


"""simple docstring"""


from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__lowerCamelCase      :int	    =		TypeVar('KT')
__lowerCamelCase      :Any	    =		TypeVar('VT')


class 			A__	(						Generic[KT, VT]):


     """simple docstring"""

     def __init__(	self:						Union[str, Any]     ,		__a:						KT | str = "root"     ,		__a:						VT | None = None	)->    List[Any]:
           lowerCamelCase					:					List[Any]        =	key
           lowerCamelCase					:					Tuple        =	value
           lowerCamelCase					:					list[Node[KT, VT]]        =	[]

     def __repr__(	self:						str	)->    str:
           return f'Node({self.key}: {self.value})'







     @property
     def 		a__						(	self:						List[Any]	)->    int:
           return len(self.forward	)


class 			A__	(						Generic[KT, VT]):


     """simple docstring"""

     def __init__(	self:						List[str]     ,		__a:						float = 0.5     ,		__a:						int = 16	)->    Dict:
           lowerCamelCase					:					Node[KT, VT]        =	Node[KT, VT]()
           lowerCamelCase					:					Optional[int]        =	0
           lowerCamelCase					:					Optional[Any]        =	p
           lowerCamelCase					:					Optional[Any]        =	max_level

     def __str__(	self:						List[str]	)->    str:
           lowerCamelCase					:					List[Any]        =	list(self	)

           if len(__a	) == 0:
                 return f'SkipList(level={self.level})'

           lowerCamelCase					:					List[Any]        =	max((len(str(__a	)	) for item in items)     ,		default=4	)
           lowerCamelCase					:					List[Any]        =	max(__a     ,		4	) + 4

           lowerCamelCase					:					List[Any]        =	self.head
           lowerCamelCase					:					Any        =	[]

           lowerCamelCase					:					Dict        =	node.forward.copy()
           lines.append(f'[{node.key}]'.ljust(__a     ,		"""-"""	) + """* """ * len(__a	)	)
           lines.append(""" """ * label_size + """| """ * len(__a	)	)

           while len(node.forward	) != 0:
                 lowerCamelCase					:					Tuple        =	node.forward[0]

                 lines.append(
                     f'[{node.key}]'.ljust(__a     ,		"""-"""	)
                     + """ """.join(str(n.key	) if n.key == node.key else """|""" for n in forwards	)	)
                 lines.append(""" """ * label_size + """| """ * len(__a	)	)
                 lowerCamelCase					:					Union[str, Any]        =	node.forward

           lines.append("""None""".ljust(__a	) + """* """ * len(__a	)	)
           return f'SkipList(level={self.level})\n' + "\n".join(__a	)

     def __iter__(	self:						List[Any]	)->    Tuple:
           lowerCamelCase					:					Tuple        =	self.head

           while len(node.forward	) != 0:
                 yield node.forward[0].key
                 lowerCamelCase					:					Dict        =	node.forward[0]

     def 		a__						(	self:						Dict	)->    int:
           lowerCamelCase					:					Any        =	1
           while random() < self.p and level < self.max_level:
                 level += 1

           return level

     def 		a__						(	self:						Tuple     ,		__a:						Optional[int]	)->    tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:
           lowerCamelCase					:					Union[str, Any]        =	[]

           lowerCamelCase					:					List[str]        =	self.head

           for i in reversed(range(self.level	)	):
                 # i < node.level - When node level is lesser than `i` decrement `i`.
                 # node.forward[i].key < key - Jumping to node with key value higher
                 #                             or equal to searched key would result
                 #                             in skipping searched key.
                 while i < node.level and node.forward[i].key < key:
                       lowerCamelCase					:					List[str]        =	node.forward[i]
                 # Each leftmost node (relative to searched node) will potentially have to
                 # be updated.
                 update_vector.append(__a	)

           update_vector.reverse()  # Note that we were inserting values in reverse order.

