


"""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


a__				:							Any						=     logging.get_logger(__name__)

a__				:							List[str]						=     {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}




class  UpperCamelCase_	( _SCREAMING_SNAKE_CASE):

	"""simple docstring"""





	snake_case__				:  Union[str, Any]    					=			"layoutlmv3"




	def __init__( self			:				Tuple			,			UpperCAmelCase__			:				Optional[int]=5_0_2_6_5			,			UpperCAmelCase__			:				Dict=7_6_8			,			UpperCAmelCase__			:				List[Any]=1_2			,			UpperCAmelCase__			:				List[str]=1_2			,			UpperCAmelCase__			:				Dict=3_0_7_2			,			UpperCAmelCase__			:				List[str]="gelu"			,			UpperCAmelCase__			:				List[str]=0.1			,			UpperCAmelCase__			:				Union[str, Any]=0.1			,			UpperCAmelCase__			:				Any=5_1_2			,			UpperCAmelCase__			:				Optional[Any]=2			,			UpperCAmelCase__			:				Tuple=0.02			,			UpperCAmelCase__			:				Union[str, Any]=1E-5			,			UpperCAmelCase__			:				Optional[Any]=1			,			UpperCAmelCase__			:				List[str]=0			,			UpperCAmelCase__			:				List[str]=2			,			UpperCAmelCase__			:				List[Any]=1_0_2_4			,			UpperCAmelCase__			:				int=1_2_8			,			UpperCAmelCase__			:				str=1_2_8			,			UpperCAmelCase__			:				Optional[int]=True			,			UpperCAmelCase__			:				List[Any]=3_2			,			UpperCAmelCase__			:				int=1_2_8			,			UpperCAmelCase__			:				Optional[int]=6_4			,			UpperCAmelCase__			:				str=2_5_6			,			UpperCAmelCase__			:				Tuple=True			,			UpperCAmelCase__			:				Dict=True			,			UpperCAmelCase__			:				Dict=True			,			UpperCAmelCase__			:				str=2_2_4			,			UpperCAmelCase__			:				List[Any]=3			,			UpperCAmelCase__			:				Any=1_6			,			UpperCAmelCase__			:				Optional[Any]=None			,			**UpperCAmelCase__			:				int			,			)       ->	Dict:
						super().__init__(
						    vocab_size=A_			,			hidden_size=A_			,			num_hidden_layers=A_			,			num_attention_heads=A_			,			intermediate_size=A_			,			hidden_act=A_			,			hidden_dropout_prob=A_			,			attention_probs_dropout_prob=A_			,			max_position_embeddings=A_			,			type_vocab_size=A_			,			initializer_range=A_			,			layer_norm_eps=A_			,			pad_token_id=A_			,			bos_token_id=A_			,			eos_token_id=A_			,			**A_			,			)
						__SCREAMING_SNAKE_CASE						   =				max_ad_position_embeddings
						__SCREAMING_SNAKE_CASE						   =				coordinate_size
						__SCREAMING_SNAKE_CASE						   =				shape_size
						__SCREAMING_SNAKE_CASE						   =				has_relative_attention_bias
						__SCREAMING_SNAKE_CASE						   =				rel_pos_bins
						__SCREAMING_SNAKE_CASE						   =				max_rel_pos
						__SCREAMING_SNAKE_CASE						   =				has_spatial_attention_bias
						__SCREAMING_SNAKE_CASE						   =				rel_ad_pos_bins
						__SCREAMING_SNAKE_CASE						   =				max_rel_ad_pos
						__SCREAMING_SNAKE_CASE						   =				text_embed
						__SCREAMING_SNAKE_CASE						   =				visual_embed
						__SCREAMING_SNAKE_CASE						   =				input_size
						__SCREAMING_SNAKE_CASE						   =				num_channels
						__SCREAMING_SNAKE_CASE						   =				patch_size
						__SCREAMING_SNAKE_CASE						   =				classifier_dropout




class  UpperCamelCase_	( _SCREAMING_SNAKE_CASE):

	"""simple docstring"""





	snake_case__				:  Union[str, Any]    					=			version.parse("1.12")




	@property
	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	Mapping[str, Mapping[int, str]]:
						# The order of inputs is different for question answering and sequence classification
						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   UpperCAmelCase_      ( self			:				int   )       ->	float:
						return 1E-5




	@property
	def   UpperCAmelCase_      ( self			:				Any   )       ->	int:
						return 1_2




	def   UpperCAmelCase_      ( self			:				Any			,			UpperCAmelCase__			:				Optional[int]			,			UpperCAmelCase__			:				Tuple = -1			,			UpperCAmelCase__			:				List[Any] = -1			,			UpperCAmelCase__			:				str = False			,			UpperCAmelCase__			:				str = None			,			UpperCAmelCase__			:				Dict = 3			,			UpperCAmelCase__			:				Optional[Any] = 4_0			,			UpperCAmelCase__			:				Any = 4_0			,			)       ->	Mapping[str, Any]:
						setattr(processor.image_processor			,			"apply_ocr"			,			A_   )

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

						# Generate dummy bounding boxes
						__SCREAMING_SNAKE_CASE						   =				[[[4_8, 8_4, 7_3, 1_2_8]]] * 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						   =				self._generate_dummy_images(A_			,			A_			,			A_			,			A_   )

						__SCREAMING_SNAKE_CASE						   =				dict(
						    processor(
						        A_			,			text=A_			,			boxes=A_			,			return_tensors=A_			,			)   )

						return inputs


from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def       A       (					)   ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase		=     {
		    'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
		    'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
		    'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
		}
		UpperCamelCase		=     Dataset.from_dict(lowercase )
		return dataset


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     get_dataset()
				UpperCamelCase		=     make_duplicate_clusters(A_				,      0.85       )
				self.assertEqual(len(duplicate_clusters[0]       )				,      2       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     get_dataset()
				UpperCamelCase	,  UpperCamelCase		=     deduplicate_dataset(A_       )
				self.assertEqual(len(A_       )				,      2       )
				print(A_       )
				self.assertEqual(duplicate_clusters[0][0]['copies']				,      2       )
				self.assertEqual(duplicate_clusters[0][0]['is_extreme']				,      A_       )
