



"""simple docstring"""


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


snake_case_				     =       logging.get_logger(__name__)







@dataclass
class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""
						__UpperCamelCase						=							[
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]






						def __init__(					self :List[str]   ,  **lowercase_ :str					)				->			Dict:
												for deprecated_arg in self.deprecated_args:
																		if deprecated_arg in kwargs:
																								UpperCAmelCase             = deprecated_arg[3:]
																								setattr(self   ,  lowercase_   ,  not kwargs.pop(lowercase_					)					)
																								logger.warning(
																								    f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
																								    f""" {positive_arg}={kwargs[positive_arg]}"""					)

												UpperCAmelCase             = kwargs.pop('torchscript'   ,  self.torchscript					)
												UpperCAmelCase             = kwargs.pop('torch_xla_tpu_print_metrics'   ,  self.torch_xla_tpu_print_metrics					)
												UpperCAmelCase             = kwargs.pop('fp16_opt_level'   ,  self.fpaa_opt_level					)
												super().__init__(**lowercase_					)

						__UpperCamelCase						=							field(default=SCREAMING_SNAKE_CASE_	,   metadata={"""help""": """Trace the models using torchscript"""}				)
						__UpperCamelCase						=							field(default=SCREAMING_SNAKE_CASE_	,   metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}				)
						__UpperCamelCase						=							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 	UpperCAmelCase__     (					self :Any					)				->			Tuple["torch.device", int]:
												requires_backends(self   ,  ['torch']					)
												logger.info('PyTorch: setting up devices'					)
												if not self.cuda:
																		UpperCAmelCase             = torch.device('cpu'					)
																		UpperCAmelCase             = 0
												elif is_torch_tpu_available():
																		UpperCAmelCase             = xm.xla_device()
																		UpperCAmelCase             = 0
												else:
																		UpperCAmelCase             = torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)
																		UpperCAmelCase             = torch.cuda.device_count()
												return device, n_gpu






						@property
						def 	UpperCAmelCase__     (					self :Tuple					)				->			Union[str, Any]:
												return is_torch_tpu_available() and self.tpu






						@property
						def 	UpperCAmelCase__     (					self :List[str]					)				->			int:
												requires_backends(self   ,  ['torch']					)
												# TODO(PVP): currently only single GPU is supported
												return torch.cuda.current_device()






						@property
						def 	UpperCAmelCase__     (					self :Tuple					)				->			"torch.device":
												requires_backends(self   ,  ['torch']					)
												return self._setup_devices[0]






						@property
						def 	UpperCAmelCase__     (					self :Dict					)				->			Dict:
												requires_backends(self   ,  ['torch']					)
												return self._setup_devices[1]






						@property
						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			List[str]:
												return self.n_gpu > 0







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__a  			=					{
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TrOCRForCausalLM',
						    'TrOCRPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




