
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[int]				):




			"""simple docstring"""


			lowerCAmelCase_						=	os.path.join(args.tf_model_dir    ,							"parameters.json"				)
			lowerCAmelCase_						=	json.loads(open(__lowerCamelCase				).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(__lowerCamelCase				).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(__lowerCamelCase				)
									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(__lowerCamelCase				)
												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(__lowerCamelCase				)
												elif key_name.endswith("/wo/kernel"				) or key_name.endswith("/wi/kernel"				):
															lowerCAmelCase_						=	key_name[-9:-7]
															for i in range(16				):
																		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(__lowerCamelCase				)
									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(__lowerCamelCase				)
												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(__lowerCamelCase				)
												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(__lowerCamelCase				)
												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(__lowerCamelCase				)
									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(__lowerCamelCase				)
												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(__lowerCamelCase				)
									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(__lowerCamelCase				)
															lowerCAmelCase_						=	"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
															lowerCAmelCase_						=	torch.tensor(__lowerCamelCase				)
															lowerCAmelCase_						=	"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
															lowerCAmelCase_						=	torch.tensor(__lowerCamelCase				)
												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(__lowerCamelCase				)
									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(__lowerCamelCase				)
												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(__lowerCamelCase				)
									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(__lowerCamelCase				)
												if key_name.startswith("model/wte"				):
															lowerCAmelCase_						=	"lm_head.weight"
															lowerCAmelCase_						=	vnp.copy()  # same in embedded
															lowerCAmelCase_						=	torch.tensor(__lowerCamelCase				)
									elif key_name.startswith("model/wob"				):
												lowerCAmelCase_						=	"final_logits_bias"
												lowerCAmelCase_						=	vnp.copy()  # same in embedded
												lowerCAmelCase_						=	state.reshape((1, -1)				)
												lowerCAmelCase_						=	torch.tensor(__lowerCamelCase				)
									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(__lowerCamelCase				)
									elif key_name == "model/dense_1/bias":
												lowerCAmelCase_						=	"model.last_project.bias"
												lowerCAmelCase_						=	vnp.copy()  # same because it is one dimensional
												lowerCAmelCase_						=	torch.tensor(__lowerCamelCase				)
			torch.save(__lowerCamelCase    ,							args.output				)


if __name__ == "__main__":
			_A   =     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")
			_A   =     parser.parse_args()
			convert_tf_gptsan_to_pt(args)





"""simple docstring"""



import logging

from transformers.configuration_utils import PretrainedConfig


UpperCAmelCase__          = logging.getLogger(__name__)

class       lowerCAmelCase__				(						A_	):
     __a						=					"""masked_bert"""







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any=30522    ,      _lowerCamelCase			:			Union[str, Any]=768    ,      _lowerCamelCase			:			Tuple=12    ,      _lowerCamelCase			:			Any=12    ,      _lowerCamelCase			:			str=3072    ,      _lowerCamelCase			:			str="gelu"    ,      _lowerCamelCase			:			int=0.1    ,      _lowerCamelCase			:			Optional[int]=0.1    ,      _lowerCamelCase			:			Dict=512    ,      _lowerCamelCase			:			List[Any]=2    ,      _lowerCamelCase			:			int=0.0_2    ,      _lowerCamelCase			:			Union[str, Any]=1e-12    ,      _lowerCamelCase			:			Union[str, Any]=0    ,      _lowerCamelCase			:			List[str]="topK"    ,      _lowerCamelCase			:			Optional[Any]="constant"    ,      _lowerCamelCase			:			Optional[Any]=0.0    ,      **_lowerCamelCase			:			str    ,      ):
       super().__init__(pad_token_id=_lowerCamelCase    ,      **_lowerCamelCase			)

       _snake_case					     =       vocab_size
       _snake_case					     =       hidden_size
       _snake_case					     =       num_hidden_layers
       _snake_case					     =       num_attention_heads
       _snake_case					     =       hidden_act
       _snake_case					     =       intermediate_size
       _snake_case					     =       hidden_dropout_prob
       _snake_case					     =       attention_probs_dropout_prob
       _snake_case					     =       max_position_embeddings
       _snake_case					     =       type_vocab_size
       _snake_case					     =       initializer_range
       _snake_case					     =       layer_norm_eps
       _snake_case					     =       pruning_method
       _snake_case					     =       mask_init
       _snake_case					     =       mask_scale



