
'''simple docstring'''
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 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			Optional[int]      )						->			str:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			Dict      )						->			Any:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						'''sshleifer/tiny-gpt2'''
				snake_case  :       Optional[Any]     			=						TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]  ,	training=UpperCamelCase__  ,	inference=UpperCamelCase__  ,	sequence_lengths=[8]  ,	batch_sizes=[1]  ,	eager_mode=UpperCamelCase__  ,	multi_process=UpperCamelCase__  ,	)
				snake_case  :       List[Any]     			=						TensorFlowBenchmark(UpperCamelCase__      )
				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       lowerCAmelCase				(      self				:			Dict      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						'''sgugger/tiny-distilbert-classification'''
				snake_case  :       List[Any]     			=						TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]  ,	training=UpperCamelCase__  ,	inference=UpperCamelCase__  ,	sequence_lengths=[8]  ,	batch_sizes=[1]  ,	multi_process=UpperCamelCase__  ,	only_pretrain_model=UpperCamelCase__  ,	)
				snake_case  :       List[Any]     			=						TensorFlowBenchmark(UpperCamelCase__      )
				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       lowerCAmelCase				(      self				:			int      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						'''sshleifer/tiny-gpt2'''
				snake_case  :       List[Any]     			=						TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]  ,	training=UpperCamelCase__  ,	inference=UpperCamelCase__  ,	sequence_lengths=[8]  ,	batch_sizes=[1]  ,	multi_process=UpperCamelCase__  ,	)
				snake_case  :       Any     			=						TensorFlowBenchmark(UpperCamelCase__      )
				snake_case  :       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       lowerCAmelCase				(      self				:			str      )						->			Any:






				"""simple docstring"""







				snake_case  :       str     			=						'''sshleifer/tiny-gpt2'''
				snake_case  :       int     			=						AutoConfig.from_pretrained(UpperCamelCase__      )
				snake_case  :       Any     			=						TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]  ,	training=UpperCamelCase__  ,	inference=UpperCamelCase__  ,	sequence_lengths=[8]  ,	batch_sizes=[1]  ,	eager_mode=UpperCamelCase__  ,	multi_process=UpperCamelCase__  ,	)
				snake_case  :       int     			=						TensorFlowBenchmark(UpperCamelCase__  ,	[config]      )
				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       lowerCAmelCase				(      self				:			Dict      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       str     			=						'''sshleifer/tiny-gpt2'''
				snake_case  :       Tuple     			=						AutoConfig.from_pretrained(UpperCamelCase__      )
				snake_case  :       Tuple     			=						TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]  ,	training=UpperCamelCase__  ,	inference=UpperCamelCase__  ,	sequence_lengths=[8]  ,	batch_sizes=[1]  ,	multi_process=UpperCamelCase__  ,	)
				snake_case  :       Dict     			=						TensorFlowBenchmark(UpperCamelCase__  ,	[config]      )
				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       lowerCAmelCase				(      self				:			Any      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						'''sshleifer/tiny-gpt2'''
				snake_case  :       List[Any]     			=						TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]  ,	training=UpperCamelCase__  ,	inference=UpperCamelCase__  ,	sequence_lengths=[8]  ,	batch_sizes=[1]  ,	multi_process=UpperCamelCase__  ,	)
				snake_case  :       Any     			=						TensorFlowBenchmark(UpperCamelCase__      )
				snake_case  :       Any     			=						benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result      )
				self.check_results_dict_not_empty(results.memory_train_result      )



