


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       lowercase_		(		_A			:    Tuple    ,					_A			:    Any    ,					_A			:    int=None						):

      """simple docstring"""




      assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
      lowerCamelCase__		:				List[str]    =  nn.Parameter(__snake_case						)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
            lowerCamelCase__		:				Tuple    =  nn.Parameter(__snake_case						)

def       lowercase_		(		_A			:    Dict    ,					_A			:    Tuple    ,					_A			:    List[Any]						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  np.asarray(weights[0]						)
      lowerCamelCase__		:				Tuple    =  np.asarray(weights[1]						)
      lowerCamelCase__		:				str    =  np.asarray(weights[2]						)

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

def       lowercase_		(		_A			:    List[Any]    ,					_A			:    List[Any]    ,					_A			:    Optional[Any]						):

      """simple docstring"""




      lowerCamelCase__		:				str    =  np.asarray(weights[0]						)
      lowerCamelCase__		:				Optional[int]    =  np.asarray(weights[1]						)
      lowerCamelCase__		:				Dict    =  np.asarray(weights[2]						)
      lowerCamelCase__		:				List[Any]    =  np.asarray(weights[3]						)

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

def       lowercase_		(		_A			:    List[str]    ,					_A			:    Any    ,					_A			:    Optional[Any]						):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  weights[0][0][0]
      lowerCamelCase__		:				Tuple    =  np.asarray(layer_norm_a[0]						)
      lowerCamelCase__		:				Any    =  np.asarray(layer_norm_a[1]						)
      set_param(
          torch_block.attention.layer_norm    ,					torch.tensor(__snake_case						)    ,					torch.tensor(__snake_case						)    ,					)

      # lsh weights + output
      lowerCamelCase__		:				Union[str, Any]    =  weights[0][1]
      if len(__snake_case						) < 4:
            set_layer_weights_in_torch_lsh(__snake_case    ,					torch_block.attention    ,					__snake_case						)
      else:
            set_layer_weights_in_torch_local(__snake_case    ,					torch_block.attention    ,					__snake_case						)

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

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

      # layernorm 2
      lowerCamelCase__		:				Any    =  np.asarray(intermediate_weights[0][0]						)
      lowerCamelCase__		:				str    =  np.asarray(intermediate_weights[0][1]						)
      set_param(
          torch_block.feed_forward.layer_norm    ,					torch.tensor(__snake_case						)    ,					torch.tensor(__snake_case						)    ,					)

      # intermediate dense
      lowerCamelCase__		:				Optional[int]    =  np.asarray(intermediate_weights[1][0]						)
      lowerCamelCase__		:				Tuple    =  np.asarray(intermediate_weights[1][1]						)
      set_param(
          torch_block.feed_forward.dense.dense    ,					torch.tensor(__snake_case						).transpose(0    ,					1						).contiguous()    ,					torch.tensor(__snake_case						)    ,					)

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

def       lowercase_		(		_A			:    List[Any]    ,					_A			:    Optional[Any]    ,					_A			:    Optional[int]						):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  torch_model.reformer

      # word embeds
      lowerCamelCase__		:				Dict    =  np.asarray(weights[1]						)
      set_param(
          torch_model_reformer.embeddings.word_embeddings    ,					torch.tensor(__snake_case						)    ,					)

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

      lowerCamelCase__		:				Any    =  weights[5]
      assert len(torch_model_reformer.encoder.layers						) * 4 == len(
          __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						):
            lowerCamelCase__		:				List[str]    =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(__snake_case    ,					__snake_case    ,					__snake_case						)

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

      # output embeddings
      lowerCamelCase__		:				Union[str, Any]    =  np.asarray(weights[9][0]						)
      lowerCamelCase__		:				Optional[Any]    =  np.asarray(weights[9][1]						)
      set_param(
          torch_model.lm_head.decoder    ,					torch.tensor(__snake_case						).transpose(0    ,					1						).contiguous()    ,					torch.tensor(__snake_case						)    ,					)

def       lowercase_		(		_A			:    str    ,					_A			:    int    ,					_A			:    List[Any]						):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  ReformerConfig.from_json_file(__snake_case						)
      print(F"Building PyTorch model from configuration: {config}"						)
      lowerCamelCase__		:				Optional[int]    =  ReformerModelWithLMHead(__snake_case						)

      with open(__snake_case    ,					"rb"						) as f:
            lowerCamelCase__		:				List[str]    =  pickle.load(__snake_case						)["weights"]

      set_model_weights_in_torch(__snake_case    ,					__snake_case    ,					config.hidden_size						)

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


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






from typing import TYPE_CHECKING

from ..utils import _LazyModule


UpperCAmelCase__   	=							{
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
  from .config import (
      EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
      OnnxConfig,
      OnnxConfigWithPast,
      OnnxSeqaSeqConfigWithPast,
      PatchingSpec,
  )
  from .convert import export, validate_model_outputs
  from .features import FeaturesManager
  from .utils import ParameterFormat, compute_serialized_parameters_size

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

