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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a__				:      Optional[Any]    		=     logging.get_logger(__name__)

a__				:      List[Any]    		=     {
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}






class     a_       (			_snake_case							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					Any						=  """bigbird_pegasus"""
       __SCREAMING_SNAKE_CASE       :					str						=  ["""past_key_values"""]
       __SCREAMING_SNAKE_CASE       :					str						=  {
           """num_attention_heads""": """encoder_attention_heads""",
           """hidden_size""": """d_model""",
           """attention_probs_dropout_prob""": """attention_dropout""",
       }






       def __init__(		self				,       _lowerCamelCase=9_6103				,       _lowerCamelCase=4096				,       _lowerCamelCase=16				,       _lowerCamelCase=4096				,       _lowerCamelCase=16				,       _lowerCamelCase=16				,       _lowerCamelCase=4096				,       _lowerCamelCase=16				,       _lowerCamelCase=0.0				,       _lowerCamelCase=0.0				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase="gelu_new"				,       _lowerCamelCase=1024				,       _lowerCamelCase=0.1				,       _lowerCamelCase=0.0				,       _lowerCamelCase=0.0				,       _lowerCamelCase=0.0_2				,       _lowerCamelCase=2				,       _lowerCamelCase=0.0				,       _lowerCamelCase=True				,       _lowerCamelCase=0				,       _lowerCamelCase=2				,       _lowerCamelCase=1				,       _lowerCamelCase="block_sparse"				,       _lowerCamelCase=64				,       _lowerCamelCase=3				,       _lowerCamelCase=False				,       **_lowerCamelCase				,       )					->int:
           SCREAMING_SNAKE_CASE				:				Tuple				      = vocab_size
           SCREAMING_SNAKE_CASE				:				Dict				      = max_position_embeddings
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = d_model
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = encoder_ffn_dim
           SCREAMING_SNAKE_CASE				:				List[Any]				      = encoder_layers
           SCREAMING_SNAKE_CASE				:				Dict				      = encoder_attention_heads
           SCREAMING_SNAKE_CASE				:				List[Any]				      = decoder_ffn_dim
           SCREAMING_SNAKE_CASE				:				List[Any]				      = decoder_layers
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = decoder_attention_heads
           SCREAMING_SNAKE_CASE				:				Dict				      = dropout
           SCREAMING_SNAKE_CASE				:				str				      = attention_dropout
           SCREAMING_SNAKE_CASE				:				Tuple				      = activation_dropout
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = activation_function
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = init_std
           SCREAMING_SNAKE_CASE				:				str				      = encoder_layerdrop
           SCREAMING_SNAKE_CASE				:				List[str]				      = decoder_layerdrop
           SCREAMING_SNAKE_CASE				:				Any				      = classifier_dropout
           SCREAMING_SNAKE_CASE				:				List[str]				      = use_cache
           SCREAMING_SNAKE_CASE				:				Dict				      = encoder_layers
           SCREAMING_SNAKE_CASE				:				str				      = scale_embedding  # scale factor will be sqrt(d_model) if True

           # extra config
           SCREAMING_SNAKE_CASE				:				List[Any]				      = attention_type
           SCREAMING_SNAKE_CASE				:				int				      = block_size
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = num_random_blocks
           SCREAMING_SNAKE_CASE				:				Tuple				      = use_bias

           super().__init__(
               pad_token_id=snake_case_				,       bos_token_id=snake_case_				,       eos_token_id=snake_case_				,       is_encoder_decoder=snake_case_				,       decoder_start_token_id=snake_case_				,       **snake_case_				,       )






class     a_       (			_snake_case							):



       """simple docstring"""






       @property
       def 	__lowerCAmelCase     (		self   )					->Any:
           if self.task in ["default", "seq2seq-lm"]:
               SCREAMING_SNAKE_CASE				:				Tuple				      = OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ]   )

               if self.use_past:
                   SCREAMING_SNAKE_CASE				:				List[str]				      = {0: "batch"}
                   SCREAMING_SNAKE_CASE				:				str				      = {0: "batch", 1: "past_decoder_sequence + sequence"}
               else:
                   SCREAMING_SNAKE_CASE				:				int				      = {0: "batch", 1: "decoder_sequence"}
                   SCREAMING_SNAKE_CASE				:				Dict				      = {0: "batch", 1: "decoder_sequence"}

