






import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch





def 					lowercase_ (    __snake_case     :							str      )     -> List[Any]:





				'''simple docstring'''


				snake_case__      :Union[str, Any]         =   os.path.join(args.tf_model_dir	,	"parameters.json"      )
				snake_case__      :List[Any]         =   json.loads(open(__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"      ):
								snake_case__      :Any         =   args.output + ".pt"
				snake_case__      :Optional[Any]         =   OrderedDict()
				with tf.device("/CPU:0"      ):
								snake_case__      :Union[str, Any]         =   tf.train.load_checkpoint(args.tf_model_dir      )
								snake_case__      :List[Any]         =   reader.get_variable_to_shape_map()
								for key_name in shapes.keys():
												snake_case__      :Tuple         =   reader.get_tensor(__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"      ):
																				snake_case__      :List[str]         =   int(key_name[9]      )
																elif key_name.startswith("pasts/out"      ):
																				snake_case__      :List[str]         =   8
																snake_case__      :Union[str, Any]         =   "model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																snake_case__      :Optional[int]         =   vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																snake_case__      :List[Any]         =   torch.tensor(__snake_case      )
												elif key_name.startswith("model/moe"      ):
																snake_case__      :Tuple         =   int(key_name[9:].split("/"      )[0]      )
																if key_name.endswith("/switch_gating/kernel"      ):
																				snake_case__      :List[str]         =   "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
																				snake_case__      :int         =   vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				snake_case__      :Any         =   torch.tensor(__snake_case      )
																elif key_name.endswith("/softmlp/kernel"      ):
																				snake_case__      :Tuple         =   "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
																				snake_case__      :List[Any]         =   vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				snake_case__      :List[str]         =   torch.tensor(__snake_case      )
																elif key_name.endswith("/wo/kernel"      ) or key_name.endswith("/wi/kernel"      ):
																				snake_case__      :List[str]         =   key_name[-9:-7]
																				for i in range(16      ):
																								snake_case__      :List[Any]         =   "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
																								snake_case__      :Optional[int]         =   (
																								    vnp[i].transpose([1, 0]      ).copy()
																								)  # In Mesh-Tensorflow, it is one array, so it is divided
																								snake_case__      :Optional[int]         =   torch.tensor(__snake_case      )
												elif key_name.startswith("model/mlp"      ):
																snake_case__      :Any         =   int(key_name[9:].split("/"      )[0]      )
																if key_name.endswith("/p1/kernel"      ):
																				snake_case__      :Dict         =   "model.blocks.%d.feed_forward.mlp.wi.weight" % player
																				snake_case__      :Optional[int]         =   vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				snake_case__      :Tuple         =   torch.tensor(__snake_case      )
																elif key_name.endswith("/p1/bias"      ):
																				snake_case__      :Dict         =   "model.blocks.%d.feed_forward.mlp.wi.bias" % player
																				snake_case__      :Any         =   vnp.copy()  # same because it is one dimensional
																				snake_case__      :Union[str, Any]         =   torch.tensor(__snake_case      )
																elif key_name.endswith("/p2/kernel"      ):
																				snake_case__      :Optional[Any]         =   "model.blocks.%d.feed_forward.mlp.wo.weight" % player
																				snake_case__      :Dict         =   vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				snake_case__      :str         =   torch.tensor(__snake_case      )
																elif key_name.endswith("/p2/bias"      ):
																				snake_case__      :Union[str, Any]         =   "model.blocks.%d.feed_forward.mlp.wo.bias" % player
																				snake_case__      :Optional[Any]         =   vnp.copy()  # same because it is one dimensional
																				snake_case__      :List[str]         =   torch.tensor(__snake_case      )
												elif key_name.startswith("model/ln"      ):
																snake_case__      :Optional[Any]         =   int(key_name[8:].split("/"      )[0]      )
																if key_name.endswith("/b"      ):
																				snake_case__      :Tuple         =   "model.blocks.%d.feed_forward.norm.bias" % player
																				snake_case__      :List[str]         =   vnp.copy()  # same because it is one dimensional
																				snake_case__      :Dict         =   torch.tensor(__snake_case      )
																elif key_name.endswith("/g"      ):
																				snake_case__      :Optional[int]         =   "model.blocks.%d.feed_forward.norm.weight" % player
																				snake_case__      :str         =   vnp.copy()  # same because it is one dimensional
																				snake_case__      :Optional[int]         =   torch.tensor(__snake_case      )