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






				"""simple docstring"""







				snake_case  :       int     			=						'''sshleifer/tiny-gpt2'''
				snake_case  :       List[Any]     			=						AutoConfig.from_pretrained(UpperCamelCase__      )
				snake_case  :       Dict     			=						TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]  ,	training=UpperCamelCase__  ,	inference=UpperCamelCase__  ,	sequence_lengths=[8]  ,	batch_sizes=[1]  ,	multi_process=UpperCamelCase__  ,	)
				snake_case  :       Any     			=						TensorFlowBenchmark(UpperCamelCase__  ,	[config]      )
				snake_case  :       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       lowerCAmelCase				(      self				:			List[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						'''patrickvonplaten/t5-tiny-random'''
				snake_case  :       Optional[Any]     			=						AutoConfig.from_pretrained(UpperCamelCase__      )
				snake_case  :       int     			=						TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]  ,	training=UpperCamelCase__  ,	inference=UpperCamelCase__  ,	sequence_lengths=[8]  ,	batch_sizes=[1]  ,	multi_process=UpperCamelCase__  ,	)
				snake_case  :       Optional[int]     			=						TensorFlowBenchmark(UpperCamelCase__  ,	configs=[config]      )
				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      )



		@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''      )      ) == 0  ,	'''Cannot do xla on CPU.'''      )
		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						'''sshleifer/tiny-gpt2'''
				snake_case  :       str     			=						TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]  ,	training=UpperCamelCase__  ,	inference=UpperCamelCase__  ,	sequence_lengths=[8]  ,	batch_sizes=[1]  ,	use_xla=UpperCamelCase__  ,	multi_process=UpperCamelCase__  ,	)
				snake_case  :       Optional[Any]     			=						TensorFlowBenchmark(UpperCamelCase__      )
				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      )



		def       lowerCAmelCase				(      self				:			Any      )						->			Any:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						'''sshleifer/tiny-gpt2'''
				with tempfile.TemporaryDirectory() as tmp_dir:
						snake_case  :       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__  ,	)
						snake_case  :       Optional[Any]     			=						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       lowerCAmelCase				(      self				:			int      )						->			int:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						'''sshleifer/tiny-gpt2'''

				def _check_summary_is_not_empty(UpperCamelCase__				:			int      ):
						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:
						snake_case  :       List[str]     			=						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__  ,	)
						snake_case  :       Any     			=						TensorFlowBenchmark(UpperCamelCase__      )
						snake_case  :       List[Any]     			=						benchmark.run()
						_check_summary_is_not_empty(result.inference_summary      )
						self.assertTrue(Path(os.path.join(UpperCamelCase__  ,	'''log.txt'''      )      ).exists()      )




'''simple docstring'''
import os
from collections.abc import Iterator




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = "."			)  ->							Iterator[str]:


		'''simple docstring'''


		for dir_path, dir_names, filenames in os.walk(SCREAMING_SNAKE_CASE__			):
				snake_case  :       Optional[Any]     			=						[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
				for filename in filenames:
						if filename == "__init__.py":
								continue
						if os.path.splitext(SCREAMING_SNAKE_CASE__			)[1] in (".py", ".ipynb"):
								yield os.path.join(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			).lstrip('''./'''			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		return F'{i * "  "}*' if i else "\n##"




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							str:


		'''simple docstring'''


		snake_case  :       Union[str, Any]     			=						old_path.split(os.sep			)
		for i, new_part in enumerate(new_path.split(os.sep			)			):
				if (i + 1 > len(SCREAMING_SNAKE_CASE__			) or old_parts[i] != new_part) and new_part:
						print(F'{md_prefix(SCREAMING_SNAKE_CASE__			)} {new_part.replace("_" ,       " "			).title()}'			)
		return new_path




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = "."			)  ->							None:


		'''simple docstring'''


		snake_case  :       List[Any]     			=						''''''
		for filepath in sorted(good_file_paths(SCREAMING_SNAKE_CASE__			)			):
				snake_case			,snake_case  :       Optional[Any]     			=						os.path.split(SCREAMING_SNAKE_CASE__			)
				if filepath != old_path:
						snake_case  :       Dict     			=						print_path(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
				snake_case  :       int     			=						(filepath.count(os.sep			) + 1) if filepath else 0
				snake_case  :       int     			=						F'{filepath}/{filename}'.replace(''' ''' ,       '''%20'''			)
				snake_case  :       int     			=						os.path.splitext(filename.replace('''_''' ,       ''' '''			).title()			)[0]
				print(F'{md_prefix(SCREAMING_SNAKE_CASE__			)} [{filename}]({url})'			)


if __name__ == "__main__":
						print_directory_md(".")


