
"""simple docstring"""






import re
import string

import numpy as np

import datasets


lowerCamelCase__ :       List[Any]						     =			"\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

lowerCamelCase__ :       int						     =			"\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

lowerCamelCase__ :       List[str]						     =			"\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,						_KWARGS_DESCRIPTION   )
class 				lowercase__(				datasets.Metric   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :str					)							->			Any:







			'''simple docstring'''




			return datasets.MetricInfo(
			    description=_DESCRIPTION		, citation=_CITATION		, inputs_description=_KWARGS_DESCRIPTION		, features=datasets.Features(
			        {
			            '''predictions''': datasets.Value('''string'''		, id='''sequence'''					),
			            '''references''': datasets.Value('''string'''		, id='''sequence'''					),
			        }					)		, reference_urls=[]		, )







	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :int=None		, lowerCamelCase_    :List[Any]=False		, lowerCamelCase_    :Union[str, Any]=False		, lowerCamelCase_    :int=False		, )							->			Any:







			'''simple docstring'''




			if regexes_to_ignore is not None:
					for s in regexes_to_ignore:
							SCREAMING_SNAKE_CASE       :     List[Any]				= np.array([re.sub(lowerCamelCase_		, ''''''		, lowerCamelCase_					) for x in predictions]					)
							SCREAMING_SNAKE_CASE       :     Any				= np.array([re.sub(lowerCamelCase_		, ''''''		, lowerCamelCase_					) for x in references]					)
			else:
					SCREAMING_SNAKE_CASE       :     Dict				= np.asarray(lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= np.asarray(lowerCamelCase_					)

			if ignore_case:
					SCREAMING_SNAKE_CASE       :     List[Any]				= np.char.lower(lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     List[str]				= np.char.lower(lowerCamelCase_					)

			if ignore_punctuation:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= string.punctuation.maketrans(''''''		, ''''''		, string.punctuation					)
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= np.char.translate(lowerCamelCase_		, table=lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     str				= np.char.translate(lowerCamelCase_		, table=lowerCamelCase_					)

			if ignore_numbers:
					SCREAMING_SNAKE_CASE       :     str				= string.digits.maketrans(''''''		, ''''''		, string.digits					)
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= np.char.translate(lowerCamelCase_		, table=lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= np.char.translate(lowerCamelCase_		, table=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     int				= predictions == references

			return {"exact_match": np.mean(lowerCamelCase_					) * 1_00}







"""simple docstring"""






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


lowerCamelCase__ :       Dict						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Dict						     =			{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """layoutlmv3"""





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







			'''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_		, )
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= max_ad_position_embeddings
			SCREAMING_SNAKE_CASE       :     List[Any]				= coordinate_size
			SCREAMING_SNAKE_CASE       :     Tuple				= shape_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= has_relative_attention_bias
			SCREAMING_SNAKE_CASE       :     List[Any]				= rel_pos_bins
			SCREAMING_SNAKE_CASE       :     int				= max_rel_pos
			SCREAMING_SNAKE_CASE       :     Any				= has_spatial_attention_bias
			SCREAMING_SNAKE_CASE       :     List[Any]				= rel_ad_pos_bins
			SCREAMING_SNAKE_CASE       :     Dict				= max_rel_ad_pos
			SCREAMING_SNAKE_CASE       :     Optional[int]				= text_embed
			SCREAMING_SNAKE_CASE       :     Any				= visual_embed
			SCREAMING_SNAKE_CASE       :     Any				= input_size
			SCREAMING_SNAKE_CASE       :     Tuple				= num_channels
			SCREAMING_SNAKE_CASE       :     List[str]				= patch_size
			SCREAMING_SNAKE_CASE       :     str				= classifier_dropout





class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  version.parse("""1.12"""   )





	@property
	def   __lowerCAmelCase (						self    :List[Any]					)							->			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   __lowerCAmelCase (						self    :Optional[int]					)							->			float:







			'''simple docstring'''




			return 1E-5





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







			'''simple docstring'''




			return 12







	def   __lowerCAmelCase (						self    :List[Any]		, 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
			SCREAMING_SNAKE_CASE       :     Dict				= 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
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= processor.tokenizer.num_special_tokens_to_add(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Any				= 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
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [[''' '''.join([processor.tokenizer.unk_token]					) * seq_length]] * batch_size

			# Generate dummy bounding boxes
			SCREAMING_SNAKE_CASE       :     int				= [[[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)
			SCREAMING_SNAKE_CASE       :     List[Any]				= self._generate_dummy_images(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     int				= dict(
			    processor(
			        lowerCamelCase_		, text=lowerCamelCase_		, boxes=lowerCamelCase_		, return_tensors=lowerCamelCase_		, )					)

			return inputs





