lowerCAmelCase						,			)
            lowercase								=      PyTorchBenchmark(__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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """sshleifer/tiny-gpt2"""
            lowercase								=      PyTorchBenchmarkArguments(
                models=[MODEL_ID]						,			training=__lowerCAmelCase						,			inference=__lowerCAmelCase						,			torchscript=__lowerCAmelCase						,			sequence_lengths=[8]						,			batch_sizes=[1]						,			multi_process=__lowerCAmelCase						,			)
            lowercase								=      PyTorchBenchmark(__lowerCAmelCase		)
            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(torch_device == """cpu"""						,			"""Cant do half precision"""		)
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """sshleifer/tiny-gpt2"""
            lowercase								=      PyTorchBenchmarkArguments(
                models=[MODEL_ID]						,			training=__lowerCAmelCase						,			inference=__lowerCAmelCase						,			fpaa=__lowerCAmelCase						,			sequence_lengths=[8]						,			batch_sizes=[1]						,			multi_process=__lowerCAmelCase						,			)
            lowercase								=      PyTorchBenchmark(__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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """sshleifer/tiny-gpt2"""
            lowercase								=      AutoConfig.from_pretrained(__lowerCAmelCase		)
            # set architectures equal to `None`
            lowercase								=      None
            lowercase								=      PyTorchBenchmarkArguments(
                models=[MODEL_ID]						,			training=__lowerCAmelCase						,			inference=__lowerCAmelCase						,			sequence_lengths=[8]						,			batch_sizes=[1]						,			multi_process=__lowerCAmelCase						,			)
            lowercase								=      PyTorchBenchmark(__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		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """sshleifer/tiny-gpt2"""
            lowercase								=      PyTorchBenchmarkArguments(
                models=[MODEL_ID]						,			training=__lowerCAmelCase						,			inference=__lowerCAmelCase						,			sequence_lengths=[8]						,			batch_sizes=[1]						,			multi_process=__lowerCAmelCase						,			)
            lowercase								=      PyTorchBenchmark(__lowerCAmelCase		)
            lowercase								=      benchmark.run()
            self.check_results_dict_not_empty(results.time_train_result		)
            self.check_results_dict_not_empty(results.memory_train_result		)

      @unittest.skipIf(torch_device == """cpu"""						,			"""Can't do half precision"""		)
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """sshleifer/tiny-gpt2"""
            lowercase								=      PyTorchBenchmarkArguments(
                models=[MODEL_ID]						,			training=__lowerCAmelCase						,			inference=__lowerCAmelCase						,			sequence_lengths=[8]						,			batch_sizes=[1]						,			fpaa=__lowerCAmelCase						,			multi_process=__lowerCAmelCase						,			)
            lowercase								=      PyTorchBenchmark(__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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """sshleifer/tiny-gpt2"""
            lowercase								=      AutoConfig.from_pretrained(__lowerCAmelCase		)
            lowercase								=      PyTorchBenchmarkArguments(
                models=[MODEL_ID]						,			training=__lowerCAmelCase						,			inference=__lowerCAmelCase						,			sequence_lengths=[8]						,			batch_sizes=[1]						,			multi_process=__lowerCAmelCase						,			)
            lowercase								=      PyTorchBenchmark(__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		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """sshleifer/tinier_bart"""
            lowercase								=      AutoConfig.from_pretrained(__lowerCAmelCase		)
            lowercase								=      PyTorchBenchmarkArguments(
                models=[MODEL_ID]						,			training=__lowerCAmelCase						,			inference=__lowerCAmelCase						,			sequence_lengths=[8]						,			batch_sizes=[1]						,			multi_process=__lowerCAmelCase						,			)
            lowercase								=      PyTorchBenchmark(__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		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """sshleifer/tiny-gpt2"""
            lowercase								=      AutoConfig.from_pretrained(__lowerCAmelCase		)
            lowercase								=      PyTorchBenchmarkArguments(
                models=[MODEL_ID]						,			training=__lowerCAmelCase						,			inference=__lowerCAmelCase						,			sequence_lengths=[8]						,			batch_sizes=[1]						,			multi_process=__lowerCAmelCase						,			)
            lowercase								=      PyTorchBenchmark(__lowerCAmelCase						,			configs=[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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """sshleifer/tinier_bart"""
            lowercase								=      AutoConfig.from_pretrained(__lowerCAmelCase		)
            lowercase								=      PyTorchBenchmarkArguments(
                models=[MODEL_ID]						,			training=__lowerCAmelCase						,			inference=__lowerCAmelCase						,			sequence_lengths=[8]						,			batch_sizes=[1]						,			multi_process=__lowerCAmelCase						,			)
            lowercase								=      PyTorchBenchmark(__lowerCAmelCase						,			configs=[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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """sshleifer/tiny-gpt2"""
            with tempfile.TemporaryDirectory() as tmp_dir:
                  lowercase								=      PyTorchBenchmarkArguments(
                      models=[MODEL_ID]						,			training=__lowerCAmelCase						,			inference=__lowerCAmelCase						,			save_to_csv=__lowerCAmelCase						,			sequence_lengths=[8]						,			batch_sizes=[1]						,			inference_time_csv_file=os.path.join(__lowerCAmelCase						,			"""inf_time.csv"""		)						,			train_memory_csv_file=os.path.join(__lowerCAmelCase						,			"""train_mem.csv"""		)						,			inference_memory_csv_file=os.path.join(__lowerCAmelCase						,			"""inf_mem.csv"""		)						,			train_time_csv_file=os.path.join(__lowerCAmelCase						,			"""train_time.csv"""		)						,			env_info_csv_file=os.path.join(__lowerCAmelCase						,			"""env.csv"""		)						,			multi_process=__lowerCAmelCase						,			)
                  lowercase								=      PyTorchBenchmark(__lowerCAmelCase		)
                  benchmark.run()
                  self.assertTrue(Path(os.path.join(__lowerCAmelCase						,			"""inf_time.csv"""		)		).exists()		)
                  self.assertTrue(Path(os.path.join(__lowerCAmelCase						,			"""train_time.csv"""		)		).exists()		)
                  self.assertTrue(Path(os.path.join(__lowerCAmelCase						,			"""inf_mem.csv"""		)		).exists()		)
                  self.assertTrue(Path(os.path.join(__lowerCAmelCase						,			"""train_mem.csv"""		)		).exists()		)
                  self.assertTrue(Path(os.path.join(__lowerCAmelCase						,			"""env.csv"""		)		).exists()		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """sshleifer/tiny-gpt2"""

