






"""simple docstring"""





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__					: Tuple=	logging.get_logger(__name__)

A__					: Tuple=	{
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class    __lowerCamelCase    (  _a  ):
   a      :				Union[str, Any] 						="marian"
   a      :				List[Any] 						=["past_key_values"]
   a      :				List[Any] 						={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

   def __init__(					self ,				snake_case_=5_8101 ,				snake_case_=None ,				snake_case_=1024 ,				snake_case_=12 ,				snake_case_=4096 ,				snake_case_=16 ,				snake_case_=12 ,				snake_case_=4096 ,				snake_case_=16 ,				snake_case_=0.0 ,				snake_case_=0.0 ,				snake_case_=True ,				snake_case_=True ,				snake_case_="gelu" ,				snake_case_=1024 ,				snake_case_=0.1 ,				snake_case_=0.0 ,				snake_case_=0.0 ,				snake_case_=0.02 ,				snake_case_=5_8100 ,				snake_case_=False ,				snake_case_=5_8100 ,				snake_case_=0 ,				snake_case_=0 ,				snake_case_=True ,				**snake_case_ ,				)		->       Optional[int]:
       UpperCamelCase__         =    vocab_size
       UpperCamelCase__         =    decoder_vocab_size or vocab_size
       UpperCamelCase__         =    max_position_embeddings
       UpperCamelCase__         =    d_model
       UpperCamelCase__         =    encoder_ffn_dim
       UpperCamelCase__         =    encoder_layers
       UpperCamelCase__         =    encoder_attention_heads
       UpperCamelCase__         =    decoder_ffn_dim
       UpperCamelCase__         =    decoder_layers
       UpperCamelCase__         =    decoder_attention_heads
       UpperCamelCase__         =    dropout
       UpperCamelCase__         =    attention_dropout
       UpperCamelCase__         =    activation_dropout
       UpperCamelCase__         =    activation_function
       UpperCamelCase__         =    init_std
       UpperCamelCase__         =    encoder_layerdrop
       UpperCamelCase__         =    decoder_layerdrop
       UpperCamelCase__         =    use_cache
       UpperCamelCase__         =    encoder_layers
       UpperCamelCase__         =    scale_embedding  # scale factor will be sqrt(d_model) if True
       UpperCamelCase__         =    share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=snake_case_ ,				eos_token_id=snake_case_ ,				is_encoder_decoder=snake_case_ ,				decoder_start_token_id=snake_case_ ,				forced_eos_token_id=snake_case_ ,				**snake_case_ ,				)







class    __lowerCamelCase    (  _a  ):

   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Mapping[str, Mapping[int, str]]:




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

           if self.use_past:
               UpperCamelCase__         =    {0: 'batch'}
               UpperCamelCase__         =    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
           else:
               UpperCamelCase__         =    {0: 'batch', 1: 'decoder_sequence'}
               UpperCamelCase__         =    {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.
           UpperCamelCase__         =    OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
               ]      )
           if self.use_past:
               UpperCamelCase__       ,						UpperCamelCase__         =    self.num_layers
               for i in range(snake_case_      ):
                   UpperCamelCase__         =    {0: 'batch', 2: 'past_sequence + sequence'}
                   UpperCamelCase__         =    {0: 'batch', 2: 'past_sequence + sequence'}
       else:
           UpperCamelCase__         =    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
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Mapping[str, Mapping[int, str]]:




       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__         =    super().outputs
       else:
           UpperCamelCase__         =    super(snake_case_ ,				self      ).outputs
           if self.use_past:
               UpperCamelCase__       ,						UpperCamelCase__         =    self.num_layers
               for i in range(snake_case_      ):
                   UpperCamelCase__         =    {0: 'batch', 2: 'past_sequence + sequence'}
                   UpperCamelCase__         =    {0: 'batch', 2: 'past_sequence + sequence'}
       return common_outputs

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = -1 ,				snake_case_ = -1 ,				snake_case_ = False ,				snake_case_ = None ,				)		->       Mapping[str, Any]:
       UpperCamelCase__         =    self._generate_dummy_inputs_for_encoder_and_decoder(
           snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )

       # Generate decoder inputs
       UpperCamelCase__         =    seq_length if not self.use_past else 1
       UpperCamelCase__         =    self._generate_dummy_inputs_for_encoder_and_decoder(
           snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )
       UpperCamelCase__         =    {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
       UpperCamelCase__         =    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
           UpperCamelCase__       ,						UpperCamelCase__         =    common_inputs['input_ids'].shape
           UpperCamelCase__         =    common_inputs['decoder_input_ids'].shape[1]
           UpperCamelCase__       ,						UpperCamelCase__         =    self.num_attention_heads
           UpperCamelCase__         =    (
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           UpperCamelCase__         =    decoder_seq_length + 3
           UpperCamelCase__         =    (
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )

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

           UpperCamelCase__         =    []
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           UpperCamelCase__       ,						UpperCamelCase__         =    self.num_layers
           UpperCamelCase__         =    min(snake_case_ ,				snake_case_      )
           UpperCamelCase__         =    max(snake_case_ ,				snake_case_      ) - min_num_layers
           UpperCamelCase__         =    '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.
           UpperCamelCase__         =    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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = -1 ,				snake_case_ = -1 ,				snake_case_ = False ,				snake_case_ = None ,				)		->       Mapping[str, Any]:
       UpperCamelCase__         =    self._generate_dummy_inputs_for_encoder_and_decoder(
           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
           UpperCamelCase__       ,						UpperCamelCase__         =    common_inputs['input_ids'].shape
           # Not using the same length for past_key_values
           UpperCamelCase__         =    seqlen + 2
           UpperCamelCase__       ,						UpperCamelCase__         =    self.num_layers
           UpperCamelCase__       ,						UpperCamelCase__         =    self.num_attention_heads
           UpperCamelCase__         =    (
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )

           UpperCamelCase__         =    common_inputs['attention_mask'].dtype
           UpperCamelCase__         =    torch.cat(
               [common_inputs['attention_mask'], torch.ones(snake_case_ ,				snake_case_ ,				dtype=snake_case_      )] ,				dim=1      )
           UpperCamelCase__         =    [
               (torch.zeros(snake_case_      ), torch.zeros(snake_case_      )) for _ in range(snake_case_      )
           ]
       return common_inputs

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = -1 ,				snake_case_ = -1 ,				snake_case_ = False ,				snake_case_ = None ,				)		->       Mapping[str, Any]:
       UpperCamelCase__         =    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
       UpperCamelCase__         =    tokenizer.num_special_tokens_to_add(snake_case_      )
       UpperCamelCase__         =    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
       UpperCamelCase__         =    [' '.join([tokenizer.unk_token]      ) * seq_length] * batch_size
       UpperCamelCase__         =    dict(tokenizer(snake_case_ ,				return_tensors=snake_case_      )      )
       return common_inputs

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




       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__         =    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_      )

       else:
           UpperCamelCase__         =    self._generate_dummy_inputs_for_causal_lm(
               snake_case_ ,				batch_size=snake_case_ ,				seq_length=snake_case_ ,				is_pair=snake_case_ ,				framework=snake_case_      )

       return common_inputs

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       int:




       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__         =    super()._flatten_past_key_values_(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )
       else:
           UpperCamelCase__         =    super(snake_case_ ,				self      )._flatten_past_key_values_(
               snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )







   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       float:
       return 1E-4












"""simple docstring"""





from __future__ import annotations

from typing import Generic, TypeVar

A__					: Any=	TypeVar("""T""")
class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    data
       UpperCamelCase__         =    self
       UpperCamelCase__         =    0







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # map from node name to the node object
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # create a new set with x as its member
       UpperCamelCase__         =    DisjointSetTreeNode(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       DisjointSetTreeNode[T]:
       # find the set x belongs to (with path-compression)
       UpperCamelCase__         =    self.map[data]
       if elem_ref != elem_ref.parent:
           UpperCamelCase__         =    self.find_set(elem_ref.parent.data      )
       return elem_ref.parent

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # helper function for union operation
       if nodea.rank > nodea.rank:
           UpperCamelCase__         =    nodea
       else:
           UpperCamelCase__         =    nodea
           if nodea.rank == nodea.rank:
               nodea.rank += 1







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # merge 2 disjoint sets
       self.link(self.find_set(snake_case_      ) ,				self.find_set(snake_case_      )      )







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # connections: map from the node to the neighbouring nodes (with weights)
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # add a node ONLY if its not present in the graph
       if node not in self.connections:
           UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       None:
       # add an edge with the given weight
       self.add_node(snake_case_      )
       self.add_node(snake_case_      )
       UpperCamelCase__         =    weight
       UpperCamelCase__         =    weight







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       GraphUndirectedWeighted[T]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    set()
       for start in self.connections:
           for end in self.connections[start]:
               if (start, end) not in seen:
                   seen.add((end, start)      )
                   edges.append((start, end, self.connections[start][end])      )
       edges.sort(key=lambda snake_case_      : x[2]      )

       # creating the disjoint set
       UpperCamelCase__         =    DisjointSetTree[T]()
       for node in self.connections:
           disjoint_set.make_set(snake_case_      )

       # MST generation
       UpperCamelCase__         =    0
       UpperCamelCase__         =    0
       UpperCamelCase__         =    GraphUndirectedWeighted[T]()
       while num_edges < len(self.connections      ) - 1:
           UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    edges[index]
           index += 1
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           if parent_u != parent_v:
               num_edges += 1
               graph.add_edge(snake_case_ ,				snake_case_ ,				snake_case_      )
               disjoint_set.union(snake_case_ ,				snake_case_      )
       return graph




