


'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def     a   (    __a     )						->			Tuple:



					'''simple docstring'''






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


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




'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=None							):

							'''simple docstring'''


							if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

							UpperCamelCase__     :Optional[int]							      =				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							UpperCamelCase__     :Any							      =				beta_t.flatten()
							while len(beta_t.shape							) < len(x.shape							):
												UpperCamelCase__     :Any							      =				beta_t.unsqueeze(-1							)
							UpperCamelCase__     :Optional[int]							      =				-0.5 * beta_t * x

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps