
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  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:str     ,   a		:Tuple						)		-> Any:
								for model_result in results.values():
															for batch_size, sequence_length in zip(model_result["bs"]     ,   model_result["ss"]						):
																						__UpperCamelCase	:   Union[str, Any]			   =					model_result["result"][batch_size][sequence_length]
																						self.assertIsNotNone(a						)







	def    _lowerCamelCase				(					self		:str						)		-> Any:
								__UpperCamelCase	:   List[str]			   =					"sshleifer/tiny-gpt2"
								__UpperCamelCase	:   Any			   =					TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]     ,   training=a     ,   inference=a     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   eager_mode=a     ,   multi_process=a     ,   )
								__UpperCamelCase	:   Union[str, Any]			   =					TensorFlowBenchmark(a						)
								__UpperCamelCase	:   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		:str						)		-> int:
								__UpperCamelCase	:   int			   =					"sgugger/tiny-distilbert-classification"
								__UpperCamelCase	:   str			   =					TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]     ,   training=a     ,   inference=a     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   multi_process=a     ,   only_pretrain_model=a     ,   )
								__UpperCamelCase	:   str			   =					TensorFlowBenchmark(a						)
								__UpperCamelCase	:   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		:str						)		-> List[str]:
								__UpperCamelCase	:   Dict			   =					"sshleifer/tiny-gpt2"
								__UpperCamelCase	:   int			   =					TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]     ,   training=a     ,   inference=a     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   multi_process=a     ,   )
								__UpperCamelCase	:   List[Any]			   =					TensorFlowBenchmark(a						)
								__UpperCamelCase	:   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		:Tuple						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					"sshleifer/tiny-gpt2"
								__UpperCamelCase	:   Any			   =					AutoConfig.from_pretrained(a						)
								__UpperCamelCase	:   int			   =					TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]     ,   training=a     ,   inference=a     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   eager_mode=a     ,   multi_process=a     ,   )
								__UpperCamelCase	:   Dict			   =					TensorFlowBenchmark(a     ,   [config]						)
								__UpperCamelCase	:   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						)		-> Optional[Any]:
								__UpperCamelCase	:   Tuple			   =					"sshleifer/tiny-gpt2"
								__UpperCamelCase	:   str			   =					AutoConfig.from_pretrained(a						)
								__UpperCamelCase	:   str			   =					TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]     ,   training=a     ,   inference=a     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   multi_process=a     ,   )
								__UpperCamelCase	:   int			   =					TensorFlowBenchmark(a     ,   [config]						)
								__UpperCamelCase	:   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		:Tuple						)		-> List[str]:
								__UpperCamelCase	:   str			   =					"sshleifer/tiny-gpt2"
								__UpperCamelCase	:   Optional[int]			   =					TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]     ,   training=a     ,   inference=a     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   multi_process=a     ,   )
								__UpperCamelCase	:   Optional[int]			   =					TensorFlowBenchmark(a						)
								__UpperCamelCase	:   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		:int						)		-> Dict:
								__UpperCamelCase	:   int			   =					"sshleifer/tiny-gpt2"
								__UpperCamelCase	:   str			   =					AutoConfig.from_pretrained(a						)
								__UpperCamelCase	:   Tuple			   =					TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]     ,   training=a     ,   inference=a     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   multi_process=a     ,   )
								__UpperCamelCase	:   int			   =					TensorFlowBenchmark(a     ,   [config]						)
								__UpperCamelCase	:   Tuple			   =					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		:Any						)		-> List[str]:
								__UpperCamelCase	:   Optional[int]			   =					"patrickvonplaten/t5-tiny-random"
								__UpperCamelCase	:   Any			   =					AutoConfig.from_pretrained(a						)
								__UpperCamelCase	:   Union[str, Any]			   =					TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]     ,   training=a     ,   inference=a     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   multi_process=a     ,   )
								__UpperCamelCase	:   Optional[Any]			   =					TensorFlowBenchmark(a     ,   configs=[config]						)
								__UpperCamelCase	:   List[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    _lowerCamelCase				(					self		:List[Any]						)		-> Union[str, Any]:
								__UpperCamelCase	:   Dict			   =					"sshleifer/tiny-gpt2"
								__UpperCamelCase	:   int			   =					TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]     ,   training=a     ,   inference=a     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   use_xla=a     ,   multi_process=a     ,   )
								__UpperCamelCase	:   Union[str, Any]			   =					TensorFlowBenchmark(a						)
								__UpperCamelCase	:   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		:List[str]						)		-> Union[str, Any]:
								__UpperCamelCase	:   Union[str, Any]			   =					"sshleifer/tiny-gpt2"
								with tempfile.TemporaryDirectory() as tmp_dir:
															__UpperCamelCase	:   Tuple			   =					TensorFlowBenchmarkArguments(
															    models=[MODEL_ID]     ,   inference=a     ,   save_to_csv=a     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   inference_time_csv_file=os.path.join(a     ,   "inf_time.csv"						)     ,   inference_memory_csv_file=os.path.join(a     ,   "inf_mem.csv"						)     ,   env_info_csv_file=os.path.join(a     ,   "env.csv"						)     ,   multi_process=a     ,   )
															__UpperCamelCase	:   Tuple			   =					TensorFlowBenchmark(a						)
															benchmark.run()
															self.assertTrue(Path(os.path.join(a     ,   "inf_time.csv"						)						).exists()						)
															self.assertTrue(Path(os.path.join(a     ,   "inf_mem.csv"						)						).exists()						)
															self.assertTrue(Path(os.path.join(a     ,   "env.csv"						)						).exists()						)







	def    _lowerCamelCase				(					self		:List[str]						)		-> Tuple:
								__UpperCamelCase	:   Optional[Any]			   =					"sshleifer/tiny-gpt2"

								def _check_summary_is_not_empty(a		:int						):
															self.assertTrue(hasattr(a     ,   "sequential"						)						)
															self.assertTrue(hasattr(a     ,   "cumulative"						)						)
															self.assertTrue(hasattr(a     ,   "current"						)						)
															self.assertTrue(hasattr(a     ,   "total"						)						)

								with tempfile.TemporaryDirectory() as tmp_dir:
															__UpperCamelCase	:   int			   =					TensorFlowBenchmarkArguments(
															    models=[MODEL_ID]     ,   inference=a     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   log_filename=os.path.join(a     ,   "log.txt"						)     ,   log_print=a     ,   trace_memory_line_by_line=a     ,   eager_mode=a     ,   multi_process=a     ,   )
															__UpperCamelCase	:   Union[str, Any]			   =					TensorFlowBenchmark(a						)
															__UpperCamelCase	:   Optional[Any]			   =					benchmark.run()
															_check_summary_is_not_empty(result.inference_summary						)
															self.assertTrue(Path(os.path.join(a     ,   "log.txt"						)						).exists()						)

from PIL import Image




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Image						,  _lowerCamelCase :			int)					->    Image:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					(259 * (level + 255)) / (255 * (259 - level))

							def contrast(_lowerCamelCase :			int) -> int:
														return int(128 + factor * (c - 128))

							return img.point(_lowerCamelCase)


if __name__ == "__main__":
		# Load image
		with Image.open('image_data/lena.jpg') as img:
				# Change contrast to 170
				lowercase  :					Tuple													=       change_contrast(img, 170)
				cont_img.save('image_data/lena_high_contrast.png', format='png')