
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 		_UpperCAmelCase   (	UpperCamelCase:     Any  ):






	"""simple docstring"""


	__lowerCAmelCase         =							os.path.join(args.tf_model_dir			,							"parameters.json"  )
	__lowerCAmelCase         =							json.loads(open(UpperCamelCase  ).read()  )
	if not params:
		raise ValueError(
		    F"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."  )
	if not args.output.endswith(".pt"  ):
		__lowerCAmelCase         =							args.output + ".pt"
	__lowerCAmelCase         =							OrderedDict()
	with tf.device("/CPU:0"  ):
		__lowerCAmelCase         =							tf.train.load_checkpoint(args.tf_model_dir  )
		__lowerCAmelCase         =							reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			__lowerCAmelCase         =							reader.get_tensor(UpperCamelCase  ).astype(np.floataa  )
			if key_name.endswith("/adam_m"  ) or key_name.endswith("/adam_v"  ):
				continue
			if key_name.startswith("pasts/"  ):
				if key_name.startswith("pasts/mlp"  ):
					__lowerCAmelCase         =							int(key_name[9]  )
				elif key_name.startswith("pasts/out"  ):
					__lowerCAmelCase         =							8
				__lowerCAmelCase         =							"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				__lowerCAmelCase         =							vnp.transpose([1, 0]  ).copy()  # Mesh-Tensorflow is a diagonal matrix
				__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
			elif key_name.startswith("model/moe"  ):
				__lowerCAmelCase         =							int(key_name[9:].split("/"  )[0]  )
				if key_name.endswith("/switch_gating/kernel"  ):
					__lowerCAmelCase         =							"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
					__lowerCAmelCase         =							vnp.transpose([1, 0]  ).copy()  # Mesh-Tensorflow is a diagonal matrix
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
				elif key_name.endswith("/softmlp/kernel"  ):
					__lowerCAmelCase         =							"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
					__lowerCAmelCase         =							vnp.transpose([1, 0]  ).copy()  # Mesh-Tensorflow is a diagonal matrix
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
				elif key_name.endswith("/wo/kernel"  ) or key_name.endswith("/wi/kernel"  ):
					__lowerCAmelCase         =							key_name[-9:-7]
					for i in range(1_6  ):
						__lowerCAmelCase         =							"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
						__lowerCAmelCase         =							(
						    vnp[i].transpose([1, 0]  ).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
			elif key_name.startswith("model/mlp"  ):
				__lowerCAmelCase         =							int(key_name[9:].split("/"  )[0]  )
				if key_name.endswith("/p1/kernel"  ):
					__lowerCAmelCase         =							"model.blocks.%d.feed_forward.mlp.wi.weight" % player
					__lowerCAmelCase         =							vnp.transpose([1, 0]  ).copy()  # Mesh-Tensorflow is a diagonal matrix
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
				elif key_name.endswith("/p1/bias"  ):
					__lowerCAmelCase         =							"model.blocks.%d.feed_forward.mlp.wi.bias" % player
					__lowerCAmelCase         =							vnp.copy()  # same because it is one dimensional
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
				elif key_name.endswith("/p2/kernel"  ):
					__lowerCAmelCase         =							"model.blocks.%d.feed_forward.mlp.wo.weight" % player
					__lowerCAmelCase         =							vnp.transpose([1, 0]  ).copy()  # Mesh-Tensorflow is a diagonal matrix
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
				elif key_name.endswith("/p2/bias"  ):
					__lowerCAmelCase         =							"model.blocks.%d.feed_forward.mlp.wo.bias" % player
					__lowerCAmelCase         =							vnp.copy()  # same because it is one dimensional
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
			elif key_name.startswith("model/ln"  ):
				__lowerCAmelCase         =							int(key_name[8:].split("/"  )[0]  )
				if key_name.endswith("/b"  ):
					__lowerCAmelCase         =							"model.blocks.%d.feed_forward.norm.bias" % player
					__lowerCAmelCase         =							vnp.copy()  # same because it is one dimensional
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
				elif key_name.endswith("/g"  ):
					__lowerCAmelCase         =							"model.blocks.%d.feed_forward.norm.weight" % player
					__lowerCAmelCase         =							vnp.copy()  # same because it is one dimensional
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
			elif key_name.startswith("model/att"  ):
