





"""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


lowerCamelCase_	:		Tuple         =   logging.get_logger(__name__)

lowerCamelCase_	:		List[str]         =   {
    'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					int             =  """mbart"""
      lowercase_ :					Any             =  ["""past_key_values"""]
      lowercase_ :					Any             =  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

      def __init__(	self   , snake_case_=5_0_2_6_5   , snake_case_=1_0_2_4   , snake_case_=1_2   , snake_case_=4_0_9_6   , snake_case_=1_6   , snake_case_=1_2   , snake_case_=4_0_9_6   , snake_case_=1_6   , snake_case_=0.0   , snake_case_=0.0   , snake_case_=True   , snake_case_=True   , snake_case_="gelu"   , snake_case_=1_0_2_4   , snake_case_=0.1   , snake_case_=0.0   , snake_case_=0.0   , snake_case_=0.02   , snake_case_=0.0   , snake_case_=False   , snake_case_=1   , snake_case_=0   , snake_case_=2   , snake_case_=2   , **snake_case_   , ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    vocab_size
        A_    :			Any									=    max_position_embeddings
        A_    :			Union[str, Any]									=    d_model
        A_    :			Any									=    encoder_ffn_dim
        A_    :			int									=    encoder_layers
        A_    :			Any									=    encoder_attention_heads
        A_    :			List[str]									=    decoder_ffn_dim
        A_    :			List[Any]									=    decoder_layers
        A_    :			Union[str, Any]									=    decoder_attention_heads
        A_    :			List[Any]									=    dropout
        A_    :			Optional[Any]									=    attention_dropout
        A_    :			Optional[int]									=    activation_dropout
        A_    :			Union[str, Any]									=    activation_function
        A_    :			Dict									=    init_std
        A_    :			List[str]									=    encoder_layerdrop
        A_    :			List[str]									=    decoder_layerdrop
        A_    :			Dict									=    classifier_dropout
        A_    :			Dict									=    use_cache
        A_    :			str									=    encoder_layers
        A_    :			List[str]									=    scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=snake_case_   , bos_token_id=snake_case_   , eos_token_id=snake_case_   , is_encoder_decoder=snake_case_   , forced_eos_token_id=snake_case_   , **snake_case_   , )


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        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_    :			Dict									=    {0: 'batch'}
            A_    :			List[str]									=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
          else:
            A_    :			Optional[int]									=    {0: 'batch', 1: 'decoder_sequence'}
            A_    :			Optional[int]									=    {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.
          A_    :			str									=    OrderedDict(
              [
                  ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                  ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
              ]    )
          if self.use_past:
            A_		,			A_    :			List[str]									=    self.num_layers
            for i in range(snake_case_    ):
              A_    :			List[str]									=    {0: 'batch', 2: 'past_sequence + sequence'}
              A_    :			Dict									=    {0: 'batch', 2: 'past_sequence + sequence'}
        else:
          A_    :			Tuple									=    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    lowerCamelCase_     (	self    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = -1   , snake_case_ = -1   , snake_case_ = False   , snake_case_ = None   , ):

        """simple docstring"""

        A_    :			List[str]									=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_    )

        # Generate decoder inputs
        A_    :			Any									=    seq_length if not self.use_past else 1
        A_    :			Tuple									=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_    )
        A_    :			Tuple									=    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
        A_    :			Tuple									=    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
          A_		,			A_    :			Optional[int]									=    common_inputs['input_ids'].shape
          A_    :			List[str]									=    common_inputs['decoder_input_ids'].shape[1]
          A_		,			A_    :			Union[str, Any]									=    self.num_attention_heads
          A_    :			Any									=    (
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          A_    :			Dict									=    decoder_seq_length + 3
          A_    :			Any									=    (
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )

          A_    :			Optional[int]									=    torch.cat(
              [common_inputs['decoder_attention_mask'], torch.ones(snake_case_   , snake_case_    )]   , dim=1    )

          A_    :			str									=    []
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          A_		,			A_    :			Optional[Any]									=    self.num_layers
          A_    :			Tuple									=    min(snake_case_   , snake_case_    )
          A_    :			Optional[int]									=    max(snake_case_   , snake_case_    ) - min_num_layers
          A_    :			Optional[int]									=    '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.
          A_    :			Optional[int]									=    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    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = -1   , snake_case_ = -1   , snake_case_ = False   , snake_case_ = None   , ):

        """simple docstring"""

        A_    :			int									=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            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
          A_		,			A_    :			Optional[int]									=    common_inputs['input_ids'].shape
          # Not using the same length for past_key_values
          A_    :			Optional[int]									=    seqlen + 2
          A_		,			A_    :			Optional[Any]									=    self.num_layers
          A_		,			A_    :			Optional[int]									=    self.num_attention_heads
          A_    :			Optional[Any]									=    (
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )

          A_    :			str									=    common_inputs['attention_mask'].dtype
          A_    :			Union[str, Any]									=    torch.cat(
              [common_inputs['attention_mask'], torch.ones(snake_case_   , snake_case_   , dtype=snake_case_    )]   , dim=1    )
          A_    :			str									=    [
              (torch.zeros(snake_case_    ), torch.zeros(snake_case_    )) for _ in range(snake_case_    )
          ]
        return common_inputs

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = -1   , snake_case_ = -1   , snake_case_ = False   , snake_case_ = None   , ):

        """simple docstring"""

        A_    :			Optional[Any]									=    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
        A_    :			List[str]									=    tokenizer.num_special_tokens_to_add(snake_case_    )
        A_    :			List[Any]									=    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
        A_    :			Any									=    [' '.join([tokenizer.unk_token]    ) * seq_length] * batch_size
        A_    :			str									=    dict(tokenizer(snake_case_   , return_tensors=snake_case_    )    )
        return common_inputs

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = -1   , snake_case_ = -1   , snake_case_ = False   , snake_case_ = None   , ):

        """simple docstring"""

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

        elif self.task == "causal-lm":
          A_    :			Tuple									=    self._generate_dummy_inputs_for_causal_lm(
              snake_case_   , batch_size=snake_case_   , seq_length=snake_case_   , is_pair=snake_case_   , framework=snake_case_    )
        else:
          A_    :			Optional[Any]									=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              snake_case_   , batch_size=snake_case_   , seq_length=snake_case_   , is_pair=snake_case_   , framework=snake_case_    )

        return common_inputs

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

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




'''simple docstring'''

from __future__ import annotations



class 		lowerCamelCase__   :




					'''simple docstring'''



					def __init__(	self					:						str  ,	UpperCamelCase_					:						int       )	->							None:



						'''simple docstring'''





						_lowercase       :							Any  	=		value
						_lowercase       :							Node | None  	=		None
						_lowercase       :							Node | None  	=		None






class 		lowerCamelCase__   :




					'''simple docstring'''



					A_         =    42




					def __init__(	self					:						List[Any]       )	->							None:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		0




					def     __UpperCAmelCase  (	self					:						str  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int       )	->							None:



						'''simple docstring'''





						if node is None:
							return

						if path_sum == self.target:
							self.paths += 1

						if node.left:
							self.depth_first_search(node.left  ,	path_sum + node.left.value       )
						if node.right:
							self.depth_first_search(node.right  ,	path_sum + node.right.value       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int | None = None       )	->							int:



						'''simple docstring'''





						if node is None:
							return 0
						if target is not None:
							_lowercase       :							Union[str, Any]  	=		target

						self.depth_first_search(UpperCamelCase_  ,	node.value       )
						self.path_sum(node.left       )
						self.path_sum(node.right       )

						return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()
