



"""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						:		int			,		UpperCamelCase						:		Dict=None						):
 # set parameter of one layer
 assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
 UpperCAmelCase	:					List[str]	=	nn.Parameter(UpperCamelCase						)
 if bias is not None:
  assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
  UpperCAmelCase	:					Optional[Any]	=	nn.Parameter(UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		List[str]						):
 # set torch weights for 1-to-1 comparison
 UpperCAmelCase	:					List[str]	=	np.asarray(weights[0]						)
 UpperCAmelCase	:					Any	=	np.asarray(weights[1]						)
 UpperCAmelCase	:					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						:		str			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Any						):
 # set torch weights for 1-to-1 comparison
 UpperCAmelCase	:					Any	=	np.asarray(weights[0]						)
 UpperCAmelCase	:					List[str]	=	np.asarray(weights[1]						)
 UpperCAmelCase	:					Tuple	=	np.asarray(weights[2]						)
 UpperCAmelCase	:					Optional[int]	=	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						:		List[str]			,		UpperCamelCase						:		List[str]			,		UpperCamelCase						:		str						):
 # layernorm 1
 UpperCAmelCase	:					Union[str, Any]	=	weights[0][0][0]
 UpperCAmelCase	:					Tuple	=	np.asarray(layer_norm_a[0]						)
 UpperCAmelCase	:					str	=	np.asarray(layer_norm_a[1]						)
 set_param(
     torch_block.attention.layer_norm			,		torch.tensor(UpperCamelCase						)			,		torch.tensor(UpperCamelCase						)			,		)

 # lsh weights + output
 UpperCAmelCase	:					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
 UpperCAmelCase	:					List[Any]	=	weights[2][0][1][2]

 # Chunked Feed Forward
 if len(UpperCamelCase						) == 4:
  UpperCAmelCase	:					List[Any]	=	intermediate_weights[2]

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

 # intermediate dense
 UpperCAmelCase	:					Optional[Any]	=	np.asarray(intermediate_weights[1][0]						)
 UpperCAmelCase	:					str	=	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
 UpperCAmelCase	:					List[Any]	=	np.asarray(intermediate_weights[4][0]						)
 UpperCAmelCase	:					Optional[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						:		Union[str, Any]			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Any						):
 # reformer model
 UpperCAmelCase	:					str	=	torch_model.reformer

 # word embeds
 UpperCAmelCase	:					Optional[Any]	=	np.asarray(weights[1]						)
 set_param(
     torch_model_reformer.embeddings.word_embeddings			,		torch.tensor(UpperCamelCase						)			,		)

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

 UpperCAmelCase	:					List[str]	=	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						):
  UpperCAmelCase	:					str	=	trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
  set_block_weights_in_torch(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)

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

 # output embeddings
 UpperCAmelCase	:					List[str]	=	np.asarray(weights[9][0]						)
 UpperCAmelCase	:					Dict	=	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						:		Optional[Any]			,		UpperCamelCase						:		str			,		UpperCamelCase						:		str						):
 # Initialise PyTorch model
 UpperCAmelCase	:					int	=	ReformerConfig.from_json_file(UpperCamelCase						)
 print(F"Building PyTorch model from configuration: {config}"						)
 UpperCAmelCase	:					List[Any]	=	ReformerModelWithLMHead(UpperCamelCase						)

 with open(UpperCamelCase			,		"""rb"""						) as f:
  UpperCAmelCase	:					List[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__":
      A:   List[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."
      )
      A:   Any								=		parser.parse_args()
      convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





"""simple docstring"""
A:   Union[str, Any]								=		{
    0: "0",
    1: "1",
    2: "2",
    3: "3",
    4: "4",
    5: "5",
    6: "6",
    7: "7",
    8: "8",
    9: "9",
    1_0: "a",
    1_1: "b",
    1_2: "c",
    1_3: "d",
    1_4: "e",
    1_5: "f",
}

def       _snake_case						(						UpperCamelCase						:		float						):
 assert type(UpperCamelCase						) in (int, float) and decimal == int(UpperCamelCase						)
 UpperCAmelCase	:					str	=	int(UpperCamelCase						)
 UpperCAmelCase	:					Optional[int]	=	""""""
 UpperCAmelCase	:					List[str]	=	False
 if decimal < 0:
  UpperCAmelCase	:					Any	=	True
  decimal *= -1
 while decimal > 0:
  UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	divmod(UpperCamelCase			,		16						)
  UpperCAmelCase	:					Union[str, Any]	=	values[remainder] + hexadecimal
 UpperCAmelCase	:					int	=	"""0x""" + hexadecimal
 if negative:
  UpperCAmelCase	:					Optional[int]	=	"""-""" + hexadecimal
 return hexadecimal


if __name__ == "__main__":
      import doctest

      doctest.testmod()