												elif key_name.startswith("model/att"      ):
																snake_case__      :Tuple         =   int(key_name[9:].split("/"      )[0]      )
																if key_name.endswith("/qkv/kernel"      ):
																				snake_case__      :List[str]         =   vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																				snake_case__      :str         =   state[:, 0, :, :]
																				snake_case__      :Optional[int]         =   state[:, 1, :, :]
																				snake_case__      :Any         =   state[:, 2, :, :]
																				snake_case__      :Dict         =   (
																				    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
																				snake_case__      :Dict         =   (
																				    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
																				snake_case__      :List[str]         =   (
																				    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
																				snake_case__      :int         =   "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
																				snake_case__      :List[Any]         =   torch.tensor(__snake_case      )
																				snake_case__      :Optional[int]         =   "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
																				snake_case__      :Union[str, Any]         =   torch.tensor(__snake_case      )
																				snake_case__      :Any         =   "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
																				snake_case__      :int         =   torch.tensor(__snake_case      )
																elif key_name.endswith("/o/kernel"      ):
																				snake_case__      :Dict         =   "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
																				snake_case__      :Union[str, Any]         =   (
																				    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				snake_case__      :Tuple         =   torch.tensor(__snake_case      )
												elif key_name.startswith("model/an"      ):
																snake_case__      :int         =   int(key_name[8:].split("/"      )[0]      )
																if key_name.endswith("/b"      ):
																				snake_case__      :str         =   "model.blocks.%d.self_attn.norm.bias" % player
																				snake_case__      :Tuple         =   vnp.copy()  # same because it is one dimensional
																				snake_case__      :Tuple         =   torch.tensor(__snake_case      )
																elif key_name.endswith("/g"      ):
																				snake_case__      :Any         =   "model.blocks.%d.self_attn.norm.weight" % player
																				snake_case__      :List[str]         =   vnp.copy()  # same because it is one dimensional
																				snake_case__      :Optional[Any]         =   torch.tensor(__snake_case      )
												elif (
												    key_name.startswith("model/wte"      )
												    or key_name.startswith("model/wpe"      )
												    or key_name.startswith("model/ete"      )
												):
																snake_case__      :List[str]         =   {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
																    key_name[-3:]
																]
																snake_case__      :Optional[Any]         =   "model.%s.weight" % nlayer
																snake_case__      :str         =   vnp.copy()  # same in embedded
																snake_case__      :str         =   torch.tensor(__snake_case      )
																if key_name.startswith("model/wte"      ):
																				snake_case__      :str         =   "lm_head.weight"
																				snake_case__      :str         =   vnp.copy()  # same in embedded
																				snake_case__      :Optional[int]         =   torch.tensor(__snake_case      )
												elif key_name.startswith("model/wob"      ):
																snake_case__      :List[str]         =   "final_logits_bias"
																snake_case__      :Any         =   vnp.copy()  # same in embedded
																snake_case__      :int         =   state.reshape((1, -1)      )
																snake_case__      :List[str]         =   torch.tensor(__snake_case      )
												elif key_name == "model/dense/kernel":
																snake_case__      :Optional[int]         =   "model.last_project.weight"
																snake_case__      :Tuple         =   vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																snake_case__      :Tuple         =   torch.tensor(__snake_case      )
												elif key_name == "model/dense_1/bias":
																snake_case__      :str         =   "model.last_project.bias"
																snake_case__      :List[str]         =   vnp.copy()  # same because it is one dimensional
																snake_case__      :int         =   torch.tensor(__snake_case      )
				torch.save(__snake_case	,	args.output      )


if __name__ == "__main__":
	__UpperCAmelCase							:   Optional[int]							=  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							:   Union[str, Any]							=  parser.parse_args()
	convert_tf_gptsan_to_pt(args)







def 					lowercase_ (    __snake_case     :							int = 10_00      )     -> int:





				'''simple docstring'''


				snake_case__      :int         =   3
				snake_case__      :int         =   0
				while a < n:
								if a % 3 == 0 or a % 5 == 0:
												result += a
								elif a % 15 == 0:
												result -= a
								a += 1
				return result


if __name__ == "__main__":
	print(F'''{solution() = }''')