

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    _lowercase				(					unittest.TestCase			):

   def     UpperCamelCase					(				self	:    Optional[int]			,						lowerCamelCase__	:    Dict       ) ->			Dict:
         """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(lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Any:
         """simple docstring"""





         A_							       =		'''sshleifer/tiny-gpt2'''
         A_							       =		TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=lowerCamelCase__			,						inference=lowerCamelCase__			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						eager_mode=lowerCamelCase__			,						multi_process=lowerCamelCase__			,						)
         A_							       =		TensorFlowBenchmark(lowerCamelCase__       )
         A_							       =		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]       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		'''sgugger/tiny-distilbert-classification'''
         A_							       =		TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=lowerCamelCase__			,						inference=lowerCamelCase__			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						multi_process=lowerCamelCase__			,						only_pretrain_model=lowerCamelCase__			,						)
         A_							       =		TensorFlowBenchmark(lowerCamelCase__       )
         A_							       =		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:
         """simple docstring"""





         A_							       =		'''sshleifer/tiny-gpt2'''
         A_							       =		TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=lowerCamelCase__			,						inference=lowerCamelCase__			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						multi_process=lowerCamelCase__			,						)
         A_							       =		TensorFlowBenchmark(lowerCamelCase__       )
         A_							       =		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]       ) ->			Any:
         """simple docstring"""





         A_							       =		'''sshleifer/tiny-gpt2'''
         A_							       =		AutoConfig.from_pretrained(lowerCamelCase__       )
         A_							       =		TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=lowerCamelCase__			,						inference=lowerCamelCase__			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						eager_mode=lowerCamelCase__			,						multi_process=lowerCamelCase__			,						)
         A_							       =		TensorFlowBenchmark(lowerCamelCase__			,						[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     UpperCamelCase					(				self	:    List[Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		'''sshleifer/tiny-gpt2'''
         A_							       =		AutoConfig.from_pretrained(lowerCamelCase__       )
         A_							       =		TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=lowerCamelCase__			,						inference=lowerCamelCase__			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						multi_process=lowerCamelCase__			,						)
         A_							       =		TensorFlowBenchmark(lowerCamelCase__			,						[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     UpperCamelCase					(				self	:    Any       ) ->			Dict:
         """simple docstring"""





         A_							       =		'''sshleifer/tiny-gpt2'''
         A_							       =		TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=lowerCamelCase__			,						inference=lowerCamelCase__			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						multi_process=lowerCamelCase__			,						)
         A_							       =		TensorFlowBenchmark(lowerCamelCase__       )
         A_							       =		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	:    List[Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		'''sshleifer/tiny-gpt2'''
         A_							       =		AutoConfig.from_pretrained(lowerCamelCase__       )
         A_							       =		TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=lowerCamelCase__			,						inference=lowerCamelCase__			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						multi_process=lowerCamelCase__			,						)
         A_							       =		TensorFlowBenchmark(lowerCamelCase__			,						[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     UpperCamelCase					(				self	:    Dict       ) ->			str:
         """simple docstring"""





         A_							       =		'''patrickvonplaten/t5-tiny-random'''
         A_							       =		AutoConfig.from_pretrained(lowerCamelCase__       )
         A_							       =		TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=lowerCamelCase__			,						inference=lowerCamelCase__			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						multi_process=lowerCamelCase__			,						)
         A_							       =		TensorFlowBenchmark(lowerCamelCase__			,						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       )

   @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''       )       ) == 0			,						'''Cannot do xla on CPU.'''       )
   def     UpperCamelCase					(				self	:    str       ) ->			Tuple:
         """simple docstring"""





         A_							       =		'''sshleifer/tiny-gpt2'''
         A_							       =		TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=lowerCamelCase__			,						inference=lowerCamelCase__			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						use_xla=lowerCamelCase__			,						multi_process=lowerCamelCase__			,						)
         A_							       =		TensorFlowBenchmark(lowerCamelCase__       )
         A_							       =		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	:    Optional[Any]       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		'''sshleifer/tiny-gpt2'''
         with tempfile.TemporaryDirectory() as tmp_dir:
               A_							       =		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__			,						)
               A_							       =		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	:    Any       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		'''sshleifer/tiny-gpt2'''

         def _check_summary_is_not_empty(lowerCamelCase__	:    Dict       ):
               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:
               A_							       =		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__			,						)
               A_							       =		TensorFlowBenchmark(lowerCamelCase__       )
               A_							       =		benchmark.run()
               _check_summary_is_not_empty(result.inference_summary       )
               self.assertTrue(Path(os.path.join(lowerCamelCase__			,						'''log.txt'''       )       ).exists()       )








from itertools import product







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		sides_number
      A_							       =		max_face_number * dice_number
      A_							       =		[0] * (max_total + 1)

      A_							       =		1
      A_							       =		range(SCREAMING_SNAKE_CASE		,				max_face_number + 1   )
      for dice_numbers in product(SCREAMING_SNAKE_CASE		,				repeat=SCREAMING_SNAKE_CASE   ):
            A_							       =		sum(SCREAMING_SNAKE_CASE   )
            totals_frequencies[total] += 1

      return totals_frequencies







def 							_lowerCamelCase   (			):
      '''simple docstring'''







      A_							       =		total_frequency_distribution(
          sides_number=4		,				dice_number=9   )
      A_							       =		total_frequency_distribution(
          sides_number=6		,				dice_number=6   )

      A_							       =		0
      A_							       =		9
      A_							       =		4 * 9
      A_							       =		6
      for peter_total in range(SCREAMING_SNAKE_CASE		,				max_peter_total + 1   ):
            peter_wins_count += peter_totals_frequencies[peter_total] * sum(
                colin_totals_frequencies[min_colin_total:peter_total]   )

      A_							       =		(4**9) * (6**6)
      A_							       =		peter_wins_count / total_games_number

      A_							       =		round(SCREAMING_SNAKE_CASE		,				ndigits=7   )

      return rounded_peter_win_probability


if __name__ == "__main__":
      print(f'{solution() = }')