				__lowerCAmelCase         =							int(key_name[9:].split("/"  )[0]  )
				if key_name.endswith("/qkv/kernel"  ):
					__lowerCAmelCase         =							vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					__lowerCAmelCase         =							state[:, 0, :, :]
					__lowerCAmelCase         =							state[:, 1, :, :]
					__lowerCAmelCase         =							state[:, 2, :, :]
					__lowerCAmelCase         =							(
					    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]  )
					    .transpose([1, 0]  )
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					__lowerCAmelCase         =							(
					    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]  )
					    .transpose([1, 0]  )
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					__lowerCAmelCase         =							(
					    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]  )
					    .transpose([1, 0]  )
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					__lowerCAmelCase         =							"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
					__lowerCAmelCase         =							"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
					__lowerCAmelCase         =							"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
				elif key_name.endswith("/o/kernel"  ):
					__lowerCAmelCase         =							"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
					__lowerCAmelCase         =							(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]  ).transpose([1, 0]  ).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
			elif key_name.startswith("model/an"  ):
				__lowerCAmelCase         =							int(key_name[8:].split("/"  )[0]  )
				if key_name.endswith("/b"  ):
					__lowerCAmelCase         =							"model.blocks.%d.self_attn.norm.bias" % player
					__lowerCAmelCase         =							vnp.copy()  # same because it is one dimensional
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
				elif key_name.endswith("/g"  ):
					__lowerCAmelCase         =							"model.blocks.%d.self_attn.norm.weight" % player
					__lowerCAmelCase         =							vnp.copy()  # same because it is one dimensional
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
			elif (
			    key_name.startswith("model/wte"  )
			    or key_name.startswith("model/wpe"  )
			    or key_name.startswith("model/ete"  )
			):
				__lowerCAmelCase         =							{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
				    key_name[-3:]
				]
				__lowerCAmelCase         =							"model.%s.weight" % nlayer
				__lowerCAmelCase         =							vnp.copy()  # same in embedded
				__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
				if key_name.startswith("model/wte"  ):
					__lowerCAmelCase         =							"lm_head.weight"
					__lowerCAmelCase         =							vnp.copy()  # same in embedded
					__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
			elif key_name.startswith("model/wob"  ):
				__lowerCAmelCase         =							"final_logits_bias"
				__lowerCAmelCase         =							vnp.copy()  # same in embedded
				__lowerCAmelCase         =							state.reshape((1, -1)  )
				__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
			elif key_name == "model/dense/kernel":
				__lowerCAmelCase         =							"model.last_project.weight"
				__lowerCAmelCase         =							vnp.transpose([1, 0]  ).copy()  # Mesh-Tensorflow is a diagonal matrix
				__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
			elif key_name == "model/dense_1/bias":
				__lowerCAmelCase         =							"model.last_project.bias"
				__lowerCAmelCase         =							vnp.copy()  # same because it is one dimensional
				__lowerCAmelCase         =							torch.tensor(UpperCamelCase  )
	torch.save(UpperCamelCase			,							args.output  )


if __name__ == "__main__":
					UpperCamelCase_   	=	argparse.ArgumentParser(
					    description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
					)
					parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
					parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
					UpperCamelCase_   	=	parser.parse_args()
					convert_tf_gptsan_to_pt(args)








from abc import ABC, abstractmethod
from argparse import ArgumentParser

class       a		(   __UpperCAmelCase					):







							@staticmethod
							@abstractmethod
							def       UpperCAmelCase__					(						snake_case__  :  ArgumentParser ):
								"""simple docstring"""







								raise NotImplementedError()







							@abstractmethod
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								raise NotImplementedError()






