



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       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=None						):





							'''simple docstring'''




							assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
							_lowerCAmelCase  :							Optional[Any]				     =					nn.Parameter(_lowerCamelCase						)
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
														_lowerCAmelCase  :							Dict				     =					nn.Parameter(_lowerCamelCase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[Any]				     =					np.asarray(weights[0]						)
							_lowerCAmelCase  :							List[Any]				     =					np.asarray(weights[1]						)
							_lowerCAmelCase  :							Optional[int]				     =					np.asarray(weights[2]						)

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




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

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[int]				     =					weights[0][0][0]
							_lowerCAmelCase  :							str				     =					np.asarray(layer_norm_a[0]						)
							_lowerCAmelCase  :							Tuple				     =					np.asarray(layer_norm_a[1]						)
							set_param(
							    torch_block.attention.layer_norm       ,				torch.tensor(_lowerCamelCase						)       ,				torch.tensor(_lowerCamelCase						)       ,				)

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

							# intermediate weighs
							_lowerCAmelCase  :							List[str]				     =					weights[2][0][1][2]

							# Chunked Feed Forward
							if len(_lowerCamelCase						) == 4:
														_lowerCAmelCase  :							Union[str, Any]				     =					intermediate_weights[2]

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

							# intermediate dense
							_lowerCAmelCase  :							List[Any]				     =					np.asarray(intermediate_weights[1][0]						)
							_lowerCAmelCase  :							Optional[Any]				     =					np.asarray(intermediate_weights[1][1]						)
							set_param(
							    torch_block.feed_forward.dense.dense       ,				torch.tensor(_lowerCamelCase						).transpose(0       ,				1						).contiguous()       ,				torch.tensor(_lowerCamelCase						)       ,				)

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[int]				     =					torch_model.reformer

							# word embeds
							_lowerCAmelCase  :							str				     =					np.asarray(weights[1]						)
							set_param(
							    torch_model_reformer.embeddings.word_embeddings       ,				torch.tensor(_lowerCamelCase						)       ,				)

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

							_lowerCAmelCase  :							int				     =					weights[5]
							assert len(torch_model_reformer.encoder.layers						) * 4 == len(
							    _lowerCamelCase						), "HF and trax model do not have the same number of layers"
							for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers						):
														_lowerCAmelCase  :							Dict				     =					trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)

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

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							int				     =					ReformerConfig.from_json_file(_lowerCamelCase						)
							print(F"Building PyTorch model from configuration: {config}"						)
							_lowerCAmelCase  :							str				     =					ReformerModelWithLMHead(_lowerCamelCase						)

							with open(_lowerCamelCase       ,				"rb"						) as f:
														_lowerCAmelCase  :							Any				     =					pickle.load(_lowerCamelCase						)["weights"]

							set_model_weights_in_torch(_lowerCamelCase       ,				_lowerCamelCase       ,				config.hidden_size						)

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


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





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


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'roc_bert'







							def __init__(     self,    __a=3_0522,    __a=768,    __a=12,    __a=12,    __a=3072,    __a="gelu",    __a=0.1,    __a=0.1,    __a=512,    __a=2,    __a=0.02,    __a=1E-12,    __a=True,    __a=0,    __a="absolute",    __a=None,    __a=True,    __a=True,    __a=768,    __a=910,    __a=512,    __a=2_4858,    __a=True,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					vocab_size
														_lowerCAmelCase  :							Optional[Any]				     =					max_position_embeddings
														_lowerCAmelCase  :							Union[str, Any]				     =					hidden_size
														_lowerCAmelCase  :							str				     =					num_hidden_layers
														_lowerCAmelCase  :							List[Any]				     =					num_attention_heads
														_lowerCAmelCase  :							int				     =					intermediate_size
														_lowerCAmelCase  :							Optional[int]				     =					hidden_act
														_lowerCAmelCase  :							Optional[Any]				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Optional[Any]				     =					attention_probs_dropout_prob
														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							Optional[Any]				     =					type_vocab_size
														_lowerCAmelCase  :							int				     =					layer_norm_eps
														_lowerCAmelCase  :							Union[str, Any]				     =					use_cache
														_lowerCAmelCase  :							Optional[int]				     =					enable_pronunciation
														_lowerCAmelCase  :							Dict				     =					enable_shape
														_lowerCAmelCase  :							Optional[Any]				     =					pronunciation_embed_dim
														_lowerCAmelCase  :							Any				     =					pronunciation_vocab_size
														_lowerCAmelCase  :							List[str]				     =					shape_embed_dim
														_lowerCAmelCase  :							int				     =					shape_vocab_size
														_lowerCAmelCase  :							Optional[int]				     =					concat_input
														_lowerCAmelCase  :							Dict				     =					position_embedding_type
														_lowerCAmelCase  :							Tuple				     =					classifier_dropout
														super().__init__(pad_token_id=__a,    **__a)
