
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



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


if __name__ == "__main__":
							lowercase__	:List[Any]										=					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")
							lowercase__	:int										=					parser.parse_args()
							convert_tf_gptsan_to_pt(args)






import os



def  UpperCamelCase					(							lowerCAmelCase__ = "input.txt"	):
					'''simple docstring'''

					with open(os.path.join(os.path.dirname(lowerCAmelCase__	)				,    lowerCAmelCase__	)	) as input_file:
										lowercase					=		[
										    [int(lowerCAmelCase__	) for element in line.split(''','''	)]
										    for line in input_file.readlines()
										]

					lowercase					=		len(lowerCAmelCase__	)
					lowercase					=		len(matrix[0]	)

					lowercase					=		[[-1 for _ in range(lowerCAmelCase__	)] for _ in range(lowerCAmelCase__	)]
					for i in range(lowerCAmelCase__	):
										lowercase					=		matrix[i][0]

					for j in range(1				,    lowerCAmelCase__	):
										for i in range(lowerCAmelCase__	):
															lowercase					=		minimal_path_sums[i][j - 1] + matrix[i][j]

										for i in range(1				,    lowerCAmelCase__	):
															lowercase					=		min(
															    minimal_path_sums[i][j]				,    minimal_path_sums[i - 1][j] + matrix[i][j]	)

										for i in range(rows - 2				,    -1				,    -1	):
															lowercase					=		min(
															    minimal_path_sums[i][j]				,    minimal_path_sums[i + 1][j] + matrix[i][j]	)

					return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums	)


if __name__ == "__main__":
							print(F'{solution() = }')