           # len(node.forward) != 0 - If current node doesn't contain any further
           #                          references then searched key is not present.
           # node.forward[0].key == key - Next node key should be equal to search key
           #                              if key is present.
           if len(node.forward	) != 0 and node.forward[0].key == key:
                 return node.forward[0], update_vector
           else:
                 return None, update_vector

     def 		a__						(	self:						Optional[Any]     ,		__a:						KT	)->    Any:
           lowerCamelCase     ,			lowerCamelCase					:					Tuple        =	self._locate_node(__a	)

           if node is not None:
                 for i, update_node in enumerate(__a	):
                       # Remove or replace all references to removed node.
                       if update_node.level > i and update_node.forward[i].key == key:
                             if node.level > i:
                                   lowerCamelCase					:					Dict        =	node.forward[i]
                             else:
                                   lowerCamelCase					:					Optional[Any]        =	update_node.forward[:i]

     def 		a__						(	self:						Tuple     ,		__a:						KT     ,		__a:						VT	)->    List[str]:
           lowerCamelCase     ,			lowerCamelCase					:					Dict        =	self._locate_node(__a	)
           if node is not None:
                 lowerCamelCase					:					Union[str, Any]        =	value
           else:
                 lowerCamelCase					:					str        =	self.random_level()

                 if level > self.level:
                       # After level increase we have to add additional nodes to head.
                       for _ in range(self.level - 1     ,		__a	):
                             update_vector.append(self.head	)
                       lowerCamelCase					:					Dict        =	level

                 lowerCamelCase					:					Union[str, Any]        =	Node(__a     ,		__a	)

                 for i, update_node in enumerate(update_vector[:level]	):
                       # Change references to pass through new node.
                       if update_node.level > i:
                             new_node.forward.append(update_node.forward[i]	)

                       if update_node.level < i + 1:
                             update_node.forward.append(__a	)
                       else:
                             lowerCamelCase					:					Tuple        =	new_node







     def 		a__						(	self:						Optional[Any]     ,		__a:						VT	)->    VT | None:
           lowerCamelCase     ,			lowerCamelCase					:					Dict        =	self._locate_node(__a	)

           if node is not None:
                 return node.value

           return None




def   snake_case		(     )   ->      str:
      lowerCamelCase					:					Optional[Any]        =	SkipList()
      skip_list.insert("""Key1"""			,				3    )
      skip_list.insert("""Key2"""			,				12    )
      skip_list.insert("""Key3"""			,				41    )
      skip_list.insert("""Key4"""			,				-19    )

      lowerCamelCase					:					Optional[int]        =	skip_list.head
      lowerCamelCase					:					int        =	{}
      while node.level != 0:
            lowerCamelCase					:					str        =	node.forward[0]
            lowerCamelCase					:					int        =	node.value

      assert len(UpperCamelCase__    ) == 4
      assert all_values["Key1"] == 3
      assert all_values["Key2"] == 12
      assert all_values["Key3"] == 41
      assert all_values["Key4"] == -19




def   snake_case		(     )   ->      Tuple:
      lowerCamelCase					:					Union[str, Any]        =	SkipList()
      skip_list.insert("""Key1"""			,				10    )
      skip_list.insert("""Key1"""			,				12    )

      skip_list.insert("""Key5"""			,				7    )
      skip_list.insert("""Key7"""			,				10    )
      skip_list.insert("""Key10"""			,				5    )

      skip_list.insert("""Key7"""			,				7    )
      skip_list.insert("""Key5"""			,				5    )
      skip_list.insert("""Key10"""			,				10    )

      lowerCamelCase					:					List[str]        =	skip_list.head
      lowerCamelCase					:					Any        =	{}
      while node.level != 0:
            lowerCamelCase					:					str        =	node.forward[0]
            lowerCamelCase					:					Tuple        =	node.value

      if len(UpperCamelCase__    ) != 4:
            print()
      assert len(UpperCamelCase__    ) == 4
      assert all_values["Key1"] == 12
      assert all_values["Key7"] == 7
      assert all_values["Key5"] == 5
      assert all_values["Key10"] == 10




def   snake_case		(     )   ->      List[Any]:
      lowerCamelCase					:					Dict        =	SkipList()
      assert skip_list.find("""Some key"""    ) is None