               if self.use_past:
                   self.fill_with_past_key_values_(snake_case_				,       direction='''inputs'''   )
           elif self.task == "causal-lm":
               # TODO: figure this case out.
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ]   )
               if self.use_past:
                   SCREAMING_SNAKE_CASE				:				Tuple				      = self.num_layers
                   for i in range(snake_case_   ):
                       SCREAMING_SNAKE_CASE				:				int				      = {0: "batch", 2: "past_sequence + sequence"}
                       SCREAMING_SNAKE_CASE				:				Optional[Any]				      = {0: "batch", 2: "past_sequence + sequence"}
           else:
               SCREAMING_SNAKE_CASE				:				List[Any]				      = OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                       ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                   ]   )

           return common_inputs






       @property
       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           if self.task in ["default", "seq2seq-lm"]:
               SCREAMING_SNAKE_CASE				:				int				      = super().outputs
           else:
               SCREAMING_SNAKE_CASE				:				str				      = super(snake_case_				,       self   ).outputs
               if self.use_past:
                   SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.num_layers
                   for i in range(snake_case_   ):
                       SCREAMING_SNAKE_CASE				:				Dict				      = {0: "batch", 2: "past_sequence + sequence"}
                       SCREAMING_SNAKE_CASE				:				str				      = {0: "batch", 2: "past_sequence + sequence"}
           return common_outputs






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase = -1				,       _lowerCamelCase = -1				,       _lowerCamelCase = False				,       _lowerCamelCase = None				,       )					->Any:
           SCREAMING_SNAKE_CASE				:				List[str]				      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               snake_case_				,       snake_case_				,       snake_case_				,       snake_case_				,       snake_case_   )

           # Generate decoder inputs
           SCREAMING_SNAKE_CASE				:				List[str]				      = seq_length if not self.use_past else 1
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               snake_case_				,       snake_case_				,       snake_case_				,       snake_case_				,       snake_case_   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dict(**snake_case_				,       **snake_case_   )

           if self.use_past:
               if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
               else:
                   import torch
               SCREAMING_SNAKE_CASE				:				Any				      = common_inputs["input_ids"].shape
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = common_inputs["decoder_input_ids"].shape[1]
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.num_attention_heads
               SCREAMING_SNAKE_CASE				:				Tuple				      = (
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = decoder_seq_length + 3
               SCREAMING_SNAKE_CASE				:				List[str]				      = (
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               SCREAMING_SNAKE_CASE				:				Tuple				      = torch.cat(
                   [common_inputs['''decoder_attention_mask'''], torch.ones(snake_case_				,       snake_case_   )]				,       dim=1   )

               SCREAMING_SNAKE_CASE				:				List[Any]				      = []
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.num_layers
               SCREAMING_SNAKE_CASE				:				Any				      = min(snake_case_				,       snake_case_   )
               SCREAMING_SNAKE_CASE				:				str				      = max(snake_case_				,       snake_case_   ) - min_num_layers
               SCREAMING_SNAKE_CASE				:				List[Any]				      = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

               for _ in range(snake_case_   ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(snake_case_   ),
                           torch.zeros(snake_case_   ),
                           torch.zeros(snake_case_   ),
                           torch.zeros(snake_case_   ),
                       )   )
               # TODO: test this.
               SCREAMING_SNAKE_CASE				:				List[Any]				      = encoder_shape if remaining_side_name == "encoder" else decoder_shape
               for _ in range(snake_case_				,       snake_case_   ):
                   common_inputs["past_key_values"].append((torch.zeros(snake_case_   ), torch.zeros(snake_case_   ))   )
           return common_inputs






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase = -1				,       _lowerCamelCase = -1				,       _lowerCamelCase = False				,       _lowerCamelCase = None				,       )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				str				      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               snake_case_				,       snake_case_				,       snake_case_				,       snake_case_				,       snake_case_   )

           if self.use_past:
               if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
               else:
                   import torch
               SCREAMING_SNAKE_CASE				:				List[Any]				      = common_inputs["input_ids"].shape
               # Not using the same length for past_key_values
               SCREAMING_SNAKE_CASE				:				List[str]				      = seqlen + 2
               SCREAMING_SNAKE_CASE				:				str				      = self.num_layers
               SCREAMING_SNAKE_CASE				:				Dict				      = self.num_attention_heads
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = (
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = common_inputs["attention_mask"].dtype
               SCREAMING_SNAKE_CASE				:				Tuple				      = torch.cat(
                   [common_inputs['''attention_mask'''], torch.ones(snake_case_				,       snake_case_				,       dtype=snake_case_   )]				,       dim=1   )
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = [
                   (torch.zeros(snake_case_   ), torch.zeros(snake_case_   )) for _ in range(snake_case_   )
               ]
           return common_inputs






