




"""simple docstring"""







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import YolosImageProcessor






class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def __init__(							self				:			Optional[Any]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			str=7						,						lowerCAmelCase				:			Dict=3						,						lowerCAmelCase				:			Any=30						,						lowerCAmelCase				:			int=400						,						lowerCAmelCase				:			List[Any]=True						,						lowerCAmelCase				:			int=None						,						lowerCAmelCase				:			str=True						,						lowerCAmelCase				:			List[Any]=[0.5, 0.5, 0.5]						,						lowerCAmelCase				:			Dict=[0.5, 0.5, 0.5]						,						lowerCAmelCase				:			Any=True						,						lowerCAmelCase				:			List[str]=1 / 255						,						lowerCAmelCase				:			Optional[int]=True						,						):
										# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
										lowerCAmelCase  =				size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
										lowerCAmelCase  =				parent
										lowerCAmelCase  =				batch_size
										lowerCAmelCase  =				num_channels
										lowerCAmelCase  =				min_resolution
										lowerCAmelCase  =				max_resolution
										lowerCAmelCase  =				do_resize
										lowerCAmelCase  =				size
										lowerCAmelCase  =				do_normalize
										lowerCAmelCase  =				image_mean
										lowerCAmelCase  =				image_std
										lowerCAmelCase  =				do_rescale
										lowerCAmelCase  =				rescale_factor
										lowerCAmelCase  =				do_pad


						def   __lowercase (							self				:			Tuple					):
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										}




						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			Optional[int]=False					):
										if not batched:
														lowerCAmelCase  =				image_inputs[0]
														if isinstance(UpperCamelCase__						,						Image.Image					):
																		lowerCAmelCase  =				image.size
														else:
																		lowerCAmelCase  =				image.shape[1], image.shape[2]
														if w < h:
																		lowerCAmelCase  =				int(self.size["""shortest_edge"""] * h / w					)
																		lowerCAmelCase  =				self.size['shortest_edge']
														elif w > h:
																		lowerCAmelCase  =				self.size['shortest_edge']
																		lowerCAmelCase  =				int(self.size["""shortest_edge"""] * w / h					)
														else:
																		lowerCAmelCase  =				self.size['shortest_edge']
																		lowerCAmelCase  =				self.size['shortest_edge']

										else:
														lowerCAmelCase  =				[]
														for image in image_inputs:
																		lowerCAmelCase  =				self.get_expected_values([image]					)
																		expected_values.append((expected_height, expected_width)					)
														lowerCAmelCase  =				max(UpperCamelCase__						,						key=lambda lowerCAmelCase					: item[0]					)[0]
														lowerCAmelCase  =				max(UpperCamelCase__						,						key=lambda lowerCAmelCase					: item[1]					)[1]

										return expected_height, expected_width






@require_torch
@require_vision
class       SCREAMING_SNAKE_CASE__					(					__A						,				unittest.TestCase					):
						_a    						=						YolosImageProcessor if is_vision_available() else None


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				YolosImageProcessingTester(self					)


						@property
						def   __lowercase (							self				:			List[str]					):
										return self.image_processor_tester.prepare_image_processor_dict()


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				self.image_processing_class(**self.image_processor_dict					)
										self.assertTrue(hasattr(UpperCamelCase__						,						"""image_mean"""					)					)
										self.assertTrue(hasattr(UpperCamelCase__						,						"""image_std"""					)					)
										self.assertTrue(hasattr(UpperCamelCase__						,						"""do_normalize"""					)					)
										self.assertTrue(hasattr(UpperCamelCase__						,						"""do_resize"""					)					)
										self.assertTrue(hasattr(UpperCamelCase__						,						"""size"""					)					)


						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				self.image_processing_class.from_dict(self.image_processor_dict					)
										self.assertEqual(image_processor.size						,						{"""shortest_edge""": 18, """longest_edge""": 1333}					)
										self.assertEqual(image_processor.do_pad						,						UpperCamelCase__					)

										lowerCAmelCase  =				self.image_processing_class.from_dict(
										    self.image_processor_dict						,						size=42						,						max_size=84						,						pad_and_return_pixel_mask=UpperCamelCase__					)
										self.assertEqual(image_processor.size						,						{"""shortest_edge""": 42, """longest_edge""": 84}					)
										self.assertEqual(image_processor.do_pad						,						UpperCamelCase__					)


						def   __lowercase (							self				:			Optional[Any]					):
										pass


						def   __lowercase (							self				:			int					):
										# Initialize image_processing
										lowerCAmelCase  =				self.image_processing_class(**self.image_processor_dict					)
										# create random PIL images
										lowerCAmelCase  =				prepare_image_inputs(self.image_processor_tester						,						equal_resolution=UpperCamelCase__					)
										for image in image_inputs:
														self.assertIsInstance(UpperCamelCase__						,						Image.Image					)

