
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 		_UpperCAmelCase (						UpperCamelCase:  str	):






	"""simple docstring"""


	__lowerCAmelCase    =				os.path.join(args.tf_model_dir      ,				"parameters.json"	)
	__lowerCAmelCase    =				json.loads(open(_lowercase	).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(_lowercase	).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(_lowercase	)
			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(_lowercase	)
				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(_lowercase	)
				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(_lowercase	)
			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(_lowercase	)
				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(_lowercase	)
				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(_lowercase	)
				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(_lowercase	)
			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(_lowercase	)
				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(_lowercase	)
			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(_lowercase	)
					__lowerCAmelCase    =				"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
					__lowerCAmelCase    =				torch.tensor(_lowercase	)
					__lowerCAmelCase    =				"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
					__lowerCAmelCase    =				torch.tensor(_lowercase	)
				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(_lowercase	)
			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(_lowercase	)
				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(_lowercase	)
			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(_lowercase	)
				if key_name.startswith("model/wte"	):
					__lowerCAmelCase    =				"lm_head.weight"
					__lowerCAmelCase    =				vnp.copy()  # same in embedded
					__lowerCAmelCase    =				torch.tensor(_lowercase	)
			elif key_name.startswith("model/wob"	):
				__lowerCAmelCase    =				"final_logits_bias"
				__lowerCAmelCase    =				vnp.copy()  # same in embedded
				__lowerCAmelCase    =				state.reshape((1, -1)	)
				__lowerCAmelCase    =				torch.tensor(_lowercase	)
			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(_lowercase	)
			elif key_name == "model/dense_1/bias":
				__lowerCAmelCase    =				"model.last_project.bias"
				__lowerCAmelCase    =				vnp.copy()  # same because it is one dimensional
				__lowerCAmelCase    =				torch.tensor(_lowercase	)
	torch.save(_lowercase      ,				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)













"""simple docstring"""




from numpy import exp, pi, sqrt






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple       ,			_lowercase		:       float = 0.0       ,			_lowercase		:       float = 1.0   )					->int:






	'''simple docstring'''






	return 1 / sqrt(2 * pi * sigma**2   ) * exp(-((x - mu) ** 2) / (2 * sigma**2)   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


