





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch







def        __lowerCamelCase (							__a       :Optional[Any]   )		->    int:






		"""simple docstring"""

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


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










import math







def        __lowerCamelCase (							)		->    None:






		"""simple docstring"""

		A__  				=       input("""Enter message: """   )
		A__  				=       int(input(F'Enter key [2-{len(__a   ) - 1}]: '   )   )
		A__  				=       input("""Encryption/Decryption [e/d]: """   )

		if mode.lower().startswith("""e"""   ):
				A__  				=       encrypt_message(__a   ,					__a   )
		elif mode.lower().startswith("""d"""   ):
				A__  				=       decrypt_message(__a   ,					__a   )

		# Append pipe symbol (vertical bar) to identify spaces at the end.
		print(F'Output:\n{text + "|"}'   )







def        __lowerCamelCase (							__a       :int   ,					__a       :str   )		->    str:






		"""simple docstring"""

		A__  				=       [""""""] * key
		for col in range(__a   ):
				A__  				=       col
				while pointer < len(__a   ):
						cipher_text[col] += message[pointer]
						pointer += key
		return "".join(__a   )







def        __lowerCamelCase (							__a       :int   ,					__a       :str   )		->    str:






		"""simple docstring"""

		A__  				=       math.ceil(len(__a   ) / key   )
		A__  				=       key
		A__  				=       (num_cols * num_rows) - len(__a   )
		A__  				=       [""""""] * num_cols
		A__  				=       0
		A__  				=       0

		for symbol in message:
				plain_text[col] += symbol
				col += 1

				if (
				    (col == num_cols)
				    or (col == num_cols - 1)
				    and (row >= num_rows - num_shaded_boxes)
				):
						A__  				=       0
						row += 1

		return "".join(__a   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()