										# Test not batched input
										lowerCAmelCase  =				image_processing(image_inputs[0]						,						return_tensors="""pt"""					).pixel_values

										lowerCAmelCase  =				self.image_processor_tester.get_expected_values(UpperCamelCase__					)

										self.assertEqual(
										    encoded_images.shape						,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,						)

										# Test batched
										lowerCAmelCase  =				self.image_processor_tester.get_expected_values(UpperCamelCase__						,						batched=UpperCamelCase__					)

										lowerCAmelCase  =				image_processing(UpperCamelCase__						,						return_tensors="""pt"""					).pixel_values
										self.assertEqual(
										    encoded_images.shape						,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )						,						)


						def   __lowercase (							self				:			str					):
										# Initialize image_processing
										lowerCAmelCase  =				self.image_processing_class(**self.image_processor_dict					)
										# create random numpy tensors
										lowerCAmelCase  =				prepare_image_inputs(self.image_processor_tester						,						equal_resolution=UpperCamelCase__						,						numpify=UpperCamelCase__					)
										for image in image_inputs:
														self.assertIsInstance(UpperCamelCase__						,						np.ndarray					)

										# Test not batched input
										lowerCAmelCase  =				image_processing(image_inputs[0]						,						return_tensors="""pt"""					).pixel_values

										lowerCAmelCase  =				self.image_processor_tester.get_expected_values(UpperCamelCase__					)

										self.assertEqual(
										    encoded_images.shape						,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,						)

										# Test batched
										lowerCAmelCase  =				image_processing(UpperCamelCase__						,						return_tensors="""pt"""					).pixel_values

										lowerCAmelCase  =				self.image_processor_tester.get_expected_values(UpperCamelCase__						,						batched=UpperCamelCase__					)

										self.assertEqual(
										    encoded_images.shape						,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )						,						)


						def   __lowercase (							self				:			List[str]					):
										# Initialize image_processing
										lowerCAmelCase  =				self.image_processing_class(**self.image_processor_dict					)
										# create random PyTorch tensors
										lowerCAmelCase  =				prepare_image_inputs(self.image_processor_tester						,						equal_resolution=UpperCamelCase__						,						torchify=UpperCamelCase__					)
										for image in image_inputs:
														self.assertIsInstance(UpperCamelCase__						,						torch.Tensor					)

										# Test not batched input
										lowerCAmelCase  =				image_processing(image_inputs[0]						,						return_tensors="""pt"""					).pixel_values

										lowerCAmelCase  =				self.image_processor_tester.get_expected_values(UpperCamelCase__					)

										self.assertEqual(
										    encoded_images.shape						,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,						)

										# Test batched
										lowerCAmelCase  =				image_processing(UpperCamelCase__						,						return_tensors="""pt"""					).pixel_values

										lowerCAmelCase  =				self.image_processor_tester.get_expected_values(UpperCamelCase__						,						batched=UpperCamelCase__					)

										self.assertEqual(
										    encoded_images.shape						,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )						,						)


						def   __lowercase (							self				:			int					):
										# Initialize image_processings
										lowerCAmelCase  =				self.image_processing_class(**self.image_processor_dict					)
										lowerCAmelCase  =				self.image_processing_class(do_resize=UpperCamelCase__						,						do_normalize=UpperCamelCase__						,						do_rescale=UpperCamelCase__					)
										# create random PyTorch tensors
										lowerCAmelCase  =				prepare_image_inputs(self.image_processor_tester						,						equal_resolution=UpperCamelCase__						,						torchify=UpperCamelCase__					)
										for image in image_inputs:
														self.assertIsInstance(UpperCamelCase__						,						torch.Tensor					)

										# Test whether the method "pad" and calling the image processor return the same tensors
										lowerCAmelCase  =				image_processing_a.pad(UpperCamelCase__						,						return_tensors="""pt"""					)
										lowerCAmelCase  =				image_processing_a(UpperCamelCase__						,						return_tensors="""pt"""					)

										self.assertTrue(
										    torch.allclose(encoded_images_with_method["""pixel_values"""]						,						encoded_images["""pixel_values"""]						,						atol=1e-4					)					)


