




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     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     Optional[Any])						->Tuple:





           '''simple docstring'''

           for model_result in results.values():
                  for batch_size, sequence_length in zip(model_result['''bs'''] ,			model_result['''ss''']):
                         A__							=   model_result['''result'''][batch_size][sequence_length]
                         self.assertIsNotNone(UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Optional[int]:





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__)
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Any:





           '''simple docstring'''

           A__							=   '''sgugger/tiny-distilbert-classification'''
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			only_pretrain_model=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__)
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->str:





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			torchscript=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__)
           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Union[str, Any]:





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			fpaa=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__)
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   AutoConfig.from_pretrained(UpperCAmelCase__)
           # set architectures equal to `None`
           A__							=   None
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__ ,			configs=[config])
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->List[str]:





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__)
           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->str:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Union[str, Any]:





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   AutoConfig.from_pretrained(UpperCAmelCase__)
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__ ,			configs=[config])
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Optional[int]:





           '''simple docstring'''

           A__							=   '''sshleifer/tinier_bart'''
           A__							=   AutoConfig.from_pretrained(UpperCAmelCase__)
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__ ,			configs=[config])
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   AutoConfig.from_pretrained(UpperCAmelCase__)
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__ ,			configs=[config])
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result)
           self.check_results_dict_not_empty(results.memory_train_result)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->List[str]:





           '''simple docstring'''

           A__							=   '''sshleifer/tinier_bart'''
           A__							=   AutoConfig.from_pretrained(UpperCAmelCase__)
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__ ,			configs=[config])
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result)
           self.check_results_dict_not_empty(results.memory_train_result)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Tuple:





           '''simple docstring'''

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





    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Tuple:





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''

           def _check_summary_is_not_empty(UpperCAmelCase__				:     Tuple):
                  self.assertTrue(hasattr(UpperCAmelCase__ ,			'''sequential'''))
                  self.assertTrue(hasattr(UpperCAmelCase__ ,			'''cumulative'''))
                  self.assertTrue(hasattr(UpperCAmelCase__ ,			'''current'''))
                  self.assertTrue(hasattr(UpperCAmelCase__ ,			'''total'''))

           with tempfile.TemporaryDirectory() as tmp_dir:
                  A__							=   PyTorchBenchmarkArguments(
                      models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			log_filename=os.path.join(UpperCAmelCase__ ,			'''log.txt''') ,			log_print=UpperCAmelCase__ ,			trace_memory_line_by_line=UpperCAmelCase__ ,			multi_process=UpperCAmelCase__ ,			)
                  A__							=   PyTorchBenchmark(UpperCAmelCase__)
                  A__							=   benchmark.run()
                  _check_summary_is_not_empty(result.inference_summary)
                  _check_summary_is_not_empty(result.train_summary)
                  self.assertTrue(Path(os.path.join(UpperCAmelCase__ ,			'''log.txt''')).exists())








from __future__ import annotations

from collections import Counter
from random import random


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Any)						->Optional[Any]:





           '''simple docstring'''

           A__							=   {}


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     str)						->None:





           '''simple docstring'''

           A__							=   {}


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     float)						->None:





           '''simple docstring'''

           if nodea not in self.connections:
                  self.add_node(UpperCAmelCase__)
           if nodea not in self.connections:
                  self.add_node(UpperCAmelCase__)
           A__							=   probability


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->list[str]:





           '''simple docstring'''

           return list(self.connections)





    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     str)						->str:





           '''simple docstring'''

           A__							=   0
           A__							=   random()

           for dest in self.connections[node]:
                  current_probability += self.connections[node][dest]
                  if current_probability > random_value:
                         return dest
           return ""







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	dict[str, int]:



       """simple docstring"""

       A__							=   MarkovChainGraphUndirectedUnweighted()

       for nodea, nodea, probability in transitions:
              graph.add_transition_probability(lowercase_			,    lowercase_			,    lowercase_						)

       A__							=   Counter(graph.get_nodes()						)
       A__							=   start

       for _ in range(lowercase_						):
              A__							=   graph.transition(lowercase_						)
              visited[node] += 1

       return visited


if __name__ == "__main__":
  import doctest

  doctest.testmod()


