
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  )					->		str:
				assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
				UpperCamelCase_:							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'''
								UpperCamelCase_:							List[str]		=  nn.Parameter(lowerCamelCase_  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Any:
				UpperCamelCase_:							Optional[Any]		=  np.asarray(weights[0]  )
				UpperCamelCase_:							int		=  np.asarray(weights[1]  )
				UpperCamelCase_:							Any		=  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  )					->		Dict:
				UpperCamelCase_:							List[Any]		=  np.asarray(weights[0]  )
				UpperCamelCase_:							Optional[int]		=  np.asarray(weights[1]  )
				UpperCamelCase_:							Union[str, Any]		=  np.asarray(weights[2]  )
				UpperCamelCase_:							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  )					->		List[Any]:
				UpperCamelCase_:							str		=  weights[0][0][0]
				UpperCamelCase_:							int		=  np.asarray(layer_norm_a[0]  )
				UpperCamelCase_:							Optional[Any]		=  np.asarray(layer_norm_a[1]  )
				set_param(
				    torch_block.attention.layer_norm       ,      torch.tensor(lowerCamelCase_  )       ,      torch.tensor(lowerCamelCase_  )       ,      )

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

				# Chunked Feed Forward
				if len(lowerCamelCase_  ) == 4:
								UpperCamelCase_:							List[Any]		=  intermediate_weights[2]

				# layernorm 2
				UpperCamelCase_:							Tuple		=  np.asarray(intermediate_weights[0][0]  )
				UpperCamelCase_:							Optional[Any]		=  np.asarray(intermediate_weights[0][1]  )
				set_param(
				    torch_block.feed_forward.layer_norm       ,      torch.tensor(lowerCamelCase_  )       ,      torch.tensor(lowerCamelCase_  )       ,      )

				# intermediate dense
				UpperCamelCase_:							Any		=  np.asarray(intermediate_weights[1][0]  )
				UpperCamelCase_:							List[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
				UpperCamelCase_:							List[Any]		=  np.asarray(intermediate_weights[4][0]  )
				UpperCamelCase_:							Union[str, 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  )					->		Tuple:
				UpperCamelCase_:							Dict		=  torch_model.reformer

				# word embeds
				UpperCamelCase_:							List[Any]		=  np.asarray(weights[1]  )
				set_param(
				    torch_model_reformer.embeddings.word_embeddings       ,      torch.tensor(lowerCamelCase_  )       ,      )

				if isinstance(weights[3]       ,      lowerCamelCase_  ):
								UpperCamelCase_:							Tuple		=  torch_model_reformer.embeddings.position_embeddings
								for emb_idx in range(len(position_embeddings.weights  )  ):
												UpperCamelCase_:							Dict		=  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'''
												UpperCamelCase_:							Optional[Any]		=  nn.Parameter(torch.tensor(lowerCamelCase_  )  )

				UpperCamelCase_:							List[Any]		=  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  ):
								UpperCamelCase_:							str		=  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
								set_block_weights_in_torch(lowerCamelCase_       ,      lowerCamelCase_       ,      lowerCamelCase_  )

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

				# output embeddings
				UpperCamelCase_:							Optional[int]		=  np.asarray(weights[9][0]  )
				UpperCamelCase_:							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  )					->		Optional[int]:
				UpperCamelCase_:							List[str]		=  ReformerConfig.from_json_file(lowerCamelCase_  )
				print(F'''Building PyTorch model from configuration: {config}'''  )
				UpperCamelCase_:							str		=  ReformerModelWithLMHead(lowerCamelCase_  )

				with open(lowerCamelCase_       ,      """rb"""  ) as f:
								UpperCamelCase_:							List[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__":
							lowerCamelCase_  :	List[str]						=					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_  :	List[Any]						=					parser.parse_args()
							convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)








import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Union[str, Any]:
				# Construct model
				if gpta_config_file == "":
								UpperCamelCase_:							Union[str, Any]		=  GPTaConfig()
				else:
								UpperCamelCase_:							Optional[Any]		=  GPTaConfig.from_json_file(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  GPTaModel(lowerCamelCase  )

				# Load weights from numpy
				load_tf_weights_in_gpta(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )

				# Save pytorch-model
				UpperCamelCase_:							int		=  pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
				UpperCamelCase_:							Any		=  pytorch_dump_folder_path + """/""" + CONFIG_NAME
				print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''  )
				torch.save(model.state_dict()       ,      lowerCamelCase  )
				print(F'''Save configuration file to {pytorch_config_dump_path}'''  )
				with open(lowerCamelCase       ,      """w"""       ,      encoding="""utf-8"""  ) as f:
								f.write(config.to_json_string()  )


if __name__ == "__main__":
							lowerCamelCase_  :	int 				=					argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--gpt2_config_file""",
							    default="""""",
							    type=str,
							    help=(
							        """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
							        """This specifies the model architecture."""
							    ),
							)
							lowerCamelCase_  :	List[str] 				=					parser.parse_args()
							convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)