						@slow
						def   __lowercase (							self				:			Union[str, Any]					):
										# prepare image and target
										lowerCAmelCase  =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
										with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""						,						"""r"""					) as f:
														lowerCAmelCase  =				json.loads(f.read()					)

										lowerCAmelCase  =				{'image_id': 3_9769, 'annotations': target}

										# encode them
										lowerCAmelCase  =				YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""					)
										lowerCAmelCase  =				image_processing(images=UpperCamelCase__						,						annotations=UpperCamelCase__						,						return_tensors="""pt"""					)

										# verify pixel values
										lowerCAmelCase  =				torch.Size([1, 3, 800, 1066]					)
										self.assertEqual(encoding["""pixel_values"""].shape						,						UpperCamelCase__					)

										lowerCAmelCase  =				torch.tensor([0.2796, 0.3138, 0.3481]					)
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]						,						UpperCamelCase__						,						atol=1e-4					)					)

										# verify area
										lowerCAmelCase  =				torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]						,						UpperCamelCase__					)					)
										# verify boxes
										lowerCAmelCase  =				torch.Size([6, 4]					)
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,						UpperCamelCase__					)
										lowerCAmelCase  =				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]						,						UpperCamelCase__						,						atol=1e-3					)					)
										# verify image_id
										lowerCAmelCase  =				torch.tensor([3_9769]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,						UpperCamelCase__					)					)
										# verify is_crowd
										lowerCAmelCase  =				torch.tensor([0, 0, 0, 0, 0, 0]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,						UpperCamelCase__					)					)
										# verify class_labels
										lowerCAmelCase  =				torch.tensor([75, 75, 63, 65, 17, 17]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,						UpperCamelCase__					)					)
										# verify orig_size
										lowerCAmelCase  =				torch.tensor([480, 640]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,						UpperCamelCase__					)					)
										# verify size
										lowerCAmelCase  =				torch.tensor([800, 1066]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,						UpperCamelCase__					)					)




						@slow
						def   __lowercase (							self				:			List[str]					):
										# prepare image, target and masks_path
										lowerCAmelCase  =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
										with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""						,						"""r"""					) as f:
														lowerCAmelCase  =				json.loads(f.read()					)

										lowerCAmelCase  =				{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

										lowerCAmelCase  =				pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""					)

										# encode them
										lowerCAmelCase  =				YolosImageProcessor(format="""coco_panoptic"""					)
										lowerCAmelCase  =				image_processing(images=UpperCamelCase__						,						annotations=UpperCamelCase__						,						masks_path=UpperCamelCase__						,						return_tensors="""pt"""					)

										# verify pixel values
										lowerCAmelCase  =				torch.Size([1, 3, 800, 1066]					)
										self.assertEqual(encoding["""pixel_values"""].shape						,						UpperCamelCase__					)

										lowerCAmelCase  =				torch.tensor([0.2796, 0.3138, 0.3481]					)
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]						,						UpperCamelCase__						,						atol=1e-4					)					)

										# verify area
										lowerCAmelCase  =				torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]						,						UpperCamelCase__					)					)
										# verify boxes
										lowerCAmelCase  =				torch.Size([6, 4]					)
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,						UpperCamelCase__					)
										lowerCAmelCase  =				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]						,						UpperCamelCase__						,						atol=1e-3					)					)
										# verify image_id
										lowerCAmelCase  =				torch.tensor([3_9769]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,						UpperCamelCase__					)					)
										# verify is_crowd
										lowerCAmelCase  =				torch.tensor([0, 0, 0, 0, 0, 0]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,						UpperCamelCase__					)					)
										# verify class_labels
										lowerCAmelCase  =				torch.tensor([17, 17, 63, 75, 75, 93]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,						UpperCamelCase__					)					)
										# verify masks
										lowerCAmelCase  =				82_2873
										self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()						,						UpperCamelCase__					)
										# verify orig_size
										lowerCAmelCase  =				torch.tensor([480, 640]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,						UpperCamelCase__					)					)
										# verify size
										lowerCAmelCase  =				torch.tensor([800, 1066]					)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,						UpperCamelCase__					)					)











"""simple docstring"""







import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
		import tensorflow as tf
		from tensorflow.python.framework.errors_impl import ResourceExhaustedError

		from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
		import pyanvml.pyanvml as nvml

a      =							logging.get_logger(__name__)





def 							lowercase					(snake_case__   : bool      ,       snake_case__   : bool )     ->  Tuple:


