




'''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


_lowercase  =			logging.get_logger(__name__)

_lowercase  =			{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}
class 						a_				(			UpperCAmelCase__						):
	lowercase_       :     List[str]			  =      '''layoutlmv3'''




	def __init__(       self     :  List[Any]       ,		__lowerCAmelCase     :  Tuple=5_0_2_6_5       ,		__lowerCAmelCase     :  int=7_6_8       ,		__lowerCAmelCase     :  List[Any]=1_2       ,		__lowerCAmelCase     :  Optional[Any]=1_2       ,		__lowerCAmelCase     :  List[str]=3_0_7_2       ,		__lowerCAmelCase     :  Any="gelu"       ,		__lowerCAmelCase     :  List[Any]=0.1       ,		__lowerCAmelCase     :  List[str]=0.1       ,		__lowerCAmelCase     :  List[Any]=5_1_2       ,		__lowerCAmelCase     :  Tuple=2       ,		__lowerCAmelCase     :  Optional[int]=0.02       ,		__lowerCAmelCase     :  Optional[Any]=1E-5       ,		__lowerCAmelCase     :  Tuple=1       ,		__lowerCAmelCase     :  str=0       ,		__lowerCAmelCase     :  List[str]=2       ,		__lowerCAmelCase     :  Any=1_0_2_4       ,		__lowerCAmelCase     :  Dict=1_2_8       ,		__lowerCAmelCase     :  Union[str, Any]=1_2_8       ,		__lowerCAmelCase     :  Union[str, Any]=True       ,		__lowerCAmelCase     :  Any=3_2       ,		__lowerCAmelCase     :  Dict=1_2_8       ,		__lowerCAmelCase     :  Optional[int]=6_4       ,		__lowerCAmelCase     :  Dict=2_5_6       ,		__lowerCAmelCase     :  int=True       ,		__lowerCAmelCase     :  Tuple=True       ,		__lowerCAmelCase     :  List[str]=True       ,		__lowerCAmelCase     :  Any=2_2_4       ,		__lowerCAmelCase     :  List[Any]=3       ,		__lowerCAmelCase     :  Tuple=1_6       ,		__lowerCAmelCase     :  str=None       ,		**__lowerCAmelCase     :  Union[str, Any]       ,		):
							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       ,		)
							__snake_case						 =   max_ad_position_embeddings
							__snake_case						 =   coordinate_size
							__snake_case						 =   shape_size
							__snake_case						 =   has_relative_attention_bias
							__snake_case						 =   rel_pos_bins
							__snake_case						 =   max_rel_pos
							__snake_case						 =   has_spatial_attention_bias
							__snake_case						 =   rel_ad_pos_bins
							__snake_case						 =   max_rel_ad_pos
							__snake_case						 =   text_embed
							__snake_case						 =   visual_embed
							__snake_case						 =   input_size
							__snake_case						 =   num_channels
							__snake_case						 =   patch_size
							__snake_case						 =   classifier_dropout





class 						a_				(			UpperCAmelCase__						):
	lowercase_       :     Any			  =      version.parse('''1.12'''						)




	@property
	def   lowercase__   (       self     :  Dict      ):
							# 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   lowercase__   (       self     :  Dict      ):
							return 1E-5




	@property
	def   lowercase__   (       self     :  Union[str, Any]      ):
							return 1_2




	def   lowercase__   (       self     :  int       ,		__lowerCAmelCase     :  "ProcessorMixin"       ,		__lowerCAmelCase     :  int = -1       ,		__lowerCAmelCase     :  int = -1       ,		__lowerCAmelCase     :  bool = False       ,		__lowerCAmelCase     :  Optional["TensorType"] = None       ,		__lowerCAmelCase     :  int = 3       ,		__lowerCAmelCase     :  int = 4_0       ,		__lowerCAmelCase     :  int = 4_0       ,		):
							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
							__snake_case						 =   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
							__snake_case						 =   processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase      )
							__snake_case						 =   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
							__snake_case						 =   [[' '.join([processor.tokenizer.unk_token]      ) * seq_length]] * batch_size

							# Generate dummy bounding boxes
							__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)
							__snake_case						 =   self._generate_dummy_images(__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase      )

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

							return inputs







'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						__snake_case						 =   [0] * len(a							)
						__snake_case						 =   []
						__snake_case						 =   []
						__snake_case						 =   0

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(a							)							):
												if indegree[i] == 0:
																		queue.append(a							)

						while queue:
												__snake_case						 =   queue.pop(0							)
												cnt += 1
												topo.append(a							)
												for x in graph[vertex]:
																		indegree[x] -= 1
																		if indegree[x] == 0:
																								queue.append(a							)

						if cnt != len(a							):
												print('Cycle exists'							)
						else:
												print(a							)


# Adjacency List of Graph
_lowercase  =			{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}






topological_sort(graph)

