






"""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


_SCREAMING_SNAKE_CASE					:				Dict					    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						{
    '''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						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     Union[str, Any]						= """marian"""
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= ["""past_key_values"""]
    SCREAMING_SNAKE_CASE						:     int						= {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






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






class      a						(					__snake_case		):






    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
    def    UpperCamelCase       (       self     :							int					)     ->		Mapping[str, Mapping[int, str]]:
      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_(__SCREAMING_SNAKE_CASE       ,						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_     ,	lowerCamelCase_         =						self.num_layers
          for i in range(__SCREAMING_SNAKE_CASE					):
            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    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		Mapping[str, Mapping[int, str]]:
      if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase_         =						super().outputs
      else:
        lowerCamelCase_         =						super(__SCREAMING_SNAKE_CASE       ,						self					).outputs
        if self.use_past:
          lowerCamelCase_     ,	lowerCamelCase_         =						self.num_layers
          for i in range(__SCREAMING_SNAKE_CASE					):
            lowerCamelCase_         =						{0: 'batch', 2: 'past_sequence + sequence'}
            lowerCamelCase_         =						{0: 'batch', 2: 'past_sequence + sequence'}
      return common_outputs






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							PreTrainedTokenizer       ,						__SCREAMING_SNAKE_CASE     :							int = -1       ,						__SCREAMING_SNAKE_CASE     :							int = -1       ,						__SCREAMING_SNAKE_CASE     :							bool = False       ,						__SCREAMING_SNAKE_CASE     :							Optional[TensorType] = None       ,						)     ->		Mapping[str, Any]:
      lowerCamelCase_         =						self._generate_dummy_inputs_for_encoder_and_decoder(
          __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      # Generate decoder inputs
      lowerCamelCase_         =						seq_length if not self.use_past else 1
      lowerCamelCase_         =						self._generate_dummy_inputs_for_encoder_and_decoder(
          __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      lowerCamelCase_         =						dict(**__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_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
        lowerCamelCase_     ,	lowerCamelCase_         =						common_inputs['input_ids'].shape
        lowerCamelCase_         =						common_inputs['decoder_input_ids'].shape[1]
        lowerCamelCase_     ,	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(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)]       ,						dim=1					)

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

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






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							PreTrainedTokenizer       ,						__SCREAMING_SNAKE_CASE     :							int = -1       ,						__SCREAMING_SNAKE_CASE     :							int = -1       ,						__SCREAMING_SNAKE_CASE     :							bool = False       ,						__SCREAMING_SNAKE_CASE     :							Optional[TensorType] = None       ,						)     ->		Mapping[str, Any]:
      lowerCamelCase_         =						self._generate_dummy_inputs_for_encoder_and_decoder(
          __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_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
        lowerCamelCase_     ,	lowerCamelCase_         =						common_inputs['input_ids'].shape
        # Not using the same length for past_key_values
        lowerCamelCase_         =						seqlen + 2
        lowerCamelCase_     ,	lowerCamelCase_         =						self.num_layers
        lowerCamelCase_     ,	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(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						dtype=__SCREAMING_SNAKE_CASE					)]       ,						dim=1					)
        lowerCamelCase_         =						[
            (torch.zeros(__SCREAMING_SNAKE_CASE					), torch.zeros(__SCREAMING_SNAKE_CASE					)) for _ in range(__SCREAMING_SNAKE_CASE					)
        ]
      return common_inputs






    def    UpperCamelCase       (       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							PreTrainedTokenizer       ,						__SCREAMING_SNAKE_CASE     :							int = -1       ,						__SCREAMING_SNAKE_CASE     :							int = -1       ,						__SCREAMING_SNAKE_CASE     :							bool = False       ,						__SCREAMING_SNAKE_CASE     :							Optional[TensorType] = None       ,						)     ->		Mapping[str, Any]:
      # 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
      lowerCamelCase_         =						compute_effective_axis_dimension(
          __SCREAMING_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
      lowerCamelCase_         =						tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						compute_effective_axis_dimension(
          __SCREAMING_SNAKE_CASE       ,						fixed_dimension=OnnxConfig.default_fixed_sequence       ,						num_token_to_add=__SCREAMING_SNAKE_CASE					)

      # Generate dummy inputs according to compute batch and sequence
      lowerCamelCase_         =						[' '.join([tokenizer.unk_token]					) * seq_length] * batch_size
      lowerCamelCase_         =						dict(tokenizer(__SCREAMING_SNAKE_CASE       ,						return_tensors=__SCREAMING_SNAKE_CASE					)					)
      return common_inputs






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							PreTrainedTokenizer       ,						__SCREAMING_SNAKE_CASE     :							int = -1       ,						__SCREAMING_SNAKE_CASE     :							int = -1       ,						__SCREAMING_SNAKE_CASE     :							bool = False       ,						__SCREAMING_SNAKE_CASE     :							Optional[TensorType] = None       ,						)     ->		Mapping[str, Any]:
      if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase_         =						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            __SCREAMING_SNAKE_CASE       ,						batch_size=__SCREAMING_SNAKE_CASE       ,						seq_length=__SCREAMING_SNAKE_CASE       ,						is_pair=__SCREAMING_SNAKE_CASE       ,						framework=__SCREAMING_SNAKE_CASE					)

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

      return common_inputs






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]					)     ->		Any:
      if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase_         =						super()._flatten_past_key_values_(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      else:
        lowerCamelCase_         =						super(__SCREAMING_SNAKE_CASE       ,						self					)._flatten_past_key_values_(
            __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)






    @property
    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		float:
      return 1e-4












"""simple docstring"""





from typing import Any






class      a						:






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		List[Any]:
      lowerCamelCase_         =						data
      lowerCamelCase_         =						None






class      a						:






    def __init__(       self     :							Union[str, Any]					)     ->		List[Any]:
      lowerCamelCase_         =						None






    def    UpperCamelCase       (       self     :							Dict					)     ->		Optional[int]:
      lowerCamelCase_         =						self.head
      while temp is not None:
        print(temp.data       ,						end=' '					)
        lowerCamelCase_         =						temp.next
      print()






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Union[str, Any]:
      lowerCamelCase_         =						Node(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.head
      lowerCamelCase_         =						new_node






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		Union[str, Any]:
      if node_data_a == node_data_a:
        return
      else:
        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        if node_a is None or node_a is None:
          return

        lowerCamelCase_     ,	lowerCamelCase_         =						node_a.data, node_a.data


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Optional[int]					    =						LinkedList()
  for i in range(5, 0, -1):
    ll.push(i)

  ll.print_list()

  ll.swap_nodes(1, 4)
  print('''After swapping''')
  ll.print_list()