				'''simple docstring'''






				def run_func(snake_case__   : Any ):
								@wraps(snake_case__ )
								def run_in_eager_mode(*snake_case__   : Optional[Any]      ,       **snake_case__   : int ):
												return func(*snake_case__      ,       **snake_case__ )

								@wraps(snake_case__ )
								@tf.function(experimental_compile=snake_case__ )
								def run_in_graph_mode(*snake_case__   : int      ,       **snake_case__   : Tuple ):
												return func(*snake_case__      ,       **snake_case__ )

								if do_eager_mode is True:
												if use_xla is not False:
																raise ValueError(
																    """Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.""" )
												return run_in_eager_mode
								else:
												return run_in_graph_mode

				return run_func





def 							lowercase					(snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  ["tf.Tensor"]:


				'''simple docstring'''






				lowerCAmelCase  =				random.Random()
				lowerCAmelCase  =				[rng.randint(0      ,       vocab_size - 1 ) for i in range(batch_size * sequence_length )]
				return tf.constant(snake_case__      ,       shape=(batch_size, sequence_length)      ,       dtype=tf.intaa )






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						42
						_a    						=						42
						_a    						=						"TensorFlow"


						@property
						def   __lowercase (							self				:			Optional[int]					):
										return tf.__version__


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										# initialize GPU on separate process
										lowerCAmelCase  =				self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)
										lowerCAmelCase  =				self._prepare_inference_func(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										return self._measure_speed(_inference					)


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)
										lowerCAmelCase  =				self._prepare_train_func(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										return self._measure_speed(_train					)


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										# initialize GPU on separate process
										if self.args.is_gpu:
														tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,						lowerCAmelCase					)
										lowerCAmelCase  =				self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)
										lowerCAmelCase  =				self._prepare_inference_func(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										return self._measure_memory(_inference					)


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										if self.args.is_gpu:
														tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,						lowerCAmelCase					)
										lowerCAmelCase  =				self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)

										lowerCAmelCase  =				self._prepare_train_func(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										return self._measure_memory(_train					)


						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.config_dict[model_name]

										if self.args.fpaa:
														raise NotImplementedError("""Mixed precision is currently not supported."""					)

										lowerCAmelCase  =				(
										    hasattr(lowerCAmelCase						,						"""architectures"""					)
										    and isinstance(config.architectures						,						lowerCAmelCase					)
										    and len(config.architectures					) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																		lowerCAmelCase  =				"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																		lowerCAmelCase  =				__import__("""transformers"""						,						fromlist=[model_class]					)
																		lowerCAmelCase  =				getattr(lowerCAmelCase						,						lowerCAmelCase					)
																		lowerCAmelCase  =				model_cls(lowerCAmelCase					)
														except ImportError:
																		raise ImportError(
																		    f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																		    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""					)
										else:
														lowerCAmelCase  =				TF_MODEL_MAPPING[config.__class__](lowerCAmelCase					)

										# encoder-decoder has vocab size saved differently
										lowerCAmelCase  =				config.vocab_size if hasattr(lowerCAmelCase						,						"""vocab_size"""					) else config.encoder.vocab_size
										lowerCAmelCase  =				random_input_ids(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)

										@run_with_tf_optimizations(self.args.eager_mode						,						self.args.use_xla					)
										def encoder_decoder_forward():
														return model(lowerCAmelCase						,						decoder_input_ids=lowerCAmelCase						,						training=lowerCAmelCase					)

										@run_with_tf_optimizations(self.args.eager_mode						,						self.args.use_xla					)
										def encoder_forward():
														return model(lowerCAmelCase						,						training=lowerCAmelCase					)

										lowerCAmelCase  =				encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

										return _inference


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.config_dict[model_name]

										if self.args.eager_mode is not False:
														raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."""					)

										if self.args.fpaa:
														raise NotImplementedError("""Mixed precision is currently not supported."""					)

										lowerCAmelCase  =				(
										    hasattr(lowerCAmelCase						,						"""architectures"""					)
										    and isinstance(config.architectures						,						lowerCAmelCase					)
										    and len(config.architectures					) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																		lowerCAmelCase  =				"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																		lowerCAmelCase  =				__import__("""transformers"""						,						fromlist=[model_class]					)
																		lowerCAmelCase  =				getattr(lowerCAmelCase						,						lowerCAmelCase					)
																		lowerCAmelCase  =				model_cls(lowerCAmelCase					)
														except ImportError:
																		raise ImportError(
																		    f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																		    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""					)
										else:
														lowerCAmelCase  =				TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCAmelCase					)

										# encoder-decoder has vocab size saved differently
										lowerCAmelCase  =				config.vocab_size if hasattr(lowerCAmelCase						,						"""vocab_size"""					) else config.encoder.vocab_size
										lowerCAmelCase  =				random_input_ids(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)

										@run_with_tf_optimizations(self.args.eager_mode						,						self.args.use_xla					)
										def encoder_decoder_train():
														lowerCAmelCase  =				model(lowerCAmelCase						,						decoder_input_ids=lowerCAmelCase						,						labels=lowerCAmelCase						,						training=lowerCAmelCase					)[0]
														lowerCAmelCase  =				tf.gradients(lowerCAmelCase						,						model.trainable_variables					)
														return gradients

										@run_with_tf_optimizations(self.args.eager_mode						,						self.args.use_xla					)
										def encoder_train():
														lowerCAmelCase  =				model(lowerCAmelCase						,						labels=lowerCAmelCase						,						training=lowerCAmelCase					)[0]
														lowerCAmelCase  =				tf.gradients(lowerCAmelCase						,						model.trainable_variables					)
														return gradients

										lowerCAmelCase  =				encoder_decoder_train if config.is_encoder_decoder else encoder_train

										return _train


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			List[str]					):
										with self.args.strategy.scope():
														try:
																		if self.args.is_tpu or self.args.use_xla:
																						# run additional 10 times to stabilize compilation for tpu
																						logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation"""					)
																						timeit.repeat(lowerCAmelCase						,						repeat=1						,						number=5					)

																		# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
																		lowerCAmelCase  =				timeit.repeat(
																		    lowerCAmelCase						,						repeat=self.args.repeat						,						number=10						,						)

																		return min(lowerCAmelCase					) / 10.0
														except ResourceExhaustedError as e:
																		self.print_fn(f'''Doesn\'t fit on GPU. {e}'''					)




						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			Callable[[], None]					):
										logger.info(
										    """Note that TensorFlow allocates more memory than """
										    """it might need to speed up computation. """
										    """The memory reported here corresponds to the memory """
										    """reported by `nvidia-smi`, which can vary depending """
										    """on total available memory on the GPU that is used."""					)
										with self.args.strategy.scope():
														try:
																		if self.args.trace_memory_line_by_line:
																						if not self.args.eager_mode:
																										raise ValueError(
																										    """`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
																										    """ consumption line by line."""					)
																						lowerCAmelCase  =				start_memory_tracing("""transformers"""					)

																		if self.args.is_tpu:
																						# tpu
																						raise NotImplementedError(
																						    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
																						    """ with `args.memory=False`"""					)
																		elif self.args.is_gpu:
																						# gpu
																						if not is_pyanvml_available():
																										logger.warning(
																										    """py3nvml not installed, we won't log GPU memory usage. """
																										    """Install py3nvml (pip install py3nvml) to log information about GPU."""					)
																										lowerCAmelCase  =				"""N/A"""
																						else:
																										logger.info(
																										    """Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
																										    """ running on the same GPU."""					)
																										# init nvml
																										nvml.nvmlInit()
																										func()
																										lowerCAmelCase  =				nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx					)
																										lowerCAmelCase  =				nvml.nvmlDeviceGetMemoryInfo(lowerCAmelCase					)
																										lowerCAmelCase  =				meminfo.used
																										lowerCAmelCase  =				Memory(lowerCAmelCase					)
																										# shutdown nvml
																										nvml.nvmlShutdown()
																		else:
																						# cpu
																						if self.args.trace_memory_line_by_line:
																										logger.info(
																										    """When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
																										    """ TensorFlow."""					)
																										lowerCAmelCase  =				None
																						else:
																										lowerCAmelCase  =				measure_peak_memory_cpu(lowerCAmelCase					)
																										lowerCAmelCase  =				Memory(lowerCAmelCase					) if isinstance(lowerCAmelCase						,						lowerCAmelCase					) else memory_bytes
																		if self.args.trace_memory_line_by_line:
																						lowerCAmelCase  =				stop_memory_tracing(lowerCAmelCase					)
																						if memory is None:
																										lowerCAmelCase  =				summary.total
																		else:
																						lowerCAmelCase  =				None

																		return memory, summary
														except ResourceExhaustedError as e:
																		self.print_fn(f'''Doesn\'t fit on GPU. {e}'''					)
																		return "N/A", None





