



"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
						import transformers
def    A_  (			_lowercase	):







						'''simple docstring'''


						random.seed(_lowercase	)
						np.random.seed(_lowercase	)
						torch.manual_seed(_lowercase	)
						torch.cuda.manual_seed_all(_lowercase	)
						# ^^ safe to call this function even if cuda is not available

class 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							List[Any]						,     snake_case:							Iterable[torch.nn.Parameter]						,     snake_case:							float = 0.9_9_9_9						,     snake_case:							float = 0.0						,     snake_case:							int = 0						,     snake_case:							bool = False						,     snake_case:							Union[float, int] = 1.0						,     snake_case:							Union[float, int] = 2 / 3						,     snake_case:							Optional[Any] = None						,     snake_case:							Dict[str, Any] = None						,     **snake_case:							Union[str, Any]						,     )   ->	str:

											if isinstance(snake_case						,     torch.nn.Module						):
																	snake_case_ :List[str]						=			(
																	    """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
																	    """Please pass the parameters of the module instead."""
																	)
																	deprecate(
																	    """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""						,     """1.0.0"""						,     snake_case						,     standard_warn=snake_case						,     )
																	snake_case_ :Any						=			parameters.parameters()

																	# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
																	snake_case_ :Optional[Any]						=			True

											if kwargs.get("""max_value"""						,     snake_case						) is not None:
																	snake_case_ :str						=			"""The `max_value` argument is deprecated. Please use `decay` instead."""
																	deprecate("""max_value"""						,     """1.0.0"""						,     snake_case						,     standard_warn=snake_case						)
																	snake_case_ :Union[str, Any]						=			kwargs["""max_value"""]

											if kwargs.get("""min_value"""						,     snake_case						) is not None:
																	snake_case_ :Dict						=			"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
																	deprecate("""min_value"""						,     """1.0.0"""						,     snake_case						,     standard_warn=snake_case						)
																	snake_case_ :str						=			kwargs["""min_value"""]

											snake_case_ :str						=			list(snake_case						)
											snake_case_ :Optional[Any]						=			[p.clone().detach() for p in parameters]

											if kwargs.get("""device"""						,     snake_case						) is not None:
																	snake_case_ :Union[str, Any]						=			"""The `device` argument is deprecated. Please use `to` instead."""
																	deprecate("""device"""						,     """1.0.0"""						,     snake_case						,     standard_warn=snake_case						)
																	self.to(device=kwargs["""device"""]						)

											snake_case_ :Any						=			None

											snake_case_ :Optional[Any]						=			decay
											snake_case_ :Any						=			min_decay
											snake_case_ :List[Any]						=			update_after_step
											snake_case_ :Optional[Any]						=			use_ema_warmup
											snake_case_ :Optional[Any]						=			inv_gamma
											snake_case_ :int						=			power
											snake_case_ :int						=			0
											snake_case_ :Dict						=			None  # set in `step()`

											snake_case_ :Dict						=			model_cls
											snake_case_ :List[Any]						=			model_config






					@classmethod
					def    lowerCAmelCase_				(       cls:							Optional[int]						,     snake_case:							Tuple						,     snake_case:							Dict						)   ->	"EMAModel":
											snake_case_,       snake_case_ :Union[str, Any]						=			model_cls.load_config(snake_case						,     return_unused_kwargs=snake_case						)
											snake_case_ :Tuple						=			model_cls.from_pretrained(snake_case						)

											snake_case_ :Any						=			cls(model.parameters()						,     model_cls=snake_case						,     model_config=model.config						)

											ema_model.load_state_dict(snake_case						)
											return ema_model






					def    lowerCAmelCase_				(       self:							Optional[Any]						,     snake_case:							List[Any]						)   ->	str:
											if self.model_cls is None:
																	raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""						)

											if self.model_config is None:
																	raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""						)

											snake_case_ :List[str]						=			self.model_cls.from_config(self.model_config						)
											snake_case_ :List[str]						=			self.state_dict()
											state_dict.pop("""shadow_params"""						,     snake_case						)

											model.register_to_config(**snake_case						)
											self.copy_to(model.parameters()						)
											model.save_pretrained(snake_case						)






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							int						)   ->	float:
											snake_case_ :List[str]						=			max(0						,     optimization_step - self.update_after_step - 1						)

											if step <= 0:
																	return 0.0

											if self.use_ema_warmup:
																	snake_case_ :Any						=			1 - (1 + step / self.inv_gamma) ** -self.power
											else:
																	snake_case_ :int						=			(1 + step) / (10 + step)

											snake_case_ :Optional[Any]						=			min(snake_case						,     self.decay						)
											# make sure decay is not smaller than min_decay
											snake_case_ :Optional[int]						=			max(snake_case						,     self.min_decay						)
											return cur_decay_value