def   snake_case		(     )   ->      Optional[int]:
      lowerCamelCase					:					Any        =	SkipList()

      skip_list.insert("""Key2"""			,				20    )
      assert skip_list.find("""Key2"""    ) == 20

      skip_list.insert("""Some Key"""			,				10    )
      skip_list.insert("""Key2"""			,				8    )
      skip_list.insert("""V"""			,				13    )

      assert skip_list.find("""Y"""    ) is None
      assert skip_list.find("""Key2"""    ) == 8
      assert skip_list.find("""Some Key"""    ) == 10
      assert skip_list.find("""V"""    ) == 13




def   snake_case		(     )   ->      Any:
      lowerCamelCase					:					Union[str, Any]        =	SkipList()
      skip_list.delete("""Some key"""    )

      assert len(skip_list.head.forward    ) == 0




def   snake_case		(     )   ->      List[Any]:
      lowerCamelCase					:					List[str]        =	SkipList()

      skip_list.insert("""Key1"""			,				12    )
      skip_list.insert("""V"""			,				13    )
      skip_list.insert("""X"""			,				14    )
      skip_list.insert("""Key2"""			,				15    )

      skip_list.delete("""V"""    )
      skip_list.delete("""Key2"""    )

      assert skip_list.find("""V"""    ) is None
      assert skip_list.find("""Key2"""    ) is None




def   snake_case		(     )   ->      List[str]:
      lowerCamelCase					:					List[str]        =	SkipList()

      skip_list.insert("""Key1"""			,				12    )
      skip_list.insert("""V"""			,				13    )
      skip_list.insert("""X"""			,				14    )
      skip_list.insert("""Key2"""			,				15    )

      skip_list.delete("""V"""    )
      assert skip_list.find("""V"""    ) is None
      assert skip_list.find("""X"""    ) == 14
      assert skip_list.find("""Key1"""    ) == 12
      assert skip_list.find("""Key2"""    ) == 15

      skip_list.delete("""X"""    )
      assert skip_list.find("""V"""    ) is None
      assert skip_list.find("""X"""    ) is None
      assert skip_list.find("""Key1"""    ) == 12
      assert skip_list.find("""Key2"""    ) == 15

      skip_list.delete("""Key1"""    )
      assert skip_list.find("""V"""    ) is None
      assert skip_list.find("""X"""    ) is None
      assert skip_list.find("""Key1"""    ) is None
      assert skip_list.find("""Key2"""    ) == 15

      skip_list.delete("""Key2"""    )
      assert skip_list.find("""V"""    ) is None
      assert skip_list.find("""X"""    ) is None
      assert skip_list.find("""Key1"""    ) is None
      assert skip_list.find("""Key2"""    ) is None




def   snake_case		(     )   ->      Tuple:
      lowerCamelCase					:					str        =	SkipList()

      skip_list.insert("""Key1"""			,				12    )
      skip_list.insert("""V"""			,				13    )
      skip_list.insert("""X"""			,				142    )
      skip_list.insert("""Key2"""			,				15    )

      skip_list.delete("""X"""    )

      def traverse_keys(UpperCamelCase__       :						Optional[int]    ):
            yield node.key
            for forward_node in node.forward:
                  yield from traverse_keys(UpperCamelCase__    )

      assert len(set(traverse_keys(skip_list.head    )    )    ) == 4




def   snake_case		(     )   ->      Any:
      def is_sorted(UpperCamelCase__       :						List[Any]    ):
            return all(next_item >= item for item, next_item in zip(UpperCamelCase__			,				lst[1:]    )    )

      lowerCamelCase					:					Union[str, Any]        =	SkipList()
      for i in range(10    ):
            skip_list.insert(UpperCamelCase__			,				UpperCamelCase__    )
      assert is_sorted(list(UpperCamelCase__    )    )
      skip_list.delete(5    )
      skip_list.delete(8    )
      skip_list.delete(2    )
      assert is_sorted(list(UpperCamelCase__    )    )
      skip_list.insert(-12			,				-12    )
      skip_list.insert(77			,				77    )
      assert is_sorted(list(UpperCamelCase__    )    )




