





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  __magic_name__  (				lowercase				,       lowercase				,       lowercase=None				)				-> Union[str, Any]:

							"""simple docstring"""



							assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
							lowercase_	:   Any  				=     nn.Parameter(lowercase				)
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
														lowercase_	:   Dict  				=     nn.Parameter(lowercase				)





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> Optional[int]:

							"""simple docstring"""



							lowercase_	:   Optional[Any]  				=     np.asarray(weights[0]				)
							lowercase_	:   List[str]  				=     np.asarray(weights[1]				)
							lowercase_	:   Tuple  				=     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  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> Dict:

							"""simple docstring"""



							lowercase_	:   Tuple  				=     np.asarray(weights[0]				)
							lowercase_	:   Optional[Any]  				=     np.asarray(weights[1]				)
							lowercase_	:   int  				=     np.asarray(weights[2]				)
							lowercase_	:   Optional[Any]  				=     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  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> Dict:

							"""simple docstring"""



							lowercase_	:   Optional[Any]  				=     weights[0][0][0]
							lowercase_	:   Dict  				=     np.asarray(layer_norm_a[0]				)
							lowercase_	:   Tuple  				=     np.asarray(layer_norm_a[1]				)
							set_param(
							    torch_block.attention.layer_norm				,       torch.tensor(lowercase				)				,       torch.tensor(lowercase				)				,       )

							# lsh weights + output
							lowercase_	:   Any  				=     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
							lowercase_	:   List[str]  				=     weights[2][0][1][2]

							# Chunked Feed Forward
							if len(lowercase				) == 4:
														lowercase_	:   Tuple  				=     intermediate_weights[2]

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

							# intermediate dense
							lowercase_	:   Dict  				=     np.asarray(intermediate_weights[1][0]				)
							lowercase_	:   List[str]  				=     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
							lowercase_	:   Dict  				=     np.asarray(intermediate_weights[4][0]				)
							lowercase_	:   Dict  				=     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  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> List[str]:

							"""simple docstring"""



							lowercase_	:   Any  				=     torch_model.reformer

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

							if isinstance(weights[3]				,       lowercase				):
														lowercase_	:   int  				=     torch_model_reformer.embeddings.position_embeddings
														for emb_idx in range(len(position_embeddings.weights				)				):
																					lowercase_	:   str  				=     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"""
																					lowercase_	:   List[Any]  				=     nn.Parameter(torch.tensor(lowercase				)				)

							lowercase_	:   Tuple  				=     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				):
														lowercase_	:   Dict  				=     trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(lowercase				,       lowercase				,       lowercase				)

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

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





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> int:

							"""simple docstring"""



							lowercase_	:   Optional[int]  				=     ReformerConfig.from_json_file(lowercase				)
							print(f"""Building PyTorch model from configuration: {config}"""				)
							lowercase_	:   Union[str, Any]  				=     ReformerModelWithLMHead(lowercase				)

							with open(lowercase				,       """rb"""				) as f:
														lowercase_	:   Dict  				=     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__":
							UpperCAmelCase_							=				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."""
							)
							UpperCAmelCase_							=				parser.parse_args()
							convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






UpperCAmelCase_							=				8.31_44_62  # Unit - J mol-1 K-1





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> float:

							"""simple docstring"""



							if moles < 0 or kelvin < 0 or volume < 0:
														raise ValueError("""Invalid inputs. Enter positive value."""				)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> float:

							"""simple docstring"""



							if moles < 0 or kelvin < 0 or pressure < 0:
														raise ValueError("""Invalid inputs. Enter positive value."""				)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
							from doctest import testmod

							testmod()