





'''simple docstring'''


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


lowerCAmelCase      :  str      							=logging.get_logger(__name__)

lowerCAmelCase      :  Union[str, Any]      							={
    '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json''',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}
class    a_     (       _lowerCAmelCase							):
     __A  				=    "blenderbot"
     __A  				=    ["past_key_values"]
     __A  				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





     def __init__(				self	:      Dict			,   lowercase	:      List[Any]=8_008			,   lowercase	:      List[str]=128			,   lowercase	:      Optional[int]=2			,   lowercase	:      Optional[int]=10_240			,   lowercase	:      Tuple=32			,   lowercase	:      int=24			,   lowercase	:      Dict=10_240			,   lowercase	:      Any=32			,   lowercase	:      Optional[int]=0.0			,   lowercase	:      Dict=0.0			,   lowercase	:      int=True			,   lowercase	:      Tuple=True			,   lowercase	:      List[str]="gelu"			,   lowercase	:      int=2_560			,   lowercase	:      Optional[Any]=0.1			,   lowercase	:      Any=0.0			,   lowercase	:      List[str]=0.0			,   lowercase	:      Optional[Any]=0.02			,   lowercase	:      Tuple=1			,   lowercase	:      Union[str, Any]=False			,   lowercase	:      str=0			,   lowercase	:      List[str]=1			,   lowercase	:      Optional[Any]=2			,   lowercase	:      Union[str, Any]=3			,   lowercase	:      List[str]=2			,   **lowercase	:      Optional[Any]			,   ):





        """simple docstring"""




        lowercase_    :str					    =				vocab_size
        lowercase_    :Any					    =				max_position_embeddings
        lowercase_    :List[Any]					    =				d_model
        lowercase_    :Union[str, Any]					    =				encoder_ffn_dim
        lowercase_    :str					    =				encoder_layers
        lowercase_    :Optional[int]					    =				encoder_attention_heads
        lowercase_    :Dict					    =				decoder_ffn_dim
        lowercase_    :str					    =				decoder_layers
        lowercase_    :int					    =				decoder_attention_heads
        lowercase_    :Union[str, Any]					    =				dropout
        lowercase_    :int					    =				attention_dropout
        lowercase_    :int					    =				activation_dropout
        lowercase_    :str					    =				activation_function
        lowercase_    :Tuple					    =				init_std
        lowercase_    :Any					    =				encoder_layerdrop
        lowercase_    :Dict					    =				decoder_layerdrop
        lowercase_    :int					    =				use_cache
        lowercase_    :Dict					    =				encoder_layers
        lowercase_    :Dict					    =				scale_embedding  # scale factor will be sqrt(d_model) if True

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







class    a_     (       _lowerCAmelCase							):





     @property
     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        if self.task in ["default", "seq2seq-lm"]:
           lowercase_    :Optional[int]					    =				OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]		)
           if self.use_past:
              lowercase_    :int					    =				{0: "batch"}
              lowercase_    :List[str]					    =				{0: "batch", 1: "past_decoder_sequence + sequence"}
           else:
              lowercase_    :Union[str, Any]					    =				{0: "batch", 1: "decoder_sequence"}
              lowercase_    :List[Any]					    =				{0: "batch", 1: "decoder_sequence"}
           if self.use_past:
              self.fill_with_past_key_values_(lowercase			,   direction="inputs"		)
        elif self.task == "causal-lm":
           lowercase_    :List[Any]					    =				OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]		)
           if self.use_past:
              lowercase_    ,							lowercase_    :Optional[int]					    =				self.num_layers
              for i in range(lowercase		):
                 lowercase_    :List[str]					    =				{0: "batch", 2: "past_sequence + sequence"}
                 lowercase_    :Any					    =				{0: "batch", 2: "past_sequence + sequence"}
        else:
           lowercase_    :str					    =				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 							lowercase__   (				self	:      Dict		):





        """simple docstring"""




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





     def 							lowercase__   (				self	:      Tuple			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        lowercase_    :int					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase			,   lowercase			,   lowercase			,   lowercase			,   lowercase		)
        # Generate decoder inputs
        lowercase_    :List[Any]					    =				seq_length if not self.use_past else 1
        lowercase_    :Optional[Any]					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase			,   lowercase			,   lowercase			,   lowercase			,   lowercase		)
        lowercase_    :Tuple					    =				{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        lowercase_    :Any					    =				dict(**lowercase			,   **lowercase		)

