





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


lowerCAmelCase_   =   logging.get_logger(__name__)

lowerCAmelCase_   =   {
    "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    (__UpperCAmelCase				):
      _SCREAMING_SNAKE_CASE					=							"""bigbird_pegasus"""
      _SCREAMING_SNAKE_CASE					=							["""past_key_values"""]
      _SCREAMING_SNAKE_CASE					=							{
          """num_attention_heads""": """encoder_attention_heads""",
          """hidden_size""": """d_model""",
          """attention_probs_dropout_prob""": """attention_dropout""",
      }



      def __init__(					self     ,  lowercase_=9_6103     ,  lowercase_=4096     ,  lowercase_=16     ,  lowercase_=4096     ,  lowercase_=16     ,  lowercase_=16     ,  lowercase_=4096     ,  lowercase_=16     ,  lowercase_=0.0     ,  lowercase_=0.0     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_="gelu_new"     ,  lowercase_=1024     ,  lowercase_=0.1     ,  lowercase_=0.0     ,  lowercase_=0.0     ,  lowercase_=0.02     ,  lowercase_=2     ,  lowercase_=0.0     ,  lowercase_=True     ,  lowercase_=0     ,  lowercase_=2     ,  lowercase_=1     ,  lowercase_="block_sparse"     ,  lowercase_=64     ,  lowercase_=3     ,  lowercase_=False     ,  **lowercase_     ,  )      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						vocab_size
             _snake_case      :							Optional[Any]     				=						max_position_embeddings
             _snake_case      :							Any     				=						d_model
             _snake_case      :							Dict     				=						encoder_ffn_dim
             _snake_case      :							Tuple     				=						encoder_layers
             _snake_case      :							Dict     				=						encoder_attention_heads
             _snake_case      :							str     				=						decoder_ffn_dim
             _snake_case      :							str     				=						decoder_layers
             _snake_case      :							Tuple     				=						decoder_attention_heads
             _snake_case      :							str     				=						dropout
             _snake_case      :							List[str]     				=						attention_dropout
             _snake_case      :							Dict     				=						activation_dropout
             _snake_case      :							str     				=						activation_function
             _snake_case      :							Union[str, Any]     				=						init_std
             _snake_case      :							Optional[int]     				=						encoder_layerdrop
             _snake_case      :							Tuple     				=						decoder_layerdrop
             _snake_case      :							Optional[int]     				=						classifier_dropout
             _snake_case      :							str     				=						use_cache
             _snake_case      :							List[Any]     				=						encoder_layers
             _snake_case      :							List[str]     				=						scale_embedding  # scale factor will be sqrt(d_model) if True

             # extra config
             _snake_case      :							Optional[Any]     				=						attention_type
             _snake_case      :							Any     				=						block_size
             _snake_case      :							Any     				=						num_random_blocks
             _snake_case      :							Optional[int]     				=						use_bias

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


class 				A    (__UpperCAmelCase				):



      @property
      def    __a (					self					)      ->   Mapping[str, Mapping[int, str]]:



             '''simple docstring'''

             if self.task in ["default", "seq2seq-lm"]:
                    _snake_case      :							Dict     				=						OrderedDict(
                        [
                            ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                            ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                        ]					)

                    if self.use_past:
                           _snake_case      :							Any     				=						{0: '''batch'''}
                           _snake_case      :							Optional[int]     				=						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
                    else:
                           _snake_case      :							Union[str, Any]     				=						{0: '''batch''', 1: '''decoder_sequence'''}
                           _snake_case      :							Any     				=						{0: '''batch''', 1: '''decoder_sequence'''}

                    if self.use_past:
                           self.fill_with_past_key_values_(lowercase_     ,  direction='''inputs'''					)
             elif self.task == "causal-lm":
                    # TODO: figure this case out.
                    _snake_case      :							Union[str, Any]     				=						OrderedDict(
                        [
                            ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                            ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                        ]					)
                    if self.use_past:
                           _snake_case				,    _snake_case      :							List[str]     				=						self.num_layers
                           for i in range(lowercase_					):
                                  _snake_case      :							Dict     				=						{0: '''batch''', 2: '''past_sequence + sequence'''}
                                  _snake_case      :							int     				=						{0: '''batch''', 2: '''past_sequence + sequence'''}
             else:
                    _snake_case      :							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    __a (					self					)      ->   Mapping[str, Mapping[int, str]]:



