



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			os.path.join(args.tf_model_dir  ,	"parameters.json"   )
	__lowercase		       =			json.loads(open(_SCREAMING_SNAKE_CASE   ).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"   ):
		__lowercase		       =			args.output + ".pt"
	__lowercase		       =			OrderedDict()
	with tf.device("/CPU:0"   ):
		__lowercase		       =			tf.train.load_checkpoint(args.tf_model_dir   )
		__lowercase		       =			reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			__lowercase		       =			reader.get_tensor(_SCREAMING_SNAKE_CASE   ).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"   ):
					__lowercase		       =			int(key_name[9]   )
				elif key_name.startswith("pasts/out"   ):
					__lowercase		       =			8
				__lowercase		       =			"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				__lowercase		       =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
				__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
			elif key_name.startswith("model/moe"   ):
				__lowercase		       =			int(key_name[9:].split("/"   )[0]   )
				if key_name.endswith("/switch_gating/kernel"   ):
					__lowercase		       =			"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
					__lowercase		       =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
				elif key_name.endswith("/softmlp/kernel"   ):
					__lowercase		       =			"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
					__lowercase		       =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
				elif key_name.endswith("/wo/kernel"   ) or key_name.endswith("/wi/kernel"   ):
					__lowercase		       =			key_name[-9:-7]
					for i in range(1_6   ):
						__lowercase		       =			"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
						__lowercase		       =			(
						    vnp[i].transpose([1, 0]   ).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
			elif key_name.startswith("model/mlp"   ):
				__lowercase		       =			int(key_name[9:].split("/"   )[0]   )
				if key_name.endswith("/p1/kernel"   ):
					__lowercase		       =			"model.blocks.%d.feed_forward.mlp.wi.weight" % player
					__lowercase		       =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
				elif key_name.endswith("/p1/bias"   ):
					__lowercase		       =			"model.blocks.%d.feed_forward.mlp.wi.bias" % player
					__lowercase		       =			vnp.copy()  # same because it is one dimensional
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
				elif key_name.endswith("/p2/kernel"   ):
					__lowercase		       =			"model.blocks.%d.feed_forward.mlp.wo.weight" % player
					__lowercase		       =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
				elif key_name.endswith("/p2/bias"   ):
					__lowercase		       =			"model.blocks.%d.feed_forward.mlp.wo.bias" % player
					__lowercase		       =			vnp.copy()  # same because it is one dimensional
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
			elif key_name.startswith("model/ln"   ):
				__lowercase		       =			int(key_name[8:].split("/"   )[0]   )
				if key_name.endswith("/b"   ):
					__lowercase		       =			"model.blocks.%d.feed_forward.norm.bias" % player
					__lowercase		       =			vnp.copy()  # same because it is one dimensional
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
				elif key_name.endswith("/g"   ):
					__lowercase		       =			"model.blocks.%d.feed_forward.norm.weight" % player
					__lowercase		       =			vnp.copy()  # same because it is one dimensional
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
			elif key_name.startswith("model/att"   ):
				__lowercase		       =			int(key_name[9:].split("/"   )[0]   )
				if key_name.endswith("/qkv/kernel"   ):
					__lowercase		       =			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					__lowercase		       =			state[:, 0, :, :]
					__lowercase		       =			state[:, 1, :, :]
					__lowercase		       =			state[:, 2, :, :]
					__lowercase		       =			(
					    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
					__lowercase		       =			(
					    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
					__lowercase		       =			(
					    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
					__lowercase		       =			"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
					__lowercase		       =			"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
					__lowercase		       =			"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
				elif key_name.endswith("/o/kernel"   ):
					__lowercase		       =			"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
					__lowercase		       =			(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]   ).transpose([1, 0]   ).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
			elif key_name.startswith("model/an"   ):
				__lowercase		       =			int(key_name[8:].split("/"   )[0]   )
				if key_name.endswith("/b"   ):
					__lowercase		       =			"model.blocks.%d.self_attn.norm.bias" % player
					__lowercase		       =			vnp.copy()  # same because it is one dimensional
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
				elif key_name.endswith("/g"   ):
					__lowercase		       =			"model.blocks.%d.self_attn.norm.weight" % player
					__lowercase		       =			vnp.copy()  # same because it is one dimensional
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
			elif (
			    key_name.startswith("model/wte"   )
			    or key_name.startswith("model/wpe"   )
			    or key_name.startswith("model/ete"   )
			):
				__lowercase		       =			{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
				    key_name[-3:]
				]
				__lowercase		       =			"model.%s.weight" % nlayer
				__lowercase		       =			vnp.copy()  # same in embedded
				__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
				if key_name.startswith("model/wte"   ):
					__lowercase		       =			"lm_head.weight"
					__lowercase		       =			vnp.copy()  # same in embedded
					__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
			elif key_name.startswith("model/wob"   ):
				__lowercase		       =			"final_logits_bias"
				__lowercase		       =			vnp.copy()  # same in embedded
				__lowercase		       =			state.reshape((1, -1)   )
				__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
			elif key_name == "model/dense/kernel":
				__lowercase		       =			"model.last_project.weight"
				__lowercase		       =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
				__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
			elif key_name == "model/dense_1/bias":
				__lowercase		       =			"model.last_project.bias"
				__lowercase		       =			vnp.copy()  # same because it is one dimensional
				__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   )
	torch.save(_SCREAMING_SNAKE_CASE  ,	args.output   )


if __name__ == "__main__":
	snake_case__					:     Tuple    		=   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""")
	snake_case__					:     Dict    		=   parser.parse_args()
	convert_tf_gptsan_to_pt(args)








from ....utils import logging


snake_case__					:     List[Any]    		=   logging.get_logger(__name__)

class   _A   ( _lowercase	):


						'''simple docstring'''





						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Dict=None							,      lowerCamelCase	:							Dict=2_048	):






							'''simple docstring'''



							__lowercase		       =			config.__dict__
							__lowercase		       =			modal_hidden_size
							if num_labels:
								__lowercase		       =			num_labels



