





'''simple docstring'''






import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
					lowerCAmelCase     :   Dict       		=      {
					    """linear""": PIL.Image.Resampling.BILINEAR,
					    """bilinear""": PIL.Image.Resampling.BILINEAR,
					    """bicubic""": PIL.Image.Resampling.BICUBIC,
					    """lanczos""": PIL.Image.Resampling.LANCZOS,
					    """nearest""": PIL.Image.Resampling.NEAREST,
					}
else:
					lowerCAmelCase     :   Dict       		=      {
					    """linear""": PIL.Image.LINEAR,
					    """bilinear""": PIL.Image.BILINEAR,
					    """bicubic""": PIL.Image.BICUBIC,
					    """lanczos""": PIL.Image.LANCZOS,
					    """nearest""": PIL.Image.NEAREST,
					}






def      __A						(							a_						:  Tuple	):
		lowerCAmelCase     :    Dict						=   (images / 2 + 0.5).clamp(0      ,1	)
		lowerCAmelCase     :    List[str]						=   images.cpu().permute(0      ,2      ,3      ,1	).float().numpy()
		lowerCAmelCase     :    Tuple						=   numpy_to_pil(snake_case__	)
		return images






def      __A						(							a_						:  Any	):
		if images.ndim == 3:
				lowerCAmelCase     :    Dict						=   images[None, ...]
		lowerCAmelCase     :    Tuple						=   (images * 2_5_5).round().astype("uint8"	)
		if images.shape[-1] == 1:
				# special case for grayscale (single channel) images
				lowerCAmelCase     :    List[str]						=   [Image.fromarray(image.squeeze()      ,mode="L"	) for image in images]
		else:
				lowerCAmelCase     :    int						=   [Image.fromarray(snake_case__	) for image in images]

		return pil_images










'''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 		lowerCamelCase  (      unittest.TestCase       ):



	def   _lowerCamelCase    (   self		,      a_	):
			for model_result in results.values():
					for batch_size, sequence_length in zip(model_result["bs"]		,      model_result["ss"]	):
							lowerCAmelCase     :    Tuple						=   model_result["result"][batch_size][sequence_length]
							self.assertIsNotNone(a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Tuple						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      eager_mode=a_		,      multi_process=a_		,      )
			lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    Optional[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	):
			lowerCAmelCase     :    Union[str, Any]						=   "sgugger/tiny-distilbert-classification"
			lowerCAmelCase     :    Tuple						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      only_pretrain_model=a_		,      )
			lowerCAmelCase     :    Any						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    str						=   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	):
			lowerCAmelCase     :    Union[str, Any]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    str						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    str						=   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	):
			lowerCAmelCase     :    List[str]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Union[str, Any]						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      eager_mode=a_		,      multi_process=a_		,      )
			lowerCAmelCase     :    Any						=   TensorFlowBenchmark(a_		,      [config]	)
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    int						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Union[str, Any]						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    Tuple						=   TensorFlowBenchmark(a_		,      [config]	)
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    Tuple						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    str						=   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	):
			lowerCAmelCase     :    List[Any]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Union[str, Any]						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Dict						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    Dict						=   TensorFlowBenchmark(a_		,      [config]	)
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    str						=   "patrickvonplaten/t5-tiny-random"
			lowerCAmelCase     :    Tuple						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Dict						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmark(a_		,      configs=[config]	)
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    str						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    List[Any]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      use_xla=a_		,      multi_process=a_		,      )
			lowerCAmelCase     :    Any						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    str						=   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	):
			lowerCAmelCase     :    Tuple						=   "sshleifer/tiny-gpt2"
			with tempfile.TemporaryDirectory() as tmp_dir:
					lowerCAmelCase     :    Optional[int]						=   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_		,      )
					lowerCAmelCase     :    Optional[Any]						=   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	):
			lowerCAmelCase     :    Any						=   "sshleifer/tiny-gpt2"

			def _check_summary_is_not_empty(a_	):
					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:
					lowerCAmelCase     :    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_		,      )
					lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmark(a_	)
					lowerCAmelCase     :    Tuple						=   benchmark.run()
					_check_summary_is_not_empty(result.inference_summary	)
					self.assertTrue(Path(os.path.join(a_		,      "log.txt"	)	).exists()	)