             '''simple docstring'''

             if self.task in ["default", "seq2seq-lm"]:
                    _snake_case      :							str     				=						super().outputs
             else:
                    _snake_case      :							Optional[Any]     				=						super(lowercase_     ,  self					).outputs
                    if self.use_past:
                           _snake_case				,    _snake_case      :							Union[str, Any]     				=						self.num_layers
                           for i in range(lowercase_					):
                                  _snake_case      :							int     				=						{0: '''batch''', 2: '''past_sequence + sequence'''}
                                  _snake_case      :							List[Any]     				=						{0: '''batch''', 2: '''past_sequence + sequence'''}
             return common_outputs



      def    __a (					self     ,  lowercase_     ,  lowercase_ = -1     ,  lowercase_ = -1     ,  lowercase_ = False     ,  lowercase_ = None     ,  )      ->   Mapping[str, Any]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)

             # Generate decoder inputs
             _snake_case      :							int     				=						seq_length if not self.use_past else 1
             _snake_case      :							Union[str, Any]     				=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)
             _snake_case      :							int     				=						{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
             _snake_case      :							int     				=						dict(**lowercase_     ,  **lowercase_					)

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

                    _snake_case      :							List[str]     				=						torch.cat(
                        [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_     ,  lowercase_					)]     ,  dim=1					)

                    _snake_case      :							Optional[int]     				=						[]
                    # If the number of encoder and decoder layers are present in the model configuration, both are considered
                    _snake_case				,    _snake_case      :							int     				=						self.num_layers
                    _snake_case      :							Tuple     				=						min(lowercase_     ,  lowercase_					)
                    _snake_case      :							Union[str, Any]     				=						max(lowercase_     ,  lowercase_					) - min_num_layers
                    _snake_case      :							Dict     				=						'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

                    for _ in range(lowercase_					):
                           common_inputs["past_key_values"].append(
                               (
                                   torch.zeros(lowercase_					),
                                   torch.zeros(lowercase_					),
                                   torch.zeros(lowercase_					),
                                   torch.zeros(lowercase_					),
                               )					)
                    # TODO: test this.
                    _snake_case      :							Optional[int]     				=						encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
                    for _ in range(lowercase_     ,  lowercase_					):
                           common_inputs["past_key_values"].append((torch.zeros(lowercase_					), torch.zeros(lowercase_					))					)
             return common_inputs



      def    __a (					self     ,  lowercase_     ,  lowercase_ = -1     ,  lowercase_ = -1     ,  lowercase_ = False     ,  lowercase_ = None     ,  )      ->   Mapping[str, Any]:



             '''simple docstring'''

             _snake_case      :							str     				=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)

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

                    _snake_case      :							List[str]     				=						common_inputs['''attention_mask'''].dtype
                    _snake_case      :							Tuple     				=						torch.cat(
                        [common_inputs['''attention_mask'''], torch.ones(lowercase_     ,  lowercase_     ,  dtype=lowercase_					)]     ,  dim=1					)
                    _snake_case      :							Dict     				=						[
                        (torch.zeros(lowercase_					), torch.zeros(lowercase_					)) for _ in range(lowercase_					)
                    ]
             return common_inputs



      def    __a (					self     ,  lowercase_     ,  lowercase_ = -1     ,  lowercase_ = -1     ,  lowercase_ = False     ,  lowercase_ = None     ,  )      ->   Mapping[str, Any]:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						compute_effective_axis_dimension(
                 lowercase_     ,  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      :							str     				=						tokenizer.num_special_tokens_to_add(lowercase_					)
             _snake_case      :							Any     				=						compute_effective_axis_dimension(
                 lowercase_     ,  fixed_dimension=OnnxConfig.default_fixed_sequence     ,  num_token_to_add=lowercase_					)

             # Generate dummy inputs according to compute batch and sequence
             _snake_case      :							int     				=						[''' '''.join([tokenizer.unk_token]					) * seq_length] * batch_size
             _snake_case      :							List[Any]     				=						dict(tokenizer(lowercase_     ,  return_tensors=lowercase_					)					)
             return common_inputs



      def    __a (					self     ,  lowercase_     ,  lowercase_ = -1     ,  lowercase_ = -1     ,  lowercase_ = False     ,  lowercase_ = None     ,  )      ->   Mapping[str, Any]:



             '''simple docstring'''

             if self.task in ["default", "seq2seq-lm"]:
                    _snake_case      :							Tuple     				=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                        lowercase_     ,  batch_size=lowercase_     ,  seq_length=lowercase_     ,  is_pair=lowercase_     ,  framework=lowercase_					)

             elif self.task == "causal-lm":
                    _snake_case      :							int     				=						self._generate_dummy_inputs_for_causal_lm(
                        lowercase_     ,  batch_size=lowercase_     ,  seq_length=lowercase_     ,  is_pair=lowercase_     ,  framework=lowercase_					)
             else:
                    _snake_case      :							Dict     				=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                        lowercase_     ,  batch_size=lowercase_     ,  seq_length=lowercase_     ,  is_pair=lowercase_     ,  framework=lowercase_					)

             return common_inputs



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   Union[str, Any]:



             '''simple docstring'''

             if self.task in ["default", "seq2seq-lm"]:
                    _snake_case      :							int     				=						super()._flatten_past_key_values_(lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)
             else:
                    _snake_case      :							Optional[int]     				=						super(lowercase_     ,  self					)._flatten_past_key_values_(
                        lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)













from __future__ import annotations

class 				A    :



      def __init__(					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						vertices
             _snake_case      :							int     				=						[[0] * vertices for _ in range(lowercase_					)]



      def    __a (					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             print('''Vertex \t Distance from Source'''					)
             for vertex in range(self.vertices					):
                    print(lowercase_     ,  '''\t\t'''     ,  distances_from_source[vertex]					)



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   int:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						1E7
             _snake_case      :							Tuple     				=						0

             # Search not nearest vertex not in the shortest path tree
             for vertex in range(self.vertices					):
                    if distances_from_source[vertex] < minimum and visited[vertex] is False:
                           _snake_case      :							Dict     				=						distances_from_source[vertex]
                           _snake_case      :							int     				=						vertex
             return min_index



      def    __a (					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						[int(1E7					)] * self.vertices  # distances from the source
             _snake_case      :							List[Any]     				=						0
             _snake_case      :							List[str]     				=						[False] * self.vertices

             for _ in range(self.vertices					):
                    _snake_case      :							List[Any]     				=						self.minimum_distance(lowercase_     ,  lowercase_					)
                    _snake_case      :							List[str]     				=						True

                    # Update dist value of the adjacent vertices
                    # of the picked vertex only if the current
                    # distance is greater than new distance and
                    # the vertex in not in the shortest path tree
                    for v in range(self.vertices					):
                           if (
                               self.graph[u][v] > 0
                               and visited[v] is False
                               and distances[v] > distances[u] + self.graph[u][v]
                           ):
                                  _snake_case      :							Any     				=						distances[u] + self.graph[u][v]

             self.print_solution(lowercase_					)


if __name__ == "__main__":
   lowerCAmelCase_   =   Graph(9)
   lowerCAmelCase_   =   [
       [0, 4, 0, 0, 0, 0, 0, 8, 0],
       [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
       [0, 8, 0, 7, 0, 4, 0, 0, 2],
       [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
       [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
       [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
       [0, 0, 0, 0, 0, 2, 0, 1, 6],
       [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
       [0, 0, 2, 0, 0, 0, 6, 7, 0],
   ]
   graph.dijkstra(0)