            def _check_summary_is_not_empty(__lowerCAmelCase		):
                  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								=      PyTorchBenchmarkArguments(
                      models=[MODEL_ID]						,			training=__lowerCAmelCase						,			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						,			multi_process=__lowerCAmelCase						,			)
                  lowercase								=      PyTorchBenchmark(__lowerCAmelCase		)
                  lowercase								=      benchmark.run()
                  _check_summary_is_not_empty(result.inference_summary		)
                  _check_summary_is_not_empty(result.train_summary		)
                  self.assertTrue(Path(os.path.join(__lowerCAmelCase						,			"""log.txt"""		)		).exists()		)




"""simple docstring"""






from copy import deepcopy





class 					_A		:

      def __init__(		self						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None		):







            """simple docstring"""



            if arr is None and size is not None:
                  lowercase								=      size
                  lowercase								=      [0] * size
            elif arr is not None:
                  self.init(__lowerCAmelCase		)
            else:
                  raise ValueError("""Either arr or size must be specified"""		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      len(__lowerCAmelCase		)
            lowercase								=      deepcopy(__lowerCAmelCase		)
            for i in range(1						,			self.size		):
                  lowercase								=      self.next_(__lowerCAmelCase		)
                  if j < self.size:
                        self.tree[j] += self.tree[i]

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.tree[:]
            for i in range(self.size - 1						,			0						,			-1		):
                  lowercase								=      self.next_(__lowerCAmelCase		)
                  if j < self.size:
                        arr[j] -= arr[i]
            return arr

      @staticmethod
      def 							A__			(		__lowerCAmelCase		):







            """simple docstring"""



            return index + (index & (-index))

      @staticmethod
      def 							A__			(		__lowerCAmelCase		):







            """simple docstring"""



            return index - (index & (-index))

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            if index == 0:
                  self.tree[0] += value
                  return
            while index < self.size:
                  self.tree[index] += value
                  lowercase								=      self.next_(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            self.add(__lowerCAmelCase						,			value - self.get(__lowerCAmelCase		)		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if right == 0:
                  return 0
            lowercase								=      self.tree[0]
            right -= 1  # make right inclusive
            while right > 0:
                  result += self.tree[right]
                  lowercase								=      self.prev(__lowerCAmelCase		)
            return result

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.prefix(__lowerCAmelCase		) - self.prefix(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.query(__lowerCAmelCase						,			index + 1		)







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            value -= self.tree[0]
            if value < 0:
                  return -1

            lowercase								=      1  # Largest power of 2 <= size
            while j * 2 < self.size:
                  j *= 2

            lowercase								=      0

            while j > 0:
                  if i + j < self.size and self.tree[i + j] <= value:
                        value -= self.tree[i + j]
                        i += j
                  j //= 2
            return i


if __name__ == "__main__":
       import doctest

       doctest.testmod()