def   snake_case		(     )   ->      str:
      for _ in range(100    ):
            # Repeat test 100 times due to the probabilistic nature of skip list
            # random values == random bugs
            test_insert()
            test_insert_overrides_existing_value()

            test_searching_empty_list_returns_none()
            test_search()

            test_deleting_item_from_empty_list_do_nothing()
            test_deleted_items_are_not_founded_by_find_method()
            test_delete_removes_only_given_key()
            test_delete_doesnt_leave_dead_nodes()

            test_iter_always_yields_sorted_values()




def   snake_case		(     )   ->      str:
      lowerCamelCase					:					Optional[Any]        =	SkipList()
      skip_list.insert(2			,				"""2"""    )
      skip_list.insert(4			,				"""4"""    )
      skip_list.insert(6			,				"""4"""    )
      skip_list.insert(4			,				"""5"""    )
      skip_list.insert(8			,				"""4"""    )
      skip_list.insert(9			,				"""4"""    )

      skip_list.delete(4    )

      print(UpperCamelCase__    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()








"""simple docstring"""


import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Optional[Any]=None    )   ->      Tuple:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
      lowerCamelCase					:					Dict        =	nn.Parameter(UpperCamelCase__    )
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
            lowerCamelCase					:					Any        =	nn.Parameter(UpperCamelCase__    )




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						Any			,				UpperCamelCase__       :						Tuple    )   ->      Union[str, Any]:
      # set torch weights for 1-to-1 comparison
      lowerCamelCase					:					Dict        =	np.asarray(weights[0]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[1]    )
      lowerCamelCase					:					List[str]        =	np.asarray(weights[2]    )

      set_param(
          torch_layer.self_attention.query_key			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.self_attention.value			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.output.dense			,				torch.tensor(UpperCamelCase__    ).view(-1			,				UpperCamelCase__    ).contiguous().transpose(0			,				1    )			,				)




def   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Any    )   ->      List[Any]:
      # set torch weights for 1-to-1 comparison
      lowerCamelCase					:					Tuple        =	np.asarray(weights[0]    )
      lowerCamelCase					:					Any        =	np.asarray(weights[1]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[2]    )
      lowerCamelCase					:					List[str]        =	np.asarray(weights[3]    )

      set_param(
          torch_layer.self_attention.query			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.self_attention.key			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.self_attention.value			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.output.dense			,				torch.tensor(UpperCamelCase__    ).view(-1			,				UpperCamelCase__    ).contiguous().transpose(0			,				1    )			,				)




def   snake_case		(     UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						Any    )   ->      Optional[Any]:
      # layernorm 1
      lowerCamelCase					:					str        =	weights[0][0][0]
      lowerCamelCase					:					Optional[int]        =	np.asarray(layer_norm_a[0]    )
      lowerCamelCase					:					Tuple        =	np.asarray(layer_norm_a[1]    )
      set_param(
          torch_block.attention.layer_norm			,				torch.tensor(UpperCamelCase__    )			,				torch.tensor(UpperCamelCase__    )			,				)

      # lsh weights + output
      lowerCamelCase					:					List[Any]        =	weights[0][1]
      if len(UpperCamelCase__    ) < 4:
            set_layer_weights_in_torch_lsh(UpperCamelCase__			,				torch_block.attention			,				UpperCamelCase__    )
      else:
            set_layer_weights_in_torch_local(UpperCamelCase__			,				torch_block.attention			,				UpperCamelCase__    )

      # intermediate weighs
      lowerCamelCase					:					int        =	weights[2][0][1][2]

      # Chunked Feed Forward
      if len(UpperCamelCase__    ) == 4:
            lowerCamelCase					:					Dict        =	intermediate_weights[2]

