


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


def       a				(      __a = ""			, )  ->     bool:



					'''simple docstring'''






					return sum(c % 2 for c in Counter(input_str.replace(''' '''			, ''''''       ).lower()       ).values()       ) < 2


def       a				(      __a = ""       )  ->     bool:



					'''simple docstring'''






					if len(__a       ) == 0:
										return True
					UpperCamelCase__       :List[Any]							 =			input_str.replace(''' '''			, ''''''       ).lower()
					# character_freq_dict: Stores the frequency of every character in the input string
					UpperCamelCase__       :dict[str, int]							 =			{}

					for character in lower_case_input_str:
										UpperCamelCase__       :Optional[int]							 =			character_freq_dict.get(__a			, 0       ) + 1
					UpperCamelCase__       :List[str]							 =			0

					for character_count in character_freq_dict.values():
										if character_count % 2:
															odd_char += 1
					if odd_char > 1:
										return False
					return True


def       a				(      __a = ""       )  ->     None:



					'''simple docstring'''






					print('''\nFor string = '''			, __a			, ''':'''       )
					print(
					    '''> can_string_be_rearranged_as_palindrome_counter()'''			, '''\tans ='''			, can_string_be_rearranged_as_palindrome_counter(__a       )			, '''\ttime ='''			, timeit(
					        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''			, setup='''import __main__ as z'''			, )			, '''seconds'''			, )
					print(
					    '''> can_string_be_rearranged_as_palindrome()'''			, '''\tans ='''			, can_string_be_rearranged_as_palindrome(__a       )			, '''\ttime ='''			, timeit(
					        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''			, setup='''import __main__ as z'''			, )			, '''seconds'''			, )


if __name__ == "__main__":
		__snake_case											=					input(
		    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
		).strip()
		benchmark(check_str)
		__snake_case											=					can_string_be_rearranged_as_palindrome_counter(check_str)
		print(F"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")