




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    :		Tuple			   =							logging.get_logger(__name__)

lowerCamelCase    :		Optional[int]			   =							{
    "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   A(						__UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						"""marian"""
 UpperCamelCase						       =						["""past_key_values"""]
 UpperCamelCase						       =						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

 def __init__(    self :  int						,							A_ :  Dict=58101						,							A_ :  List[Any]=None						,							A_ :  Any=1024						,							A_ :  int=12						,							A_ :  Tuple=4096						,							A_ :  Tuple=16						,							A_ :  Union[str, Any]=12						,							A_ :  Any=4096						,							A_ :  List[str]=16						,							A_ :  Union[str, Any]=0.0						,							A_ :  Optional[Any]=0.0						,							A_ :  Optional[int]=True						,							A_ :  str=True						,							A_ :  Any="gelu"						,							A_ :  Any=1024						,							A_ :  List[str]=0.1						,							A_ :  int=0.0						,							A_ :  Any=0.0						,							A_ :  Union[str, Any]=0.02						,							A_ :  List[str]=58100						,							A_ :  str=False						,							A_ :  Optional[int]=58100						,							A_ :  Optional[Any]=0						,							A_ :  Optional[Any]=0						,							A_ :  str=True						,							**A_ :  List[str]						,							)       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   decoder_vocab_size or vocab_size
       lowerCamelCase_							=   max_position_embeddings
       lowerCamelCase_							=   d_model
       lowerCamelCase_							=   encoder_ffn_dim
       lowerCamelCase_							=   encoder_layers
       lowerCamelCase_							=   encoder_attention_heads
       lowerCamelCase_							=   decoder_ffn_dim
       lowerCamelCase_							=   decoder_layers
       lowerCamelCase_							=   decoder_attention_heads
       lowerCamelCase_							=   dropout
       lowerCamelCase_							=   attention_dropout
       lowerCamelCase_							=   activation_dropout
       lowerCamelCase_							=   activation_function
       lowerCamelCase_							=   init_std
       lowerCamelCase_							=   encoder_layerdrop
       lowerCamelCase_							=   decoder_layerdrop
       lowerCamelCase_							=   use_cache
       lowerCamelCase_							=   encoder_layers
       lowerCamelCase_							=   scale_embedding  # scale factor will be sqrt(d_model) if True
       lowerCamelCase_							=   share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=A_						,							eos_token_id=A_						,							is_encoder_decoder=A_						,							decoder_start_token_id=A_						,							forced_eos_token_id=A_						,							**A_						,							)







class   A(						__UpperCamelCase       ):



 '''simple docstring'''


 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
 def     a__			(    self :  str  )       ->     int:

       """simple docstring"""






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

             if self.use_past:
                   lowerCamelCase_							=   {0: """batch"""}
                   lowerCamelCase_							=   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
             else:
                   lowerCamelCase_							=   {0: """batch""", 1: """decoder_sequence"""}
                   lowerCamelCase_							=   {0: """batch""", 1: """decoder_sequence"""}

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

       """simple docstring"""






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

 def     a__			(    self :  Optional[int]						,							A_ :  PreTrainedTokenizer						,							A_ :  int = -1						,							A_ :  int = -1						,							A_ :  bool = False						,							A_ :  Optional[TensorType] = None						,							)       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   self._generate_dummy_inputs_for_encoder_and_decoder(
           A_						,							A_						,							A_						,							A_						,							A_  )

       # Generate decoder inputs
       lowerCamelCase_							=   seq_length if not self.use_past else 1
       lowerCamelCase_							=   self._generate_dummy_inputs_for_encoder_and_decoder(
           A_						,							A_						,							A_						,							A_						,							A_  )
       lowerCamelCase_							=   {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       lowerCamelCase_							=   dict(**A_						,							**A_  )

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

             lowerCamelCase_							=   torch.cat(
                 [common_inputs['decoder_attention_mask'], torch.ones(A_						,							A_  )]						,							dim=1  )

             lowerCamelCase_							=   []
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             lowerCamelCase_							=   self.num_layers
             lowerCamelCase_							=   min(A_						,							A_  )
             lowerCamelCase_							=   max(A_						,							A_  ) - min_num_layers
             lowerCamelCase_							=   """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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

 def     a__			(    self :  Optional[Any]						,							A_ :  PreTrainedTokenizer						,							A_ :  int = -1						,							A_ :  int = -1						,							A_ :  bool = False						,							A_ :  Optional[TensorType] = None						,							)       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   self._generate_dummy_inputs_for_encoder_and_decoder(
           A_						,							A_						,							A_						,							A_						,							A_  )

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

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

 def     a__			(    self :  str						,							A_ :  PreTrainedTokenizer						,							A_ :  int = -1						,							A_ :  int = -1						,							A_ :  bool = False						,							A_ :  Optional[TensorType] = None						,							)       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   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
       lowerCamelCase_							=   tokenizer.num_special_tokens_to_add(A_  )
       lowerCamelCase_							=   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
       lowerCamelCase_							=   [""" """.join([tokenizer.unk_token]  ) * seq_length] * batch_size
       lowerCamelCase_							=   dict(tokenizer(A_						,							return_tensors=A_  )  )
       return common_inputs

 def     a__			(    self :  str						,							A_ :  PreTrainedTokenizer						,							A_ :  int = -1						,							A_ :  int = -1						,							A_ :  bool = False						,							A_ :  Optional[TensorType] = None						,							)       ->     str:

       """simple docstring"""






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

       else:
             lowerCamelCase_							=   self._generate_dummy_inputs_for_causal_lm(
                 A_						,							batch_size=A_						,							seq_length=A_						,							is_pair=A_						,							framework=A_  )

       return common_inputs

 def     a__			(    self :  Any						,							A_ :  List[Any]						,							A_ :  Optional[int]						,							A_ :  List[str]						,							A_ :  List[str]  )       ->     Dict:

       """simple docstring"""






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





 @property
 def     a__			(    self :  List[str]  )       ->     Any:

       """simple docstring"""






       return 1E-4









from __future__ import annotations

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	dict     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_					,    lowerCamelCase_							=   set(lowercase					), [start]

      while stack:
            lowerCamelCase_							=   stack.pop()
            explored.add(lowercase					)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]					):
                  if adj not in explored:
                        stack.append(lowercase					)
      return explored


lowerCamelCase    :		int			   =       {
    "A": ["B", "C", "D"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B", "D"],
    "E": ["B", "F"],
    "F": ["C", "E", "G"],
    "G": ["F"],
}

if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(depth_first_search(G, "A"))



