
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def 		UpperCAmelCase     (      a_		)						->      List[Any]:







				"""simple docstring"""



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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE				: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')
					SCREAMING_SNAKE_CASE				:Optional[Any]									=							parser.parse_args()
					convert_tf_gptsan_to_pt(args)




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     int:




						'''simple docstring'''







						return 1 if input_a == input_a else 0




def     __SCREAMING_SNAKE_CASE							(       )		->     None:




						'''simple docstring'''







						assert xnor_gate(0      ,       0      ) == 1
						assert xnor_gate(0      ,       1      ) == 0
						assert xnor_gate(1      ,       0      ) == 0
						assert xnor_gate(1      ,       1      ) == 1


if __name__ == "__main__":
				print(xnor_gate(0, 0))
				print(xnor_gate(0, 1))
				print(xnor_gate(1, 0))
				print(xnor_gate(1, 1))

