


from __future__ import annotations







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase      ):
       snake_case_ =   TypeError(
           '''Matrices must be formed from a list of zero or more lists containing at '''
           '''least one and the same number of values, each of which must be of type '''
           '''int or float.'''      )
       if len(_UpperCAmelCase      ) != 0:
             snake_case_ =   len(rows[0]      )
             if cols == 0:
                   raise error
             for row in rows:
                   if len(_UpperCAmelCase      ) != cols:
                         raise error
                   for value in row:
                         if not isinstance(_UpperCAmelCase	,   (int, float)      ):
                               raise error
             snake_case_ =   rows
       else:
             snake_case_ =   []



 def 		UpperCamelCase__  (			self      ):
       return [[row[i] for row in self.rows] for i in range(len(self.rows[0]      )      )]



 @property
 def 		UpperCamelCase__  (			self      ):
       return len(self.rows      )



 @property
 def 		UpperCamelCase__  (			self      ):
       return len(self.rows[0]      )



 @property
 def 		UpperCamelCase__  (			self      ):
       return (self.num_rows, self.num_columns)



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.order[0] == self.order[1]



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   [
           [0 if column_num != row_num else 1 for column_num in range(self.num_rows      )]
           for row_num in range(self.num_rows      )
       ]
       return Matrix(_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       if not self.is_square:
             return 0
       if self.order == (0, 0):
             return 1
       if self.order == (1, 1):
             return int(self.rows[0][0]      )
       if self.order == (2, 2):
             return int(
                 (self.rows[0][0] * self.rows[1][1])
                 - (self.rows[0][1] * self.rows[1][0])      )
       else:
             return sum(
                 self.rows[0][column] * self.cofactors().rows[0][column]
                 for column in range(self.num_columns      )      )



 def 		UpperCamelCase__  (			self      ):
       return bool(self.determinant()      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   [
           [
               self.rows[other_row][other_column]
               for other_column in range(self.num_columns      )
               if other_column != column
           ]
           for other_row in range(self.num_rows      )
           if other_row != row
       ]
       return Matrix(_UpperCAmelCase      ).determinant()



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       if (row + column) % 2 == 0:
             return self.get_minor(_UpperCAmelCase	,   _UpperCAmelCase      )
       return -1 * self.get_minor(_UpperCAmelCase	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       return Matrix(
           [
               [self.get_minor(_UpperCAmelCase	,   _UpperCAmelCase      ) for column in range(self.num_columns      )]
               for row in range(self.num_rows      )
           ]      )



 def 		UpperCamelCase__  (			self      ):
       return Matrix(
           [
               [
                   self.minors().rows[row][column]
                   if (row + column) % 2 == 0
                   else self.minors().rows[row][column] * -1
                   for column in range(self.minors().num_columns      )
               ]
               for row in range(self.minors().num_rows      )
           ]      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   [
           [self.cofactors().rows[column][row] for column in range(self.num_columns      )]
           for row in range(self.num_rows      )
       ]
       return Matrix(_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.determinant()
       if not determinant:
             raise TypeError('''Only matrices with a non-zero determinant have an inverse'''      )
       return self.adjugate() * (1 / determinant)



 def __repr__(			self      ):
       return str(self.rows      )



 def __str__(			self      ):
       if self.num_rows == 0:
             return "[]"
       if self.num_rows == 1:
             return "[[" + ". ".join(str(self.rows[0]      )      ) + "]]"
       return (
           "["
           + "\n ".join(
               [
                   '''[''' + '''. '''.join([str(_UpperCAmelCase      ) for value in row]      ) + '''.]'''
                   for row in self.rows
               ]      )
           + "]"
       )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
       snake_case_ =   TypeError('''Row must be a list containing all ints and/or floats'''      )
       if not isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             raise type_error
       for value in row:
             if not isinstance(_UpperCAmelCase	,   (int, float)      ):
                   raise type_error
       if len(_UpperCAmelCase      ) != self.num_columns:
             raise ValueError(
                 '''Row must be equal in length to the other rows in the matrix'''      )
       if position is None:
             self.rows.append(_UpperCAmelCase      )
       else:
             snake_case_ =   self.rows[0:position] + [row] + self.rows[position:]



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
       snake_case_ =   TypeError(
           '''Column must be a list containing all ints and/or floats'''      )
       if not isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             raise type_error
       for value in column:
             if not isinstance(_UpperCAmelCase	,   (int, float)      ):
                   raise type_error
       if len(_UpperCAmelCase      ) != self.num_rows:
             raise ValueError(
                 '''Column must be equal in length to the other columns in the matrix'''      )
       if position is None:
             snake_case_ =   [self.rows[i] + [column[i]] for i in range(self.num_rows      )]
       else:
             snake_case_ =   [
                 self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                 for i in range(self.num_rows      )
             ]



 def __eq__(			self	,   _UpperCAmelCase      ):
       if not isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             return NotImplemented
       return self.rows == other.rows



 def __ne__(			self	,   _UpperCAmelCase      ):
       return not self == other



 def __neg__(			self      ):
       return self * -1



 def __add__(			self	,   _UpperCAmelCase      ):
       if self.order != other.order:
             raise ValueError('''Addition requires matrices of the same order'''      )
       return Matrix(
           [
               [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns      )]
               for i in range(self.num_rows      )
           ]      )



 def __sub__(			self	,   _UpperCAmelCase      ):
       if self.order != other.order:
             raise ValueError('''Subtraction requires matrices of the same order'''      )
       return Matrix(
           [
               [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns      )]
               for i in range(self.num_rows      )
           ]      )



 def __mul__(			self	,   _UpperCAmelCase      ):
       if isinstance(_UpperCAmelCase	,   (int, float)      ):
             return Matrix(
                 [[int(element * other      ) for element in row] for row in self.rows]      )
       elif isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             if self.num_columns != other.num_rows:
                   raise ValueError(
                       '''The number of columns in the first matrix must '''
                       '''be equal to the number of rows in the second'''      )
             return Matrix(
                 [
                     [Matrix.dot_product(_UpperCAmelCase	,   _UpperCAmelCase      ) for column in other.columns()]
                     for row in self.rows
                 ]      )
       else:
             raise TypeError(
                 '''A Matrix can only be multiplied by an int, float, or another matrix'''      )



 def __pow__(			self	,   _UpperCAmelCase      ):
       if not isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             raise TypeError('''A Matrix can only be raised to the power of an int'''      )
       if not self.is_square:
             raise ValueError('''Only square matrices can be raised to a power'''      )
       if other == 0:
             return self.identity()
       if other < 0:
             if self.is_invertable():
                   return self.inverse() ** (-other)
             raise ValueError(
                 '''Only invertable matrices can be raised to a negative power'''      )
       snake_case_ =   self
       for _ in range(other - 1      ):
             result *= self
       return result





 @classmethod
 def 		UpperCamelCase__  (			cls	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       return sum(row[i] * column[i] for i in range(len(_UpperCAmelCase      )      )      )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      )->						Any:




      """simple docstring"""

      assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
      snake_case_ =   nn.Parameter(SCREAMING_SNAKE_CASE      )
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
            snake_case_ =   nn.Parameter(SCREAMING_SNAKE_CASE      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   np.asarray(weights[0]      )
      snake_case_ =   np.asarray(weights[1]      )
      snake_case_ =   np.asarray(weights[2]      )

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   np.asarray(weights[0]      )
      snake_case_ =   np.asarray(weights[1]      )
      snake_case_ =   np.asarray(weights[2]      )
      snake_case_ =   np.asarray(weights[3]      )

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   weights[0][0][0]
      snake_case_ =   np.asarray(layer_norm_a[0]      )
      snake_case_ =   np.asarray(layer_norm_a[1]      )
      set_param(
          torch_block.attention.layer_norm      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

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

      # intermediate weighs
      snake_case_ =   weights[2][0][1][2]

      # Chunked Feed Forward
      if len(SCREAMING_SNAKE_CASE      ) == 4:
            snake_case_ =   intermediate_weights[2]

      # layernorm 2
      snake_case_ =   np.asarray(intermediate_weights[0][0]      )
      snake_case_ =   np.asarray(intermediate_weights[0][1]      )
      set_param(
          torch_block.feed_forward.layer_norm      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      # intermediate dense
      snake_case_ =   np.asarray(intermediate_weights[1][0]      )
      snake_case_ =   np.asarray(intermediate_weights[1][1]      )
      set_param(
          torch_block.feed_forward.dense.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(0      ,      1      ).contiguous()      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      # intermediate out
      snake_case_ =   np.asarray(intermediate_weights[4][0]      )
      snake_case_ =   np.asarray(intermediate_weights[4][1]      )
      set_param(
          torch_block.feed_forward.output.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(0      ,      1      ).contiguous()      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      snake_case_ =   torch_model.reformer

      # word embeds
      snake_case_ =   np.asarray(weights[1]      )
      set_param(
          torch_model_reformer.embeddings.word_embeddings      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      if isinstance(weights[3]      ,      SCREAMING_SNAKE_CASE      ):
            snake_case_ =   torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights      )      ):
                  snake_case_ =   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'''
                  snake_case_ =   nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE      )      )

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

      # output layer norm
      snake_case_ =   np.asarray(weights[7][0]      )
      snake_case_ =   np.asarray(weights[7][1]      )
      set_param(
          torch_model_reformer.encoder.layer_norm      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      # output embeddings
      snake_case_ =   np.asarray(weights[9][0]      )
      snake_case_ =   np.asarray(weights[9][1]      )
      set_param(
          torch_model.lm_head.decoder      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(0      ,      1      ).contiguous()      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   ReformerConfig.from_json_file(SCREAMING_SNAKE_CASE      )
      print(f'''Building PyTorch model from configuration: {config}'''      )
      snake_case_ =   ReformerModelWithLMHead(SCREAMING_SNAKE_CASE      )

      with open(SCREAMING_SNAKE_CASE      ,      '''rb'''      ) as f:
            snake_case_ =   pickle.load(SCREAMING_SNAKE_CASE      )['''weights''']

      set_model_weights_in_torch(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      config.hidden_size      )

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


if __name__ == "__main__":
   UpperCAmelCase    =					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."""
   )
   UpperCAmelCase    =					parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)