

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


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


if __name__ == "__main__":
					lowerCAmelCase_			      =      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_			      =      parser.parse_args()
					convert_tf_gptsan_to_pt(args)







__magic_name__   :  str        =							8.314_4598
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			float:
      """simple docstring"""

      if temperature < 0:
            raise Exception('Temperature cannot be less than 0 K')
      if molar_mass <= 0:
            raise Exception('Molar mass cannot be less than or equal to 0 kg/mol')
      else:
            return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
       import doctest

       # run doctest
       doctest.testmod()

       # example
       __magic_name__   :  List[Any]        =							300
       __magic_name__   :  Union[str, Any]        =							28
       __magic_name__   :  str        =							rms_speed_of_molecule(temperature, molar_mass)
       print(F'Vrms of Nitrogen gas at 300 K is {vrms} m/s')



