


"""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


UpperCAmelCase__					=logging.get_logger(__name__)

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





class  lowerCamelCase__    (		_a     ):
      a			:  int           =		"""layoutlmv3"""
      def __init__(		self						:						str   ,							A_						:						Dict=5_0_2_6_5   ,							A_						:						List[Any]=7_6_8   ,							A_						:						List[Any]=1_2   ,							A_						:						List[str]=1_2   ,							A_						:						Optional[Any]=3_0_7_2   ,							A_						:						int="gelu"   ,							A_						:						List[str]=0.1   ,							A_						:						List[Any]=0.1   ,							A_						:						int=5_1_2   ,							A_						:						List[str]=2   ,							A_						:						List[str]=0.02   ,							A_						:						Union[str, Any]=1e-5   ,							A_						:						int=1   ,							A_						:						Optional[Any]=0   ,							A_						:						Union[str, Any]=2   ,							A_						:						Optional[Any]=1_0_2_4   ,							A_						:						Optional[int]=1_2_8   ,							A_						:						Optional[int]=1_2_8   ,							A_						:						str=True   ,							A_						:						List[Any]=3_2   ,							A_						:						List[str]=1_2_8   ,							A_						:						Any=6_4   ,							A_						:						Any=2_5_6   ,							A_						:						Union[str, Any]=True   ,							A_						:						Any=True   ,							A_						:						Optional[Any]=True   ,							A_						:						Optional[int]=2_2_4   ,							A_						:						Tuple=3   ,							A_						:						Optional[Any]=1_6   ,							A_						:						Tuple=None   ,							**A_						:						Union[str, Any]   ,							):






       '''simple docstring'''


       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_   ,							)
       __lowercase						=						max_ad_position_embeddings
       __lowercase						=						coordinate_size
       __lowercase						=						shape_size
       __lowercase						=						has_relative_attention_bias
       __lowercase						=						rel_pos_bins
       __lowercase						=						max_rel_pos
       __lowercase						=						has_spatial_attention_bias
       __lowercase						=						rel_ad_pos_bins
       __lowercase						=						max_rel_ad_pos
       __lowercase						=						text_embed
       __lowercase						=						visual_embed
       __lowercase						=						input_size
       __lowercase						=						num_channels
       __lowercase						=						patch_size
       __lowercase						=						classifier_dropout






class  lowerCamelCase__    (		_a     ):
      a			:  str           =		version.parse("""1.12"""     )
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[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 SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       return 1e-5
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       return 1_2






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						"ProcessorMixin"   ,							A_						:						int = -1   ,							A_						:						int = -1   ,							A_						:						bool = False   ,							A_						:						Optional["TensorType"] = None   ,							A_						:						int = 3   ,							A_						:						int = 4_0   ,							A_						:						int = 4_0   ,							):






       '''simple docstring'''


       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
       __lowercase						=						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
       __lowercase						=						processor.tokenizer.num_special_tokens_to_add(A_				)
       __lowercase						=						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
       __lowercase						=						[[""" """.join([processor.tokenizer.unk_token]				) * seq_length]] * batch_size

       # Generate dummy bounding boxes
       __lowercase						=						[[[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)
       __lowercase						=						self._generate_dummy_images(A_   ,							A_   ,							A_   ,							A_				)

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

       return inputs




"""simple docstring"""

from __future__ import annotations


def lowerCAmelCase_   (					UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			Any			):






 """simple docstring"""







 print(f'''Vertex\tShortest Distance from vertex {src}'''			)
 for i, d in enumerate(UpperCamelCase__			):
  print(f'''{i}\t\t{d}'''			)


def lowerCAmelCase_   (					UpperCamelCase__						:			list[dict[str, int]]					, UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			int			):






 """simple docstring"""







 for j in range(UpperCamelCase__			):
  __lowercase				,		__lowercase				,		__lowercase						=						(graph[j][k] for k in ["""src""", """dst""", """weight"""])
  if distance[u] != float("""inf"""			) and distance[u] + w < distance[v]:
   return True
 return False


def lowerCAmelCase_   (					UpperCamelCase__						:			list[dict[str, int]]					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						[float("""inf"""			)] * vertex_count
 __lowercase						=						0.0

 for _ in range(vertex_count - 1			):
  for j in range(UpperCamelCase__			):
   __lowercase				,		__lowercase				,		__lowercase						=						(graph[j][k] for k in ["""src""", """dst""", """weight"""])

   if distance[u] != float("""inf"""			) and distance[u] + w < distance[v]:
    __lowercase						=						distance[u] + w

 __lowercase						=						check_negative_cycle(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)
 if negative_cycle_exists:
  raise Exception("""Negative cycle found"""			)

 return distance


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       UpperCAmelCase__					=int(input("Enter number of vertices: ").strip())
       UpperCAmelCase__					=int(input("Enter number of edges: ").strip())

       UpperCAmelCase__					=[{} for _ in range(E)]

       for i in range(E):
              print("Edge ", i + 1)
              UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__					=(
                  int(x)
                  for x in input("Enter source, destination, weight: ").strip().split(" ")
              )
              UpperCAmelCase__					={"src": src, "dst": dest, "weight": weight}

       UpperCAmelCase__					=int(input("\nEnter shortest path source:").strip())
       UpperCAmelCase__					=bellman_ford(graph, V, E, source)
       print_distance(shortest_distance, 0)
