




"""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       (							lowercase_      ,			lowercase_      ,			lowercase_=None						)						->		Tuple:
 # set parameter of one layer
 assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
 A__	=    nn.Parameter(lowercase_						)
 if bias is not None:
  assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
  A__	=    nn.Parameter(lowercase_						)







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		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]						)

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Union[str, Any]:
 # 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(lowercase_						).transpose(1      ,			2						).contiguous().view(-1      ,			lowercase_						)      ,			)
 set_param(
     torch_layer.self_attention.key      ,			torch.tensor(lowercase_						).transpose(1      ,			2						).contiguous().view(-1      ,			lowercase_						)      ,			)
 set_param(
     torch_layer.self_attention.value      ,			torch.tensor(lowercase_						).transpose(1      ,			2						).contiguous().view(-1      ,			lowercase_						)      ,			)
 set_param(
     torch_layer.output.dense      ,			torch.tensor(lowercase_						).view(-1      ,			lowercase_						).contiguous().transpose(0      ,			1						)      ,			)







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Optional[Any]:
 # 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(lowercase_						)      ,			torch.tensor(lowercase_						)      ,			)

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

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

 # Chunked Feed Forward
 if len(lowercase_						) == 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(lowercase_						)      ,			torch.tensor(lowercase_						)      ,			)

 # 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(lowercase_						).transpose(0      ,			1						).contiguous()      ,			torch.tensor(lowercase_						)      ,			)

 # 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(lowercase_						).transpose(0      ,			1						).contiguous()      ,			torch.tensor(lowercase_						)      ,			)







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Optional[Any]:
 # reformer model
 A__	=    torch_model.reformer

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

 if isinstance(weights[3]      ,			lowercase_						):
  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(lowercase_						)						)

 A__	=    weights[5]
 assert len(torch_model_reformer.encoder.layers						) * 4 == len(
     lowercase_						), "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(lowercase_      ,			lowercase_      ,			lowercase_						)

 # 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(lowercase_						)      ,			torch.tensor(lowercase_						)      ,			)

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		List[Any]:
 # Initialise PyTorch model
 A__	=    ReformerConfig.from_json_file(lowercase_						)
 print(f"""Building PyTorch model from configuration: {config}"""						)
 A__	=    ReformerModelWithLMHead(lowercase_						)

 with open(lowercase_      ,			"rb"						) as f:
  A__	=    pickle.load(lowercase_						)["weights"]

 set_model_weights_in_torch(lowercase_      ,			lowercase_      ,			config.hidden_size						)

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


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











"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''lilt'''




    def __init__(   self						:	List[str]     ,			snake_case_						:	Any=30_522     ,			snake_case_						:	Optional[Any]=768     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Any=3_072     ,			snake_case_						:	List[str]="gelu"     ,			snake_case_						:	Union[str, Any]=0.1     ,			snake_case_						:	List[Any]=0.1     ,			snake_case_						:	Any=512     ,			snake_case_						:	Optional[Any]=2     ,			snake_case_						:	List[str]=0.02     ,			snake_case_						:	Optional[Any]=1e-12     ,			snake_case_						:	List[Any]=0     ,			snake_case_						:	Any="absolute"     ,			snake_case_						:	str=None     ,			snake_case_						:	int=4     ,			snake_case_						:	int=1_024     ,			**snake_case_						:	Tuple     ,			)				->							List[Any]:




     '''simple docstring'''

     super().__init__(pad_token_id=snake_case_     ,			**snake_case_				)

     A__	=    vocab_size
     A__	=    hidden_size
     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    hidden_act
     A__	=    intermediate_size
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    max_position_embeddings
     A__	=    type_vocab_size
     A__	=    initializer_range
     A__	=    layer_norm_eps
     A__	=    position_embedding_type
     A__	=    classifier_dropout
     A__	=    channel_shrink_ratio
     A__	=    max_ad_position_embeddings





