

'''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    A_  ( snake_case      ,       snake_case      ,       snake_case=None ):
  # set parameter of one layer
  assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
  SCREAMING_SNAKE_CASE: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'''
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					nn.Parameter(snake_case )





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

  # lsh weights + output
  SCREAMING_SNAKE_CASE:Tuple						      =					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
  SCREAMING_SNAKE_CASE:Tuple						      =					weights[2][0][1][2]

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

  # layernorm 2
  SCREAMING_SNAKE_CASE:Dict						      =					np.asarray(intermediate_weights[0][0] )
  SCREAMING_SNAKE_CASE:List[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
  SCREAMING_SNAKE_CASE:Any						      =					np.asarray(intermediate_weights[1][0] )
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					np.asarray(intermediate_weights[4][0] )
  SCREAMING_SNAKE_CASE:List[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    A_  ( snake_case      ,       snake_case      ,       snake_case ):
  # reformer model
  SCREAMING_SNAKE_CASE:Any						      =					torch_model.reformer

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

  if isinstance(weights[3]      ,       snake_case ):
    SCREAMING_SNAKE_CASE:Dict						      =					torch_model_reformer.embeddings.position_embeddings
    for emb_idx in range(len(position_embeddings.weights ) ):
      SCREAMING_SNAKE_CASE:Union[str, 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'''
      SCREAMING_SNAKE_CASE:List[Any]						      =					nn.Parameter(torch.tensor(snake_case ) )

  SCREAMING_SNAKE_CASE:List[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 ):
    SCREAMING_SNAKE_CASE:List[Any]						      =					trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
    set_block_weights_in_torch(snake_case      ,       snake_case      ,       snake_case )

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

  # output embeddings
  SCREAMING_SNAKE_CASE:Optional[int]						      =					np.asarray(weights[9][0] )
  SCREAMING_SNAKE_CASE:int						      =					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    A_  ( snake_case      ,       snake_case      ,       snake_case ):
  # Initialise PyTorch model
  SCREAMING_SNAKE_CASE:Dict						      =					ReformerConfig.from_json_file(snake_case )
  print(F'''Building PyTorch model from configuration: {config}''' )
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					ReformerModelWithLMHead(snake_case )

  with open(snake_case      ,       "rb" ) as f:
    SCREAMING_SNAKE_CASE:Dict						      =					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_       						=							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_       						=							parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)









'''simple docstring'''

import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
A_       						=							[
    "kernels/rwkv/wkv_cuda.cu",
    "kernels/rwkv/wkv_op.cpp",
    "kernels/deformable_detr/ms_deform_attn.h",
    "kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh",
    "models/graphormer/algos_graphormer.pyx",
]





def    A_  ( snake_case ):
  # Test all the extensions added in the setup
  for file in FILES_TO_FIND:
    if not (transformers_path / file).exists():
      return False
  return True


if __name__ == "__main__":
    A_       						=							argparse.ArgumentParser()
    parser.add_argument("--check_lib", action="store_true", help="Whether to check the build or the actual package.")
    A_       						=							parser.parse_args()
    if args.check_lib:
        A_       						=							importlib.import_module("transformers")
        A_       						=							Path(transformers_module.__file__).parent
    else:
        A_       						=							Path.cwd() / "build/lib/transformers"
    if not test_custom_files_are_present(transformers_path):
        raise ValueError("The built release does not contain the custom files. Fix this before going further!")






