


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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=None     ):
							# set parameter of one layer
							assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
							__snake_case		:      List[Any]										=       nn.Parameter(__lowerCamelCase     )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
														__snake_case		:      Any										=       nn.Parameter(__lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							# set torch weights for 1-to-1 comparison
							__snake_case		:      List[Any]										=       np.asarray(weights[0]     )
							__snake_case		:      Optional[int]										=       np.asarray(weights[1]     )
							__snake_case		:      Union[str, 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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							# set torch weights for 1-to-1 comparison
							__snake_case		:      int										=       np.asarray(weights[0]     )
							__snake_case		:      Any										=       np.asarray(weights[1]     )
							__snake_case		:      List[Any]										=       np.asarray(weights[2]     )
							__snake_case		:      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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							# layernorm 1
							__snake_case		:      List[Any]										=       weights[0][0][0]
							__snake_case		:      Any										=       np.asarray(layer_norm_a[0]     )
							__snake_case		:      int										=       np.asarray(layer_norm_a[1]     )
							set_param(
							    torch_block.attention.layer_norm							,     torch.tensor(__lowerCamelCase     )							,     torch.tensor(__lowerCamelCase     )							,     )

							# lsh weights + output
							__snake_case		:      Union[str, Any]										=       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
							__snake_case		:      Optional[int]										=       weights[2][0][1][2]

							# Chunked Feed Forward
							if len(__lowerCamelCase     ) == 4:
														__snake_case		:      Dict										=       intermediate_weights[2]

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

							# intermediate dense
							__snake_case		:      Optional[int]										=       np.asarray(intermediate_weights[1][0]     )
							__snake_case		:      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
							__snake_case		:      Union[str, Any]										=       np.asarray(intermediate_weights[4][0]     )
							__snake_case		:      List[str]										=       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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							# reformer model
							__snake_case		:      Tuple										=       torch_model.reformer

							# word embeds
							__snake_case		:      Union[str, Any]										=       np.asarray(weights[1]     )
							set_param(
							    torch_model_reformer.embeddings.word_embeddings							,     torch.tensor(__lowerCamelCase     )							,     )

							if isinstance(weights[3]							,     __lowerCamelCase     ):
														__snake_case		:      List[Any]										=       torch_model_reformer.embeddings.position_embeddings
														for emb_idx in range(len(position_embeddings.weights     )     ):
																					__snake_case		:      Union[str, Any]										=       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'
																					__snake_case		:      Optional[int]										=       nn.Parameter(torch.tensor(__lowerCamelCase     )     )

							__snake_case		:      Tuple										=       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     ):
														__snake_case		:      Optional[Any]										=       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )

							# output layer norm
							__snake_case		:      Optional[int]										=       np.asarray(weights[7][0]     )
							__snake_case		:      List[str]										=       np.asarray(weights[7][1]     )
							set_param(
							    torch_model_reformer.encoder.layer_norm							,     torch.tensor(__lowerCamelCase     )							,     torch.tensor(__lowerCamelCase     )							,     )

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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							# Initialise PyTorch model
							__snake_case		:      List[Any]										=       ReformerConfig.from_json_file(__lowerCamelCase     )
							print(F'Building PyTorch model from configuration: {config}'     )
							__snake_case		:      Optional[Any]										=       ReformerModelWithLMHead(__lowerCamelCase     )

							with open(__lowerCamelCase							,     "rb"     ) as f:
														__snake_case		:      int										=       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					:		int      =    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					:		Union[str, Any]      =    parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_snake_case					:		Union[str, Any]      =    ["small", "medium", "large"]

_snake_case					:		List[Any]      =    "lm_head.decoder.weight"
_snake_case					:		Optional[Any]      =    "lm_head.weight"







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Tuple										=       torch.load(__lowerCamelCase     )
							__snake_case		:      Dict										=       d.pop(__lowerCamelCase     )
							os.makedirs(__lowerCamelCase							,     exist_ok=__lowerCamelCase     )
							torch.save(__lowerCamelCase							,     os.path.join(__lowerCamelCase							,     __lowerCamelCase     )     )


if __name__ == "__main__":
			_snake_case					:		Dict      =    argparse.ArgumentParser()
			parser.add_argument("--dialogpt_path", default=".", type=str)
			_snake_case					:		Any      =    parser.parse_args()
			for MODEL in DIALOGPT_MODELS:
						_snake_case					:		Dict      =    os.path.join(args.dialogpt_path, f'''{MODEL}_ft.pkl''')
						_snake_case					:		List[str]      =    f'''./DialoGPT-{MODEL}'''
						convert_dialogpt_checkpoint(
						    checkpoint_path,
						    pytorch_dump_folder_path,
						)

