





'''simple docstring'''


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch







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


if __name__ == "__main__":
		lowerCAmelCase_   :   Optional[int]       =					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_   :   List[str]       =					parser.parse_args()
		convert_tf_gptsan_to_pt(args)










'''simple docstring'''


import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script






@require_cpu
class       UpperCamelCase__  (		unittest.TestCase     ):







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				debug_launcher(test_script.main		)







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				debug_launcher(test_ops.main		)



