





import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


A    :					Dict =      logging.get_logger(__name__)







def    __lowerCamelCase					(							__a   :int=None    ,     __a   :Optional[Any]=None    )		->				int:






		"""simple docstring"""

		return field(default_factory=lambda: default    ,     metadata=__a    )






@dataclass
class   A			:
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    list_field(
			    default=[]   ,     metadata={
			        '''help''': (
			            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
			            ''' of all available models'''
			        )
			    }   ,     )

			__lowerCamelCase	:       List[int]							=    list_field(
			    default=[8]   ,     metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}    )

			__lowerCamelCase	:       List[int]							=    list_field(
			    default=[8, 32, 128, 512]   ,     metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}   ,     )

			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Use FP16 to accelerate inference.'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Benchmark training of model'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Verbose memory tracing'''}    )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
			    }   ,     )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Trace memory line by line'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Save result to a CSV file'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Save all print statements in a log file'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to print environment information'''}    )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': (
			            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
			            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
			            ''' for debugging / testing and on TPU.'''
			        )
			    }   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''inference_time_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving time results to csv.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''inference_memory_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''train_time_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''train_memory_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''env_info_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving environment information.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''log_{round(time()    )}.csv'''   ,     metadata={'''help''': '''Log filename used if print statements are saved in log.'''}   ,     )
			__lowerCamelCase	:       int							=    field(default=3   ,     metadata={'''help''': '''Times an experiment will be run.'''}    )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': (
			            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
			            ''' model weights.'''
			        )
			    }   ,     )






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







					"""simple docstring"""






					warnings.warn(
					    f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
					    """ are deprecated in general and it is advised to use external Benchmarking libraries """
					    """ to benchmark Transformer models."""							,	__lowerCAmelCase							,	)






			def    a_       (			self   :    Union[str, Any]   )	->	List[str]:







					"""simple docstring"""






					return json.dumps(dataclasses.asdict(self   )							,	indent=2   )






			@property
			def    a_       (			self   :    Tuple   )	->	List[str]:







					"""simple docstring"""






					if len(self.models   ) <= 0:
							raise ValueError(
							    """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
							    """ bert-base-cased` or `args.models = ['bert-base-cased']."""   )
					return self.models







			@property
			def    a_       (			self   :    Union[str, Any]   )	->	Optional[Any]:







					"""simple docstring"""






					if not self.multi_process:
							return False
					elif self.is_tpu:
							logger.info("""Multiprocessing is currently not possible on TPU."""   )
							return False
					else:
							return True










from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


A    :					str =      logging.get_logger(__name__)

A    :					Union[str, Any] =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Optional[Any]							=    '''layoutlmv3'''






			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    Optional[int]=5_02_65							,	__lowerCAmelCase   :    Tuple=7_68							,	__lowerCAmelCase   :    Union[str, Any]=12							,	__lowerCAmelCase   :    Any=12							,	__lowerCAmelCase   :    List[Any]=30_72							,	__lowerCAmelCase   :    Optional[int]="gelu"							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[Any]=0.1							,	__lowerCAmelCase   :    Dict=5_12							,	__lowerCAmelCase   :    Any=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-5							,	__lowerCAmelCase   :    List[str]=1							,	__lowerCAmelCase   :    int=0							,	__lowerCAmelCase   :    Dict=2							,	__lowerCAmelCase   :    Tuple=10_24							,	__lowerCAmelCase   :    List[str]=1_28							,	__lowerCAmelCase   :    Optional[int]=1_28							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Optional[Any]=32							,	__lowerCAmelCase   :    Any=1_28							,	__lowerCAmelCase   :    str=64							,	__lowerCAmelCase   :    Optional[int]=2_56							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    int=2_24							,	__lowerCAmelCase   :    Dict=3							,	__lowerCAmelCase   :    List[Any]=16							,	__lowerCAmelCase   :    Dict=None							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	Dict:







					"""simple docstring"""






					super().__init__(
					    vocab_size=__lowerCAmelCase							,	hidden_size=__lowerCAmelCase							,	num_hidden_layers=__lowerCAmelCase							,	num_attention_heads=__lowerCAmelCase							,	intermediate_size=__lowerCAmelCase							,	hidden_act=__lowerCAmelCase							,	hidden_dropout_prob=__lowerCAmelCase							,	attention_probs_dropout_prob=__lowerCAmelCase							,	max_position_embeddings=__lowerCAmelCase							,	type_vocab_size=__lowerCAmelCase							,	initializer_range=__lowerCAmelCase							,	layer_norm_eps=__lowerCAmelCase							,	pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase							,	)
					A__        =   max_ad_position_embeddings
					A__        =   coordinate_size
					A__        =   shape_size
					A__        =   has_relative_attention_bias
					A__        =   rel_pos_bins
					A__        =   max_rel_pos
					A__        =   has_spatial_attention_bias
					A__        =   rel_ad_pos_bins
					A__        =   max_rel_ad_pos
					A__        =   text_embed
					A__        =   visual_embed
					A__        =   input_size
					A__        =   num_channels
					A__        =   patch_size
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    version.parse('''1.12'''    )






			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task in ["question-answering", "sequence-classification"]:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]   )
					else:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
							    ]   )






			@property
			def    a_       (			self   :    Optional[int]   )	->	float:







					"""simple docstring"""






					return 1e-5






			@property
			def    a_       (			self   :    Tuple   )	->	int:







					"""simple docstring"""






					return 12







			def    a_       (			self   :    str							,	__lowerCAmelCase   :    "ProcessorMixin"							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    bool = False							,	__lowerCAmelCase   :    Optional["TensorType"] = None							,	__lowerCAmelCase   :    int = 3							,	__lowerCAmelCase   :    int = 40							,	__lowerCAmelCase   :    int = 40							,	)	->	Mapping[str, Any]:







					"""simple docstring"""






					setattr(processor.image_processor							,	"""apply_ocr"""							,	__lowerCAmelCase   )

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	fixed_dimension=OnnxConfig.default_fixed_batch							,	num_token_to_add=0   )
					# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
					A__        =   processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase   )
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	fixed_dimension=OnnxConfig.default_fixed_sequence							,	num_token_to_add=__lowerCAmelCase   )
					# Generate dummy inputs according to compute batch and sequence
					A__        =   [[""" """.join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

					# Generate dummy bounding boxes
					A__        =   [[[48, 84, 73, 1_28]]] * batch_size

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
					A__        =   self._generate_dummy_images(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

					A__        =   dict(
					    processor(
					        __lowerCAmelCase							,	text=__lowerCAmelCase							,	boxes=__lowerCAmelCase							,	return_tensors=__lowerCAmelCase							,	)   )

					return inputs



