






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
	import torch

if is_torch_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm


A	:		int    =       logging.get_logger(__name__)



@dataclass
class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			[
					    '''no_inference''',
					    '''no_cuda''',
					    '''no_tpu''',
					    '''no_speed''',
					    '''no_memory''',
					    '''no_env_print''',
					    '''no_multi_process''',
					]

					def __init__(self   :						Tuple				, **_UpperCAmelCase   :						Optional[int]		)						->       List[str]:




							"""simple docstring"""


							for deprecated_arg in self.deprecated_args:
									if deprecated_arg in kwargs:
											lowercase__					      =		deprecated_arg[3:]
											setattr(self				, _UpperCAmelCase				, not kwargs.pop(_UpperCAmelCase		)		)
											logger.warning(
											    f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
											    f''' {positive_arg}={kwargs[positive_arg]}'''		)

							lowercase__					      =		kwargs.pop("""torchscript"""				, self.torchscript		)
							lowercase__					      =		kwargs.pop("""torch_xla_tpu_print_metrics"""				, self.torch_xla_tpu_print_metrics		)
							lowercase__					      =		kwargs.pop("""fp16_opt_level"""				, self.fpaa_opt_level		)
							super().__init__(**_UpperCAmelCase		)

					A__      =			field(default=UpperCAmelCase__			,  metadata={'''help''': '''Trace the models using torchscript'''}							)
					A__      =			field(default=UpperCAmelCase__			,  metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}							)
					A__      =			field(
					    default='''O1'''			,  metadata={
					        '''help''': (
					            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
					            '''See details at https://nvidia.github.io/apex/amp.html'''
					        )
					    }			,  )

					@cached_property
					def   lowerCamelCase__					(self   :						str		)						->       Tuple["torch.device", int]:




							"""simple docstring"""


							requires_backends(self				, ["""torch"""]		)
							logger.info("""PyTorch: setting up devices"""		)
							if not self.cuda:
									lowercase__					      =		torch.device("""cpu"""		)
									lowercase__					      =		0
							elif is_torch_tpu_available():
									lowercase__					      =		xm.xla_device()
									lowercase__					      =		0
							else:
									lowercase__					      =		torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""		)
									lowercase__					      =		torch.cuda.device_count()
							return device, n_gpu

					@property
					def   lowerCamelCase__					(self   :						int		)						->       str:




							"""simple docstring"""


							return is_torch_tpu_available() and self.tpu

					@property
					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       int:




							"""simple docstring"""


							requires_backends(self				, ["""torch"""]		)
							# TODO(PVP): currently only single GPU is supported
							return torch.cuda.current_device()

					@property
					def   lowerCamelCase__					(self   :						str		)						->       "torch.device":




							"""simple docstring"""


							requires_backends(self				, ["""torch"""]		)
							return self._setup_devices[0]

					@property
					def   lowerCamelCase__					(self   :						int		)						->       List[Any]:




							"""simple docstring"""


							requires_backends(self				, ["""torch"""]		)
							return self._setup_devices[1]


					@property
					def   lowerCamelCase__					(self   :						Dict		)						->       Any:




							"""simple docstring"""


							return self.n_gpu > 0





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import TFXLMRobertaModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@slow
					def   lowerCamelCase__					(self   :						Any		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base"""		)

							lowercase__					      =		{
							    """input_ids""": tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]				, dtype=tf.intaa		),  # "My dog is cute"
							    """attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]				, dtype=tf.intaa		),
							}

							lowercase__					      =		model(_UpperCAmelCase		)["""last_hidden_state"""]
							lowercase__					      =		tf.TensorShape((1, 6, 768)		)
							self.assertEqual(output.shape				, _UpperCAmelCase		)
							# compare the actual values for a slice.
							lowercase__					      =		tf.convert_to_tensor(
							    [
							        [
							            [0.0_681_762, 0.10_894_451, 0.06_772_504],
							            [-0.06_423_668, 0.02_366_615, 0.04_329_344],
							            [-0.06_057_295, 0.09_974_135, -0.00_070_584],
							        ]
							    ]				, dtype=tf.floataa				, )

							self.assertTrue(np.allclose(output[:, :3, :3].numpy()				, expected_slice.numpy()				, atol=1E-4		)		)