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase = -1				,       _lowerCamelCase = -1				,       _lowerCamelCase = False				,       _lowerCamelCase = None				,       )					->int:
           # Copied from OnnxConfig.generate_dummy_inputs
           # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           SCREAMING_SNAKE_CASE				:				List[Any]				      = compute_effective_axis_dimension(
               snake_case_				,       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]				      = tokenizer.num_special_tokens_to_add(snake_case_   )
           SCREAMING_SNAKE_CASE				:				Any				      = compute_effective_axis_dimension(
               snake_case_				,       fixed_dimension=OnnxConfig.default_fixed_sequence				,       num_token_to_add=snake_case_   )

           # Generate dummy inputs according to compute batch and sequence
           SCREAMING_SNAKE_CASE				:				List[str]				      = [" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
           SCREAMING_SNAKE_CASE				:				Tuple				      = dict(tokenizer(snake_case_				,       return_tensors=snake_case_   )   )
           return common_inputs






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase = -1				,       _lowerCamelCase = -1				,       _lowerCamelCase = False				,       _lowerCamelCase = None				,       )					->Union[str, Any]:
           if self.task in ["default", "seq2seq-lm"]:
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   snake_case_				,       batch_size=snake_case_				,       seq_length=snake_case_				,       is_pair=snake_case_				,       framework=snake_case_   )

           elif self.task == "causal-lm":
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self._generate_dummy_inputs_for_causal_lm(
                   snake_case_				,       batch_size=snake_case_				,       seq_length=snake_case_				,       is_pair=snake_case_				,       framework=snake_case_   )
           else:
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                   snake_case_				,       batch_size=snake_case_				,       seq_length=snake_case_				,       is_pair=snake_case_				,       framework=snake_case_   )

           return common_inputs






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->Any:
           if self.task in ["default", "seq2seq-lm"]:
               SCREAMING_SNAKE_CASE				:				int				      = super()._flatten_past_key_values_(snake_case_				,       snake_case_				,       snake_case_				,       snake_case_   )
           else:
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = super(snake_case_				,       self   )._flatten_past_key_values_(
                   snake_case_				,       snake_case_				,       snake_case_				,       snake_case_   )


"""simple docstring"""
from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
_a   :      List[str]       =				{
    """1""": ["""2""", """3""", """4""", """5"""],
    """2""": ["""1""", """3""", """4""", """5"""],
    """3""": ["""1""", """2""", """4""", """5""", """10"""],
    """4""": ["""1""", """2""", """3""", """5""", """6"""],
    """5""": ["""1""", """2""", """3""", """4""", """7"""],
    """6""": ["""7""", """8""", """9""", """10""", """4"""],
    """7""": ["""6""", """8""", """9""", """10""", """5"""],
    """8""": ["""6""", """7""", """9""", """10"""],
    """9""": ["""6""", """7""", """8""", """10"""],
    """10""": ["""6""", """7""", """8""", """9""", """3"""],
}
def 					a__ ( a :					dict[str, list[str]]		):

  """simple docstring"""







  _snake_case  :    List[Any]            = {node: {node} for node in graph}

  _snake_case  :    str            = {node: graph[node][:] for node in graph}

  while len(a		) > 2:
    # Choose a random edge.
    _snake_case  :    str            = random.choice(list(graph_copy.keys()		)		)
    _snake_case  :    Union[str, Any]            = random.choice(graph_copy[u]		)

    # Contract edge (u, v) to new node uv
    _snake_case  :    Union[str, Any]            = u + v
    _snake_case  :    Union[str, Any]            = list(set(graph_copy[u] + graph_copy[v]		)		)
    uv_neighbors.remove(a		)
    uv_neighbors.remove(a		)
    _snake_case  :    Union[str, Any]            = uv_neighbors
    for neighbor in uv_neighbors:
      graph_copy[neighbor].append(a		)

    _snake_case  :    Dict            = set(contracted_nodes[u].union(contracted_nodes[v]		)		)

    # Remove nodes u and v.
    del graph_copy[u]
    del graph_copy[v]
    for neighbor in uv_neighbors:
      if u in graph_copy[neighbor]:
        graph_copy[neighbor].remove(a		)
      if v in graph_copy[neighbor]:
        graph_copy[neighbor].remove(a		)

    # Find cutset.
  _snake_case  :    List[Any]            = [contracted_nodes[node] for node in graph_copy]
  return {
      (node, neighbor)
      for node in groups[0]
      for neighbor in graph[node]
      if neighbor in groups[1]
  }


if __name__ == "__main__":
    print(partition_graph(TEST_GRAPH))

