


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 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	str					)		->      List[str]:
         for model_result in results.values():
            for batch_size, sequence_length in zip(model_result['bs']					,							model_result['ss']					):
               a_ :		Union[str, Any]							=  model_result["""result"""][batch_size][sequence_length]
               self.assertIsNotNone(_UpperCamelCase					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Union[str, Any]:
         a_ :		List[Any]							=  """sshleifer/tiny-gpt2"""
         a_ :		Optional[Any]							=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]					,							training=_UpperCamelCase					,							inference=_UpperCamelCase					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							eager_mode=_UpperCamelCase					,							multi_process=_UpperCamelCase					,							)
         a_ :		Optional[Any]							=  TensorFlowBenchmark(_UpperCamelCase					)
         a_ :		Any							=  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    :	Union[str, Any]					)		->      Tuple:
         a_ :		Union[str, Any]							=  """sgugger/tiny-distilbert-classification"""
         a_ :		List[Any]							=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]					,							training=_UpperCamelCase					,							inference=_UpperCamelCase					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_UpperCamelCase					,							only_pretrain_model=_UpperCamelCase					,							)
         a_ :		List[str]							=  TensorFlowBenchmark(_UpperCamelCase					)
         a_ :		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 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Dict:
         a_ :		Any							=  """sshleifer/tiny-gpt2"""
         a_ :		Optional[Any]							=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]					,							training=_UpperCamelCase					,							inference=_UpperCamelCase					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_UpperCamelCase					,							)
         a_ :		Any							=  TensorFlowBenchmark(_UpperCamelCase					)
         a_ :		Any							=  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]					)		->      str:
         a_ :		Optional[Any]							=  """sshleifer/tiny-gpt2"""
         a_ :		Tuple							=  AutoConfig.from_pretrained(_UpperCamelCase					)
         a_ :		Optional[int]							=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]					,							training=_UpperCamelCase					,							inference=_UpperCamelCase					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							eager_mode=_UpperCamelCase					,							multi_process=_UpperCamelCase					,							)
         a_ :		Dict							=  TensorFlowBenchmark(_UpperCamelCase					,							[config]					)
         a_ :		Union[str, Any]							=  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					)		->      Tuple:
         a_ :		Union[str, Any]							=  """sshleifer/tiny-gpt2"""
         a_ :		Union[str, Any]							=  AutoConfig.from_pretrained(_UpperCamelCase					)
         a_ :		Tuple							=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]					,							training=_UpperCamelCase					,							inference=_UpperCamelCase					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_UpperCamelCase					,							)
         a_ :		Any							=  TensorFlowBenchmark(_UpperCamelCase					,							[config]					)
         a_ :		str							=  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[str]					)		->      Dict:
         a_ :		Dict							=  """sshleifer/tiny-gpt2"""
         a_ :		Dict							=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]					,							training=_UpperCamelCase					,							inference=_UpperCamelCase					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_UpperCamelCase					,							)
         a_ :		Tuple							=  TensorFlowBenchmark(_UpperCamelCase					)
         a_ :		Tuple							=  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    :	Dict					)		->      List[str]:
         a_ :		Tuple							=  """sshleifer/tiny-gpt2"""
         a_ :		Tuple							=  AutoConfig.from_pretrained(_UpperCamelCase					)
         a_ :		Optional[Any]							=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]					,							training=_UpperCamelCase					,							inference=_UpperCamelCase					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_UpperCamelCase					,							)
         a_ :		Dict							=  TensorFlowBenchmark(_UpperCamelCase					,							[config]					)
         a_ :		Optional[int]							=  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    :	Optional[int]					)		->      Dict:
         a_ :		Dict							=  """patrickvonplaten/t5-tiny-random"""
         a_ :		Dict							=  AutoConfig.from_pretrained(_UpperCamelCase					)
         a_ :		Tuple							=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]					,							training=_UpperCamelCase					,							inference=_UpperCamelCase					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_UpperCamelCase					,							)
         a_ :		Dict							=  TensorFlowBenchmark(_UpperCamelCase					,							configs=[config]					)
         a_ :		Optional[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 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Optional[Any]:
         a_ :		List[Any]							=  """sshleifer/tiny-gpt2"""
         a_ :		Tuple							=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]					,							training=_UpperCamelCase					,							inference=_UpperCamelCase					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							use_xla=_UpperCamelCase					,							multi_process=_UpperCamelCase					,							)
         a_ :		Tuple							=  TensorFlowBenchmark(_UpperCamelCase					)
         a_ :		int							=  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					)		->      int:
         a_ :		str							=  """sshleifer/tiny-gpt2"""
         with tempfile.TemporaryDirectory() as tmp_dir:
            a_ :		List[Any]							=  TensorFlowBenchmarkArguments(
                models=[MODEL_ID]					,							inference=_UpperCamelCase					,							save_to_csv=_UpperCamelCase					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							inference_time_csv_file=os.path.join(_UpperCamelCase					,							'inf_time.csv'					)					,							inference_memory_csv_file=os.path.join(_UpperCamelCase					,							'inf_mem.csv'					)					,							env_info_csv_file=os.path.join(_UpperCamelCase					,							'env.csv'					)					,							multi_process=_UpperCamelCase					,							)
            a_ :		Optional[int]							=  TensorFlowBenchmark(_UpperCamelCase					)
            benchmark.run()
            self.assertTrue(Path(os.path.join(_UpperCamelCase					,							'inf_time.csv'					)					).exists()					)
            self.assertTrue(Path(os.path.join(_UpperCamelCase					,							'inf_mem.csv'					)					).exists()					)
            self.assertTrue(Path(os.path.join(_UpperCamelCase					,							'env.csv'					)					).exists()					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Tuple:
         a_ :		List[Any]							=  """sshleifer/tiny-gpt2"""