        if self.use_past:
           if not is_torch_available():
              raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
           else:
              import torch
           lowercase_    ,							lowercase_    :int					    =				common_inputs["input_ids"].shape
           lowercase_    :Tuple					    =				common_inputs["decoder_input_ids"].shape[1]
           lowercase_    ,							lowercase_    :Tuple					    =				self.num_attention_heads
           lowercase_    :List[Any]					    =				(
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           lowercase_    :List[Any]					    =				decoder_seq_length
           lowercase_    :str					    =				(
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )
           lowercase_    :Optional[int]					    =				torch.cat(
               [common_inputs["decoder_attention_mask"], torch.ones(lowercase			,   lowercase		)]			,   dim=1		)
           lowercase_    :int					    =				[]
           lowercase_    ,							lowercase_    :Tuple					    =				self.num_layers

           for _ in range(lowercase		):
              common_inputs["past_key_values"].append(
                  (
                      torch.zeros(lowercase		),
                      torch.zeros(lowercase		),
                      torch.zeros(lowercase		),
                      torch.zeros(lowercase		),
                  )		)
        return common_inputs





     def 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        lowercase_    :str					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase			,   lowercase			,   lowercase			,   lowercase			,   lowercase		)

        if self.use_past:
           if not is_torch_available():
              raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
           else:
              import torch
           lowercase_    ,							lowercase_    :List[str]					    =				common_inputs["input_ids"].shape
           lowercase_    :int					    =				seqlen
           lowercase_    ,							lowercase_    :int					    =				self.num_layers
           lowercase_    ,							lowercase_    :Union[str, Any]					    =				self.num_attention_heads
           lowercase_    :Any					    =				(
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           lowercase_    :Optional[Any]					    =				common_inputs["attention_mask"].dtype
           lowercase_    :List[Any]					    =				torch.cat(
               [common_inputs["attention_mask"], torch.ones(lowercase			,   lowercase			,   dtype=lowercase		)]			,   dim=1		)
           lowercase_    :Any					    =				[
               (torch.zeros(lowercase		), torch.zeros(lowercase		)) for _ in range(lowercase		)
           ]
        return common_inputs





     def 							lowercase__   (				self	:      Dict			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				compute_effective_axis_dimension(
            lowercase			,   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
        lowercase_    :Optional[Any]					    =				tokenizer.num_special_tokens_to_add(lowercase		)
        lowercase_    :int					    =				compute_effective_axis_dimension(
            lowercase			,   fixed_dimension=OnnxConfig.default_fixed_sequence			,   num_token_to_add=lowercase		)

        # Generate dummy inputs according to compute batch and sequence
        lowercase_    :int					    =				[" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
        lowercase_    :Dict					    =				dict(tokenizer(lowercase			,   return_tensors=lowercase		)		)
        return common_inputs





     def 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




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

        elif self.task == "causal-lm":
           lowercase_    :Optional[Any]					    =				self._generate_dummy_inputs_for_causal_lm(
               lowercase			,   batch_size=lowercase			,   seq_length=lowercase			,   is_pair=lowercase			,   framework=lowercase		)
        else:
           lowercase_    :List[str]					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowercase			,   batch_size=lowercase			,   seq_length=lowercase			,   is_pair=lowercase			,   framework=lowercase		)

        return common_inputs





     def 							lowercase__   (				self	:      Optional[int]			,   lowercase	:      str			,   lowercase	:      Union[str, Any]			,   lowercase	:      int			,   lowercase	:      List[Any]		):





        """simple docstring"""




        if self.task in ["default", "seq2seq-lm"]:
           lowercase_    :Optional[int]					    =				super()._flatten_past_key_values_(lowercase			,   lowercase			,   lowercase			,   lowercase		)
        else:
           lowercase_    :Tuple					    =				super(lowercase			,   self		)._flatten_past_key_values_(
               lowercase			,   lowercase			,   lowercase			,   lowercase		)







     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      Mapping[str, Mapping[int, str]]			,   lowercase	:      str		):





        """simple docstring"""




        if direction not in ["inputs", "outputs"]:
           raise ValueError(F'direction must either be "inputs" or "outputs", but {direction} was given'		)

        lowercase_    :Any					    =				"past_key_values" if direction == "inputs" else "present"
        lowercase_    ,							lowercase_    :Optional[int]					    =				self.num_layers

        lowercase_    :str					    =				"past_encoder_sequence"
        lowercase_    :Tuple					    =				"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

        for i in range(lowercase		):
           lowercase_    :Dict					    =				{0: "batch", 2: decoder_sequence}
           lowercase_    :Optional[int]					    =				{0: "batch", 2: decoder_sequence}
           lowercase_    :Optional[int]					    =				{0: "batch", 2: encoder_sequence}
           lowercase_    :int					    =				{0: "batch", 2: encoder_sequence}









'''simple docstring'''


class    a_     :





     def __init__(				self	:      str			,   lowercase	:      List[Any]		):





        """simple docstring"""




        lowercase_    :List[str]					    =				val
        # Number of nodes in left subtree
        lowercase_    :Optional[int]					    =				0
        lowercase_    :List[str]					    =				None
        lowercase_    :List[Any]					    =				None
        lowercase_    :Optional[Any]					    =				None







     def 							lowercase__   (				self	:      Union[str, Any]			,   lowercase	:      Dict		):





