


import heapq
def 				__lowerCamelCase						( UpperCAmelCase_						:		dict						):




      """simple docstring"""





      a						:list[list] 							=       []

      # for each node and his adjacency list add them and the rank of the node to queue
      # using heapq module the queue will be filled like a Priority Queue
      # heapq works with a min priority queue, so I used -1*len(v) to build it
      for key, value in graph.items():
            # O(log(n))
            heapq.heappush(UpperCAmelCase_ ,  [-1 * len(UpperCAmelCase_						), (key, value)]						)

      # chosen_vertices = set of chosen vertices
      a						:Optional[Any] 							=       set()

      # while queue isn't empty and there are still edges
      #   (queue[0][0] is the rank of the node with max rank)
      while queue and queue[0][0] != 0:
            # extract vertex with max rank from queue and add it to chosen_vertices
            a						:Tuple 							=       heapq.heappop(UpperCAmelCase_						)[1][0]
            chosen_vertices.add(UpperCAmelCase_						)

            # Remove all arcs adjacent to argmax
            for elem in queue:
                  # if v haven't adjacent node, skip
                  if elem[0] == 0:
                        continue
                  # if argmax is reachable from elem
                  # remove argmax from elem's adjacent list and update his rank
                  if argmax in elem[1][1]:
                        a						:Optional[Any] 							=       elem[1][1].index(UpperCAmelCase_						)
                        del elem[1][1][index]
                        elem[0] += 1
        # re-order the queue
            heapq.heapify(UpperCAmelCase_						)
      return chosen_vertices


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     snake_case				:    Optional[Any]       				=     {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
     print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")









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

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


snake_case				:    List[str]       				=     logging.get_logger(__name__)

snake_case				:    int       				=     {
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'blenderbot-small'
 SCREAMING_SNAKE_CASE__      =       ['past_key_values']
 SCREAMING_SNAKE_CASE__      =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




 def __init__(		self					,  _lowerCamelCase=5_0265					,  _lowerCamelCase=512					,  _lowerCamelCase=8					,  _lowerCamelCase=2048					,  _lowerCamelCase=16					,  _lowerCamelCase=8					,  _lowerCamelCase=2048					,  _lowerCamelCase=16					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.0					,  _lowerCamelCase=True					,  _lowerCamelCase=True					,  _lowerCamelCase="gelu"					,  _lowerCamelCase=512					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.02					,  _lowerCamelCase=1					,  _lowerCamelCase=False					,  _lowerCamelCase=0					,  _lowerCamelCase=1					,  _lowerCamelCase=2					,  _lowerCamelCase=2					,  **_lowerCamelCase					,  ):
       a						:Dict 							=       vocab_size
       a						:Optional[Any] 							=       max_position_embeddings
       a						:str 							=       d_model
       a						:Any 							=       encoder_ffn_dim
       a						:Optional[int] 							=       encoder_layers
       a						:List[str] 							=       encoder_attention_heads
       a						:List[str] 							=       decoder_ffn_dim
       a						:Optional[int] 							=       decoder_layers
       a						:str 							=       decoder_attention_heads
       a						:List[str] 							=       dropout
       a						:Optional[int] 							=       attention_dropout
       a						:Dict 							=       activation_dropout
       a						:List[str] 							=       activation_function
       a						:List[Any] 							=       init_std
       a						:Optional[int] 							=       encoder_layerdrop
       a						:Tuple 							=       decoder_layerdrop
       a						:List[str] 							=       use_cache
       a						:int 							=       encoder_layers
       a						:Union[str, Any] 							=       scale_embedding  # scale factor will be sqrt(d_model) if True

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







class 		_snake_case  (      _snake_case							):




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

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

             if self.use_past:
                   self.fill_with_past_key_values_(_lowerCamelCase					,  direction='''inputs'''    )
       elif self.task == "causal-lm":
             # TODO: figure this case out.
             a						:Optional[int] 							=       OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]    )
             if self.use_past:
                   a							,				a						:str 							=       self.num_layers
                   for i in range(_lowerCamelCase    ):
                         a						:List[Any] 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
                         a						:List[str] 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
       else:
             a						:Optional[int] 							=       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        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:List[Any] 							=       super().outputs
       else:
             a						:Union[str, Any] 							=       super(_lowerCamelCase					,  self    ).outputs
             if self.use_past:
                   a							,				a						:int 							=       self.num_layers
                   for i in range(_lowerCamelCase    ):
                         a						:int 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
                         a						:Optional[Any] 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
       return common_outputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       a						:Tuple 							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )

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

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

             a						:Union[str, Any] 							=       torch.cat(
                 [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCamelCase					,  _lowerCamelCase    )]					,  dim=1    )

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

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




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       a						:Tuple 							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )

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

             a						:Tuple 							=       common_inputs['''attention_mask'''].dtype
             a						:Any 							=       torch.cat(
                 [common_inputs['''attention_mask'''], torch.ones(_lowerCamelCase					,  _lowerCamelCase					,  dtype=_lowerCamelCase    )]					,  dim=1    )
             a						:Any 							=       [
                 (torch.zeros(_lowerCamelCase    ), torch.zeros(_lowerCamelCase    )) for _ in range(_lowerCamelCase    )
             ]
       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       # 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
       a						:Optional[Any] 							=       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
       a						:Optional[int] 							=       tokenizer.num_special_tokens_to_add(_lowerCamelCase    )
       a						:Tuple 							=       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
       a						:List[str] 							=       [''' '''.join([tokenizer.unk_token]    ) * seq_length] * batch_size
       a						:Dict 							=       dict(tokenizer(_lowerCamelCase					,  return_tensors=_lowerCamelCase    )    )
       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:Tuple 							=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _lowerCamelCase					,  batch_size=_lowerCamelCase					,  seq_length=_lowerCamelCase					,  is_pair=_lowerCamelCase					,  framework=_lowerCamelCase    )

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

       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:Optional[int] 							=       super()._flatten_past_key_values_(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       else:
             a						:Any 							=       super(_lowerCamelCase					,  self    )._flatten_past_key_values_(
                 _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )





