

'''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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__=None				)			->			str:
       # set parameter of one layer
       assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
       A_						=      nn.Parameter(UpperCAmelCase__				)
       if bias is not None:
              assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
              A_						=      nn.Parameter(UpperCAmelCase__				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			str:
       # set torch weights for 1-to-1 comparison
       A_						=      np.asarray(weights[0]				)
       A_						=      np.asarray(weights[1]				)
       A_						=      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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Optional[int]:
       # set torch weights for 1-to-1 comparison
       A_						=      np.asarray(weights[0]				)
       A_						=      np.asarray(weights[1]				)
       A_						=      np.asarray(weights[2]				)
       A_						=      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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Optional[int]:
       # layernorm 1
       A_						=      weights[0][0][0]
       A_						=      np.asarray(layer_norm_a[0]				)
       A_						=      np.asarray(layer_norm_a[1]				)
       set_param(
           torch_block.attention.layer_norm,       torch.tensor(UpperCAmelCase__				),       torch.tensor(UpperCAmelCase__				),       )

       # lsh weights + output
       A_						=      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
       A_						=      weights[2][0][1][2]

       # Chunked Feed Forward
       if len(UpperCAmelCase__				) == 4:
              A_						=      intermediate_weights[2]

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

       # intermediate dense
       A_						=      np.asarray(intermediate_weights[1][0]				)
       A_						=      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
       A_						=      np.asarray(intermediate_weights[4][0]				)
       A_						=      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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			List[str]:
       # reformer model
       A_						=      torch_model.reformer

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

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

       A_						=      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				):
              A_						=      trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
              set_block_weights_in_torch(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

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

       # output embeddings
       A_						=      np.asarray(weights[9][0]				)
       A_						=      np.asarray(weights[9][1]				)
       set_param(
           torch_model.lm_head.decoder,       torch.tensor(UpperCAmelCase__				).transpose(0,       1				).contiguous(),       torch.tensor(UpperCAmelCase__				),       )
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Any:
       # Initialise PyTorch model
       A_						=      ReformerConfig.from_json_file(UpperCAmelCase__				)
       print(F'''Building PyTorch model from configuration: {config}'''				)
       A_						=      ReformerModelWithLMHead(UpperCAmelCase__				)

       with open(UpperCAmelCase__,       """rb"""				) as f:
              A_						=      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__":
      __lowerCamelCase		       =    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		       =    parser.parse_args()
      convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCamelCase		       =    logging.get_logger(__name__)


class 					A__      (      _snake_case			):



    def __init__(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      None:





           '''simple docstring'''
           warnings.warn(
               """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
               """ use BeitImageProcessor instead."""		,				UpperCamelCase__		,				)
           super().__init__(*UpperCamelCase__		,				**UpperCamelCase__       )