        """simple docstring"""




        assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

        if self.val < other.val:
           lowercase_    :List[Any]					    =				self.right
           lowercase_    :str					    =				None
           if self.right:
              lowercase_    :Optional[int]					    =				other
           lowercase_    :Optional[Any]					    =				other
           lowercase_    :str					    =				self.left_tree_size * 2 + 1
           return self
        else:
           lowercase_    :Any					    =				other.right
           lowercase_    :Any					    =				None
           if other.right:
              lowercase_    :List[Any]					    =				self
           lowercase_    :Union[str, Any]					    =				self
           lowercase_    :Optional[int]					    =				other.left_tree_size * 2 + 1
           return other







class    a_     :





     def __init__(				self	:      Optional[Any]			,   lowercase	:      Any=None			,   lowercase	:      Optional[Any]=None			,   lowercase	:      int=0		):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				heap_size
        lowercase_    :List[str]					    =				bottom_root
        lowercase_    :Dict					    =				min_node





     def 							lowercase__   (				self	:      List[str]			,   lowercase	:      List[Any]		):





        """simple docstring"""




        if other.size == 0:
           return None
        if self.size == 0:
           lowercase_    :Dict					    =				other.size
           lowercase_    :int					    =				other.bottom_root
           lowercase_    :Tuple					    =				other.min_node
           return None
        # Update size
        lowercase_    :Optional[int]					    =				self.size + other.size

        # Update min.node
        if self.min_node.val > other.min_node.val:
           lowercase_    :int					    =				other.min_node
        # Merge

        # Order roots by left_subtree_size
        lowercase_    :Any					    =				[]
        lowercase_    ,							lowercase_    :List[str]					    =				self.bottom_root, other.bottom_root
        while i or j:
           if i and ((not j) or i.left_tree_size < j.left_tree_size):
              combined_roots_list.append((i, True)		)
              lowercase_    :str					    =				i.parent
           else:
              combined_roots_list.append((j, False)		)
              lowercase_    :Dict					    =				j.parent
        # Insert links between them
        for i in range(len(lowercase		) - 1		):
           if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
              lowercase_    :Dict					    =				combined_roots_list[i + 1][0]
              lowercase_    :str					    =				combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
        lowercase_    :int					    =				combined_roots_list[0][0]
        while i.parent:
           if (
               (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
           ) or (
               i.left_tree_size == i.parent.left_tree_size
               and i.left_tree_size != i.parent.parent.left_tree_size
           ):
              # Neighbouring Nodes
              lowercase_    :List[Any]					    =				i.left
              lowercase_    :Any					    =				i.parent.parent

              # Merging trees
              lowercase_    :Any					    =				i.merge_trees(i.parent		)

              # Updating links
              lowercase_    :List[Any]					    =				previous_node
              lowercase_    :int					    =				next_node
              if previous_node:
                 lowercase_    :int					    =				i
              if next_node:
                 lowercase_    :str					    =				i
           else:
              lowercase_    :str					    =				i.parent
        # Updating self.bottom_root
        while i.left:
           lowercase_    :List[str]					    =				i.left
        lowercase_    :List[str]					    =				i

        # Update other
        lowercase_    :Any					    =				self.size
        lowercase_    :Optional[Any]					    =				self.bottom_root
        lowercase_    :int					    =				self.min_node

        # Return the merged heap
        return self





     def 							lowercase__   (				self	:      int			,   lowercase	:      List[Any]		):





        """simple docstring"""




        if self.size == 0:
           lowercase_    :Dict					    =				Node(lowercase		)
           lowercase_    :Optional[Any]					    =				1
           lowercase_    :Optional[Any]					    =				self.bottom_root
        else:
           # Create new node
           lowercase_    :Union[str, Any]					    =				Node(lowercase		)

           # Update size
           self.size += 1

           # update min_node
           if val < self.min_node.val:
              lowercase_    :Union[str, Any]					    =				new_node
           # Put new_node as a bottom_root in heap
           lowercase_    :int					    =				new_node
           lowercase_    :List[str]					    =				self.bottom_root
           lowercase_    :Any					    =				new_node

           # Consecutively merge roots with same left_tree_size
           while (
               self.bottom_root.parent
               and self.bottom_root.left_tree_size
               == self.bottom_root.parent.left_tree_size
           ):
              # Next node
              lowercase_    :int					    =				self.bottom_root.parent.parent

              # Merge
              lowercase_    :Union[str, Any]					    =				self.bottom_root.merge_trees(self.bottom_root.parent		)

              # Update Links
              lowercase_    :Optional[Any]					    =				next_node
              lowercase_    :Any					    =				None
              if next_node:
                 lowercase_    :Union[str, Any]					    =				self.bottom_root





     def 							lowercase__   (				self	:      str		):





