


'''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 _SCREAMING_SNAKE_CASE (A    ,					A    ,					A=None			)			->			Any:






       """simple docstring"""







       assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
       lowercase__      =					nn.Parameter(A			)
       if bias is not None:
              assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
              lowercase__      =					nn.Parameter(A			)

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			List[str]:






       """simple docstring"""







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

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

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			Dict:






       """simple docstring"""







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

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

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			List[str]:






       """simple docstring"""







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

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

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

       # Chunked Feed Forward
       if len(A			) == 4:
              lowercase__      =					intermediate_weights[2]

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

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

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

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			Optional[int]:






       """simple docstring"""







       lowercase__      =					torch_model.reformer

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

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

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

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

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

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			List[str]:






       """simple docstring"""







       lowercase__      =					ReformerConfig.from_json_file(A			)
       print(f"Building PyTorch model from configuration: {config}"			)
       lowercase__      =					ReformerModelWithLMHead(A			)

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

       set_model_weights_in_torch(A    ,					A    ,					config.hidden_size			)

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


if __name__ == "__main__":
 lowerCamelCase				:  Optional[Any]    		=      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				:  Any    		=      parser.parse_args()
 convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''

from __future__ import annotations

from typing import Any
class     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       Optional[Any]  ,		UpperCamelCase       :       int = 6					):

             '''simple docstring'''







             lowercase__      =					None
             lowercase__      =					None
             self.create_linked_list(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







             lowercase__      =					Node()
             lowercase__      =					current_node
             lowercase__      =					current_node
             lowercase__      =					current_node
             for _ in range(1  ,		UpperCamelCase					):
                    lowercase__      =					Node()
                    lowercase__      =					current_node
                    lowercase__      =					previous_node
                    lowercase__      =					current_node
             lowercase__      =					self.front
             lowercase__      =					previous_node



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             return (
                 self.front == self.rear
                 and self.front is not None
                 and self.front.data is None
             )



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             self.check_can_perform_operation()
             return self.front.data if self.front else None



      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       Any					):

             '''simple docstring'''







             if self.rear is None:
                    return

             self.check_is_full()
             if not self.is_empty():
                    lowercase__      =					self.rear.next
             if self.rear:
                    lowercase__      =					data



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             self.check_can_perform_operation()
             if self.rear is None or self.front is None:
                    return None
             if self.front == self.rear:
                    lowercase__      =					self.front.data
                    lowercase__      =					None
                    return data

             lowercase__      =					self.front
             lowercase__      =					old_front.next
             lowercase__      =					old_front.data
             lowercase__      =					None
             return data



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             if self.is_empty():
                    raise Exception('''Empty Queue'''					)






      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             if self.rear and self.rear.next == self.front:
                    raise Exception('''Full Queue'''					)






class     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       Any					):

             '''simple docstring'''







             lowercase__      =					None
             lowercase__      =					None
             lowercase__      =					None


if __name__ == "__main__":
 import doctest

 doctest.testmod()