         def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE__    :	Union[str, Any]					):
            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_ :		Union[str, Any]							=  TensorFlowBenchmarkArguments(
                models=[MODEL_ID]					,							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					,							eager_mode=_UpperCamelCase					,							multi_process=_UpperCamelCase					,							)
            a_ :		Any							=  TensorFlowBenchmark(_UpperCamelCase					)
            a_ :		Dict							=  benchmark.run()
            _check_summary_is_not_empty(result.inference_summary					)
            self.assertTrue(Path(os.path.join(_UpperCamelCase					,							'log.txt'					)					).exists()					)






import json
import sys







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]  ,   __A			:      List[str]    )     ->		Tuple:

       """simple docstring"""

       with open(__A  ,   encoding='utf-8'    ) as f:
              a_ :		Union[str, Any]							=  json.load(__A    )

       a_ :		Any							=  ['<details>', '<summary>Show updated benchmarks!</summary>', ' ']

       for benchmark_name in sorted(__A    ):
              a_ :		List[str]							=  results[benchmark_name]

              a_ :		int							=  benchmark_name.split('/'    )[-1]
              output_md.append(F"""### Benchmark: {benchmark_file_name}"""    )

              a_ :		Any							=  '| metric |'
              a_ :		Optional[Any]							=  '|--------|'
              a_ :		int							=  '| new / old (diff) |'
              for metric_name in sorted(__A    ):
                     a_ :		List[Any]							=  benchmark_res[metric_name]
                     a_ :		int							=  metric_vals['new']
                     a_ :		Union[str, Any]							=  metric_vals.get('old'  ,   __A    )
                     a_ :		Optional[int]							=  metric_vals.get('diff'  ,   __A    )

                     a_ :		str							=  F""" {new_val:f}""" if isinstance(__A  ,   (int, float)    ) else 'None'

                     if old_val is not None:
                            val_str += F""" / {old_val:f}""" if isinstance(__A  ,   (int, float)    ) else "None"
                     if dif_val is not None:
                            val_str += F""" ({dif_val:f})""" if isinstance(__A  ,   (int, float)    ) else "None"

                     title += " " + metric_name + " |"
                     lines += "---|"
                     value += val_str + " |"

              output_md += [title, lines, value, " "]

       output_md.append('</details>'    )

       with open(__A  ,   'w'  ,   encoding='utf-8'    ) as f:
              f.writelines('\n'.join(__A    )    )


if __name__ == "__main__":
   UpperCAmelCase_				:			int   =	sys.argv[1]
   UpperCAmelCase_				:			Any   =	sys.argv[2]

   format_json_to_md(input_json_file, output_md_file)