        """simple docstring"""




        return self.min_node.val





     def 							lowercase__   (				self	:      Optional[int]		):





        """simple docstring"""




        return self.size == 0





     def 							lowercase__   (				self	:      str		):





        """simple docstring"""




        lowercase_    :str					    =				self.min_node.val

        # Last element in heap corner case
        if self.size == 1:
           # Update size
           lowercase_    :Dict					    =				0

           # Update bottom root
           lowercase_    :int					    =				None

           # Update min_node
           lowercase_    :Tuple					    =				None

           return min_value
        # No right subtree corner case
        # The structure of the tree implies that this should be the bottom root
        # and there is at least one other root
        if self.min_node.right is None:
           # Update size
           self.size -= 1

           # Update bottom root
           lowercase_    :Optional[Any]					    =				self.bottom_root.parent
           lowercase_    :Tuple					    =				None

           # Update min_node
           lowercase_    :Dict					    =				self.bottom_root
           lowercase_    :List[str]					    =				self.bottom_root.parent
           while i:
              if i.val < self.min_node.val:
                 lowercase_    :Union[str, Any]					    =				i
              lowercase_    :Dict					    =				i.parent
           return min_value
        # General case
        # Find the BinomialHeap of the right subtree of min_node
        lowercase_    :List[str]					    =				self.min_node.right
        lowercase_    :Dict					    =				None
        lowercase_    :str					    =				bottom_of_new
        lowercase_    :Optional[int]					    =				1

        # Size, min_node and bottom_root
        while bottom_of_new.left:
           lowercase_    :List[Any]					    =				size_of_new * 2 + 1
           lowercase_    :Union[str, Any]					    =				bottom_of_new.left
           if bottom_of_new.val < min_of_new.val:
              lowercase_    :Optional[int]					    =				bottom_of_new
        # Corner case of single root on top left path
        if (not self.min_node.left) and (not self.min_node.parent):
           lowercase_    :Optional[int]					    =				size_of_new
           lowercase_    :Union[str, Any]					    =				bottom_of_new
           lowercase_    :Dict					    =				min_of_new
           # print("Single root, multiple nodes case")
           return min_value
        # Remaining cases
        # Construct heap of right subtree
        lowercase_    :Tuple					    =				BinomialHeap(
            bottom_root=lowercase			,   min_node=lowercase			,   heap_size=lowercase		)

        # Update size
        lowercase_    :Dict					    =				self.size - 1 - size_of_new

        # Neighbour nodes
        lowercase_    :Optional[Any]					    =				self.min_node.left
        lowercase_    :str					    =				self.min_node.parent

        # Initialize new bottom_root and min_node
        lowercase_    :int					    =				previous_node or next_node
        lowercase_    :Any					    =				next_node

        # Update links of previous_node and search below for new min_node and
        # bottom_root
        if previous_node:
           lowercase_    :int					    =				next_node

           # Update bottom_root and search for min_node below
           lowercase_    :str					    =				previous_node
           lowercase_    :List[Any]					    =				previous_node
           while self.bottom_root.left:
              lowercase_    :List[str]					    =				self.bottom_root.left
              if self.bottom_root.val < self.min_node.val:
                 lowercase_    :List[Any]					    =				self.bottom_root
        if next_node:
           lowercase_    :List[Any]					    =				previous_node

           # Search for new min_node above min_node
           lowercase_    :Dict					    =				next_node
           while i:
              if i.val < self.min_node.val:
                 lowercase_    :Optional[Any]					    =				i
              lowercase_    :str					    =				i.parent
        # Merge heaps
        self.merge_heaps(lowercase		)

        return min_value





     def 							lowercase__   (				self	:      Dict		):





        """simple docstring"""




        lowercase_    :Optional[Any]					    =				self.bottom_root
        while top_root.parent:
           lowercase_    :int					    =				top_root.parent
        # preorder
        lowercase_    :List[str]					    =				[]
        self.__traversal(lowercase			,   lowercase		)
        return heap_pre_order





     def 							lowercase__   (				self	:      Union[str, Any]			,   lowercase	:      List[Any]			,   lowercase	:      List[str]			,   lowercase	:      Dict=0		):





        """simple docstring"""




        if curr_node:
           preorder.append((curr_node.val, level)		)
           self.__traversal(curr_node.left			,   lowercase			,   level + 1		)
           self.__traversal(curr_node.right			,   lowercase			,   level + 1		)
        else:
           preorder.append(("#", level)		)







     def __str__(				self	:      Optional[int]		):





        """simple docstring"""




        if self.is_empty():
           return ""
        lowercase_    :int					    =				self.pre_order()

        return "\n".join(("-" * level + str(lowercase		)) for value, level in preorder_heap		)


# Unit Tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()


