





"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str   )					->str:






	'''simple docstring'''






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


if __name__ == "__main__":
		a		:					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''')
		a		:					Any	      =     parser.parse_args()
		convert_tf_gptsan_to_pt(args)









"""simple docstring"""




class 			__UpperCamelCase							:  # Public class to implement a graph






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					None:
								a :						int      =							row
								a :						Tuple      =							col
								a :						Optional[int]      =							graph






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					bool:
								return (
								    0 <= i < self.ROW
								    and 0 <= j < self.COL
								    and not visited[i][j]
								    and self.graph[i][j]
								)






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					None:
								# Checking all 8 elements surrounding nth element
								a :						Optional[Any]      =							[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
								a :						str      =							[-1, 0, 1, -1, 1, -1, 0, 1]
								a :						str      =							True  # Make those cells visited
								for k in range(8 ):
									if self.is_safe(i + row_nbr[k] ,   j + col_nbr[k] ,   lowerCAmelCase__ ):
										self.diffs(i + row_nbr[k] ,   j + col_nbr[k] ,   lowerCAmelCase__ )






							def        __a  (  self )      ->					int:  # And finally, count all islands.
								a :						Union[str, Any]      =							[[False for j in range(self.COL )] for i in range(self.ROW )]
								a :						Dict      =							0
								for i in range(self.ROW ):
									for j in range(self.COL ):
										if visited[i][j] is False and self.graph[i][j] == 1:
											self.diffs(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )
											count += 1
								return count