      # layernorm 2
      lowerCamelCase					:					Optional[int]        =	np.asarray(intermediate_weights[0][0]    )
      lowerCamelCase					:					Tuple        =	np.asarray(intermediate_weights[0][1]    )
      set_param(
          torch_block.feed_forward.layer_norm			,				torch.tensor(UpperCamelCase__    )			,				torch.tensor(UpperCamelCase__    )			,				)

      # intermediate dense
      lowerCamelCase					:					Optional[Any]        =	np.asarray(intermediate_weights[1][0]    )
      lowerCamelCase					:					Union[str, Any]        =	np.asarray(intermediate_weights[1][1]    )
      set_param(
          torch_block.feed_forward.dense.dense			,				torch.tensor(UpperCamelCase__    ).transpose(0			,				1    ).contiguous()			,				torch.tensor(UpperCamelCase__    )			,				)

      # intermediate out
      lowerCamelCase					:					Optional[int]        =	np.asarray(intermediate_weights[4][0]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(intermediate_weights[4][1]    )
      set_param(
          torch_block.feed_forward.output.dense			,				torch.tensor(UpperCamelCase__    ).transpose(0			,				1    ).contiguous()			,				torch.tensor(UpperCamelCase__    )			,				)




def   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						int    )   ->      List[Any]:
      # reformer model
      lowerCamelCase					:					List[Any]        =	torch_model.reformer

      # word embeds
      lowerCamelCase					:					Union[str, Any]        =	np.asarray(weights[1]    )
      set_param(
          torch_model_reformer.embeddings.word_embeddings			,				torch.tensor(UpperCamelCase__    )			,				)

      if isinstance(weights[3]			,				UpperCamelCase__    ):
            lowerCamelCase					:					Union[str, Any]        =	torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights    )    ):
                  lowerCamelCase					:					str        =	np.asarray(weights[3][emb_idx][0]    )
                  assert (
                      position_embeddings.weights[emb_idx].shape == emb_weights.shape
                  ), F'{position_embeddings[emb_idx]} emb does not match'
                  lowerCamelCase					:					Dict        =	nn.Parameter(torch.tensor(UpperCamelCase__    )    )

      lowerCamelCase					:					int        =	weights[5]
      assert len(torch_model_reformer.encoder.layers    ) * 4 == len(
          UpperCamelCase__    ), "HF and trax model do not have the same number of layers"
      for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers    ):
            lowerCamelCase					:					Dict        =	trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      # output layer norm
      lowerCamelCase					:					Any        =	np.asarray(weights[7][0]    )
      lowerCamelCase					:					List[str]        =	np.asarray(weights[7][1]    )
      set_param(
          torch_model_reformer.encoder.layer_norm			,				torch.tensor(UpperCamelCase__    )			,				torch.tensor(UpperCamelCase__    )			,				)

      # output embeddings
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[9][0]    )
      lowerCamelCase					:					Optional[int]        =	np.asarray(weights[9][1]    )
      set_param(
          torch_model.lm_head.decoder			,				torch.tensor(UpperCamelCase__    ).transpose(0			,				1    ).contiguous()			,				torch.tensor(UpperCamelCase__    )			,				)




def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						str    )   ->      Optional[int]:
      # Initialise PyTorch model
      lowerCamelCase					:					Union[str, Any]        =	ReformerConfig.from_json_file(UpperCamelCase__    )
      print(F'Building PyTorch model from configuration: {config}'    )
      lowerCamelCase					:					str        =	ReformerModelWithLMHead(UpperCamelCase__    )

      with open(UpperCamelCase__			,				"""rb"""    ) as f:
            lowerCamelCase					:					str        =	pickle.load(UpperCamelCase__    )["""weights"""]

      set_model_weights_in_torch(UpperCamelCase__			,				UpperCamelCase__			,				config.hidden_size    )

      # Save pytorch-model
      print(F'Save PyTorch model to {pytorch_dump_path}'    )
      torch.save(model.state_dict()			,				UpperCamelCase__    )


if __name__ == "__main__":
    __lowerCamelCase      :Dict	    =		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained Reformer model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    __lowerCamelCase      :Optional[int]	    =		parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