					@torch.no_grad()
					def    lowerCAmelCase_				(       self:							int						,     snake_case:							Iterable[torch.nn.Parameter]						)   ->	Optional[int]:
											if isinstance(snake_case						,     torch.nn.Module						):
																	snake_case_ :Tuple						=			(
																	    """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
																	    """Please pass the parameters of the module instead."""
																	)
																	deprecate(
																	    """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""						,     """1.0.0"""						,     snake_case						,     standard_warn=snake_case						,     )
																	snake_case_ :Any						=			parameters.parameters()

											snake_case_ :str						=			list(snake_case						)

											self.optimization_step += 1

											# Compute the decay factor for the exponential moving average.
											snake_case_ :Union[str, Any]						=			self.get_decay(self.optimization_step						)
											snake_case_ :Optional[Any]						=			decay
											snake_case_ :Union[str, Any]						=			1 - decay

											snake_case_ :Tuple						=			contextlib.nullcontext
											if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																	import deepspeed

											for s_param, param in zip(self.shadow_params						,     snake_case						):
																	if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																							snake_case_ :List[Any]						=			deepspeed.zero.GatheredParameters(snake_case						,     modifier_rank=snake_case						)

																	with context_manager():
																							if param.requires_grad:
																													s_param.sub_(one_minus_decay * (s_param - param)						)
																							else:
																													s_param.copy_(snake_case						)






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							Iterable[torch.nn.Parameter]						)   ->	None:
											snake_case_ :int						=			list(snake_case						)
											for s_param, param in zip(self.shadow_params						,     snake_case						):
																	param.data.copy_(s_param.to(param.device						).data						)






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							Optional[Any]=None						,     snake_case:							List[str]=None						)   ->	None:
											snake_case_ :List[str]						=			[
											    p.to(device=snake_case						,     dtype=snake_case						) if p.is_floating_point() else p.to(device=snake_case						)
											    for p in self.shadow_params
											]






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	dict:
											return {
											    "decay": self.decay,
											    "min_decay": self.min_decay,
											    "optimization_step": self.optimization_step,
											    "update_after_step": self.update_after_step,
											    "use_ema_warmup": self.use_ema_warmup,
											    "inv_gamma": self.inv_gamma,
											    "power": self.power,
											    "shadow_params": self.shadow_params,
											}






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							Iterable[torch.nn.Parameter]						)   ->	None:
											snake_case_ :Optional[Any]						=			[param.detach().cpu().clone() for param in parameters]






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							Iterable[torch.nn.Parameter]						)   ->	None:
											if self.temp_stored_params is None:
																	raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""						)
											for c_param, param in zip(self.temp_stored_params						,     snake_case						):
																	param.data.copy_(c_param.data						)

											# Better memory-wise.
											snake_case_ :Optional[int]						=			None






					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							dict						)   ->	None:
											snake_case_ :Optional[Any]						=			copy.deepcopy(snake_case						)

											snake_case_ :Tuple						=			state_dict.get("""decay"""						,     self.decay						)
											if self.decay < 0.0 or self.decay > 1.0:
																	raise ValueError("""Decay must be between 0 and 1"""						)

											snake_case_ :Union[str, Any]						=			state_dict.get("""min_decay"""						,     self.min_decay						)
											if not isinstance(self.min_decay						,     snake_case						):
																	raise ValueError("""Invalid min_decay"""						)

											snake_case_ :List[str]						=			state_dict.get("""optimization_step"""						,     self.optimization_step						)
											if not isinstance(self.optimization_step						,     snake_case						):
																	raise ValueError("""Invalid optimization_step"""						)

											snake_case_ :Union[str, Any]						=			state_dict.get("""update_after_step"""						,     self.update_after_step						)
											if not isinstance(self.update_after_step						,     snake_case						):
																	raise ValueError("""Invalid update_after_step"""						)

											snake_case_ :str						=			state_dict.get("""use_ema_warmup"""						,     self.use_ema_warmup						)
											if not isinstance(self.use_ema_warmup						,     snake_case						):
																	raise ValueError("""Invalid use_ema_warmup"""						)

											snake_case_ :int						=			state_dict.get("""inv_gamma"""						,     self.inv_gamma						)
											if not isinstance(self.inv_gamma						,     (float, int)						):
																	raise ValueError("""Invalid inv_gamma"""						)

											snake_case_ :Union[str, Any]						=			state_dict.get("""power"""						,     self.power						)
											if not isinstance(self.power						,     (float, int)						):
																	raise ValueError("""Invalid power"""						)

											snake_case_ :Optional[int]						=			state_dict.get("""shadow_params"""						,     snake_case						)
											if shadow_params is not None:
																	snake_case_ :Tuple						=			shadow_params
																	if not isinstance(self.shadow_params						,     snake_case						):
																							raise ValueError("""shadow_params must be a list"""						)
																	if not all(isinstance(snake_case						,     torch.Tensor						) for p in self.shadow_params						):
																							raise ValueError("""shadow_params must all be Tensors"""						)





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

import numpy as np
import tensorflow as tf
import torch
def    A_  (			_lowercase	):







						'''simple docstring'''


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