





'''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 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     str   )					->       Any:







													"""simple docstring"""
													for model_result in results.values():
																			for batch_size, sequence_length in zip(model_result['''bs''']       ,     model_result['''ss''']   ):
																									UpperCAmelCase_			:      Optional[Any]     						=  model_result['''result'''][batch_size][sequence_length]
																									self.assertIsNotNone(__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  '''sshleifer/tiny-gpt2'''
													UpperCAmelCase_			:      str     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     eager_mode=__magic_name__       ,     multi_process=__magic_name__       ,     )
													UpperCAmelCase_			:      Dict     						=  TensorFlowBenchmark(__magic_name__   )
													UpperCAmelCase_			:      List[str]     						=  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]   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  '''sgugger/tiny-distilbert-classification'''
													UpperCAmelCase_			:      int     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=__magic_name__       ,     only_pretrain_model=__magic_name__       ,     )
													UpperCAmelCase_			:      Any     						=  TensorFlowBenchmark(__magic_name__   )
													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     UpperCAmelCase__	(							self							:     Optional[int]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  '''sshleifer/tiny-gpt2'''
													UpperCAmelCase_			:      Any     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=__magic_name__       ,     )
													UpperCAmelCase_			:      Optional[Any]     						=  TensorFlowBenchmark(__magic_name__   )
													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     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  '''sshleifer/tiny-gpt2'''
													UpperCAmelCase_			:      List[str]     						=  AutoConfig.from_pretrained(__magic_name__   )
													UpperCAmelCase_			:      Tuple     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     eager_mode=__magic_name__       ,     multi_process=__magic_name__       ,     )
													UpperCAmelCase_			:      str     						=  TensorFlowBenchmark(__magic_name__       ,     [config]   )
													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     UpperCAmelCase__	(							self							:     Dict   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  '''sshleifer/tiny-gpt2'''
													UpperCAmelCase_			:      List[Any]     						=  AutoConfig.from_pretrained(__magic_name__   )
													UpperCAmelCase_			:      str     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=__magic_name__       ,     )
													UpperCAmelCase_			:      Optional[Any]     						=  TensorFlowBenchmark(__magic_name__       ,     [config]   )
													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     UpperCAmelCase__	(							self							:     Any   )					->       List[str]:







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







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  '''sshleifer/tiny-gpt2'''
													UpperCAmelCase_			:      Dict     						=  AutoConfig.from_pretrained(__magic_name__   )
													UpperCAmelCase_			:      Tuple     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=__magic_name__       ,     )
													UpperCAmelCase_			:      List[str]     						=  TensorFlowBenchmark(__magic_name__       ,     [config]   )
													UpperCAmelCase_			:      str     						=  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   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  '''patrickvonplaten/t5-tiny-random'''
													UpperCAmelCase_			:      Union[str, Any]     						=  AutoConfig.from_pretrained(__magic_name__   )
													UpperCAmelCase_			:      Union[str, Any]     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=__magic_name__       ,     )
													UpperCAmelCase_			:      List[Any]     						=  TensorFlowBenchmark(__magic_name__       ,     configs=[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   )

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







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  '''sshleifer/tiny-gpt2'''
													UpperCAmelCase_			:      Tuple     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     use_xla=__magic_name__       ,     multi_process=__magic_name__       ,     )
													UpperCAmelCase_			:      Dict     						=  TensorFlowBenchmark(__magic_name__   )
													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   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  '''sshleifer/tiny-gpt2'''
													with tempfile.TemporaryDirectory() as tmp_dir:
																			UpperCAmelCase_			:      Union[str, Any]     						=  TensorFlowBenchmarkArguments(
																			    models=[MODEL_ID]       ,     inference=__magic_name__       ,     save_to_csv=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     inference_time_csv_file=os.path.join(__magic_name__       ,     '''inf_time.csv'''   )       ,     inference_memory_csv_file=os.path.join(__magic_name__       ,     '''inf_mem.csv'''   )       ,     env_info_csv_file=os.path.join(__magic_name__       ,     '''env.csv'''   )       ,     multi_process=__magic_name__       ,     )
																			UpperCAmelCase_			:      Optional[int]     						=  TensorFlowBenchmark(__magic_name__   )
																			benchmark.run()
																			self.assertTrue(Path(os.path.join(__magic_name__       ,     '''inf_time.csv'''   )   ).exists()   )
																			self.assertTrue(Path(os.path.join(__magic_name__       ,     '''inf_mem.csv'''   )   ).exists()   )
																			self.assertTrue(Path(os.path.join(__magic_name__       ,     '''env.csv'''   )   ).exists()   )



							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  '''sshleifer/tiny-gpt2'''

													def _check_summary_is_not_empty(__magic_name__							:     str   ):
																			self.assertTrue(hasattr(__magic_name__       ,     '''sequential'''   )   )
																			self.assertTrue(hasattr(__magic_name__       ,     '''cumulative'''   )   )
																			self.assertTrue(hasattr(__magic_name__       ,     '''current'''   )   )
																			self.assertTrue(hasattr(__magic_name__       ,     '''total'''   )   )

													with tempfile.TemporaryDirectory() as tmp_dir:
																			UpperCAmelCase_			:      str     						=  TensorFlowBenchmarkArguments(
																			    models=[MODEL_ID]       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     log_filename=os.path.join(__magic_name__       ,     '''log.txt'''   )       ,     log_print=__magic_name__       ,     trace_memory_line_by_line=__magic_name__       ,     eager_mode=__magic_name__       ,     multi_process=__magic_name__       ,     )
																			UpperCAmelCase_			:      Dict     						=  TensorFlowBenchmark(__magic_name__   )
																			UpperCAmelCase_			:      Dict     						=  benchmark.run()
																			_check_summary_is_not_empty(result.inference_summary   )
																			self.assertTrue(Path(os.path.join(__magic_name__       ,     '''log.txt'''   )   ).exists()   )












'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case_     :       Union[str, Any]						=  logging.get_logger(__name__)


class 					__a      (lowerCamelCase     ):
							__a					:   Tuple									=       ["pixel_values"]

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     int = 32       ,     __magic_name__							:     Union[str, Any]=PILImageResampling.BILINEAR       ,     __magic_name__							:     bool = True       ,     **__magic_name__							:     List[str]       ,     )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  do_resize
													UpperCAmelCase_			:      Tuple     						=  do_rescale
													UpperCAmelCase_			:      List[Any]     						=  size_divisor
													UpperCAmelCase_			:      Any     						=  resample
													super().__init__(**__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     int       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Tuple   )					->       np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      List[str]     						=  get_image_size(__magic_name__   )
													# Rounds the height and width down to the closest multiple of size_divisor
													UpperCAmelCase_			:      Dict     						=  height // size_divisor * size_divisor
													UpperCAmelCase_			:      Dict     						=  width // size_divisor * size_divisor
													UpperCAmelCase_			:      Any     						=  resize(__magic_name__       ,     (new_h, new_w)       ,     resample=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )
													return image

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     float       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Optional[Any]   )					->       np.ndarray:







													"""simple docstring"""
													return rescale(image=__magic_name__       ,     scale=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )



							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[int] = None       ,     __magic_name__							:     Any=None       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[Union[TensorType, str]] = None       ,     __magic_name__							:     ChannelDimension = ChannelDimension.FIRST       ,     **__magic_name__							:     Tuple       ,     )					->       BatchFeature:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_			:      str     						=  do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_			:      Any     						=  size_divisor if size_divisor is not None else self.size_divisor
													UpperCAmelCase_			:      Dict     						=  resample if resample is not None else self.resample

													if do_resize and size_divisor is None:
																			raise ValueError('''size_divisor is required for resizing'''   )

													UpperCAmelCase_			:      Optional[int]     						=  make_list_of_images(__magic_name__   )

													if not valid_images(__magic_name__   ):
																			raise ValueError('''Invalid image(s)'''   )

													# All transformations expect numpy arrays.
													UpperCAmelCase_			:      List[str]     						=  [to_numpy_array(__magic_name__   ) for img in images]

													if do_resize:
																			UpperCAmelCase_			:      str     						=  [self.resize(__magic_name__       ,     size_divisor=__magic_name__       ,     resample=__magic_name__   ) for image in images]

													if do_rescale:
																			UpperCAmelCase_			:      Tuple     						=  [self.rescale(__magic_name__       ,     scale=1 / 2_55   ) for image in images]

													UpperCAmelCase_			:      Union[str, Any]     						=  [to_channel_dimension_format(__magic_name__       ,     __magic_name__   ) for image in images]

													UpperCAmelCase_			:      int     						=  {'''pixel_values''': images}
													return BatchFeature(data=__magic_name__       ,     tensor_type=__magic_name__   )





